Technology that can cure cancer is a feasible and realistic outcome, but we’re not there yet. Here’s what needs to happen to move the needle on artificial intelligence.
Artificial intelligence is being touted as a panacea for everything from curing cancer to preventing crime. However, to achieve such goals, the A.I. industry must first focus on the smaller, more practical solutions before it takes on bigger challenges–a process that involves working through countless iterations.
The road to autonomous cars began with the invention of the wheel. It continued with the development of the horse-drawn carriage, the creation of the car, and hundreds of thousands of subsequent automotive advancements that spanned over a period of 133 years. It took this many years and many more models to create today’s connected cars with their sophisticated telematics and advanced driver assistance systems. Despite the promise of materializing in the foreseeable future, fully-autonomous cars will require many more iterations until they become a reality.
For A.I. innovation to be practical, companies have to first embrace the low-hanging fruit, which is, providing solutions that relieve people from mundane and repetitive tasks, while increasing productivity and return on investment. A.I. can be easily applied to tedious tasks such as sales-flow automation and advertising airchecks. It is for these reasons that change-management leaders like Deloitte are encouraging businesses to “start with the boring.”
These solutions are called “one-time” (1x) AI transformations. They represent pragmatic tools that satisfy immediate needs while promoting strategic objectives.
The importance of these transformations cannot be underestimated. Studies show that businesses that do not integrate such 1x AI into their operations will fall behind in productivity and overall competitiveness. In contrast, businesses that invest in such practical A.I. solutions are expected to boost employment by 10 percent and revenues by 38 percent during the next five years, according to a recent report from Accenture.
Two-fold (2x) AI transformations take things a step further by looking at the bigger picture.
In litigation, 2x AI can take terabytes of unstructured and structured data from hundreds of thousands of sources and quickly extract, review, and analyze it with great accuracy. Lawyers can gain insights, uncover trends, and better predict the outcome of litigation.
In broadcasting, networks can leverage 2x AI to evaluate how the content of their news segments compares to their competitors’. A network also can learn which topics, and even expressions, resonate best with its audience, enabling them to optimize programming accordingly.
The type of A.I. used in all these examples is called artificial narrow intelligence (ANI)–it pertains to solutions that target a specific task, executing it better than humans, thus, augmenting human tasks and capabilities.
But this is just the tip of the iceberg in terms of what A.I. can offer. Things get truly exciting when organizations engage in 10-times (10x) AI transformations.
The key to such transformations lies in two technologies: artificial general intelligence (AGI) and artificial superintelligence (ASI). AGI is defined as a machine that can perform any intellectual task as well as a human does. Artificial superintelligence goes beyond AGI by delivering machines with intellectual capabilities that are superior to humans’.
Attaining this level of A.I. not only requires patience but a re-thinking of the nature of the technology and a complete reshaping of business practices.
Using 10x AI, legal systems will be able to find case law to ensure the criminals are tried justly, reducing wrongful conviction errors to a minimum, if not eliminating them entirely. Lawyers will be able to know which specific evidence and precedence they should use to optimize their chances of prevailing in a lawsuit and bringing justice to light.
In medicine, 2x image detection enables detection of cancerous growths at their very initial stages. 10x AI-based medicine will be truly preventative through cross-referencing many data sources ranging from personal data, such as DNA to external environmental factors, such as pollution.
In retail, 10x AI will enable brands to offer customized and optimized shopping experiences that will convert to the highest degree. Information, such as customers’ faces, moods, geo-location in the store or area, purchasing history up to the specific item and price point, the likelihood of purchasing certain items at specific moments, discount or loyalty programs that can be triggered, and more, will all be cross-referenced to achieve the most refined resolutions into customer behavior.
The list of ways that 10x transforms business processes goes on and on in every field imaginable.
True innovation happens when businesses re-think and re-frame needs, disrupt industries, and inspire. However, reaching the 10x summit requires a long and incremental climb. To get to the top of the A.I. mountain, technology providers must undergo innumerable iterations–from 1x to 10x-to address and solve business and technology challenges.
This article originally appeared on Inc.