Despite nearly 60 years of experimentation with artificial intelligence, mankind still hasn’t achieved the goal of creating a machine that can perform all cognitive tasks as well as humans can, also called artificial general intelligence.
In fact, the very notion of AI also is open to widely different interpretations. These days it seems like everything is being called AI, from specialized applications like computer vision systems that detect very specific objects, to mass-market digital assistants like Apple’s Siri, to robots and drones, to ambitious efforts to apply human-level artificial intelligence to solve major problems, like treating disease.
Though some organizations are making significant R&D investments in AGI, most AI companies and startups are currently focused on ANI (artificial narrow intelligence) to perform specific tasks like logo recognition, facial detection and transcription. There is an abundance of engines that perform narrow, specialized processes as point solutions — there are more than 5,000 ANI algorithms available today, with that number set to rise to the millions during the next five years.
ANI is the first step in the journey toward AGI
Humans don’t have just one sense; we possess at least nine — and we need them all to navigate and understand the world. What’s more, the human brain is adept at using these senses in combination, like detecting a large truck is nearby both by hearing the characteristic sound and by feeling the intense vibration. While technology exists today that can target individual tasks such as these human senses, the AI market today is rife with incomplete and single-point offerings. These are then populated by a multiplying horde of algorithms, each with a different purpose, capability and specialty. Currently the AI landscape is confusing and limited. So, what’s needed to bring order out of this chaos?
Businesses incorporating cognitive processing require a concrete, intuitive way to use multiple ANI cognitive engines at once. Deploying several engines in concert results in efficient data processing that can be analyzed and reviewed to produce actionable insights for real life use cases. Businesses need this ability to leverage a combined, robust suite of AI engines in order to make decisions that impact the business’s bottom line.
Data continues to grow, so let’s put machine learning to use
With the number of commercially-available cognitive engines expanding exponentially, this approach will enable coordinated AI to rapidly overtake and even surpass human capabilities. Moreover, with the AI market set to expand by nearly a factor of 60 from 2016 to 2025, this method represents the best way for companies to capitalize on the industry’s growth potential.
Gartner surmises, by 2019, 50% of analytic queries will be generated using search, natural-language processing, voice or autogenerated. More simply: users will deploy a variety of engines to gather insights across the incredible number of data around them.
Source:: Business 2 Community