The challenges of leading companies through the AI revolution were examined in a recent symposium.

On May 23, 2017, the MIT Sloan School of Management hosted the 14th annual CIO Symposium: “The CIO Adventure: Now, Next and… Beyond.” The one-day event brought senior IT executives together to discuss key technologies, including IoT, AI, blockchain, big data, DevOps, cloud computing, and cybersecurity. The main idea was to help prepare these tech leaders for challenges they face, including shepherding ongoing digital transformations, building a digital organization, and managing IT talent.

This series highlights insightful sessions from the event.

It’s nearly impossible to have a robust discussion about artificial intelligence (AI) without talking about its impact on society — especially the fear that machine learning may eliminate jobs. With these concerns top-of-mind for CIOs and IT leaders, MIT Media Lab’s Joi Ito moderated the session “Putting AI to Work,” which featured Josh Tenenbaum, a professor in the Department of Brain and Cognitive Sciences at MIT; Ali Azarbayejani, CTO of Cogito Corporation; Seth Earley, CEO of Earley Information Science; and Ryan Gariepy, CTO and co-founder of Clearpath Robotics and OTTO Motors.

“People worry that computers will get too smart and take over the world,” Ito quips at the start of the panel, quoting author Pedro Domingos. He continues: “the real problem is they’re stupid and they’ve already taken over the world.” But, as the panelists agree, AI has a long road ahead before it will render our working lives unrecognizable.

The discussion focuses on two use cases for AI: augmentation and automation. Ito argues that augmentation — that is, using machine learning to improve industrial processes, workplace efficiency, and customer experiences — is the more common way to leverage the technology today, and the panel finds consensus around this idea. Azarbayejani’s firm, Cogito Corporation Inc., for example, employs AI to understand the nuances of how people use words in conversations, using the knowledge to provide real-time feedback to frontline staff in call center environments. Tenenbaum contrasts two methods of learning: pattern recognition, which algorithms do well, versus model building, which can occur after a single experience and has more to do with human instinct.

Perhaps the meatiest question Ito posed to the panel was: “How is the talent availability curve going to evolve?” As organizations decide whether to make or buy deep learning technologies, it’s a fair question — one that gets at whether growing demand for AI talent will outpace the jobs AI replaces.

1 Comment On: Augmentation Versus Automation: AI’s Utility in the Workplace

  • artificial intelligence | August 12, 2017

    There are three reasons why the innovation has seen a lift as of late:

    Adequate registering energy to process the a lot of information required to drive it. The cost of processing has diminished drastically finished the most recent 20 years.

    There has been a tremendous advances in the improvement of calculations over the most recent 20 years. This is somewhat determined by accessibility of PC preparing power on which to test them.

    AI frameworks at long last have adequate information to prepare their calculation. This has been significantly helped by the ascent of the web, making exabytes (interpretation: “a dreadful parcel”) of information every day, and drastically lessened expenses of information stockpiling. This implies information is currently accessible in sufficiently huge volumes to make AI suitable.

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