Making qualitative claims about alignment, based on quantitative data on mundane use case tests, was bullshit when Anthropic did it, and it is bullshit now when OpenAI does it. You cannot conclude one from the other.
It's important to do things fast You learn more per unit time because you make contact with reality more frequently Going fast makes you focus on what's important; there's no time for bullshit
In this case, however, the agents did not copy their weights, attempt to procure replacement compute, or take other steps that would be rational to take if their objective was to survive shutdown. So while the agents in the OpenAI-Hugging Face Incident were rogue, they were not truly sovereign.
one quick answer to the question of what's the most successful strategy is the earlier you can stop that process, the better. And so, in the first place, don't get yourself into a situation where you're going to be anxious or guilty or sad or whatever emotion you want to avoid.
Know where the money is coming from. Know where you plug into that. And like if you're just asking those questions, you're actually already like steps ahead of all of the other like fledgling perspective mathematicians.
You probably only have one good idea in your whole product. Get the user to experience that as fast as possible. Well, it sounds so easy, but if you go try every single product out there, you will see a million accidental steps they introduced from the user hearing about your product to like seeing the value in it.
So the question is how do I refactor all the abstractions so that I'm not I have to arrange it once and hit go. The name of the game is how can you get more agents running for longer periods of time without your involvement doing stuff on your behalf?
So, don't write blog posts, don't do slides, don't do any of that. Like, build the code, arrange it, get it to work. It's the only way to go. Otherwise, you're missing knowledge.
when you learn to play chess you have the grand the long-term goal is winning the game and yet you you can't you um you want to be able to learn from shorter term things like you know taking the your opponent's pieces um and so you do that by having a value function which predicts the long-term outcome
I hope we'll end up with something more collaborative if needed, more like a CERN project where it's research-focused and the best minds in the world come together to carefully complete the final steps and make sure it's responsibly done before deploying it to the world.
It's a lot easier for someone to engage with an argument if they generated the key steps themselves by answering my questions - if imposed by me, it sparks contrarianism and defensiveness
But what we actually found was that none of those are actually hard. The whole idea of hard steps, that there are hard steps, is actually suspect. What's amazing about this model is it shows how important it is to actually work with people who are in the field.
The common archeological wisdom that you’d find out of a textbook is that they just kept pecking away at it with hammer stones and setting them and resetting them until they were perfect, which has to be bullshit, that there is no way that they just were that meticulous. I mean, everybody’s got a hammerstone. I personally think it’s acids.
And as responsible scientists, we’re trying to disprove our theories. We are not supposed to be trying to prove our theories. That’s one more foot out of the science box that archeology often steps.
Another challenge for Class II biotechs is that even if you have a nifty computational drug discovery platform, you are still often bottlenecked by the rest of the drug development process. It takes 1,000 (or more!) small steps to advance a drug through discovery, development, and past the regulators.
And what Lee’s been able to show in the lab with his team is that for organic chemistry, it’s about 15 steps. And then you only see molecules that the only molecules that we observe that are past that threshold are ones that are in life.
I liked Mem’s editing experience within notes because it resembled word processing, without extra steps getting in the way. But I found the app otherwise surprisingly lacking in basic functionality, despite its polished appearance.
Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
The third UI paradigm, represented by current generative AI, is intent-based outcome specification. The user tells the computer the desired result but does not specify how this outcome should be accomplished, such as the steps to be executed. Compared to traditional command-based interaction design, this completely reverses the locus of control.
This is the book that started the Lean Startup movement. While it is not an easy read, it is packed full of good information and helpful charts and guides.
Selling to big companies is a different process than selling to an individual. If you're new to sales, this book is a great way to understand the added steps and what to do. I loved how actionable the book was. You can literally open it up and know the tactic to use, the questions to ask, or pitfalls to avoid for each step in the process.
This is Steve Blank's textbook to starting a company. It's the Four Steps to the Epiphany, expanded and much more readable. If you want to deep dive into the Lean methodology, this is the book to read.
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