Startup Sentent can link one million processors from excess data center capacity for deep learning artificial intelligence

A private company called Sentient with only about 70 employees, says it can cheaply assemble even larger computing systems to power artificial-intelligence software.

Sentient’s power comes from linking up hundreds of thousands of computers over the Internet to work together as if they were a single machine. The company won’t say exactly where all the machines it taps into are. But many are idle inside data centers, the warehouse-like facilities that power Internet services such as websites and mobile apps, says Babak Hodjat, cofounder and chief scientist at Sentient. The company pays a data-center operator to make use of its spare machines.

Data centers often have significant numbers of idle machines because they are built to handle surges in demand, such as a rush of sales on Black Friday. Sentient has created software that connects machines in different places over the Internet and puts them to work running machine-learning software as if they were one very powerful computer. That software is designed to keep data encrypted as much as possible so that what Sentient is working on–perhaps for a client–is kept confidential.

Sentient can get up to one million processor cores working together on the same problem for months at a time, says Adam Beberg, principal architect for distributed computing at the company. Google’s biggest machine-learning systems don’t reach that scale, he says.

Sentient was founded in 2007 and has received over $140 million in investment funding, with just over $100 million of that received late last year. The company has so far focused on using its technology to power a machine-learning technique known as evolutionary algorithms. That involves “breeding” a solution to a problem from an initial population of many slightly different algorithms. The best performers of the first generation are used to form the basis of the next, and over successive generations the solutions get better and better.

Sentient currently earns some revenue from operating financial-trading algorithms created by running its evolutionary process for months at a time on hundreds of thousands of processors. But the company now plans to use its infrastructure to offer services targeted at industries such as health care or online commerce, says Hodjat. Companies in those industries would theoretically pay Sentient for those products.

An interview with Babak Hodjat, Co-founder, Chief Scientist and Nigel Duffy, Chief Technology Officer.

Q. What is new about Sentient’s approach to scaling its AI?
A (Principal Architect, Distributed Computing, Adam Beberg). If you ask a cloud provider for a million cores, they will laugh, then respond with a date a few years from now when those cores might be available. What we’re doing is harvesting idle cycles wherever they are, to solve problems with AI at a scale unheard of in the industry. AI is a new frontier and the problems we’re solving to make a system like this work are really difficult to solve. We’re having to rethink and re-imagine how distributed systems work from the ground up to enable the AI and that’s a fun problem to solve.

Q. How big is Sentient’s system and to how many cores can it scale to?
A. We’re designing the next version of our system to scale over millions of cores coordinated across different geographic regions, for months at a time. It’s also designed to handle general computation, not just the types of work needed for the AI.

Q. What types of challenges did Sentient have building this system and how were they overcome?
A. The biggest technical challenges are optimizing data movement across different geographic locations, security and data privacy issues, and making the entire system operate in the most efficient way possible by moving the computation to the data. These are problems that don’t exist when running at a cloud provider or a dedicated data center, but solving them allows us to work at unprecedented scale.

SOURCES – Sentient, Technology Review, Youtube