An important use for computer vision and deep learning is self driving cars. Perception and Computer Vision forms about 80% of the work that Self Driving Cars do to drive around. If you want to improve your deep learning skills, this is a great topic to learn about. We just published a deep learning course on the freeCodeCamp.org Sakshay is a machine learning engineer and an excellent teacher.
Earlier this month, DeepMind presented a new "generalist" AI model called Gato. The model can play the video game Atari, caption images, chat, and stack blocks with a real robot arm, the Alphabet-owned AI lab announced. All in all, Gato can do hundreds of different tasks. But while Gato is undeniably fascinating, in the week since its release some researchers have got a bit carried away. One of DeepMind's top researchers and a coauthor of the Gato paper, Nando de Freitas, couldn't contain his excitement.
According to Gartner, AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision-making, and take action. In essence, the concept of AI centres on enabling computer systems to think and act in a more'human' way, by learning from and responding to the vast amounts of information they're able to use. AI is already transforming our everyday lives. From the AI features on our smartphones such as built-in smart assistants, to the AI-curated content and recommendations on our social media feeds and streaming services. As the name suggests, machine learning is based on the idea that systems can learn from data to automate and improve how things are done – by using advanced algorithms (a set of rules or instructions) to analyse data, identify patterns and make decisions and recommendations based on what they find.
The ultimate achievement to some in the AI industry is creating a system with artificial general intelligence (AGI), or the ability to understand and learn any task that a human can. Long relegated to the domain of science fiction, it's been suggested that AGI would bring about systems with the ability to reason, plan, learn, represent knowledge, and communicate in natural language. Not every expert is convinced that AGI is a realistic goal -- or even possible. Gato is what DeepMind describes as a "general-purpose" system, a system that can be taught to perform many different types of tasks. Researchers at DeepMind trained Gato to complete 604, to be exact, including captioning images, engaging in dialogue, stacking blocks with a real robot arm, and playing Atari games. Jack Hessel, a research scientist at the Allen Institute for AI, points out that a single AI system that can solve many tasks isn't new.
Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. During the training phase of Gato, data from different tasks and modalities are serialised into a flat sequence of tokens, batched, and processed by a transformer neural network similar to a large language model. The loss is masked so that Gato only predicts action and text targets.
Humanity has been waiting for self-driving cars for several decades. Thanks to the extremely fast evolution of technology, this idea recently went from "possible" to "commercially available in a Tesla". Deep learning is one of the main technologies that enabled self-driving. It's a versatile tool that can solve almost any problem – it can be used in physics, for example, the proton-proton collision in the Large Hadron Collider, just as well as in Google Lens to classify pictures. Deep learning is a technology that can help solve almost any type of science or engineering problem. CNN is the primary algorithm that these systems use to recognize and classify different parts of the road, and to make appropriate decisions. Along the way, we'll see how Tesla, Waymo, and Nvidia use CNN algorithms to make their cars driverless or autonomous. The first self-driving car was invented in 1989, it was the Automatic Land Vehicle in Neural Network (ALVINN). It used neural networks to detect lines, segment the environment, navigate itself, and drive. It worked well, but it was limited by slow processing powers and insufficient data.
Artificial intelligence has revolutionized every aspect of our lives in this technological age. When we look at self-driving cars, smartphones, electronics, and robotics around us, we can easily discover the opportunities we can create by integrating AI. Besides, next-generation AI processors are much more powerful and can do more image processing, machine vision, machine learning, deep learning and artificial neural networks. The list of top AI chip manufacturers also shows interest from big players such as Intel, Apple, and Nvidia in this industry, positioning them as key competitors in the AI chip market. Therefore, it is easy to assume that we can expect significant growth in AI technology over the next few years with the major tech giants participating.
Understanding and manipulating articulated objects such as doors and drawers is a key skill for robots in human environments. However, it is difficult to train systems that generalize to variations of those objects. The sensory signal comes from an Azure Kinect depth camera, and the agent is a Sawyer BLACK robot. A novel per-point representation of the articulation structure of an object is proposed, called 3D Articulation Flow. A newly-developed 3D vision neural network architecture takes as input a static 3D point cloud and predicts the 3D Articulation Flow of the input under articulation motion.
As robots begin to dominate our everyday lives, Massachusetts Institute of Technology(MIT) researchers have taught a robot how to learn a new pick-and-place task with the help of human demonstrations. The human demonstrations help to "reprogramme" the robot in random poses which it has never encountered earlier. The new technology allows the robot to quickly learn a new skill in 10 to 15 minutes. According to the researchers, the new technology uses a "neural network" to reconstruct 3D images, the system quickly grasps what the neural network has learned and then executes it. The new technology is set to be a gamechanger in e-commerce warehouse storage where robots have to perform a variety of tasks like storing mugs upside down and in various places.