In general in the US, life saving or emergency medical care is administered without regard for the patient's ability to pay. Hospitals are already subsidized or compensated in various ways for this. The real issue is preventative or precautionary care. If Americans had that for free, like with the NHS, there would be fewer $XXX,XXX debts later in life.

If I study Einstein and learn to do a really good impression, the statement "Einstein often sounds like karmacondon" will be true. That does not make me Einstein.

1) Access to capital, normally through family or a family friend. I've worked at several companies where the main thing the CEO brought to the table was that someone trusted him or her enough to invest millions of dollars.

2) At least 5 years working a 9-5 job in the target industry and the associated social connections and experience. This eliminates most college students, sadly.

3) Something unique that enables the execution of the idea. This is normally a relationship or insider knowledge. The answer to "Why you?" can't be "Because I had the idea".

The most common exception I see to this list is when both founders have the same level of passion for solving a given problem. If you have to explain the opportunity and get someone else interested in it, it could be a tough road.

That said, don't lose hope. It's a big world. People meet and things happen.

All of the AI generated media has this quality where I can immediately tell that it's ai, and that becomes my dominant thought. I see these things on social media and think "oh, another ai pic" and keep scrolling. I've yet to be confused about whether something is ai generated or real for more than several seconds.

Consistency and continuity still seem to be a major issues. It would be very difficult to tell a story using Sora because details and the overall style would change from scene to scene. This is also true of the newest image models.

Many people think that Sora is the second coming, and I hope it turns out to have a major impact on all of our lives. But right now it's looking to have about the same impact that DALL-E has had so far.

Machine learning is different because every implementation has a known error rate. If your application has a measured 80% accuracy then 20% of cases WILL have an error. You don't know which 20% and you don't get to choose. There's no way to notice a problem and immediately fix it, like you can with almost every other kind of engineering. At best you can expand your dataset, incorporate new models, fix actual bugs in the code. Doing those things could increase the accuracy up to, say, 85%. This means there will be fewer errors overall, but the one that you happened to notice may or may not still be there. There's no way to directly intervene.

I see a lot of people who are new to the field struggle with this. There are many ways to improve models and handle edge cases. But not being able to fix a problem that's in front of you takes some getting used to.

There isn't a bigger, more interesting story here. This is in fact a very common story that plays out at many software companies. The board of openai ended up making a decision that destroyed billions of dollars worth of brand value and good will. That's all there is to it.

I get that this is a "just for fun" hypothesis, which is why I have just for fun questions like what incentive does anyone have to keep clearly observed ai risk a secret during such a public situation?

I imagine that the board wants to go back to that or something like it.

The board's decisions may or may not turn out to be correct in hindsight. But it's very difficult to say that this was a good example of leadership or decision making.

I don't think that this is the problem. From what I know, hackernews doesn't hire any marketing experts, psychologists or focus groups. It doesn't even support images. I was more addicted to this site than any other, despite the lack of psychological tricks.

And there are many sites that DO employ full psychological warfare teams that I completely ignore. Tinder seemed fully committed to forcing repetitive user engagement. And I dropped that site after about two days, psychologists or not. If all it took to force engagement was a certain list of UI tricks, every funded social site would be able to do it.

I don't think there's a single root cause or an off switch for social media. This phenomenon is here to stay, for better or worse. I think that we will adapt as a species but there's no going back.

I'm not entering my openai key to a random website. My billing is tied to that.

This really makes it difficult to demo the product.

People that run equipment rental stores probably have a facebook group or professional association. If you can befriend someone influential in one of those, it might be a good place to get started.

The grandparent post seems to believe that the issue is algorithmic complexity and programming aptitude. Personally, I think that all the major LLMs are using the same basic transformer architecture with relatively minor differences in code.

GPT is trained on more data with more parameters than any open source model. The size does matter, far more than the software does. In my experience with data science, the best programmers in the world can only do so much if they are operating with 1/10th the scale of data. That applies to any problem.

Humans love to think of multi agent systems as being like a team of people. It's much more like a writer imagining different characters and how they would respond. When George RR Martin imagines all 500 characters in Game of Thrones, there is a lot of diversity of perspective and thought there. But all of that is coming from one intelligence and doesn't represent a collaboration in any traditional sense.

You can use multiple agents, or split a lot of information across multiple requests to one agent. The result is the same. Some problems require a full understanding of the whole picture.