Sentences with phrase «kinds of machine learning»

Instead, he said that the new chip, in conjunction with other chips, can be used for very specific kinds of machine learning techniques, although he didn't elaborate further.

What kind of machine learning are you using?

There's supposed to be another lower - powered computer in the goggles, which handles world detection and includes some kind of machine learning capabilities.

SharpestMinds aims to create a new kind of machine learning - driven service that can identify tech talent early, get them in the door at startups and eventually lead them to a potential hire for a problem they're interested in — and not just the appeal of the massive scale that Google and others can offer.

The major commercial application of this kind of machine learning is advertising, «and Google's got that locked up,» he told the E-Commerce Times.

There's no doubt Huawei will be talking up the AI potential of the new P20, P20 Plus and P20 Lite too — the Kirin 970 is specially engineered to better deal with the kind of machine learning tasks required for mobile AI.

Not exact matches

AI and machine learning is starting to synchronize with CRM software to create new kinds of customer relationships.

The enterprise chatbot - building solution uses machine learning technology to develop data - driven applications for businesses of all kinds.

Video processing, machine learning algorithms, and networking are all places where big companies use FPGAs to their advantage, but what happens if smaller hobbyists start taking that kind of customizable processing power into their garages?

The rebranding was a kind of pledge of sorts, that Tribune would catch up with current technology and start using machine learning and artificial intelligence in its «content monetization engine.»

The gym is also equipped with a non-traditional kind of scoreboard, which will showcase developers who come up with the best learning techniques for the machines.

We already know the kinds of things that are possible when machine learning is combined with large amounts of high quality medical data.

Equally if not more important, scientists are using the classifications made by Zooniverse participants to develop more accurate machine - learning algorithms so that computers will be able to do this kind of work in the future.See for yourself: zooniverse.org

Even app developers who don't understand the inner workings of machine - learning algorithms can easily get this kind of code online to build sensor - sniffing programs.

For a machine - learning algorithm that exhibits this kind of discrimination, Hardt's team suggested switching some of the program's past decisions until each demographic gets erroneous outputs at the same rate.

People kind of take that for granted, but the oil industry didn't have the magic of big data, machine - learning type stuff in the past, and now they do.

In the future, studies on brain decoding and machine learning will create possibilities of communication regardless any kind of written or spoken language.

And so, hardware, not only begets the capability to create new kinds of software like machine learning, but also is creating new ways to sense, measure and control the world.

Just getting started While other groups have used machine learning to come up with predictions about where different kinds of metallic glass can be found, Mehta said, «The unique thing we have done is to rapidly verify our predictions with experimental measurements and then repeatedly cycle the results back into the next round of machine learning and experiments.»

The Once dating app is the most disruptive app since Tinder, revolutionizing the online dating space by offering a novel value proposition: quality over quantity with advanced machine learning that provides users with one special match per day and a first - of - its - kind rating system that allows women to rate men following a date.

The first few times you fight a new machine will involve a steep learning curve, and failure remains a very real possibility even after you feel like you've mastered a particular kind of foe.

Global Fishing Watch runs this information — more than 22 million points of information per day — through machine learning classifiers to determine the type of ship (e.g., cargo, tug, sail, fishing), what kind of fishing gear (longline, purse seine, trawl) they're using and where they're fishing based on their movement patterns.

That's a kind of fundamental principle of all of mathematics, physics, machine learning, risk assessment.

And while thinking about all of this a colleague * was kind enough to send around a link to a recent post by Brian Sheppard over on the Legal Rebels blog called, «Does machine - learning - powered software make good research decisions?

While AI is in effect «language agnostic», i.e. a machine learning system can be taught to look for patterns in any kind of text, those training it naturally have to be proficient and know what they are looking for.

Artificial Lawyer caught up with the Toronto - based founders and asked them about their new creation and what kind of impact using NLP and machine learning in this way would have on global compliance.

There are literally dozens of components that go into AI systems — from computer vision and machine learning systems to process the world and decide what to do, through to the hardware mechanics to execute those decisions — and Boston Dynamics is one of the few synthesizing all of them in one place successfully, which begs all kinds of questions about what these robots could do in a few years» time, versus what they should be doing.

Researchers across academia and industry have applied called machine learning techniques — a method of computer programming that allows the programme to change when exposed to new data — to train models that can zero in on a person's playstyle, predict what the player will do in the future, and the kinds of problems that might hinder the player from enjoying the game.

But even beyond the porn angle, the machine learning technology that makes these kind of manipulated videos work will only grow more sophisticated over time, making it even harder for internet - goers to determine what's real and what's fabricated.

The concept of «smart home» is bolstered (or shoved down our throat) by all the tech giants by introducing all different kind of personal home gadgets that can act as your personal chef, keep your home secure, replace a nanny, search for information online, play your favorite tunes, and learn over time through machine learning.

When highlighting text, Google's machine learning figures out what kind of data you've selected and offers relevant contextual options — for example, a shortcut to the Dialer app for phone numbers, or Google Maps for addresses.

The endless streams of data generated by applications lends its name to this paradigm, but also brings some hard to deal with requirements to the table: How do you deal with querying semantics and implementation when your data is not finite, what kind of processing can you do on such data, and how do you combine it with data from other sources or feed it to your machine learning pipelines, and do this at production scale?

Phones don't strictly need these kinds of specialized chips to handle machine learning.

What was at first a bizarre string of content cards has expanded to become a voice - and text - friendly assistant, the kind that tells you things it thinks, based on machine learning, you need to know.

Google CEO Sundar Pichai has announced Google Lens, a new kind of smart camera technology that combines Google's machine learning with what you're pointing your smartphone at.

This kind of power is crucial when you are the company that is powering incredible new machine learning, AR apps and immersive 3D games, and making them the norm for everyone, rather than the company that releases expensive models that no - one buys and waits for Apple to innovate, take the future, and widely distribute it.

Plus, it's also coming from a position of relative weakness in the market, in terms of both the maturity of AI and machine learning prowess that's necessary for this kind of smart camera feature to work well, as well as simply shipping devices.