Hide table of contents
This is a linkpost for https://youtu.be/NqmUBZQhOYw

This video is based on this article. @jai has written both the original article and the script for the video. 

Script:

The ACM Turing Award is the highest distinction in computer science, comparable to the Nobel Prize. In 2018 it was awarded to three pioneers of the deep learning revolution: Geoffrey Hinton, Yoshua Bengio, and Yann LeCun.

In May 2023, Geoffrey Hinton left Google so that he could speak openly about the dangers of advanced AI, agreeing that “it could figure out how to kill humans” and saying “it's not clear to me that we can solve this problem.”

Later that month, Yoshua Bengio wrote a blog post titled "How Rogue AIs may Arise",  in which he defined a "rogue AI" as "an autonomous AI system that could behave in ways that would be catastrophically harmful to a large fraction of humans, potentially endangering our societies and even our species or the biosphere."

Yann LeCun continues to refer to thoseanyone suggesting that we're facing severe and imminent risk as “professional scaremongers” and says it's a “simple fact” that “the people who are terrified of AGI are rarely the people who actually build AI models.”

LeCun is a highly accomplished researcher, but in light of Bengio and Hinton's recent comments it's clear that he's misrepresenting the field whether he realizes it or not. There is not a consensus among professional researchers that AI research is safe. Rather, there is considerable and growing concern that advanced AI could pose extreme risks, and this concern is shared by not only both of LeCun's award co-recipients, but the headsleaders of all three leading AI labs (OpenAI, Anthropic, and Google DeepMind):

Demis Hassabis, CEO of DeepMind, said in an interview with Time Magazine: "When it comes to very powerful technologies—and obviously AI is going to be one of the most powerful ever—we need to be careful. Not everybody is thinking about those things. It’s like experimentalists, many of whom don’t realize they’re holding dangerous material."

Anthropic, in their public statement "Core Views on AI Safety", says: “One particularly important dimension of uncertainty is how difficult it will be to develop advanced AI systems that are broadly safe and pose little risk to humans. Developing such systems could lie anywhere on the spectrum from very easy to impossible.”

And OpenAI, in their blog post "Planning for AGI and Beyond", says "Some people in the AI field think the risks of AGI (and successor systems) are fictitious; we would be delighted if they turn out to be right, but we are going to operate as if these risks are existential." Sam Altman, the current CEO of OpenAI, once said "Development of superhuman machine intelligence (SMI) is probably the greatest threat to the continued existence of humanity. "

There are objections one could raise to the idea that advanced AI poses significant risk to humanity, but "it's a fringe idea that actual AI experts do not take seriously" is no longer among them. Instead, a growing share of experts are echoing the conclusion reached by Alan Turing, considered by many to be the father of computer science and artificial intelligence, back in 1951: "[I]t seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. [...] At some stage therefore we should have to expect the machines to take control." 


 

33

0
0

Reactions

0
0

More posts like this

No comments on this post yet.
Be the first to respond.
More from Writer
Curated and popular this week
 ·  · 10m read
 · 
I wrote this to try to explain the key thing going on with AI right now to a broader audience. Feedback welcome. Most people think of AI as a pattern-matching chatbot – good at writing emails, terrible at real thinking. They've missed something huge. In 2024, while many declared AI was reaching a plateau, it was actually entering a new paradigm: learning to reason using reinforcement learning. This approach isn’t limited by data, so could deliver beyond-human capabilities in coding and scientific reasoning within two years. Here's a simple introduction to how it works, and why it's the most important development that most people have missed. The new paradigm: reinforcement learning People sometimes say “chatGPT is just next token prediction on the internet”. But that’s never been quite true. Raw next token prediction produces outputs that are regularly crazy. GPT only became useful with the addition of what’s called “reinforcement learning from human feedback” (RLHF): 1. The model produces outputs 2. Humans rate those outputs for helpfulness 3. The model is adjusted in a way expected to get a higher rating A model that’s under RLHF hasn’t been trained only to predict next tokens, it’s been trained to produce whatever output is most helpful to human raters. Think of the initial large language model (LLM) as containing a foundation of knowledge and concepts. Reinforcement learning is what enables that structure to be turned to a specific end. Now AI companies are using reinforcement learning in a powerful new way – training models to reason step-by-step: 1. Show the model a problem like a math puzzle. 2. Ask it to produce a chain of reasoning to solve the problem (“chain of thought”).[1] 3. If the answer is correct, adjust the model to be more like that (“reinforcement”).[2] 4. Repeat thousands of times. Before 2023 this didn’t seem to work. If each step of reasoning is too unreliable, then the chains quickly go wrong. Without getting close to co
 ·  · 1m read
 · 
JamesÖz
 ·  · 3m read
 · 
Why it’s important to fill out this consultation The UK Government is currently consulting on allowing insects to be fed to chickens and pigs. This is worrying as the government explicitly says changes would “enable investment in the insect protein sector”. Given the likely sentience of insects (see this summary of recent research), and that median predictions estimate that 3.9 trillion insects will be killed annually by 2030, we think it’s crucial to try to limit this huge source of animal suffering.  Overview * Link to complete the consultation: HERE. You can see the context of the consultation here. * How long it takes to fill it out: 5-10 minutes (5 questions total with only 1 of them requiring a written answer) * Deadline to respond: April 1st 2025 * What else you can do: Share the consultation document far and wide!  * You can use the UK Voters for Animals GPT to help draft your responses. * If you want to hear about other high-impact ways to use your political voice to help animals, sign up for the UK Voters for Animals newsletter. There is an option to be contacted only for very time-sensitive opportunities like this one, which we expect will happen less than 6 times a year. See guidance on submitting in a Google Doc Questions and suggested responses: It is helpful to have a lot of variation between responses. As such, please feel free to add your own reasoning for your responses or, in addition to animal welfare reasons for opposing insects as feed, include non-animal welfare reasons e.g., health implications, concerns about farming intensification, or the climate implications of using insects for feed.    Question 7 on the consultation: Do you agree with allowing poultry processed animal protein in porcine feed?  Suggested response: No (up to you if you want to elaborate further).  We think it’s useful to say no to all questions in the consultation, particularly as changing these rules means that meat producers can make more profit from sel