Deepmind GATO: new AI model

DeepMind is one of the most well-known AI companies dedicated to the advancement of artificial intelligence. With several programs, it aims to offer new ideas and improvements in machine learning, engineering, simulation, and computer infrastructure. The remarkable all-in-one machine learning kit has recently gained popularity in the worldwide tech market.Gato as the agent is known, is deepmind's generalist AI that can perform many different tasks that humans can do, without carving a niche for itself as an expert on one task. Gato can perform more than 600 different tasks, such as playing video games, captioning images and moving real-world robotic arms. Gato is a multi-modal, multi-task, multi-embodiment generalist policy.
If future AI systems are as intelligent as humans, they will need to use different skills and pieces of information to complete various tasks in different situations. In other words, they need general intelligence as humans do. This type of system is called "artificial general intelligence" (AGI). AGI systems could lead to many great new ideas, but they could also become more intelligent than humans and "superintelligent." If researchers didn't align a superintelligent system, it could be hard or even impossible to control and predict its behaviour, leaving humans vulnerable.Gato is a single neural network that can do many different things. DeepMind says it can "play Atari, caption images, chat, stack blocks with a real robot arm, and do much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens." It doesn't have intelligence as humans do yet, but it does have some general abilities.
Gato's training methodology differs slightly from those of other well-known AI agents. For example, the AI system AlphaGo, which beat the world champion Go player Lee Sedol in 2016, was trained mainly by a form of trial and error called reinforcement learning (RL). Expert Go players showed AlphaGo how to play the game when researchers first trained it, but in the next version, AlphaGo Zero. AlphaGo Zero learned how to play games only by playing them itself.Gato was on various control tasks by calculating the mean of 50 results. Researchers compared these averages to the results of trained and fine-tuned expert agents for each given control task. It is essential to note that Gato was also on language, vision, and robotics data, represented in the model. 

Deepmind

DeepMind is an artificial intelligence technology that uses machine learning to solve problems that computers haven't traditionally been able to tackle, such as beating humans at the game Go and predicting the myriad ways in which proteins can fold themselves into functional shapes. DeepMind's tech is already used in real-world applications. For example, it plays a role in slashing energy use at computing data centers and optimizing phone battery life. The company DeepMind began as a London-based startup in 2010 and was acquired by Google in 2014. It's now a subsidiary of Alphabet Inc., the parent company of Google. In September 2022, scientists from DeepMind won the $3 million Breakthrough Prize for their work on the protein-prediction program AlphaFold. 
deepmind science: DeepMind's system is an artificial neural network. That means it's organized as a network of nodes, mimicking the way neurons connect to one another in the brain. Specifically, DeepMind uses a convolutional neural network, which is organized similarly to the human visual cortex, the part of the brain that processes visual information. The advantage of this kind of network is that, using a series of filters and large amounts of training data, the system can pick out particular features from those data. For instance, in image recognition, certain nodes become adept at recognizing a specific feature — for example, an eye or, in audio data, a particular combination of sounds. DeepMind uses raw pixel data as input and learns from experience. The AI uses deep learning on a convolutional neural network, with a model-free reinforcement learning technique called Q-learning.
deepmind hardware: Google DeepMind AI is a software-based AI system that can run on various types of hardware. The specific hardware used by DeepMind can vary depending on the specific application or project being run.Regarding AlphaGO, it was developed by Google DeepMind and was specifically designed to play the board game Go. The hardware used in AlphaGO included a combination of processors and graphics processing units (GPUs) to perform the complex calculations required for game strategy and decision-making.

GATO: A generalist agent

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. DeepMind called Gato a “generalist” might have made it a victim of the AI sector’s excessive hype around AGI. The AI systems of today are called “narrow,” meaning they can only do a specific, restricted set of tasks such as generate text. Some technologists, including some at DeepMind, think that one day humans will develop “broader” AI systems that will be able to function as well as or even better than humans. Though some call this artificial general intelligence, others say it is like "belief in magic.“ Many top researchers, such as Meta’s chief AI scientist Yann LeCun, question whether it is even possible at all.
Gato is a “generalist” in the sense that it can do many different things at the same time. But that is a world apart from a “general” AI that can meaningfully adapt to new tasks that are different from what the model was trained on, says MIT’s Andreas: “We’re still quite far from being able to do that.”

How does Gato work

In a paper detailing Gato, the researchers sought to apply a similar approach found in large-scale language modeling. Gato was trained on data covering different tasks and modalities. This data was serialized into a flat sequence of tokens which was then 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, the paper reads. Upon deployment, a prompt is tokenized, which forms an initial sequence. The environment yields the first observation – which again, is tokenized and appended to the sequence. Gato then samples the action vector autoregressively, one token at a time. Once all tokens comprising the action vector have been sampled, the action is decoded and sent to the environment which steps and yields a new observation. Then the procedure repeats. The DeepMind researchers suggest the model always sees all previous observations and actions within its context window of 1024 tokens.

What is the capital of France?; The system itself is built on a sizable dataset comprising data from both simulated and real-world environments. It was also built using several natural language and image datasets. While Gato can do a host of tasks compared with other AI systems, it appears to struggle to get everything right. In their 40-page paper, the DeepMind researchers showcased examples of some of the tasks it performs. In terms of dialogue, the system provides a relevant response, but tends to be "often superficial or factually incorrect.”For example, when asked what the capital of France is, the system replies ‘Marseille’ on occasion and Paris on another. The research team suggests that such inaccuracies “could likely be improved with further scaling.” Gato also struggles with memory constraints, much to the detriment of learning to adapt to a new task via conditioning on a prompt, like demonstrations of desired behavior. The researchers opted to fine-tune the agent’s parameters on a limited number of demonstrations of a single task and then evaluate the fine-tuned model’s performance in the environment.
Judge me by my size, do you?; Though sizable, 364 million and 1.18 billion are dwarfed in terms of size by the language models that inspired Gato in the first place. Arguably the most famous language model, Open AI’s GPT-3 weighs in at 175 billion parameters. On par with GPT-3 in terms of size is the newly released OPT-175B from Meta. Both however are dwarfed by the 204 billion Hyperclova model from Naver, DeepMind's Gopher which has 280 billion parameters and MT-NLP or Megatron, from Microsoft and Nvidia, which boasts 530 billion. The world’s largest language model belongs to WuDao-2.0 with Chinese researchers claiming it has 1.75 trillion parameters. Google Brain previously developed an AI language model with 1.6 trillion parameters, using what it called Switch Transformers. However, neither of these two were monolithic transformer models, preventing a meaningful ‘apples-to-apples’ comparison.

Deepmind alpha

AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure.The program is designed as a deep learning system. AlphaFold AI software has had two major versions. A team of researchers that used AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. The program was particularly successful at predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing template structures were available from proteins with a partially similar sequence.

How successful is Gato at the tasks it can perform?