Five challenges facing general AI -

Five challenges facing general AI


SAN FRANCISCO — Try to find a technology conference or trade show where everybody is not talking about artificial intelligence. Go ahead: Try. But not at this week’s Design Automation Conference (DAC).

Dario Gil

Dario Gil

The DAC keynote on Tuesday was “AI is the new IT,” offered by Dario Gil, vice president of AI and IBM Q at IBM Research. Gil presented a helicopter’s-eye view of the technology’s current topography, identifying key areas as the industry strives to broaden AI’s turf.

Gil looked back to 2012, a pivotal year. That was when the development of a deep convolutional neural net in the ImageNet Challenge proved to be a breakthrough in visual object recognition algorithms. Dramatic increases in labeled data and compute power, along with more progress in algorithms, have fueled the deep-learning revolution further.

Many industry segments are hot for AI. One way to measure this trend is to look at the enrollment of students in introductory courses on machine learning, noted Gil. Traditionally, such classes attracted 30 to 40 students, he said. Now, more than 1,000 have signed up at Stanford and 700 at MIT.

Narrow AI
AI, as we know it today, is being applied to language translation, speech transcription, object detection, and face recognition. Gil calls this “a narrow form of AI” in which AI runs a single task in a single domain.

Nonetheless, AI is already spreading like wildfire across many industry segments. “There are hundreds of applications, and the list is quite long,” said Gil. IBM is tracking AI challenges in a spectrum of applications that range from design automation, industrial, healthcare, and visual inspection to customer care, marketing/business, IoT, and compliance.

In IC design, for example, machine learning is already used to optimize synthesis flow. Advances in AI can now “automate the decisions of skilled designers,” according to Gil.

Machine learning applied to IBM 22-nm z13 system (Source: IBM)  Click here for larger image

Machine learning applied to IBM 22-nm z13 system (Source: IBM)

A good example is when IBM developed its z and Power server microprocessor chips using a 22-nm process. Experience has taught IBM that machine learning can be effective to “automate synthesis flow parameter tuning, capture knowledge from expert designers, and learn from prior design runs.”

Using machine learning for synthesis flow optimization (Source: IBM)  Click here for larger image

Using machine learning for synthesis flow optimization (Source: IBM)

Such efforts have shown the promises of AI. But throughout this speech, Gil cautioned, “We are just at the infancy.”


The horizon between narrow AI and broader AI (and, ultimately, general AI) is “still quite far away.”

Ultimately, Gil noted, “we must create a system that can learn and read, move automation across domains, and learn across arbitrary spaces. This still remains a very difficult problem.”

As for AI running a single task in a single domain with enough labeled data, Gil said, “We have no doubt that AI can achieve superhuman accuracy performance.” The challenge is how narrow AI can evolve into a broader form. Gil explained the dilemma: The moment you need to perform another task in another domain, you need to build a new neural network from scratch and clean it up. What the world needs, he added, is AI that can grow across tasks and domains.

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