Machine Learning, Artificial Intelligence

Updated 28-Jun-2024

• See also Deep Speech, Tesseract

Recent Items on ML

• IBM's Ginny Rommety gave a compelling keynote at CES on AI, as well as answering a great set of questions on Bloomberg Technology.
• 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. 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.

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).