AI journalism has a hype problem. To move past the cycle of reacting to each story, @sayashk and I analyzed over 50 pieces from 5 prominent outlets. We've compiled a list of recurring pitfalls that readers should watch out for and journalists should avoid. https://t.co/uHFUpHMZ6U
— Arvind Narayanan (@random_walker) September 30, 2022
This is the first tweet in the tweet thread.
Pay attention to audience interaction at 7:07 to the end.
We collect human preference annotations for news summaries generated by current SOTA and zero-shot GPT-3 models. For multiple settings (generic + keyword) and datasets (CNN + BBC), GPT-3 summaries beat prior fine-tuned models!
— Tanya Goyal (@tanyaagoyal) September 27, 2022
[2/6] pic.twitter.com/BiFHDh0nZa
This also means we can now break away from noisy benchmark datasets, e.g. XSum, that (we observe) cannot produce systems for real settings. Instead, actual use cases and not data availability can now dictate future research directions (task goals, domains, etc.)
— Tanya Goyal (@tanyaagoyal) September 27, 2022
[4/6]
Browse examples of generated summaries and human annotations at: https://t.co/vcSeVl5Zwj
— Tanya Goyal (@tanyaagoyal) September 27, 2022
[6/6]
Jeffrey Funk - Gary N. Smith, No, AI probably won’t revolutionize drug development, Salon, September 24.
The opening paragraphs:
Drug development is expensive, time consuming, and risky. A typical new drug costs billions of dollars to develop and requires more than ten years of work — yet only about 0.02% of the drugs in development ever making it to market.
Some claim that AI, or artificial intelligence, will revolutionize drug development by ushering in much shorter development times and drastically lower costs. Many scientists and business consultants are especially optimistic about AI's ability to predict the shapes of nearly every known protein using DeepMind's AlphaFold, an artificial intelligence tool developed by Google parent company Alphabet; predicting this information with great detail would be key to quickly developing drugs. As one AI company boasts, "We … firmly believe that AI has the potential to transform the drug discovery process to achieve time and cost efficiencies."
Later:
The relative unimportance of AI in COVID development is consistent with the conclusion of many scientists that AI is not about to revolutionize drug development. The biggest problem is that clinical trials are the longest and typically most expensive part of the process, and AI cannot replace actual trials. Even AI's impact on drug discovery may be limited. A Science op-ed recently argued that, "[AI] doesn't make as much difference to drug discovery as many stories and press releases have had it…. Protein structure prediction is a hard problem, but even harder ones remain."
An often crucial part of drug development is to determine whether a drug binds to the candidate protein, something that MIT researchers have shown that AlphaFold cannot do and DeepMind admits AlphaFold can't do: "Predicting drug binding is probably one of the most difficult tasks in biology: these are many-atom interactions between complex molecules with many potential conformations, and the aim of docking is to pinpoint just one of them." [...]
One huge hurdle for all AI data mining algorithms is that the data deluge has made the number of promising-but-coincidental patterns waiting to be discovered far, far larger than the number of useful relationships — which means that the probability that a discovered pattern is truly useful is very close to zero.
There is more at the link.
The NYTimes has another article that feeds my interest in changing attitudes toward work: Lisa Rabasa Roepe, Millennials Want to Retire at 50. How to Afford It Is Another Matter, Sept. 24, 2022. It opens:
Although Devangi Patel, 33, has been working as a cardiothoracic anesthesiologist at a large medical center outside Atlanta for only two years, her goal is to afford to walk away from her job at 50.
“That, to me, is the American dream,” she said.
Dr. Patel is not alone in her quest to become financially independent — and at a relatively early age. It seems that a generational shift is well underway: Many millennial workers don’t aspire to retire in their mid- or late 60s, like their parents. Instead, many with professional careers are seeking to leave their jobs by 50 and work for themselves or take a lower-paying role that is more aligned with their interests, studies are showing and financial advisers are finding.
“I want to get to a point where I don’t have to work for money anymore, and I can work for pleasure,” Dr. Patel said.
But reaching that goal has been harder than Dr. Patel anticipated. Although she contributes to a 401(k) and a Roth individual retirement account, invests in stocks with a brokerage account and maxes out her health savings account, she is also paying off a $250,000 loan for medical school and paying for her wedding in December.
There’s more at the link.
"We’ve torn down the old gods and replaced them with an idolatry of innovators. And this religion is most deeply embedded in the culture of technology," @profgalloway writes: https://t.co/Lrj9jsJTjV
— The Atlantic (@TheAtlantic) September 25, 2022
Two weeks ago I published another essay at 3 Quarks Daily:
It’s a decent essay, in particular I like the capsule history I sketch for the emergence of AI Doom as an issue. But it’s hardly my last word on the subject.
* * * * *
Here’s a TEDx talk from 2017 by a high school student, Andrew Zeitler, entitled, “The Truth Behind Artificial Intelligence.”
It opens with two quick versions of the future, one in which AI is the center of a world of luxury and enjoyment and the other in which AI destroys the human race. At about 14:08 he says the technological singularity will arrive in 2029. He concludes by quoting Edsgar Dijkstra: “The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.”
He's bright and engaged but, as far as I can tell, does not have deep technical knowledge of AI. He presents these ideas as obvious truths. I have no idea how many people share his beliefs. But relatively few people are in a position to think these matters through with any degree of sophistication.
I have no idea when these issues will be resolved. But I suspect that, at this level, they are issues being driven by symbolic concerns, not technical ones. We’re working through a cultural mythology for the 21st century.
* * * * *
This is epistemic theater, but also PR (signaling). For those purposes the contents of the essays are irrelevant. How much more such theater is in store for us?
We think it's possible that we're totally out of it on AGI.
— Leopold Aschenbrenner (@leopoldasch) September 23, 2022
And we want to learn about it as quickly as because it would change how we allocate 100s of millions of $ (or more).
Enter by Dec 23! pic.twitter.com/AxA9wM7ZAo
Bump: from June 2017. This sort of thing is on my mind these days.
This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it. Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.)We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. What is to be done?
So if most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software — China or the United States — to essentially become that country’s economic dependent, taking in welfare subsidies in exchange for letting the “parent” nation’s A.I. companies continue to profit from the dependent country’s users. Such economic arrangements would reshape today’s geopolitical alliances.
