The AI Hype Train has left the station. You know you’re in the middle of a hype cycle when products and companies start using a term regardless of whether or not their product incorporates any of that technology. This is where we currently are with regards to the market for AI products and services. While there is a lot of great, new innovation that’s pushing the industry forward towards more intelligent systems capable of many of the challenging areas that have previously not been able to be solved due to extreme complexity or the need for human labor, there are just as many companies who are using the term AI as more of a marketing ploy or a way to raise money.

Many firms are claiming to be AI-enabled when all they have done is put some thin capability provided by a third-party library or API that doesn’t really transform their existing product into something that is inherently different with that new intelligent technology. One of the biggest offenders of this AI-as-a-buzzword is the entire Robotic Process Automation (RPA) market. While automation is they key word in this phrase, this corner of the process automation market somehow is identified not with the traditional workflow automation and Business Process automation or management (BPM) space that has existed for decades, but rather the more widely hype and strongly invested in AI space. This whole category is currently attempting to rebrand itself as intelligent and AI-enabled because they’ve added OCR or some other add-on. While automation is valuable and provides a ROI in and of itself, there’s no reason to conflate automation activities with intelligent activities. Automation is not intelligence.

What is automation?

Automation is not a bad word. The primary movement of the industrial revolution was to take much of what we were doing at the time with manual labor and automate that work so that we could achieve significantly greater productivity, quality of life, and transform society as a result. Automation is the process of applying technology to some repeatable task or process so that the task or process can be accomplished with predictable repeatability, lower total cost of operation, increased safety, and provide better efficiency. This is what we demand of most of our technology, and technology has delivered that value. In fact, technology continues to deliver increasingly greater value to enterprises and individuals, squeezing more efficiency and capabilities and increasing productivity on a daily basis. So, automation is good. There’s nothing bad about it.

Mechanization and the unleashing of power from steam and electricity, the development of the assembly line and factorization of manufacturing, and the evolution of computing and the Internet have truly revolutionized the way we work, live, and exist. However, while these are fundamentally potent and transformative technologies, they are not intelligent technologies. We can’t walk up to a steam engine and ask it to recognize who we are or answer a random question or even learn from its experiences. A web server is just a web server no matter how many times it’s served the same content to the same sort of people. Automation has provided enormous and fundamental value to society. However it is different than the value we are seeking from intelligent systems, because automation is not intelligence. Today In: Innovation

Intelligence is much more than automation

Humans demand more from intelligent systems than simply repeating or simplifying a repetitive task that requires zero cognitive skills. From the beginnings of what researchers have been attempting to do with AI, we’ve been striving for systems that can understand and comprehend their surroundings, learn from their experiences, make judgements and decisions that are based on rational thinking, handle new situations and apply their learning from previous experience, and perhaps even address bigger questions of self-awareness, consciousness, and more. These are complex problems AI researchers are trying to solve, and fundamental questions of cognition including self-awareness and reasoning.

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Human intelligence precedes civilization; artificial and superhuman intelligence, however, will redefine it. Current research in artificial general intelligence (AGI) and intelligence enhancement (IE) seek to remove human error from their most ambitious technological quests. On the one hand, using evolutionary algorithms, AGI aims to develop a fully automated, increasingly independent, gradually cognitive, and eventually conscious artificial being. On the other hand, using neurotechnology, IE intends to create a super-intelligent and inherently different human being capable to counteract the inexorable ascension of machines in the next few years.

But what is the limit of such scientific enterprises? If we develop a conscious artificial being or a super-intelligent human being, what rights then prevail: human rights, artificial- or superhuman- rights? How far should we go to satisfy our intellectual curiosity, our ability to innovate, or other less noble yet often prevailing reasons such as productivity, greed, or power?

Has the time come to develop – in addition to individual human rights (e.g., equality, liberty, human dignity) – a new generation of collective human rights (e.g., equal technological access, human-life preservation, reciprocal income equality, brain-privacy) directed at protecting humans from humans, humans from superhumans, and humanity from extinction?

Nothing threatens us more than our decisions. Although our technological progress may lead us to think we live in a modern civilized world, the circular development of human society (e.g., going from supporting Nazis in the 30s to supporting Nazis in 2018) reveals a rather regressive and inhuman tendency.

Knowing that machines will eventually replace most humans as factor of production — which will deprive people across the planet of vital sources of income and increase the already exponential gap of income inequality — how is it we seem to prioritize economic factors such as reduction of labor, health care, insurance, and litigation costs over human rights apprehensions while downplaying existential concerns?

For instance, according to a recent report issued by the World Economic Forum, in the next four years, machines are expected to take over up to 42 percent of all tasks currently being performed by humans. Yet responses to this threat go from enhancing brain-capacity and providing universal basic income to reskilling current-and-future work force — this as millions across the world are still training for jobs that will soon disappear.

Evolutionary algorithms bring AI’s greatest risk yet: machine learning from which AGI’s full development may result. In 2014, Professor Steven Hawking warned about this risk by indicating that: “the development of full artificial intelligence could spell the end of the human race.” Nevertheless, more recently, in an Op-ed published on the Canadian newspaper the Globe and Mail, cognitive psychologist and Harvard Professor, Steven Pinker, dismissed AI risks to humanity as “apocalyptic thinking.”

Although Pinker’s larger argument is reasonable, it suffers from a major fatal flaw: his risk assessment on AI technology hinges on the human not machine cognitive behavior. Pinker’s analysis focuses mainly on human nature, bias, and decision-making processes; factors that may become gradually exogenous in AGI’s evolutionary algorithms and self-learned cognitive functions.

As MIT Professor Erik Brynjolfssen explains, AI machine learning provides machines with sometimes a million-fold improvement in their performance enabling them to solve problems on their own. That is, outside human supervision, despite human nature and, ergo the risk.

AGI appears also increasingly juxtaposed to human functionality. In fact, self-driving cars, trucks, trains, boats, and planes as well as customer service, bartender, waiter, firefighter, police, mower, farmer, chef, dentist, medical assistant, lawyer, and journalist robots are being introduced as cost-efficient and more reliable options.

Make no mistake, it is not about whether these technologies should or will develop — particularly when, in many ways, they already have. It is about a defining balance society must make between its scientific and economic ambitions and its existential reasons.

Notwithstanding pundits’ estimations, we cannot really predict how fast and what such intelligences will learn, not when our prime goal was to create independent and improved intelligences. After all, is not that the very risk we are assuming?

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