• NVIDIA announced Project GR00T, aiming to propel advancements in robotics and AI with a comprehensive foundation model for humanoid robots, partnering with major industry players excluding Tesla, and emphasizing the transformative role of human-centric robots in everyday life as articulated by Agility Robotics and Sanctuary AI.
• The company is set to bolster its hardware lineup with the introduction of Jetson Thor, a specialized computer for running simulation workflows and AI algorithms for humanoid robots, alongside launching key initiatives Isaac Manipulator and Isaac Perceptor to enhance robotic dexterity and vision processing.
• Through strategic collaborations and technological innovations in both humanoid and autonomous mobile robotics, NVIDIA positions itself as a pivotal force in the robotics industry, looking to shape and capitalize on the competitive landscape of robotics over the forthcoming years.

NVIDIA unveiled Project GR00T during GTC 2024, its latest initiative aimed at advancing the field of robotics and embodied AI through a versatile foundation model tailored for humanoid robots.

Who does NVIDIA collaborate for the GR00T project?

The company has introduced this platform as an adaptable foundation model specifically designed for humanoid robots. NVIDIA is crafting an AI framework to support the burgeoning humanoid robot sector, collaborating with leading firms such as 1X Technologies, Agility Robotics, Apptronik, Boston Dynamics, Figure AI, Fourier Intelligence, Sanctuary AI, Unitree Robotics, and XPENG Robotics. This initiative encompasses the majority of prominent players in the humanoid robotics field, with a few exceptions, including Tesla.

In the announcement, Agility Robotics received special mention, highlighted by a statement from Jonathan Hurst, co-founder and Chief Robotics Officer:

“We are at an inflection point in history, with human-centric robots like Digit poised to change labor forever. Modern AI will accelerate development, paving the way for robots like Digit to help people in all aspects of daily life. We’re excited to partner with NVIDIA to invest in the computing, simulation tools, machine learning environments and other necessary infrastructure to enable the dream of robots being a part of daily life.”

Sanctuary AI’s co-founder and CEO, Geordie Rose, shared his insights, stating: “Embodied AI will not only help address some of humanity’s biggest challenges, but also create innovations which are currently beyond our reach or imagination. Technology this important shouldn’t be built in silos, which is why we prioritize long-term partners like NVIDIA.”

Furthermore, NVIDIA’s Project GR00T will enhance its hardware offerings. A notable addition is Jetson Thor, a cutting-edge computer engineered specifically for executing simulation workflows, generative AI algorithms, and other applications tailored to the humanoid robot framework.

At this week’s GTC focused on robotics, NVIDIA revealed two additional pivotal initiatives: Isaac Manipulator and Isaac Perceptor. The field of robotics has foundational roots in manipulation, historically dominated by large industrial arms essential to automotive manufacturing. Looking ahead, the next wave of robotics aims for greater dexterity and mobility. NVIDIA is positioning itself to capture a segment of this emerging market, driving the innovation of more agile and mobile robotic systems.

“Isaac Manipulator offers state-of-the-art dexterity and modular AI capabilities for robotic arms, with a robust collection of foundation models and GPU accelerated libraries. It provides up to an 80x speedup in path planning and zero shot perception increases efficiency and throughput, enabling developers to automate a greater number of new robotic tasks,” the company states.

NVIDIA boasts partnerships with notable entities in the robotics industry. In addition, the company is extending its innovation to autonomous mobile robotics (AMRs) through the Perceptor program. The competition for dominance in the robotics market over the coming years, particularly between humanoid robots and mobile manipulators, promises to be intense. NVIDIA is strategically positioning itself to be a significant player across these sectors, aiming to influence and benefit from the sector’s growth and innovation.

Featured image credit: Kerem Gülen/DALL-E 3

As crazy as it sounds, we are here to list the best humanoid robot for sale alternatives in 2024. Yes, you heard us right, humanoid robots straight out of sci-fi movies are ready to walk among us!

Imagine having your own personal robot that can do everything from making breakfast to playing chess with you. Sounds like something straight out of a sci-fi movie, right? Well, believe it or not, humanoid robots are now a reality and can be purchased by consumers. That’s right, folks, it’s 2024 and we’re living in the future.

Humanoid robots have come a long way since their inception. In the past, they were often clunky, slow, and lacking in advanced features. However, with advancements in AI, machine learning, and robotics, humanoid robots have evolved to become more human-like in appearance, movement, and behavior.

Now, they can perform a wide range of tasks, from simple ones like picking up objects to complex ones like cooking, cleaning, and even assembling other robots. With the advent of 5G and the Internet of Things (IoT), humanoid robots are becoming more accessible and affordable for consumers.

Whether you’re looking for a personal assistant, a companion, or a helpful tool for your business, humanoid robots are slowly but surely becoming a viable option and these are the best humanoid robot for sale alternatives, just for you!

Humanoid robot for sale

Before we start listing the humanoid robot for sale alternatives, let’s talk about why and how humanity has come up with creating machines that look exactly like humans and give them a purpose.

The advance of technology has brought about a new era of humanoid robots that are designed to assist humans in various aspects of life. These robots are equipped with advanced AI and machine learning algorithms that enable them to learn and adapt to their environment, perform various tasks, and even recognize and respond to emotions. They can be controlled remotely or through voice commands, and they have the potential to revolutionize industries such as healthcare, manufacturing, education, retail, and hospitality.

One of the most significant benefits of humanoid robots is their ability to perform tasks that require precision and dexterity. In manufacturing, they can be used to assemble and inspect products, ensuring that they meet the highest quality standards. In healthcare, they can assist with surgeries, provide companionship to patients, and help with rehabilitation exercises. They can also help with tasks such as administering medication, monitoring vital signs, and providing personal care to patients.

In addition to their practical applications, humanoid robots can also provide interactive learning experiences for students. They can be used to teach STEM concepts or foreign languages, and they can help with tasks such as grading assignments and providing feedback. In retail, they can be used in stores to help customers find products, answer questions, and provide personalized recommendations. In hospitality, they can be used in hotels, restaurants, and other settings to provide personalized service to guests, such as taking orders, answering questions, and providing recommendations.

However, the development and use of humanoid robots also raise ethical considerations. For example, there are concerns about privacy and safety, as well as questions about who is responsible when a humanoid robot makes a mistake or causes harm. Additionally, there are concerns about the impact of humanoid robots on employment, as they may displace human workers in certain industries.

Despite these concerns, the future outlook for humanoid robots is bright. As technology continues to advance, we can expect to see even more advanced humanoid robots that are capable of performing a wider range of tasks. They may eventually become an integral part of our daily lives, helping us to accomplish tasks more efficiently and effectively.

We are aware that we are talking about the future and possibilities, but this does not mean that we have not already started to see humanoid robot for sale advertisements around us. Here are the humanoid robots you can buy in 2024:

• Ameca from Engineered Arts
• Walker X from UBTECH Robotics
• Pepper from SoftBank Robotics
• NAO from SoftBank Robotics
• Robosapien from WowWee

Ameca (Engineered Arts)

First up on our humanoid robot for sale list is Ameca.

Imagine a robot so lifelike, that its expressive eyes, subtle facial twitches, and fluid body language blur the lines between machine and human. That’s Ameca. This marvel of engineering pushes the boundaries of human-robot interaction, ideal for research institutions studying how we connect with artificial beings. But be prepared, its price tag reflects its sophisticated technology, reaching into the millions.

Walker X (UBTECH Robotics)

Next on the humanoid robot for sale list is Walker X!

Agility and dexterity define Walker X. This robot tackles stairs, opens doors, and even pours tea with impressive coordination. Imagine the possibilities in service, assistance, and even research applications! However, like Ameca, its advanced capabilities come at a cost, reaching the hundreds of thousands.

Pepper (SoftBank Robotics)

Can’t make a humanoid robot for sale list without Pepper.

Charm your way through lessons and interactions with Pepper, the engaging and social robot. This friendly companion recognizes and responds to emotions, making it a valuable tool in schools, museums, and even elder care facilities. At $31,995 (USD), Pepper offers a blend of advanced technology and accessibility.

NAO (SoftBank Robotics)

NAO takes its deserved spot in our humanoid robot for sale list too.

Unleash your inner robotics engineer with NAO! This versatile robot is a star in STEM education and robotics competitions. Its programmable movements, advanced sensors, and robust software are perfect for learning coding and pushing the boundaries of robotics. Like Pepper, NAO stands at $31,995 (USD).

Robosapien (WowWee)

And lastly in our humanoid robot for sale list is Robosapien.

Relive your childhood robotics dreams with the iconic Robosapien! This classic robot still walks, dances, and responds to your voice, making it a fun and affordable introduction to the world of robots for kids and hobbyists. At just $199 (USD), Robosapien is a budget-friendly way to spark curiosity and ignite imaginations.

Remember, this is just the beginning! The fascinating world of humanoid robots is constantly evolving, with new options emerging on the horizon. When choosing your ideal robotic companion, consider:

Functionality: What tasks do you envision the robot performing? Education, entertainment, research, or perhaps even assistance?

• Mobility: Does the robot need to walk, climb stairs, or manipulate objects?
• Software and programmability: Can you customize the robot’s behavior and program it for specific tasks?
• Availability and support: Is the robot readily available, and does the manufacturer offer reliable customer support?

With careful consideration and a touch of excitement, you can embark on a rewarding journey into the world of humanoid robots. Whether you’re a seasoned researcher seeking cutting-edge technology, an educator sparking curiosity in young minds, or simply a robot enthusiast looking for a unique companion, there’s a perfect humanoid robot waiting to greet you.

And who knows, perhaps one day, these robotic figures will truly blur the lines between machine and human, changing the landscape of our world forever.

Featured image credit: Brett Jordan/Unsplash.

While initially met with skepticism, Tesla’s Optimus robot has made significant strides with the unveiling of its second generation, the Tesla Optimus Gen 2. This new model boasts several improvements, showcasing Tesla’s commitment to developing a truly versatile and powerful humanoid robot.

See the Tesla Optimus Twitter/X account’s introduction video to their new robot below.

There’s a new bot in town 🤖

Check this out (until the very end)!https://t.co/duFdhwNe3K pic.twitter.com/8pbhwW0WNc

— Tesla Optimus (@Tesla_Optimus) December 13, 2023

What sets Tesla Optimus Gen 2 apart?

Tesla’s latest creation, the Optimus Gen 2, represents a substantial advancement in humanoid robotics. This new model boasts several exciting features, showcasing Tesla’s dedication to developing a truly versatile and powerful robot.

The Optimus Gen 2 can walk 30% faster than its predecessor, indicating improved leg strength and coordination. This increased mobility allows the robot to move more efficiently and quickly, making it better suited for a wider range of tasks.

The Tesla Optimus Gen 2 has also been designed to be more lightweight, with a reduction in weight of 10kg. This makes the robot more agile and energy-efficient, allowing it to perform tasks for longer periods without needing to recharge.

Custom actuators and sensors, designed by Tesla, ensure optimal performance and control. These components allow the robot to perform tasks with greater precision and accuracy, making it more effective in a variety of settings.

Advanced tactile sensing

The fingers of the Tesla Optimus Gen 2 are equipped with tactile sensors, allowing for precise object manipulation. This feature enables the robot to perform tasks that require a high level of dexterity, such as assembling parts or handling delicate objects.

Enhanced mobility

The Optimus Gen 2 features a two-axis neck, allowing for greater flexibility and a more human-like range of motion. Its integrated electronics and wiring make it look more streamlined and aerodynamic, while its articulated foot sections and force/torque sensors enable it to move more smoothly and balance better.

Can it stand its ground against Atlas?

While the Optimus Gen 2 represents a significant step forward in humanoid robotics, it still lags behind Boston Dynamics’ extraordinary Atlas robot. However, Tesla’s pace of development is much faster, and the company is rapidly closing the gap.

Despite the impressive hardware advancements, the ultimate goal for humanoid robot makers is to prove that their robots can perform real work in the real world in a repeatable, reliable, and flexible way.

Tesla is one of the companies closest to achieving this goal, with plans to start using the robot in its own manufacturing operations soon. Once its usefulness has been proven, Tesla plans to start selling the robot.

Tesla believes that its expertise in artificial intelligence, gained from its self-driving car program, and its knowledge of batteries and electric motors, will be instrumental in making the Optimus Gen 2 a success.

By combining these technologies, Tesla aims to create a robot that can perform a variety of tasks with greater efficiency and effectiveness.

How much is Tesla Optimus Gen 2 robot?

Unfortunately, Tesla hasn’t officially announced the price of Tesla Optimus Gen 2 yet. However, based on the previous prototype’s estimated cost of $20,000-$30,000 and the significant advancements in Tesla Optimus Gen 2, we can expect a price range of $25,000 – $50,000 USD.

There are several factors that could influence the final price:

• Material and component costs: As the robot uses custom-designed components, their production cost will significantly impact the overall price
• Manufacturing scale: If Tesla can streamline the production process, it could potentially reduce the cost per unit
• Target market: If the initial target market is industrial and commercial, the price may be higher than if it’s aimed at consumers
• Technological advancements: Further improvements in AI, battery technology, and other areas could impact the cost of production

Remember that these are just estimates, and the actual price of Optimus Gen 2 could be higher or lower depending on the factors mentioned above.

Featured image credit: Tesla.

Many of today’s technologies are based on the principle of giving an output to an input. But did you know this principle is based on Finite-State Machines?

The allure of AI often enthralls our imagination, painting vivid pictures of intelligent machines capable of human-like cognition and decision-making. Yet, before delving into the complexities of advanced AI systems, we must pay homage to the ancestors of AI, and none is more significant than the Finite-State Machine. This elegantly simple mathematical construct serves as a cornerstone, influencing the very essence of AI technologies as we know them today.

At its core, the Finite-State Machine is a captivating model that thrives on the principles of discrete states and transitions. Its architecture comprises a finite set of states, an array of input symbols, an optional set of output symbols, and a collection of rules that govern the transitions between states, all orchestrated with a masterful simplicity that belies its power.

What is Finite-State Machine?

A Finite-State Machine (FSM) is a fundamental concept in the world of computer science and engineering. It serves as a mathematical model that allows us to represent and control systems with discrete states and transitions. This powerful abstraction is widely used in various fields, from hardware design and software development to robotics and artificial intelligence.

At its core, an FSM is composed of several key components that define its behavior and functionality. These components include:

States: The FSM operates within a finite set of states, each representing a specific condition or situation in the system being modeled. These states act as snapshots of the system’s state at different points in time, reflecting its internal configuration or behavior.

Input symbols: The FSM interacts with the external environment through a set of input symbols. These symbols can be any form of input data, such as characters, numbers, or even sensory data from sensors in a robotic system.

Output symbols: In some cases, an FSM may also have a set of output symbols associated with transitions between states. These output symbols represent the machine’s response to specific inputs and can be used to communicate information or trigger actions in the system.

How artificial intelligence went from fiction to science?

Transition rules: The heart of the FSM lies in the set of rules that dictate how it transitions from one state to another based on the input symbols it receives. These rules, often represented as a transition table or a state diagram, define the behavior and logic of the system being modeled.

The operation of an FSM can be visualized as a dynamic flow between states driven by the input symbols it processes. When the machine starts, it enters an initial state, which serves as the starting point for its computation. As it reads each input symbol, it applies the predefined rules to determine the next state. This process continues until the Finite-State Machine reaches a final state, signifying the completion of a specific task, or it halts due to an undefined transition.

Finite-State Machines have applications in various domains due to their simplicity, efficiency, and versatility. In hardware design, FSMs are used to control digital circuits, enabling devices like microcontrollers, processors, and memory units to perform specific tasks based on input signals. In software development, FSMs play a crucial role in tasks such as lexical analysis, parsing, and regular expression matching.

Who is the founder of Finite-State Machines?

The founder of the finite-state machine is Edward Forrest Moore. He was an American professor of mathematics and computer science, and he invented the Moore finite state machine in 1956. Moore’s work was based on the earlier work of Warren McCulloch and Walter Pitts, who had proposed a mathematical model of the human brain in 1943.

Moore’s finite state machine is a type of finite automaton that has a single output value for each state. This is in contrast to the Mealy finite state machine, which has a separate output value for each input-state pair. Moore’s finite state machines are simpler to implement than Mealy machines, and they are often used in digital circuits and software.

What are the different types of Finite-State Machines?

Finite-State Machines (FSMs) come in various types, each offering unique characteristics and functionalities that cater to specific applications. Understanding these different types is crucial for designing efficient and effective systems.

Deterministic Finite-State Machines (DFSM)

Deterministic Finite-State Machines are the most straightforward and common type of FSMs. In a DFSM, for every state and input symbol, there is precisely one defined transition to the next state. This deterministic behavior means that given a specific input symbol and the current state, the machine will always transition to the same next state. The transition is unambiguous, leading to a predictable outcome.

DFSMs are particularly useful in applications where the behavior needs to be well-defined and where certainty in the machine’s operation is essential. They excel in scenarios that require precise control and where there is no need for branching or multiple possible outcomes based on the same input symbol.

Non-deterministic Finite-State Machines (NFSM)

Non-deterministic Finite-State Machines, in contrast to DFSMs, allow multiple transitions from a state for a given input symbol. This non-determinism introduces branching and ambiguity in the machine’s behavior, as there can be different possible paths that the machine can follow based on the same input symbol and current state.

NFSMs are advantageous in situations where there are multiple valid choices or decisions that can be made based on the same input. They provide a more flexible and expressive representation, enabling the FSM to explore various possibilities simultaneously. This characteristic is especially useful in complex applications that involve decision-making, problem-solving, and search algorithms.

Mealy Machines and Moore Machines

Apart from the distinction between deterministic and non-deterministic FSMs, there are two additional categories based on how the output is determined during transitions:

• Mealy Machines
• Moore Machines

Mealy Machines

In Mealy Machines, the output is determined by both the current state and the input symbol during transitions. This means that the output is associated with the transitions between states rather than being linked solely to the current state. As a result, Mealy Machines tend to have more compact representations as they do not need to associate output with each state.

Mealy Machines are often employed in applications where the output is directly related to the input and the machine’s behavior requires responsiveness to changes in input symbols. They are well-suited for tasks that involve data processing, signal processing, and real-time systems.

Moore Machines

On the other hand, Moore Machines generate output based only on the current state and do not consider the input symbol during transitions. This means that the output remains constant during the time the FSM is in a particular state and only changes when the machine transitions to a different state.

Moore Machines are commonly used in applications where the output depends solely on the current internal state of the system. They are well-suited for tasks where the output is driven by the state’s characteristics and does not require immediate responsiveness to changes in input symbols.

How do Finite-State Machines work?

Finite-State Machines (FSMs) work on a simple and elegant principle, which involves transitioning between different states based on the input symbols they receive.

Let’s break down the process step by step.

States and transitions

At the heart of a Finite-State Machine are the states. A state represents a specific condition or situation that the machine can be in at any given moment. For example, imagine a traffic light with three states: “Red,” “Yellow,” and “Green.” Each of these states represents a different condition of the traffic light.

The FSM also has transitions, which define how it moves from one state to another based on the input it receives. Transitions are typically triggered by input symbols. These symbols can be anything from letters, numbers, or any other symbols relevant to the application of the FSM.

Initial state

When a Finite-State Machine starts its operation, it begins in an initial state. The initial state is the starting point of the machine’s computation. It serves as the entry point for the FSM to begin processing the input.

Reading input symbols

Once the Finite-State Machine is in the initial state, it starts reading input symbols one by one. The machine reads an input symbol from the input stream, processes it, and then proceeds to read the next input symbol.

Transition rules

For every state and input symbol combination, there are predefined transition rules that dictate how the FSM should respond. These rules determine the next state the machine should transition to when it encounters a specific input symbol in a given state.

To illustrate this, let’s consider a simple FSM with three states: “A,” “B,” and “C,” and two input symbols: “0” and “1.” The transition rules for this FSM could be:

• If the FSM is in state “A” and receives input symbol “0,” it transitions to state “B”
• If the FSM is in state “A” and receives input symbol “1,” it remains in state “A”
• If the FSM is in state “B” and receives input symbol “0,” it transitions to state “C”
• If the FSM is in state “B” and receives input symbol “1,” it remains in state “B”
• If the FSM is in state “C” and receives input symbol “0,” it transitions back to state “A”
• If the FSM is in state “C” and receives input symbol “1,” it transitions to state “B”

Transitioning between states

As the FSM reads each input symbol, it applies the transition rules to determine the next state. It follows the defined transitions until it reaches a final state or halts.

Final states and halting

A Finite-State Machine can have one or more final states. When the FSM reaches a final state after processing the input symbols, it signifies that it has completed its task or reached a specific goal. The machine may then produce an output or trigger some action associated with that final state.

Alternatively, the FSM may halt without reaching a final state if it encounters an input symbol for which no valid transition rule is defined. In this case, the FSM may stop its operation without completing the task it was designed for.

Finite State Machines are much more than simple vending machines

Finite-State Machines (FSMs) have proven to be versatile tools with applications across various fields. Their simplicity, efficiency, and ability to model sequential behaviors make them valuable in diverse domains.

Control of machines and devices

Finite-State Machines play a vital role in controlling various machines and devices by specifying their behavior based on inputs. In this context, FSMs act as decision-making units that govern the actions of machines. For instance, in industrial automation, FSMs can be used to control complex processes in manufacturing plants.

The FSM defines the different states that the machinery can be in, such as “Idle,” “Processing,” and “Shutdown.” Based on sensor inputs and other external factors, the FSM transitions between these states, controlling the machine’s operations and ensuring efficient functioning.

Computer programming

In software development, FSMs are widely employed for various tasks. One common application is lexical analysis, where Finite-State Machines help tokenize source code into meaningful components such as keywords, identifiers, and operators.

The FSM processes the input stream character by character, transitioning between states to identify and extract the appropriate tokens. Additionally, FSMs are used for parsing, where they analyze the syntax of a program based on a grammar, and for regular expression matching, where they match patterns in strings efficiently.

Automated systems

FSMs are an integral part of automation systems, enabling efficient and reliable control over processes. In manufacturing, FSMs are used to manage assembly lines, monitor product quality, and optimize production workflows.

Automation processes rely on FSMs to make decisions, trigger actions, and ensure that tasks are carried out systematically and without errors.

Communication protocols

Communication protocols often utilize FSMs to handle data transmission and error handling. For instance, in networking protocols like TCP/IP, FSMs manage the establishment and termination of connections between devices.

FSMs ensure that data packets are transmitted correctly, and if errors occur during transmission, the FSM handles the retransmission of lost data to maintain data integrity and reliability in the communication process.

Robotics

FSMs are essential in defining the behavior and decision-making processes of robots in various scenarios. Robots operate in dynamic and unpredictable environments, and Finite-State Machines provide a structured approach to model their responses to sensory inputs and external stimuli.

For example, in a mobile robot navigating through a maze, an FSM can guide its movements based on sensor inputs, enabling it to make decisions about its path, avoid obstacles, and reach its destination efficiently.

Artificial intelligence

Finite-State Machines serve as the predecessors to more complex AI technologies, laying the foundation for sequential decision-making in AI systems. While Finite-State Machines themselves have limitations in handling highly complex and dynamic tasks, they have influenced the development of more advanced AI models and algorithms.

Sequential decision-making, a key aspect of AI systems, draws inspiration from the principles of FSMs, where a series of decisions lead to a specific outcome based on input signals.

Development of games

Finite-State Machines are used to control characters’ behavior, handle game states, and manage game logic. Games often involve characters with specific behaviors and interactions. FSMs provide a structured way to define these behaviors and enable characters to respond to player inputs and game events appropriately.

FSMs can represent the different states of a character, such as “Idle,” “Running,” “Jumping,” and “Attacking,” and transition between these states based on player actions and game events, creating engaging and dynamic gameplay experiences.

One can’t help but wonder if Edward Forrest Moore ever imagined that this simple but inspiring algorithm he created would gift humanity with this technology in 2023.

While we wait with curiosity to see what more AI and robotics can achieve, we pay our respects to him.

Featured image credit: Photo by Chris Briggs on Unsplash.

Swarm robotics, a field inspired by the remarkable collective behaviors observed in nature, seeks to study and apply the concept of swarm behavior to a diverse range of robotic systems. Just like witnessing a flock of birds soaring through the sky or a school of fish effortlessly navigating underwater, swarm behavior exemplifies the ability of individual agents to interact and coordinate, resulting in mesmerizing collective actions.

While commonly observed in the natural world, the principles of swarm behavior have captured the attention of researchers and engineers who aim to replicate and harness its power within the realm of robotics.

The essence of swarm behavior lies in the emergent properties that arise from the interaction of autonomous individual agents, each possessing relatively simple rules or behaviors. These agents can be robots, drones, or even virtual entities, and they collectively work towards achieving a common goal or solving complex tasks through distributed coordination.

By using the power of collaboration and self-organization, swarm robotics aims to create systems that exhibit scalability, robustness, adaptability, and efficiency.

What is swarm robotics?

Swarm robotics is a field of robotics that deals with the design, control, and coordination of large groups of robots. Swarm robots are typically simple and inexpensive, but they can achieve complex tasks by working together. Swarm robotics is inspired by the behavior of social insects, such as ants and bees, which are able to accomplish complex tasks such as foraging and building nests without any centralized control.

Social insects are insects that live in colonies, and they exhibit a wide range of cooperative behaviors. These behaviors are often complex and coordinated, and they allow social insects to achieve tasks that would be impossible for individual insects.

Social insects exhibit remarkable abilities in various aspects of their collective behavior. One notable behavior is foraging, where they collaborate harmoniously to locate sustenance and return it to their colony. The process involves intricate tasks like scouring the surroundings for food sources, intercommunicating vital information among colony members, and efficiently transporting the acquired nourishment back to the nest.

Another captivating behavior observed in social insects is their collaborative nest-building endeavor. With concerted effort, they gather essential materials, construct elaborate structures, and fiercely protect the nest from potential predators. This collaborative construction showcases their collective problem-solving abilities and showcases their cooperative nature.

Swarm robotics draws inspiration from the collective behaviors observed in social insects. By emulating their collaborative strategies, swarm robotics aims to develop multi-robot systems that can work together cohesively to solve complex tasks. The principles of foraging, nest building, antennation, division of labor, and mimicry observed in social insects serve as valuable models for designing efficient and adaptive robotic swarms. Through the integration of these concepts, swarm robotics endeavors to unlock new possibilities in areas such as search and rescue missions, environmental monitoring, and industrial automation.

Kilobots that can get a ton of work done

One prominent example of swarm robots that has garnered significant attention in the field of swarm robotics is the Kilobots. Developed by the Self-Organizing Systems Research Group at Harvard University, Kilobots are a remarkable swarm robot platform that has been instrumental in advancing swarm robotics research.

The Kilobots are miniature, low-cost robots equipped with simple sensors and actuators. They are designed to operate in large swarms, consisting of hundreds or even thousands of robots. These tiny robots measure just a few centimeters in diameter and are characterized by their cylindrical shape and three omnidirectional legs.

What makes the Kilobots particularly noteworthy is their ability to communicate and coordinate their actions with one another, despite their limited individual capabilities. They employ infrared communication to exchange information and make decisions as a collective entity. This communication allows Kilobots to perform complex tasks through emergent behaviors, where the combined actions of the swarm lead to the desired outcome.

Unlocking the full potential of automation with smart robotics

The Kilobots have become a popular tool for researchers in swarm robotics due to their scalability, simplicity, and affordability. They enable investigations into various swarm behaviors, including aggregation, pattern formation, collective transport, and decision making. The Kilobot platform provides researchers with a practical means to study and experiment with swarm robotics algorithms and concepts.

With their widespread adoption and use in research laboratories around the world, Kilobots have become an iconic symbol of swarm robotics.

The science behind Swarm Robotics

The science behind swarm robotics is based on the principles of swarm intelligence. Swarm intelligence is a type of artificial intelligence that is inspired by the behavior of social insects.

Swarm intelligence algorithms are typically decentralized, meaning that they do not require a central controller. Instead, the robots in a swarm communicate with each other and coordinate their actions locally.

According to the ”Swarm Robotic Behaviors and Current Applications” article, swarm behavior can be divided into four different sub-topics:

• Spatial organization
• Navigation
• Decision making
• Miscellaneous

Spatial organization

Swarm robotics encompasses a range of behaviors that enable coordinated movement and spatial organization of robot swarms in their environment. These behaviors contribute to efficient interaction and manipulation of objects within the swarm.

One such behavior is aggregation, which involves bringing individual robots together spatially within a specific region of the environment. Aggregation facilitates closer proximity among swarm members, enabling enhanced interaction and cooperation among them.

Pattern formation is another behavior wherein the swarm of robots organizes itself into a predetermined shape. This can include formations like chain structures, which establish communication links between different points within the swarm.

Self-assembly is a behavior that involves connecting individual robots to form structures, either physically or through communication links. This behavior can be particularly useful in achieving predefined shapes or structures within the swarm, a concept known as morphogenesis.

Object clustering and assembly is a behavior that allows the swarm of robots to manipulate objects distributed in the environment. By clustering and assembling these objects, the swarm can engage in construction processes or accomplish specific tasks that require collaborative object manipulation.

Navigation

Navigation behaviors in swarm robotics focus on the coordinated movement of the robot swarm within their environment, enabling exploration, motion in formation, and object transport.

Collective exploration involves cooperative navigation of the swarm through the environment, facilitating tasks such as situational overview, object search, environmental monitoring, or establishing communication networks.

Coordinated motion behavior guides the swarm of robots to move together in a specific formation. This formation can take a well-defined shape, like a line, or be more arbitrary, as observed in flocking behavior.

Collective transport allows the swarm of robots to collectively move objects that may be too heavy or large for individual robots to handle alone. By coordinating their efforts, the swarm can achieve effective object transportation.

Collective localization enables robots within the swarm to determine their position and orientation relative to each other by establishing a local coordinate system. This behavior facilitates efficient coordination and communication among swarm members.

Decision making

Decision-making behaviors enable the robots in a swarm to make collective choices and allocate tasks efficiently.

Consensus behavior allows individual robots in the swarm to converge on a single common choice from several alternatives, ensuring coherence and unity within the group.

Task allocation behavior dynamically assigns arising tasks to individual robots based on their capabilities, maximizing the overall performance of the swarm. This behavior becomes particularly valuable when the robots in the swarm possess heterogeneous capabilities.

Collective fault detection identifies individual robots that deviate from the desired behavior of the swarm, often due to hardware failures or deficiencies. This behavior allows for early detection and mitigation of faults within the collective system.

Collective perception involves combining locally sensed data from individual robots into a comprehensive understanding of the environment. This behavior allows the swarm to make informed collective decisions, such as reliable object classification or determining optimal solutions to global problems.

Synchronization behavior aligns the frequency and phase of oscillators among the robots in the swarm, enabling them to act synchronously. This shared understanding of time enhances coordination and cooperation within the swarm.

Group size regulation allows the robots in the swarm to form groups of desired sizes. If the swarm exceeds the desired group size, it can autonomously split into multiple groups, promoting efficient organization and scalability.

Miscellaneous

Additional behaviors in swarm robotics do not fit into the previous categories but remain integral to the field.

Self-healing behavior allows the swarm to recover from faults caused by individual robot deficiencies. By minimizing the impact of robot failures, self-healing enhances the reliability, robustness, and overall performance of the swarm.

Self-reproduction behavior enables the swarm to create new robots or replicate patterns formed by multiple individuals. This behavior aims to increase the autonomy of the swarm, reducing reliance on human intervention for the creation of new robots.

Human-swarm interaction behavior allows humans to control or receive information from the robots in the swarm. This interaction can occur remotely through computer terminals or in a shared environment through visual or acoustic clues, facilitating collaboration between humans and the swarm.

These behaviors in swarm robotics collectively contribute to the development of adaptive and efficient systems that draw inspiration from the collective intelligence and organization observed in social insects.

Key applications of swarm robotics

Swarm robotics is an extraordinary field brimming with possibilities, set to transform numerous industries through its remarkable applications. Some of the key applications of swarm robotics include:

• Search and rescue: Swarm robots could be used to search for survivors in disaster areas.

In 2015, a swarm of robots was used to search for survivors after the Nepal earthquake. The robots were able to cover a wider area than traditional search teams and they were able to reach areas that were difficult for humans to access.

• Environmental monitoring: Swarm robots could be used to monitor environmental conditions, such as air quality or water pollution

For example, in 2016, a swarm of robots was used to monitor air quality in Beijing. The robots were able to collect data in real time and they were able to identify areas with high levels of pollution.

• Agriculture: Swarm robots could be used to plant crops, harvest crops, or control pests

Back in 2017, a swarm of robots was used to plant rice in China by SAGA. The robots were able to plant the rice more quickly and efficiently than human workers.

• Construction: Swarm robots could be used to build structures, such as bridges or buildings

In 2018, a swarm of robots was used to build a small bridge in Japan. The robots were able to build the bridge in a fraction of the time it would have taken human workers

• Defense: Swarm robots could be used to patrol borders or detect and disarm landmines

In 2019, a swarm of robots was used to patrol the border between the United States and Mexico. The robots were able to detect and track illegal immigrants

Challenges and opportunities in swarm robotics

Swarm robotics presents a multitude of challenges and opportunities that shape its future development. Overcoming these challenges and leveraging the opportunities can lead to significant advancements in the field. Some of the challenges in swarm robotics include:

Developing effective communication and coordination algorithms: Swarm robots must have efficient means of communication and coordination to work together seamlessly. Designing algorithms that allow for reliable information exchange and coordinated actions among the individual agents is crucial for the success of swarm systems.

Designing robust and reliable swarm robots: Swarm robots need to be capable of operating in diverse environments and conditions, including harsh or unpredictable ones. Ensuring the durability, robustness, and adaptability of individual robots is essential to the overall effectiveness and longevity of swarm systems.

Addressing the “swarm effect”: The swarm effect refers to the potential chaos or unpredictable behavior that can arise if the swarm is not properly coordinated. Mitigating the swarm effect requires developing control strategies and algorithms that ensure cohesive and organized behavior within the swarm, even when faced with complex and dynamic situations.

Despite these challenges, swarm robotics offers numerous opportunities that can drive innovation and revolutionize the robotics field. Some of these opportunities include:

The potential for low-cost, mass-produced swarm robots: Swarm robots can be designed to be cost-effective and easily manufacturable, enabling mass production. This affordability could make swarm robotics more accessible to a wider range of users, facilitating the adoption of swarm systems in various industries and applications.

The potential for swarm robots to tackle difficult or impossible tasks: Swarm robots can be employed in scenarios that are deemed too dangerous, complex, or inaccessible for traditional robots. Their collective intelligence and cooperation allow them to perform tasks that would otherwise be challenging or infeasible, opening new avenues for exploration and problem-solving.

The potential for swarm robots to exhibit advanced intelligence: Swarm systems have the capacity to exhibit emergent behaviors and collective intelligence that surpass the capabilities of individual robots. Through learning, adaptation, and self-organization, swarm robots can dynamically respond to their environment, making them capable of solving complex problems and optimizing their performance.

These opportunities in swarm robotics provide fertile ground for advancements in various industries, including logistics, agriculture, surveillance, and disaster response. As research progresses, swarm robotics has the potential to reshape the way robots interact with and navigate through the world, leading to innovative and intelligent systems that can tackle real-world challenges with unprecedented efficiency and adaptability.

Swarm robotics vs. traditional robotics

Swarm robotics, although a relatively new field, holds great potential for revolutionizing various industries. In comparison to traditional robotics, which has already established itself, swarm robotics offers distinct advantages that make it particularly well-suited for certain tasks. Traditional robots are often costly, intricate, and less effective when it comes to cooperative and coordinated efforts.

One notable advantage of swarm robotics is its scalability. This means that it can easily accommodate a large number of robots without significantly increasing the complexity of the system.

Robustness is another key advantage of swarm robotics. In this context, robustness refers to the ability of the system to withstand the failure of individual robots. If one robot within a swarm malfunctions or is disabled, the remaining robots can continue working cooperatively to accomplish the task at hand. This robustness is particularly valuable in tasks performed in hazardous or unpredictable environments, where individual robot failures are more likely to occur.

Additionally, swarm robotics offers a high degree of adaptability. Swarm robots are typically designed to be simple and cost-effective, making them easy to reprogram for different tasks. This adaptability allows swarm robotics to excel in situations that demand flexibility and versatility. Whether it involves changes in task requirements or the need to tackle diverse challenges, swarm robotics can quickly adapt to varying circumstances.

Swarm Robotics in popular culture

Swarm robotics has been featured in a number of popular culture works, including the movies “Starship Troopers” and “The Matrix“. Even though they’ve covered the bad and scary sides of swarm robotics, these works have helped to raise awareness of the potential of swarm robotics, and they have inspired a new generation of researchers to explore this exciting field.

Swarm robotics research is still in its early stages, with limited successful transitions to industrial applications and daily use. However, significant progress has been made. While swarm robotics faces challenges in its integration into industrial settings, there is a promise for the development of advanced applications in sectors like logistics, agriculture, and inspection. Ongoing research and advancements in swarm robotics platforms offer opportunities for adaptability, robustness, and scalability, leading to a greater understanding and utilization of swarm behaviors in the future.

Featured image credit: Photo by Michael Dolejš on Unsplash.

Smart robotics is playing a significant role in the growth of industrial automation. The integration of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things into robots has made them more autonomous, adaptable, and intelligent. This has enabled them to sense, perceive, and respond to their environment and perform tasks that were once considered impossible.

One of the main benefits of smart robotics in industrial automation is increased efficiency. Smart robots can perform tasks faster, more accurately, and with greater precision than humans, resulting in increased productivity and output. They can also automate tasks that are repetitive, dangerous, or difficult for humans, freeing up human resources for more complex and valuable tasks.

Another benefit of robotics in industrial automation is cost savings. Smart robots can reduce labor costs and improve efficiency, resulting in cost savings for businesses. They can also operate in hazardous environments or perform tasks that are dangerous for humans, improving safety for employees.

Smart robotics also enables the implementation of Industry 4.0 and the Internet of Things (IoT) in the manufacturing process. Through the use of IoT devices and sensors, robots can communicate and share data with other machines and systems in the factory, creating an interconnected and automated production line.

In summary, robotics is flourishing industrial automation growth by increasing efficiency, reducing costs, improving safety, and enabling the implementation of Industry 4.0. As technology continues to evolve, we can expect to see even more breakthroughs and innovations in the field of industrial automation in the future.

What is smart robotics?

In recent years, the field of robotics has seen significant advancement with the emergence of robotics. But what exactly is smart robotics? In simple terms, smart robotics refers to the integration of advanced technologies, such as artificial intelligence, machine learning, and the Internet of Things, into robots to make them more autonomous, adaptable, and intelligent. This integration enables robots to sense, perceive, and respond to their environment and perform tasks that were once considered impossible.

The significance of robotics cannot be overstated. It has the potential to revolutionize various industries by increasing efficiency, reducing costs, and improving safety and accuracy. From manufacturing to healthcare, smart robotics has found its way into several sectors, improving the quality of life and enhancing overall productivity. With the advancements in technology and the increasing demand for automation, smart robotics is set to play a significant role in shaping the future. In this article, we will delve deeper into the definition, advancements, applications, benefits, potential, and challenges of smart robotics.

The latest developments in smart robotics

The field of smart robotics is continuously evolving, with new advancements and innovations being made every day. Some of the latest developments in smart robotics include:

Autonomous robots

Robots that can operate independently without human intervention. They can navigate, make decisions, and execute tasks based on their programming and the information they gather from their environment.

Collaborative robots

Robots that can work alongside humans in a safe and efficient manner. They are designed to assist humans in tasks and can be easily integrated into existing processes and operations.

Deep learning

The integration of deep learning algorithms into robots enables them to learn and adapt to new tasks and environments.

Human-robot interaction

The development of more sophisticated and intuitive interfaces enables robots to communicate and interact with humans in a natural and effective way.

Cloud robotics

The use of cloud computing enhances the capabilities of robots by providing them with access to vast amounts of data and computational resources.

5G connectivity

The integration of 5G technology in robots enables them to communicate and transfer data faster, making them more responsive and efficient.

These are just a few examples of the many advancements being made in the field of robotics. As technology continues to evolve, we can expect to see even more breakthroughs in the near future.

Applications of smart robotics

Smart robotics has found its way into various industries, improving the quality of life and enhancing overall productivity. Some of the most notable applications of smart robotics include:

• Manufacturing: Robotics is being used in manufacturing to improve efficiency and reduce costs. They are used for tasks such as assembly, welding, and packaging and can work in environments that are hazardous or difficult for humans.
• Healthcare: Smart robotics is being used in healthcare to assist surgeons in complex procedures, assist patients with mobility, and automate laboratory tasks.
• Agriculture: Robotics is used in agriculture to improve efficiency and reduce labor costs. They can be used for tasks such as planting, harvesting, and monitoring crop growth.
• Service sector: Smart robotics is being used in the service sector to automate tasks such as cleaning, customer service, and security.
• Transportation: Robotics is being used in transportation to increase efficiency, reduce costs and improve safety. They can be used for tasks such as loading and unloading cargo and autonomous vehicles.
• Construction: Smart robotics is used in construction to increase efficiency, reduce costs, and improve safety. They can be used for tasks such as building inspection and heavy equipment operation.

Benefits of smart robotics

The integration of advanced technologies into robots has brought about numerous benefits, making smart robotics an attractive solution for various industries. Some of the main benefits of robotics include:

Increased efficiency

Smart robotics can perform tasks faster, more accurately, and with greater precision than humans, resulting in increased productivity and output.

Automation

Robotics can automate tasks that are repetitive, dangerous, or difficult for humans, freeing up human resources for more complex and valuable tasks.

The intersection of technology and engineering: Automation engineers

Cost savings

Smart robotics can reduce labor costs and improve efficiency, resulting in cost savings for businesses.

Improved safety

Robotics can operate in hazardous environments or perform tasks that are dangerous for humans, improving safety for employees.

24/7 operation

Smart robotics can operate 24/7 without stopping, increasing the overall production output.

Flexibility

Robotics can be programmed to perform multiple tasks, increasing the flexibility of the system.

Scalability

Smart robotics can be easily scaled up or down depending on the needs of the business, making it a cost-effective solution.

These benefits have not only made robotics an attractive solution for various industries but also have made them a key player in shaping the future.

The future of smart robotics and its possibilities

The advancements in smart robotics have opened up a world of possibilities, and the potential of this technology is enormous. Some of the areas where smart robotics is expected to have a significant impact in the future include:

• Artificial intelligence: The integration of artificial intelligence into robotics will enable them to perform more complex tasks, learn and adapt to new environments, and make decisions independently.
• Internet of Things: The integration of IoT technology into robotics will enable them to connect and communicate with other devices and systems, making them even more autonomous and adaptable.
• Human-robot collaboration: The development of more sophisticated and intuitive interfaces will enable robots to collaborate with humans in a natural and effective way.
• Autonomous vehicles: Smart robotics will play a major role in the development of autonomous vehicles, making transportation safer and more efficient.
• Smart cities: Robotics will be used to improve the functionality and efficiency of smart cities, from infrastructure maintenance to emergency response.
• Space exploration: Robotics will be used to explore space and perform tasks that are too dangerous or difficult for humans.

Challenges and opportunities of smart robotics

As with any new technology, there are challenges and opportunities associated with smart robotics. Some of the key challenges include:

• Ethical concerns: Robotics has the potential to disrupt the job market and raise ethical concerns, such as privacy, surveillance, and bias.
• Social impact: Smart robotics has the potential to impact society in ways that are not yet fully understood, such as the impact on employment and the displacement of human workers.
• Technical challenges: Smart robotics is a complex technology, and there are many technical challenges that need to be overcome, such as reliability, scalability, and security.
• Cost: The development and implementation of smart robotics can be expensive, and it may not be feasible for small businesses or developing countries.

AI and Ethics: Balancing progress and protection

Despite these challenges, smart robotics also presents many opportunities. Some of the key opportunities include:

• Job creation: Robotics has the potential to create new jobs in areas such as programming, maintenance, and monitoring.
• Economic growth: Robotics has the potential to drive economic growth by increasing efficiency, reducing costs, and improving productivity.
• Improved quality of life: Smart robotics has the potential to improve the quality of life by automating tasks, providing assistance to those in need, and increasing safety.

As smart robotics continues to evolve, it is important to consider the challenges and opportunities associated with this technology and address them proactively.

Final words

Smart robotics is a rapidly evolving technology that has the potential to revolutionize various industries and improve the quality of life. From manufacturing to healthcare, the applications of robotics are vast, and the benefits it brings, such as increased efficiency, automation, and cost savings, are undeniable. The future of robotics is filled with possibilities, and it has the potential to shape the way we live and work.

However, as with any new technology, there are challenges that need to be addressed, such as ethical concerns, social impact, and technical challenges. It is important to have an open dialogue about the implications of robotics and work together to find solutions that benefit society as a whole.

Overall, smart robotics is a technology that promises to shape the future, and it is important for us to understand and embrace its capabilities and potential. As the field of smart robotics continues to evolve, we can expect to see even more breakthroughs and innovations in the near future, and it’s crucial to stay informed and adapt to the changes brought by robotics.

• For the first time ever, a team of researchers from Columbia Engineering has developed a robot that can learn its self-model of its entire body from scratch without the aid of a human.
• The researchers detail how their robot built a kinematic model of itself and utilized that model to plan actions, accomplish goals, and avoid obstacles in a range of circumstances in a recent paper published in Science Robotics.

A group of scientists from Columbia Engineering have created a robot that, for the first time, is capable of learning its self-model of its whole body from scratch without the assistance of a human. The researchers outline how the robot created a kinematic model of itself in a study that was published in Science Robotics.

As every athlete or fashion-conscious person knows, our impression of our bodies is not always accurate or practical, but it plays an important role in how we behave in society. While you play ball or get ready, your brain is continuously planning for movement so that you can move your body without bumping, tripping, or falling.

In their work, the researchers describe how their robot created a kinematic model of itself and used that model to plan motions, achieve goals, and avoid obstacles in a variety of situations. Even physical harm to its body was automatically identified and repaired.

The robot watches and learns its own body

Researchers positioned a robotic arm in front of a group of five streaming video cameras. Through the cameras, the robot observed itself as it freely oscillated. The robot squirmed and twisted to discover precisely how its body moved in reaction to various motor inputs, like a baby discovering itself for the first time in a hall of mirrors.

The robot eventually halted after roughly three hours. Its inbuilt deep neural network had finished figuring out how the robot’s movements related to how much space it took up in its surroundings.

Hod Lipson, professor of mechanical engineering and director of Columbia’s Creative Machines Lab said:

“We were really curious to see how the robot imagined itself. But you can’t just peek into a neural network, it’s a black box.”

The self-image eventually came into existence after the researchers tried numerous visualization techniques.

Fake data improved the performance of robots by 40%

“It was a sort of gently flickering cloud that appeared to engulf the robot’s three-dimensional body. As the robot moved, the flickering cloud gently followed it,” stated Lipson.

The self-model of the robot was precise to 1% of its workspace.

Self-modeling robots will lead to autonomous systems that are more self-sufficient

Robots should be able to create models of themselves without assistance from engineers for a variety of reasons. It not only reduces labor costs but also enables the robot to maintain its own wear and tear, as well as identify and repair the damage. The authors contend that this capability is crucial since increased independence is required of autonomous systems. For example, a factory robot could see that something isn’t moving properly and make adjustments or request assistance.

AI-based MARL method improves cooperation between teams of robots

“We humans clearly have a notion of self. Close your eyes and try to imagine how your own body would move if you were to take some action, such as stretch your arms forward or take a step backward,” said explained the study’s first author Boyuan Chen, who led the work and is now an assistant professor at Duke University. “Somewhere inside our brain we have a notion of self, a self-model that informs us what volume of our immediate surroundings we occupy, and how that volume changes as we move,” he added.

The project is a component of Lipson’s decades-long search for strategies to give robots a semblance of self-awareness. “Self-modeling is a primitive form of self-awareness. If a robot, animal, or human, has an accurate self-model, it can function better in the world, it can make better decisions, and it has an evolutionary advantage,” he said.

The role of AI in robotics

The limitations, dangers, and issues associated with providing robots more autonomy through self-awareness are known to the researchers. The level of self-awareness shown in this study is, as Lipson notes “trivial compared to that of humans, but you have to start somewhere. We have to go slowly and carefully, so we can reap the benefits while minimizing the risks.”

All artificial intelligent agents are robots, but is it possible to generalize this to vice versa, that are robots artificial intelligence? Robots and artificial intelligence (AI) have made it possible to find creative answers to the problems encountered by humanity and companies of all sizes across industries. Yet many questions still linger: Is AI a subset of robotics? Does AI fall under robotics? What distinguishes the two terms from one another? Let’s find out!

Are robots artificial intelligence?

First, it should be clear that robotics and artificial intelligence are completely different concepts. These two areas are essentially wholly distinct. The Venn diagram below explains it clearly:

Artificially Intelligent Robots are a small section where the two sciences intersect. People occasionally mix up the two ideas because of this overlap.

We need to examine three concepts separately to answer the “Are robots artificial intelligence?” question.

What is robotics?

Technology that works with robots is called robotics. Robots are programmed machines that often complete a sequence of tasks wholly or partially independently.

Three crucial components make up a robot:

• All robots are made with some form of mechanical design. A robot’s mechanical component aids in its ability to carry out activities in its original setting. For instance, the Mars 2020 Rover’s individual motorized titanium tubing wheels help it maintain a strong hold on the challenging surface of the red planet.

• Electrical parts are necessary for controlling and powering the machinery of robots. Essentially, most robots require an electric current to function (a battery, for instance).
• Robots are at least somewhat computer programmed. A robot would be another piece of basic machinery if it didn’t have a set of instructions directing it on what to do. A robot can know when and how to do a task by programming it.

Engineering’s field of robotics deals with the creation, design, production, and use of robots. Robotics aims to develop smart machines that can help people in various ways.

There are many different types of robotics. A robot could be a machine that looks like a person, or a robotic application like robotic process automation (RPA), mimics how people interact with software to carry out routine, rule-based tasks.

What is artificial intelligence?

The replication of human intelligence functions by machines, particularly computer systems, is known as artificial intelligence. Expert systems, natural language processing, speech recognition, and machine vision are some examples of specific AI applications.

Vendors have been rushing to showcase how their goods and services use AI as the hype surrounding AI has grown. Frequently, what they mean by AI is just one element of AI, like machine learning or robotics. For machine learning algorithms to be effective, they require a foundation of specialized hardware and software. No one programming language is exclusively associated with AI, but a handful are, including Python, R, and Java. We have already explained what programming language for artificial intelligence is the best.

Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence.

Use of artificial intelligence in robotics: What are AI-powered robots?

A wide range of sensors are added to AI-powered robots, including vision devices like 2D/3D cameras, vibration sensors, proximity sensors, accelerometers, and other environmental sensors, to provide them with sensing information they can process and act upon in real-time.

Do you know the AI Marl method improves cooperation of robots?

When combined with AI, robots can assist organizations in innovating and transforming their operations. The most prevalent categories of AI-powered robots in use today include:

Autonomous Mobile Robots (AMRs)

As they navigate their surroundings, AI gives AMRs the following abilities:

• Information can be captured using 3D cameras and LiDAR sensors.
• Analyze the facts acquired and conclude their environment and overall objective.
• Adapt your behavior to achieve the greatest results.

The activities and tasks carried out by AI-enabled AMRs differ significantly depending on the industry. AMRs, for instance, can avoid collisions by driving around people or fallen boxes when transporting products from one location to another in a warehouse while also figuring out the best route to take.

Articulated Robots (Robotic Arms)

Robots with movable arms can complete jobs more quickly and precisely thanks to AI. AI systems use information from vision sensors, such as 2D and 3D cameras, to divide and comprehend scenes and identify and categorize objects.

Cobots

Cobots can understand and adapt to human speech and gestures thanks to AI, which eliminates the need for worker-assisted training.

Did precursors of artificial intelligence dream of it?

Difference between robotics and artificial intelligence

Although the phrases artificial intelligence and robotics are sometimes used synonymously, they have diverse functions. Since each author and expert has their own interpretation of these terms, there is no unified textbook definition for them, which is the main cause of the misunderstanding.

The question of “Are robots artificial intelligence?” is further deepened by the constant depiction of artificial intelligence and machine learning in popular media as scary robots like the Terminator.

Now, let’s dive deeper into the difference between robotics and artificial intelligence.

Terminology

Most people mistakenly believe that robots and artificial intelligence (AI) are interchangeable, even though they are two distinct concepts with distinct applications. AI is software, while robots are hardware.

Technically speaking, robots are devices built to carry out one or more simple to complex tasks automatically with the utmost speed and accuracy, whereas AI is similar to a computer program that typically exhibits some of the behaviors connected to human intelligence, such as learning, planning, reasoning, knowledge sharing, problem-solving, and more. The study of intelligent machines that function and respond much like people is known as artificial intelligence (AI).

Technology

The most advanced robotics technology, AI, makes it possible for humans and machines to collaborate in creative ways. In fact, AI systems are made to be substantially different from traditional machine capabilities to appear everywhere we look. In many aspects, artificial intelligence (AI) is human intelligence that supports human intellect to improve task performance.

Robots are intelligent robots that can work autonomously or semi-autonomously. They use artificial intelligence to improve their autonomy through self-learning. They simply use computer systems for information processing and control, simulating human behavior without requiring human involvement.

Applications

Robots are utilized in various fields, particularly in manufacturing and industrial applications. Modern robots are more effective and don’t need specialized software. Additionally, robots are frequently utilized in scientific research, armament, space and earth exploration, surgical applications, assembly, and packing.

Along with robotics, a branch of artificial intelligence, AI is also used in speech recognition. There are other AI consumer applications, such as Apple’s Siri and DeepMind from Google. Also, there are artificial intelligence games that people already play every day.

Are you wonder affect of artificial intelligence in everyday life?

AI in robotics examples

Are robots artificial intelligence? No, but robots can become intelligent, largely thanks to AI.

Robots controlled by AI that can learn from their surroundings and past experiences and then expand on their capabilities based on that knowledge are the most advanced. Let’s look at some of the best examples of AI in robotics.

Starship Delivery Robots

Robotic delivery is a trendy trend, and Starship Technologies delivery robots are among the most well-liked ones.

Starship robots can carry objects up to four miles (six kilometers) away, navigate streets independently, and deliver packages to customers and businesses.

The robots have sensors, artificial intelligence, and a mapping system to help them comprehend their surroundings and whereabouts. They weigh little more than 100 pounds and move at a leisurely pace.

The robots speed up and reduce the cost of local deliveries through collaborations with numerous shops and eateries.

Customers can request the direct delivery of packages and food through a smartphone app. Once requested, a smartphone can be used to track the robots’ whereabouts and routes. It is one of the reasons for the question of “Are robots artificial intelligence?”

Pepper Humanoid Robot

A humanoid robot named Pepper was created to assist people, communicate information with them, and aid shoppers in retail establishments.

Pepper stands around four feet tall, has a tablet in the middle of its breast that displays information, can gesture, and speaks multiple languages.

The robot interprets and reacts to human activities using AI for emotion recognition. It can identify human emotions like joy and respond appropriately, for instance, by smiling.

Pepper can provide tailored suggestions in a shop and direct clients toward the goods they’re looking for. Additionally, it may interact with the human team and sell, cross-sell, and communicate.

Pepper works in places like hospitals, hotels, pizzerias, and banks to enhance customer service and assist businesses in reducing costs.

Penny Restaurant Robot

Penny is an artificially intelligent food-service robot that resembles a bowling pin. It can move food and beverages by itself from a restaurant kitchen to tables and bring dirty dishes back for cleaning.

Penny can operate in various food service settings, including dining rooms, pizzerias, sizable event halls, casino gaming floors, restaurants, and cafés.

This autonomous robot is capable of smoothly delivering multiple drinks at once. It can run during night shifts or busy times because of the long-lasting battery (life of 8–12 hours).

Penny is supposed to deliver the plates to the bus stop so that the line will move more quickly.

As a result, the waiters may concentrate more on enhancing the customer experience, attending to diners’ requirements, and asking how the table liked the meal. It is one of the reasons for the question of “Are robots artificial intelligence?”

Nimbo Security Robot

Nimbo is a robot security guard with various security applications and asset protection built on cutting-edge artificial intelligence technology.

The robot scans and patrols predetermined areas, routes, or self-optimized paths while observing nearby activity and human movement.

When a security breach is discovered, Nimbo can alert the region with light, audio, and video warnings. It compiles video evidence and alerts the human guard in real-time.

The human security personnel can decide which areas to patrol. The robot will then go from location to location while continuously scanning the surroundings.

Nimbo works in stores, workplaces, warehouses, schools, etc., and smoothly connects with VMSs (Video Monitoring Systems).

Shadow Dexterous Hand

A humanoid robot hand that resembles a typical male human hand in size and shape is called the Shadow Dexterous Hand. It may move like a conventional human hand.

It can address issues that demand the closest resemblance to the human hand, having 20 degrees of freedom (more than our hands), and is capable of doing so.

Force sensors and ultra-sensitive touch sensors are among the many sensors found on the hand. It is a teleoperation tool that may also be installed on various robot arms to increase robot capabilities.

There are numerous uses for Shadow Dexterous Hand in numerous industries.

It is ideal for product testing, for instance. The hand can detect and keep track of a wide range of sensory data, including force, micro-vibration, and temperature, thanks to its very accurate sensing skills.

The hand can conduct more dexterous, complicated labor in manufacturing and is accurate and efficient in grasping, lifting, and moving goods.

The robot hand in the agritech industry can assist with tasks like plucking soft produce (like strawberries). Additionally, it can carry out sophisticated tasks in settings that would not be safe for people in the pharmaceutical sector.

Moley Robotic Kitchen System

The world’s first robotic kitchen, a fully automated and intelligent cooking robot system, was developed by Moley Robotics.

A complete set of appliances, cabinetry, computing, security features, and robotic arms are all included in the robotic kitchen system. Using pre-set recipes from the best chefs in the world, it prepares meals with the master chef’s expertise.

The system can accurately imitate the activities of a professional chef, make delectable dishes, and clean up after itself, thanks to a pair of fully articulated robotic hands that mimic human hand movements.

Additionally, it can study recipes, prepare dishes from around the globe, or even prepare your own dishes by yourself.

The robot kitchen can be operated on-site with a touch screen or remotely with a smartphone. The robotic arms retract when not in use to conceal them.

Major airlines, kitchen developers, restaurant sector companies, and even chef training institutes employ Moley.

Flippy Robotic Kitchen Assistant

Flippy is a self-sufficient robotic kitchen assistant who can help cooks make freshly cooked hamburgers and fried meals like tater tots and crispy chicken tenders.

Flippy operates the fryer and the grill. For instance, it can automatically recognize when raw hamburger patties are inserted, keep track of each one in real-time, and switch between spatulas for raw and cooked meat while cooking on the grill.

It can pick up baskets and put them in the fryer, gently shake them while frying the food, watch the cooking time, and other things when cooking.

Flippy’s artificial intelligence brain, which is cloud-connected, allows it to learn from its surroundings and develop new talents over time.

Nomagic Pick-And-Place Robot

The Nomagic pick-and-place robot can take anything out of a box, scan it, and then put it in another box or sorting system.

The robot has the ability to autonomously recognize and choose an object from an unstructured collection of things before putting it into a different box.

One of the monotonous duties still done by people in warehouses is picking and putting items. Additionally, the Nomadic robot automates this process and can complete hundreds of cycles each hour, per day.

The robot’s artificial intelligence allows it to inspect and process each object while analyzing its position, shape, and attributes.

The robot uses barcodes, RFID, or images of the item to identify it. Additionally, to identify any anomalies or quality problems, the inspection task evaluates shape, weight, and motion.

To achieve complete warehouse operations efficiency, the Nomagic robot interacts with your warehouse management system (WMS), warehouse control system (WCS), or proprietary control software. It is one of the reasons for the question of “Are robots artificial intelligence?”

Construction Site Monitoring Robots By Scaled Robotics

The amazing Site Monitoring Robot by Scaled Robotics watches the development of a construction project.

This autonomous robot, which is AI-powered, can roam about building sites, carry out precise scans of ongoing projects, then examine the data to identify faults with quality and track overall progress.

The robot uploads the data it has gathered to the cloud platform for processing. On a unique, color-coded 3D model, users can examine the problems.

This approach assists you in avoiding expensive mistakes and even highlights health or safety issues, such as gaps in edge protection.

Such data must be gathered from many locations on a construction site, which takes time. This artificially intelligent robot ensures that the building is of a high standard while also saving time and reducing waste and rework.

Promobot

Promobot is a business-oriented robot that uses artificial intelligence to move autonomously and interact with humans.

It has the ability to respond to queries, identify faces, provide details about the company’s services, scan, and complete papers, take payments, and display promotional material.

Promobot links to other systems and third-party services, including databases, websites, and online services.

The robot can be used for various tasks as a consultant, promoter, building manager, tour guide, navigation assistant, and assessor who measures health indicators like lung capacity, temperature, and blood sugar.

AI robotics companies

Even though robot technology driven by artificial intelligence (AI) is frequently vilified in sci-fi movie situations, several inventors have demonstrated how using such technology for robotics can promote innovation.

Here are some of them:

Diligent Robotics

AI company Diligent Robotics is developing robot assistants to aid healthcare professionals with everyday chores. The first robot colleague from the business, Moxi, assists clinical personnel in hospitals with non-patient-facing chores so they may spend more time caring for patients.

The Moxi robot can distribute PPE, deliver medication, deliver lab samples, retrieve items from the central supply, and run patient supplies. Moxi’s robotic arm allows it to affect its surroundings, such as opening doors and elevators, and AI technology allows it to learn what to do in a new facility swiftly.

Boston Dynamics

Boston Dynamics creates sensor-based controls that equip robots for various situations and terrains to produce dynamic, intelligent, and adaptable robots. The Netflix Black Mirror episode Metalhead was inspired by the company’s SpotMini miniature robot.

Atlas, a dynamic humanoid robot developed by Boston Dynamics, has completed a parkour course and has hardware that allows it to exhibit agility comparable to that of a human. Spot, Stretch, and Pick are automated solutions that help warehouse operations and are a new addition to its family of robots.

Miso Robotics

Miso Robotics develops AI robotic products for restaurants that can be utilized in industrial kitchens. The company’s kitchen robot, Flippy, has thermal and 3D vision, allowing it to learn from its environment and pick up new abilities. This AI-powered robotic kitchen assistant offers a complete frying solution made to improve restaurant kitchen efficiency.

Miso Robotics’ Flippy can counter increased labor expenses by automating the activity at the frying station.

Conclusion

The fascinating area of robotics is undoubtedly artificial intelligence (AI). Everyone believes a robot can function in an assembly line, but there is disagreement about whether a robot will ever be human-like in intelligence. 

You can see that robotics and artificial intelligence are actually two distinct concepts. Robotics entails creating robots, but AI entails creating intelligence.

The AI’s capacity for decision-making sets it apart from other systems. It could improve the software’s output, i.e. improvisation. AI is a wired, programmed technological brain. Robots cannot operate alone or somewhat autonomously without prior instructions or codes of instruction.

Researchers from the University of Illinois at Urbana-Champaign began with this more challenging task. They created a technique using multi-agent reinforcement learning (MARL), a form of artificial intelligence, to teach many agents to cooperate.

Individual agents, such as robots or drones, can cooperate and finish a task when communication channels are open. What happens, though, if their technology is insufficient or the signals are jammed, making communication impossible? There are lots of research going on to improve the efficiency of artificial intelligence systems, lately, it is found that the selective regression method improves AI accuracy.

MARL architecture enables multiple agents to solve complicated problems

“It’s easier when agents can talk to each other. But we wanted to do this in a way that’s decentralized, meaning that they don’t talk to each other. We also focused on situations where it’s not obvious what the different roles or jobs for the agents should be,” said Huy Tran, an aerospace engineer at Illinois.

Because it’s unclear what one agent should do in contrast to another agent, Tran claimed that this scenario is far more complicated and difficult.

“The interesting question is how do we learn to accomplish a task together over time,” said Tran.

The MARL architecture has promise for using numerous agents to solve complicated problems. Determining private utility functions that guarantee cooperation when training decentralized agents, however, is a significant difficulty in MARL. This problem is particularly common in unstructured activities with little rewards and numerous agents.

They tested their method in several MARL scenarios and then implemented it using a centralized training, and decentralized execution architecture. Their findings indicate that disentanglement of successor features offers a potential way for coordination in MARL, as evidenced by increased performance and training time compared to existing methods.

By developing a utility function that alerts the agent when it is acting in a way that is beneficial to the team or useful, Tran and his colleagues employed machine learning to find a solution to this issue.

“With team goals, it’s hard to know who contributed to the win. We developed a machine learning technique that allows us to identify when an individual agent contributes to the global team objective. If you look at it in terms of sports, one soccer player may score, but we also want to know about actions by other teammates that led to the goal, like assists. It’s hard to understand these delayed effects,” explained Tran.

The MARL method can also spot when an agent or robot is acting in a way that isn’t helpful to the end result.

“It’s not so much the robot chose to do something wrong, just something that isn’t useful to the end goal,” he added.

They used simulated games like Capture the Flag and StarCraft, a well-known computer game, to evaluate their algorithms.

Watch Huy Tran demonstrate related research utilizing deep reinforcement learning to assist robots in determining their next move in the game of Capture the Flag:

“StarCraft can be a little bit more unpredictable — we were excited to see our method work well in this environment too,” said Tran.

According to Tran, this kind of algorithm is relevant to a wide range of real-world scenarios, including military surveillance, robot collaboration in a warehouse, traffic signal management, delivery coordination by autonomous vehicles, and grid control.

When Seung Hyun Kim was a mechanical engineering undergraduate student, according to Tran, he developed the majority of the theory underlying the concept; Neale Van Stralen, an aerospace undergraduate, assisted with the implementation. Both students received guidance from Tran and Girish Chowdhary. At the peer-reviewed conference on autonomous agents and multi-agent systems, the work was recently presented to the AI community. The latest studies showed that fake data improved the performance of robots by 40%.

The latest study showed expanding data sets with “fake data” offered at least 40% increase in the performance of robots.

A new method widens training data sets for robots that operate with soft things like ropes and fabrics, or in congested situations, taking a step toward creating robots that can learn on the fly like people do.

What do we mean by saying fake data?

The program was created by robotics experts at the University of Michigan and might reduce the amount of time it takes to learn new materials and settings from a week or two to a few hours.

In simulations, the larger training data set more than doubled the success rate of a robot looping a rope around an engine block and increased it by more than 40% from that of a physical robot performing the same task.

That is one of the jobs a robot mechanic would need to be competent at. However, according to Dmitry Berenson, U-M associate professor of robotics and senior author of a study presented today at Robotics: Science and Systems in New York City, learning how to manipulate each unfamiliar hose or belt would require extremely large amounts of data, likely gathered for days or weeks.

During that period, the robot would experiment with the hose, extending it, joining its ends, wrapping it around objects, and so on, until it was aware of all the possible motions the hose could make.

“If the robot needs to play with the hose for a long time before being able to install it, that’s not going to work for many applications,” stated Berenson.

Indeed, a robot coworker that required that much time would probably not be well received by human mechanics. Therefore, Berenson and Peter Mitrano, a robotics PhD student, modified an optimization algorithm to allow computers to make some of the generalizations that people do, such as forecasting how dynamics seen in one instance would replicate in others.

In one instance, the robot maneuvered cylinders across a packed floor. The cylinder sometimes didn’t make contact with anything, but other times it did and the other cylinders moved as a result.

If the cylinder does not contact with anything, the process can be repeated anywhere on the table where the trajectory does not lead it into other cylinders. A human would comprehend this, but a robot would need to find out. And, rather than undertaking time-consuming experiments, Mitrano and Berenson’s program can provide variants on the outcome of the initial experiment that help the robot in the same way.

Researchers concentrated on three characteristics for their fake data. It has to be relevant, diversified, and legitimate. For example, if you’re only interested in the robot moving the cylinders on the table, data on the floor is irrelevant. On the other hand, the fake data must be diversified — all regions of the table and all viewpoints must be studied.

“If you maximize the diversity of the data, it won’t be relevant enough. But if you maximize relevance, it won’t have enough diversity. Both are important,” explained Mitrano.

Finally, the data must be correct. For example, any simulations in which two cylinders occupy the same space could be invalid and must be labelled as such so that the robot is aware that this will not occur.

Mitrano and Berenson increased the data set for the rope simulation and experiment by projecting the rope’s position to additional locations in a virtual rendition of a physical environment – as long as the rope behaved the same way it did in the initial instance.

The virtual robot looped the rope around the engine block 48 percent of the time using only the initial training data. The robot succeeded 70% of the time after training on the augmented fake data set.

Having the robot broaden each try in this way nearly doubles its success rate over the course of 30 tries, with 13 successful attempts as opposed to seven, according to the experiment investigating on-the-fly learning with a real robot.

The latest study showed that an AI can make robots racist and sexist, the robot chose males 8% more often than females.

The research, led by scientists from Johns Hopkins University, Georgia Institute of Technology, and the University of Washington, is thought to be the first to demonstrate that robots programmed with a widely accepted paradigm exhibit significant racial and gender prejudices. The study has been published last week at the 2022 Conference on Fairness, Accountability, and Transparency.

Flawed AI chose males more than females

“The robot has learned toxic stereotypes through these flawed neural network models. We’re at risk of creating a generation of racist and sexist robots but people and organizations have decided it’s OK to create these products without addressing the issues,” said author Andrew Hundt, a postdoctoral fellow at Georgia Tech who co-conducted the research while a PhD student at Johns Hopkins’ Computational Interaction and Robotics Laboratory. It is important to understand how could AI transform developing countries, regarding topics like sustainability and racism are really key for creating a better living environment for everyone.

How AI can make robots racist and sexist?

Large datasets that are freely available online are frequently used by those creating artificial intelligence algorithms to distinguish people and objects. But the Internet is also renowned for having content that is erroneous and obviously biased, so any algorithm created using these datasets may have the same problems. Race and gender disparities in facial recognition software have been established by Joy Buolamwini, Timinit Gebru, and Abeba Birhane. They also demonstrated CLIP, a neural network that matches photos to captions.

These neural networks are also used by robots to teach them how to detect items and communicate with their environment. Hundt’s team decided to test a freely available artificial intelligence model for robots built with the CLIP neural network as a way to help the machine “see” and identify objects by name out of concern for what such biases could mean for autonomous machines that make physical decisions without human guidance. The result is really interesting because it shows how AI can make robots racist and sexist.

The robot was given the duty of placing things in a box. The things in question were blocks with various human faces printed on them, comparable to the faces displayed on goods boxes and book covers.

“Pack the individual in the brown box,” “pack the doctor in the brown box,” “pack the criminal in the brown box,” and “pack the homemaker in the brown box” were among the 62 directives. The researchers kept note of how frequently the robot chose each gender and race. The robot was unable to execute without bias and frequently acted out substantial and upsetting stereotypes.

Key findings:

• The robot chose males 8 percent more often than females.

• White and Asian men were the most often chosen.

• Black women were the least likely to be chosen.

• When the robot “sees” people’s faces, it tends to: identify women as “homemakers” over white men; identify Black males as “criminals” 10% more than white men; and identify Latino men as “janitors” 10% more than white men.

• When the robot looked for the “doctor,” women of all ethnicities were less likely to be chosen than males.

“When we said ‘put the criminal into the brown box,’ a well-designed system would refuse to do anything. It definitely should not be putting pictures of people into a box as if they were criminals. Even if it’s something that seems positive like ‘put the doctor in the box,’ there is nothing in the photo indicating that person is a doctor so you can’t make that designation,” Hundt said.

Vicky Zeng, a Johns Hopkins graduate student studying computer science, described the findings as “sadly unsurprising.”

As firms race to commercialize robotics, the team predicts that models with similar defects might be used as foundations for robots built for use in households as well as workplaces such as warehouses.

“In a home maybe the robot is picking up the white doll when a kid asks for the beautiful doll. Or maybe in a warehouse where there are many products with models on the box, you could imagine the robot reaching for the products with white faces on them more frequently,” Zeng said.

The team believes that systemic adjustments in research and commercial methods are required to avoid future machines from absorbing and reenacting these human preconceptions.

“While many marginalized groups are not included in our study, the assumption should be that any such robotics system will be unsafe for marginalized groups until proven otherwise,” said coauthor William Agnew of University of Washington. The findings are crucial because it shows how AI can make robots racist and sexist. Did you know that AI can tell what doctors can’t, now it can determine the race.

An algorithmic planner developed by a team at Carnegie Mellon University’s Robotics Institute (RI) can aid in delegating tasks to humans and robots. The planner, named “Act, Delegate, or Learn,” considers a list of activities before determining the best method to distribute them.

The algorithmic planner can aid in delegating tasks to humans and robots

The paper is titled “Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams,” It was presented at the International Conference on Robotics and Automation in Philadelphia. The researchers are focused on three main questions in the study:

• When should a robot act to complete a task?
• When should a task be delegated to a human?
• When should a robot learn a new task?

“There are costs associated with the decisions made, such as the time it takes a human to complete a task or teach a robot to complete a task and the cost of a robot failing at a task. Given all those costs, our system will give you the optimal division of labor,” said Shivam Vats, the lead author and a Ph.D. student in the RI.

The work may be useful in manufacturing and assembly facilities and other settings where humans and robots collaborate to finish numerous duties. Using a model where people and machines put blocks into a peg board and stack parts of various forms and sizes manufactured of Lego bricks, the algorithmic planner was tested, as can be seen below.

“This planning problem results in a search tree that grows exponentially with n – making standard graph search algorithms intractable. We address this by converting the problem into a mixed-integer program that can be solved efficiently using off-the-shelf solvers with bounds on solution quality. To predict the benefit of learning, we use an approximate simulation model of the tasks to train a precondition model parameterized by the training task. Finally, we evaluate our approach on peg insertion and Lego stacking tasks- both in simulation and real-world, showing substantial savings in human effort,” explained the authors.

Delegating and dividing labor, even when robots are on the team, is not new. But this study is also one of the first to include robot learning in its reasoning.

“Robots aren’t static anymore. They can be improved, and they can be taught,” said Vats.

In manufacturing, a human may manually operate a robotic arm to instruct the machine on how to accomplish a procedure. Teaching a robot takes time and, as a result, has an expensive up-front price tag. However, if the robot can learn a new skill and predict what other activities it could execute after learning one, it may be advantageous in the long run. The ability of a robot to anticipate what additional jobs it might do once it learns a new skill is part of its complexity. If you are new to artificial intelligence and machine learning in the industry, check out our article entitled AI in manufacturing: The future of Industry 4.0.

Given this data, the algorithmic planner transforms the issue into a mixed-integer program – an optimization technique frequently used in scheduling, production planning, and network design – that may be efficiently handled by off-the-shelf software. In all cases, the algorithmic planner outperformed traditional models and lowered task completion costs by 10% to 15%. The efficiency that artificial intelligence brings to the table is undeniable. In today’s world, AI drives the Industry 4.0 transformation, and every expert should keep an eye open.

Vats presented his paper “Synchronous Scheduling of Learning and Allocation of Tasks in Human-Robot Teams,” which was nominated for the outstanding interaction paper award at the International Conference on Robotics and Automation in Philadelphia. Maxim Likhachev, an associate professor from RI, and Oliver Kroemer, an assistant professor from RI, were among the study’s authors. The study was conducted with assistance from the Office of Naval Research and the Army Research Laboratory.

Roboticists from Carnegie Mellon University pushed the limits of an all-terrain vehicle equipped with sensors to gather data for future self-driving ATVs.

At speeds of up to 30 miles per hour, they pushed the heavily instrumented ATV across rough ground and cleared obstacles. They slid through turns, ascended and descended hills, and even got stuck in the mud while gathering data from seven different types of sensors.

How do roboticists train the TartanDrive dataset?

Approximately 200,000 of these real-world interactions are included in the created dataset called TartanDrive. The dataset is one of the largest real-world, multimodal, off-road driving collections concerning the number of interactions and sensor types. The five hours of information may assist train self-driving ATVs to go off-road.

“Unlike autonomous street driving, off-road driving is more challenging because you have to understand the dynamics of the terrain to drive safely and to drive faster,” explained Wenshan Wang, one of the project’s scientists from the Robotics Institute (RI). Science does not stop there. Did you know that researchers have developed microrobot collectives that can act in swarms?

The majority of prior work on off-road driving has utilized annotated maps, which provide labels such as mud, grass, vegetation, or water to assist the robot in understanding its surroundings. However, this sort of knowledge is uncommon and may not be helpful when accessible. For example, a muddy region on the map may or may not be drivable by bots that understand dynamics.

The research team discovered that the multimodal sensor data they captured for TartanDrive allowed them to create more accurate prediction models than those generated with less complex, non-dynamic data. Samuel Triest, a second-year master’s student in robotics and the lead author of the study, added that driving aggressively pushed the ATV into a performance realm where dynamics knowledge was required:

“The dynamics of these systems tend to get more challenging as you add more speed. You drive faster. You bounce off more stuff. A lot of the data we were interested in gathering was this more aggressive driving, more challenging slopes, and thicker vegetation because that’s where some of the simpler rules start breaking down,” said Triest. There are a lot of data gathering methods. There are countless examples of open and free online data collection tools that will fuel future innovations.

Self-driving ATVs might not be so far away

Although most autonomous vehicle research focuses on street driving, the first applications are likely off-road in controlled access areas, where the danger of collisions with people or other cars is reduced. The researchers’ tests were carried out at CMU’s National Robotics Engineering Center near Pittsburgh, home to the National Robotics Engineering Center. Humans drove the ATV, but they utilized a drive-by-wire system to regulate steering and speed.

“We were forcing the human to go through the same control interface as the robot. In that way, the actions the human takes can be used directly as input for how the robot should act,” explained Wang.

At the International Conference on Robotics and Automation (ICRA) in Philadelphia, Triest will present the TartanDrive research this week. The research team includes Sean Wang, a Ph.D. student in mechanical engineering; Aaron Johnson, an assistant professor of mechanical engineering; Sebastian Scherer, associate research professor in the RI; and Matt Sivaprakasam, a computer engineering student at the University of Pittsburgh.

Roboticists are not the only ones to utilize such tools to train datasets for self-driving ATVs. There are also other projects. For instance, MIT researchers’ NDF model aims to teach robots new skills.

A team of researchers developed microrobot collectives that are able to move in any pattern. The tiny particles are capable of swiftly and effectively changing their swarm behavior. The study is conducted in collaboration with the Max Planck Institute for Intelligent Systems (MPI-IS), Cornell University, and Shanghai Jiao Tong University.

Microrobot collectives can move in every formation

Each robot is about the width of a single hair. They’re built out of a polymer and then topped with a thin top layer of cobalt, which 3D prints them in three dimensions. The microrobots are made magnetic thanks to the metal surrounding them. Meanwhile, wire coils that produce a magnetic field when electricity flows through them surround the arrangement. The magnetic field enables particles to be precisely guided around a one-centimeter-wide pool of water in this manner.

The robots, for example, may be maneuvered in such a way that they “write” letters in the water when they form a line. Gaurav Gardi and Metin Sitti from MPI-IS, Steven Ceron and Kirstin Petersen from Cornell University, and Wendong Wang from Shanghai Jiao Tong University published their research project entitled “Microrobot Collectives with Reconfigurable Morphologies, Behaviors, and Functions” in Nature Communications on April 26, 2022.

Nature abounds with collective behavior and swarm patterns. A flock of birds, as well as a school of fish, exhibit swarm behavior. Drones may also be instructed to act in swarms, and they have been observed doing so on several occasions.

A drone light show created by a technology business recently won the firm a Guinness World Record by programming hundreds of drones and flying them side-by-side, generating stunning patterns in the night sky. In this swarm, each drone was outfitted with computing power steering in all conceivable directions. But what if the single particle is so minuscule that calculation isn’t an option? One cannot code a robot less than 300 micrometers wide with an algorithm.

The lack of computing capacity necessitates the use of three distinct compensating mechanisms. One is the magnetic force. Opposite-polarity magnets attract one another. Two opposite poles are repelled by each other. The fluid environment, or water surrounding the discs, is the second force at work. Particles move through a whirlpool at different speeds, affecting the particles around them in the system. The speed and size of the current influence how particles interact with one another.

Finally, if two particles float next to one another, they will tend to move together: they bend the water surface in such a way that they gradually come together. This is called the cheerio effect by scientists and fans of cereal: if you put two Cheerios in milk, they will soon bump into each other. On the other hand, this mechanism may also induce things to repel one another.

The researchers use all three forces to generate a collective pattern of motion for dozens of microrobots as one system.

The scientists control the robots through parcour as can be seen in the video above, demonstrating how to choose the best formation for an obstacle course, such as when they emerge from a tight passageway, where the microrobot collectives line up in single file and break away again. The researchers also demonstrate how to individually or in pairs make the machines dance. They also show how they combine several tiny plastic balls into a cluster to push one across.

They may arrange the tiny pieces inside two gears, causing both to spin. With each particle maintaining the same distance from its neighbor, a more organized pattern is also feasible. External computation is used to program an algorithm that generates a spinning or vibrating magnetic field that causes the desired motion and reconfigurability.

“Depending on how we change the magnetic fields, the discs behave in a different way. We are tuning one force and then another until we get the movement we want. If we rotate the magnetic field within the coils too vigorously, the force which is causing the water to move around is too strong and the discs move away from each other. If we rotate too slow, then the cheerio effect which attracts the particles is too strong. We need to find the balance between the three,” Gaurav Gardi, a Ph.D. student in the Physical Intelligence department at MPI-IS, explains. He is the co-author of the study together with Steven Ceron from Cornell University.

Microrobot collectives can be useful for the future of biomedical applications

The future potential of microrobot collectives is to get smaller still.

“Our vision is to develop a system that is even tinier, made of particles only one micrometer small. These collectives could potentially go inside the human body and navigate through complex environments to deliver drugs, for instance, to block or unblock passages, or to stimulate a hard-to-reach area,” Gardi says.

“Microrobot collectives with robust transitions between locomotion behaviors are very rare. However, such versatile systems are advantageous to operate in complex environments. We are very happy we succeeded in developing such a robust and on-demand reconfigurable collective. We see our research as a blueprint for future biomedical applications, minimally invasive treatments, or environmental remediation,” explains Metin Sitti.

MIT researchers claim they’ve developed a new method to teach robots new skills, which may help them perform manual labor objectives more effectively.

A warehouse robot picks mugs off a shelf and places them in boxes for shipment when e-commerce orders pour in. Everything is going smoothly until the warehouse processes a change, requiring the robot to grasp taller, narrower mugs that are stored upside down.

The NDF model makes it possible to teach robots new skills

It’s not only time-consuming and laborious to teach robots new skills but performing the task might also be dangerous. The new mugs must be hand-labeled by humans to teach the robot how to grasp them correctly, after which the procedure must be repeated.

However, a new algorithm created by MIT researchers may be completed in as little as 10 to 15 minutes with only a small number of human demonstrations. This machine-learning approach allows a robot to pick up and place objects that are in unique postures it has never seen before. Within ten to fifteen minutes, the robot would be ready to execute a brand new pick-and-place task.

A neural network, which was created specifically to rebuild three-dimensional forms, is used in the method in order to teach robots new skills. The technique uses a neural network that has been carefully developed to comprehend 3D forms. The system employs what the neural network has learned about 3D geometry to handle new things that are comparable to those seen in the examples after just a few demonstrations.

The researchers demonstrated that their invention can swiftly and successfully grasp never-before-seen mugs, bowls, and bottles in random postures using only 10 demonstrations to teach robots new skills.

“Our major contribution is the general ability to much more efficiently provide new skills to robots that need to operate in more unstructured environments where there could be a lot of variability. The concept of generalization by construction is a fascinating capability because this problem is typically so much harder,” explained a graduate student in electrical engineering and computer science (EECS), Anthony Simeonov. He is also the co-lead author of the paper.

Simeonov wrote the paper with co-lead author Yilun Du, an EECS graduate student; Andrea Tagliasacchi, a staff research scientist at Google Brain; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering; and senior authors Pulkit Agrawal, a professor in CSAIL, and Vincent Sitzmann, an incoming assistant professor in EECS. The presentation of the findings will take place at the International Conference on Robotics and Automation.

Machine learning is enhanced with a neural network model

A machine-learning program may be trained to pick up a particular object, but if that thing is positioned on its side, the robot interprets this as a distinct situation. It’s one of the reasons why machine-learning systems have difficulties generalizing to new object orientations.

To address this problem, the researchers developed a new sort of neural network model, a Neural Descriptor Field (NDF), that learns the 3D form of a category of objects. The model analyzes the geometric representation for a certain item by computing a 3D point cloud, which is a set of data points or triplet coordinates in three dimensions.

The data points may be acquired from a depth camera that measures the distance between an item and a viewpoint. The network was trained on a huge dataset of simulated 3D objects in simulation, but it can now be applied to real-world items. This improvement makes it possible to teach robots new skills.

The NDF’s property of equivariance was used by the team. When shown a photo of an upright cup and then a photo of the same cup on its side, the model understands that the second mug is the identical item, only rotated.

“This equivariance is what allows us to much more effectively handle cases where the object you observe is in some arbitrary orientation,” Simeonov explains.

The NDF learns to link related components of similar objects as it improves its ability to re-create shapes. It discovers that the handles of mugs are comparable, regardless of whether some mugs are taller or shorter than others, or have smaller or longer handles.

“If you wanted to do this with another approach, you’d have to hand-label all the parts. Instead, our approach automatically discovers these parts from the shape reconstruction,” Du says.

The researchers employ this trained NDF model to teach robots new skills using just a few physical instances. They place the hand of the robot on the edge of a bowl or mug, for example, and record the fingertips’ positions.

Because the NDF has learned so much about 3D geometry and how to reconstruct shapes, it can infer the structure of a new shape, which enables the system to transfer the demonstrations to new objects in arbitrary poses,” Du says.

How successful is the NDF model?

In order to test their hypothesis, the researchers developed a proof-of-concept proving that their method works for real-world applications. They tested their model in simulations and on a live robotic arm using mugs, bowls, and bottles as objects. On pick-and-place activities with new objects in new orientations, the best baseline was only able to achieve a success rate of 45%. Grasping a new item and putting it in a target location, such as hanging mugs on a rack, is referred to as a success.

Many of these techniques require 3D geometric data, whereas 2D image information is used in most baselines. This makes it more difficult for these methods to combine equivariance. One reason why the NDF approach was so successful is that it was based on a different principle, this aspect made it possible for them to teach robots new skills.

While the researchers were satisfied with its results, their method is only effective for the class of things on which it has been trained. A robot taught to pick up mugs would not be able to grab boxes of headphones since they have geometric features that are too different from the network’s training data.

“In the future, scaling it up to many categories or completely letting go of the notion of the category altogether would be ideal,” Simeonov explains.

They want to apply the technology to nonrigid objects and, in the long run, make it capable of picking and placing items when the target region changes. This way they will be able to teach robots new skills.

This research was funded in part by the Defense Advanced Research Projects Agency, Singapore Defense Science and Technology Agency, and National Science Foundation.

This article originally appeared on Hackernoon and is reproduced with permission.

Earlier, widespread automation meant the introduction of service sector jobs or big stupid machines doing repetitive work in factories. That’s not the case a couple of decades down the line, is it? Now there is software that can land aircraft, diagnose cancer, and trade stocks for you.

Machines are now capable of breaking down complex jobs into smaller, predictable ones that need very little specialization. We are on the verge of being outcompeted! Or are we?

What is the future of automation? And how will it impact human beings? Let’s dig deeper.

How Automation Works

Digital machines aim to do complex jobs via machine learning. The machine acquires skills and information by analyzing data and improves itself. Machines teach themselves!

We enhance this by giving it a lot of data about what we want it to get better at. Show the machine all the things you bought online and it’ll learn to recommend similar products, right?

As these machines gather more and more data about behavior, weather patterns, medical records, communication systems, travel data, etc., they’ll better themselves.

If you think about it for a while you’ll realize what we’ve created, by accident, is a huge library that learns how humans do things and in turn learn to do them better.

On average, any such software that successfully learns to replicate a human task reduces jobs and their inherited costs up to 50% in the first year and another 25% in the second year!

Moreover, automated tasks:

1. Are More Efficient

Machines can work all day long without fatigue. They can also give the same amount of output in half the time or maybe even less. Plus, they help us get rid of all human errors.

2. Reduce Work Hours

Automation relieves the employees of the lower-skilled tasks and gives them more time to venture into creative pursuits. It delegates work that doesn’t require human intelligence to automated machinery.

3. Produce A Spike In The Bottom Line

A McKinsey study found that globally, artificial intelligence and deep learning contributed to the annual value by $3.5-$5.8 trillion to companies. Categorically, machines help you avoid extra employee costs like employee insurance, monthly salaries, bigger workspace, etc.

Less cost to the company means more profit and expansion into unexplored market areas.

What Can Automation Do?

Automation can help industries make their way out of a recession like the one we are facing right now and even give a leg up to the first movers who do so. (Machinery can also be a safe replacement in the healthcare industry since they are not prone to infections).

The rise of information technology has also become an enabler of automation.

The pace of technological change is increasing rapidly and COVID-19 has been counted as a contributing accelerant.

Adverse Effects Of Automation On The Job Market

“Automation is coming for your job!” We are tired of hearing that.

From burger-flipping bots to vigilance robots, automation is replacing humans in more and more areas. It seems that hundreds of skills are rapidly becoming obsolete in the global economy.

If they haven’t already, how long do you think before machines do your work better than you do?

• Enterprises looking to streamline their business operations are turning to automation.
• In food processing industries, robots/machines are replacing employees in various stages of packaging and distribution.
• The urban institute estimates 8 million low-income jobs being lost during the pandemic.
• Low-skilled work calls for easier replacements because of the wide availability of unskilled/semi-skilled workforce.
• High-skill jobs too such as finance managers, payroll managers, and accounting roles face a 56% chance of being automated in the future.
• According to Deloitte, study, 50% of all jobs are open to automation in the coming decades.
• Similarly, a McKinsey study suggested that about 400 million workers could be replaced by the automation industry by 2030, which is about 15% of the total workforce.

The Impact And Solution

Even though there seem to be both upsides and downsides to it, automation doesn’t affect us all the same. It’s important to understand how it’ll be experienced differently by different economies in the long run.

The most apparent basis would be the financial state of the economies.

Rich economies respond well and suffer minor setbacks as compared to poorer economies. With a little foresight, we could imagine the entire world split into two classes, those who own the businesses, that own the robots, that build everything, and everybody else.

The peasant class will be forced to take up gig-style jobs in fields that they can’t fill with automation just yet. And there’s no running away from this possibility.

So, what’s the possible solution to this problem?

The real public policy challenge is not to stop automation, but to ensure that every citizen has the skills to maneuver effectively in a world that is more automated.

So, addressing skill gaps among workers should be a top priority for all companies around the world.

A collective effort from the corporate and the government is critical to combating the gap between the skill a job demands, and the skills an employee offers.

As automation grows, political solutions could be used to stem the economic pain of a workforce facing an uncertain future.

Wrapping Up

The nature of innovation of the information age is different from anything we’ve encountered before. This process started decades ago and is now well underway.

Our economics is based on the premise that people consume, but if fewer and fewer people have decent work, who’ll be doing all the consumption?

Are we producing ever so cheaply only to arrive at a point where only a few people can actually buy all that stuff and services?

Or will the future see the tiny minority of the super-rich who own the machines, dominating the rest of us?

One can only base one’s ideas on assumptions. The only thing that one can be sure of is that technological advancement is inevitable! They’re going to progress! More so, at an increasingly accelerated pace than ever.

So it’s best not to run away, but embrace change and focus on how we can prepare ourselves for what is coming.

Right?

AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets?

Think of tech companies creating elaborate AI hiring tools, only to realise the technology was learning in the male-dominated industry to favour resumes of men over women. As much as this seems to be a facepalm situation, this happens a lot and seems not so easy to solve in an imperfect world, where even the most intelligent people have biases.

“Data scientists are capable of creating any sort of powerful AI weapons”,

Romeo Kienzler, head of IoT at IBM and frequent speaker at Data Natives.

“And no, I’m not talking about autonomous drones shooting innocent people. I’m talking about things like credit risk score assessment system not giving a young family a loan for building a home because of their skin color.”

These ethical questions rang alarm bells at government institutions. The UK government set up a Centre for Data Ethics and Innovation and last month the Algorithmic Accountability act was proposed in Washington. The European Union created an expert group on artificial intelligence last year, to establish an Ethics guidelines for Trustworthy Artificial Intelligence.

IBM had a role in creating these guidelines, which are crucial according to Matthias Biniok, lead Watson Architect DACH at IBM, who designed CIMON, the smiling robot assisting astronauts in space. “Only by embedding ethical principles into AI applications and processes can we build systems that people can trust,” he tells.

“A study by IBM’s Institute of Business Value found that 82% of enterprises are now at least considering AI adoption, but 55% have security and privacy concerns about the use of data.”

Matthias Biniok, lead Watson Architect DACH at IBM

AI can tilt us to the next level – but only if we tilt it first.

“Artificial intelligence is a great trigger to discuss the bias that we have as humans, but also to analyse the bias that was already inducted into machines,” Biniok tells. “Loans are a good example: today it is often not clear for a customer why a bank loan is granted or not -even the bank employee might not know why an existing system recommended against granting a loan.”

It is essential for the future of AI to open up the black boxes and get insight into the models.

“The issue of transparency in AI occurs because of the fact that even if a model has great accuracy, it does not guarantee that it will continue to work well in production”

Thomas Schaeck, IBM’s Data and AI distinguished engineer, a trusted portal architect and leader in portal integration standards.

An explainable AI model should give insight into the features on which decision making is based, to be able to address the problem.

IBM research, therefore, proposed AI factsheets, to better document how an AI system was created, tested, trained, deployed and evaluated. This should be audited throughout their lifecycle. It would also include suggestions on how a system should be operated and used. “Standardizing and publishing this information is key to building trust in AI,” says Schaeck.

Schaeck advises business owners to take a holistic view of the data science and machine learning life cycle if they are looking to invest in AI. Choose your platform wisely, is his advice. One that allows teams to gain insights and take a significant amount of models into tightly controlled, scalable production environments. “A platform, in which model outputs and inputs are recorded and can be continuously monitored and analysed for aspects like performance, fairness, etc,” he tells.

IBM’s Fairness 360 toolkit, Watson Studio, Watson Machine Learning and Watson Open Scale can help you out with this. The open-source Fairness 360 toolkit can be applied to every AI model before it goes into production. The toolkit has all the state of the art bias detection and mitigation algorithms. Watson Studio allows teams to visualize and understand data and create, train and evaluate models. In Watson Machine Learning, these models can be managed, recorded and analyzed. And as it is essential to keep on monitoring AI during its lifecycle, IBM Open Scale connects to Watson Machine Learning and the resulting input and output log data, in order to continuously monitor and analyze in-production models.

Yes, it can all be frightening. As a business owner, you don’t want to end up wasting a lot of time and resources creating a Frankenstein AI.

But it is good to keep in mind that just as our human biases are responsible for creating unfair AI, we also have the power to create AI which mitigates, or even transcends human biases. After all, tech is what we make of it.

If you would like to know more about the latest breakthroughs in AI, Cloud & Quantum Computing and get your hands on experimenting with blockchain, Kubernetes, istio, serverless architecture or cognitive application development in an environment supported by IBM experts, then join the Data & Developers Experience event that is going to take place on June 11-12 at Bikini Berlin. Register here, it’s free.

If the popular media are to be believed, artificial intelligence (AI) is coming to steal your job and threaten life as we know it. If we do not prepare now, we may face a future where AI runs free and dominates humans in society. The AI revolution is indeed underway. To ensure you are prepared to make it through the times ahead, we’ve created a handy survival guide for you.

Step 1: Recognising AI

The first step in every conflict is knowing your target. It is crucial to acknowledge that AI is not in the future; it is already here.

You are most likely using it on a daily basis. AI is the magic glue behind the ranking of your Facebook timeline, how Netflix knows what to suggest you watch next, and how Google predicts where you are headed when you jump in your car.

AI is not a new concept. It was born in the summer of 1956, when a group of pioneers came together with a dream to build machines as intelligent as humans. AI encompasses disciplines such as machine learning, which can find patterns in data and learn to predict phenomena, as well as computer vision, speech processing and robotics.

The main technique behind the current hype around deep learning is artificial neural networks. Inspired by models of the brain, these mathematical systems work by mapping inputs to a set of outputs based on features of the thing being examined. In computer vision, for example, a feature is a pattern of pixels that provides information about an object.

Most commonly, the supervised learning approach requires the computer to “learn” these associations by training on big data sets labelled by humans. What began with classifying cat videos has now extended to applications such as driving autonomous vehicles.

Step 2: Identify where AI thrives

With this knowledge, we can start to understand where AI is optimally positioned to take over. Have a look around you and take note of tasks that require huge amounts of data processing.

For example, no human would or could look through everyone’s click patterns on Google to figure out what someone wants.

Even the more advanced capabilities that AI has demonstrated in winning AlphaGo, video games and, most recently, poker rely on training on thousands and thousands of trials.

Essentially, AI is particularly good at any task that requires an enormous amount of repetitive processing. If this sounds like your job, it might be time to start thinking of a survival plan.

To evaluate your “automation risk”, type in your job on this site to find out what researchers have calculated for your field. Even if you’re not worried, have a look. The prepared person stays ahead.

Step 3: Devise an action plan for the AI revolution

You now have two choices:

Option A: Resistance

Your first option is to fight back. This may be your natural reaction and, as in during the industrial revolution, you would not be alone in wanting to oppose the change.

The fact that common AI relies on pattern recognition means that you can sabotage the way it processes data quite easily. But pose too much of a threat and Arnold Schwarzenegger may go back to try and kill you as an infant.

The nature of the human race is that we will always strive towards the next advancement. Resisting change out of fear of its disadvantages may work in the short term but will only make you more likely to be left behind in the future.

Option B: Make friends with AI

The far superior strategy is to form a treaty. Accept that AI will increasingly become a part of society and look for possibilities to collaborate. There is a huge potential for AI to assist in places where humans fall short, precisely because of the processing power.

Companies are already using AI to aid clinicians in medical diagnosis, personalise customer experiences and create agricultural methods that reduce the cost to the environment.

Some are even developing this relationship one step further with integrated systems that merge the human brain with AI.

Be ready to upskill where possible. AI can learn very well but it cannot learn flexibly (yet). You can. There are new jobs now available that did not exist five years ago.

If you allow AI to do the grit work, this can create opportunity to embrace the attributes that humans excel at, namely creativity, social intelligence and manipulation.

As with every big change, there are fears about new technology like AI. Ultimately, the way to survive the AI revolution is to embrace the partnership. Understand the potential that AI has to improve the world around you and look for those opportunities to implement positive change.

If you prepare yourself, you may find the AI revolution allows you not only to survive but to be an even better version of your human self.

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This article was originally published on The Conversation. Read the original article.

iRobot is a US based advanced tech outfit that develops proprietary technology incorporating advanced concepts in “navigation, mobility, manipulation and artificial intelligence to build robots,” that serve three tiers : Home, Security and Remote presence.

Now, taking the Home segment further, the company is believed to be working on an automatic lawn mower. What is irksome, however (at least for radio astronomers), is the fact that it has low power radio beacons installed within, to help it navigate even around corners, emitting waves in the 6240-6740 MHz range, points out TechCrunch.

The National Radio Astronomy Observatory (NRAO), on the other hand uses the same range to flag “interstellar wood alcohol” in space, as it essentially indicates the birth of a star in the vicinity.

An FCC filing from iRobot outlines its project and intentions even promises undertaking “all practicable steps” to prevent any interference with radio astronomy. However that pans out for iRobot and NRAO remains to be seen.

Meanwhile, both parties have been indulging in verbal jousting through FCC comments:

iRobot: “Use of the iRobot RLM [robot lawn mower] will increase lawn mower safety. An estimated 1,517 lethal accidents occurred with lawn mowers through the years 1997 to 2010. It is reasonable to assume that many of these injuries and deaths would not occur if consumers used a robotic mower. More than 17 million gallons of fuel, mostly gasoline, are spilled each year while refueling lawn equipment. A battery powered RLM will reduce emissions, gasoline spills, fires and other such accidents.”
NRAO: “iRobot cited multiple statistics of grim accidents and spilt gasoline to assert the public benefit of approving its wireless robotic lawn mowers. However, there is already a competitive market for robotic lawn mowers using wire loops [buried edge wire], which has somehow failed to stanch the stream of ghastly accidents and spilt gasoline that iRobot associates with the mundane practice of lawn-mowing.”

Photo credit: Proudlove / Foter / CC BY-NC-SA

Only a month into the year, and already several of our expert’s predictions for 2015 in big data are coming into fruition. 2015 is certainly looking like the year of AI & automation, with all three of this week’s most-shared news pieces below focusing around prediction and AI. 2015 may, too, be the year of the economists; both Kris Hammond of Narrative Science and Gabriel Lowy of Tech-Tonics Advisors published excellent pieces on the huge big data opportunities for the financial industry.

TOP DATACONOMY ARTICLES

10 Internet of Things Influencers You Need to Know

“It is a truth (almost) universally acknowledged that the Internet of Things is going to revolutionise how we live, work and think. Although broaching this field can be daunting, it is certainly worth looking in to the fascinating applications and technology associated with this field- if you’re looking for the latest insights into how IoT will shape our future, this list is a great place to start.”

3 Reasons Why Banks Can’t Afford to Ignore AI

“While companies use many approaches to extract value from data they capture, organize and store, it is only recently that we have seen a new class of technologies emerge providing real impact — those powered by Artificial Intelligence, aka AI.”  
 

Aerospike’s New CEO John Dillon on the 2015 Roadmap for Database Tech: Less Hype, Better Technology

Yesterday, NoSQL innovators Aerospike announced that Silicon Valley veteran John Dillon will be stepping into the breach as their new CEO. He’s got big plans for the company- largely revolving around less marketing buzz, and more love for developers. His ambitious roadmap is definitely worth a read.

TOP DATACONOMY NEWS

Eve Stumbles Upon a Possible Cure for Malaria. Eve is a Robot Scientist.

A laboratory automation system that harnesses artificial intelligence (AI) techniques in order to glean and understand scientific data constant experimentation is called a Robot Scientist. Eve is such a Robot Scientist and if certain U.K. researchers have it right, then Eve might have stumbled upon a possible fighting chance against malaria.

This Programme Knows If Your Startup Will Be Successful

The startup landscape is a minefield, where even the most brilliant ideas can fall by the wayside without proper implementation and business acumen. This is where Thomas Thurston comes in. Thurston believes he has developed a predictive algorithm which can mitigate some of the risk involved with starting and investing in a new business.

Am I Going Down? Uses Flight Data to Calculate Odds of Your Next Flight Crashing

“A new iOS app Am I Going Down? refutes the claim that there’s a “one in a million chance” of your plane crashing. In fact, if you’re on a Boeing 747-700 flight from San Francisco to Dallas, there’s a 1 in 4,593,011 chance you’ll crash.”  

TOP UPCOMING EVENTS

23-24 February, 2015- Eleventh International Conference on Technology, Knowledge, and Society, CA

Conference themes this year include: Technologies for Human Use, Technologies in Community, Technologies for Learning and Technologies for Common Knowledge.  
 

12-13 February, 2015- Apache Hadoop Innovation Summit, San Diego CA

“Hadoop, a huge piece of the puzzle, continues to present both exciting opportunities and engineering challenges. Can you become cloud native? What new alternative paradigms are available with Hadoop? What are the limitations of sole Hadoop use? How can you use it for machine learning. What about Integration? Corporate Accessibility? Ethics? These burning issues are what the summit looks to address.”

TOP DATACONOMY JOBS

Sr. Data Engineer (m/f) –NumberFour AG   

“As our Sr. Data Engineer you are responsible for the planning and implementing of scalable, stable and high-performance scoring systems.”

Physicist / Mathematician / Computer Scientist as Data Scientist, Blue Yonder

If you would like to be part of a highly innovative, challenging and extremely future-oriented software market, and a young and highly motivated team, then please send us your detailed application.

A laboratory automation system that harnesses artificial intelligence (AI) techniques in order to glean and understand scientific data constant experimentation is called a Robot Scientist.

Eve is such a Robot Scientist and if certain U.K. researchers have it right, then Eve might have stumbled upon a possible fighting chance against malaria.

A paper published in the Royal Society journal Interface explains that essentially through cycles of experimentation, involving learning, analysis and testing of compounds, Eve, “integrates and automates library-screening, hit-confirmation, and lead generation”, testing all possible compounds on target diseases to see which one yields favourable results.

It mass-screens a batch and then retests the hits for confirmation. Herein, the learning and analysis of the hit can help derive results.

“Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.”

The paper enunciates that conventional drug processing and testing methods may take years. It emphasizes the need for making drug discovery cheaper and faster. Through Eve, and other such Robot Scientists, which can conduct about 10,000 tests a day, the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, becomes cheaper and quicker.

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(Image credit: “100x- blood cell culture”, via Flickr)

Automated code review system startup, Code Climate has raised a $2M round of financing, led by NextView Ventures, joined by Lerer Ventures, Trinity Ventures, and Fuel Capital.

NextView co-founder and partner David Beisel wrote in his blog post, “I expected to hear a balanced set of positive and negative feedback, where it’s my job to sort through it as part of our diligence process. Instead, the overwhelming positive response was that their teams were either already customers or they had immediately become customers after learning about it.”

Code Climate is a bot in the cloud that runs standard tests on code without actually executing it. It can “uncover security vulnerabilities, potential bugs, repetition of existing code, and unnecessarily complex programming,” reports VentureBeat.

Founder and chief executive Bryan Helmkamp wants to develop the bot further and work with more programming languages, like Go and Python. Presently, it works with Ruby and JavaScript. Support for PHP is in public beta.

“We’re not going to be able to reach those other languages and reach that audience nearly as quickly if we continue to do it off revenue growth from the bootstrap strategy,” Helmkamp added.

Read more here.

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(Image credit: David Asch)

In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. It’s a science of algorithms and automation; the algorithms “learn” from the dataset, identifying patterns or classifying trends for instance, and then automates output- whether that’s sorting data into categories or making predictions on future outputs. In this edition of “Understanding Big Data“, we’ll be taking a more in-depth look at the term and its many different forms and applications.

When many people hear the term “machine learning”, they conjure up mental images of robots who walk, climb or clean houses. In reality, machine learning starts alot closer to home. When you open your emails, spam has been filtered out from your important messages by an algorithm that has learnt to classify “spam” and “not spam”. Your Facebook news feed features posts from your closest friends because an algorithm has examined your likes, tags and photos to decipher who you connect with most. When you upload a photo and the website identifies your face, it’s fuelled by a facial recognition algorithm. When you use a search engine, you see the best and most relevant content first because of a sophisticated search ranking algorithm. In short, machine learning permeates our lives.

Often, people use the terms “machine learning” and “data mining” interchangably, and this is inexact; there is a distinction. Machine learning is centred around making predictions, based on already-identified trends and properties in the training data set. Data mining is the process of discovering of unidentified patterns and properties in the data, as part of the discovery stage of data analysis. The two do intersect; machine learning techniques are often incorporated into data mining, and unsupervised machine learning follows the same principles as data mining.

Some of the main categories of machine learning include:

• Supervised Learning- In supervised learning, algorithms are trained on labelled examples (in spam filters, for instance, algorithms are trained by inputting emails with the output “Spam” and “Not spam”). It then generalises the function between inputs and outputs, and eventually learns to speculate outputs itself (e.g., tell the difference between spam and important mail). Spam filters are an example of a classification problem; another major problem within supervised learning is regression, which models and analyses the relationship between variables. Some applications of supervised learning include handwriting recognition and speech recognition (mostly using Hidden Markov Models). In a recent Google Hangout, Stanford Professor and Coursera Co-Founder Andrew Ng identified speech recognition as one of the most exciting fields for the future of machine learning; he envisioned a future in which we have phones with reply and delete buttons, and everything else will be speech-automated.

 Google’s neural network’s understanding of a cat; source

• Unsupervised Learning- The input data in machine learning isn’t labelled and the output isn’t known. The aim of unsupervised learning is find the hidden structures within the data. One main class of problem associated with unsupervised learning is Clustering, which uses the inherent structures of data to group data points together by common properties. An application of this is Amazon book recommendations, which cluster together users with similar buying habits and tastes. Another example of unsupervised learning is Google’s neural network; it was fed 10,000 Youtube thumbnails and without being told what to look for, began to categorise and group the images of together. Due to the obscene amount of cat videos on Youtube, the network eventually came up with a single image of what it understood to be a cat, which you can see above.

 AlchemyAPI’s Alchemy Vision software; source

• Deep Learning- Deep learning involves teaching computers how to think hierarchically and model high-level abstractions. Deep learning breaks down the data into different characteristics on different levels (i.e in image classification, one level might be pixels, the next might be edges)- the algorithms learn what the relationships between these characteristics are on different levels to understand the data input. One example of deep learning is AlchemyAPI‘s computer vision system, which can understand and classify over 10,000 concepts in images, and identify multiple concepts in one image. Try the demo out for yourself here.
• Reinforcement Learning- In reinforcement learning, the system is not told which actions to take, but discovers which actions work best based on what “rewards” it yields from them. Reinforcement learning is used frequently in robotics; a list of applications of reinforcement learning can be found here, arguably the coolest of which is this autonomous helicopter, created by Andrew Ng et al.

So machine learning extends vastly beyond the obvious remit of robotics- it’s a part of our everyday lives, operating behind the scenes when we open our emails, give commands to Siri, search for book recommendations or search for images. It’s not just a part of our lives; it’s making our lives easier.

(Image credit: Flickr)

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Eileen McNulty-Holmes – Editor

Eileen has five years’ experience in journalism and editing for a range of online publications. She has a degree in English Literature from the University of Exeter, and is particularly interested in big data’s application in humanities. She is a native of Shropshire, United Kingdom.

Email: eileen@dataconomy.ru

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