The lasting wealth of most gold rushes isn’t found in quick strikes, though those exist, but in the long-term infrastructure built in the race for riches. While the banking scandal that precipitated the economic collapse of 2008 left only pain in its wake, the current AI frenzy in Silicon Valley will probably, sooner or later, bring some good things to life–or some such simulacrum of life–even if there will also be a lot of disappointed investors. From Richard Waters at the Financial Times:
“The latest AI dawn owes much to new programming techniques for approximating ‘intelligence’ in machines. Foremost among these is machine learning, which involves training machines to identify patterns and make predictions by crunching vast amounts of data. But like other promising new ideas that inspire a rash of start-ups, there is a risk that many companies drawn to the field will struggle to find profitable uses for the technology.
‘A lot of these AI platforms are like Swiss army knives,’ says Tim Tuttle, chief executive of Expect Labs, which recently raised $13m. ‘They can do a lot of things, but it’s not clear what the high-value ones will be.’
The result, he says, is a ‘wild-west mentality’ in the industry, as entrepreneurs race to apply AI to every computing problem they can think of.
‘I don’t think machine learning, as a standalone technology, is a valuable business,’ adds [Context Relevant’s Stephen] Purpura. ‘A lot of these things will get acquired.’
Artificial intelligence, machine learning, deep learning, neural networks: building machines that tackle problems that were previously believed to be solvable only by the human brain has given rise to a range of techniques and jargon.
The hope that AI will be more than just another passing tech fad is based on its broader potential. Like ‘big data,’ the phrase refers not just to a single technology or use but an approach that could have wide applications.”