Why are people building AI systems article

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Automating tasks

McKinsey estimate of 0.1 to 0.6% growth rate in productivity (!?) which leads to a value addition of $2.6 to 4.4 Trillion dollars.

  • This is a disputed figure. Original McKinsey article estimates use cases such as hiring which people are quite skeptical about because of problems with fairness or hallucinations.
  • Earlier AI systems were thought to have good use cases in operations and supply chain but those were more compute/calculation based optimizations which are now not considered as relevant because of generative AI.
  • Customer Operations, Sales, Software Engineering, R&D. (only SWE and R&D seem legit).

Replacing Human workers

e.g. Devin or the idea of a “drop in” agent as mentioned by Leopold Aschenbrenner

Non Economic Motivations

  1. Positive externalities of technology development
  2. National Interests (proprietary models and when AGI comes its going to be like nuclear power).
  3. Fun Stuff / Solving their own problems.

Compute Trends across three eras of ML article

Better ML systems come from:

  1. Better Algorithms.
  2. Better Data.
  3. More Compute.

This article aims to analyse training compute trends.

Research Methodology

How is compute trained:

  1. Counting number of operations.
  2. Estimating through Runtime - requires assumptions about GPUs and system used for training - more uncertain.

Only the “important models” (influence, number of citations) were considered that pushed the SoTA.

Results

  1. Old era doubling time ~20 months.
  2. DL era (~2010-12) doubling time of around 6 months.
  3. Large Model Era (~2016) doubling time of around 10 months.

Scaling Laws article

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Compute, Data, Parameters. Neural Language models scale with a consistent power law wrt to these. Transformer architecture doesn’t converge.

Paper on AI prediction

High-level machine intelligence (HLMI) is achieved when unaided machines can accomplish every task better and more cheaply than human workers.

3.6 Intelligence Explosion

Since 2016 a majority of respondents have thought that it’s either “quite likely,” “likely,” or an “about even chance” that technological progress becomes more than an order of magnitude faster within 5 years of HLMI being achieved.

3.7 Future Systems

There were areas of agreement:

  1. Find unexpected ways to achieve goals (82.3% of respondents)
  2. Be able to talk like a human expert on most topics (81.4%)
  3. Frequently behave in ways that are surprising to humans (69.1%)

Interpretability / Explainable AI systems

Only ~20 percent think that it’s likely that for typical state-of-the-art AI systems in 2028, it will be possible for users to know the true reasons for systems making a particular choice.

Prediction Exercise

My Predictions:

  1. Event: Win Putnam Math Contest. Prediction: 75% confidence by 2027. Reasoning: We have already done pretty decent on IMO benchmarks.
  2. Event: Simple Python Code given spec. Prediction: 100% confidence 2023. Reasoning: Claude 3 Opus and GPT-4 pretty much solve this.
  3. Event: Finetuning an LLM. Prediction: 75% confidence 2028. Reasoning: With good prompting it is almost able to do so.

Partner Predictions:

  1. Vid from diff angle. Prediction: 75% confidence by 2028. Reasoning: 2020 panoptic scene graph, SoRA.

How will AI affect your job Exercise

  1. Reading research papers or textbooks, coding, solving physics problems, interacting with others.
  2. In 10 years: Better comprehension, significantly fewer bugs in coding, good progress in research-level problems, significant AI-generated text/speech.
  3. Technology adoption would be quite slow. Even with all the developments there would be a significant transition delay.
  4. AI would exacerbate the rockstar effect considerably but also empower creatives.

Question for the week: Who is liable for the damages if an AI system generates something that wouldn’t have been possible otherwise?