The term Artificial Intelligence (AI) was coined by John McCarthy, although he thought it to be a misnomer later. “I should have named it machine intelligence”, he said on more than one occasions. Since the 1950s, a great deal of the original optimism and unrealistic expectations has gone out of AI and has been replaced with a degree of realism. The aim of the study of AI is no longer to create a robot as intelligent as a human, but rather to use algorithms, heuristics, and methodologies based on the ways in which the human brain solves problems. With the rise of distributed computing the power of super-computers has been brought to the masses, enabling them to use machine intelligence in such a fast and cost-effective way which was never possible before. More recently Cloud computing has brought this enormous machine wisdom to various businesses providing them with an option to outsource their complex problems and get them solved in the Cloud. I call it “Outsourced Intelligence”.
Take any business problem. From supply-chain management systems to “what-if scenario” business simulations; from data trend analytics to data warehouse classification algorithms…. just type the name of the problem, Google it; and you will get a bunch of solutions in the Cloud tailored for the particular problem you have at hand. In many cases these solutions will offer seemingly complete package with end-to-end data-integration. The code in the Cloud will provide the intelligence for your problem.
But will you ever know that exactly what logic is going to be used to solve you particular problem? Just try to dig into this. Browse through the literature, query the sales representatives and dig deep through the provided documentation. Chances are that this key information would be missing.
But isn’t this be the most important question that what the algorithm lies behind the intelligence and how accurately it would behave under your particular system and workload conditions. You may be led to believe that this information is the “secret sauce” which cannot be revealed. After all these “secret sauces” are the fiercely guarded business secrets which are not be be shared. But what about more general information about the algorithm….what about some information on the class of algorithm, its performance and accuracy, the comprises it makes and the constraints it relaxes, any insight into its performance upper and lower bounds, its comparison with optimal, its objective function formulation, if Integer Programming or Linear Programming is in place… and the proof that heuristics will actually work, if any heuristics are being used.
Recently I was surprised to know that some of the biggest names in IT industry are using this Outsourced Intelligence without having any knowledge of the key metrics for their critical supply chain management solutions. No doubt algorithms should be able to provide a solution in any case that will work… but how optimum that solution would be, is another story. Few seem to care but.. beware…“harder work can offset lower IQ” is only true for human intelligence. For machine intelligence even few comprises in the algorithms may have huge unwanted implications once in a while.