20 Bullets on Artificial Intelligence
On Artificial Intelligence:
- The prior two decades’ work on digitization of big data sets, infrastructure to manage big data sets, and paradigms to compute over big data sets is the primary causal driver explaining this era’s emphasis first on ‘Data Science’ then on ‘AI’.
- Once we have digitized data and made data programmatically available, the obvious next step is to lever it for future automation and prediction making. As our predictive capabilities give the optics of greater ‘intelligence’ we evolve our vocabulary from ‘Data Science’ to ‘Artificial Intelligence’. In reality there is no strong distinction between the two, rather only the perception of novelty and difficulty. Novelty and difficulty are normative to time; today’s ‘Artificial Intelligence’ is tomorrow’s ‘Data Science’
AI that learns from data is called Machine Learning. Traditionally ML has taken raw data...
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