Where are the Opportunities for Machine Learning Startups?

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Machine learning has permeated data-driven businesses, which means almost all businesses. Here are a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch.

Machine Learning and AI are fast becoming ubiquitous in data driven businesses, that is to say, an awful lot of businesses. Here I choose a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch. It’s not uncharted territory — if I could think of the next killer application, I’d be trying to do it!

‘Pick and shovel’ plays

So-called after the California Gold Rush where the purveyors of picks and shovels made a killing (whereas the outcome for prospectors was mixed), the picks and shovels of machine intelligence are hardware, data feeds,and (arguably) the algorithms themselves.

  1. It’s striking that algorithm developments for machine intelligence have been overwhelmingly open source. Of course there are exceptions — last year, Oxford University filed a patent on an efficient alternative to backprop called the Feedback Alignment Algorithm (page 14) — I wonder how they intend to commercialise it? High quality SaaS offerings that make it easy for people to utilise learning algorithms will find eager customers, and MetaMind, bringing cutting-edge deep learning to your dataset, is one such with all the right credentials. Another initiative I like is The Automatic Statistician project, which searches models to discover the best explanation for your data using Bayesian inference. The Curious AI Company, a General AI company whose first venture is waste sorting (the very definition of unsexy but lucrative), reportedly aims to sell its AI software as a toolkit.
  2. Large corporations have access to huge datasets and can acquire more (pace IBM’s recent acquisition of The Weather Channel data assets for $1.5bn). But focus to date has been on the low-hanging fruit such as social or ecommerce data, so there’s still opportunity where data is harder to acquire and/or to label. Affectiva’s database of facial emotional responses is in this category, as are Pallas Ludens (end to end data annotation service), and opensensors.io (adding value to public sources of sensor data). Genome and medical image data — subject to some knotty privacy issues — will enable personalised treatment and care, and better diagnostics. On that note, it will be interesting to see howGenomics England proceeds with its industry engagement.

Read the source article at Analytics, Data Mining, and Data Science