Category Archives: Machine Learning

Southwest Research Institute (SwRI) and the Lyle School of Engineering at Southern Methodist University (SMU) announced the Seed Projects Aligning Research, Knowledge, and Skills (SPARKS) joint program, which aims to strengthen and cultivate long-term research collaboration between the organizations.

Research topics will vary for the annual funding cycles. The inaugural program selections will apply machine learning a subset of artificial intelligence (AI) to solve industry problems. A peer review panel selected two proposals for the 2020 cycle, with each receiving $125,000 in funding for a one-year term.

Our plan for the SPARKS program is not only to foster a close collaboration between our two organizations but, more importantly, to also make a long-lasting impact in our collective areas of research, said Lyle Dean Marc P. Christensen. With the growing demand for AI tools in industry, machine learning was an obvious theme for the programs inaugural year.

The first selected project is a proof of concept that will lay the groundwork for drawing relevant data from satellite and other sources to assess timely surface moisture conditions applicable to other research. SwRI will extract satellite, terrain and weather data that will be used by SMU Lyle to develop machine learning functions that can rapidly process these immense quantities of data. The interpreted data can then be applied to research for municipalities, water management authorities, agricultural entities and others to produce, for example, fire prediction tools and maps of soil or vegetation water content. Dr. Stuart Stothoff of SwRI and Dr. Ginger Alford of SMU Lyle are principal investigators of Enhanced Time-resolution Backscatter Maps Using Satellite Radar Data and Machine Learning.

The second project tackles an issue related to the variability of renewable energy from wind and solar power systems: effective management of renewable energy supplies to keep the power grid stable. To help resolve this challenge, the SwRI-SMU Lyle team will use advanced machine learning techniques to model and control battery energy storage systems. These improved battery storage systems, which would automatically and strategically push or draw power instantly in response to grid frequency deviations, could potentially be integrated with commercial products and tools to help regulate the grid. Principal investigators of Machine Learning-powered Battery Storage Modeling and Control for Fast Frequency Regulation Service are Dr. Jianhui Wang of SMU Lyle and Yaxi Liu of SwRI.

To some extent, the SPARKS program complements our internal research efforts, which are designed to advance technologies and processes so they can be directly applied to industry programs, said Executive Vice President and COO Walt Downing of SwRI. We expect the 2020 selections to do just that, greatly advancing the areas of environmental management and energy storage and supply.

The program will fund up to three projects each year, seeking to bridge the gap between basic and applied research.

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SwRI, SMU fund SPARKS program to explore collaborative research and apply machine learning to industry problems - TechStartups.com

One of the biggest challenges for developers of natural language systems is accounting for the many and varied ways people express themselves. There is a reason many technology companies would rather we all spoke in simple terms, it makes humans easier to understand and narrows down the chances of machines getting it wrong.

But its hardly the engaging conversational experience that people expect of AI.

Language has evolved over many centuries. As various nations colonised and traded with other nations so our language whatever your native tongue is changed. And thanks to radio, TV, and the internet its continuing to expand every day.

Among the hundreds of new words added to the Merriam Webster dictionary in 2019 was Vacay: a shortening of vacation; Haircut: a new sense was added meaning a reduction in the value of an asset; and Dad joke: a corny pun normally told by fathers.

In a conversation, we as humans would probably be able to deduce what someone meant, even if wed never heard a word or expression before. Machines? Not so much. Or at least, not if they are reliant solely on machine learning for their natural language understanding.

While adding domain specialism such as a product name or industry terminology to an application overcomes a machine recognising some specific words, understanding all of the general everyday phrases people use in between those words is where the real challenge lies.

Most commercial natural language development tools today dont offer the intelligent, humanlike, experience that customers expect in automated conversations. One of the reasons is because they rely on pattern matching words using machine learning.

Although humans - at a basic level - pattern match words too, our brains add a much higher level of reasoning to allow us to do a better job of interpreting what the person meant by considering the words used, their order, synonyms and more, plus understanding when words such as book is being used as a verb or a noun. One might say we add our own more flexible form of linguistic modelling.

As humans, we can zoom in on the vocabulary that is relevant to the current discussion. So, when someone asks a question using a phrasing weve not heard before, we can extrapolate from what we do know, to understand what is meant. Even if weve never heard a particular word before, we can guess with a high degree of accuracy what it means.

But when it comes to machines, most statisticians will tell you that accuracy isnt a great metric. Its too easily skewed by the data its based on. Instead of accuracy, they use precision and recall. In simple terms precision is about quality. It marks the number of times you were actually correct with your prediction. Recall is about quantity, the number of times you predicted correctly out of all of the possibilities.

The vast majority of conversational AI development tools available today rely purely on machine learning. However, machine learning isnt great at precision, not without massive amounts of data on which to build its model. The end result is that the developer has to code in each and every way someone might ask a question. Not a task for the faint hearted when you consider there are at least 22 ways to say yes in the English language.

Some development tools rely on linguistic modelling, which is great at precision, because it understands sentence constructs and the common ways a particular type of question is phrased, but often doesnt stack up to machine learnings recall ability. This is because linguistic modelling is based on binary rules. They either match or they dont, which means inputs with minor deviations such as word ordering or spelling mistakes will be missed.

Machine learning on the other hand provides a probability on how much the input matches with the training data for a particular intent class and is therefore less sensitive to minor variations. Used alone, neither system is conducive to delivering a highly engaging conversation.

However, by taking a hybrid approach to conversational AI development, enterprises can benefit from the best of both worlds. Rules increase the precision of understanding, while machine learning delivers greater recall by recovering the data missed by the rules.

Not only does this significantly speed up the development process, it also allows for the application to deal with examples it has never seen before. In addition, it reduces the number of customers sent to a safety net such as a live chat agent, merely because theyve phrased their question slightly differently.

By enabling the conversational AI development platform to decide where each model is used, the performance of the conversational system can be optimised even further. Making it easier for the developer to build robust applications by automatically mixing and matching the underlying technology to achieve the best results, while allowing technology to more easily understand humans no matter what words we choose to use.

Andy Peart, CMSO, Artificial Solutions

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How to handle the unexpected in conversational AI - ITProPortal

The high bandwidth demands created by our mobile and smart devices, data storage, and cloud computing centers is growing by leaps and bounds. And the ubiquity of fiber optics is a big part of this. Analysts are predicting the global fiber optics market will be worth $9 billion USD by 2025. Muchof this will be driven by the aforementioned technologies but also by new technologies such as VR/AR.

But none will have more impact than machine learning. The compute power needed and the demand for machine learning performance is driving more and more developers to move AI applications to the edge and away from the cloud. One of those companies is Luminous Computing, a machine learning startup that has set itself on the lofty goal of leveraging photonics to fit the computing power of the world's largest supercomputers onto a single chip for AI processing.

Ahead of his DesignCon 2020 keynote, The Future of Fiber Optic Communications: Datacenter & Mobile, Chris Cole, vice president of systems engineering at Luminous Computing, spoke with DesignCon brand director Suzanne Deffree about the rapid changes coming to data centers and mobile.

Check out the video interview below, where Cole discusses how fiber optics and machine learning are transforming each other, how new technologies like Silicon Photonics (SiPh) and co-packaging play into the communications landscape, why you can't be religious about technology, and more.

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How Machine Learning Is Changing The Future Of Fiber Optics - DesignNews

One Technologies Partners with Arrikto to Uniquely Tailor its ScoreSense Consumer Credit Platform to Each Individual Customer

DALLAS, Jan. 30, 2020 /PRNewswire/ --To provide customers with the most personalized credit experience possible, One Technologies, LLC has partnered with data management innovator Arrikto Inc. (https://www.arrikto.com/)to incorporate Machine Learning (ML) into its ScoreSense credit platform.

ScoreSense, http://www.ScoreSense.com (PRNewsfoto/One Technologies, LLC)

"To truly empower consumers to take control of their financial future, we must rely on insights from real datanot on assumptions and guesswork," said Halim Kucur, Chief Product Officer at One Technologies, LLC. The innovations we have introduced provide data-driven intelligence about customers' needs and wants before they know this information themselves."

"ScoreSense delivers state-of-the-art credit information through their ongoing investment in the most cutting-edge machine learning products the industry has to offer," said Constantinos Venetsanopoulos, Founder and CEO of Arrikto Inc. "Our partnership has been a big success because One Technologies aligns seamlessly with the most forward-looking developers in the ML space and understands the tremendous value of data for serving customers better."

ScoreSense (https://www.scoresense.com) serves as a one-stop digital resource where consumers can access credit scores and reports from all three main credit bureausTransUnion, Equifax, and Experianand comprehensively pinpoint the factors which are most affecting their credit.

About One Technologies

One Technologies, LLC harnesses the power of technology, analytics and its people to create solutions that empower consumers to make more informed decisions about their financial lives. The firm's consumer credit products include ScoreSense, which enables members to seamlessly access, interact with, and understand their credit profiles from all three main bureaus using a single application. The ScoreSense platform is continually updated to give members deeper insights, personalized tools and one-on-one Customer Care support that can help them make the most sense of their credit.

One Technologies is headquartered in Dallas and was established in October 2000. For more information, please visit https://onetechnologies.net/.

Media Contact

Laura MarvinJConnelly for One Technologies646-922-7774 OT@jconnelly.com

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ScoreSense Leverages Machine Learning to Take Its Customer Experience to the Next Level - Yahoo Finance

Payoneer uses Iguazio to move from detection to prevention of fraud with predictive machine learning models served in real-time.

Iguazio, the data science platform for real time machine learning applications, today announced that Payoneer, the digital payment platform empowering businesses around the world to grow globally, has selected Iguazios platform to provide its 4 million customers with a safer payment experience. By deploying Iguazio, Payoneer moved from a reactive fraud detection method to proactive prevention with real-time machine learning and predictive analytics.

Payoneer overcomes the challenge of detecting fraud within complex networks with sophisticated algorithms tracking multiple parameters, including account creation times and name changes. However, prior to using Iguazio, fraud was detected retroactively, enabling customers to only block users after damage had already been done. Payoneer is now able to take the same sophisticated machine learning models built offline and serve them in real-time against fresh data. This ensures immediate prevention of fraud and money laundering with predictive machine learning models identifying suspicious patterns continuously. The cooperation was facilitated by Belocal, a leading Data and IT solution integrator for mid and enterprise companies.

"Weve tackled one of our most elusive challenges with real-time predictive models, making fraud attacks almost impossible on Payoneer" noted Yaron Weiss, VP Corporate Security and Global IT Operations (CISO) at Payoneer. "With Iguazios Data Science Platform, we built a scalable and reliable system which adapts to new threats and enables us to prevent fraud with minimum false positives".

"Payoneer is leading innovation in the industry of digital payments and we are proud to be a part of it" said Asaf Somekh, CEO, Iguazio. "Were glad to see Payoneer accelerating its ability to develop new machine learning based services, increasing the impact of data science on the business."

"Payoneer and Iguazio are a great example of technology innovation applied in real-world use-cases and addressing real market gaps" said Hugo Georlette, CEO, Belocal. "We are eager to continue selling and implementing Iguazios Data Science Platform to make business impact across multiple industries."

Iguazios Data Science Platform enables Payoneer to bring its most intelligent data science strategies to life. Designed to provide a simple cloud experience deployed anywhere, it includes a low latency serverless framework, a real-time multi-model data engine and a modern Python eco-system running over Kubernetes.

Earlier today, Iguazio also announced having raised $24M from existing and new investors, including Samsung SDS and Kensington Capital Partners. The new funding will be used to drive future product innovation and support global expansion into new and existing markets.

About Iguazio

The Iguazio Data Science Platform enables enterprises to develop, deploy and manage AI applications at scale. With Iguazio, companies can run AI models in real time, deploy them anywhere; multi-cloud, on-prem or edge, and bring to life their most ambitious data-driven strategies. Enterprises spanning a wide range of verticals, including financial services, manufacturing, telecoms and gaming, use Iguazio to create business impact through a multitude of real-time use cases. Iguazio is backed by top financial and strategic investors including Samsung, Verizon, Bosch, CME Group, and Dell. The company is led by serial entrepreneurs and a diverse team of innovators in the USA, UK, Singapore and Israel. Find out more on http://www.iguazio.com

About Belocal

Since its inception in 2006, Belocal has experienced consistent and sustainable growth by developing strong long-term relationships with its technology partners and by providing tremendous value to its clients. We pride ourselves on delivering the most innovative technology solutions enabling our customers to lead their market segments and stay ahead of the competition. At Belocal, we pride ourselves in our ability to listen, our attention to detail and our expertise in innovation. Such strengths have enabled us to develop new solutions and services, to suit the changing needs of our clients and acquire new businesses by tailoring all our solutions and services to the specific needs of each client.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200127005311/en/

Contacts

Iguazio Media Contact:Sahar Dolev-Blitental, +972.73.321.0401press@iguazio.com

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Iguazio Deployed by Payoneer to Prevent Fraud with Real-time Machine Learning - Yahoo Finance

NEW YORK, Jan. 30, 2020 /PRNewswire/ -- Itiviti, a leading technology, and service provider to financial institutions worldwide, has signed an exclusive partnership agreement with Imandra Inc., the AI pioneer behind the Imandra automated reasoning engine.

Imandra's technology will initially be applied to improving the onboarding process for our clients to Itiviti's Managed FIX global connectivity platform, with further plans to swiftly expand the AI capabilities across a number of our software solutions and services.

Imandra is the world-leader in cloud-scale automated reasoning, and has pioneered scalable symbolic AI for financial algorithms. Imandra's technology brings deep advances relied upon in safety-critical industries such as avionics and autonomous vehicles to the financial markets. Imandra is relied upon by top investment banks for the design, testing and governance of highly regulated trading systems. In 2019, the company expanded outside financial services and is currently under contract with the US Department of Defense for applications of Imandra to safety-critical algorithms.

"Partnerships are integral to Itiviti's overall strategy, by partnering with cutting edge companies like Imandra we can remain at the forefront of technology innovation and continue to develop quality solutions to support our clients. Generally, client onboarding has been a neglected area within the industry for many years, but we believe working with Imandra we can raise the level of automation for testing and QA, while significantly reducing onboarding bottlenecks for our clients. Other areas we are actively exploring to benefit from AI are within the Compliance and Analytics space. We are very excited to be working with Imandra." said Linda Middleditch, EVP, Head of Product Strategy, Itiviti Group.

"This partnership will capture the tremendous opportunities within financial markets for removing manual work and applying much-needed rigorous scientific techniques toward testing of safety critical infrastructure," said Denis Ignatovich, co-founder and co-CEO of Imandra. "We look forward to helping Itiviti empower clients to take full advantage of their solutions, while adding key capabilities." Dr Grant Passmore, co-founder and co-CEO of Imandra, further added, "This partnership is the culmination of many years of deep R&D and we're thrilled to partner with Itiviti to bring our technology to global financial markets on a massive scale."

About Itiviti

Itiviti enables financial institutions worldwide to transform their trading and capture tomorrow. With innovative technology, deep expertise and a dedication to service, we help customers seize market opportunities and guide them through regulatory change.

Top-tier banks, brokers, trading firms and institutional investors rely on Itiviti's solutions to service their clients, connect to markets, trade smarter in all asset classes by consolidating trading platforms and leverage automation to move faster.

A global technology and service provider, we offer the most innovative, consistent, and reliable connectivity and trading solutions available.

With presence in all major financial centres and serving around 2,000 clients in over 50 countries, Itiviti delivers on a global scale.

For more information, please visitwww.itiviti.com.

Itiviti is owned by Nordic Capital.

About Imandra

Imandra Inc. (www.imandra.ai) is the world-leader in cloud-scale automated reasoning, democratizing deep advances in algorithm analysis and symbolic AI for making algorithms safe, explainable and fair. Imandra has been deep in R&D and industrial pilots over the past 5 years and has recently closed its $5mm Seed round led by several top deep-tech investors in US and UK. Imandra is headquartered in Austin, TX, and has offices in the UK and continental Europe.

For further information, please contact:

Itiviti

Linda Middleditch, EVPHead of Product StrategyTel +44 796 82 126 24Email: linda.middleditch@itiviti.com

George RosenbergerHead of Product StrategyClient Connectivity ServiceTel: + Email: george.rosenberger@itiviti.com

Christine BlinkeEVP, Head of Marketing & CommunicationsTel. +46 739 01 02 01Email: christine.blinke@itiviti.com

Imandra

Denis Ignatovich, co-CEOTel: +44 20 3773 6225Email: denis@imandra.ai

Grant Passmoreco-CEOTel: +1 512 629 4038Email: grant@imandra.ai

This information was brought to you by Cision http://news.cision.com

https://news.cision.com/itiviti-group-ab/r/itiviti-partners-with-ai-innovator-imandra-to-integrate-machine-learning-into-client-onboarding-and-,c3021540

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Itiviti Partners With AI Innovator Imandra to Integrate Machine Learning Into Client Onboarding and Testing Tools - PRNewswire