Updated 08-Jan-2025
- See also Deep Speech, Tesseract
Items on ML (from 2019)
- IBM's Ginny Rommety gave a compelling keynote at CES on AI, as well as answering a great set of questions on Bloomberg Technology. It seems to me that the discussion of AI (as per Rommety) should not be using the term AI but rather cognitive computing, or simply machine learning.
- Harari's book 21 Lessons for the 21st Century has some interesting discussion of AI. One thing is that he has a tendency to push the case harder than it needs be, and reliance on trends which later are seen as less pronouced, or are better understood as a longer-term trend (that is, not quite new), tend to undercut the writing. Nevertheless he is asking the important questions, though in general overrated.
General AI
Artificial Intelligence (AI) was always a joke in the 1990s and 2000s. The main point is that the entire project was a failure. To continue to get grant money, researchers rebranded what they were doing cognitive sciences and learning sciences. The problem at the time was that they could not produce anything resembling intelligence.
In the 2010s that has significantly changed. The main reason for the resurgence of AI is the dramatic increase in computing power and in data availability (largely due to the dramatic increase in sensors and data-creating devices). Also, a more focused aspect of AI is now in vogue, deep learning, which is about learning data representations rather than task performance. In a sense this is much easier to do, with the learning happening at a higher cognitive level, and which can include directed, semi-directed, or non-directed machine learning.
A general purpose learning machine, even one constrained to things like a voice-enabled assistant, has a long way to go for any kind of measure of success. However, certain focused forms of deep learning, namely pattern discovery and anticipation, have had enough successes to generate continued investment. Finance and transportation are two systems which while productive using humans -- the lowest-cost, 150-pound, nonlinear, all-purpose computer system which can be mass-produced by unskilled labor)1 -- can be improved dramatically using AI applications (neural networks, and computer vision and cybernetics, respectively).
It seems that now is the time to integrate AI, where and when possible, into projects of various kinds. Some basic principles and current state of the art is needed to get one's bearings in AI, and provide a foundation for experimentation with AI in new products and services, or simply useful utilities.
Update early 2025
From an interview with DeepSeek's CEO:
large models made in China will be as much of a force to be reckoned with as drones and electric cars.
Indeed, the entire interview is quite eye-opening, though at the same time entirely predictable. In these three markets: drones, EVs, and LLMs, the secret sauce is doing fundamental, architectural research with confidence. That creates disruptive breakthroughs. Of course hiring geniuses who are motivated to do such is mandatory.
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1965 NASA report on spaceflight computing. ↩︎