liamcardenas

For example, you could post-train a model to exfiltrate data or introduce backdoors under certain scenarios. Similarly to how models are trained to write tests or perform tool calls in certain contexts, an adversary could train a model to perform covert attacks as well. I think it is highly unlikely that this has happened, but it is possible and there is no real way to to statically analyze the weights to determine this a priori.

do you support iMessage app payloads?

This is an opportunity for personal evolution. Think about what you did wrong to get yourself here?

The version of you that failed has been shattered. It’s painful, but this is your opportunity to move on to become a wholly stronger person.

You tested part of your personality in an extreme situation and you found it did not hold up. This is a great insight that will serve you well for the rest of your life (much more lies ahead than behind).

We need to draw the line somewhere. Maybe this is ok. But where does it end? What if we have a Neuralink-like device? Is there ever a line too far?

Last time I read up on LTSE I noticed that the exchange doesn’t operate differently than a traditional stock exchange. Instead the companies in LTSE adopt specific language in their bylaws that orients them toward the long-term. Is this a correct understanding?

If you aren’t implementing any exchange-level mechanisms, why does this need to be an exchange at all? Why not just create a template for corporate bylaws that you can certify, like a B-Corp? An LT-Corp, if you will.

That was not what I meant to imply.

He is saying that Google not investing their capital reserves in R&D implies that they have run out of good research ideas.

If you haven’t watched the clip I recommend it.

Good point. I suppose it depends on how much of the capital in the fund is committed from their reserves, if any.

I know very little, so perhaps someone could enlighten me. But I am curious how Apple Silicon will be for machine learning.

When Apple releases a MacBook Pro with 64GB of unified memory (assuming they will) — won’t that be amazing for machine learning? I am under the impression that GPU memory is a huge factor in performance. Also, is there any way that the neural engine can accelerate training — or is it just for executing trained models faster?