#AI#ML#Artificialintelligence#Machinelearning

Machine-Learning Pioneer Says Stop Calling Everything AI

Michael I. Jordan, a pioneer in the machine learning (ML) field, explains the critical difference between artificial intelligence (AI) and ML

|Apr 7|magazine8 min read

One of the leading researchers in the field of artificial intelligence and machine learning recently issued a call to the world of technology: to stop labelling everything as ‘AI’. Michael I. Jordan stated that while AI systems do show some aspects of human intelligence and a human-level of competence in very low-level pattern recognition skills, they are only imitating human intelligence on a cognitive level ─ in essence, AI, in its infancy, is still a far cry from the reality of being human. 

Jordan, a professor in the department of electrical engineering and computer science and the department of statistics at the University of California, Berkeley, is considered by many as one of the foremost authorities on AI and ML. He is credited with transforming unsupervised machine learning from a collection of algorithms to an intellectually coherent field. So this isn’t his first rodeo when it comes to putting down AI. 

Machine Learning’s Superiority

Nowadays, Jordan’s frustration comes from something that many in the field of AI and ML share: their collective irritation at the mislabelling of machine learning. Oftentimes, it seems to be the case that when most people talk about Ai, they actually mean ML ─ they just don’t understand the difference.

“People are getting confused about the meaning of AI in discussions of technology trends – that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans,” Jordan said.

In a previous article on Medium titled “AI – The Revolution Hasn’t Happened Yet”, Jordan said this of ML: “ML is an algorithmic field that blends ideas from statistics, computer science, and many other disciplines to design algorithms that process data, make predictions, and help make decisions.”

The problem with AI is that it’s regularly misconstrued by Hollywood and other filmmaking industries that like to glamourise the technologies’ potential world-conquering capabilities. They continuously portray AI as a competitive force that will overtake humans in a questionable, certainly fictional, race for survival between man and machine. 

“While the science-fiction discussions about AI and superintelligence are fun, they are a distraction,” he says. “There’s not been enough focus on the real problem, which is building planetary-scale machine learning-based systems that actually work, deliver value to humans, and do not amplify inequities.”

In essence, the reality is that ML is the technology that changes our lives on a daily basis; while AI might be present in the workplace, automating previously manual, incredibly mundane tasks, and building links for interconnected devices, it isn’t the be-all and end-all that technologists and companies often portray it as. 

The Future of AI and ML

“For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations,” Jordan writes. “We will need well-thought-out interactions of humans and computers to solve our most pressing problems. We need to understand that the intelligent behaviour of large-scale systems arises as much from the interactions among agents as from the intelligence of individual agents.”

Moreover, he emphasises, human happiness should not be an afterthought when developing technology. “We have a real opportunity to conceive of something historically new: a human-centric engineering discipline,” he writes.

In the most ironic of turns, Jordan’s call-to-reality makes one thing come to mind: if companies and innovators stop focusing on the development of artificial intelligence, we’d probably be better off. Right now, AI is just serving as a distraction and a false saviour when the powers that be could actually make our lives far better through the judicious application of data science and machine learning. 

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