A close look reveals that the newest systems, including DeepMind’s much-hyped Gato, are still stymied by the same old problems.
Category: robotics/AI – Page 1,458
AI translates maths problems into code to make them easier to solve
An artificial intelligence can translate maths problems written in plain English to formal code, making them easier for computers to solve in a crucial step towards building a machine capable of discovering new maths.
Axon halts plans to make a drone equipped with a Taser
Axon has paused work on a project to build drones equipped with its Tasers. A majority of its artificial intelligence ethics board quit after the plan was announced last week.
Nine of the 12 members said in a resignation letter that, just a few weeks ago, the board voted 8–4 to recommend that Axon shouldn’t move forward with a pilot study for a Taser-equipped drone concept. “In that limited conception, the Taser-equipped drone was to be used only in situations in which it might avoid a police officer using a firearm, thereby potentially saving a life,” the nine board members wrote. They noted Axon might decline to follow that recommendation and were working on a report regarding measures the company should have in place were it to move forward.
The nine individuals said they were blindsided by an announcement from the company last Thursday — nine days after 19 elementary school students and two teachers were killed in a mass shooting in Uvalde, Texas — about starting development of such a drone. It had an aim of “incapacitating an active shooter in less than 60 seconds.” Axon said it “asked the board to re-engage and consider issuing further guidance and feedback on this capability.”
Ionic Liquid-Based Reservoir Computers: Efficient and Flexible Edge Computing
Researchers from Japan design a tunable physical reservoir device based on dielectric relaxation at an electrode-ionic liquid interface.
In the near future, more and more artificial intelligence processing will need to take place on the edge — close to the user and where the data is collected rather than on a distant computer server. This will require high-speed data processing with low power consumption. Physical reservoir computing is an attractive platform for this purpose, and a new breakthrough from scientists in Japan just made this much more flexible and practical.
Physical reservoir computing (PRC), which relies on the transient response of physical systems, is an attractive machine learning framework that can perform high-speed processing of time-series signals at low power. However, PRC systems have low tunability, limiting the signals it can process. Now, researchers from Japan present ionic liquids as an easily tunable physical reservoir device that can be optimized to process signals over a broad range of timescales by simply changing their viscosity.
Is DeepMind’s Gato the world’s first AGI?
Artificial general intelligence (AGI) is back in the news thanks to the recent introduction of Gato from DeepMind. As much as anything, AGI invokes images of the Skynet (of Terminator lore) that was originally designed as threat analysis software for the military, but it quickly came to see humanity as the enemy. While fictional, this should give us pause, especially as militaries around the world are pursuing AI-based weapons.
However, Gato does not appear to raise any of these concerns. The deep learning transformer model is described as a “generalist agent” and purports to perform 604 distinct and mostly mundane tasks with varying modalities, observations and action specifications. It has been referred to as the Swiss Army Knife of AI models. It is clearly much more general than other AI systems developed thus far and in that regard appears to be a step towards AGI.
Multimodal systems are not new — as evidenced by GPT-3 and others. What is arguably new is the intent. By design, GPT-3 was intended to be a large language model for text generation. That it could also produce images from captions, generate programming code and other functions were add-on benefits that emerged after the fact and often to the surprise of AI experts.