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.
Just how much is out there? Analyst firm IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes, ten times the 16.1 zettabytes generated last year. The need for cognitive processing abounds, as humans alone are no longer able to keep up with the demand for insightful analysis from all of this data
The solution to an intelligent future: Collaboration
The key to realizing this vision will be the development of an open AI ecosystem that makes the vast array of cognitive engines available in a single online marketplace. Such a marketplace could yield lucrative business opportunities for algorithm developers, application developers and end users.
Much in the way that Salesforce.com’s AppExhange and Apple’s App Store spurred significant investment in software development and led to the rise of the app economy, the establishment of a structured marketplace for AI will be key to facilitating widespread custom development based on a transparent and well-defined economic model.
For companies hoping to harness the capabilities of cognitive engines, this situation presents major challenges. Companies have specific needs that may change over time. These needs could be best served by one of the multitude of algorithms on the market — or a combination of different algorithms.
For example, imagine a company that’s building a robot intended to look and act human. This effort would require one algorithm to make the robot talk, another to allow it to walk, a third to enable it to perceive and understand the world around it — and so on. The difficulty involved with identifying the right engines to perform each task would be daunting, with a high likelihood that the company wouldn’t pick the best engines for each task. While AGI needed to accomplish this remains a potential technology of the future, ANI can deliver on the promise of AI today if properly orchestrated.
This can be accomplished by aggregating cognitive computing algorithms into a single software, utilizing a range of developers extending that platform to a variety of computing environments and developing a transparent app ecosystem. This system or organization corrals the stampede of algorithms, places them into an group and deploys them to deliver real value that meets customers’ specific AI needs.
More simply: When AI remains an open field, with best-of-breed systems working cohesively together, ANI can work towards AGI. With this approach, it’s possible to create AI technology with human-like capabilities .
This article originally appeared in Machine Learnings newsletter