In today’s column, I am going to do a deep dive into what is meant by the oft-mentioned terms known as Artificial Intelligence (AI) and Artificial General Intelligence (AGI).
I walk you thru the likes of AI and AGI and what they mean, including a recently posted proposal by Google DeepMind about encompassing levels of autonomy. Good stuff.
Researchers are utilizing a reinforcement learning method known as ‘curiosity-driven’ training for tasks like calling the elevator, opening doors, and sorting boxes.
Swiss-Mile’s ANYmal robot is being trained to perform practical tasks by manipulating its wheeled legs and arms.
The robots offer a modular, multi-functional tool may help to efficiently and economically execute a spectrum of tasks on the lunar surface.
The Japanese startup was selected as part of 14 firms selected by DARPA for its LunA-10 study.
The alliance aims to open-source the development of artificial intelligence and take on the bad boys of AI, Microsoft, OpenAI, and Google.
Major names in the technology industry, such as IBM, Meta, and many others who seemed to have been left out of the race to develop artificial intelligence (AI) models, have now teamed up to form the AI Alliance.
The collaborative effort also includes government and research organizations and a few startups that will work together to “support open innovation and open science in AI”, a press release from IBM about the alliance said.
Ever since OpenAI released ChatGPT last year, technology companies have been caught in a frenzy to release to own AI models that can deliver text, visual, and even audio content with the help of machine learning. Although these models are still far from attaining artificial general intelligence (AGI), a handful of names have taken the lead in this arena.
A team of AI researchers at Google’s DeepMind project have developed a type of AI system that is able to demonstrate social learning capabilities. In their paper published in the journal Nature Communications, the group describes how they developed an AI application that showed it was capable of learning new skills in a virtual world by copying the actions of an implanted “expert.”
Most AI systems, such as ChatGPT, gain their knowledge through exposure to huge amounts of data, such as from repositories on the Internet. But such an approach, those in the industry have noted, is not very efficient. Therefore many in the field continue to look for other ways to teach AI systems to learn.
One of the most popular approaches used by researchers is to attempt to mimic the process by which humans learn. Like traditional AI apps, humans learn by exposure to known elements in an environment and by following the examples of others who know what they are doing. But unlike AI apps, humans pick things up without the need for huge numbers of examples. A child can learn to play the game of Jacks, for example, after watching others play for just a few minutes—an example of cultural transmission. In this new effort, the research team has attempted to replicate this process using AI constrained to a virtual world.