For six decades machine learning (ML) was poised to take off because members of the ’artificial intelligentsia’ had already come up with the theoretical models that could make it work. The problem was that they were waiting for rich data sets and affordable ’accelerated computing’ technology to ignite it.
These are now becoming more available, and amid a swirl of hype, ML - i.e., software that becomes smarter as it trains itself on large amounts of data - has gone mainstream, and within five years its deployment will be essential to the survival of companies of all shapes and sizes across all sectors.
For many investors, ML=AI; ML is an AI technology that allows machines to learn by using algorithms to interpret data from connected ’things’ to predict outcomes and learn from successes and failures.
There are many other AI technologies - from image recognition to natural language processing (NLP), gesture control, context awareness, and predictive APIs - but ML is where most of the investment community’s funding has flowed in recent years. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.
Many companies, like Alphabet, have already become ’AI-first’ companies, with machine learning at their core. At the same time, many ML techniques are getting commoditized by being open sourced and pre-packaged into developer toolkits that anyone can use.
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