Apr 7, 2021

Machine-Learning Pioneer Says Stop Calling Everything AI

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

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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Jun 10, 2021

Google is using AI to design faster and improved processors

2 min
Google scientists claim their new method of designing Google’s AI accelerators has the potential to save thousands of hours of human effort

Engineers at Google are now using artificial intelligence (AI) to design faster and more efficient processors, and then using its chip designs to develop the next generation of specialised computers that run the same type of AI algorithms.

Google designs its own computer chips rather than buying commercial products, this allows the company to optimise the chips to run its own software, but the process is time-consuming and expensive, usually taking two to three years to develop.

Floorplanning, a stage of chip design, involves taking the finalised circuit diagram of a new chip and arranging the components into an efficient layout for manufacturing. Although the functional design of the chip is complete at this point, the layout can have a huge impact on speed and power consumption. 

Previously floorplanning has been a highly manual and time-consuming task, says Anna Goldie at Google. Teams would split larger chips into blocks and work on parts in parallel, fiddling around to find small refinements, she says.

Fast chip design

In a new paper, Googlers Azalia Mirhoseini and Anna Goldie, and their colleagues, describe a deep reinforcement-learning system that can create floorplans in under six hours. 

They have created a convolutional neural network system that performs the macro block placement by itself within hours to achieve an optimal layout; the standard cells are automatically placed in the gaps by other software. This ML system should be able to produce an ideal floorplan far faster than humans at the controls. The neural network gradually improves its placement skills as it gains experience, according to the AI scientists. 

In their paper, the Googlers said their neural network is "capable of generalising across chips — meaning that it can learn from experience to become both better and faster at placing new chips — allowing chip designers to be assisted by artificial agents with more experience than any human could ever gain."

Generating a floorplan can take less than a second using a pre-trained neural net, and with up to a few hours of fine-tuning the network, the software can match or beat a human at floorplan design, according to the paper, depending on which metric you use.

"Our method was used to design the next generation of Google’s artificial-intelligence accelerators, and has the potential to save thousands of hours of human effort for each new generation," the Googlers wrote. "Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.

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