This, too, is actually addressed in the article - the whole point is that you're being forced to participate/speculate through this rapid inclusion in indices and mutual funds, because he's had the indices change the rules for him. So no, it's not that simple.

Your crappy source is vague in what consumption pattern constitutes a risk and actually cites a better source that supports the idea that acute alcohol consumption reduces paracetamol toxicity. https://www.biorxiv.org/content/10.1101/2020.07.07.191916v1....

That's a mathematical model, but this relationship between the two is what I was taught in medical school and it is still supported by the science. There's plenty of other sources, I just picked that one because your article cites it. Just search for "paracetamol ethanol" on Google Scholar.

Data scientists were appreciated largely because of their ability to create models that unlock business value. Model creation was a dark magic that you needed strong mathematical skills to perform - or at least that's the image, even if in reality you just slap XGBoost on a problem and call it a day. Data scientists were enablers and value creators.

With GenAI, value creation is apparently done by the LLM provider and whoever in your company calls the API, which could really be any engineering team. Coaxing the right behavior out of the LLM is a bit of black magic in itself, but it's not something that requires deep mathematical knowledge. Knowing how gradients are calculated in a decoder-only transformer doesn't really help you make the LLM follow instructions. In fact, all your business stakeholders are constantly prompting chatbots themselves, so even if you provide some expertise here they will just see you as someone doing the same thing they do when they summarize an email.

So that leaves the part the OP discusses: evaluation and monitoring. These are not sexy tasks and from the point of view of business stakeholders they are not the primary value add. In fact, they are barriers that get in the way of taking the POC someone slapped together in Copilot (it works!) and putting that solution in production. It's not even strictly necessary if you just want to move fast and break things. Appreciation for this kind of work is most present in large risk-averse companies, but even there it can be tricky to convince management that this is a job that needs to be done by a highly paid statistician with a graduate degree.

What's the way forward? Convince management that people with the job title "data scientist" should be allowed to gatekeep building LLM solutions? Maybe I'm overestimating how good the average AI-aware software engineer is at this stuff, but I don't see the professional moat.

That's why I say that is an upper bound - we know that it _has_ happened under those circumstances, so the minimum time needed is not more than that. If we reran the simulation it could indeed very well be much faster.

I agree that 20 watts can be enough to support intelligence and if we can figure out how to get there, it will take us much less time than a billion years. I also think that on the compute side for developing the AGI we should count all the PhD brains churning away at it right now :)

The human brain is not a 20-watt computer ("100 watts per day" is not right) that learns from scratch on 2 petabytes of data. State manipulations performed in the brain can be more efficient than what we do in silicon. More importantly, its internal workings are the result of billions of years of evolution, and continue to change over the course of our lives. The learning a human does over its lifetime is assisted greatly by the reality of the physical body and the ability to interact with the real world to the extent that our body allows. Even then, we do not learn from scratch. We go through a curriculum that has been refined over millennia, building on knowledge and skills that were cultivated by our ancestors.

An upper bound of compute needed to develop AGI that we can take from the human brain is not 20 watts and 2 petabytes of data, it is 4 billion years of evolution in a big and complex environment at molecular-level fidelity. Finding a tighter upper bound is left as an exercise for the reader.

"The key is ensuring that any future cuts at NASA are not indiscriminate. If and when Jared Isaacman is confirmed by the US Senate as the next NASA administrator, it will be up to him and his team to make the programmatic decisions about which parts of the agency are carrying their weight and which are being carried, which investments carry NASA into the future, and which ones drag it into the past. If these future cuts are smart and position NASA for the future, this could all be worth it. If not, then the beloved agency that dares to explore may never recover."

Long-term alcohol abusers will develop more of this enzyme, so they are more likely to get liver damage from paracetamol though.