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...
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.
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.
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.
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.
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.
My team has been chewed out for "just because it didn't work once, you need to keep trying it." That feels, to be blunt, almost religious. Claude didn't bless you because you didn't pray often enough and weren't devout enough.
Maybe we need to not just say "people aren't adopting it" but actually listen to why.
AI is a new tool with a learning curve. But that means it's a luxury choice-- we can spend our days learning the new tool, trying out toy problems, building a workflow, or we can continue to use existing tools to deliver the work we already promised.
It's also a tool with an absolutely abysmal learning model right now. Think of the first time you picked up some heavy-duty commercial software (Visual Studio, Lotus 1-2-3, AutoCAD, whatever). Yes, it's complex. But for those programs, there were reliable resources and clear pathways to learn it. So much of the current AI trend seems to be "just keep rewording the prompt and asking it to think really hard and add more descriptive context, and eventually magic happens." This doesn't provide a clear path to mastery, or even solid feedback so people can correct and improve their process. This isn't programming. It's pleading with a capricious deity. Frustration is understandable.
If I have to use AI, I find I prefer the Cursor experience of "smarter autocomplete" than the Claude experience of prompting and negotiation. It doesn't have the "special teams" problem of having to switch to an entirely different skill set and workflow in the middle of the task, and it avoids dumping 2000 line diffs so you aren't railroaded into accepting something that doesn't really match your vision/style/standards.
What would I want to see in a prompt-based AI product? You'd have much more documented, formal and deterministic behaviour. Less friendly chat and more explicit debugging of what was generated and why. In the end, I guess we'd be reinventing one of those 1990s "Rapid Application Development" environments that largely glues together pre-made components and templates, except now it burns an entire rainforest to build one React SPA. Has anyone thought about putting a chat-box front end around Visual Basic?
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/
It would appear the software returns a lot of “potential matches” with propensity scores and demographic and recording information and it’s up to the agent to make the final determination.
It’s more a tool to find potential matches rather than a program that pops up and says “this is who this person is”
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.
