Why man has become so curious about Artificial intelligence

Man has a curious relationship with technology. We are fascinated and feared by it in equal parts. Take the case of the spinning jenny—a great invention which spawned the Industrial Revolution. This textile-making machinery was supposed to make the mills more productive. Some Nineteenth century English workers saw it differently. They smashed the equipment and burnt down factories fearing that this new invention will destroy their jobs and livelihoods. Time proved that their anguish was short-sighted.

Today, artificial intelligence (AI) is the latest scare. Report after dense report forecasts a jobless future with AI eliminating most of the jobs. Some futurists believe that AI will relegate humans to a perpetual underclass. As always, the reality presents a mixed picture. To understand how AI affects our future, we should first understand what AI is and why we are embracing and fearing it simultaneously.

This two-part explainer is intended to clear the air around AI and help people understand it better. It may be basic reading for most of us, but our intent is to explain what AI really is in plain language.

What is Artificial intelligence?

AI is suitcase word—it is stuffed with many meanings. Hollywood’s take on AI, with its preference for robot heroes and gravity-defying action, muddled its meaning in the minds of the general public. To simplify, AI refers to a system that is capable of thinking and acting like humans. Whether it is a robot or a software program does not matter. Machines are good at taking instructions, but they are not good at thinking for themselves—like we can. For true AI to be possible, machines should be able to learn and think autonomously by finding patterns, which calls for a ton of data and huge computing power. Even then, a machine brain would be a poor match for the human brain.

Any promising new technology attracts a lot of hype. The same was true of AI too, which had a spirited start in the late 1950s. In the 60s, leading AI researchers declared that machines will be as smart as human beings in a decade. It turned out that it was not so easy and by the mid-70s, the mood turned pessimistic as some AI projects failed to deliver on their advertised promise. Soon, funding was cut and interest in the area waned leading to a period that’s referred to as the ‘AI winter’. By 1993, things started looking up again. When IBM’s chess program, Deep Blue beat Gary Kasparov in 1997, it caught headlines and the AI spring began. See below an infographic on the evolution of AI from Vertex Ventures—a VC firm:

In 1965, Gordon Moore, the co-founder of Intel, made a prediction that computer chips—essentially the brains of computers—double in power every two years. Called the Moore’s law, this prediction turned out to be incredibly accurate. This improvement is made possible by advances in chip design that allow more transistors to be crammed into a square inch of an integrated circuit. Today, the availability of large-scale computing power is driving a new wave of AI research, as are advances in machine learning techniques and the availability of immense data troves. Think of all our online activity and the data streamed by sensors that are everywhere. AI systems have started getting better. They are also moving out of research labs and into our daily lives with greater speed.

Narrow AI vs Full AI

Whether we know it or not, AI is a regular feature of our daily lives. When we book a cab via Uber, the AI-enabled algorithm determines what price to show you based on the real-time demand for rides in that area. Facebook’s algorithm will rank and sort your feed based on several parameters to surface stuff it thinks you may find interesting. Video suggestions on YouTube and shopping recommendations on Amazon are made by intelligent algorithms. These are examples of what is called as ‘Narrow AI’. These algorithms do a specific task better than humans, but they do not have general intelligence in the sense that we do. Most of us embrace Narrow AI without hesitation because its presence improves the experience of using a product or service.

Some ambitious researchers are working on building machines with the general intelligence of the kind that humans possess. They are trying to develop artificial general intelligence (AGI)—also called ‘Full AI’—which would allow machines to perform all the intellectual tasks that humans can, like abstract thinking, natural language communication and continuous learning. One of the ways researchers are seeking to achieve Full AI is by mimicking the structure of our brains. Deep learning—a subset of machine learning—is an example of this approach. Still, for all the breakthroughs, we are nowhere close to replicating the intelligence of a 5-year-old. Given the exponential increase in computing power though, experts believe that we are not too far from that day. The below illustration by Chris Noessel—a UI expert and author—explains the difference between Narrow AI, Full AI and Super AI:

  

  

As things stand currently, computers are good at doing things that we find difficult, but they are miserable at things that are a breeze for us. Computers can multiply two six-digit numbers in a fraction of a second. But throw a ball at a robot to catch and it will stumble and freeze. Since the rules of chess and the combination of moves can be programmed, it is easy to create an algorithm that can beat a grandmaster. That’s Narrow AI. On the other hand, it is exceedingly difficult to create a program that can engage in friendly small talk or detect sarcasm. There is good reason to believe that Full AI has a long uphill road to tread. Since technological advance represents just one factor in the development of AI, more computing power does not always mean better AI