chriskanan

And all those reports of Claude when asked without a system prompt what its name was in Chinese it often would say Qwen or Deepseek, etc. I'd love Anthropic to say they aren't distilling and taking from every model out there, because I'm sure they are. As my mom would say, "the pot calling the kettle black." At least Alibaba and other Chinese companies are giving back to the AI community with detailed scientific papers on how their systems work and releasing open-weight or opensource models. I believe Anthropic has released nothing, and given that they had originally configured Fable to sabotage ML related work because only they can be trusted to do it safely, is just anti-science and anti-aligned with what I would consider good human values. They are way too sanctimonious and I don't trust them at all.

They are doing ta tremendous amount of novel research where American AI companies have "war rooms" to study their papers and models and American labs publish next to nothing. They have to often do more with less. As an AI researcher, Chinese labs are doing tremendous benefit to science whereas some American companies (and I'm American) seem to think only they are able to do AI research responsibility (I've been working on neural networks for 25+ years). I'm pretty sure Fable sabotaged my research codebase (see the news stories about this).

Did you ever watch Star Trek: The Next Generation? The current trajectory is like the Ship's Computer. It know everything humanity has learned and can do a lot. But it can't explore and lacks desires and agency. That's why they made a big deal about the character Data being an entirely new kind of AI. Of course Star Trek has a very different economic system and there is a book called Trekenomics about that. So optimistically people live for themselves and don't persue labor they despise. Half of Americans hate their jobs and live for the dream of retirement when they get to actually do what they want.... But they don't have the same energy anymore.

Jobs are an invention of humanity. About 50% of people dislike their job. People spend much of their lives working. Poverty and inequality are a choice made by society if society chooses poorly.

Slightly more nuanced in that the reciprocal reviewer may have been essentially forced to sign despite having other commitments or may not have even been the lead contributor. Nowadays if a student submits a side project to a top-tier conference then it is required that if any authors have significant publication count in top-tier venues, then one must be a mandatory reviewer. Then one must sign that agreement. Students need to publish, much less so for me, where I really want to publish big innovations rather than increments, but now I get all these mandatory reviewer emails demanding I review for a conference because a student has my name on the paper and I'm the most senior, but I may have just seeded the idea or helped them in significant ways. However, many times those are not my passion projects and is just something a student did that I helped with, but now all AI conferences are demanding I review or hurt a student, where I'm the middle author.

But if anything, I think the whole anti-LLM review philosophy is wrong. If anything we need multiple deep background and research analyses of papers. So many papers are trash or are publishing what has already been done or are missing things. The volume of AI papers makes it impossible for a human alone to really critique work because hundreds of new papers come out a day.

I had lunch with Yann last August, about a week after Alex Wang became his "boss." I asked him how he felt about that, and at the time he told me he would give it a month or two and see how it goes, and then figure out if he should stay or find employment elsewhere. I told him he ought to just create his own company if he decides to leave Meta to chase his own dream, rather than work on the dream's of others.

That said, while I 100% agree with him that LLM's won't lead to human-like intelligence (I think AGI is now an overloaded term, but Yann uses it in its original definition), I'm not fully on board with his world model strategy as the path forward.

I can see some promise with diffusion LLMs, but getting them comparable to the frontier is going to require a ton of work and these closed source solutions probably won't really invigorate the field to find breakthroughs. It is too bad that they are following the path of OpenAI with closed models without details as far as I can tell.

This has been a major problem in hospitals where there are many false alarms and often concurrent alarms for hospital beds: https://pmc.ncbi.nlm.nih.gov/articles/PMC3928208/

Same here. I’m an AI professor, but every time I wanted to try out an idea in my very limited time, I’d spend it all setting things up rather than focusing on the research. It has enabled me to do my own research again rather than relying solely on PhD students. I’ve been able to unblock my students and pursue my own projects, whereas before there were not enough hours in the day.

I think we need to distinguish among kinds of AGI, as the term has become overloaded and redefined over time. I'd argue we need to retire the term and use more appropriate terminology to distinguish between economic automation and human-like synthetic minds. I wrote a post about this here: https://syntheticminds.substack.com/p/retiring-agi-two-paths...

See this study, which is consistent with your thesis: https://www.eurekalert.org/news-releases/814485

Essentially, it claims that modern humans and our ancestors starting with Homo habilis were primarily carnivores for 2 million years. We moved back to an omnivorous diet starting around 85,000 years ago after killing off the megafauna, is the hypothesis.

That's the practical reason for why one might care. Keep in mind that the solar system is rotating around the galaxy, so over time different stars become closer or farther away.

As the Kurzesagt video points out, a supernova within 100 light-years would make space travel very difficult for humans and machines due to the immense amount of radiation for many years.

Still, I think the primary value is in expanding our understanding of science and the nature of the universe and our location within it.

A Type II supernova within 26 light-years of Earth is estimated to destroy more than half of the Earth's ozone layer. Some have argued that supernovas within 250-100 light-years can have a significant impact on Earth's environment, increase cancer rates, and kill a lot of plankton. They can potentially cause ice ages and extinctions. Within 25 light-years, we are within a supernova's "kill range." Fortunately, nothing should go supernova close to us for a long time.

Wikipedia article: https://en.wikipedia.org/wiki/Near-Earth_supernova

Kurzgesagt video on the impact on Earth of supernovas at varying distances: https://www.youtube.com/watch?v=q4DF3j4saCE

Read the paper. The media is not providing a lot of missing context. The paper points out problems like leadership failures for those efforts, lack of employee buy-in (potentially because they use their personal LLM), etc.

A huge fraction of people at my work use LLMs, but only a small fraction use the LLM they provided. Almost everyone is using a personal license

This is so shortsighted. The US needs a huge increase in its electricity generation capabilities, and nowadays, rewnewables, especially solar, are the cheapest option.

This video from a few days ago analyzes the issue: https://www.youtube.com/watch?v=2tNp2vsxEzk

Regardless of climate change issues, the anti-renewable policy doesn't seem to make any sense from an economic, growth, or national security standpoint. It even is contrary to the anti-regulation and pro-capitalism _stated_ stance of the administration.

That's my assessment of the report as well.... really, some news truly is "fake" where they are pushing a narrative that they think will drive clicks and eyeballs, and the media is severely misrepresenting what is in this report.

The failure is not AI, but that a lot of existing employees are not adopting the tools or at least not adopting the tools provided by their company. The "Shadow AI economy" they discuss is a real issue: People are just using their personal subscriptions to LLMs rather than internal company offerings. My university made an enterprise version of ChatGPT available to all students, faculty, and staff so that it can be used with data that should not be used with cloud-based LLMs, but it lacks a lot of features and has many limitations compared to, for example, GPT-5. So, adoption and retention of users of that system is relatively low, which is almost surely due to its limitations compared to cloud-based options. Most use-cases don't necessarily involve data that would be illegal to use with a cloud-based system.

Where is the actual paper that makes these claims? I'm seeing this story repeated all over today, but the link doesn't actually seem to go to the study.

I am not going to trust it without actually going over the paper.

Even then, if it isn't peer-reviewed and properly vetted, I still wouldn't necessarily trust it. The MIT study on AI's impact on scientific discovery that made a big splash a year ago was fraudulent even though it was peer reviewed (so I'd really like to know about the veracity of the data): https://www.ndtv.com/science/mit-retracts-popular-study-clai...

Sam Altman way oversold GPT-5's capabilities, in that it doesn't feel like a big leap in capability from a user's perspective; however, the a idea of a trainable dynamic router enabling them to run inference using a lot less compute (in aggregate) to me seems like a major win. Just not necessarily a win for the user (a win for the electric grid and making OpenAI's models more cost competitive).

If they are going to do this, they really ought to corroborate the face recognition with fingerprints. Many people have unrelated doppelgangers, even if an AI algorithm was near perfect: https://twinstrangers.net/

Is there a list of the papers that were flagged as doing this?

A lot of people are reviewing with LLMs, despite it being banned. I don't entirely blame people nowadays... the person inclined to review using LLMs without double checking everything is probably someone who would have given a generic terrible review anyway.

A lot of conferences now require that one or even all authors who submit to the conference review for it, but they may be very unqualified. I've been told that I must review for conferences where some collaborators are submitting a paper and I helped, but I really don't know much about the field. I also have to be pretty picky with the venues I review for nowadays, just because my time is way too limited.

Conference reviewing has always been rife with problems, where the majority of reviewers wait until the last day which means they aren't going to do a very good job evaluating 5-10 papers.

This will be huge in the next decade and powered by AI. There are so many competitors, currently, that it is hard to know who the winners will be. Nvidia is already angling for humanoid robotics with its investments.

The game is only about 30 hours and has no micro transactions. It is addictive until you beat it. Easily game of the year.

Different gyms have very different cultures. Try going to different ones to see if there is one you like. For example, Gold's Gym has a lot of bodybuilders whereas I've found the YMCA is mostly older folks trying to stay active.

What I do is to always set the context by giving the my "background" and some papers as reading material such that I've conditioned the model for whatever topic that will be discussed as the first step.

They address this in the AlphaEvolve paper:

"While AI Co-Scientist represents scientific hypotheses and their evaluation criteria in natural language, AlphaEvolve focuses on evolving code, and directs evolution using programmatic evaluation functions. This choice enables us to substantially sidestep LLM hallucinations, which allows AlphaEvolve to carry on the evolution process for a large number of time steps."

If they only studied remote work around the time of COVID, I'm not sure if findings will generalize. I think the pandemic caused a lot of people to reassess their lives and careers, and I don't know if increases in new venture creation can be entirely attributed to remote work.

My dishwasher from the 1990s dried much better than the one I got in 2021, which required rinsing each dish after washing to get rid of the soap taste. It then broke after only 1.5 years. My new one is better, but still leaves dishes pretty wet and I still have to rinse a lot of cups to get rid of the soap residue.

I've been thinking deeply about a lot of the same topics via teaching a course on AGI this semester (defining it, conflicting definitions, socioeconomic impact, etc. That said, I disagree with him vehemently regarding some of his opinions such as that open weight models should be banned. He equates the weights of models as equivalent to fissile material for building nuclear bombs.

On some topics, I 100% agree with him, e.g., on the issues with value alignment (humans disagree). I devoted an entire lecture to that in my course.

I also think he doesn't understand the romance of space exploration and why, in the very long term, that is critical (learning cosmology is one of my hobbies).

This is exactly how I feel. I use an AI powered email client and I specifically requested this to its dev team a year ago and they were pretty dismissive.

Are there any email clients with this function?

That was my initial reaction as well, but I think that isn't fair upon looking more closely. Equation 1 of their paper is a unifying equation such that different choices for the terms result in the various classical and new algorithms.

I still wouldn't call it a periodic table, though.