Ever since the late 1950s, when pioneering IBM researcher Arthur Samuel trained the world's first self-learning computer to play a mean game of checkers, the future has promised a widespread emergence of intelligent machines.
That future is here.
Machine learning, the computing methodology Samuel introduced to the world, is now seen as mature, effective and -- thanks to a variety of new offerings -- readily accessible to the channel.
"For consulting companies like ours, this is the opportunity of a lifetime," said Dj Das, CEO of Third Eye Consulting Services and Solutions, a big data and analytics solution provider in Santa Clara, Calif., that partners with four major hyper-scale cloud providers: IBM, Microsoft, Google and Amazon Web Services.
That opportunity has presented itself in a diverse spectrum of use cases, from optimizing supply chains, predicting customer buying patterns, diagnosing illnesses, detecting fraud, recognizing text and images, and improving IT performance and security.
"The clients I talk to, the partners I talk to, they understand that this is going to be a disrupter," Ed Harbour, IBM's vice president for implementations for Watson, Big Blue's cloud-based cognitive computing platform, told CRN. "And whether they choose to embrace it to their advantage or whether they don't is probably going to determine the outcome of how their businesses go forward."
IBM is staking much of its future on cognitive computing, an analytic approach that mimics human thought processes of which machine learning is an essential component.
But IBM's challenge won't be to simply prove the value and viability of the technology. Big Blue is operating in an increasingly crowded market that includes hyper-scale powerhouses like Google, Microsoft and Amazon Web Services, all of which have launched platforms in the past year and a half that offer machine-learning libraries and tools as cloud services.
Where pre-cloud implementations of the technology were expensive and difficult to use, the public cloud platforms have drastically reduced the cost of entry, making it practical for solution providers to introduce machine learning for consumption at far greater scale.
"That's why they call it the ‘democratization of machine learning,'" said Orion Gebremedhin, director of technology for data and analytics at Neudesic, a Microsoft partner based in Irvine, Calif.
With libraries of prepackaged algorithms, often in the form of neural networks, combined with tools for building predictive models and big data technologies such as Hadoop and Spark, those cloud platforms are poised to usher in a wave of self-learning applications, offering businesses insight that makes them more effective and efficient.