Artificial Intelligence: Where Machines Are Smarter

 

Artificial Intelligence: Where Machines Are Smarter

The use of AI is a phrase that many people are using this time in time. Artificial Intelligence disguises the truth that humans are often as befuddled by AI as society is. Some say that the current era revolves around Artificial Intelligence, but there is much misunderstanding. There are many in academia and industry who believe that AI has the potential to surpass human intelligence and create Machine Superintelligence. The idea is frightening for some, but it's fascinating for others. People often overlook the dangers that exist in AI. One of the most common problems is that humans are confident AI will make accurate decisions, but they fail to account for malfunctioning machines and poor data. In the digital era, industries are being disrupted by Artificial Intelligence (AI). AI is not just a fad but it also has a major impact on how marketers will do their job in the future. Marketers need to be prepared for the changes ahead and learn how to use AI to stay ahead of the curve. We live in a world where innovation is driven by technology and AI has been at the forefront of innovation. It’s time for marketers to get with the times.

Most of what is being called “AI” today, particularly in the public sphere, is what has been called “Machine Learning” (ML) for the past several decades. ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines (see below) to design algorithms that process data, make predictions and help make decisions. We now come to a critical issue: Is working on classical human-imitative AI the best or only way to focus on these larger challenges? Some of the most heralded recent success stories of ML have in fact been in areas associated with human-imitative AI — areas such as computer vision, speech recognition, game-playing and robotics. So perhaps we should simply await further progress in domains such as these. First, although one would not know it from reading the newspapers, success in human-imitative AI has in fact been limited — we are very far from realizing human-imitative AI aspirations. Unfortunately the thrill (and fear) of making even limited progress on human-imitative AI gives rise to levels of over-exuberance and media attention that is not present in other areas of engineering.

A related argument is that human intelligence is the only kind of intelligence that we know, and that we should aim to mimic it as a first step. But humans are in fact not very good at some kinds of reasoning — we have our lapses, biases and limitations. Moreover, critically, we did not evolve to perform the kinds of large-scale decision-making that modern II systems must not face, nor to cope with the kinds of uncertainty that arise in II contexts. One could argue that an AI system would not only imitate human intelligence, but also “correct” it, and would also scale to arbitrarily large problems. But we are now in the realm of science fiction — such speculative arguments, while entertaining in the setting of fiction, should not be our principal strategy going forward in the face of the critical IA and II problems that are beginning to emerge. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda.

AI will also remain quite essential because, for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. We will need well-thought-out interactions between humans and computers to solve our most pressing problems. And we will want computers to trigger new levels of human creativity, not replace human creativity Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. The term “engineering” is often invoked in a narrow sense — in academia and beyond — with overtones of cold, affectless machinery, and negative connotations of loss of control by humans. But an engineering discipline can be what we want it to be. I will resist giving this emerging discipline a name, but if the acronym “AI” continues to be used as placeholder nomenclature going forward, let’s be aware of the very real limitations of this placeholder. Let’s broaden our scope, tone down the hype, and recognize the serious challenges ahead.

“Why choose from a thousand job applications when AI can do it for you?”

~Chirag Ferwani