Four trends that changed AI in 2023

Existential threat has turn out to be one of many biggest memes in AI. The speculation is that at some point we are going to construct an AI that’s far smarter than humans, and this might result in grave penalties. It’s an ideology championed by many in Silicon Valley, together with Ilya Sutskever, OpenAI’s chief scientist, who performed a pivotal position in ousting OpenAI CEO Sam Altman (after which reinstating him a number of days later). 

However not everybody agrees with this concept. Meta’s AI leaders Yann LeCun and Joelle Pineau have stated that these fears are “ridiculous” and the dialog about AI dangers has turn out to be “unhinged.” Many different energy gamers in AI, akin to researcher Joy Buolamwini, say that specializing in hypothetical dangers distracts from the very actual harms AI is inflicting as we speak. 

Nonetheless, the elevated consideration on the expertise’s potential to trigger excessive hurt has prompted many necessary conversations about AI coverage and animated lawmakers everywhere in the world to take motion. 

4. The times of the AI Wild West are over

Due to ChatGPT, everybody from the US Senate to the G7 was talking about AI coverage and regulation this yr. In early December, European lawmakers wrapped up a busy coverage yr after they agreed on the AI Act, which is able to introduce binding guidelines and requirements on the best way to develop the riskiest AI extra responsibly. It should additionally ban sure “unacceptable” purposes of AI, akin to police use of facial recognition in public locations. 

The White Home, in the meantime, launched an executive order on AI, plus voluntary commitments from main AI firms. Its efforts aimed to convey extra transparency and requirements for AI and gave lots of freedom to businesses to adapt AI guidelines to suit their sectors. 

One concrete coverage proposal that acquired lots of consideration was watermarks—invisible indicators in textual content and pictures that may be detected by computer systems, with the intention to flag AI-generated content material. These may very well be used to trace plagiarism or assist combat disinformation, and this yr we noticed analysis that succeeded in making use of them to AI-generated text and images.

It wasn’t simply lawmakers that have been busy, however attorneys too. We noticed a record number of  lawsuits, as artists and writers argued that AI firms had scraped their intellectual property with out their consent and with no compensation. In an thrilling counter-offensive, researchers on the College of Chicago developed Nightshade, a brand new data-poisoning device that lets artists combat again in opposition to generative AI by messing up coaching information in ways in which may trigger critical harm to image-generating AI fashions. There’s a resistance brewing, and I anticipate extra grassroots efforts to shift tech’s energy steadiness subsequent yr. 

Deeper Studying

Now we all know what OpenAI’s superalignment workforce has been as much as

OpenAI has introduced the primary outcomes from its superalignment workforce, its in-house initiative devoted to stopping a superintelligence—a hypothetical future AI that may outsmart people—from going rogue. The workforce is led by chief scientist Ilya Sutskever, who was a part of the group that simply final month fired OpenAI’s CEO, Sam Altman, solely to reinstate him a number of days later.

Enterprise as standard: Not like most of the firm’s bulletins, this heralds no massive breakthrough. In a low-key analysis paper, the workforce describes a method that lets a much less highly effective massive language mannequin supervise a extra highly effective one—and means that this could be a small step towards determining how people may supervise superhuman machines. Read more from Will Douglas Heaven. 

Bits and Bytes

Google DeepMind used a big language mannequin to unravel an unsolvable math downside
In a paper printed in Nature, the corporate says it’s the first time a big language mannequin has been used to find an answer to a long-standing scientific puzzle—producing verifiable and useful new info that didn’t beforehand exist. (MIT Technology Review)