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This is why AI has yet to reshape most businesses

The art of making perfumes and colognes hasn’t changed much since the 1880s, when synthetic ingredients began to be used.

Daub hired IBM to design a computer system that would pore over massive amounts of information—the formulas of existing fragrances, consumer data, regulatory information, on and on—and then suggest new formulations for particular markets.

Plus, the company is still wrestling with costly IT upgrades that have been necessary to pump data into Philyra from disparate record-­keeping systems while keeping some of the information confidential from the perfumers themselves.

“We are nowhere near having AI firmly and completely established in our enterprise system.” The perfume business is hardly the only one to adopt machine learning without seeing rapid change.

AI might eventually transform the economy—by making new products and new business models possible, by predicting things humans couldn’t have foreseen, and by relieving employees of drudgery.

Such productivity gains are largest at the biggest and richest companies, which can afford to spend heavily on the talent and technology infrastructure necessary to make AI work well.

Last September, a data scientist named Peter Skomoroch tweeted: “As a rule of thumb, you can expect the transition of your enterprise company to machine learning will be about 100x harder than your transition to mobile.” It had the ring of a joke, but Skomoroch wasn’t kidding.

“I think there’s a lot of pain out there—inflated expectations,” says Skomoroch, who is CEO of SkipFlag, a business that says it can turn a company’s internal communications into a knowledge base for employees.

“AI and machine learning are seen as magic fairy dust.” Among the biggest obstacles is getting disparate record-keeping systems to talk to each other.

It recently rolled out a conversational software agent called Livi, which uses natural-­language technology from a startup called Avaamo to assist patients who call UC Health or use the website.

It took a year and a half to deploy Livi, largely because of the IT headaches involved with linking the software to patient medical records, insurance-billing data, and other hospital systems.

Some big retailers, for instance, save supply-chain records and consumer transactions in separate systems, neither of which is connected to broader data storehouses.

That explains why the most common uses of AI so far have involved business processes that are siloed but nonetheless have abundant data, such as computer security or fraud detection at banks.

That’s what’s slowing down AI or machine-learning adoption.” Fluor, a huge engineering company, spent about four years working with IBM to develop an artificial-intelligence system to monitor massive construction projects that can cost billions of dollars and involve thousands of workers.

The system inhales both numeric and natural-language data and alerts Fluor’s project managers about problems that might later cause delays or cost overruns.

“Hybrid corn was the invention of a method of inventing, a method of breeding superior corn for specific localities,” Griliches wrote in a landmark paper in 1957.

But the adoption curve was nowhere near as steep in places like Texas and Alabama, where hybrids were introduced later and covered about half of corn acreage in the early 1950s.

The users’ key question is not, as it is for technologists, “What can the technology do?” but “How much will we benefit from investing in it?” Today machine learning is undergirding every aspect of the operations of companies like Facebook, Google, and Amazon and many startups.

The Pentagon Doubles Down on AI–and Wants Help from Big Tech

In the 1960s, the Department of Defense began shoveling money toward a small group of researchers with a then-fringe idea: making machines intelligent.

The plan depends on the Pentagon working closely with the tech industry to source the algorithms and cloud computing power needed to run AI projects.

Last year thousands of Google employees protested against the company’s work on Project Maven, which was intended to demonstrate how the US military could benefit from tapping commercially available AI technology.

The pushback against Google’s work on a program using algorithms to identify objects in video from drones prompted the company to decide not to renew the contract.

In budget requests last summer, the Air Force and Marine Corps described plans to make wider use of Maven algorithms, including putting them “on multiple unmanned aerial vehicles,” and using them to identify targets based on data from drones carrying special cameras that can monitor up to 40 square miles of territory at a time.

That likely means more contracts like JEDI, a cloud contract worth more than $10 billion that is expected to be announced in coming months, with Amazon and Oracle among the leading bidders.

An IBM spokesperson pointed to the company’s existing $135 million Army contract that involves applying AI to predict equipment faults, and said IBM hopes to work more with the Defense Department on the technology.

Brian Roach, managing director for regulated industries at SAP North America, said the company works with all branches of the armed services and is interested in supporting all kinds of future government AI programs.

“They’re going to try to reach out and convince as many companies as possible to work with the department on some of those issues.” The JAIC could soon have plenty of money to hand out to tech companies, and may even set up shop in Silicon Valley.

Shanahan said Tuesday that the JAIC’s future budget isn’t finalized, but a Pentagon budget request document from June forecast the center’s budget at $89 million in 2019, and $414 million in 2020.

Shanahan said algorithms will be trained to identify fire lines on video shot from planes flying over wildfires, extracting data that can speed up efforts to fight the fires.

The humanitarian program will also work on new ways to process satellite photos, drawing on a dataset created for a Pentagon competition offering $100,000 for algorithms that could identify structures such as damaged buildings and utility trucks.

In 2016, it staged an odd, $55 million, contest in Las Vegas ballroom in which bots competed for a $2 million prize by hacking one another, while patching their own flaws.

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