少点错误 2024年10月24日
Big tech transitions are slow (with implications for AI)
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文章以蒸汽引擎的发展为例,探讨了新技术从发明到广泛应用所需的漫长过程及多种因素。同时提及AI的发展,认为其虽会带来巨大收益,但也需时间逐步实现,与其他技术的发展有相似之处。

🎈蒸汽引擎的发明:1712 年托马斯·纽科门建造了第一台实用蒸汽引擎,起初用于矿井抽水,但存在诸多问题,如运动方式适合抽水但不适合转动,效率低,引擎沉重,燃料问题等。

💡蒸汽引擎的改进:发明家詹姆斯·瓦特等对蒸汽引擎进行改进,使其能产生平稳旋转运动,提高效率,高压技术的发展使引擎可用于交通工具,新型燃料的出现减少污染。

🤔AI 的发展:认为 AI 会提高生产力和促进增长,但像蒸汽引擎等技术的发展一样,需要时间来完善,目前 AI 在一些方面表现出色,在某些关键领域仍需改进。

📈技术发展的共性:各种新技术从发明到完全取代旧技术都需要数十年或数百年,初始模型需在功率、效率和可靠性方面改进,适应不同使用场景,推动整个系统重新设计。

Published on October 24, 2024 2:25 PM GMT

The first practical steam engine was built by Thomas Newcomen in 1712. It was used to pump water out of mines.

“Old Bess,” London Science Museum Photo by the author

An astute observer might have looked at this and said: “It’s clear where this is going. The engine will power everything: factories, ships, carriages. Horses will become obsolete!”

This person would have been right—but they might have been surprised to find, two hundred years later, that we were still using horses to plow fields.

Sacaton Indian Reservation, early 1900s. Library of Congress

In fact, it took about a hundred years for engines to be used for transportation, in steamships and locomotives, both invented in the early 1800s. It took more than fifty years just for engines to be widely used in factories.

What happened? Many factors, including:

Not only did the transition take a long time, it produced counterintuitive effects. At first, the use of draft horses did not decline: it increased. Railroads provide long-haul transportation, but not the last mile to farms and houses, so while they substitute for some usage of horses, they are complementary to much of it. An agricultural census from 1860 commented on the “extraordinary increase in the number of horses,” noting that paradoxically “railroads tend to increase their number and value.” A similar story has been told about how computers, at first, increased the demand for paper.

Engines are not the only case of a relatively slow transition. Electric motors, for instance, were invented in the late 1800s, but didn’t transform factory production until about fifty years later. Part of the reason was that to take advantage of electricity, you can’t just substitute a big central electric motor in place of a steam or gas engine. Instead, you need to redesign the entire factory and all the equipment in it to use a decentralized set of motors, one powering each machine. Then you need to take advantage of that to change the factory layout: instead of lining up machines along a central power shaft as in the old system, you can now reorganize them for efficiency according to the flow of materials and work.

All of these transitions may have been inevitable, given the laws of physics and economics, but they took decades or centuries from the first practical invention to fully obsoleting older technologies. The initial models have to be improved in power, efficiency, and reliability; they start out suitable for some use cases and only later are adapted to others; they force entire systems to be redesigned to accommodate them.

At Progress Conference 2024 last weekend, Tyler Cowen and Dwarkesh Patel discussed AI timelines, and Tyler seemed to think that AI would eventually lead to large gains in productivity and growth, but that it would take longer than most people in AI are anticipating, with only modest gains in the next few years. The history of other transitions makes me think he is right. I think we already see the pattern fitting: AI is great for some use cases (coding assistant, image generator) and not yet suitable for others, especially where reliability is critical. It is still being adapted to reference external data sources or to use tools such as the browser. It still has little memory and scant ability to plan or to fact-check. All of these things will come with time, and most if not all of them are being actively worked on, but they will make the transition gradual and “jagged.” As Dario Amodei suggested recently, AI will be limited by physical reality, the need for data, the intrinsic complexity of certain problems, and social constraints. Not everything has the same “marginal returns to intelligence.”

I expect AI to drive a lot of growth. I even believe in the possibility of it inaugurating the next era of humanity, an “intelligence age” to follow the stone age, agricultural age, and industrial age. Economic growth in the stone age was measured in basis points; in the agricultural age, fractions of a percent; in the industrial age, single-digit percentage points—so sustained double-digit growth in the intelligence age seems not-crazy. But also, all of those transitions took a long time. True, they were faster each time, following the general pattern that progress accelerates. But agriculture took thousands of years to spread, and industry (as described above) took centuries. My guess is the intelligence transition will take decades.



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蒸汽引擎 AI 发展 技术改进 发展共性
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