A platform, in technology, is essentially a piece of software that other companies build on and that consumers cannot do without. Become the platform and huge profits will follow. Microsoft dominated personal computers because its Windows software became the center of the consumer software world. Google has come to dominate the Internet through its ubiquitous search bar. If true believers in A.I. are correct that this long-promised technology is ready for the mainstream, the company that controls A.I. could steer the tech industry for years to come.I then tweeted "Can a company control AI? More likely to become a commodity." The major machine learning algorithms are public knowledge and one can find a number of open-source implementations including Google's own TensorFlow that powered AlphaGo. What's to stop a start-up from implementing their own machine learning tools on the cloud?
Some of my readers' comments forced me to rethink my hasty tweet. First, Google, Microsoft and Amazon can create ML infrastructure, cloud hardware that optimizes computational power and storage for machine learning algorithms to get a level of data analysis that one couldn't replicate in software alone.
More importantly, Google etc. have access to huge amounts of data. Cloud companies can provide pretrained machine learning algorithms. Google provides image classification, voice transcription and translation. Microsoft offers face and emotion detection and speech and text analysis. One could imagine, in the absence of privacy issues, Google taking your customer data, matching with data that Google already has on the same customers to draw new inferences about how to market to those customers better.
With almost all our computing heading to the cloud, cloud computing providers will continue to compete, and provide continuing better tools in machine learning and beyond. Eventually will one company "control AI"? That would surprise me but we may still end up with an AI oligarchy.
I like your thoughts on this one. First, no one controls machine learning, and then cloud infrastructure could become a key element in machine learning.
ReplyDeleteThere's some subtleties here as well. The most important thing about machine learning is how you set up the problem that learning is applied to. Small changes can dramatically affect the outcome. This is true regardless of the infrastructure that does the AI.
Software functionality for AI and it's API is a key element also. If someone produces breakthrough algorithms, and these aren't made public, then they could controller the market.
Infrastructure includes hardware. If hardware is developed, where it's feasible for folks to do AI in house, then cloud computing isn't nessisary.
It's a matter of how much resources are needed to solve useful problems and what hardware becomes available. For example, if a single machine equiped with gpgpu, or analog neurons, or whatever the hardware will be, can tackle a big job, then companies may do this in house, but if this requires 100 machines all with special hardware, then they may tend to always rely on cloud services.
Most commodities I can think of are provided by few companies. Commodities enable economies of scale. So the largest players have an edge.
ReplyDelete"So the largest players have an edge"
DeleteSure, this is the very point of the post, with the minor aside that they also have an edge to screw you! :-)
...asks one type of machine of another to others...
ReplyDeleteI think computer and mathematics has close relationship with each other. http://www.zeepedia.com/answers/question-category/computer-science/ & its various languages are based on mathematics. So by using different software, one could solve different complex problems and this site providing this facility to its user to learn something new about mathematics by using different computer skills.
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