The fallacy of thinking intelligence as software

During the  Enlightenment the human body was thought of as a kind of clock. That was because the dominant technology of the day was mechanical engineering.Christiaan Huygens invented the pendulum clock in the 1600s, and in the following decades and centuries, all across Europe, the miraculous ticking of interconnected gears and springs felt akin to the periodic and cyclical nature of the human biology, and indeed of the whole universe. God was thought of as an architect, or an engineer. Everything in the cosmos was placed in perfect relation to everything else; an idea referred in philosophy as “determinism”. The human brain was mechanical too, and excreted thoughts – as other machines exhaled gases or fumes or fluids – and was powered by a mystical “soul”. This metaphor mutated by the late 20th century, as western societies rejected religion and adopted a new form of technology: computers.

Computers seemed to do “smart” things, like manipulating numbers, which was something that only humans were able to do till then. Computers did so by codifying a calculating process into a “program” that could then be “executed” on a machine. The program was called “software” and the machine “hardware”. The “smart” part of computing lay in the software, because that’s where the knowledge of solving a problem resided. The hardware was important of course, and necessary, but one could imagine all kinds of hardware, not necessary built with silicon chips and electronics, but with billiard balls, light bulbs, paper clips, whatever. This curious juxtaposition between hardware and software led to the following conclusion: that we can engineer intelligent behaviour as long as we code the right programs (or “algorithms”); executing those algorithms was of secondary importance and independent of the physical substrate. As long as you had a smart algorithm you had intelligence, not unlike having a smart genie that you could then place inside any bottle, or lamp, you liked.

Thinking of intelligence as something independent of the physical substrate (the “hardware”) was an idea that originated in computing and nowadays dominates our everyday thinking. We are using the computing metaphor in our everyday speech, as if it was a given. Our brains are the “hardware”, and our minds the “software”. We are thinking of Artificial Intelligence as computers becoming more and more “intelligent” because of algorithms.

The computer metaphor has led people like Stephen Hawing and Max Tegmark suggest that the future of humanity is to transfer our intelligence and consciousness to computers; to “upload” our consciousness and free ourselves from the frailty and perishable nature of biological bodies; thus bequeathing the keys of biological, and cosmic, evolution to our computer descendants. This is the main thesis of Life 3.0, the new book by Max Tegmark, although the idea is not new and was also explored in the “Anthropic Principle” by John Barrow and Frank Tippler published in 1988.

But of course, such thinking is fallacious. That’s because these otherwise very smart people confuse the computer metaphor of software versus hardware as the real thing. Like people in the Enlightenment who thought of the human body as a clock powered by an immaterial soul, Tegmark et al are regarding the self as an immaterial algorithm trapped inside a biological prison. Such thinking is also irrational because it has not being substantiated by any scientific evidence. In fact, the contrary is true: neuroscience and neurobiology show that intelligence is inextricable from the physical aspects of the brain. “We” are not an algorithm. We are unitary biological creatures.

Confusing metaphor with reality would have been unremarkable if it was not for how it frames the current debate on Artificial Intelligence. When powerful, successful and highly intelligent people adopt the metaphor when speaking publicly about the future of AI they offer validation to a fallacy that could have serious consequences in the economy, society and politics.  Artificial Intelligence is not intelligence but an imitation of intelligence. It is imitation because it fools us into believing it is the real thing. This idea of “imitation” is fundamental in AI, and was put forward since the beginning from none other than Alan Turing. In his “Imitation Game” paper he suggests how a computer could fool us into believing it was a human.

Once we adopt the computer metaphor without thinking then we render ourselves incapable of distinguishing between reality and the imitation of reality. As a result we are talking about AI “ethics”, or AI “bias”, as if they were real. They are not. Machines cannot have ethics, or uphold values, or have opinions or preferences. These words only have meaning to creatures like us, with the ability of self-refection. It is because we can examine the content and meaning of our thinking that we can decide between right and wrong. Self-refection is a property of biology. Machines cannot have self-reflection, and that is what will forever differentiate them from us.

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AI and the C-suite

Full transcript of my interview with Lisa Morgan for her Information Week article.

Generally speaking, how aware of AI does the average C suite have to be these days and why?  What should their attitude about it be?

The C-suite is becoming increasingly aware of the impact that AI will have on business processes, product development and, crucially, on HR. Big technology provides such as Microsoft, Google and IBM are pushing the AI agenda aggressively, and embedding AI across their applications; something that is catching the attention of CIOs and CTOs across the corporate world. The attitude of C-execs should be to add AI as a top strategic priority. This time technology will move faster than ever; and the laggards will pay a hefty price.

You hear the Doomsday hype and regardless of one’s view, it’s apparent that AI capabilities are expanding and so are the use cases.  How might AI impact the C suite from organizational and management perspectives?

AI will have profound impact across the organisation. More specifically, it will impact three areas. First, business processes; by automating many tasks currently performed by humans. Secondly, on product and services development; by adding an element of intelligence and intuitive human/machine interactivity. Finally, on data insights; by managing the exponentially increasing deluge of data, particularly so in the emerging era of IoT (Internet of things)

From a competitive POV, how can AI distinguish a company and how much easier or difficult will be for the laggards to catch up?

As suggested in (2) above, first adopters will gain a quantum leap in competitive advantage by embracing AI. This technology will separate the companies with leaders who are quick to see and act on the opportunity – and succeed – and those who will fall helplessly behind. Unlike previous technological disruptions AI will not be a slowly accelerating ride. It will be like a spaceship switching to light speed overdrive. The laggards will be in danger of becoming irrelevant overnight.

We’re constantly hearing about men, machines, and where the lines should be drawn.  Where are we now on the continuum of human assist, advisement, and automation/autonomy and how do you see that evolving in the near future?

There is a clear trend towards machines becoming more intelligent so that humans can work more intelligently with them. Although machines will increasingly gain more autonomy, they will do so within the human space and within human norms and ethics. Whether they are robots working alongside us on the assembly line, or intelligent interfaces which carry out the most tedious cognitive tasks, machine intelligence will be our most trusted colleague in the future.

More organizations are using machine learning to solve problems faster and at scale.  Some are innovators, some are consumers, some are both.  At what point does it become obvious that AI is impacting or will impact the corporate culture?

Corporate culture is essentially about behaviour; or how employees conduct themselves while aligning with the company’s goals. The successful companies of the future will be the ones that encourage innovation and the empowerment of their people. This is because the most positive, and most disruptive, impact of AI will be the empowerment of humans to be more productive and more creative. Savvy corporate leaders get that and are ready to shift gears towards more employee empowerment, more organisation agility, and more data-driven management.

What are the characteristics of companies that are in a better position to gain a strategic advantage of AI and what are the characteristics of companies who are likely to fall short of their own expectations?

Companies that have embarked on rethinking their business models in a more digital fashion are the ones who are better prepared for the 4th Industrial revolution. AI takes digital transformation to another level, so naturally those companies that are already investing on innovation and digital are better positioned to adopt AI.

How will AI affect the workforce?  Which types of roles are more likely to benefit from AI assistance/advisement and which roles are more likely to become obsolete, given that AI is constantly becoming more sophisticated and helping bona fide experts?

AI will have a major impact on the workforce, particularly white-collar workers. It is estimated that 60% of jobs will have 30% of their tasks automated (McKinsey research). The impact will be most severe on entry level jobs, challenging how young graduates will enter the labour market (Willis Towers Watson research). The current trend of companies becoming leaner by shedding full-time employees and replacing them with contractors using digital talent platforms will persist. In the future, most working people will be working as contractors using digital platforms to get work and AI to support them; this will give more opportunity for a better work/life balance, but will also make our working lives less secure.

How does and how will AI affect the decision-making process?

By providing better data insights and predictive analytics (as alluded in 2), AI will significantly enhance decision-making and transform management into a more data-driven process but also one that is more creative.

Is there any advice you’d like to offer or best practices you’d like to mention that you haven’t yet talked about?

Identify opportunities in the three impact areas of AI (products and services; business processes; data insights) and start upping your game.