@EUCommission

I don’t know if this account is actually monitored, or just a publishing place, but you may have noticed that this post has received almost overwhelmingly negative responses.

You could disregard this as Mastodon bias, but keep in mind that the biggest bias on Mastodon is that people who understand and built core parts of the information technology that you use every day are massively over represented. This is probably the only place you will get a lot of replies from people who both understand technology and do not have a financial incentive to hype things to get large amounts of government funding.

EDIT: I should add, I used machine learning during my PhD and there are a lot of problems for which it is a really good fit. But, in the current climate, it’s generally safe to interpret ‘AI’ as meaning ‘machine learning applied to a problem where machine learning is the wrong solution’. It isn’t a technology, it’s a branding term, and it’s a branding term used almost exclusively for things that have no social benefit.

@fuji @david_chisnall @EUCommission

Organizations have been implementing AI for years by identifying what human tasks that can be safely done by AI at no risk to the company. More or less every single modern organization of a moderately large size that relies to a large degree on digital infrastructure does this now, either directly or indirectly through the tools and services that they use. And if they don't, then their suppliers and vendors do.

@davidsonsr @fuji @EUCommission

This is true only if you conflate 'AI' with 'automation'. Companies trying to sell 'AI' like it when you do this, but if 'AI' includes anything that a computer does then it's a meaningless term.

@david_chisnall @fuji @EUCommission

I'm talking about LLMs/reasoning models enabling software to make decisions based on natural language instead of programmatic instructions. I'd say that this is what's commonly understood to be the meaning of the term "AI" in business contexts. Isn't that what we're talking about?

@davidsonsr @fuji @EUCommission

So what are these use cases? Replacing customer support with a chatbot that makes up policies, can't answer questions, and drives away customers? Meeting summary systems that invert the conclusion of the meeting? Note taking for doctors that fabricates conditions and cancels essential prescriptions?

Machine-learning systems work really nicely in situations where either the result can be checked instantly and cheaply, or where the cost of a wrong answer is vastly lower than the benefit of a correct answer. Very few natural-language processing tasks have this property.

LLMs have had hundreds of billions of dollars spent on them, and are not yet profitable. No company can offer them to customers at a price that customers are willing to pay and which covers the costs. And, even with that level of subsidy, it has made zero measurable impact on the GDP of the USA.

If a technology has failed to deliver anything of value to the economy after sinking a hundred billion, the rational thing to do is not say 'we must also throw money down this hole'. It is to say 'other countries, please keep wasting your economic potential! We will invest in things that actually deliver!' (Or, at least, in things that haven't yet been shown to not deliver).

@david_chisnall @fuji @EUCommission

We're discussing a diffuse economic impact, so you're not going to see many concentrated labor displacements or sweeping gains. Companies are reporting being able to conduct AI improvements at scale with fine-grained tasks, but the ratio at which it displaces, pressures or complements labor differs depending on the context. What's important to acknowledge is that this ratio is changing as companies are continuously optimizing for AI implementation.

@david_chisnall @fuji @EUCommission

There are some quantifiable indicators like direct labor displacements in professions like freelancing, language and content work, what seems to be declines in junior and entry-level hiring in AI-exposed companies and industry surveys and labor data indicating that AI is significantly commoditizing skills in some professions. We're possibly going to see more of this as the Service-as-a-software model is emerging.

@david_chisnall @fuji @EUCommission

For anecdotal real-world examples of how AI is being used, there's invoice and document processing (ingestion and scanning), document drafting (legal, corporate and technical writing), content reviewing (code, contracts or otherwise), monitoring (credit underwriting, fraud detection), technical inspection (defect detection, report processing) and so on. Companies see marginal to significant improvements related to many of these AI implementations.