It's easy to lose sight of the visceral fear and uncertainty that pervaded the early days of the pandemic.
With each iteration, AI becomes both student and teacher, trapped in an echo chamber of its own creation.
From Wendy's ill-fated foray into dynamic pricing to the prices of Ticketmaster and Live Nation, the line between innovation and exploitation grows increasingly blurred.
The shame and blame game for COVID continues, with Senator Rand and Elon Musk suggesting that Dr. Fauci go on trial. But what I personally find disconcerting, although not surprising, is that few people remember the fear of those early days.
“Makari and Friedman write that “many people don’t regularly recall the details of the early pandemic—how walking down a crowded street inspired terror, how sirens wailed like clockwork in cities, or how one had to worry about inadvertently killing grandparents when visiting them.”
From Slow Boring, 17 thoughts four years after Covid
One of the problems with AI is that it learns from the content of the Internet, and as the Internet continues to fill with AI-generated content, AI learns nothing new; it just reinforces itself.
“Right after the blockbuster release of GPT-4, the latest artificial intelligence model from OpenAI and one of the most advanced in existence, the language of scientific research began to mutate. Especially within the field of AI itself. … A study published this month examined scientists’ peer reviews — researchers’ official pronouncements on others’ work that form the bedrock of scientific progress — across a number of high-profile and prestigious scientific conferences studying AI, At one such conference, those peer reviews used the word “meticulous” more than 34 times as often as reviews did the previous year. Use of “commendable” was around 10 times as frequent, and “intricate,” 11 times. Other major conferences showed similar patterns.”
From the NY Times, A.I.-Generated Garbage Is Polluting Our Culture
In other news, Wendy’s proposed and swiftly withdrew a plan for dynamic pricing at the take-out window. I have mentioned that Amazon has used dynamic pricing based on who, what, and where a purchase has been made for some time. Dynamic pricing involves large datasets and algorithmic manipulation. As Tyler Cohen at Marginal Revolution points out, software, especially the new generation of AI, can also collude on pricing – more than a hallucination, perhaps a violation of the law.
“The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically GPT-4. We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions (“prompts”) may increase collusion. These results extend to auction settings. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover regulatory challenges unique to LLM-based pricing agents.”
And this problem exists right now concerning Ticket Master and Live Nation. Here is the take on Ticket Master from Vox, The problem with Ticketmaster, explained not by Taylor Swift and from Matt Stoller on Big, Explosive New Documents Unearthed On Live Nation/Ticketmaster
