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AI. Measuring Progress and Preparing

The phrase “Silicon Valley” was first seen in print around 1970 but the origins and heritage of the region stretch back to when Stanford University, NASA Ames and IBM Research in San Jose were doing pioneering work on silicon-based transistors, hard disks, and other foundations of computing.

IBM now has 15 global research laboratories with around 3000 researches, IBM invests billions in R&D, paid for in part by significant patent licensing — IBM has been #1 in the world for over 27 years in the production of patents, more than any other company.

How do we know this? : Jim suggests that we should pose and address the following questions: Deep Learning for AI Pattern Recognition depends on massive amounts of ‘labelled data’ and computing power (available since ~2012).

Labelled data is input and output pairs (such as a sound and word, image and word, English sentence and French sentence, or road scene and car control settings).

This, coupled with ML developments, will stimulate and enable the growth of personal and digital assistants which will become commercially viable from about 2020 and in widespread use within 20 years.

Shorter-term risks are more realistic, and include de-skilling of the workforce and lower cost of certain attacks, for example, spear-fishing, allowing bad actors to automate tasks that were previously labour intensive.

To prepare to get the benefits and avoid the risks, this is what Jim tells his students, to provoke their thinking about the cognitive era: 2015 — about nine months to build a formative Q&A system — 40% accuracy 2025: Watson will be able to rapidly ingest any textbooks and produce a Q&A system 2035 — as above, but rivals C-level (average) faculty performance on questions 2035 — approaching peta-scale of compute power costs for about $1000 2035 — nearly everyone has a cognitive mediator that knows them in many ways better than they know themselves 2055 — nearly everyone has 100 cognitive assistants that “work for them.” In 2015 we were at the beginning of the beginning of the cognitive era.

In 2035, we will be at the end of the beginning (1/1000 one brain power equivalent), easy to generate average faculty level performance on questions in a textbook.

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