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.
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.
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.
Industries are rapidly advancing. With growing adaptation and accessibility of state-of-the-art technologies, various industries’ production innovation and R&D technology are becoming very advanced, albeit more complex. However, with technologies getting more complex, they are also getting easier to adapt. So laden with numerous possibilities and opportunities, industries are adopting digital technologies in their industrial application to reap these lucrative advantages as deep learning boosts robot picking flexibility.
The ultimate pursuit of automation in industries and production goes through the path of intelligent and smart robots. With more demanding industries, newer and better robots can perform various industrial applications more smoothly and efficiently. But as industries expand their reach into more fields/sectors, they need robots to achieve even more different tasks in different environments.
This broad spectrum of need for the usability of robots leads to robotic technology not being able to keep up with the demand. Hence, traditional methods and approaches to robotics must be let go to introduce new and better techniques to robotic technology. Within the advent of digital technology lies more possibilities for robotics that are even unseen before.
Digital technologies and platforms like Robotic Simulation Services, Offline Programming, Augmented Reality, Virtual Reality, and Artificial Intelligence take the world by storm. They are now in integration or development for almost every industry possible. The robotics industry also is not lagging in this aspect, with robotic manufacturers or various services providers already utilizing these technologies to propel robotics further. Deep learning is one of the technologies in use, with much anticipation and exciting possibilities, within the robotic industry.
Let's talk about Deep Learning
Deep learning is a type of Artificial Intelligence, or more so a kind of Machine Learning approach. In the broader AI paradigm, Machine Learning is a subset of AI that refers to an AI system that can learn with the help of data instead of developers having to code it. ML is an approach to AI that enables various algorithms to remember from data, i.e., training data consisting of input and output data, to infer a pattern or a “knowledge” in the input data about the output. With this knowledge, ML algorithms can effectively predict the outcomes with the analysis of input data.
Deep Learning is a similar approach. It's a family of algorithms in the machine learning paradigm based upon Artificial Neural Networks (ANNs). These ANNs in deep learning can perform representation learning. Representation learning is a method in which systems detect or infer a pattern or representation, i.e., features in the input data for feature detection or classification. Hence, computer science also defines it as feature learning since it detects features from raw data and uses them to perform some specific task.
Deep learning boosts robotic picking flexibility with its data by effectively imitating how intelligent creatures like humans gain knowledge and do certain things. In deep understanding, a system takes in input data and tries to infer a pattern or detect some specific feature in that data. This “learning” approach is known as deep learning. Furthermore, education can also be either supervised, unsupervised or semi-supervised.
These are various deep learning architectures that researchers combine up with various other computer techniques and technologies to enable different features and functions in robotics: deep neural networks, recurrent neural networks, convolutional neural networks. Deep reinforcement learning and deep belief networks are various architectures in deep learning—robotic technology pairs up these architectures with different hardware and technologies to build various robotic functions.
For instance, robotic researchers and developers use convolutional neural networks for computer vision with cameras and other sensors to give visual information like depth. Likewise, different architectures enable different computer application fields like speech recognition, natural language processing, image analysis, bioinformatics, etc. Moreover, these applications are often in use for various purposes within other industrial areas.
Why Deep Learning Boosts Robotic Picking Flexibility?
In robotics, one of the most complex things to perfect is its ability to pick things up. For human beings, picking items seems very easy. However, seemingly effortless things with biological creatures are not always similar to robotics and computer systems.
Thus, although it may seem that picking items up is easy, it is not the case. The complex interworking of different systems together to perform even a simple task is very hard for computers. For instance, to first pick things up, you need to know what you are picking.
This part is usually straightforward since, for example, you can tell a computer that the stuff it's gathering is in a specific location. But the hard part comes when it's doing the actual picking. For example, how is it even going to pick the object? Even in a single production environment, there are a variety of things with different shapes and sizes. In addition, objects have different textures, structures, and a specific suitable picking spot.
We can undoubtedly program a robot to utilize information about a particular object and a suitable method to pick the thing, but programming it to select it is challenging. Relatively, programming a robot to choose only a single type of object can be easy, but you would need other robots for different kinds of things/products. So this is certainly not an effective method to accomplish this.
Furthermore, products and objects may behave differently in different environments, creating complexities in ways deep learning boosts picking flexibilities. For instance, a product with a smooth surface can be slippery to grab or hold onto in a humid environment. Moreover, picking other objects in different backgrounds requires the robot developer to program the robot for various environments and various things. Along with this, considering the wide range of products, this problem quickly becomes substantially huge.
One of the enormous complexities we are not even exploring yet remains motor skills. Programming a robot to perform specific motor skills and functions is one of the vastest complexities of the robot development paradigm. Even to grant them specific motor functions is very hard. That's why it's a huge deal, even if a robot can perform simple tasks like holding a cup, walking, etc. However, now you can certainly deal with these problems through various means.
For instance, a robot that needs to move can have wheels. A robot that does not have to move but grab onto things can have arms on a fixed body. But these solutions are also tough to implement. Add this to the use case, such as a moving robot that has to move on an uneven surface or a wrong road or even locations where there are no roads, i.e., hills, rocky places, etc. Then this problem becomes substantially more challenging. Similarly, for industrial robots, picking different products and objects is also a complex problem due to different environments and types of things it has to deal with in a particular manner.
Apart from these problems, one primary concern is how deep learning boosts robotic picking flexibility, computer vision. A robot needs to see the object it's picking up. Recognizing a thing insight is a significant feat of computer vision that is currently possible with a massive range of solutions available. But simply recognizing an object is enough to interact with the thing. The robot has to know what object it's looking at and determine how it will pick it up. It again involves problems regarding the size, shape, texture, and structure of the object or product.
In hindsight of all these problems, an industrial robot capable of gripping and interacting with different types of objects or products with other characteristics and properties in different conditions or environments is tough to build. Consequently, it is one of the biggest problems in the industrial robotic plane. It is where deep learning comes into play.
We can use various deep learning techniques to teach a system to recognize and interact with an object. Using deep learning methods, we can use data from multiple production sites, companies, and industries of interaction and manipulation of various things and products for training the system. This data can effectively help a deep learning model to “learn” how to pick different objects in different environments in various particular ways.
The initial data can come from systems already proficient in picking and dealing with objects, which would help in how deep learning boosts robotic picking flexibility. For instance, there is data with humans picking up things. These specialized robots pick only a specific object or interact with them, or even human operators that operate machines to pick up different objects. After data collection of these types, a robot with a deep learning system can go through a training process to effectively learn how to replicate the task or perform it more efficiently.
With this, data collection is complete from a specific specialized robot and for different machines. Moreover, developers and researchers can share and augment such data for training there be used robots for broader use cases and even interact and manipulate objects they are yet to interact with. The possibilities are endless as deep learning boosts robot picking flexibility. As a result, developers can build with a wide range of picking flexibility that can help an industry drive itself towards the end goal of automation. It is why companies like FS Studio provide various services regarding robots and AI tools like deep learning. With decades of collective experience and knowledge with a wide range of expertise, FS Studio provides deep learning services for various robots and other innovative services like Robot Simulation Services, Offline Programming Solutions, and the integration of innovative technologies like AR and VR in different systems.
Industries are using Computer vision to improve health and workplace safety.
Computer vision has been an area of interest in recent years for several reasons. Motion capture, facial recognition, and pattern matching are all areas that have seen tremendous growth in the last decade. There is also a huge market for computer vision technology to improve workplace safety by reducing hazardous environments and accidents.
Safety is a massive concern in the workplace. According to Economic Policy Institute, injuries cost businesses $192 billion every year, and lost productivity can result in lost revenue.
New technology may help make work environments safer and more efficient by providing data on how people use their hands while working and when they might be at risk for injury or exhaustion. It could also help reduce accidents caused by repetitive stress injuries, which happen when someone repeatedly does the same movement without taking breaks.
Let's take a look at some examples where Computer Vision is used to improve health and workplace safety
Forklift accidents come with a high risk of injury, damage, and disruption to the workplace. To avoid these outcomes, we can use computer vision technology which alerts us when forklifts are moving in the wrong directions or zones that may lead to an accident.
When operating a forklift, it is essential for pedestrians at worksites to follow safety rules such as wearing reflective clothing to be visible from all sides on dimly lit grounds where there's no natural light.
According to McCue, there has been an increase in forklift accidents and fatalities in recent years.
Forklifts account for 85 deaths every year - a 28% Increase since 2011. Forklift accidents that result in serious injury total 34,900 annually, while non-serious injuries related to fork-lifting reach 61,800 each year.
The most common incident is when a forklift overturns, which accounts for 24% of all incidents. These statistics are startling but can be avoided by following safety procedures such as staying away from moving parts and loading areas unless authorized or instructed to do so.
With the help of Computer Vision and Deep Learning, there are ways that humans can be safer in their work environment. For example, in a study conducted by Google's research lab "Deep Mind," researchers found that just one day after deploying these technologies on forklift trucks at an industrial site, errors reduced dramatically from 64 to 8 per hour without any noticeable change for workers or machines.
Imagine if your car could sense a collision before you even knew it happened. That's the potential of Deep Learning: this technology can detect an incident and learn from these events to prevent future ones.
For example, imagine that after analyzing video footage and data collected by other sensors on-site following an accident involving a forklift colliding with column supports for product storage racks.
The system can identify patterns in how collisions happen to provide warnings about what needs more attention to reduce risks associated with them happening again at all costs!
Computer vision systems are a helpful new tool in the workplace. They can detect the type of lifting equipment used and identify different types of loads.
The computer system also monitors how employees use their tools to provide real-time warnings if they walk under an insecure suspended load.
AI and computer vision systems can monitor loads on an elevated platform. For example, they will detect whether workers are wearing PPE, using the equipment correctly, and over-crowding on scaffolding. These eyes can also warn against entry into exclusion zones that could potentially harm people in the area below such a situation.
Computer vision can detect fires within 10-15 seconds to give a timely warning. The system integrates with local buzzers, PA systems, display screens, email and SMS notifications, and push alerts, so people know the danger in time.
It also allows for quick rescue during emergencies by monitoring people trapped or stuck around the site during an emergency such as a fire.
In a world where machines and robotics are increasingly prevalent, safety is of the utmost priority. AI systems can detect when employees enter hazardous zones or near dangerous machinery. Next, the system will send real-time warnings to prevent accidents.
Machine operators will get alerts as they may overlook an employee nearby because they are focused on other things. In this way, there will be fewer chances of accidents occurring by monitoring maintenance levels as well.
This safeguard would prevent accidents from occurring between workers who are unaware that they have strayed into the unsafe territory by accident, which might cause injury to themselves and others around them before it's too late.
With instant alerts for potential hazards coming through via our state-of-the-art system, you'll always know where your staff members are at any given time, so there will never be another lost life because someone didn't get out of the way fast enough when something terrible happened nearby without anyone noticing sooner
Today, there is a new technology that can monitor the use of PPE at work. It includes safety helmets, gloves, eye protection, and more!
Developers need to program AI and computer vision systems to track whether or not you are using this equipment correctly.
Manufacturers should make sure each employee has their kind of personal protective gear. In addition, the workers should be aware of health risks while working with hazardous substances like asbestos which could cause cancer if exposure continues for years without proper protection.
Modern workplaces are increasingly implementing facial recognition software to minimize the amount of time spent monitoring their employees. Still, it's a complex system to implement without already having existing CCTV infrastructure.
However, the cost-effectiveness and low-level investment in AI solutions make them more appealing for businesses looking for quick ways to upgrade their security with minimal capital expenditure.
AI computer vision use cases: Image Segmentation of Scans in Public Health
Modern medicine relies heavily on the study of images, scans, and photographs. Computer vision technologies promise to simplify this process and prevent false diagnoses and reduce treatment costs.
Computer vision cannot replace medical professionals but instead, work alongside them as a helpful tool. For example, image segmentation can help diagnose by identifying relevant areas on 2D or 3D scans and colorizing those portions so that doctors can skim through black-and-white images more quickly when looking for something specific.
CT Scans are an essential tool for medical professionals when it comes to identifying infections.
Scientists used image segmentation to identify The COVID-19 pandemic. In addition, image segmentation is a helpful way it detects suspicious areas on CT scans.
It helps physicians and scientists deduce how long a patient has had their infection, where they contracted it from (if possible), or what stage the disease is in that body part.
This research will be essential in aiding those who contract this type of virus and helping researchers find cures by studying past cases more closely than ever before.
One of the most promising developments in healthcare is computer vision.
This technology makes it easier to diagnose and monitor disease. In addition, scientists can use the data generated from the process in tests and experiments on other subjects.
Researchers can spend more time on experiments rather than handling tedious tasks such as data collection when they have access to collected images from patients' MRI scans that machine-learning algorithms have processed.
AI computer vision use cases: Measuring blood loss accurately
The Orlando Health Winnie Palmer Hospital for Women and Babies has found a way to save mothers from postpartum hemorrhaging by using computer vision.
In the past, nurses would manually count surgical sponges after childbirth to keep track of blood loss - but now, all they need is an iPad with this AI-powered tool that analyzes pictures taken during surgery.
This app can measure how much fluid was lost before or after birth, preventing women from bleeding out when giving birth.
Imagine not knowing how much blood you've lost after childbirth. But, thanks to new technology, that's no longer the case for mothers at one hospital where 14 thousand births occur every year.
This groundbreaking computer vision has helped doctors estimate more accurately and treat patients accordingly when they need medical attention post-delivery!
AI computer vision use cases: Timely identification of diseases
Biomedical research is a complex field to be in because it often requires foresight of what will happen. As we all know, sometimes identifying conditions early on can save lives, while other times it might just prolong them.
Deep down inside, though, everyone wants their loved ones and friends alive and well for as long as possible!
AI-like pattern recognition tools will help doctors diagnose patients much earlier. Treatment plans could start immediately before things get out of control, if at any point along the journey.
Several computer vision technologies have improved health and safety in specific industries in the last decade. One such example is using facial recognition software for security purposes at airports or other public spaces.
Another is reducing hazardous environments by giving workers real-time information about their surroundings that can help prevent accidents from happening.
What are your thoughts on how you might use computer vision to improve health and workplace safety? Let us know!