Sentences with phrase «learning algorithms»

Legalist will then use machine learning algorithms to mine the data for novel insights on how a lawyer compares.

«The knowledge graph grows and relationships are created over time as additional content is processed by our machine learning algorithms,» Pfeifer said.

Machine Learning algorithms have also successfully discovered certain rules that are being used to combine or omit certain clauses in legal documents.

In other words, nobody had to look for or enter the relevant data as the Machine Learning algorithms were able to do it.

And, as mentioned many times on this screen, technology, and specifically technologies that employ machine learning algorithms, are changing the way we do everything, including the practice of law.

Its technology combines machine and deep - learning algorithms, text analytics, and expert knowledge to enhance contract reviews.

Katherine Bourzac writes, «machine learning algorithms can more quickly identify and cluster the debris that comets leave in their wake.

Lawfty is a unique combination of law firm and technology company that partners with personal injury attorneys throughout the country, utilizing machine learning algorithms to identify and attract potential clients.

This applies to all our topic modeling and machine learning algorithms.

We can not blindly rah rah our way toward an AI future without taking a close look at the processes that often manifest themselves to us as a «black box» full of machine learning algorithms.

This is essentially what Lexum is currently working on in an alliance with MILA (Montreal Institute for Learning Algorithms).

The second makes use of large statistical datasets that can be easier and cheaper to access, and applies processing power in the form of machine learning algorithms to detect anomalies and determine when failures might occur.

Rachel Buxton, a post-doctoral researcher at Colorado State University's Warner College of Natural Resources and lead author of the study, led a team of researchers that recorded sounds at 492 sites across the country in order to quantify the extent of noise pollution in the U.S. Using baseline sound levels for each study area established by machine learning algorithms that took into account geospatial features of the area, the researchers determined that anthropogenic noise pollution exceeds three decibels (dB), essentially doubling background sound levels, in 63 percent of the nation's protected areas.

Challenges to this approach include the need for innovation in machine learning algorithms and balancing the computational cost of high - resolution simulations with the value of the information they provide.

I've recently been reading David MacKay's 2003 book, Information Theory, Inference, and Learning Algorithms.

Scientific, computational, and mathematical challenges need to be confronted to realize such an ESM, for example, developing parameterizations suitable for automated learning, and learning algorithms suitable for ESMs.

They cunningly manipulate the masses with the use of self - learning algorithms and streamlined filter bubbles.

Uniquely, games and channels are tagged according to their genres and publishers, while content is flagged as esports using machine - learning algorithms.

Vetrax technology is powered by proprietary machine learning algorithms that process the data collected by the sensor to deliver actionable insights for veterinarians» treatment protocols.

It aims to bring superior returns at low risk by combining cloud technologies with machine - learning algorithms.

The idea behind the browser is impressive enough to be worth bragging about, but the fact that the eventual results rely on Amazon's machine learning algorithms means that it would inevitably take time to get the best out of it.

It's an Alexa - enabled device equipped with a depth - sensing camera and LED lighting that will take pictures of your outfit, via voice command, and offer fashion advice through style - trained machine learning algorithms.

Finally, Silk leverages the collaborative filtering techniques and machine learning algorithms Amazon has built over the last 15 years to power features such as «customers who bought this also bought...» As Silk serves up millions of page views every day, it learns more about the individual sites it renders and where users go next.

Effectively applying AI involves extensive manual effort to develop and tune many different types of machine learning and deep learning algorithms (e.g. automatic speech recognition, natural language understanding, image classification), collect and clean the training data, and train and tune the machine learning models.

«By combining our traditional published content and databases with machine learning algorithms, we can support chemists in innovative research as they advance world knowledge.»

This is another case arguing for the necessity of modern machine learning algorithms to be transparent and accountable at each computational, decision making step, especially when the results were impactful to life - changing human decision making.

IADLearning uses machine learning algorithms and big data analysis techniques to create content recommendations and to derive predictive analytics.

Machine learning algorithms, when combined with the contextual knowledge of researchers and practitioners, offer service providers nuanced estimates of risk and opportunities to refine their efforts.

Then figure out which operations can be managed by Machine Learning algorithms in the future.

Using machine learning algorithms, we're able to adapt the user experience based on prior skill and behavior within the app, creating a tailored curriculum.

Not only pre-processing tools, but the machine learning products also offer a large number of machine learning algorithms as well.

No matter how sophisticated our adaptive learning algorithms and software become, however, blending online and face - to - face instruction does not directly address the other ways in which the field seems to conceive of «personalized» learning.

Luna is a blockchain dating platform that uses machine learning algorithms for advanced sorting.

But as machine learning algorithms become more accurate and accessible than ever, dating companies will be able to learn more precisely who we are and who we «should» go on dates with.

According to researchers at Bar - Ilan University in Israel led by Prof. Ido Kanter, the theory promises to transform our understanding of brain dysfunction and may lead to advanced, faster, deep - learning algorithms.

Researchers at Google, Harvard University, and Gladstone Institutes have developed and tested new deep - learning algorithms that can identify details in terabytes of bioimages, replacing slow, less - accurate manual labeling methods.

Because of the lab's speed and volume, Hodak says that some companies are tracking their experimental runs with machine learning algorithms, which then analyze the results and churn out suggestions for the next set.

Bian Li passed his defense with the title «Structure prediction and variant interpretation of membrane proteins aided by machine learning algorithms»

The phrase «intelligent interactive systems» describes many of Gajos's interests: understanding how intelligent technologies can enable novel ways of interacting with computation and in the new challenges that human abilities, limitations and preferences create for machine learning algorithms embedded in interactive systems.

Challenges to this approach include the need for innovation in machine learning algorithms and balancing the computational cost of high - resolution simulations with the value of the information they provide.

The implicit independent feature assumption in most classifier learning algorithms is thus violated.

In a first for machine - learning algorithms, a new piece of software developed at Caltech can predict behavior of bacteria by reading the content of a gene.

His research interests include brain decoding, learning algorithms for diffusion MRI data, joint analysis of multiple neuroimaging data sources, active learning and Bayesian inference.

Lawrence Livermore National Laboratory researchers Priyadip Ray (left) and Brenden Petersen and their teams, using machine learning algorithms, have developed computer models that can more accurately characterize a patient's progression through stages of sepsis and better predict mortality risk by integrating past medical history, real - time vital signs and other diagnostics.

Founded by data scientists, clinicians, and microbiologists from MIT and OpenBiome, Finch uses machine - learning algorithms informed by high - throughput molecular data to reverse engineer successful experiences with fecal transplantation.

While the models haven't been used clinically yet, researchers said the machine learning algorithms have the potential to substantially improve diagnostics and triaging, resulting in improved treatments for sepsis, which kills at least 250,000 Americans each year, according to the Centers for Disease Control and Prevention (CDC).

«Our latest work is unique insofar as it identifies concept alterations that are associated with suicidal ideation and behavior, using machine - learning algorithms to assess the neural representation of specific concepts related to suicide,» Just said.

He compares PassGAN to AlphaGo, the Google DeepMind program that recently beat a human champion at the board game Go using deep learning algorithms.

In a follow - up study, the researchers were able to examine the word associations of a group of 324 participants and, by using machine - learning algorithms, predict their political leanings and what party they belonged to, as well as which candidate they were likely to vote for in the presidential elections.

It is an extension of TAMER that uses deep learning — a class of machine learning algorithms that are loosely inspired by the brain to provide a robot the ability to learn how to perform tasks by viewing video streams in a short amount of time with a human trainer.