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January 7 - February 6, 2025
John McCarthy, who coined the term “artificial intelligence”:
this is exactly what the scaling hypothesis predicts is coming and what we see the first signs of in today’s systems.
I believe the debate about whether and when the Singularity will be achieved is a colossal red herring.
Singularity,
When AI could display humanlike conversational abilities for a lengthy period of time, such that a human interlocutor couldn’t tell they were speaking to a machine, the test would be passed: the AI, conversationally akin to a human, deemed intelligent.
systems are already close to passing the Turing test.
I think it will be done with a few minor human interventions within the next year, and probably fully autonomously within three to five years.
The challenge is in advancing what AI developers call hierarchical planning, stitching multiple goals and subgoals and capabilities into a seamless process toward a singular end.
AI and AGI are both parts of the everyday discussion, but we need a concept encapsulating a middle layer in which the Modern Turing Test is achieved but before systems display runaway “superintelligence.”
Step back and consider what’s happening on the scale of a decade or a century. We really are at a turning point in the history of humanity.
Extending the work of Rosalind Franklin and Maurice Wilkins, James Watson and Francis Crick discovered the structure of DNA,
one of the world’s first biotech companies, Genentech, in 1976.
One catalyst was the Human Genome Project. This was a thirteen-year, multibillion-dollar endeavor that gathered together thousands of scientists from across the world, in private and public institutions, with a single goal: unlocking the three billion letters of genetic information making up the human genome.
CRISPR gene editing (the acronym stands for clustered regularly interspaced short palindromic repeats) is perhaps the best-known example of how we can directly intervene in genetics.
Cas9, an enzyme acting as a pair of finely tuned DNA scissors,
New techniques like Craspase, a CRISPR tool working with RNA and proteins rather than DNA, might allow for safer therapeutic interventions than conventional methods.
The London DNA Foundry housed at Imperial College London claims it can create and test fifteen thousand different genetic designs in a single morning.
enzymatic synthesis
Stanford bioengineer Drew Endy, “Biology is the ultimate distributed manufacturing platform.”
General-purpose technologies are accelerants. Invention sparks invention. Waves lay the ground for further scientific and technological experimentation, nudging open the doors of possibility.
The future of agriculture, as John Deere sees it, involves autonomous tractors and combines that operate independently, following a field’s GPS coordinates and using an array of sensors to make automatic, real-time alterations to harvesting, maximizing yield and minimizing waste.
It’s the computational equivalent of moving from a flat, black-and-white film into full color and three dimensions, unleashing a world of algorithmic possibility.
Like AI and biotech, quantum computing helps speed up other elements of the wave.
nuclear fusion.
Fusion power involves the release of energy when isotopes of hydrogen collide and fuse to form helium, a process long considered the holy grail of energy production.
inertial confinement, which involves compressing pellets of hydrogen-rich material with lasers and
heating them to 100 million degrees to create a fleeting fusion reaction.
One viable quantum computer could render the world’s entire encryption infrastructure redundant.
physicist César Hidalgo argues that configurations of matter are significant because of the information they contain.
The more powerful the computational base, the more tractable this becomes.
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.
The challenger, a Western firm, London based, American owned, had just marched into an ancient, iconic, cherished game, literally put its flag in the turf, and obliterated the home team.
The first driver has to do with what I experienced with AlphaGo: great power competition. Technological rivalry is a geopolitical reality. Indeed it always has been. Nations feel the existential need to keep up with their peers. Innovation is power. Second comes a global research ecosystem with its ingrained rituals rewarding open publication, curiosity, and the pursuit of new ideas at all costs. Then come the immense financial gains from technology and the urgent need to tackle our global social challenges. And the final driver is perhaps the most human of all: ego.
Quantum computing is an area of notable Chinese expertise.
“Even if we are not actually in an arms race, we must assume ‘they’ think we are, and therefore we must ourselves race to achieve a decisive strategic advantage since this new technological wave might completely rebalance global power.” This attitude becomes a self-fulfilling prophecy.
myopic.
Atmanirbhar Bharat (Self-Reliant India)
Because these technologies are getting cheaper and simpler to use even as they get more powerful, more nations can engage at the frontier.
Access to computation is likely the biggest bottleneck,
India’s billion-strong biometric identification system, Aadhaar,
there is no central authority controlling what technologies get developed, who does it, and for what purpose; technology is an orchestra with no conductor. Yet this single fact could end up being the most significant of the twenty-first century.
Raw curiosity, the quest for truth, the importance of openness, evidence-based peer review—these are core values for scientific and technological research.
Openness is science and technology’s cardinal ideology.
Audrey Kurth Cronin calls “open technological innovation.” A global system of developing knowledge and technology is now so sprawling and open that it’s almost impossible to steer, govern, or, if need be, shut down.
Junior researchers are especially liable to be judged—and hired—on their publication record, publicly viewable on platforms like Google Scholar.
GitHub has 190 million repositories of code,
Amazon’s R&D budget alone is $78 billion, which would be the ninth biggest in the world if it were a country.
Alphabet, Apple, Huawei, Meta, and Microsoft all spend well in excess of $20 billion a year on R&D.
Today’s world is optimized for curiosity, sharing, and research at a pace never seen before.
And this, the potential for profit, is built on something even more long-lasting and robust: raw demand. People both want and need the fruits of technology.
