TWENTY-TWO: The Fear
âWhen I shared Jensenâs objections with Bengio, he grew agitated. âOf course, there is no data!â he said. âHumanity hasnât disappeared yet! Are we going to wait until we have repeated the death of humanity multiple times to decide that, oh, now we have data?!â He made a good point. All the data in the world would not have predicted the breakthrough of AlexNet or the success of the transformer architecture. Twice in ten years, AI had experienced unforeseeable and permanent upgrades to its capabilities. Bengio did not think that the current models posed an immediate threat to human lifeâbut what about the next breakthrough? No one could say what it might bring or when it might happen.
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The Thinking Machine - Stephen Witt
Introduction:
âThis is the story of how a niche vendor of video game hardware became the most valuable company in the world. It is the story of a stubborn entrepreneur who pushed his radical vision for computing for thirty years, in the process becoming one of the wealthiest men alive. It is the story of a revolution in silicon and the small group of renegade engineers who defied Wall Street to make it happen. And it is the story of the birth of an awesome and terrifying new category of artificial intelligence, whose long-term implications for the human species cannot be known. At the center of this story is a propulsive, mercurial, brilliant, and extraordinarily dedicated man. His name is Jensen Huang, and his thirty-two-year tenure is the longest of any technology CEO in the S&P 500.
Huang is a visionary inventor whose familiarity with the inner workings of electronic circuitry approaches a kind of intimacy. He reasons from first principles about what microchips can do today, then gambles with great conviction on what they will do tomorrow.
TWENTY-ONE: Jensen
âJensen contradicted himself frequently, sometimes offering opposing viewpoints within the same interview. He wasnât playing devilâs advocate, exactlyâhe just liked to attack ideas from both sides. âHeâs not trying to be a politician,â Horstmann said. âHeâs not trying to stay on message. Heâs trying to process real-time input, and heâs willing to entertain a contradictory thought for a while.â What might appear to be a definitive pronouncement was often just Jensen thinking out loud. Only once he started to repeat himself was it time to pay attention. When an idea really struck Jensen, it slowly built up steam over a period of days or even weeks. It cycled into his vocabulary and was repeated at every meeting. Concepts like the âzero-billion-dollar marketâ or the âspeed of lightâ hadnât come to Jensen in a flash; theyâd arrived as polished nuggets of wisdom after spending months being tossed in the rock tumbler of his mind. Having arrived, they were then drilled so thoroughly into his employees that his staff sometimes sounded like characters from The Manchurian Candidate, repeating Jensenâs catchphrases verbatim with a glassy look in their eyes. Even employees who hadnât worked at Nvidia for years could still recite the catechism from memory.
TWENTY-THREE: The Thinking Machine
âLooking back, it became clear to me that Jensen had wanted to lose his temper; heâd made a conscious decision to thrash me. Once the performance had started, his fury was genuine, but it was all in service of a larger point he wanted to make. It wasnât just that Jensen didnât read science fictionâit was that he actually hated science fiction. He was a
serious man.
The reason that Jensen had succeeded in fields where others had failedâparallel computing, AI, the Omniverseâwas precisely because he didnât tolerate airy speculation about the future. He examined technologies coldly, from first principles, swayed neither by optimism nor fear but only by a cold and patient sense of business logic that he alone could push to the outer limits of corporate foresight. Beyond that he did not look or care to imagine. The potential for human extinction was not a question of corporate strategy and thus, to him, was as foolish as drawing a dragon on the unexplored portion of the map.
My conversation with Brockman and Sutskever continued on in circles until we ran out the clock after forty-five minutes. I tried with little success to get more concrete details on what exactly they were trying to buildâwhich by nature, they explained, they couldnât knowâand why then, if they couldnât know, they were so confident it would be beneficial.
At one point, I tried a different approach, asking them instead to give examples of the downsides of the technology. This was a pillar of OpenAIâs founding mythology: The lab had to build good AGI before someone else built a bad one.
In reality, the analogies to intelligence are once again anthropomorphizing and exaggerating the capabilities of the technology. While Hinton and other deep learning absolutists predicted that the shortfalls of neural networks compared with humans would go away at sufficient scale, the challenges have in fact persisted and, by many accounts, only gotten worse.