Let’s be honest, most of what the general population knows about “AI” primarily derives from what they’ve seen in the movies or gathered from mass media misrepresentations around artificial intelligence. John McCarthy coined the term ‘Artificial Intelligence’ back in the 1950s, and while the end-goals for the technology now oscillate based on the solutions businesses require, the base objective of accomplishing AI has stayed fairly consistent since its origins: humans trying to get machines to think like humans.
Naturally, this proposition has some humans nervous because historically, cinema has predominantly taught us that this accomplishment can only end badly. iRobot, Minority Report, Terminator, The Matrix, Ex Machina and many more, have constructed a narrative in our heads that says once those robots finally achieve, what the industry calls “Strong AI” or “Artificial General Intelligence” (AGI), the end result could only equate to an existential war between humans and machines.
Not to mention, we are constantly encircled by largely generalized representations of artificial intelligence like IBM’s Watson and voice-controlled digital assistants like Alexa, Siri, and Cortana. While systems such as these are very good at what they do, their capabilities are just scratching the surface of AI technology.
Moreover, if you were to query the general population about artificial intelligence, many people would evoke mental images of malicious cyborgs and half-human-half-machine killer robots; or the supposition that it will all begin with robots stealing jobs and making humans irrelevant. Quite the overblown escalation. The reality of AI probably seems a lot less fantastical; so be it.
Most modern AI tech solutions are considered ‘narrow’ AI; solutions that cannot be effortlessly utilized to solve different problems in varying scenarios or implement their capabilities to multiple undertakings at once. Machine learning and numeric methods are great foundational tools for artificial intelligence – but on their own, they aren’t enough to power an explainable, trustworthy solution.
In contrast to the stand-alone conventional methods implemented by the majority of AI software companies, Beyond Limits combines the formidable building blocks of numeric (machine learning) approaches with symbolic (knowledge-based reasoning) techniques to arrive at Cognitive AI solutions. Unlike black-box machine learning methods, this pioneering Cognitive AI technology is also explainable (XAI). We build solutions you can trust for comprehensive insights into the reasoning behind their recommendations; transparent technology that empowers people to make better, faster, more confident decisions.
Our systems become trusted partners to human decision-makers, making sense of their environments while analyzing real-time data and scrutinizing that information in conjunction with human knowledge and expertise. They reason like humans to improve processes, formulate hypotheses, analyze concepts, anticipate complications, and execute remedies that resolve complex problems.
Our AI solutions are intended to strengthen human talent, proficiency, and opportunity – in reciprocal collaboration, like advisors. Humans can work with our solutions in this seamless manner via trust-generating interactive audit trails that clearly illustrate how they come to their conclusions. This transparency permits people to easily examine the system’s reasoning path for arriving at an answer, determine whether it makes sense, and then take action to mitigate complex problems accordingly. By contrast, stand-alone conventional AI techniques only offer a one-dimensional response without explaining the reason behind that recommendation.
Beyond Limits is dedicated to building pragmatic solutions to problems in support of people and their professional goals. We uphold that the purpose of an AI solution is not to substitute humans or take their jobs. Many humans perform numerous extraneous tasks at their jobs day in and day out; our solutions ensure that the responsibility of AI is to adopt arduous, monotonous and repetitive tasks that are often carried out in isolated or dangerous circumstances, so people can focus on more high-value undertakings.
It’s critical to have a realistic view of emerging technologies so we don’t become jaded, and inadvertently stifle new initiatives that could take us to that next level of potential. Being open-minded, ignoring clichés, and asking probing questions that lead to more conscientious understandings will also, ultimately, help us more effectively and confidently guide AI to the most useful utility possible.
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