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Were long past the point of questioning whether machines can learn. The question now is how do they learn? Machine learning, a subset of artificial intelligence, is the study of computer algorithms that improve automatically through experience. That means a machine can learn, independent of human programming. Los Alamos National Laboratory staff scientist Nga Thi Thuy Nguyen-Fotiadis is an expert on machine learning, and at 5:30 p.m. on Monday, Dec. 14, she hosts the virtual presentation Deep focus: Techniques for image recognition in machine learning, as part of the Bradbury Science Museums (1350 Central Ave., Los Alamos, 505-667-4444, lanl.gov/museum) Science on Tap lecture series. Nguyen-Fotiadis is a member of LANLs Information Sciences Group, whose Computer, Computational, and Statistical Sciences division studies fields that are central to scientific discovery and innovation. Learn about the differences between LANLs Trinity supercomputer and the human brain, and how algorithms determine recommendations for your nightly viewing pleasure on Netflix and the like. The talk is a free WebEx virtual event. Follow the link from the Bradburys event page at lanl.gov/museum/events/calendar/2020/12 /calendar-sot-nguyen-fotaidis.php to register.

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Information gathering: A WebEx talk on machine learning - Santa Fe New Mexican

SANTA CLARA, Calif.--(BUSINESS WIRE)--LeanTaaS, Inc., a Silicon Valley software innovator that increases patient access and transforms operational performance for healthcare providers, announced a $130 million Series D funding round led by Insight Partners with participation from Goldman Sachs. The funds will be used to invest in building out the existing suite of products (iQueue for Operating Rooms, iQueue for Infusion Centers and iQueue for Inpatient Beds) as well as scaling the engineering, product and GoToMarket teams, and expanding the iQueue platform to include new products.

LeanTaaS is uniquely positioned to help hospitals and health systems across the country face the mounting operational and financial pressures exacerbated by the coronavirus. This funding will allow us to continue to grow and expand our impact while helping healthcare organizations deliver better care at a lower cost, said Mohan Giridharadas, founder and CEO of LeanTaaS. Our company momentum over the past several years - including greater than 50% revenue growth in 2020 and negative churn despite a difficult macro environment - reflects the increasing demand for scalable predictive analytics solutions that optimize how health systems increase operational utilization and efficiency. It also highlights how weve been able to develop and maintain deep partnerships with 100+ health systems and 300+ hospitals in order to keep them resilient and agile in the face of uncertain demand and supply conditions.

With this investment, LeanTaaS has raised more than $250 million in aggregate, including more than $150 million from Insight Partners. As part of the transaction, Insight Partners Jeff Horing and Jon Rosenbaum and Goldman Sachs Antoine Munfa will join LeanTaaS Board of Directors.

Healthcare operations in the U.S. are increasingly complex and under immense pressure to innovate; this has only been exacerbated by the prioritization of unique demands from the current pandemic, said Jeff Horing, co-founder and Managing Director at Insight Partners. Even under these unprecedented circumstances, LeanTaaS has demonstrated the effectiveness of its ML-driven platform in optimizing how hospitals and health systems manage expensive, scarce resources like infusion center chairs, operating rooms, and inpatient beds. After leading the companys Series B and C rounds, we have formed a deep partnership with Mohan and team. We look forward to continuing to help LeanTaaS scale its market presence and customer impact.

Although health systems across the country have invested in cutting-edge medical equipment and infrastructure, they cannot maximize the use of such assets and increase operational efficiencies to improve their bottom lines with human based scheduling or unsophisticated tools. LeanTaaS develops specialized software that increases patient access to medical care by optimizing how health systems schedule and allocate the use of expensive, constrained resources. By using LeanTaaS product solutions, healthcare systems can harness the power of sophisticated, AI/ML-driven software to improve operational efficiencies, increase access, and reduce costs.

We continue to be impressed by the LeanTaaS team. As hospitals and health systems begin to look toward a post-COVID-19 world, the agility and resilience LeanTaaS solutions provide will be key to restoring and growing their operations, said Antoine Munfa, Managing Director of Goldman Sachs Growth.

LeanTaaS solutions have now been deployed in more than 300 hospitals across the U.S., including five of the 10 largest health networks and 12 of the top 20 hospitals in the U.S. according to U.S. News & World Report. These hospitals use the iQueue platform to optimize capacity utilization in infusion centers, operating rooms, and inpatient beds. iQueue for Infusion Centers is used by 7,500+ chairs across 300+ infusion centers including 70 percent of the National Comprehensive Cancer Network and more than 50 percent of National Cancer Institute hospitals. iQueue for Operating Rooms is used by more than 1,750 ORs across 34 health systems to perform more surgical cases during business hours, increase competitiveness in the marketplace, and improve the patient experience.

I am excited about LeanTaaS' continued growth and market validation. As healthcare moves into the digital age, iQueue overcomes the inherent deficiencies in capacity planning and optimization found in EHRs. We are very excited to partner with LeanTaaS and implement iQueue for Operating Rooms, said Dr. Rob Ferguson, System Medical Director, Surgical Operations, Intermountain Healthcare.

Concurrent with the funding, LeanTaaS announced that Niloy Sanyal, the former CMO at Omnicell and GE Digital, would be joining as its new Chief Marketing Officer. Also, Sanjeev Agrawal has been designated as LeanTaaS Chief Operating Officer in addition to his current role as the President. "We are excited to welcome Niloy to LeanTaaS. His breadth and depth of experience will help us accelerate our growth as the industry evolves to a more data driven way of making decisions," said Agrawal.

About LeanTaaSLeanTaaS provides software solutions that combine lean principles, predictive analytics, and machine learning to transform hospital and infusion center operations. The companys software is being used by over 100 health systems across the nation which all rely on the iQueue cloud-based solutions to increase patient access, decrease wait times, reduce healthcare delivery costs, and improve revenue. LeanTaaS is based in Santa Clara, California, and Charlotte, North Carolina. For more information about LeanTaaS, please visit https://leantaas.com/, and connect on Twitter, Facebook and LinkedIn.

About Insight PartnersInsight Partners is a leading global venture capital and private equity firm investing in high-growth technology and software ScaleUp companies that are driving transformative change in their industries. Founded in 1995, Insight Partners has invested in more than 400 companies worldwide and has raised through a series of funds more than $30 billion in capital commitments. Insights mission is to find, fund, and work successfully with visionary executives, providing them with practical, hands-on software expertise to foster long-term success. Across its people and its portfolio, Insight encourages a culture around a belief that ScaleUp companies and growth create opportunity for all. For more information on Insight and all its investments, visit insightpartners.com or follow us on Twitter @insightpartners.

About Goldman Sachs GrowthFounded in 1869, The Goldman Sachs Group, Inc. is a leading global investment banking, securities and investment management firm. Goldman Sachs Merchant Banking Division (MBD) is the primary center for the firms long-term principal investing activity. As part of MBD, Goldman Sachs Growth is the dedicated growth equity team within Goldman Sachs, with over 25 years of investing history, over $8 billion of assets under management, and 9 offices globally.

LeanTaaS and iQueue are trademarks of LeanTaaS. All other brand names and product names are trademarks or registered trademarks of their respective companies.

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LeanTaaS Raises $130 Million to Strengthen Its Machine Learning Software Platform to Continue Helping Hospitals Achieve Operational Excellence -...

NEW YORK--(BUSINESS WIRE)--Runway, a start-up building the next generation of digital creative tools, announced today an $8.5 million Series A led by Amplify Partners with participation from Lux Capital and Compound Ventures. Using machine learning, the company is pioneering image and video creation techniques for synthetic content manipulation and media editing, allowing users to create and generate content with cutting-edge AI and graphics technology. With an active and growing community, Runway has emerged as a leader in the future of content production.

Deep learning techniques are bringing a new paradigm to content creation with synthetic media and automation, Runway founder Cristobal Valenzuela explained. With Runway, were building the backbone of that creative revolution, allowing creators to do things that were impossible until very recently.

Today, Runways user community includes designers, filmmakers, and other creative professionals at R/GA, New Balance, Google, and IBM. A favorite among educators, Runway has been incorporated into the design curriculum at NYU, RISD, and MIT. So far, Runway users have trained more than 50,000 AI models, uploaded over 24 million files to the platform, and run more than 900,000 models.

By making sophisticated machine learning algorithms unimaginably accessible, Runway challenges a designers own visual and muscle memories, pushing them out of their comfort zones to create unexpected, innovative work, said Onur Yuce Gun, Creative Manager of Computation Design at New Balance.

Most recently, Runway released Green Screen, a web tool that uses machine learning to automate the process of rotoscoping, saving users significant time when they want to remove objects from a background. Professional editors and content creators are using Green Screen to edit content faster and expand the visual effects tools of their projects.

The ways in which we distribute content have changed radically in recent years; however, the tools that creative professionals use to make content have not, said Sarah Catanzaro, Partner at Amplify Partners. For the first time in decades, creatives now have a radically better suite of tools to generate and edit images, video, and other media with AI. Runway is not only automating routine work for creatives, but also enabling new forms of perception and creative expression. We are thrilled to partner with a talented team as they develop the next-gen creative toolkit.

The investment brings Runways total funding since launching to $10.5M and will help Runway to hire within their research and engineering teams as they continue building cutting-edge synthetic media tools while growing their community of creative users.

Valenzuela co-founded Runway with Anastasis Germanidis and Alejandro Matamala, all graduates of NYUs Interactive Telecommunications Program.

About Runway: Runway is building the next generation of creative tools that makes machine learning easy and accessible for all types of creatives. With an active and growing community, Runway is pioneering how content and media are created. With a focus on video automation and synthetic media, Runway reduces the costs of creating visual media across creative industries. To learn more and sign up for a free account visit http://www.runwayml.com.

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Runway Raises $8.5M Series A to Build the Next Generation of Creative Tools - Business Wire

Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.

Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.

In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.

Today, examples of machine learning are all around us. Digital assistants search the web and play music in response to our voice commands. Websites recommend products and movies and songs based on what we bought, watched, or listened to before. Robots vacuum our floors while we do . . . something better with our time. Spam detectors stop unwanted emails from reaching our inboxes. Medical image analysis systems help doctors spot tumors they might have missed. And the first self-driving cars are hitting the road.

We can expect more. As big data keeps getting bigger, as computing becomes more powerful and affordable, and as data scientists keep developing more capable algorithms, machine learning will drive greater and greater efficiency in our personal and work lives.

There are four basic steps for building a machine learning application (or model). These are typically performed by data scientists working closely with the business professionals for whom the model is being developed.

Training data is a data set representative of the data the machine learning model will ingest to solve the problem its designed to solve. In some cases, the training data is labeled datatagged to call out features and classifications the model will need to identify. Other data is unlabeled, and the model will need to extract those features and assign classifications on its own.

In either case, the training data needs to be properly preparedrandomized, de-duped, and checked for imbalances or biases that could impact the training. It should also be divided into two subsets: the training subset, which will be used to train the application, and the evaluation subset, used to test and refine it.

Again, an algorithm is a set of statistical processing steps. The type of algorithm depends on the type (labeled or unlabeled) and amount of data in the training data set and on the type of problem to be solved.

Common types of machine learning algorithms for use with labeled data include the following:

Algorithms for use with unlabeled data include the following:

Training the algorithm is an iterative processit involves running variables through the algorithm, comparing the output with the results it should have produced, adjusting weights and biases within the algorithm that might yield a more accurate result, and running the variables again until the algorithm returns the correct result most of the time. The resulting trained, accurate algorithm is the machine learning modelan important distinction to note, because 'algorithm' and 'model' are incorrectly used interchangeably, even by machine learning mavens.

The final step is to use the model with new data and, in the best case, for it to improve in accuracy and effectiveness over time. Where the new data comes from will depend on the problem being solved. For example, a machine learning model designed to identify spam will ingest email messages, whereas a machine learning model that drives a robot vacuum cleaner will ingest data resulting from real-world interaction with moved furniture or new objects in the room.

Machine learningmethods (also called machine learning styles) fall into three primary categories.

Supervised machine learning trains itself on a labeled dataset. That is, the data is labeled with information that the machine learning model is being built to determine and that may even be classified in ways the model is supposed to classify data. For example, a computer vision model designed to identify purebred German Shepherd dogs might be trained on a data set of various labeled dog images.

Supervised machine learning requires less training data than other machine learningmethods and makes training easier because the results of the model can be compared to actual labeled results. But, properly labeled data is expensive to prepare, and there's the danger of overfitting, or creating a model so closely tied and biased to the training data that it doesn't handle variations in new data accurately.

Learn more about supervised learning.

Unsupervised machine learning ingests unlabeled datalots and lots of itand uses algorithms to extract meaningful features needed to label, sort, and classify the data in real-time, without human intervention. Unsupervised learning is less about automating decisions and predictions, and more about identifying patterns and relationships in data that humans would miss. Take spam detection, for examplepeople generate more email than a team of data scientists could ever hope to label or classify in their lifetimes. An unsupervised learning algorithm can analyze huge volumes of emails and uncover the features and patterns that indicate spam (and keep getting better at flagging spam over time).

Learn more about unsupervised learning.

Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled dataset to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.

Reinforcement machine learning is a behavioral machinelearning model that is similar to supervised learning, but the algorithm isnt trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.

The IBM Watson system that won the Jeopardy! challenge in 2011 makes a good example. The system used reinforcement learning to decide whether to attempt an answer (or question, as it were), which square to select on the board, and how much to wagerespecially on daily doubles.

Learn more about reinforcement learning.

Deep learning is a subset of machine learning (all deep learning is machine learning, but not all machine learning is deep learning). Deep learning algorithms define an artificial neural network that is designed to learn the way the human brain learns. Deep learning models require large amounts of data that pass through multiple layers of calculations, applying weights and biases in each successive layer to continually adjust and improve the outcomes.

Deep learning models are typically unsupervised or semi-supervised. Reinforcement learning models can also be deep learning models. Certain types of deep learning modelsincluding convolutional neural networks (CNNs) and recurrent neural networks (RNNs)are driving progress in areas such as computer vision, natural language processing (including speech recognition), and self-driving cars.

See the blog post AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: Whats the Difference? for a closer look at how the different concepts relate.

Learn more about deep learning.

As noted at the outset, machine learning is everywhere. Here are just a few examples of machine learning you might encounter every day:

IBM Watson Machine Learning supports the machine learning lifecycle end to end. It is available in a range of offerings that let you build machine learning models wherever your data lives and deploy them anywhere in your hybrid multicloud environment.

IBM Watson Machine Learning on IBM Cloud Pak for Data helps enterprise data science and AI teams speed AI development and deployment anywhere, on a cloud native data and AI platform. IBM Watson Machine Learning Cloud, a managed service in the IBM Cloud environment, is the fastest way to move models from experimentation on the desktop to deployment for production workloads. For smaller teams looking to scale machine learning deployments, IBM Watson Machine Learning Server offers simple installation on any private or public cloud.

To get started, sign up for an IBMid and create your IBM Cloud account.

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What is Machine Learning? | IBM

Learning Curve

Artificial intelligence has been a hot technology area in recent years and machine learning, a subset of AI, is one of the most important segments of the whole AI arena.

Machine learning is the development of intelligent algorithms and statistical models that improve software through experience without the need to explicitly code those improvements. A predictive analysis application, for example, can become more accurate over time through the use of machine learning.

But machine learning has its challenges. Developing machine-learning models and systems requires a confluence of data science, data engineering and development skills. Obtaining and managing the data needed to develop and train machine-learning models is a significant task. And implementing machine-learning technology within real-world production systems can be a major hurdle.

Heres a look at a dozen startup companies, some that have been around for a few years and some just getting off the ground, that are addressing the challenges associated with machine learning.

AI.Reverie

Top Executive: Daeil Kim, Co-Founder, CEO

Headquarters: New York

AI.Reverie develops AI and machine -earning technology for data generation, data labeling and data enhancement tasks for the advancement of computer vision. The companys simulation platform is used to help acquire, curate and annotate the large amounts of data needed to train computer vision algorithms and improve AI applications.

In October AI.Reverie was named a Gartner Cool Vendor in AI core technologies.

Anodot

Top Executive: David Drai, Co-Founder, CEO

Headquarters: Redwood City, Calif.

Anodots Deep 360 autonomous business monitoring platform uses machine learning to continuously monitor business metrics, detect significant anomalies and help forecast business performance.

Anodots algorithms have a contextual understanding of business metrics, providing real-time alerts that help users cut incident costs by as much as 80 percent.

Anodot has been granted patents for technology and algorithms in such areas as anomaly score, seasonality and correlation. Earlier this year the company raised $35 million in Series C funding, bringing its total funding to $62.5 million.

BigML

Top Executive: Francisco Martin, Co-Founder, CEO

Headquarters: Corvallis, Ore.

BigML offers a comprehensive, managed machine-learning platform for easily building and sharing datasets and data models, and making highly automated, data-driven decisions. The companys programmable, scalable machine -earning platform automates classification, regression, time series forecasting, cluster analysis, anomaly detection, association discovery and topic modeling tasks.

The BigML Preferred Partner Program supports referral partners and partners that sell BigML and oversee implementation projects. Partner A1 Digital, for example, has developed a retail application on the BigML platform that helps retailers predict sales cannibalizationwhen promotions or other marketing activity for one product can lead to reduced demand for other products.

StormForge

Top Executive: Matt Provo, Founder, CEO

Headquarters: Cambridge, Mass.

StormForge provides machine learning-based, cloud-native application testing and performance optimization software that helps organizations optimize application performance in Kubernetes.

StormForge was founded under the name Carbon Relay and developed its Red Sky Ops tools that DevOps teams use to manage a large variety of application configurations in Kubernetes, automatically tuning them for optimized performance no matter what IT environment theyre operating in.

This week the company acquired German company Stormforger and its performance testing-as-a-platform technology. The company has rebranded as StormForge and renamed its integrated product the StormForge Platform, a comprehensive system for DevOps and IT professionals that can proactively and automatically test, analyze, configure, optimize and release containerized applications.

In February the company said that it had raised $63 million in a funding round from Insight Partners.

Comet.ML

Top Executive: Gideon Mendels, Co-Founder, CEO

Headquarters: New York

Comet.ML provides a cloud-hosted machine-learning platform for building reliable machine-learning models that help data scientists and AI teams track datasets, code changes, experimentation history and production models.

Launched in 2017, Comet.ML has raised $6.8 million in venture financing, including $4.5 million in April 2020.

Dataiku

Top Executive: Florian Douetteau, Co-Founder, CEO

Headquarters: New York

Dataikus goal with its Dataiku DSS (Data Science Studio) platform is to move AI and machine-learning use beyond lab experiments into widespread use within data-driven businesses. Dataiku DSS is used by data analysts and data scientists for a range of machine-learning, data science and data analysis tasks.

In August Dataiku raised an impressive $100 million in a Series D round of funding, bringing its total financing to $247 million.

Dataikus partner ecosystem includes analytics consultants, service partners, technology partners and VARs.

DotData

Top Executive: Ryohei Fujimaki, Founder, CEO

Headquarters: San Mateo, Calif.

DotData says its DotData Enterprise machine-learning and data science platform is capable of reducing AI and business intelligence development projects from months to days. The companys goal is to make data science processes simple enough that almost anyone, not just data scientists, can benefit from them.

The DotData platform is based on the companys AutoML 2.0 engine that performs full-cycle automation of machine-learning and data science tasks. In July the company debuted DotData Stream, a containerized AI/ML model that enables real-time predictive capabilities.

Eightfold.AI

Top Executive: Ashutosh Garg, Co-Founder, CEO

Headquarters: Mountain View, Calif.

Eightfold.AI develops the Talent Intelligence Platform, a human resource management system that utilizes AI deep learning and machine-learning technology for talent acquisition, management, development, experience and diversity. The Eightfold system, for example, uses AI and ML to better match candidate skills with job requirements and improves employee diversity by reducing unconscious bias.

In late October Eightfold.AI announced a $125 million Series round of financing, putting the startups value at more than $1 billion.

H2O.ai

Top Executive: Sri Ambati, Co-Founder, CEO

Headquarters: Mountain View, Calif.

H2O.ai wants to democratize the use of artificial intelligence for a wide range of users.

The companys H2O open-source AI and machine-learning platform, H2O AI Driverless automatic machine-learning software, H20 MLOps and other tools are used to deploy AI-based applications in financial services, insurance, health care, telecommunications, retail, pharmaceutical and digital marketing.

H2O.ai recently teamed up with data science platform developer KNIME to integrate Driverless AI for AutoMl with KNIME Server for workflow management across the entire data science life cyclefrom data access to optimization and deployment.

Iguazio

Top Executive: Asaf Somekh, Co-Founder, CEO

Headquarters: New York

The Iguazio Data Science Platform for real-time machine learning applications automates and accelerates machine-learning workflow pipelines, helping businesses develop, deploy and manage AI applications at scale that improve business outcomeswhat the company calls MLOps.

In early 2020 Iguazio raised $24 million in new financing, bringing its total funding to $72 million.

OctoML

Top Executive: Luis Ceze, Co-Founder, CEO

Headquarters: Seattle

OctoMLs Software-as-a-Service Octomizer makes it easier for businesses and organizations to put deep learning models into production more quickly on different CPU and GPU hardware, including at the edge and in the cloud.

OctoML was founded by the team that developed the Apache TVM machine-learning compiler stack project at the University of Washingtons Paul G. Allen School of Computer Science & Engineering. OctoMLs Octomizer is based on the TVM stack.

Tecton

Top Executive: Mike Del Balso, Co-Founder, CEO

Headquarters: San Francisco

Tecton just emerged from stealth in April 2020 with its data platform for machine learning that enables data scientists to turn raw data into production-ready machine-learning features. The startups technology is designed to help businesses and organizations harness and refine vast amounts of data into the predictive signals that feed machine-learning models.

The companys three founders: CEO Mike Del Balso, CTO Kevin Stumpf and Engineering Vice President Jeremy Hermann previously worked together at Uber where they developed the companys Michaelangelo machine-learning platform the ride-sharing company used to scale its operations to thousands of production models serving millions of transactions per second, according to Tecton.

The company started with $25 million in seed and Series A funding co-led by Andreessen Horowitz and Sequoia.

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The 12 Coolest Machine-Learning Startups Of 2020 - CRN