Andy Kessler

WSJ: AI Simply Needs a Kill Switch

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https://www.wsj.com/articles/ai-simply-needs-a-kill-switch-regulations-biden-commerce-innovation-musk-pause-spacex-767ae62a

The best lesson for artificial intelligence may be Thursday’s “rapid unscheduled disassembly” of SpaceX’s Starship rocket, aborted four minutes after launch. With ChatGPT prompting speculation about mankind’s destruction, you should know that techies have obsessed seemingly forever over what’s known as the Paper Clip Theory—the idea that if you told an artificial-intelligence system to maximize the production of paper clips, it would fill the whole world with paper clips. Another version of the theory, Strawberry Fields Forever, has AI using every piece of available dirt to grow strawberries. Scary, right? So are “Halloween” movies.

Not to be outdone, decision theorist (huh?) Eliezer Yudkowsky recently wrote in Time magazine that the “most likely result of building a superhumanly smart AI” is that “literally everyone on Earth will die.” Literally everyone! That’s ludicrous, as is most clickbait these days. Sam Altman, CEO of ChatGPT creator OpenAI, told podcaster Lex Fridman, “There is some chance of that.” C’mon now.

Apparently, Pandora’s box has opened and is spewing its evils, which ignores all the good uses of large language models that will transform software coding, the workplace, education and more. Sadly, our geniuses in government appear to be the remaining few who still read Time magazine. So bring on the regulators to shut the box heroically.

Earlier this month, the Commerce Department initiated the process of regulating artificial intelligence, with Assistant Secretary Alan Davidson suggesting, “We know that we need to put some guardrails in place to make sure that they are being used responsibly.” Bad idea. Guardrails are for children bowling at birthday parties. AI is in its infancy, and we don’t yet know how it will change industries and society. Don’t freeze it now.

If the U.S. regulates AI, research will just move somewhere that doesn’t regulate it, maybe the Bahamas, where the unkempt coders of the future could keep cranking away. Or worse, China. Google CEO Sundar Pichai told CBS’s “60 Minutes” that he wants global AI regulation. Elon Musk and a group of AI experts wrote an open letter calling for an immediate six-month pause of “giant AI experiments.” Isn’t it interesting that those who need to catch up are pushing for a pause?

We don’t need onerous new regulations. There are already laws on the books…

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WSJ: A Wrench Thrown Into Capitalism

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https://www.wsj.com/articles/a-wrench-thrown-into-capitalism-philanthropy-investment-technology-musk-attention-innovation-climate-5b319142

I’m pretty sure we’ve hit peak absurdity. I recently read about a climate author “investigating how shade can be a powerful civic resource to protect us from climate change.” Shade! This from the announcement of a new “climate cohort” at a well-regarded philanthropy. It reveals a flaw in capitalism.

Not a single scientist can be found in the climate cohort—why bother?—but instead authors, educators, designers, a poet and, hmmm, a chef. There is a “data ecologist,” but that almost sounds made up. Reading further, apparently the lack of shade is racist and a product of inequality. Of course it is.

Hey look, the wealthy can do what they like with their money—often paradoxically amassed by delivering productivity-enhancing products. I’ve noted before that there are only four things you can do with your money: spend it, pay taxes, invest it or give it away. The flaw in capitalism is that only one of these helps the world in the long term.

In the short term, spending is great for the economy, but it’s hard to blow a billion dollars at Costco or buy 3,000 Lamborghinis. Paying taxes funnels money to unproductive government spending and should be kept to a minimum. Even investing has turned absurd: An Oklahoma City brand designer prompted ChatGPT: “You have $100, and your goal is to turn that into as much money as possible in the shortest time possible, without doing anything illegal.” ChatGPT instructed him to launch “Green Gadget Guru” and promote “eco-friendly gadgets and sustainable living.” Climate is lucrative!

Anyway, of the four, philanthropy is the most misunderstood. Yes, many philanthropies do great stuff, especially on a local level. And it sure feels good for the giver. We all get that warm fuzzy feeling—a psychic reward—when we donate to causes. Help your fellow human. Fund schools. Gild the opera. But does that giving do any lasting good? Sadly, only sometimes.

Andrew Carnegie funded libraries.

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WSJ: Turn Off the Fed Bubble Machine

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https://www.wsj.com/articles/turn-off-the-fed-bubble-machine-inflation-federal-funds-interest-rates-stimulus-regulation-keynes-supply-a90c462

“The Lawrence Welk Show,” which ran from 1951 to 1982, was known for its schmaltzy “champagne music” and a TV screen full of bubbles to open most shows. The interest-rate-slashing Federal Reserve has been running the bubble machine ever since. Now, with inflation feeling like it’s rolling over as the economy slows, it’s time to shut the machine down.

Since the fall of 2008, with a brief respite in 2019, the real federal-funds rate has been negative, meaning interest rates have been below inflation (and still are) and the Fed has been accommodative. Accommodating what? Well, in the false hope of boosting aggregate demand and fighting deflation, they’ve accommodated bubbles, bubbles everywhere.

Deflation is only a ghost, often mistaken for prices dropping naturally and to our benefit. Productivity, doing more with less, lowers prices. Think of chips, iPhones and $538 for a 75-inch 4K smart TV at Walmart. Why fight it?

Ask the Fed. Faced with this productive trend, it keeps trying to stimulate aggregate demand by dropping interest rates or buying bonds. Instead the central bank ends up inflating prices for various assets and unproductive things that don’t naturally drop in price, like housing, healthcare and education. This sets up the next bubble and downturn and bank bailouts which the Fed fights with even more interest rate cuts, churning the crank on the Welkian machine. Stop doing this!

Don’t worry, central bankers would reassure, they keep a sharp eye on consumer inflation indexes. But even former Fed Chairman Alan Greenspan admitted they’re flawed, noting in 2019, “We have a problem with measuring inflation.” Even through mid-2021, inflation numbers looked subdued, but didn’t the Fed notice the asset inflation that rate cuts were causing elsewhere?

Stocks were flying. Even the junkiest bonds had low yields.

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WSJ: Does AI Dream of Electric Sheep?

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https://www.wsj.com/articles/does-ai-dream-of-electric-sheep-neural-networks-chatgpt-sleep-memory-overfitting-research-5ab42f1b

I had a weird dream last night. I’m sure you’ve said that many times. But why? From Sigmund Freud to Carl Jung to Calvin Hall, dreams have been researched ad nauseam and there’s still no good answer to what shapes them. Unconscious desires? Keeping neurons active? Memory consolidation? Psychosexual wish fulfillment? No one knows. Even Freud supposedly noted, “Sometimes a cigar is just a cigar.”

But clues are starting to emerge from artificial intelligence and its use of brain-mimicking neural networks. In 1968, Philip K. Dick published the novel “Do Androids Dream of Electric Sheep?”—on which the 1982 movie “Blade Runner” was based. Turns out the answer is yes.

First, a quick and oversimplified primer on neural networks. They contain digital layers of interconnected nodes, modeled on the human brain’s neurons and synapse connections, which are great at finding patterns. Based on numerical weightings in each node after scanning millions of photos, a layer might conclude that a roundish object with eyes, nose and mouth is a face. Then that data moves onto the next layer, which looks for patterns in those patterns. Perhaps it determines whether it’s a cat’s or dog’s face. Then another layer finds patterns of patterns of patterns until, for example, it can identify the dog’s breed as a Siberian Husky. And so on.

Theorized in 1943, trainable neural networks have been around since 1958, when psychologist Frank Rosenblatt demonstrated the Perceptron machine at the Cornell Aeronautical Laboratory. But only in the past decade has AI scaled to the masses and gained the ability to understand your voice or recognize faces in photos.

What changed? Faster processors and cheaper memory certainly helped, but neural networks in the past often got stuck by “overfitting” data to a conclusion, such as determining that everything with feline-shaped eyes is a cat. Or concluding that dinosaurs built Stonehenge. We do that too when we knock on wood or wear our lucky socks because our team won last time we did. Computers can be superstitious like us.

In 1970 Finnish student Seppo Linnainmaa proposed “back-propagation”—again oversimplifying here—sending errors backward through neural-network layers to generalize the weightings and better find patterns.

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