AI Expansion: 6 Key Reads for the Week Ahead

This week’s edition of my newsletter Exponential View lasers in on AI . There is so much going on, and this year could be a turning point. A year that delivers a significant shock to the system. I’ll be covering these issues in the coming weeks and months — for now, here are 6 key AI reads to start the week strong.

1. AI & productivity

Noy and Zhang found that the use of ChatGPT for writing tasks made participants perform better, and much faster.?Essentially, exposure to generative AI increases efficiency and job satisfaction .

The most surprising effect, however, is that it is an equaliser: it benefited the worst performers more, thereby reducing the inequality between workers. It’s worth noting that this is a working paper, and hasn’t been through a rigorous peer review yet.

In an?essay last week, I compared ChatGPT to a rather mediocre human graduate . Turns out, the chatbot might help mediocre graduates become excellent ones!

See also:?Ethan Mollick on how to get the best out of Bing AI, while working around its quirks . Bing AI is more valuable as an analytic engine than a search engine.

2. Price goes down, use goes up.?

The price of OpenAI’s ChatGPT API is low: reflecting a?drop of 96.6% for similar capabilities in less than a year . The cheapest version (GPT-3.5-turbo) is not as good as its the best available model (text-DaVinci-003), but it’s 10% the price per token. That is probably good enough for many applications. Using the ChatGPT API, it would cost $4.3 to process—and learn from—the entire Harry Potter series . It would take an average human reader about 72 hours of non-stop reading.

3. Horse race

Given the feverish uptake of LLMs like GPT-3, why did Deepmind not get out of the gate first??Jonathan Godwin, a former Deepmind engineer, argues it comes down to company culture . OpenAI is an engineering organisation, whereas Deepmind is a research organisation. My view is this is credible: the engineering company ships products, such as ChatGPT. Products are evolving answers to open-ended questions. A research company builds answers to more well-defined research questions. AlphaFold addresses the protein folding problem. Product/engineering companies ship, learn, ship, learn, ship, learn. A far cry from the discipline of scientific research.

4. Mind reading

Japanese researchers Takagi and Nishimoto used Stable Diffusion to reconstruct images from brain activity with astonishing precision. The paper is too not yet peer-reviewed, but it shows the accomplishment of a technology that has been in the works for years.?In EV#138 of November 2017, I wrote :

Purdue researchers have paired up high-fidelity fMRI scans ?with deep learning networks to decode videos people are watching, from brain activity alone.?This is approaching real-time mind-reading.

5. Problematic parrots?

Mind reading it might be, mind understanding it is not.?Computational linguist Emily Bender warns that AI’s linguistic abilities trick us into thinking that these systems understand and create meaning . See also,?this great introduction to neuro-symbolic AI . I?discussed the subject extensively with contrarian neuroscientist and entrepreneur Gary Marcus on my podcast .

6. The EU grapples with managing the risks of generative AI?

In my book, I introduced the idea of the exponential gap : slow, rigid organisations struggle to keep up with exponential technologies. The EU has surprisingly woken up to many of the challenges of generative AI. As the group considers?whether generative AI requires specific regulatory intervention. Big tech is pushing back. ?My view, which I’ll detail in a?future members’ commentary , is that we need to act quickly to put in place some sort of standards or expectations for these powerful technologies and the applications built on them. A starting point will be widespread discussion and the willingness of those building such tools to engage in that discussion. That won’t be sufficient and more will be needed.