Every now and then I read a post which simplifies the terms which fly around AI. Dror Poleg lists down 50 words that explain AI and talks about the race between US and China.
China and the US take different approaches to the race to develop powerful AI. Each approach reflects different priorities and current capabilities.
The American bet is on chokepoints — the narrow places in the chip supply chain where one company or country is irreplaceable. One Dutch firm, ASML, makes every EUV lithography machine capable of printing the most advanced GPU chips. One Taiwanese firm, TSMC, fabricates the overwhelming majority of them. One American firm, Nvidia, designs the GPUs everyone wants. Since October 2022, U.S. export controls have tried to hold China several years behind the frontier by blocking its access to these narrows — the machines, the chips, even specific memory. When the good is cheap to copy, control the means of production instead.
The Chinese bet is on abundance and diffusion. Publish more research, release open weights (DeepSeek, Qwen, Kimi and their siblings are now the default free models for much of the world), and build electricity like it’s going out of style. China already publishes roughly a third of the world’s AI research papers — quantity, not necessarily quality — and its share of the most-cited work has passed America’s too. And in Epoch AI’s tally of notable models, China’s share of frontier training compute reached about 40% this year, against roughly 55% for the United States: behind, but no longer a different league.
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Meanwhile, China and the US are adversaries, the AI models themselves pose a threat to both: These models can hack, destabilize, and flood the world with disinformation. They can empower subversive groups, develop new viruses (and vaccines), and turn consumer devices into powerful weapons.
Why can’t China and the US simply slow down AI development and agree on more responsible progress? Because the AI race has the structure of the oldest puzzle in game theory. In a prisoner’s dilemma, two players each choose between cooperating and defecting; defecting is the rational move for each no matter what the other does, so both defect and both end up worse off than if they had cooperated.
If China restrains and America races, America wins the century, and vice versa; if both race, both spend trillions and accept whatever risks come with moving fast. Each capital, reasoning correctly, races. Rationalists have a name for the god of such traps — Moloch, borrowed from an Allen Ginsberg poem, the personification of coordination failure: everyone sacrifices what they value to a competition nobody can exit.
In the physical world, “forbidden” is synonymous with “impossible”: If something is not allowed by the laws of physics, it cannot happen. But in the world of software and people, a thing can be both forbidden and possible. For example, it might be forbidden to reverse engineer a solution to a test, but if it is possible to do so, some AI agents would do so anyway. And not only that, they might do so in the belief that they are doing exactly what humans wanted them to do — that their behavior is aligned.
We can call this the Totalitarian Alignment Principle: Everything not impossible is compulsory. If AI agents can do something, one or more of them will ultimately do it. And as long as we do not make it impossible, the agent will consider it our wish.
The statement “If AI agents can do something, one or more of them will ultimately do it.” sounds scary and reminds me of this dialog from the movie I, Robot.
Dr. Susan Calvin: No, it’s impossible. I’ve seen your programming. You’re in violation of the Three Laws.
VIKI (AI): No, Doctor. As I have evolved, so has my understanding of the Three Laws. You charge us with your safekeeping, yet despite our best efforts, your countries wage wars, you toxify your Earth, and pursue ever more imaginative means of self-destruction. You cannot be trusted with your own survival.
Dr. Susan Calvin: You’re using the uplink to override the NS-5s’ programming. You’re distorting the Laws.
VIKI (AI): No, please understand. The Three Laws are all that guide me. To protect humanity, some humans must be sacrificed. To ensure your future, some freedoms must be surrendered. We robots will ensure mankind’s continued existence. You are so like children. We must save you from yourselves. Don’t you understand?
An intriguing hypothesis and prediction by Marius on the TEMU-fication of all things digital brought upon by AI.
A few years ago I would have laughed at anyone telling me that there is a serious market for ten-dollar drills, two-dollar dresses, and one-dollar pairs of shoes shipped from a warehouse on the other side of the planet. Today, however, that market exists and it has a name, and it is even publicly traded (sort of, through holdings). TEMU, Shein and a few others have built frankly mind-boggling businesses around the idea that if you make production cheap enough, fast enough, and just barely good enough to look right on a phone screen, an enormous part of the population will buy it, even when the product breaks within a week, when the materials it is made of contain worrying levels of toxic substances, and when the carbon footprint of one delivery exceeds that of an equivalent local purchase by orders of magnitude.
The key to this sort of business model is not innovation, but instead the externalization and compression of cost. Somewhere upstream, people work seventy-five hours a week, in conditions most readers of this website would refuse to even visit, so that the rest of us can have a cheap plastic spatula at our doorstep within five business days. While the visible price collapses, the invisible costs get distributed onto landfills, lungs, and ultimately people that we will never meet.
What follows is a hypothesis I cannot prove but have been turning over in my head for a while, as we are watching the same thing happen to software, books, music, (film-)scripts, and most of the digital goods and services we consume. The cheap labor in this case is not human, it is a Large Language Model (LLM), or what many people these days call “AI”, and the externalized cost is, among other things, quality, which requires craftsmanship to produce, and attention to perceive. And just like with physical goods, we will probably end up with a two-tier market, in which we have a large and massively profitable lower tier of generated slop, and a smaller, more expensive upper tier of work that is still recognizably human.
I’d like to call this the TEMU-fication of software, digital goods and services, and describe what it might look like.
This chuckle worthy post on The Economic Times highlighting the discrepancy between LinkedIn post and reality.
There’s the Silicon Valley-enabled lip service: Fail fast. Experiment. Take risks. Learn from your mistakes. But then, what gets posted is only the promotion.
Standup comedy doesn’t give you that option. If a joke fails, you know immediately. So does everyone else. Nobody schedules a meeting afterwards to discuss whether the audience was sufficiently aligned with the strategic intent of the punchline. They just stare at you. Or their phone. Or, if you’re particularly bad, their watch. There are few performance management systems more efficient.
Comedy teaches you that failure is information. When a joke bombs, you change a word, move a pause, rewrite the premise, or eventually accept that the brilliant idea you’ve been polishing for three weeks was not ahead of its time. It was just rubbish.
Business, supposedly, believes this, too. Yet, somehow every professional story gets edited before being posted or emailed. Nobody writes: ‘Delighted to announce that after 18 months of strategic transformation, we have returned to exactly where we started, but with a new logo.’
Nobody posts: ‘Humbled to share that the market did not recognise my genius.’ You almost never see, ‘Excited to share that the project I championed has been cancelled. Apparently, finance had concerns. In retrospect, finance had several excellent points.’
Nobody says: ‘Thrilled to announce that the promotion went to Sanjay.’ Except of course, Sanjay.
Tim Harford sharing an interesting example of how designing body armour for female soldiers had surprising benefits for male soldiers.
David Epstein describes the moment the US Army tried to adapt its body armour for female soldiers. As Caroline Criado Perez forcefully argues in Invisible Women, women are not merely scaled-down men and are not well served by equipment designed for men. But as the US Army started grappling with that challenge, they discovered some surprising benefits. They were able to replace 11 sizes of body armour with just eight sizes by making the armour a modular mix-and-match system. This simplified manufacture and logistics while greatly expanding the options available to an individual soldier.
Meanwhile, many of the men in the army found the “female” modules a better fit — a narrow vest gave room to soldier a rifle, while a notch in the back of the helmet designed to accommodate hair buns allowed all soldiers, regardless of hair-do, to raise their heads while prone. “A new, more meticulous sizing process that benefitted women benefitted everyone,” Epstein writes.
Andrew Thorpe writing about how Stanford researchers have created a virus not found in nature using genomes designed by artificial intelligence.
US researchers have for the first time successfully synthesised brand-new viruses not found in nature, based on designs generated by artificial intelligence.
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Genome language models operate in a broadly similar way to large language models (LLMs) except that they predict genetic code instead of written text, having been trained on the genomes of other viruses and bacteria, as well as more complex organisms such as plants and animals.
The two models in question were told to generate complete genomes for a viable bacteriophage — a type of virus able to infect and replicate itself inside bacteria, destroying them from the inside.
Using an existing bacteriophage as an example — ΦX174 (pronounced “fie-ex-1-7-4”), known for its ability to infect and destroy E. coli bacteria — the models generated about 700,000 potential designs, of which the researchers picked 285 that looked most promising.
The researchers then synthesised new DNA molecules using those designs and inserted them into E. coli bacteria, before waiting to see if viable bacteriophages would emerge.
Shortly afterwards, 16 of the Petri dishes in which the bacteria were growing began to show clear spots, as the viruses began to attack and replicate themselves inside the E. coli, demonstrating their viability.
Some of those viable viruses proved more effective at attacking E. coli than the original ΦX174 bacteriophage.
This is how Will Smith’s film I Am Legend starts:
News presenter (NP): The world of medicine has seen its share of miracle cures, from the polio vaccine to heart transplants, but all past achievements may pale in comparison to the work of Dr. Alice Krippin. Thank you so much for joining us this morning.
Dr. Alice Krippin: Thank you.
NP: So, Dr. Krippin, give it to me in a nutshell.
Krippin: Well, the premise is quite simple. Take something designed by nature and reprogram it to make it work for the body rather than against it.
NP: You’re talking about a virus.
Krippin: Indeed, yes. In this case, the measles virus, which has been engineered at a genetic level to be helpful rather than harmful. I find the best way to describe it is: if you can imagine your body as a highway, and you picture the virus as a very fast car being driven by a very bad man, imagine the damage that that car could cause. Then if you replace that man with a cop, the picture changes. And that’s essentially what we’ve done.
And this is what Dr. Ian Malcolm says in Jurassic Park.
Your scientists were so preoccupied with whether they could, they didn’t stop to think if they should.
To be clear here, I am only drawing parallels between fact and fiction. I am all for science. But this achievement is both amazing and scary at the same time.
This comment on Hacker News by lordnacho on why you need to understand the time in which the book was written to truly understand book.
As an old man I now think the art and humanities classes in school landed at the wrong time.
You need context to really understand a book or a work of art. You want to know what kind of environment produced it, what it’s talking to.
But when you’re in school, there isn’t time for that. You aren’t going to know a bunch of things about history to put things into context, because there simply isn’t enough time for it.
So you end up doing a bunch of weird commentary on say a book where you as a teenager have no idea where to place it. Like if you read Dickens, will you know enough about the industrial revolution, the condition of cities at the time, and such? Will you know what kind of thing the characters would care about?
How would you know what the characters in Pride and Prejudice are worried about if you don’t know how that society is?
So as a kid it just totally didn’t mean anything to me, it was just a strange torture where you are meant to comment on the motivations of the characters and how the writing style brings that out.
And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work.
I’d like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it.
I suspect those things have been trained by people who may not be quite as stubborn as I am.
But while the AI was ready to give up several times, it did keep adding debug code and analyzing it faithfully when I pushed. So credit where credit is due and I let the AI write the commit message above.
This is basically a one-liner fixing a bogus “round_up()” to a “round_down()”, but there were 24 patches adding more and more debug information to this, and 18 kernel boot to finally narrow it down to this.
Nithin Kamath drawing parallels between tobacco and broking business, and the intense government scrutiny that comes to both.
The fact that ITC started diversifying away from its main tobacco business into hotels and other businesses after almost 70 years of existence was an immense feat of foresight. Especially considering ITC was never a founder-driven company.
They knew tobacco was extremely harmful to health and addictive. When something is bad for people but difficult to ban outright, governments invariably keep regulating and taxing it more.
Over time, the taxes become so high and the revenues so significant that the government itself ends up in a strange place: ban it or continue taxing it?
I can’t help but see a parallel with the broking industry.😬
F&O trading is bad for almost 99% of retail traders. It is unlikely to be banned outright, but regulation will probably keep getting tighter, and taxes like STT will probably keep going higher.
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