Artificial Intelligence (AI) is a transformative technology. Not only can it enable autonomy and machines that can make intelligent decisions, but it can also even reinvent the technological wheels of various industries. Robotics, being an emergent technology to enable autonomy, AI is a beautiful tool that can help flourish the true capability of robotics technology. And Google's AI partner, DeepMind is reinventing robotics once again.
Today, AI is around us everywhere. From different apps to different devices/gadgets and various services we use, AI mainly integrates with these apps, devices/gadgets, or services. With this, AI provides us a superior experience of use with devices capable of making intelligent decisions and predictions. Moreover, AI is very persistent in modern life, with AI in various voice assistants, recommendation systems in services from e-commerce sites to media consumption platforms, and intelligent solutions to make predictions or autonomous decisions.
With these services and devices, AI has already become an integral part of our lives. Therefore, it is only natural that industries and companies use AI to boost their company performance on the consumer and product development and innovation front in such a scenario. One of these industries where AI has much potential is the robotics industry.
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The robotics industry in itself is revolutionary, with capabilities to enable autonomy in industries. However, the endeavors of enterprises and various industries pose a massive challenge for robotics to fulfill them alone. So developers and researchers worldwide are trying to embed AI into robotics technology to usher the robotic industry to a new level.
With the help of AI, robots will not only be intelligent, but they will also be more capable and efficient. They will be able to form elegant solutions and make intelligent decisions. Moreover, they will be able to control and move a physical body which is very hard to program and build from the ground up. Furthermore, with the decision-making and prediction prowess of the system with convergence of robotics and AI, revolutionary and even unseen developments are possible.
DeepMind is reinventing robotics, and its developers have certainly caught up with this revolutionary possibility. The search giant Google's AI partner, DeepMind, is now working on this problem of convergence of AI with robotics. Raia Hadsell, the head of robotics at DeepMind, said, "I would say those robotics as a field is probably ten years behind where computer vision is." It demonstrates the lack of distinct development in robotics even when tech-like computer vision embedded in robots is already very far ahead.
The problem lying here is, though, more complex. Alphabet Inc, the parent company of Google and DeepMind, understands this daunting AI incorporation with robotics. More daunting challenges and longstanding problems remain in the Robotics-AI paradigm alongside challenges of gathering adequate and proper data for various AI algorithms to train and test them.
For instance, problems like training an AI system to learn new tasks without forgetting the old one? How to prepare an AI to apply the skills it knows for a new task? These problems remain primarily unsolved, but DeepMind is reinventing robotics to tackle the issues.
DeepMind is mainly successful with its previous endeavors with AlphaGO, WaveRNN, AlphaStar, and AlphaFold. However, with various breakthroughs and revolutionary developments, DeepMind is now turning towards these more complex problems with AI and Robotics.
However, a more fundamental problem remains in robotics. With their AlphaGO AI, DeepMind is reinventing robotics and successfully trained it through the data from hundreds of thousands of games of Go among humans. Apart from this, additional data with millions of games of AlphaGO AI playing with itself was also in use for its training.
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However, to train a robot, such an abundance of data is not available. Hadsell remarks that this is a huge problem and notes that for AI like AlphaGO, AI can simulate thousands of games in a few minutes with parallel jobs in numerous CPUs. However, for training a robot, for instance, if picking up a cup takes 3 seconds to perform, it will take a whole minute to just train 20 cases of this action.
Pair this problem with other problems like the use of bipedal robots to accomplish the same task. You will be dealing with a whole lot more than just picking up the cup. This problem is enormous, even unsolvable, in the physical world. However, OpenAI, an AI research and development company in San Francisco, has found a way out with robotic simulations.
Since physically training a robot is rigid, slow, and expensive, OpenAI solves this problem using simulation technology. For example, the researchers at OpenAI built a 3D simulation environment to train a robot hand to solve a Rubik's cube. This strategy to train robots in a simulation environment proved fruitful when they installed this AI in a real-world robot hand, and it worked.
Despite the success of OpenAI, Hudsell notes that the simulations are too perfect. She goes on to explain, "Imagine two robot hands in simulation, trying to put a cellphone together." The robot might eventually succeed with millions of training iterations but with other "hacks" of the perfect simulation environment.
"They might eventually discover that by throwing all the pieces up in the air with exactly the right amount of force. With exactly the right amount of spin, that they can build the cellphone in a few seconds," Hudshell says. The cellphone pieces will fall precisely where the robot wants them, eventually building a phone with this method. It might work in a perfect simulation environment, but this will never work in a complex and messy reality. Hence, the technology still has its limitations.
For now, however, you can settle with random noise and imperfections in the simulations. However, Hudsell explains that "You can add noise and randomness artificially. But no contemporary simulation is good enough to recreate even a small slice of reality truly."
Furthermore, another more profound problem with AI remains. Hadsell says that catastrophic forgetting, an AI problem, is what interests him the most. It is not only a problem in robotics but a complexity in the whole AI paradigm. Simply put, catastrophic forgetting is when an AI learns to perfect some task. It tends to forget it when you train the same AI to perform another task. For instance, an AI that learns to walk perfectly fails when training to pick a cup.
This problem is a major persistent problem in the Robot-AI paradigm. The whole AI paradigm suffers from this complexity. For instance, you train an AI to distinguish a dog and a cat through computer vision using a picture. However, when you use this same AI to prepare it for classification between a bus and car, all its previous training becomes useless. So now it will train and adjust its "learning" to differentiate between a bus and a car. When it becomes adept in doing so, it may even gain great accuracy. However, at this point, it will lose its previous ability to distinguish between a dog and a cat. Hence, effectively "forgetting" is training.
To work around this problem, Hadsell prefers an approach of elastic weight consolidation. In this approach, you task the AI to assess some essential nodes or weights (in a neural network). Or "learnings" and freeze this "knowledge" to make it interchangeable even if it is training for some other task. For instance, after training an AI to its maximum accuracy for distinguishing between cats, dogs, and you, task the AI to freeze its most important "learnings" or weights that it uses to determine these animals. Hadsell notes that you can even freeze a small number of consequences, say only 5%, and then train the AI for another classification task. This time says for classification of car and a dog.
With this, the AI can effectively learn to perform multiple tasks. Although it may not be perfect, it will still do remarkably better than completely "forgetting," as in the previous case.
However, this also presents another problem: as the AI learns multiple tasks, more and more of its neurons will freeze. As a result, it would create less and less flexibility for the AI to learn something new. Nevertheless, Hudsell this problem is also mitigable by a technique of "progress and compress."
After learning new tasks, a neural network AI can freeze its neural network and store it in memory/storage to get ready to learn new jobs in a completely new neural network. Thus, it will enable an AI to utilize knowledge from previous tasks to understand and solve new tasks but will not use knowledge from new functions in its primary operations.
However, another fundamental problem remains. Suppose you want a robot that can perform multiple tasks and works. In that case, you will have to train the AI inside the robot in each of these tasks separately in a broad range of scenarios, conditions, and environments. However, a general intelligence AI robot that can perform multiple tasks and continuously learn new things is complex and challenging. DeepMind is reinventing robotics and now working continuously to solve these AI-Robot problems. Like DeepMind, FS Studio is also hard at work with its collective experience and knowledge over decades. FS Studio is also improving its services like Robotic Simulation Services, Offline Programming, and Digital Twins for reinventing the paradigm of robotic research and development with AI at its center.
From everyday market consumers to innovative technologies like robotic simulation services, offline robot programming, AI, AR, and VR, one thing is for sure, the robotic technology in the future will reach places and fields that are unforeseen even today. So, researchers and market enthusiasts have already started to predict what the industry will be like in the future. Hovering over thousands of ideas and scenarios, they have come down to these top three predictions for the robotic industry.
The Robotics industry is continuously evolving and growing. Researchers estimate that the market for the robotic industry globally in 2020 was more than 27 Billion US Dollars. This figure, however, has high expectations to grow astronomically to more than 74 Billion US Dollars by 2026. Researchers also pair this expectation with an annual growth rate of 17.45%, which again believes it will grow more.
The mainstream market also reflects this growing influence of robotics. The demand for robots and robotic technology is increasing in industries and factories, and regular consumer space. It shows that the robotic industry will become more and more mainstream with its uses to be making places even in fields that we cannot foresee today.
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With the COVID-19 pandemic, industry and consumer trends are shifting. During the pandemic, automation and remote operations experienced a boom that saw changing needs among manufacturers and consumers. In addition, people working from home, communication technology was on top of its game, with industries relating to remote communications increasing in value and influence.
It also brings together the sensing technology along it. With automation of tasks, even daily tasks being in demand, the robotic industry and the consumer industry focus on automation and sensing technology that enables it. Moreover, with automation comes data. Hence the data-driven industries like cloud technology are also increasing. Today’s data industry is so big that the tech giants of the current world are determinants of the amount of data they control and can process.
Another significant technology in communication, the 5G technology, is also a rave among consumers and industry alike. With this, the robotic industry is also taking advantage of 5G technology, with robots being more capable of high-speed communication and being more data-driven than ever.
We can compile all this information and trends of the current world into three things: Mainstream consumer space, Automation, and the data-driven industry and communication and sensing technology.
The demand for robotic and other state-of-the-art technology is increasing in the mainstream market. As a result, consumers are getting warier with these technologies and are willing to invest in them. It shows that the mainstream consumer market is undoubtedly aware that robotics technology is the future.
Furthermore, with or without the pandemic, communication and sensing technology is increasing in adoption and innovation, giving the green light to the predictions for the robotics industry. But due to the pandemic, it experienced a rapid increase in its adoption and development. Moreover, with people working from home and companies emphasizing remote working, communication technology is experiencing a high rise in demand. It is no different in robotic technology. Since robots integrate other technologies that are very advanced and highly complex, communication and networking will experience colossal development.
Consumers will expect their devices to be able to communicate with them more seamlessly. Furthermore, every use case of any robotic technology will want to fully utilize this advancement in communication technology to enable different possibilities. With high-speed communication possible, fleets of robots will communicate more efficiently and rapidly, creating even more use cases. Furthermore, Fleets of communicating robots capable of working together as a unit to complete specific tasks together will also be a high possibility with newer communication standards like 5G.
Along with communication comes sensor technology. With sensors getting smaller with more efficiency but less power, it will be possible to use them even in unforeseen places and use cases. Furthermore, with home security systems improving daily and technologies like computer vision and natural language progressing, sensors adept at these technologies will also enhance more. So naturally, the robotics industry will also take advantage of this.
Since the robotic industry is mainly based around sensors and their capabilities, with the increasing efficiency of sensors, it will be possible to include more significant, more capable sensors in any robot.
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Predictions for the robotic industry are getting wilder; however, the accomplishments don’t fail to amaze us. Like the battery technology is improving further, and these sensors are getting more and more power-efficient, it is almost certain that we will use various kinds of sensors in different fields that are even seen as not possible today. For instance, take our phones, for example. Mobile technology is improving at such a fast pace that with each increasing year or two, people feel obliged to upgrade their phones to a newer model since they have started to feel old even if they are only a year or two old.
Since phones are getting smarter, so are the sensors inside them. A smartphone has numerous sensors, from cameras to accelerators to some phones even having LiDAR sensors in them. Compare this advancement to only a decade back, when phones with even a camera were tough to find. It acts as a testament to how far sensing technology has come and is improving at a fast pace. Of course, this also applies to robotic technology.
With sensors getting more efficient, smaller, more powerful while being more power-efficient, it will be possible for robot developers to pack more robust and accurate sensors in their robots. It will enable more probabilities. Furthermore, with sensors comes to their data. Sensors are devices that extract enormous amounts of data. However, to process and handle this, data-driven technologies are promptly evolving, if not even more.
The data-driven industry is evolving at a pace that exceeded the predictions for robotic industries made before the pandemic. With almost all kinds of technology now capable of dealing with data, manufacturers are constantly packing their products with more data-driven features, thanks to the efficiency of processing units getting better. The data industry is so important today that the top tech leaders of the current world are determinants of the efficient utilization of data technology; with devices capable of collecting large amounts of data, whether, through sensors or user interactions, data-driven applications are certainly thriving.
With data comes technologies like Machine Learning, Deep Learning, and Artificial Intelligence (AI) applications. With AI comes the automation of the industry. The Robotics industry is undoubtedly at the forefront of automation technology, with humans having a vision of automated robots way back. However, what’s even more exciting about this data-driven technology is that it helps a robot have practical and smart applications and even helps to develop and build robots.
Innovative technologies like Simulations, AR, and VR will thrive under the data-driven industry after all these technologies rely heavily upon data. But with data-driven technology developing at a rapid rate, these technologies are also improving very fast. Moreover, simulations are now capable of imitating real-world environments and phenomena with very accurate physics engines. Robotic development is also possible with these technologies, especially since the robotic industry is a costly industry due to its high risk for humans and economic benefits and resource consumption.
Robotic research and development usually require many resources and skills willing to take a risk with high-value components, and research is for waste. Furthermore, since simulations and digital technologies like Robotic Simulation Services or Offline Robot Programming Software Platforms are mainstream, the future robotic industry will depend on these technologies.
With various advantages like rapid prototyping, faster and efficient designing process, fewer resources, and fewer requirements of highly skilled personnel, simulation technology will thrive in the future for the robotic industry. The robotic industry will design, test, develop, and research robotics inside simulations with technologies like digital twins.
The predictions for the robotic industry also indicate that the industries and production sites will be using technologies like Offline Robot Programming Platforms for teaching and programming robots, resulting in fewer downtimes and progressing more smoothly. It is because the robotic industry will have its core lying in digital technologies like these.
Robots of the future will also focus more on the human-robot collaboration where robots will be more capable of working together with humans. For this, integrating technologies like AR and VR in robotics and AI will be crucial. AR and VR will allow the robotic industry to venture towards complete digital premises along with remote technology.
Compiling all this information and trends in the world today, we can be sure that the future of the robotic industry looks to be very promising. From everyday market consumers to innovative technologies like robotic simulation services, offline robot programming, AI, AR, VR, one thing is for sure, the robotic technology in the future will reach places and fields that are unforeseen even today. With this, the top 3 most significant predictions for the robotic industry are:
