Who couldn’t use some time-lapsing, truth-seeing artificial intelligence models at work? Such models analyze whole object-storage buckets full of text or images. They answer difficult queries in minutes, sparing users weeks or months of tedious work. Sign me up.
The trouble is, not all companies have a dream team of data-science Ph.D.s on hand to tailor custom AI solutions. Vendors are increasingly promising instant insight via applications with baked-in AI. But the latter might not be as spot-on as models custom-built by in-house experts.
Is there any middle ground offering customization with a barrier to entry that data laymen can clear? There is — if users can manage just a smidgen of coding.
DimensionalMechanics Inc. allows customers to build highly sophisticated AI models on its platform. It has its own built-in AI, dubbed “the oracle” — DimensionalMechanics is filled with fervid fans of The Matrix. The oracle guides users as they build their models.
“It has a vast knowledge base,” said Rajeev Dutt (pictured), co-founder, president and chief executive officer of DimensionalMechanics. “It has a lot of additional machine-learning components and things like that that essentially allow it to adapt and learn based on the kind of problem you’re trying to solve.”
The word “language” can instantly scare off people with zero machine-learning or coding experience. But DimensionalMechanics’s NeoPulse Modelling Language, or NML, is nothing they’ll need to go back to college for, according to Dutt.
He has seen people with no tech chops, like university professors, build AI models with NML. Sometimes they get the job done with as few as 14 lines of code. “We had a high school student who spent about a week learning it,” he said. “A week later, she was ready to start coding, and she had built her first models using that.”
Dutt spoke with Jeff Frick, in an interview Media’s mobile live streaming studio, during the AWS Marketplace and Service Catalog Experience Hub event in Las Vegas. They discussed how businesses are using DimensionalMechanics’s platform to build accurate AI models more cheaply.
This week, the report spotlights DimensionalMechanics in its Startup of the Week feature.
The big data trend that exploded in the industry several years ago left many disappointed. The software that vendors and open-source communities brought out didn’t yield jaw-dropping gains for the majority of users. Today, 85 percent of big data projects in enterprises fail, according to Gartner analyst Nick Heudecker.
So why do enterprises keep hacking away at such projects? Because the potential of big data to improve all kinds of business outcomes is clear. Now the industry at least has a better picture of what successful big data projects look like. It’s not just a data lake; it’s not a collection of halfway-decent analytics tools. The endgame of big data is AI — real, instant answers to questions via trained models and appropriate actions triggered by data, information and events.
We’re now seeing technologies bring users closer to the AI endgame right out of the gate.
“One of the most disappointing things to me in our industry is that most of the AI projects have boiled down to a chatbot,” Matt Lancaster, tech architecture science associate director at Accenture LLP, recently told in a report. “They can’t actually do anything.”
Event-driven architectures that react to single events rather than complex sequences can deliver AI for better end-user experiences, according to Lancaster.
Solutions that allow users to attach AI to applications like Lego blocks are also surfacing. Amazon Web Services Inc. made a number of announcements around easy machine learning and AI add-ons at re: Invent in Las Vegas in November.
“You now have a simple sort of application which can even do the some of the basic build these modern AI-powered apps,” Jerry Chen, a partner at Greylock Partners, recently told in a report. “In the future, can you build a [software as a service] application entirely on Amazon, Azure or Google without any custom code?”
Perhaps. For now, some DimensionalMechanics users are building highly accurate AI models for business use cases. The way they hug the curves of their unique problem and data make a dash of coding easily worth the effort.