Artificial Intelligence Looms Larger in the Corporate World

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From The Wall Street Journal:

Artificial intelligence, long a subject of fanciful forecasts, is starting to enter the corporate world in a much bigger way, as costs decline and the need increases to identify patterns within ever-growing troves of business data.

Once a mainstay of startups and big-tech firms such as International Business Machines Corp. and Alphabet Inc., technologies such as machine learning are taking a larger role inside corporate giants including American International Group and Fannie Mae, which are deploying AI to automate and augment tasks previously done by humans alone.

Chief information officers say the technology helps them complete routine tasks faster and often without human help, saving money while freeing their employees to focus on value-added activities.

But as the technology becomes both less expensive and smarter, and more advanced technologies continue to emerge, companies will extend AI use beyond routine jobs to aid in decision making and spot trends and patterns that wouldn’t be evident to the sharpest data scientist.

. . . .

Less expensive, more abundant data storage, increased processing power and advances in deep-learning technology could lower the cost of artificial intelligence and make it possible for machines to learn with minimal programming from humans.

One common deep-learning tool, the neural network, uses layers of interconnected nodes to roughly mimic the operations of the human brain.

Nova Spivack, founder of AI startup Bottlenose, said the latest versions of deep learning employ hundreds of layers of neural networks. That power can be used in areas such as weak-signal detection, or the ability to spot trends more quickly.

. . . .

AIG said it recently deployed five “virtual engineers” inside its IT infrastructure that work 24 hours a day collecting and analyzing system performance data and spotting network device outages. They work alongside human engineers to learn patterns in the network data and eventually act on their own to solve technical problems.

A network device outage, for example, typically would go to a queue and take human engineers about 3½ hours to address, an AIG spokeswoman said. Using the virtual assistants, nicknamed “co-bots,” there is no queue and most incidents can be fixed within 10 minutes, she said. If a machine can’t solve a problem on its own, it is kicked back to a human engineer.

Link to the rest at The Wall Street Journal (Link may expire)

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8 thoughts on “Artificial Intelligence Looms Larger in the Corporate World”

    • Basically, we’re creating computer systems where we literally have no idea how they work or what they’ll do, and then basing significant business or safety decisions on them.

      What could possibly go wrong?

      • The routines called AI today were called Expert Systems not too many years ago. Before that they were called just code.

        If I find out you are letting software make a pay-no-pay decision on my account, I’m pulling the plug on you and taking my business elsewhere.

        There will come a day when software is up to the task but that day is not today.

  2. “If a machine can’t solve a problem on its own, it is kicked back to a human engineer.”

    We’ve been building systems that know how to fix themselves for decades, and it hasn’t required ‘artificial intelligence’, just remote reboot capability.

    10 If ( deviceFailed() )
    20 reboot_count = tryToRebootAFewTimes()
    30 If ( reboot_count > 3 )
    40 PANIC
    50 Endif
    60 Endif

    • Exactly. The technology has gotten so much better than “hold your nose and reboot”. Nowadays, a diagnostic system monitors hundreds, maybe thousands, of factors like ambient temperature, load levels, software error logs, patch history, past failure records, and remedial actions and slams them together into a huge self-learning matrix that picks the solution that worked best in the past in the situation most resembling the current state. The amazing thing is that, like a self-driving car, it works. I’ve been out of the industry for a few years, but five years ago, self learning network system diagnostics were making great progress.

      These are a different kind of AI, not like the old Prolog “horne clause” expert systems of 30 years ago. Expert systems attempted to capture the skills of a human expert. Theses systems draw their own conclusions from observation. IT systems are good subjects because they are heavily instrumented and their activity is typically logged.

      I would be careful about investing in a career in IT operations.

  4. It’ll be nice when these guys figure out the difference between an ‘AI’ and an over-built calculator …

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