IoT and big data brought together in commercial use cases

Google CEO Sundar Pichai last week lit up his first earnings call with accounts of a solid third quarter and machine learning dreams that could permeate all the company's products.

The Q3 2015 report was the first in which Google reported earnings through its recently formed parent company, Alphabet.

As the call drew to a close, investors asked Pichai and Ruth Porat, CFO of Google and Alphabet, about the company's vision for machine learning.

Google's investments in artificial intelligence and machine learning are a priority, Pichai said.

The company has been rethinking everything it has been doing, and doing so through the transformative field of machine learning, he said. Google has been investing in machine learning for a while and believes it is "state of the art" in that area.

"The progress, particularly in the last two years, has been pretty dramatic," Pichai said. "We're thoughtfully applying it across all of our products, be it search, be it ads, be it YouTube and Play. We are in the early days, but you'll see it in a systematic manner think about how we can apply machine learning all these areas."

Machine Vision

One of Google's most publicized uses of machine learning has been in developing machine vision for its driverless cars.

The company also put neck hairs on end when it showed trippy pictures of what the dreams of its AI looked like.

It's difficult to think of areas where machine learning wouldn't be valuable, according to Charles King, principal analyst for Pund-IT.

"In essence, what machine learning provides are ways for computerized processes to learn as they go, thus improving performance over time and through experience," he told TechNewsWorld.

"As a result, ML could be applied in virtually any industrial situation, from enhancing the time and results of product design processes to monitoring and maintaining complex machines," King said.

It's easy to imagine Google leveraging machine learning to bolster everything from ad placement and their results to driverless cars and their performance, he said.

While promising, machine learning isn't without serious challenges. It tends to require "hugely complex compute resources" and, occasionally, the ability to respond in real time with virtually zero room for error, King said.

"That's at the leading edge of where computing is today," he said. "You could say that Google is well acquainted with that territory, but it's a place where the price of failure tends to be very high."

Conversely, the rewards for success are so lucrative that it's natural for Google and others to step up their explorations into machine learning and artificial intelligence, King stated. "For the winners, the results will be similar to what those who struck it rich during the California gold rush experienced."

Google Plus

Q3 earnings were brimming with positive details, Google and Alphabet reported. Google racked in $18.7 billion in revenues during its third quarter, about a 20 percent increase year over year.

The pluses extended from the top of the accounting sheets to the very bottom and, according to industry analyst Jeff Kagan.

"Google continues to look golden," he told TechNewsWorld. "This is a large and growing company. It's very complex to stay on top of. However, the bottom-line numbers continue to took terrific at this time."