rjdagost

Meta has spent massive sums of money to train these models and they've released the models to the public. You can fine-tune the models. You can see the source code and the architecture of the model. The EULA is commercially-friendly.

You are free to quibble over how truly "open source" these models are, but I am very thankful that Meta has released them.

SAAB was financially failing when GM bought them. It's tough to blame GM for trying to impose financial discipline on a money-bleeding operation.

Then there's the question of how the electric grid will sustain this significant new increase in demand. As with mandates banning gas cars, the grid supply and stability is always regarded as a mere detail to be sorted out later.

Slack is great in small doses, but a complete time-waster beyond a certain threshold. I don't have ADHD, but I still have had problems with Slack. Don't feel bad about shutting all notifications off, or, better yet, closing the app entirely, and only opening it once or twice a day. You will almost instantly feel better. Some people may get annoyed that you're not instantly available, but if you're not able to get your work done then many more people will get annoyed. There is no perfect solution.

I have worked in both semiconductor manufacturing and drug discovery industries. Reliably and profitably producing 5 nm chips is an extreme engineering challenge, but- it is an engineering challenge. Drug discovery is a question of science and requires a fundamentally different mindset that semiconductor manufacturing. Human biology is much more complicated than manufacturing chips (and that is extremely complicated); drug discovery is about "unknown unknowns". Discovering a drug that has the intended effects without causing terrible adverse effects is something that some of the best-funded companies on the planet struggle with.

If there's one thing I learned with biomedical data modeling and machine learning, it's that "it's complicated". For biomedical scenarios, getting more data is often not simple at all. This is especially the case for rare diseases. For areas like drug discovery, getting a single new data point (for example, the effect of a drug candidate in human clinical settings) may require a huge expenditure of time and money. Biomedical results are often plagued with confounding variables, hidden and invisible, and simply adding in more data without detection and consideration of these bias sources can be disastrous. For example, measurements from lab #1 may show persistent errors not present in lab #2, and simply adding in more data blindly from lab #1 can make for worse models.

My conclusion is that you really need domain knowledge to know if you're fooling yourself with your great-looking modeling results. There's no simple statistical test to tell you if your data is acceptable or not.

One of the big differences between the US and Europe in this regard is the much larger proportion of bicyclists in Europe. In Europe, the larger number of bicyclists trains you as a driver to always be on the lookout for them. Driving past a bicyclist in the US is a much rarer event. Drivers should of course always be vigilant for anything in their way, but as a practical matter it's easier to mentally drift off when bicyclists are much less common.

With this kind of uncertainty, is it any wonder that many people don't trust medical science when we are told to behave in a certain way, or when we are told to take a certain medical treatment?

How many years have we been told that saturated fat is the nutritional devil? This is a great example of why censorship of "misinformation" is not justified- who is to say what constitutes misinformation?

There is PhD level math involved. And yet, ML (deep learning in particular) is much more of an empirical endeavor than many would like to admit. A deep understanding of the underlying mathematics does not necessarily give you a better model. Modern models are so complicated that no one can reason through them. Parameter spaces are non-convex and fully of ugly pathologies that make neat and tidy analysis methods useless.

From one perspective, it is disheartening that a deep understanding of the underlying methods doesn't necessarily win the day. From another, it is quite remarkable that having good implementation skills and a methodical mindset can get you quite far.

Calling YOLOv5 a "fraud" is a bit harsh. It has many excellent aspects for practitioners: easy to use, fast inference time, scalable model architecture, and it has many helpful utilities built-in for model deployment. In my experience, in real use-cases the models achieve about the same precision / recall / mAP as well as "state of the art" methods that report better stats on benchmarks.

Welcome to computer vision / machine learning research!

What I'm hearing you say, loud and clear, is that you no longer have a sense of a purpose in life. Drugs (legal or otherwise) will not fill that void for you. I do not believe that you can rekindle a sense of purpose just by thinking about it. I would advise you to try new things that sound interesting to you, and keep exploring until you find something you can pour your heart and soul into. Probably something different from, or at least not directly related to, tech.

I share your frustration. If you're trying to be realistic and straight-forward about the limitations of AI/ML, you're getting little interest from investors. Here's an anecdote. I was at an "AI in drug discovery" conference 2 years ago. One of the presenters, the founder of a drug discovery start-up, emphatically made the claim in a talk that "we should deliberately overhype AI in drug discovery, to raise awareness of what we as an industry can accomplish". I was gobsmacked by this. And yet, in the social mixer afterwards, the investors I spoke with LIKED this approach- they said the boastful founder was bold and visionary, and they didn't care that he was exaggerating the capabilities of his company. So, that's why all we hear is hype- founders are just responding to investor incentives.

That's also why I work as a freelance consultant and not as a founder. I think that autonomous driving (rather, the lack of such) is going to be what triggers the next AI winter. Too much money and hype, too little results for too long- the rope is wearing quite thin from what I can see.

Your suspicion is largely correct, in my experience. I've worked as a consultant on a number of different AI / ML projects for start-ups. Most aren't doing anything all that new or groundbreaking from an ML point of view. Their real innovation is usually more about applying ML to industries / areas where it hasn't been used much before. But that doesn't get the investor dollars flowing in, so the founders try to make it seem like they have some radical new ML breakthrough. And in recent times, it has worked.

If there's one thing I've learned from the dotcom crash and the 2008 collapse, it's this: when the crap hits the fan, cash is king. Start-ups without profitable business models are extremely vulnerable. All of the pitches about exponential growth and glorious future profits carry little weight when investor psychology turns negative. So if you have a large pile of investor capital and you have investors who can't / won't claw their money back, you may be OK. But don't kid yourself that you are on an easier path.