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 4 trends that modified AI in 2023

 4 trends that modified AI in 2023

This has been certainly one of the craziest years in AI in an extended time: countless product launches, boardroom coups, intense policy debates about AI doom, and a race to seek out the following big thing. But we’ve also seen concrete tools and policies geared toward getting the AI sector to behave more responsibly and hold powerful players accountable. That provides me lots of hope for the long run of AI. 

Here’s what 2023 taught me: 

1. Generative AI left the lab with a vengeance, however it’s not clear where it would go next

The 12 months began with Big Tech going all in on generative AI. The runaway success of OpenAI’s ChatGPT prompted every major tech company to release its own version. This 12 months might go down in history because the 12 months we saw probably the most AI launches: Meta’s LLaMA 2, Google’s Bard chatbot and Gemini, Baidu’s Ernie Bot, OpenAI’s GPT-4, and a handful of other models, including one from a French open-source challenger, Mistral. 

But despite the initial hype, we haven’t seen any AI applications grow to be an overnight success. Microsoft and Google pitched powerful AI-powered search, however it turned out to be more of a dud than a killer app. The elemental flaws in language models, comparable to the indisputable fact that they continuously make stuff up, led to some embarrassing (and, let’s be honest, hilarious) gaffes. Microsoft’s Bing would continuously reply to people’s questions with conspiracy theories, and suggested that a Latest York Times reporter leave his wife. Google’s Bard generated factually incorrect answers for its marketing campaign, which wiped $100 billion off the corporate’s share price.

There’s now a frenetic hunt for a well-liked AI product that everybody will wish to adopt. Each OpenAI and Google are experimenting with allowing corporations and developers to create customized AI chatbots and letting people construct their very own applications using AI—no coding skills needed. Perhaps generative AI will find yourself embedded in boring but useful tools to assist us boost our productivity at work. It’d take the shape of AI assistants—perhaps with voice capabilities—and coding support. Next 12 months shall be crucial in determining the actual value of generative AI.

2. We learned quite a bit about how language models actually work, but we still know little or no

Though tech corporations are rolling out large language models into products at a frenetic pace, there remains to be quite a bit we don’t find out about how they work. They make stuff up and have severe gender and ethnic biases. This 12 months we also came upon that different language models generate texts with different political biases, and that they make great tools for hacking people’s private information. Text-to-image models may be prompted to spit out copyrighted images and pictures of real people, they usually can easily be tricked into generating disturbing images. It’s been great to see a lot research into the failings of those models, because this might take us a step closer to understanding why they behave the best way they do, and ultimately fix them.

Generative models may be very unpredictable, and this 12 months there have been plenty of attempts to attempt to make them behave as their creators want them to. OpenAI shared that it uses a way called reinforcement learning from human feedback, which uses feedback from users to assist guide ChatGPT to more desirable answers. A study from the AI lab Anthropic showed how easy natural-language instructions can steer large language models to make their results less toxic. But sadly, lots of these attempts find yourself being quick fixes reasonably than everlasting ones. Then there are misguided approaches like banning seemingly innocuous words comparable to “placenta” from image-generating AI systems to avoid producing gore. Tech corporations provide you with workarounds like these because they don’t know why models generate the content they do. 

We also got a greater sense of AI’s true carbon footprint. Generating a picture using a robust AI model takes as much energy as fully charging your smartphone, researchers on the AI startup Hugging Face and Carnegie Mellon University found. Until now, the precise amount of energy generative AI uses has been a missing piece of the puzzle. More research into this might help us shift the best way we use AI to be more sustainable. 

3. AI doomerism went mainstream

Chatter concerning the possibility that AI poses an existential risk to humans became familiar this 12 months. A whole bunch of scientists, business leaders, and policymakers have spoken up, from deep-learning pioneers Geoffrey Hinton and Yoshua Bengio to the CEOs of top AI firms, comparable to Sam Altman and Demis Hassabis, to the California congressman Ted Lieu and the previous president of Estonia Kersti Kaljulaid.

Existential risk has grow to be certainly one of the biggest memes in AI. The hypothesis is that sooner or later we are going to construct an AI that’s far smarter than humans, and this may lead to grave consequences. It’s an ideology championed by many in Silicon Valley, including Ilya Sutskever, OpenAI’s chief scientist, who played a pivotal role in ousting OpenAI CEO Sam Altman (after which reinstating him just a few days later). 

But not everyone agrees with this concept. Meta’s AI leaders Yann LeCun and Joelle Pineau have said that these fears are “ridiculous” and the conversation about AI risks has grow to be “unhinged.” Many other power players in AI, comparable to researcher Joy Buolamwini, say that specializing in hypothetical risks distracts from the very real harms AI is causing today. 

Nevertheless, the increased attention on the technology’s potential to cause extreme harm has prompted many vital conversations about AI policy and animated lawmakers all around the world to take motion. 

4. The times of the AI Wild West are over

Because of ChatGPT, everyone from the US Senate to the G7 was talking about AI policy and regulation this 12 months. In early December, European lawmakers wrapped up a busy policy 12 months once they agreed on the AI Act, which can introduce binding rules and standards on develop the riskiest AI more responsibly. It can also ban certain “unacceptable” applications of AI, comparable to police use of facial recognition in public places. 

The White House, meanwhile, introduced an executive order on AI, plus voluntary commitments from leading AI corporations. Its efforts aimed to bring more transparency and standards for AI and gave lots of freedom to agencies to adapt AI rules to suit their sectors. 

One concrete policy proposal that got lots of attention was watermarks—invisible signals in text and pictures that may be detected by computers, to be able to flag AI-generated content. These might be used to trace plagiarism or help fight disinformation, and this 12 months we saw research that succeeded in applying them to AI-generated text and images.

It wasn’t just lawmakers that were busy, but lawyers too. We saw a record variety of  lawsuits, as artists and writers argued that AI corporations had scraped their mental property without their consent and with no compensation. In an exciting counter-offensive, researchers on the University of Chicago developed Nightshade, a brand new data-poisoning tool that lets artists fight back against generative AI by messing up training data in ways that would cause serious damage to image-generating AI models. There’s a resistance brewing, and I expect more grassroots efforts to shift tech’s power balance next 12 months. 

Deeper Learning

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

OpenAI has announced the primary results from its superalignment team, its in-house initiative dedicated to stopping a superintelligence—a hypothetical future AI that may outsmart humans—from going rogue. The team is led by chief scientist Ilya Sutskever, who was a part of the group that just last month fired OpenAI’s CEO, Sam Altman, only to reinstate him just a few days later.

Business as usual: Unlike lots of the company’s announcements, this heralds no big breakthrough. In a low-key research paper, the team describes a way that lets a less powerful large language model supervise a more powerful one—and suggests that this could be a small step toward determining how humans might supervise superhuman machines. Read more from Will Douglas Heaven. 

Bits and Bytes

Google DeepMind used a big language model to unravel an unsolvable math problem
In a paper published in Nature, the corporate says it’s the primary time a big language model has been used to find an answer to a long-standing scientific puzzle—producing verifiable and helpful recent information that didn’t previously exist. (MIT Technology Review)

This recent system can teach a robot a straightforward household task inside 20 minutes
A brand new open-source system, called Dobb-E, was trained using data collected from real homes. It may well help to show a robot open an air fryer, close a door, or straighten a cushion, amongst other tasks. It could also help the sphere of robotics overcome certainly one of its biggest challenges: a scarcity of coaching data.  (MIT Technology Review)

ChatGPT is popping the web into plumbing
German media giant Axel Springer, which owns Politico and Business Insider, announced a partnership with OpenAI, by which the tech company will give you the option to make use of its news articles as training data and the news organizations will give you the option to make use of ChatGPT to do summaries of stories. This column has a clever point: tech corporations are increasingly becoming gatekeepers for online content, and journalism is just “plumbing for a digital faucet.” (The Atlantic)

Meet the previous French official pushing for looser AI rules after joining startup Mistral
A profile of Mistral AI cofounder Cédric O, who was once France’s digital minister. Before joining France’s AI unicorn, he was a vocal proponent of strict laws for tech, but he lobbied hard against rules within the AI Act that might have restricted Mistral’s models. He was successful: the corporate’s models don’t meet the computing threshold set by the law, and its open-source models are also exempt from transparency obligations. (Bloomberg) 


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