Enterprise AI and the ‘valley of death’

Like humans, algorithms rely on examples to learn new concepts, but not at the same pace or level of understanding. For example, a child can quickly learn the difference between a cat and a squirrel, if shown pictures of each. An algorithm may require data scientists to feed it thousands of pictures before it can make the same distinction. Moreover, the model recognizes only as many types of cats and squirrels as it has been shown.

This is a problem for organizations. As they ingest more data—from web apps, mobile apps, IoT devices, customers, vendors, and partners—training machine learning models becomes more expensive and complex.

Though low-data learning has been around for decades, researchers are pushing the technology further than was previously possible. A seminal 2015 paper by Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum, published in the journal Science, describes a model that learns the alphabet as efficiently as children do.

Pal says low-data learning could have important business benefits, allowing models to react quickly as the business context evolves.

“Tools like low-data learning can make your workforce more productive,” he says. “In an enterprise AI context, the most prevalent use-case of low-data learning would be for a customer to adapt a pretrained ‘out-of-the-box’ model to their own business context, using their own datasets while minimizing labeling effort.”

Another promising trend in AI research involves training machines to write software in response to simple voice commands. “This has been a holy grail for a long time,” says Pal. “Coming up with a programming language so flexible that it allows you to program even if you don’t know the programming language.”

It is not hard to imagine a future where almost anyone will be able to execute rudimentary programming tasks using natural language and the spoken word. The ATG team has developed an experimental model called PICARD, inspired by the well-known Star Trek character, that enables this scenario. (PICARD is an acronym for “Parsing Incrementally for Constrained Auto-Regressive Decoding.”)

“Let’s say you want to ask how often a delivery is late,” says Pal. “Instead of writing the code, you can simply say it, and the model translates from text to SQL. Or you’ll be able to say ‘Send me an alert any time I get a sale of this kind of product,’ and that’s it.”

Nicolas Chapados, ATG’s vice president of research, says his group is extending these language models into so-called foundation models, which “are good not just at representing texts, but also at capturing the relationship between related modalities such as text and images.” When a researcher inputs a text description of an image, for example, the model may have the ability to generate that image.

“The kinds of things that people once thought AI should do—really get the nuances of language—are becoming possible,” Chapados says.

As AI gets smarter and faster, researchers are using it to help humans make better decisions more quickly. The next step is human decision support, which involves using AI to augment our ability to make strategic decisions and forecast their long-term impact.

As organizations take in more data, decision-making grows increasingly complex. Researchers are building algorithms that can parse large volumes of data that are constantly evolving—something even the smartest human experts can’t do. AI models can then plot the likelihood an event will cause a second event.

For example, ATG researchers are building AI that can look at an IT environment, predict whether service issues might occur and determine a possible cause. The model will either flag the IT team to address the issue or automatically fix the issue itself.