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AlphaGo initially learned by watching 150,000 games played by human experts.
At root, the primary driver of all of these new technologies is material—the ever-growing manipulation of their atomic elements. Then, starting in the mid-twentieth century, technology began to operate at a higher level of abstraction. At the heart of this shift was the realization that information is a core property of the universe.
AI is enabling us to replicate speech and language, vision and reasoning. Foundational breakthroughs in synthetic
Our new powers to control bits and genes feed back into the material, allowing extraordinary control of the world around
us even down to the atomic level.
AI and synthetic biology are the coming wave’s central general-purpose technologies, a bundle of technologies with unusually powerful ramifications surrounds them, encompassing quantum computing, robotics, nanotechnology, and the potential for abundant energy, among others.
AlexNet. AlexNet was powered by the resurgence
AlexNet was built by the legendary researcher Geoffrey Hinton and two of his students, Alex Krizhevsky and Ilya Sutskever, at the University of Toronto.
ImageNet Large Scale Visual Recognition Challenge, an annual competition designed by the Stanford professor Fei-Fei Li to focus the field’s
efforts around a simple goal: identifying the primary object in an image. Each year competing teams would test their best models against one another, often beating the previous year’s submissions b...
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AI; soon it will be many more, and almost everywhere it will make experiences more efficient, faster, more useful, and frictionless.
A single strand of human hair is ninety thousand nanometers thick;
1971 an average transistor was already just ten thousand nanometers thick. Today the most advanced chips are manufactured at three nanometers.
in AI training we can just keep connecting larger and larger arrays of chips, daisy-chaining them into massively parallel supercomputers.
The human brain is said to contain around 100 billion neurons with 100 trillion connections between them—it
EleutherAI, a grassroots coalition of independent researchers, has made a series of large language models completely open-source, readily available to hundreds of thousands of users.
This ease of access and ability to adapt and customize, often in a matter of weeks, is a prominent feature of the coming wave. Indeed, nimble creators working with efficient systems,
curated data sets, and quick iterations can already quickly rival the most well-resourced developers.
Stable Diffusion lets anyone produce bespoke and ultrarealistic images, for free, on a laptop. The same will soon be possible for audio clips and even video generation.
was convinced that the future of computing was conversational.
DNA Script are commercializing DNA printers that train and adapt enzymes to build de novo, or completely new, molecules.
Synthia.
In the words of Elliot Hershberg, “What if we could grow what we wanted locally? What if our supply chain was just biology?”
Proteins are the building blocks of life.
Simply knowing the DNA sequence isn’t enough to know how a protein works. Instead, you need to understand how it folds. Its shape, formed by this knotted folding, is core to its function: collagen in our tendons has a rope-like structure, while enzymes have pockets to hold the molecules they act on. And yet, in advance, there was no means of knowing how this would happen. If you used traditional brute-force computation, which involves systematically trying all the possibilities, it might take longer than the age of the known universe to run through all the possible shapes of a given protein.
In 1993, they decided to set up a biannual competition—called Critical Assessment for Structure Prediction (CASP)—to see who could crack the
protein folding problem.
Then, at CASP13 in 2018, held at a palm-fringed resort in Cancún, a rank outsider entrant arrived at the competition, with zero track record, and beat ninety-eight established teams. The winning team was DeepMind’s. Called AlphaFold, the project started during a weeklong experimental
Our team used deep generative neural networks to predict how the proteins might fold based on their DNA, training on a set of known proteins and extrapolating from there. The new models were better able to guess the distance and angles of pairs of amino acids.
In 2022, AlphaFold2 was opened up for public use. The result has been an explosion of the world’s most advanced machine learning tools, deployed in both fundamental and applied biological research: an “earthquake,” in the words of one researcher.
Pong. It likely won’t be too long before neural “laces” made from carbon nanotubes plug us directly into the digital world.
Welcome to the age of biomachines and biocomputers, where strands of DNA perform calculations and artificial cells are put to work. Where machines come alive. Welcome to the age of synthetic life.
2019, Google announced that it had reached “quantum supremacy.”
Chilled to a temperature colder than the coldest parts of outer space, Google’s machine used an understanding of quantum mechanics to complete a calculation in seconds that would, it said, have taken a conventional computer ten thousand years. It had just fifty-three “qubits,” or quantum bits, the core units of quantum computing.
Quantum computing has far-reaching implications. For instance, the cryptography underlying everything from email security to cryptocurrencies would suddenly be at risk, in an impending event those in the field call “Q-Day.”
gossamer
If the last wave reduced the costs of broadcasting information, this one reduces the costs of acting on it, giving rise to technologies that go from sequencing
to synthesis, reading to writing, editing to creating, imitating conversations to leading them. In this, it is qualitatively different from every previous wave, despite all the big claims made about the transformative power of the internet. This kind of power is even harder to centralize and oversee; this wave is not just a deepening and acceleration of history’s pattern, then, but also a sharp break from
each is surrounded by challenges technical, ethical, and social.
None is complete.
Ukrainian militia was called Aerorozvidka.
A ragtag volunteer band of drone hobbyists, software engineers, management consultants, and soldiers, they were amateurs, designing, building, and modifying their own drones in real time, much like a start-up. A
A thousand-strong group of nonmilitary elite programmers and computer scientists banded together in an organization called Delta to bring advanced AI and robotics capabilities to the army, using machine learning to identify targets, monitor Russian tactics, and even suggest strategies.
Emerging technologies have always created new threats, redistributed power, and removed barriers to entry. Cannons meant a small force could destroy castles and level armies. A few
colonial soldiers with advanced weapons could massacre thousands of indigenous people. The printing press meant a single workshop might produce thousands of pamphlets—spreading ideas with an ease that medieval monks copying books by hand could scarcely fathom. Steam power enabled single factories to do the work of entire towns. The internet took this capacity to a new peak: a single tweet or image might travel the world
These developments represent a colossal transfer of power away from traditional states and militaries toward anyone with the capacity, and motivation, to deploy these devices. There is no obvious reason why a single operator, with enough wherewithal, could not control a swarm of thousands of drones.
The very scale and interconnectedness of the coming wave create new systemic vulnerabilities: one point of failure can quickly cascade around the world. The
less localized a technology, the less easily it can be contained—and vice versa.
Its risks extend to entire societies, making it not so much a blunt tool as a lever with global consequences. Just as globalized and highly connected markets transmit contagion in a financial crisis, so with technology.
A paradox of the coming wave is that its technologies are largely beyond our ability to comprehend at a granular level yet still within our ability to create and use. In AI, the neural networks moving toward autonomy are, at present, not explainable.
