Sentences with phrase «training algorithm uses»

DNAFit's unique, peer - reviewed genetic training algorithm uses genetic insights to bring your fitness goals into focus, whether you're at the gym or at home.

Although firms like Google and Uber are teaching their software by physically driving millions of miles in the real world, they also train their algorithms using pre-recorded footage of traffic.

Nakatsuji and his team trained their algorithm using almost 190,000 questions and 770,000 answers from the Oshiete goo forum.

Alarie: use what the courts say to check whether the algorithm is right; train the algorithm using 70 % of the data then use the remaining 30 % to see if algorithm can predict what the court decided; achieving over 90 % success rate; not making any «normative claims» about how things should be decided; rather predicting what the courts are likely to say

Not exact matches

IBM is using machine learning algorithms to train robots to better associate appropriate gestures and tones with phrases.

Similarly, Carbon3D uses algorithms to help train its 3D printers to make better prints, said DeSimone.

Strategy: Trained on 70,000 10 - K reports, our A.I. algorithm uses natural language processing to detect semantic fields around initial keywords.

Restless Bandit, named for a problem in decision theory, has aggregated more than 100 million job descriptions and 30 million resumes to train its algorithms, which are now being used by such clients as Gannett, Adidas, Applebee's, and comScore.

The resulting dataset was then used to train a classifier algorithm that gives any headline posted on Facebook a «clickbait» score based on patterns.

Facebook used this collection to train its machine learning algorithms.

The project is detailed in the contract as a seven step process — with Kogan's company, GSR, generating an initial seed sample (though it does not specify how large this is here) using «online panels»; analyzing this seed training data using its own «psychometric inventories» to try to determine personality categories; the next step is Kogan's personality quiz app being deployed on Facebook to gather the full dataset from respondents and also to scrape a subset of data from their Facebook friends (here it notes: «upon consent of the respondent, the GS Technology scrapes and retains the respondent's Facebook profile and a quantity of data on that respondent's Facebook friends»); step 4 involves the psychometric data from the seed sample, plus the Facebook profile data and friend data all being run through proprietary modeling algorithms — which the contract specifies are based on using Facebook likes to predict personality scores, with the stated aim of predicting the «psychological, dispositional and / or attitudinal facets of each Facebook record»; this then generates a series of scores per Facebook profile; step 6 is to match these psychometrically scored profiles with voter record data held by SCL — with the goal of matching (and thus scoring) at least 2M voter records for targeting voters across the 11 states; the final step is for matched records to be returned to SCL, which would then be in a position to craft messages to voters based on their modeled psychometric scores.

No doubt, it is a dismaying picture that confronts us: British company SCL Group, operating under the brand name Cambridge Analytica with the supervision of Steve Bannon, obtained data collected from Facebook by Cambridge University academic Alexandr Kogan, and used systems built by data scientist and whistleblower - to - be Chris Wylie to train its microtargeting algorithms to nudge scores of already - angry voters towards electing Donald Trump and leaving the European Union — a set of experiments largely bankrolled by US hedge - fund billionaire Robert Mercer, 90 % owner of Cambridge Analytica.

The feature could be seen as Facebook being transparent in how it is training its algorithms to detect misleading headlines, but is also an example of using its user base as, essentially, a rich well of free data into which it can dip its bucket any time it wants and on its own terms.

This subsample, with no particular statistical property, represents a training set that will be used by the second step of the algorithm to classify all the unread documents (the test set).

No doubt, it is a dismaying picture that confronts us: British company SCL Group, operating under the brand name Cambridge Analytica with the supervision of Steve Bannon, obtained data collected from Facebook by Cambridge University academic Alexandr Kogan, and used systems built by data scientist and whistleblower - to - be Chris Wylie to train its microtargeting algorithms to nudge scores of already - angry voters towards electing Donald Trump and leaving the European Union — a set of experiments largely bankrolled by US hedge - fund billionaire Robert Mercer, 90 % owner of Cambridge Analytica.

Companies trawl the web to gather billions of images and use them to train an algorithm inspired by neurons in the brain, called a deep neural network.

The team trained an algorithm solely using GTA V and tested it against the same algorithm trained on real - world images.

Zwicker and his colleagues can «train» their algorithm by exposing it to a large database of high - quality, uncorrupted images widely used for research with artificial neural networks.

The team had shown that an algorithm could be trained to recognise the intention to move both hands, but the next step was to see whether they could train the algorithm without the monkey initially using its own arms.

Like the Rosetta Stone that scholars used to decode hieroglyphics, researchers trained the algorithm with more than 4,600 T cell receptors and then used it to correctly assign 81 percent of the human T cells and 78 percent of mouse T cells to one of 10 different viral epitopes.

This part of the experiment is crucial, because it demonstrates that the algorithm can be trained using thoughts alone — a vital step if people without the use of their limbs are to use the technology.

Krishna Shenoy, the Lim professor of electrical engineering, bioengineering and neurobiology at Stanford and an investigator of the Howard Hughes Medical Institute, noted, «This substantial reduction in training time is remarkable as it requires the new algorithm to make very efficient use of neural data coming in for so little time, under a minute in some cases, and helps point the way toward further advances of real - world importance.»

Using a high - throughput data set, they trained a random forest algorithm to predict which specific palladium catalysts would best tolerate isoxazoles (cyclic structures with an N — O bond) during C — N bond formation.

The researchers asked users on Amazon's Mechanical Turk crowdsourcing site to generate possible help messages for different scenarios, which were then used to train a new algorithm.

These games are used to train the algorithm that guides Tell Me Dave, so when it's later faced with commands like «boil some water» or «cook the ramen» in a real kitchen, it can come up with the appropriate actions.

An algorithm trained on real streets can create realistic - looking imaginary ones, and could one day be used to generate highly realistic video game worlds

Typically, scientists use a set of manually validated cell images to «train» the software's learning algorithms on the desired morphological traits.

Using a training set of images of people from the Web, the machine learning algorithms mastered identifying a human figure and nine anatomical sections, such as torso, upper left arm or lower right leg.

To try to remove bias, researchers altered the training data used to teach three algorithms: an income predictor, a credit scorer and a judge of promotion - worthiness.

Machine learning is the process by which software developers train an AI algorithm, using massive amounts of data relevant to the task at hand.

These ratings were then used to train a machine - learning algorithm to extract a single score from the measured values that would faithfully reflect the perceptual judgement of the volunteers.

The plan is to find out if Microsoft's gaming sensor, combined with computer - vision algorithms trained to detect behavioural abnormalities, can be used to automate the early diagnosis of autism.

Verum then used these classifications to train an algorithm to autonomously recognize each kind of passage in papers outside this sample group.

«To go beyond this we use modern machine - learning methods where you don't necessarily know how a computer has made a decision about a particular sound, but by training it, which means showing it lots of previous examples, we can encourage a computer algorithm to generalise from those.»

The algorithm uses a type of classifier known as Random Forests, which uses 21 different features to detect bots, and the classifier itself is trained by the original dataset annotated by the human annotators.

Relevant calls, like the subtle grunt of the critically endangered coquí llanero (Eleutherodactylus juanariveroi, pictured), are singled out using a computer algorithm, which scientists can train to identify any species they choose.

With minimal training, health care providers can use a special camera to take a picture of the back of the patient's retina, which an algorithm then analyzes to look for the disease.

To determine spectral indices, we use a machine algorithm that allows to train a model representing flux at each wavelength as a function of exoplanet parameters.

FaceDate users are able to train the mobile app by uploading photos of faces they find attractive, and the app will provide matches, using a face recognition algorithm.

These guys have used these new machine learning techniques to train algorithms to recognize the artist and style of a fine - art painting with an accuracy that has never been achieved before.

The use of TAR have been accepted by courts in various jurisdictions - firstly with several US cases, then in early 2016 in the UK with the High Court Phrrho Investments Ltd v MWB Property LTD case [2016] EWHC 256 (Cth), and finally in December 2016 in Australia in a decision of the Supreme Court of Victoria (McConnell Dowell Constructors (Aust) Pty Ltd v Santam Ltd & Ors (No 1)[2016] VSC 734), and orders in a Federal Court of Australia matter (Money Max Int v QBE Insurance, VID513 / 2015) relating to the TAR algorithms used and methodology in the training and validation.

An iterative method that uses completely random document samples to train the machine learning algorithm until the desired result is achieved.

«Our goal is to help solve this problem now by using human beings — trained, experienced journalists — who will operate under a transparent, accountable process to apply basic common sense to a growing scourge that clearly can not be solved by algorithms,» explained Brill, the author of two best - selling books, who has won multiple National Magazine awards and, among other journalism enterprises, founded The American Lawyer, Court TV, and Brill's Content magazine.

The «truth» the authors ** are exploring in their research relates to the quality of the «gold standards» used to train, test and measure the success of machine learning algorithms.

However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front - end automation right now.

This is the lamest algorithm you can use, trained on a small sample with small resolution with off - the - shelf tools that are actually not made for what we are asking them to do.

Project Road Runner, an initiative by the Microsoft Garage division, intends to train algorithms that are used for autonomous driving cars by using real - world simulation and deep learning.

Using computer vision, Echo Look can help you decide what shirt or dress to wear by taking a picture and running it through style - trained machine learning algorithms.

Once a user grants such access, algorithms trawl through likes and posts to train statistical models that use such «digital footprints» to predict personality types.

Using the same parameters as in the paper, he ran his own quick experiment, and found that the VGG - Face algorithm trained on pictures of people making a «sad» face could identify the emotion «sad» elsewhere.