On AI, Data Science, & Causal Models

Google seems to have just revealed its latest text-to-video diffusion model, Google Lumiere, just as the debate over fake images and videos heats up, with the following note:

𝘚𝘰𝘤𝘪𝘦𝘵𝘢𝘭 𝘐𝘮𝘱𝘢𝘤𝘵
𝘖𝘶𝘳 𝘱𝘳𝘪𝘮𝘢𝘳𝘺 𝘨𝘰𝘢𝘭 𝘪𝘯 𝘵𝘩𝘪𝘴 𝘸𝘰𝘳𝘬 𝘪𝘴 𝘵𝘰 𝘦𝘯𝘢𝘣𝘭𝘦 𝘯𝘰𝘷𝘪𝘤𝘦 𝘶𝘴𝘦𝘳𝘴 𝘵𝘰 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦 𝘷𝘪𝘴𝘶𝘢𝘭 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘪𝘯 𝘢𝘯 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘢𝘯𝘥 𝘧𝘭𝘦𝘹𝘪𝘣𝘭𝘦 𝘸𝘢𝘺. 𝘏𝘰𝘸𝘦𝘷𝘦𝘳, 𝘵𝘩𝘦𝘳𝘦 𝘪𝘴 𝘢 𝘳𝘪𝘴𝘬 𝘰𝘧 𝘮𝘪𝘴𝘶𝘴𝘦 𝘧𝘰𝘳 𝘤𝘳𝘦𝘢𝘵𝘪𝘯𝘨 𝘧𝘢𝘬𝘦 𝘰𝘳 𝘩𝘢𝘳𝘮𝘧𝘶𝘭 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘸𝘪𝘵𝘩 𝘰𝘶𝘳 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺, 𝘢𝘯𝘥 𝘸𝘦 𝘣𝘦𝘭𝘪𝘦𝘷𝘦 𝘵𝘩𝘢𝘵 𝘪𝘵 𝘪𝘴 𝘤𝘳𝘶𝘤𝘪𝘢𝘭 𝘵𝘰 𝘥𝘦𝘷𝘦𝘭𝘰𝘱 𝘢𝘯𝘥 𝘢𝘱𝘱𝘭𝘺 𝘵𝘰𝘰𝘭𝘴 𝘧𝘰𝘳 𝘥𝘦𝘵𝘦𝘤𝘵𝘪𝘯𝘨 𝘣𝘪𝘢𝘴𝘦𝘴 𝘢𝘯𝘥 𝘮𝘢𝘭𝘪𝘤𝘪𝘰𝘶𝘴 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦𝘴 𝘪𝘯 𝘰𝘳𝘥𝘦𝘳 𝘵𝘰 𝘦𝘯𝘴𝘶𝘳𝘦 𝘢 𝘴𝘢𝘧𝘦 𝘢𝘯𝘥 𝘧𝘢𝘪𝘳 𝘶𝘴𝘦.

This is the only paragraph in the paper on safe and fair use. The model output certainly looks impressive, but, without a concrete discussion of ideas and guardrails for safe and fair use, this reads like nothing more than a checkbox to avoid bad publicity from the likely consequences.

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In a sample of 6.4 billion sentences in 90 languages from the Web, this study finds that 57.1% is low-quality machine translation. In addition, it is the low quality content produced in English (to generate ad revenue) that is translated en masse into other languages (again, to generate ad revenue).

The study discusses the negative implications for the training of large language models (garbage in, garbage out), but the increasingly poor quality of public web content is concerning nevertheless.

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In this Q&A about Walmart’s custom-trained, proprietary “My Assistant” language model, I saw an excerpt from another article in which Walmart’s Head of People Product uses Excel as an analogy for generative models.

“𝘈𝘤𝘤𝘰𝘳𝘥𝘪𝘯𝘨 𝘵𝘰 𝘗𝘦𝘵𝘦𝘳𝘴𝘰𝘯, 𝘢𝘯𝘺 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘈𝘐 𝘳𝘰𝘭𝘭𝘰𝘶𝘵 𝘪𝘴 𝘨𝘰𝘪𝘯𝘨 𝘵𝘰 𝘦𝘯𝘤𝘰𝘶𝘯𝘵𝘦𝘳 𝘢 𝘤𝘩𝘢𝘯𝘨𝘦 𝘤𝘶𝘳𝘷𝘦 𝘯𝘰𝘵 𝘶𝘯𝘭𝘪𝘬𝘦 𝘸𝘩𝘢𝘵 𝘔𝘪𝘤𝘳𝘰𝘴𝘰𝘧𝘵 𝘌𝘹𝘤𝘦𝘭 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦𝘥 𝘪𝘯 𝘵𝘩𝘦 1980𝘴 𝘣𝘦𝘧𝘰𝘳𝘦 𝘣𝘦𝘪𝘯𝘨 𝘢𝘤𝘤𝘦𝘱𝘵𝘦𝘥 𝘢𝘴 𝘤𝘰𝘳𝘱𝘰𝘳𝘢𝘵𝘦 𝘨𝘰𝘴𝘱𝘦𝘭. 𝘚𝘪𝘮𝘪𝘭𝘢𝘳 𝘵𝘰 𝘩𝘰𝘸 𝘦𝘢𝘳𝘭𝘺 𝘶𝘴𝘦𝘳𝘴 𝘰𝘧 𝘔𝘪𝘤𝘳𝘰𝘴𝘰𝘧𝘵 𝘌𝘹𝘤𝘦𝘭 𝘩𝘢𝘥 𝘵𝘰 𝘣𝘦 𝘵𝘳𝘢𝘪𝘯𝘦𝘥 𝘵𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘩𝘰𝘸 𝘵𝘰 𝘩𝘢𝘳𝘯𝘦𝘴𝘴 𝘵𝘩𝘦 𝘱𝘰𝘸𝘦𝘳 𝘰𝘧 𝘢 𝘗𝘪𝘷𝘰𝘵𝘛𝘢𝘣𝘭𝘦 𝘢𝘯𝘥 𝘝𝘓𝘖𝘖𝘒𝘜𝘗 𝘧𝘰𝘳𝘮𝘶𝘭𝘢𝘴, 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘈𝘐 𝘶𝘴𝘦𝘳𝘴 𝘩𝘢𝘷𝘦 𝘵𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘱𝘳𝘰𝘮𝘱𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘩𝘪𝘨𝘩-𝘪𝘮𝘱𝘢𝘤𝘵 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦𝘴 𝘵𝘰 𝘵𝘳𝘶𝘭𝘺 𝘩𝘢𝘳𝘯𝘦𝘴𝘴 𝘪𝘵𝘴 𝘱𝘰𝘸𝘦𝘳.”

2024 will be the year that more companies adopt generative models as an aid to their employees. But it is interesting to use an analogy to deterministic functions like PivotTable and VLOOKUP to drive the adoption of a black box model with probabilistic outputs. Let’s see how that plays out for Walmart.

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The use of AI algorithms for drug discovery is one of the most promising areas for its societal value. Historically, most deep learning approaches in this area have used black box models, providing little insight into discoveries.

A recent study published in Nature uses explainable graph neural networks to address the urgent need for new antibiotics due to the ongoing antibiotic resistance crisis.

The study begins with the testing and labeling of 39,312 compounds,

– which become training data for four ensembles of graph neural networks,
– which make predictions for a total of 12,076,365 compounds in the test set (hits vs. non-hits based on antibiotic activity and cytotoxicity),
– of which 3,646 compounds are selected based on the probability that they will act as antibiotics without being toxic to humans,
– which are then reduced to 283 compounds by a series of empirical steps,
– and to 4 compounds by experimental testing,
– and of the 4, two “drug-like” compounds are tested in mice,

and one of the two is found to be effective against MRSA infections in this controlled experiment, thus closing the causal loop.

This is a great application of combining explainable predictive models with causal identification, and demonstrates that machine learning models used in high-stakes areas can be explainable without compromising performance.

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The dose-response work of Callaway, Goodman-Bacon, and Pedro Sant’Anna seems to be coming along nicely. If you haven’t had enough of the parallel trends assumption, get ready for the “strong” parallel trends assumption!

“𝘐𝘯 𝘵𝘩𝘪𝘴 𝘱𝘢𝘱𝘦𝘳, 𝘸𝘦 𝘥𝘪𝘴𝘤𝘶𝘴𝘴 𝘢𝘯 𝘢𝘭𝘵𝘦𝘳𝘯𝘢𝘵𝘪𝘷𝘦 𝘣𝘶𝘵 𝘵𝘺𝘱𝘪𝘤𝘢𝘭𝘭𝘺 𝘴𝘵𝘳𝘰𝘯𝘨𝘦𝘳 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯, 𝘸𝘩𝘪𝘤𝘩 𝘸𝘦 𝘤𝘢𝘭𝘭 𝘴𝘵𝘳𝘰𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘵𝘳𝘦𝘯𝘥𝘴. 𝘚𝘵𝘳𝘰𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘵𝘳𝘦𝘯𝘥𝘴 𝘰𝘧𝘵𝘦𝘯 𝘳𝘦𝘴𝘵𝘳𝘪𝘤𝘵𝘴 𝘵𝘳𝘦𝘢𝘵𝘮𝘦𝘯𝘵 𝘦𝘧𝘧𝘦𝘤𝘵 𝘩𝘦𝘵𝘦𝘳𝘰𝘨𝘦𝘯𝘦𝘪𝘵𝘺 𝘢𝘯𝘥 𝘫𝘶𝘴𝘵𝘪𝘧𝘪𝘦𝘴 𝘤𝘰𝘮𝘱𝘢𝘳𝘪𝘯𝘨 𝘥𝘰𝘴𝘦 𝘨𝘳𝘰𝘶𝘱𝘴. 𝘐𝘯𝘵𝘶𝘪𝘵𝘪𝘷𝘦𝘭𝘺, 𝘵𝘰 𝘣𝘦 𝘢 𝘨𝘰𝘰𝘥 𝘤𝘰𝘶𝘯𝘵𝘦𝘳𝘧𝘢𝘤𝘵𝘶𝘢𝘭, 𝘭𝘰𝘸𝘦𝘳-𝘥𝘰𝘴𝘦 𝘶𝘯𝘪𝘵𝘴 𝘮𝘶𝘴𝘵 𝘳𝘦𝘧𝘭𝘦𝘤𝘵 𝘩𝘰𝘸 𝘩𝘪𝘨𝘩𝘦𝘳-𝘥𝘰𝘴𝘦 𝘶𝘯𝘪𝘵𝘴’ 𝘰𝘶𝘵𝘤𝘰𝘮𝘦𝘴 𝘸𝘰𝘶𝘭𝘥 𝘩𝘢𝘷𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘥 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘵𝘳𝘦𝘢𝘵𝘮𝘦𝘯𝘵 𝘢𝘯𝘥 𝘢𝘵 𝘵𝘩𝘦 𝘭𝘰𝘸𝘦𝘳 𝘭𝘦𝘷𝘦𝘭 𝘰𝘧 𝘵𝘩𝘦 𝘵𝘳𝘦𝘢𝘵𝘮𝘦𝘯𝘵. 𝘞𝘦 𝘴𝘩𝘰𝘸 𝘵𝘩𝘢𝘵 𝘸𝘩𝘦𝘯 𝘰𝘯𝘦 𝘰𝘯𝘭𝘺 𝘪𝘮𝘱𝘰𝘴𝘦𝘴 𝘵𝘩𝘦 “𝘴𝘵𝘢𝘯𝘥𝘢𝘳𝘥” 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘵𝘳𝘦𝘯𝘥𝘴 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯, 𝘤𝘰𝘮𝘱𝘢𝘳𝘪𝘴𝘰𝘯𝘴 𝘢𝘤𝘳𝘰𝘴𝘴 𝘵𝘳𝘦𝘢𝘵𝘮𝘦𝘯𝘵 𝘥𝘰𝘴𝘢𝘨𝘦𝘴 𝘢𝘳𝘦 “𝘤𝘰𝘯𝘵𝘢𝘮𝘪𝘯𝘢𝘵𝘦𝘥” 𝘸𝘪𝘵𝘩 𝘴𝘦𝘭𝘦𝘤𝘵𝘪𝘰𝘯 𝘣𝘪𝘢𝘴 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘵𝘰 𝘵𝘳𝘦𝘢𝘵𝘮𝘦𝘯𝘵 𝘦𝘧𝘧𝘦𝘤𝘵 𝘩𝘦𝘵𝘦𝘳𝘰𝘨𝘦𝘯𝘦𝘪𝘵𝘺. 𝘛𝘩𝘶𝘴, 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘢𝘥𝘥𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦, 𝘤𝘰𝘮𝘱𝘢𝘳𝘪𝘴𝘰𝘯 𝘢𝘤𝘳𝘰𝘴𝘴 𝘥𝘰𝘴𝘢𝘨𝘦𝘴 𝘮𝘢𝘺 𝘯𝘰𝘵 𝘪𝘥𝘦𝘯𝘵𝘪𝘧𝘺 𝘤𝘢𝘶𝘴𝘢𝘭 𝘦𝘧𝘧𝘦𝘤𝘵𝘴. 𝘛𝘩𝘦 𝘱𝘭𝘢𝘶𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺 𝘰𝘧 𝘴𝘵𝘳𝘰𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘵𝘳𝘦𝘯𝘥𝘴 𝘥𝘦𝘱𝘦𝘯𝘥𝘴 𝘰𝘯 𝘵𝘩𝘦 𝘦𝘮𝘱𝘪𝘳𝘪𝘤𝘢𝘭 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘰𝘧 𝘵𝘩𝘦 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴, 𝘢𝘯𝘥 𝘸𝘦 𝘥𝘪𝘴𝘤𝘶𝘴𝘴 𝘴𝘰𝘮𝘦 𝘧𝘢𝘭𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘦𝘴 𝘵𝘩𝘢𝘵 𝘤𝘢𝘯 𝘣𝘦 𝘶𝘴𝘦𝘥 𝘵𝘰 𝘢𝘴𝘴𝘦𝘴𝘴 𝘪𝘵.”

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2024 will be the year of lawsuits against generative AI companies. We’ve already had the GitHub Copilot case over assisted coding (https://lnkd.in/eH8Ap-eJ) and the Anthropic case over AI lyrics (https://lnkd.in/eY6UF9Cn). Now the Times joins the fray (https://lnkd.in/e8wHHMzx), and more are likely to follow.

So far, Gen AI companies have defended themselves by arguing fair use and transformative use – that their models create something substantially new and serve a different purpose than the original (thus doesn’t substitute the original, as in Google Books). But recent Supreme Court decisions such as Warhol v. Goldsmith made clear that transformative use claims face high bars.

What might come next?
– New business models for content licensing
– Restrictions on public access to some internal models
– Calls for updated copyright laws and content use regulations
– Technical solutions like attribution, data provenance, and content tagging
– What else?

In our latest short post at Data Duets, Duygu Dagli and I share a recent paper (and an R package) that offers a better solution.

How often do you log transform your data? How do you deal with zeros? Do you just add 1 or some other constant before taking the log?

In our latest short post, Gorkem Turgut (G.T.) Ozer and I share a recent paper (and an R package) that offers a better solution!

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The founding event of artificial intelligence as a field is considered to be the 1956 Dartmouth Workshop in Hanover, New Hampshire.

The proposal listed seven areas of focus for AI: automation of higher-level functions, language models, neural networks, computational efficiency, self-learning, abstraction and generalization from sensor data, and creativity.

These were all revolutionary ideas at the time (and still are), but the one that stands out to me the most is creativity:

“𝘈 𝘧𝘢𝘪𝘳𝘭𝘺 𝘢𝘵𝘵𝘳𝘢𝘤𝘵𝘪𝘷𝘦 𝘢𝘯𝘥 𝘺𝘦𝘵 𝘤𝘭𝘦𝘢𝘳𝘭𝘺 𝘪𝘯𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘤𝘰𝘯𝘫𝘦𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘢𝘯𝘥 𝘶𝘯𝘪𝘮𝘢𝘨𝘪𝘯𝘢𝘵𝘪𝘷𝘦 𝘤𝘰𝘮𝘱𝘦𝘵𝘦𝘯𝘵 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘭𝘪𝘦𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘪𝘯𝘫𝘦𝘤𝘵𝘪𝘰𝘯 𝘰𝘧 𝘢 𝘴𝘰𝘮𝘦 𝘳𝘢𝘯𝘥𝘰𝘮𝘯𝘦𝘴𝘴.”

Today, most generative AI models seem to follow this idea of injecting some randomness. But can a touch of randomness turn 𝘶𝘯𝘪𝘮𝘢𝘨𝘪𝘯𝘢𝘵𝘪𝘷𝘦 𝘤𝘰𝘮𝘱𝘦𝘵𝘦𝘯𝘵 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 into 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘪𝘵𝘺? Well, this 𝘤𝘭𝘦𝘢𝘳𝘭𝘺 𝘪𝘴 𝘢𝘯 𝘪𝘯𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘤𝘰𝘯𝘫𝘦𝘤𝘵𝘶𝘳𝘦.

Randomness alone can’t make a model imaginative. Imagination requires an understanding of cause-effect relationships and counterfactual reasoning.

𝘈𝘯 𝘈𝘐 𝘮𝘰𝘥𝘦𝘭 𝘤𝘢𝘯 𝘱𝘦𝘳𝘧𝘦𝘤𝘵𝘭𝘺 𝘳𝘦𝘱𝘳𝘰𝘥𝘶𝘤𝘦 𝘵𝘩𝘦 𝘤𝘰𝘭𝘰𝘳𝘴 𝘰𝘧 𝘢 𝘴𝘶𝘯𝘴𝘦𝘵 𝘪𝘯 𝘱𝘪𝘹𝘦𝘭𝘴, 𝘺𝘦𝘵 𝘪𝘵 𝘸𝘰𝘶𝘭𝘥 𝘧𝘢𝘪𝘭 𝘵𝘰 𝘨𝘳𝘢𝘴𝘱 𝘵𝘩𝘦 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘴𝘱𝘢𝘳𝘬 𝘵𝘩𝘢𝘵 𝘵𝘶𝘳𝘯𝘴 𝘢 𝘮𝘦𝘳𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦 𝘪𝘯𝘵𝘰 𝘢 𝘸𝘰𝘳𝘬 𝘰𝘧 𝘢𝘳𝘵.

That’s why the more exciting potential today seems to lie in creative human input to a model, or in using the output of the model as input to creative human brain.

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Daniel Millimet and Marc F. Bellemare work on an interesting paper on the feasibility of assuming that fixed effects are fixed over long periods in causal inference models. They highlight an overlooked reality that fixed effects may fail to control for unobserved heterogeneity over long periods of time.

One lesson for causal identification using long panels is to think twice before assuming that fixed effects will take care of unobserved heterogeneity.

More on this is in our short post with Duygu Dagli at Data Duets. She uses rapid gentrification as an example. The short format is a new idea to post more often.

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This is the tag line on boomy.com, a generative AI-based music platform.

We have entered an era where everyone seems to be able to 𝘤𝘳𝘦𝘢𝘵𝘦 𝘰𝘳𝘪𝘨𝘪𝘯𝘢𝘭 𝘟𝘟𝘟 𝘪𝘯 𝘴𝘦𝘤𝘰𝘯𝘥𝘴, 𝘦𝘷𝘦𝘯 𝘪𝘧 𝘵𝘩𝘦𝘺’𝘷𝘦 𝘯𝘦𝘷𝘦𝘳 𝘤𝘳𝘦𝘢𝘵𝘦𝘥 𝘟𝘟𝘟 𝘣𝘦𝘧𝘰𝘳𝘦, a fact that leads to the need for a new vocabulary, since “original” is defined as

– created directly and personally by a particular artist
– not dependent on other people’s ideas

Meanwhile, “Boomy artists have created 18,047,099 original songs” and that 𝘴𝘰𝘶𝘯𝘥𝘴 great.

Imagination (of counterfactuals) draws on domain knowledge and creativity and is key to causal reasoning; it is also where humans continue to outperform algorithms. What about the role of data?

𝘖𝘯𝘦 𝘵𝘳𝘪𝘤𝘬 𝘪𝘴 𝘵𝘰 𝘶𝘴𝘦 𝘥𝘢𝘵𝘢 𝘢𝘴 𝘢 𝘩𝘦𝘭𝘱𝘧𝘶𝘭 𝘨𝘶𝘪𝘥𝘦, 𝘯𝘰𝘵 𝘵𝘩𝘦 𝘰𝘯𝘭𝘺 𝘨𝘶𝘪𝘥𝘦. 𝘐𝘵’𝘴 𝘭𝘪𝘬𝘦 𝘶𝘴𝘪𝘯𝘨 𝘢 𝘮𝘢𝘱. 𝘈 𝘮𝘢𝘱 𝘤𝘢𝘯 𝘵𝘦𝘭𝘭 𝘺𝘰𝘶 𝘸𝘩𝘦𝘳𝘦 𝘵𝘩𝘦 𝘴𝘵𝘳𝘦𝘦𝘵𝘴 𝘢𝘳𝘦, 𝘣𝘶𝘵 𝘪𝘵 𝘤𝘢𝘯’𝘵 𝘵𝘦𝘭𝘭 𝘺𝘰𝘶 𝘪𝘧 𝘵𝘩𝘦𝘳𝘦’𝘴 𝘢 𝘱𝘢𝘳𝘢𝘥𝘦. 𝘐𝘵 𝘤𝘢𝘯’𝘵 𝘵𝘦𝘭𝘭 𝘺𝘰𝘶 𝘸𝘩𝘢𝘵 𝘦𝘭𝘴𝘦 𝘮𝘪𝘨𝘩𝘵 𝘩𝘢𝘷𝘦 𝘣𝘦𝘦𝘯 𝘩𝘢𝘱𝘱𝘦𝘯𝘪𝘯𝘨 𝘦𝘪𝘵𝘩𝘦𝘳. 𝘠𝘰𝘶 𝘩𝘢𝘷𝘦 𝘵𝘰 𝘭𝘰𝘰𝘬 𝘶𝘱 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘮𝘢𝘱 𝘢𝘯𝘥 𝘶𝘴𝘦 𝘺𝘰𝘶𝘳 𝘦𝘺𝘦𝘴 𝘢𝘯𝘥 𝘺𝘰𝘶𝘳 𝘣𝘳𝘢𝘪𝘯.

This map distorts the data on the relative positions of the continents, but it uses the data correctly on their location and size. It’s just as wrong as any other world map, but it makes one look up, think, and imagine.

What might have happened if
– Central America was in the arctic zone and Siberia was subtropical
– Cuba was off the east coast of Canada and the USA
– Japan was off the coasts of Portugal and Spain
– North Korea was part of South Korea
– Taiwan was next to France

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Gilbert may have a point here, except for the part about how markets work. This is a popular free market product with a growing number of customers. Today I heard that ChatGPT Plus now has a waiting list. I also find Gilbert’s statement a bit overdramatic, but that’s beside the point. LLMs are useful tools if their limitations are well defined, communicated, and acknowledged, and if the lingering issues of copyright and privacy are resolved.

But if not, and if we continue to treat a computer model as if it had some kind of consciousness and general intelligence, progress will be painful. I’ve expressed this concern several times, using Searle’s Chinese Room argument and pointing out the dangers of so-called “hallucinations” in the hands of well-meaning users who don’t always know what they don’t know.

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I started coding in C and used C# before switching to Java (those were the dark days). Today, R and Python make up my daily stack (and I dabble in Julia for fun). Now, in retrospect, one habit I wish the data science community had developed better is exception handling. More often than not, I come across code that is doomed to crash (hey, it’s called an exception for a reason).

The next time you code to analyze data, remember this video of division by zero. This is the pain and suffering your computer goes through when your denominator hits zero. This is also a reminder to appreciate (not complain about) the errors and warnings in R and Python (my former and future students, hear me?).

Received the video in personal communication and happy to add a source.

…because experts are more likely to know what they don’t know. When users don’t know what they don’t know, so-called “hallucinations” are less likely to be detected and this seems to be a growing problem, likely exacerbated by the Dunning-Kruger effect. Well, this is my take on them.

In the study cited in the article, several LLM models are asked to summarize news articles to measure how often they “hallucinated” or made up facts.

The LLM models showed different rates of “hallucination”, with OpenAI having the lowest (about 3%), followed by Meta (about 5%), Anthropic’s Claude 2 system (over 8%), and Google’s Palm chat with the highest (27%).

Source

𝘈𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘺𝘰𝘶𝘳 𝘥𝘢𝘵𝘢 𝘸𝘪𝘵𝘩 𝘢 𝘮𝘪𝘹 𝘰𝘧 𝘴𝘬𝘦𝘱𝘵𝘪𝘤𝘪𝘴𝘮 𝘢𝘯𝘥 𝘤𝘶𝘳𝘪𝘰𝘴𝘪𝘵𝘺. 𝘛𝘳𝘦𝘢𝘵 𝘥𝘢𝘵𝘢 𝘢𝘴 𝘢 𝘴𝘵𝘰𝘳𝘺𝘵𝘦𝘭𝘭𝘦𝘳 𝘵𝘩𝘢𝘵 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘥𝘪𝘳𝘦𝘤𝘵𝘭𝘺 𝘵𝘦𝘭𝘭 𝘵𝘩𝘦 𝘵𝘳𝘶𝘵𝘩, 𝘣𝘶𝘵 𝘰𝘧𝘧𝘦𝘳𝘴 𝘤𝘭𝘶𝘦𝘴 𝘵𝘩𝘢𝘵, 𝘸𝘪𝘵𝘩 𝘳𝘪𝘨𝘰𝘳𝘰𝘶𝘴 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘢𝘯𝘥 𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨, 𝘳𝘦𝘷𝘦𝘢𝘭 𝘵𝘩𝘦 𝘥𝘦𝘦𝘱𝘦𝘳 𝘯𝘢𝘳𝘳𝘢𝘵𝘪𝘷𝘦. 𝘊𝘶𝘭𝘵𝘪𝘷𝘢𝘵𝘦 𝘵𝘩𝘦 𝘢𝘳𝘵 𝘰𝘧 𝘢𝘴𝘬𝘪𝘯𝘨 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 -𝘯𝘰𝘵 𝘰𝘯𝘭𝘺 𝘰𝘧 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢, 𝘣𝘶𝘵 𝘢𝘭𝘴𝘰 𝘰𝘧 𝘵𝘩𝘦 𝘴𝘵𝘢𝘬𝘦𝘩𝘰𝘭𝘥𝘦𝘳𝘴. 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘵𝘩𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘢𝘯𝘥 𝘶𝘯𝘥𝘦𝘳𝘭𝘺𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴 𝘵𝘩𝘢𝘵 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢 𝘵𝘰 𝘢𝘷𝘰𝘪𝘥 𝘮𝘢𝘬𝘪𝘯𝘨 𝘪𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘰𝘯 𝘴𝘦𝘦𝘮𝘪𝘯𝘨 𝘴𝘪𝘨𝘯𝘢𝘭𝘴 𝘵𝘩𝘢𝘵 𝘮𝘢𝘺 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘣𝘦 𝘯𝘰𝘪𝘴𝘦. 𝘈𝘯𝘥 𝘯𝘦𝘷𝘦𝘳 𝘧𝘰𝘳𝘨𝘦𝘵 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘴𝘵𝘰𝘳𝘺𝘵𝘦𝘭𝘭𝘪𝘯𝘨; 𝘺𝘰𝘶𝘳 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘳𝘦 𝘰𝘯𝘭𝘺 𝘢𝘴 𝘷𝘢𝘭𝘶𝘢𝘣𝘭𝘦 𝘢𝘴 𝘺𝘰𝘶𝘳 𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘵𝘰 𝘦𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘤𝘢𝘵𝘦 𝘵𝘩𝘦𝘮.

The Executive Order defines “AI” as:
“a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.”

This means that the scope is not limited to generative AI, which is good. Using “AI” as an umbrella term may still not be a good idea for the reasons my assistant lists below but I hope this is a first step in the right direction.

“𝘈𝘐” 𝘢𝘴 𝘢 𝘣𝘭𝘢𝘯𝘬𝘦𝘵 𝘵𝘦𝘳𝘮

𝘖𝘯 𝘵𝘩𝘦 𝘱𝘰𝘴𝘪𝘵𝘪𝘷𝘦 𝘴𝘪𝘥𝘦, 𝘪𝘵 𝘴𝘪𝘮𝘱𝘭𝘪𝘧𝘪𝘦𝘴 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘣𝘺 𝘨𝘳𝘰𝘶𝘱𝘪𝘯𝘨 𝘵𝘰𝘨𝘦𝘵𝘩𝘦𝘳 𝘢 𝘸𝘪𝘥𝘦 𝘳𝘢𝘯𝘨𝘦 𝘰𝘧 𝘮𝘰𝘥𝘦𝘭𝘴 𝘵𝘩𝘢𝘵 𝘦𝘮𝘶𝘭𝘢𝘵𝘦 𝘩𝘶𝘮𝘢𝘯 𝘤𝘰𝘨𝘯𝘪𝘵𝘪𝘷𝘦 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴 𝘴𝘶𝘤𝘩 𝘢𝘴 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨, 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘴𝘰𝘭𝘷𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘱𝘢𝘵𝘵𝘦𝘳𝘯 𝘳𝘦𝘤𝘰𝘨𝘯𝘪𝘵𝘪𝘰𝘯. 𝘛𝘩𝘪𝘴 𝘴𝘪𝘮𝘱𝘭𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘤𝘢𝘯 𝘣𝘦 𝘣𝘦𝘯𝘦𝘧𝘪𝘤𝘪𝘢𝘭 𝘧𝘰𝘳 𝘦𝘥𝘶𝘤𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘱𝘶𝘳𝘱𝘰𝘴𝘦𝘴, 𝘱𝘰𝘭𝘪𝘤𝘺𝘮𝘢𝘬𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘱𝘳𝘰𝘮𝘰𝘵𝘪𝘯𝘨 𝘱𝘶𝘣𝘭𝘪𝘤 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨. 𝘐𝘵 𝘱𝘳𝘰𝘷𝘪𝘥𝘦𝘴 𝘢 𝘤𝘰𝘯𝘷𝘦𝘯𝘪𝘦𝘯𝘵 𝘴𝘩𝘰𝘳𝘵𝘩𝘢𝘯𝘥 𝘧𝘰𝘳 𝘥𝘪𝘴𝘤𝘶𝘴𝘴𝘪𝘯𝘨 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯𝘴 𝘳𝘢𝘯𝘨𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘴𝘪𝘮𝘱𝘭𝘦 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮𝘴 𝘵𝘰 𝘤𝘰𝘮𝘱𝘭𝘦𝘹 𝘯𝘦𝘶𝘳𝘢𝘭 𝘯𝘦𝘵𝘸𝘰𝘳𝘬𝘴 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘨𝘦𝘵𝘵𝘪𝘯𝘨 𝘣𝘰𝘨𝘨𝘦𝘥 𝘥𝘰𝘸𝘯 𝘪𝘯 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘥𝘦𝘵𝘢𝘪𝘭𝘴.

𝘖𝘯 𝘵𝘩𝘦 𝘥𝘰𝘸𝘯𝘴𝘪𝘥𝘦, 𝘩𝘰𝘸𝘦𝘷𝘦𝘳, 𝘵𝘩𝘦 𝘵𝘦𝘳𝘮 𝘤𝘢𝘯 𝘣𝘦 𝘮𝘪𝘴𝘭𝘦𝘢𝘥𝘪𝘯𝘨 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘰𝘧 𝘪𝘵𝘴 𝘣𝘳𝘰𝘢𝘥 𝘴𝘤𝘰𝘱𝘦 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘱𝘶𝘣𝘭𝘪𝘤’𝘴 𝘷𝘢𝘳𝘺𝘪𝘯𝘨 𝘪𝘯𝘵𝘦𝘳𝘱𝘳𝘦𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘸𝘩𝘢𝘵 𝘈𝘐 𝘦𝘯𝘤𝘰𝘮𝘱𝘢𝘴𝘴𝘦𝘴. 𝘐𝘵 𝘤𝘢𝘯 𝘤𝘰𝘯𝘧𝘭𝘢𝘵𝘦 𝘳𝘶𝘥𝘪𝘮𝘦𝘯𝘵𝘢𝘳𝘺 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘸𝘪𝘵𝘩 𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘥 𝘮𝘢𝘤𝘩𝘪𝘯𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴, 𝘭𝘦𝘢𝘥𝘪𝘯𝘨 𝘵𝘰 𝘪𝘯𝘧𝘭𝘢𝘵𝘦𝘥 𝘦𝘹𝘱𝘦𝘤𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘳 𝘶𝘯𝘥𝘶𝘦 𝘧𝘦𝘢𝘳. 𝘐𝘯 𝘢𝘥𝘥𝘪𝘵𝘪𝘰𝘯, 𝘵𝘩𝘦 𝘣𝘳𝘰𝘢𝘥 𝘶𝘴𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘵𝘦𝘳𝘮 𝘤𝘢𝘯 𝘰𝘣𝘴𝘤𝘶𝘳𝘦 𝘵𝘩𝘦 𝘯𝘶𝘢𝘯𝘤𝘦𝘥 𝘦𝘵𝘩𝘪𝘤𝘢𝘭, 𝘭𝘦𝘨𝘢𝘭, 𝘢𝘯𝘥 𝘴𝘰𝘤𝘪𝘰𝘦𝘤𝘰𝘯𝘰𝘮𝘪𝘤 𝘪𝘮𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘴𝘱𝘦𝘤𝘪𝘧𝘪𝘤 𝘵𝘰 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘈𝘐 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴, 𝘵𝘩𝘦𝘳𝘦𝘣𝘺 𝘩𝘪𝘯𝘥𝘦𝘳𝘪𝘯𝘨 𝘧𝘰𝘤𝘶𝘴𝘦𝘥 𝘥𝘦𝘣𝘢𝘵𝘦 𝘢𝘯𝘥 𝘵𝘩𝘰𝘶𝘨𝘩𝘵𝘧𝘶𝘭 𝘳𝘦𝘨𝘶𝘭𝘢𝘵𝘪𝘰𝘯. 𝘛𝘩𝘦 𝘣𝘭𝘢𝘯𝘬𝘦𝘵 𝘵𝘦𝘳𝘮 𝘤𝘢𝘯 𝘢𝘭𝘴𝘰 𝘰𝘣𝘴𝘤𝘶𝘳𝘦 𝘵𝘩𝘦 𝘴𝘪𝘨𝘯𝘪𝘧𝘪𝘤𝘢𝘯𝘵 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴 𝘢𝘯𝘥 𝘳𝘪𝘴𝘬𝘴 𝘰𝘧 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘈𝘐 𝘮𝘰𝘥𝘦𝘭𝘴, 𝘱𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭𝘭𝘺 𝘭𝘦𝘢𝘥𝘪𝘯𝘨 𝘵𝘰 𝘢 𝘰𝘯𝘦-𝘴𝘪𝘻𝘦-𝘧𝘪𝘵𝘴-𝘢𝘭𝘭 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘵𝘰 𝘱𝘰𝘭𝘪𝘤𝘺 𝘢𝘯𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦.

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Every time I fly, I am struck by how archaic airport baggage handling still is. Sure, the airline industry is infamous for maintaining its legacy Fortran and Cobol software, but that’s because aviation is a pioneer in using computers to run its operations. Meanwhile, baggage handling remains a bottleneck in air travel because the process is highly manual and inefficient (except for using the same conveyor system that seems to have been in use since 1971).

When robots take over (or assist with) baggage handling, overall passenger satisfaction is likely to improve. Increased use of robots to solve such low-stakes bottleneck problems may also help the public perception of robots.

evoBot looks like one of the robots that can solve this problem. The robot achieves excellent balance using an inverted pendulum design, and can reach speeds of 37 mph and carry over 220 pounds. Pretty impressive.

Not shown in the video, but it can also lift luggage off the ground and deliver it to its destination (airplane or the 1971 conveyor belt). Munich Airport seems to have tested it already. I hope to see it in action soon.

If you ask it, its answer is “Yes.” If you ask it if it “understands” Chinese, its answer is again “Yes” without hesitation. Searle’s 1980 Chinese Room argument is more relevant than ever in the age of LLMs:

𝘚𝘶𝘱𝘱𝘰𝘴𝘦 𝘢 𝘮𝘰𝘥𝘦𝘭 (𝘣𝘰𝘹 𝘪𝘯 𝘵𝘩𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦) 𝘵𝘩𝘢𝘵 𝘣𝘦𝘩𝘢𝘷𝘦𝘴 𝘢𝘴 𝘪𝘧 𝘪𝘵 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘊𝘩𝘪𝘯𝘦𝘴𝘦. 𝘐𝘵 𝘵𝘢𝘬𝘦𝘴 𝘊𝘩𝘪𝘯𝘦𝘴𝘦 𝘤𝘩𝘢𝘳𝘢𝘤𝘵𝘦𝘳𝘴 𝘢𝘴 𝘪𝘯𝘱𝘶𝘵 𝘢𝘯𝘥 𝘱𝘳𝘰𝘥𝘶𝘤𝘦𝘴 𝘰𝘵𝘩𝘦𝘳 𝘊𝘩𝘪𝘯𝘦𝘴𝘦 𝘤𝘩𝘢𝘳𝘢𝘤𝘵𝘦𝘳𝘴 𝘢𝘴 𝘰𝘶𝘵𝘱𝘶𝘵. 𝘛𝘩𝘪𝘴 𝘮𝘰𝘥𝘦𝘭 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘴 𝘪𝘵𝘴 𝘵𝘢𝘴𝘬 𝘴𝘰 𝘤𝘰𝘯𝘷𝘪𝘯𝘤𝘪𝘯𝘨𝘭𝘺 𝘵𝘩𝘢𝘵 𝘪𝘵 𝘤𝘰𝘮𝘧𝘰𝘳𝘵𝘢𝘣𝘭𝘺 𝘱𝘢𝘴𝘴𝘦𝘴 𝘵𝘩𝘦 𝘛𝘶𝘳𝘪𝘯𝘨 𝘵𝘦𝘴𝘵: 𝘪𝘵 𝘤𝘰𝘯𝘷𝘪𝘯𝘤𝘦𝘴 𝘢 𝘩𝘶𝘮𝘢𝘯 𝘊𝘩𝘪𝘯𝘦𝘴𝘦 𝘴𝘱𝘦𝘢𝘬𝘦𝘳 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴 𝘪𝘵𝘴𝘦𝘭𝘧 𝘢 𝘭𝘪𝘷𝘦 𝘊𝘩𝘪𝘯𝘦𝘴𝘦 𝘴𝘱𝘦𝘢𝘬𝘦𝘳. 𝘛𝘰 𝘢𝘭𝘭 𝘰𝘧 𝘵𝘩𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦 𝘱𝘦𝘳𝘴𝘰𝘯 𝘢𝘴𝘬𝘴, 𝘪𝘵 𝘮𝘢𝘬𝘦𝘴 𝘢𝘱𝘱𝘳𝘰𝘱𝘳𝘪𝘢𝘵𝘦 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘦𝘴, 𝘴𝘶𝘤𝘩 𝘵𝘩𝘢𝘵 𝘢𝘯𝘺 𝘊𝘩𝘪𝘯𝘦𝘴𝘦 𝘴𝘱𝘦𝘢𝘬𝘦𝘳 𝘸𝘰𝘶𝘭𝘥 𝘣𝘦 𝘤𝘰𝘯𝘷𝘪𝘯𝘤𝘦𝘥 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦𝘺 𝘢𝘳𝘦 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘵𝘰 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘊𝘩𝘪𝘯𝘦𝘴𝘦-𝘴𝘱𝘦𝘢𝘬𝘪𝘯𝘨 𝘩𝘶𝘮𝘢𝘯 𝘣𝘦𝘪𝘯𝘨. 𝘐𝘯 𝘵𝘩𝘪𝘴 𝘤𝘢𝘴𝘦, 𝘥𝘰𝘦𝘴 𝘵𝘩𝘦 𝘮𝘢𝘤𝘩𝘪𝘯𝘦 𝘭𝘪𝘵𝘦𝘳𝘢𝘭𝘭𝘺 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘊𝘩𝘪𝘯𝘦𝘴𝘦? 𝘖𝘳 𝘪𝘴 𝘪𝘵 𝘮𝘦𝘳𝘦𝘭𝘺 𝘴𝘪𝘮𝘶𝘭𝘢𝘵𝘪𝘯𝘨 𝘵𝘩𝘦 𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘵𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘊𝘩𝘪𝘯𝘦𝘴𝘦?

More recently in his book, Searle linked his original argument to consciousness, but that’s probably a higher bar than needed to reason that ChatGPT is a box that has no idea what it’s talking about.

I took several rides in Google’s Waymo robotaxi. This is a short video of the experience, which is great, almost flawless. One problem is that it gets boring really fast.

About half the time during my trip, I used Uber or Lyft instead of a Waymo, and I met a professional dancer, a retired chef, a compliance officer, a criminal justice expert, an Amazon truck driver, and a painter.

I had really fun conversations that touched on “AI” and dance music, the best old school restaurants in town, the private equity fundraising process, cybersecurity, privacy, more “AI” and so on. As a bonus, almost all of the conversations included some useful, local information about the city.

None of the robotaxi rides had any of this, serendipity was nonexistent. The robot feels friendly, sure, but that’s about it. The longer the ride, the more boring it gets.

Replacing influencers with generative AI looks like a great use case. The real question is whether influencers who promote “AI” will also be replaced by AI.

Some details:
With just a few minutes of sample video from the person to be cloned and a payment of $1,000, brands can clone a human streamer to work 24/7.

The AI videobots may already be having some economic impact: the average salary for livestream hosts in China is down 20% from 2022 (just another YoY figure, not a causal effect).

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