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The advent of artificial general intelligence (AGI), a milestone signified by machines becoming intellectual equals with their human counterparts, is nearer than we think. This tipping point will transform most aspects of daily life and the technical path to achieving it is becoming clearer and for some scientists, more narrowly defined.
Modern machine learning algorithms are currently capable of mastering only narrowly-defined cognitive challenges, like playing chess. So long as the training corpus is large enough and the problem space is sufficiently narrow, most machine learning algorithms will quickly learn to outperform humans, at a fraction of the cost and at superhuman speeds. However, the sufficiently narrow qualifier has proven to be a significant roadblock to data scientists and has held the evolution of machine learning at the artificial narrow intelligence (ANI) levels.
Two competing, yet also complementary solutions are actively being developed to bridge this gap from ANI to AGI.
One path is focused on modifying the machine learning algorithms to eliminate the “narrowness” constraint, coupled with massively increasing the size of the training data sets. The alternative path involves teaching machine learning algorithms to collaborate with one another and to replicate themselves, with each new copy trained to master a unique yet narrow skill. So while scientific research diverges for the moment, the advent of AGI will most likely depend on breakthroughs on both fronts and the integration of the two.
In the meantime, hundreds of companies big and small now offer thousands of artificial narrow intelligence (ANI) cognitive engines, each of which can perform a single AI task, such as translation, transcription or object recognition. The engines already are used in multitudes of products, from personal digital assistants that understand spoken words, to facial-recognition systems that can authenticate smartphone users.
While the AGI and ANI paths may seem to be incompatible, they actually are leading to the same destination.
By combining the thousands of cognitive engines and orchestrating their capabilities to apply the best technology to the task at hand, ANI can approximate the capabilities of AGI.
There’s strength in numbers when it comes to ANI, with each new engine arriving on the market bringing the world a bit closer to AGI. And the number of engines available on the market is growing explosively, from just five in 2012, to about 5,500 today. I’m predicting this will grow to several million in the next three to five years, as the machines learn to replicate and train themselves.
Each of these engines refines an existing capability or adds a new one. Employing multiple engines of the same class improves the precision of cognitive processing, delivering a higher quality translation of a speech, for example. When engines of different classes are used in combination, such as transcription and object recognition, AI can correlate the various types of processing to perceive things in a more sophisticated, multidimensional manner. This mirrors the way that humans use all their senses in combination to observe the world.
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Source:: Business 2 Community