Maybe someday Altman’s ideas about AI will prove out, but for now, his approach is textbook Silicon Valley mythmaking.

But research is showing that AI generation may be even more resource-intensive than originally thought. Imagine that you want to ask an AI program to write up a 100-word email for you. You get an almost instant response, but what you don’t see are the intensive computing resources that went into creating that email. At the AI data center, generating just two of those emails could use as much energy as a full charge on the latest iPhone. And according to a Pew Research Center study, that 100-word email could use up a whole bottle of water for the cooling that’s needed at data centers.

Brillant texte de Ted Chiang sur les différences fondamentales entre la création artistique telle que pratiquée par un humain et la génération de texte, d'images ou de sons par un LLM (Large Language Model).

Art is notoriously hard to define, and so are the differences between good art and bad art. But let me offer a generalization: art is something that results from making a lot of choices. This might be easiest to explain if we use fiction writing as an example. When you are writing fiction, you are—consciously or unconsciously—making a choice about almost every word you type; to oversimplify, we can imagine that a ten-thousand-word short story requires something on the order of ten thousand choices. When you give a generative-A.I. program a prompt, you are making very few choices; if you supply a hundred-word prompt, you have made on the order of a hundred choices.

Le vrai risque des soit-disant "IA" : non pas une révolte des machine qui voudraient soudainement détruire l'humanité, mais l'automatisation de tâches administratives par des systèmes aveugles et inhumains, qui écrasent les individus et contre lesquels il est pratiquement impossible de faire appel. Le tout fondé sur la croyance (absolument fausse) qu'un algorithme est nécessairement neutre et objectif.

Companies may unintentionally hurt their sales by including the words “artificial intelligence” when describing their offerings that use the technology, according to a study led by Washington State University researchers.

Of course, generative AI is an impressive technology, and it provides tremendous opportunities for improving productivity in a number of tasks. But because the hype has gone so far ahead of reality, the setbacks of the technology in 2024 will be more memorable.

Rather than solving the problems raised by employers’ methods, however, the use of automated job-hunting only served to set off an AI arms race that has no obvious conclusion. ZipRecruiter’s quarterly New Hires Survey reported that in Q1 of this year, more than half of all applicants admitted using AI to assist their efforts. Hiring managers, flooded with more applications than ever before, took the next logical step of seeking out AI that can detect submissions forged by AI. Naturally, prospective employees responded by turning to AI that could defeat AI detectors. Employers moved on to AI that can conduct entire interviews. The applicants can cruise past this hurdle by using specialized AI assistants that provide souped-up answers to an interviewer’s questions in real time. Around and around we go, with no end in sight.

One of the world's largest investment banks wonders if generative AI will be worth the huge investment and hype: "will this large spend ever pay off?"

Vidéo de la chaîne YouTube Computerphile qui explore l'hypothèse (papier scientifique à l'appui) selon laquelle l'IA pourrait bientôt atteindre un plateau en terme de performances —contrairement à l'idée (très complaisante et pas du tout étayée) d'une progression linéaire illimitée, voire exponentielle.

97 travailleurs Kényans cosignent une lettre ouverte demandant au président des États-Unis, Joe Biden, de mettre fin « aux conditions de travail qui s'apparentent à de l'esclavage moderne » dans l'industrie du numérique.