I'll start:
* There are balanced perspectives here, i don't care to side with one group. I value experts of all fields, because the problem is multi-dimensional, but they all have very solid qualifications:
Ed Zitron (Journalist):
Probably the most charismatic and vocal critic of the AI industry i've found to be worth listening to regularly. He is sort of just a journalist, but has extensive public relations work in the tech industry. The key point here is that he doesn't deny that LLMs are useful or deny the pattern of growth (Not anymore atleast). He simply thinks the business models around the industry are completely full of bullshit and are lying or misleading people on multiple fronts, but unlike many people sharing his space, he has the numbers and reports to back up his cynicism. Not an expert in AI or the technology, but a voice of real value in financial analysis and media around the topic. When he goes on shows, podcasts and other media platforms, he very rarely steps outside his lane, and is highly intentivized to jump on criticisms. My only concern is that with an established audience of critics, and predisposition to hang around Anti-Ai groups, he might eventually start appealing to that audience too hard and lose credibility.
Demis Hassibis (Prominent google AI-researcher)/ Mustafa Suleyman:
Co-founder of Google's Deepmind project, computer scientist and Ai-researcher (Him and the other Co-founder shared a Noble Prize in 2024 for Alphafold which has massive implications for medical and genetic research).
One of the main reasons i'd shout out almost any Google AI expert as your main reference for the leading AI research and status is because they are the absolute safest company in this whole arrangement (look up their safety researchers for example and what the people leaving their projects are saying/doing). They win whether or not they have AI, every model uses google, and they have a well established international presence that isn't relying on AGI or a business model that requires constant media attention. Demis is a significant voice in Google's AI projects with a very well balanced collection of opinions with slightly less investment than his counterpart Mustafa. Very optimistic on the rate of progress and outcomes, including the idea that we might have a helpful version of AGI far sooner than people think, but well aware that there are issues that need immediate attention and warns of serious failures being very possible. The key to his optimism is pragmatic. There's no monetary value in an unsafe product and companies will take actions in accordance with the issues they encounter (but perhaps not ahead of time). He believes this should be an international project with international regulation, and thinks we should be considering more democratic decision making in the development of AI capabilities. The combination of wanting international action, whilst believing progress is faster than most critics would allow, leaves him very concerned with the commercial and geopolitical race that might throw out the caution he believes would otherwise have confidence in. Given their connection, i'm also interested in seeing Mustafa Suleyman (CEO of Google AI, cofounder of deepmind), and what conflict might arise between these two in the future (if any, given Google's stability). They both don't like the idea of viewing AI as conscious or aiming for consciousness, and seem realistic, balanced, but to some, too heavily incentivized to display a media profile that contrasts with the frontier models and display optimism that maintains a healthy market value.
Geoffrey Hinton (Neural Network expert):
One of the Godfathers of AI, computer scientist/cognitive psychologist, Noble prize laureate for work on neural networks (A major underlying principle in modern LLMs). Also worked for google as recently as 2023, in an effort to have more freedom to talk on AI risk. Has incredibly valuable insight on the nature of a neural network and why some people might be underestimating what these systems are actually capable of, drawing rather compelling comparisons against human learning, exploring the nature of what it ACTUALLY means to "Predict language". Believes the systems are already "intelligent", already experiencing subjective thoughts, and will continue to become more so (even if they remain LLMs) and that concerningly, we have no method of controlling entities more intelligent than us (Which is almost definitely the trajectory of the technology overall). However, he is also not involved in modern LLM projects or AI (although he is still well-read), and presents a bias on the topic of neural networks. I have heard modern researchers involved in the frontier projects specifically address his takes on neural networks in LLMs and effectively say "His understanding is outdated and too theoretical". Still, if you need someone to be giving you the doomsday prompt, this is the man i credit the most for thought-provoking and concerning evaluations of the potential of current AI (Given that its pointless to make comments on future AI without context).
David Krakauer (Complexity scientist):
One of the most well-spoken genius-level scientists you're going to come across. Evolutionary biologist, "complexity" scientist and President of the Santa Fe Institute (With computer science undergrad and history). For context on why you should listen to him, basically, Santa Fe is genius-only territory, where unconventional thinking is rewarded. They are explicitly aimed at tackling complex, adaptaptive systems where your typical specialized science research groups would struggle, encouraging cross-disciplinary thoughts and interaction. Evolution is also a very relevant topic when thinking about AI's capabilities in comparison to humans. His primary concern is not the death of humans by AI hands or even it controlling us, but the death of our intelligence and the way our reliance upon it might cripple humanity for generations to come. In contrast to Geoffrey, he is INCREDIBLY skeptical of LLM and neural network intelligence, and has a high degree of confidence in our ability to tackle the problem of a rogue AI. But thinks even a non-malicious AI will lead to many issues in the future.
There is a high degree of informed philosophy in his approach, and that grants him a very relevant position of reference on the issues, though subject to the same flaws as any other person in his position.
Terrence Tao (mathematician)/ ANY field-leading mathematician:
Widely regarding as one of the best mathematicians of this last generation, and a fields medal recipient that actively uses AI for mathematics and a University professor
The point of including him (or any other world-class mathematician) was not as a AI-expert, but as a benchmark for the tech in a field where the software/tech is unusually useful and likely one of its strongest areas to develop given recent news. Especially relevant to offer when we have a bunch of random internet people making claims about AI and maths with no context outside the news "AI solves a bunch of difficult equations". Being a professor, working with students is also relevant here.
Terrence believes that AI will transform mathematics, but makes clear that whilst the technology is genuinely useful, he does not feel the AI is transforming things for the better currently and that anything it solves still needs mathematicians understand the concepts in detail. He is very relaxed and unswerving in the stance that "solving" the problem is different from understanding why the solution matters, and is worried that Ai-generated findings will overwhelm human institutions and result in a gradual loss of quality in mathematical capability across both students and the field at wide. He initially had compared AI to having an average graduate assistant and felt only mildly impressed by the tool, as it could take cost more time than it saved. However, he now believes it's ready for serious use and they save him a noticeable amount of time. His criticism remains that these systems need verification and that verification is not a simple problem either. His stance is best summed up with "it's a problem, but it's a good problem to have". However, the recent string of math solutions have made him more concerned that the results show the AI-companies are not actually interested in supporting the scientific community to genuinely understand, validate and develop the results into something meaningful and useful (And now feels openAI cannot be trusted due to them taking research-findings without consent from mathematicians in order to solve problems that the mathematicians had already made signficant progress on), but acknowledges the results are significant and innovative.
Anyone in the media space will see immediately that i've intentionally left out some very high profile names and people leaving the frontier companies. The issue is simply that I have not found either; their stances to be rigorously defendable, applicable to all AI/companies, and/or their circumstances are suspect. There are always parallel incentives in this conversation, particularly to people who gain prominence quickly, and most of the big names in the doomsday camps have admitted to hyperbole for the sake of generating a discussion and getting political/media attention. The prime example, Roman Yampolskiy has good points which mirror Geoffrey's, but one of these is a leading expert with nuanced discussions on current and theoretical possibilities, the other is a regular expert who by his own admission is not interested in nuance, just the outcome of the discussion, and understands that he needs to be as dramatic as possible without losing credibility, if he wants people to take action. Ei. Generate fear>generate understanding for people like him.
Would love more voices if you have good ones!
