Joscha Bach and Jim start by talking about the difference between mind & brain, and the body & environment’s connection to mind & emotions. Joscha then offers his views on some popular consciousness theories & thinkers: consciousness as frequency, Global Workspace Theory, Integrated Information Theory, Functionalism, Daniel Dennet, and Roger Penrose. While covering these theories & thinkers they talk about GPT-3, learning & memory, what it means to understand, intuitive vs analytical intelligence, dreaming vs reality, attention & agents, psychedelics, magical phenomena, areas worth exploring to improve AI, and much more.
Facebook (now Meta) popularized the Silicon Valley ethos with the saying “Move fast and break things”. This approach might have worked when disrupting the social media business, but it’s causing all sorts of problems for them as well as other major AI players. Breaking things and moving fast might be the reason why so many AI projects are failing. According to an MIT study, over 85% of AI projects fail to deliver their stated objectives, and 70% of data science projects never make it to fruition. Clearly moving fast and breaking things doesn’t work if you’re not getting closer to success.
There’s a difference between Iterating to Success and Breaking Things.
The oft-cited Silicon Valley ethos of “Move fast and break things” isn’t working that well for AI.
Researchers have developed a machine learning algorithm that could help reduce charging times and prolong battery life in electric vehicles by predicting how different driving patterns affect battery performance, improving safety and reliability.
The researchers, from the University of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power electric vehicles by suggesting routes and driving patterns that minimize battery degradation and charging times.
The team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.
A team of researchers at the Max Planck Institute for Intelligent Systems, working with a pair of colleagues from the Harbin Institute of Technology, has developed a tiny actuated gearbox that can be used to give very tiny robots more power. In their paper published in the journal Science Robotics, the group describes how their gearbox works and the power improvements observed in several types of tiny robots.
Over the past several years, scientists have been working toward the development of tiny robots that can be injected into the human body to carry out medical procedures. The hope is that such robots can be sent to find and destroy cancerous tumors, for example. Such tiny robots are too small to carry their own power plant; thus, they must be manipulated using an external magnetic field. Unfortunately, as the robots grow ever tinier, their power diminishes as they have too little mass. In this new effort, the researchers have found a way to increase the power of the tiny robots using a tiny gearbox that helps them become stronger.
The gearbox comes with a magnet on its end to harness the power in a magnetic field via the gears in the box. And the gearbox is able to magnify the power of a robot using clever features including elastic components and mechanical linkages.
Three-dimensional computing-in-memory circuits based on vertical resistive random-access memory and complementary metal–oxide–semiconductor technologies can be used to create efficient hardware for artificial neural networks.
A team of researchers at Google’s Deep Mind London project, has taught animated players how to play a realistic version of soccer on a computer screen. In their paper published in the journal Science Robotics, the group describes teaching the animated players to play as solo players and also in teams.
For several years, robot engineers have been working diligently to create robots capable of playing soccer. Such work has resulted in competition between various groups to see who can devise the best robot players. And that has led to the creation of RoboCup, which has several leagues, both in the real world and simulated. In this new effort, the researchers applied a new degree of artificial intelligence programming and learning networks to teach simulated robots how to play soccer without ever giving them the rules.
The idea behind the new approach is to get simulated soccer players to learn to play the game the same way humans do—by watching how others do it. It also involved starting from pretty much ground zero. The simulated players first had to learn how to walk, then to run and kick a ball around. At each new level, the AI systems were shown video of real-world soccer players, which allowed them to learn not just the basics of soccer playing, but to mimic the way professional athletes move as they engage in high level sporting events.
Two of America’s top chipmakers have been ordered to stop selling some of their technology to China that can be used for artificial intelligence.
Nvidia (NVDA) and AMD (AMD) said Wednesday that they had been told by the US government to halt exports of certain high-performance chips to the world’s second largest economy.
In a regulatory filing, Nvidia said that it had been told by US officials that the requirement was due to a potential risk of the products being used by, or diverted to, a “military end user.”
North American robot sales continued to set records for year-over-year growth, according to the Association for Advancing Automation. FANUC also saw strong demand.
Since iPhones are one of Apple’s primary revenue streams, they may be cautious about releasing a product that may encroach on their own turf. However, as we’ll suggest below, it may not be an either/or situation for users.
