Sooner or later, an AI agent couldn’t solely recommend issues to do and locations to remain on my honeymoon; it might additionally go a step additional than ChatGPT and e book flights for me. It might keep in mind my preferences and funds for inns and solely suggest lodging that matched my standards. It may additionally keep in mind what I appreciated to do on previous journeys, and recommend very particular issues to do tailor-made to these tastes. It’d even request bookings for eating places on my behalf.
Sadly for my honeymoon, right now’s AI methods lack the form of reasoning, planning, and reminiscence wanted. It’s nonetheless early days for these methods, and there are loads of unsolved analysis questions. However who is aware of—perhaps for our tenth anniversary journey?
Deeper Studying
A technique to let robots study by listening will make them extra helpful
Most AI-powered robots right now use cameras to grasp their environment and study new duties, however it’s changing into simpler to coach robots with sound too, serving to them adapt to duties and environments the place visibility is proscribed.
Sound on: Researchers at Stanford College examined how way more profitable a robotic could be if it’s able to “listening.” They selected 4 duties: flipping a bagel in a pan, erasing a whiteboard, placing two Velcro strips collectively, and pouring cube out of a cup. In every activity, sounds supplied clues that cameras or tactile sensors battle with, like understanding if the eraser is correctly contacting the whiteboard or whether or not the cup comprises cube. When utilizing imaginative and prescient alone within the final take a look at, the robotic may inform 27% of the time whether or not there have been cube within the cup, however that rose to 94% when sound was included. Read more from James O’Donnell.
Bits and Bytes
AI lie detectors are higher than people at recognizing lies
Researchers on the College of Würzburg in Germany discovered that an AI system was considerably higher at recognizing fabricated statements than people. People normally solely get it proper round half the time, however the AI may spot if a press release was true or false in 67% of instances. Nevertheless, lie detection is a controversial and unreliable know-how, and it’s debatable whether or not we must always even be utilizing it within the first place. (MIT Technology Review)
A hacker stole secrets and techniques from OpenAI
A hacker managed to entry OpenAI’s inner messaging methods and steal details about its AI know-how. The corporate believes the hacker was a personal particular person, however the incident raised fears amongst OpenAI staff that China may steal the corporate’s know-how too. (The New York Times)
AI has vastly elevated Google’s emissions over the previous 5 years
Google stated its greenhouse-gas emissions totaled 14.3 million metric tons of carbon dioxide equal all through 2023. That is 48% increased than in 2019, the corporate stated. That is principally as a consequence of Google’s huge push towards AI, which is able to probably make it tougher to hit its objective of eliminating carbon emissions by 2030. That is an totally miserable instance of how our societies prioritize revenue over the local weather emergency we’re in. (Bloomberg)
Why a $14 billion startup is hiring PhDs to coach AI methods from their residing rooms
An fascinating learn in regards to the shift occurring in AI and information work. Scale AI has beforehand employed low-paid information staff in international locations equivalent to India and the Philippines to annotate information that’s used to coach AI. However the large growth in language fashions has prompted Scale to rent extremely expert contractors within the US with the required experience to assist practice these fashions. This highlights simply how essential information work actually is to AI. (The Information)
A brand new “moral” AI music generator can’t write a midway first rate music
Copyright is without doubt one of the thorniest problems going through AI right now. Simply final week I wrote about how AI companies are being forced to cough up for high-quality coaching information to construct highly effective AI. This story illustrates why this issues. This story is about an “moral” AI music generator, which solely used a restricted information set of licensed music. However with out high-quality information, it’s not in a position to generate something even near first rate. (Wired)
