Situational Awareness

Leopold Aschenbrenner (previously at OpenAI) offers some unique perspectives on the future of AI. His paper “situational awareness” paints a picture of an inevitable AI-Manhatten project.

If you want to look into his arguments, here are some different formats:

It’s hard to summarize that much material. But here are my notes on the main points he argues:

  • Geopolitics will undoubtedly be at play once we get close to AGI; and definitely when ASI is at play.
  • Most people talk about AI as a project of corporate research labs (which it currently is), but as capabilities improve, it will be impossible for the national security apparatus to ignore.
  • Simple scaling arguments suggest we will reach AGI in ~2-3 years, unless we hit a barrier (he lists many). Of course, we may well hit a barrier; but caution requires us to plan assuming AGI could be very near.
  • Once you have AGI, you will achieve ASI very quickly. One of the easiest jobs to automate with AGIs will be AI research, so you will suddenly have an army of tireless AI researchers making exponential improvements. This is probably enough to go from AGI to ASI within a year.
  • Obviously, whoever controls ASI will have a massive geopolitical advantage (superhuman cyber-warfare, autonomous drone swarms, rapid development of new WMDs, optimal allocation of resources, etc.).
  • The US nuclear arsenal, the bedrock of recent global peace and security, will become essentially obsolete.
  • The corporate labs are operating like startups, with almost no regard for security. They need to transition to a strong security mindset sooner rather than later. Some of the key insights for building AGI and ASI are likely being developed right now. And those insights are not being safeguarded.
  • Obviously (within this mindset) open-sourcing anything would be irresponsible. Everything must be kept secret.
  • Western democracies are on the cusp of making a serious error, wherein they cede control of AI (and thus AGI and thus ASI and thus the future of the species) to an authoritarian regime.
  • We are very soon going to see major geopolitics (including espionage, assassinations, combat, bombing datacenters, etc.) focused on AI; as soon as more leaders “wake up” to what’s going on.
  • So, the US will aggressively pursue but lock-down AI research. It is a strategic asset. The US will invest in an enormous (multi-trillion $) Manhattan-style project to develop AGI first.
  • This will involve building massive compute clusters on US soil, investing in the research enterprise, locking it down using nuclear-weapons caliber security, and building tons of power plants (including bypassing clean energy laws if that’s what it takes to deliver the required power).
  • So, the near-future will be a contentious time period, with greater hostilities between countries and a greater threat to democracy.

His opinions are mostly predictions, but he is also prescriptive in the sense that he believes the West (and the US in particular) need to win this. I don’t agree with all his claims, but many of his points are hard to argue against. He is indeed correct that most of the general discussion on AI (across many ‘sides’) is missing some key points.

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AI News 2024-06-06

Research Insights

  • Guiding a Diffusion Model with a Bad Version of Itself combines an image model with an intentionally worse version of itself, and shows how this combination can be used for image synthesis that better balances coherence vs. diversity. (Despite neural methods being largely “block boxes”, results like this show that we do actually understand enough about internals to make meaningful interventions.)
  • LLMs are notoriously bad at math. A new preprint investigates fixing that: Transformers Can Do Arithmetic with the Right Embeddings.
    • The model can do 100-digit addition (99% accuracy) after being trained on 20-digit numbers. Capabilities also adapted to multiplication. The trick is to enforce an embedding that explicitly captures the position of digits within a number. So numerical representations are first-class during tokenization (conceptually similar to the Polymathic xVal number encoding).
    • Of course LLMs can just call external functions to make sure math gets done correctly. But I like the idea of crafting the LLMs themselves to correctly embody basic concepts from math and logic, as it might generalize to improved performance on a range of other planning/deliberation activities.

Audio

  • Machine translation has been scaled to 200 languages. The impressive part is that many of these languages have very little training data. The point is that the model can learn language structure from the well-represented languages, and generalize to the languages will less training data.

Avatars

AI audio/video avatars are advancing rapidly. (So this is your periodic reminder to be increasingly skeptical of videos you see, and of random phone calls from loved ones asking you for money.)

  • Synthesia EXPRESS-1 avatars show emotions that match the text.
  • HeyGen has also demonstrated that they can apply their AI avatar trick (resync lip motions in an existing video to match a new script) to videos where the person is in motion. One of the main use-cases is converting videos to other languages; so this broadens the range of content that can be targeted. Of course one can also use it to nefariously change what someone said in an otherwise very-non-AI-looking video.
  • V-Express improves further on virtual avatars (generates video aligned with an audio track, based on a single photo).
  • ChatTTS is a text-to-speech system that is remarkably good, including being able to add natural-sounding pauses, laughs, etc. Open source, so you can run it all locally if you want.

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How to break apart Python pathlib Paths?

Python pathlib is the modern way to handle file paths. But I always forget how to break apart a path into components (directory part, filename part, etc.). This image is a cheat-sheet for working with Path, breaking it apart into root, directory path, filename, suffix, etc.

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How to convert dates/times in Python?

Working with dates and times in Python often involves converting between the various possible representations. Here is a graphic to quickly lookup how to convert between the different formats (epoch, struct_time, Python datetime object, string representation, and matplotlib date convention).

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Quarantine Email

Awhile ago, I wrote some code to graph my email behavior (in the spirit of Stephen Wolfram). I have been teleworking for the last 11 weeks (due to COVID19), and was curious to see how this shows up in my email behavior. First let’s start with a baseline by looking at average behavior over the last 5 years.

This is a stacked plot, with email I send (purple, bottom), and email I receive (blue for internal email, green for external).

There are many caveats. This is work email only. I am (intentionally) only measuring archived email, and exclude spam or any email that I delete rather than archive. This leads to the ‘sent’ (which is always saved) being much larger than ‘received’ (since I only archive things that are useful, not the thousands of emails generated every time someone needs to schedule a meeting). The data has some artifacts related to changes in how the lab has managed email over the years (and changes in my threshold for delete vs. archive). Nevertheless, it gives us something to consider.

The overall increase in email volume over time is apparent. The dip at the end of December each year of course coincides with holidays. We can also look at the average distribution of email over the course of a week:

Emails obviously come in mostly during working hours (though there is plenty of off-hours traffic from automated systems, other time zones, and colleagues who just aren’t sensible). My email sending follows clear patterns, including how I set aside weekday morning to handle backlogs of low-priority requests. Now we can compare during-quarantine to before-quarantine.

What changes do we see? I am receiving less external email than usual (not surprising, given how many collaborations are currently on hold). Email both sent and received is less contained to normal working hours. This is due both to crisis-management activities, and the blending of work and life.

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New Layout

I have moved the website over to WordPress.

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