Deep Learning: Machine Learning becomes ‘Intelligent’, Artificially or Otherwise?

‘Deep Learning’ is the new big thing. ‘Old Fashioned’ Academic AI from the second half of the C20th has passed through the last decades of a ‘narrower’ focus of Data Mining or Knowledge Discovery and Machine Learning to blossom in to what is now touted as a new, vibrant age.

There is too much here to comment on in anything like a sane manner, and this is intended to be a very brief summary of my own ‘non-expert’ position, so I’ll be very brief with a few examples:

  1. The internet and technology giants are splashing cash on ‘Artificial Intelligence’ or ‘Machine Intelligence’, see Yahoo, IBM, Microsoft, Google and Facebook. That much money can’t be wrong, can it?
  2. Where the giants have trod, the VC world has followed. See this single VC post for the ‘Machine Intelligence’ landscape here from the end of 2014. They all can’t be wrong, either, surely?
  3. Goverments have ‘quietly’ been doing their own thing anyway for security, military, and logistical purposes anyway regardless of the public involvement or awareness. See the FBI NGI here, or anything that DARPA funds.

What’s also interesting is the emergence of the ‘old school’ academics among the individuals who are leading this ‘new’ (read ‘old’!) era. This is I believe down to the fact that the skills and knowledge required to be a ‘master’ in this area are intensely academic and rare in themselves, and that the ‘Deep Learning’ technologies that are currently being worked on have a continuing evolution from the early Neural Nets through to the Convolutional or ‘learning’ approaches that represent the ‘state of the art’ today. See first Hinton, LeCun, Ng as three eminent examples who have been ‘appropriated’ or ‘acquired’ or ‘assimilated’ by commercial operations. Ng’s website is the ‘outlier’, the others are gloriously and happily ‘old school’. Demis Hassibis‘s journey to Google is slightly different – not a mainstream academic at all but games developer- but with his skills he could have been. Other leading research figures in the last decade or so include Bengio, Bottou, Ciresan. See any footnotes in published research or NIPS for further lists of individuals.

As a one-time academic of sorts, it is or will be fascinating to see how this ‘cohort’ of researchers react to ‘suddenly’ being thrust in to the spotlight of a broader and more commercial world.

Moving on from this aside, it is worth pointing out that it is a new, vibrant age. The reinforcement loops at play particularly now with the internet / webscale technology giants and their real-world need for ‘intelligent applications’, at speed and at scale, has shifted the landscape and a few paradigms with it.

One  other clear outcome at play in recent years has been the inflation of ‘Data Scientist’ wages, perhaps long overdue, and the related ‘talent search’ or ‘fill a room with Machine Learning PhDs and wait for acquisition’ approach to company formation. Machine Learning or Data Science qualifications and the courses offered to support these by existing academic institutions or online at Coursera are I would imagine highly prized.

I’m going to write separately on particular areas or approaches of interest.

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