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Blacklight Solutions Unveils Software to Simplify Business Analytics with AI and Machine Learning – PRNewswire

AUSTIN, Texas, Aug. 5, 2020 /PRNewswire/ -- Blacklight Solutions, an applied analytics company based in Texas, introduced today a simplified business analytics platform that allows small to mid-market businesses to implement artificial intelligence and machine learning with code free transformation, aggregation, blending and mixing of multiple data sources. Blacklight software empowers companies to increase efficiency by using machine learning and artificial intelligence for business processes with a team of experts guiding this metamorphosis.

"Small and mid-size firms need a simpler way to leverage these technologies for growth in the way large enterprises have." said Chance Coble, Blacklight Solutions CEO. "We are thrilled to bring an easy pay-as-you-go solution along with the expertise to guide them and help them succeed."

Blacklight Solutions believes that now more than ever companies need business analytics solutions that can increase sales, enhance productivity, and improve risk control. Blacklight software gives small to mid-market businesses an opportunity to implement the latest technology and create insightful digital products without requiring a dedicated team or familiarity with coding languages. Blacklight Solutions provides each client with a team of experts to help guide their journey in becoming evidence-based decision makers.

Capabilities and Benefits for Users

Blacklight is a cloud-based system that is built to scale with your business as it grows. It is the simplest way to create business analytics solutions that users can then sell to their customers. Users have the added ability to create dashboards and embed them in client facing portals. Additionally, users are enabled to grow and improve cash flow by creating data products that their customers can subscribe to resulting in generated revenue. Blacklight software also features an alerting system that notifies designated users when changes in data or anomalies occur.

"Blacklight brought the strategy, expertise and software that made analytics a solution for us to achieve new business objectives and grow sales," said Deren Koldwyn, CEO, Avannis, Blacklight Solutions client.

Blacklight software brings the full power of business analytics to companies that are looking for digital transformations and want to move fast. Blacklight Solutions is the only full-service solution that provides empowering software combined with the insight and strategy necessary for impactful analytics implementations. To learn more about Blacklight Solutions' offerings visit http://www.blacklightsolutions.com.

About Blacklight Solutions

Blacklight Solutions is an analytics firm focused on helping mid-market companies accelerate their growth. Founded in 2009, Blacklight Solutions has spent over a decade helping organizations solve business problems by putting their data to work to generate revenue, increase efficiency and improve customer relationships.

Media Contact:

Bailey Steinhauser979.966.8170[emailprotected]

SOURCE Blacklight Solutions

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AI is learning when it should and shouldnt defer to a human – MIT Technology Review

The context: Studies show that when people and AI systems work together, they can outperform either one acting alone. Medical diagnostic systems are often checked over by human doctors, and content moderation systems filter what they can before requiring human assistance. But algorithms are rarely designed to optimize for this AI-to-human handover. If they were, the AI system would only defer to its human counterpart if the person could actually make a better decision.

The research: Researchers at MITs Computer Science and AI Laboratory (CSAIL) have now developed an AI system to do this kind of optimization based on strengths and weaknesses of the human collaborator. It uses two separate machine-learning models; one makes the actual decision, whether thats diagnosing a patient or removing a social media post, and one predicts whether the AI or human is the better decision maker.

The latter model, which the researchers call the rejector, iteratively improves its predictions based on each decision makers track record over time. It can also take into account factors beyond performance, including a persons time constraints or a doctors access to sensitive patient information not available to the AI system.

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Moderna Announced Partnership With Amazon Web Services for Their Analytics and Machine Learning Services – Science Times

The $29 billion biotech company Modernahas announced on Wednesday, August 5, that they will be partnering with Amazon Web Servicesto become their preferred cloud partner.

Moderna is currently considered the lead COVID-19 vaccine developer as it is the first company to reach the third phase of vaccine development in late July.

(Photo : Getty Images)CAMBRIDGE, MASSACHUSETTS - MAY 08: A view of Moderna headquarters on May 08, 2020 in Cambridge, Massachusetts. Moderna was given FDA approval to continue to phase 2 of Coronavirus (COVID-19) vaccine trials with 600 participants. (Photo by Maddie Meyer/Getty Images)

Read Also: 'Very Low' Dose Moderna COVID-19 Vaccine Elicits Immune Response with No Side Effect, First Human Trial Show

Vaccine development could take years of research and lab testing before it can be administered to people. As one of the leading companies who joined the race for a COVID-19 vaccine, Moderna gave 30,000 peoplelast week their first vaccine candidate that reached phase 3 of testing the United States.

At present, Moderna has been using AWS to run its everyday operations in accounting and inventory management and also to power its production facility, robotic tools, and engineering systems. According to the press release by the biotech company, this allows them to achieve greater efficiency and visibility across its operations.

Moderna CEO Stphane Bancel said that with AWS, the company's researchers could have the ability to quickly design and perform experiments and, in no time, uncover novel insights to produce faster life-saving treatments.

Modernizing IT infrastructures through the use of artificial intelligenceis one of the things that biotech companies, such as Moderna, are looking into helping them in the race of developing new medicines and treatments.

The race for a COVID-19 vaccine has made the biotechnology sector a sought-after market these days. Like AWS, its rival Microsoft Azurehas recently inked a big cloud and artificial intelligence deal with drugmaker Novartis as well.

According to biotech analyst Michael Yee, the vaccine test results could be made public in October.

Read Next: Is Moderna Coronavirus Vaccine Leading the Race? Early Trials Show the Jab Gives Off Immunity

Moderna Therapeutics' co-founder and chairman, Dr. Noubar Afeyan, said that the biotech company is the first US firm to enter Phase 3 of a clinical trial for their candidate COVID-19 vaccine.

The blind trial will include 30,000 volunteers in which half of them will receive Moderna's drug, and the other half will receive a placebo of sodium and water. The volunteers are 18 years old and older who are interested in participating in the clinical trial.

Afeyan said that the Food and Drug Administration's authorization would be based on how fast some 150 cases of the infection occur. If the trial proves to be successful, those people who received the vaccine should have a disproportionately lower number of cases than those who received the placebo.

At the end of the day, the FDAmust ensure that the vaccine meets all the necessary safety and efficacy measures. The administration mandated at least 50% protection value for any vaccine before considering authorizing them.

Moreover, Moderna hopes to have authorization from the FDA by the last quarter of 2020. Afeyan said that they expect to have 500 million to 1 billion doses of their vaccine ready for distribution once they get the FDA authorization.

Read More: Moderna COVID-19 Vaccine Trial Volunteer Suffered 'Severe Adverse Reaction'

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STMicroelectronics Releases STM32 Condition-Monitoring Function Pack Leveraging Tools from Cartesiam for Simplified Machine Learning – ELE Times

STMicroelectronicshas released a free STM32 software function pack that lets users quickly build, train, and deployintelligent edge devices for industrial condition monitoringusing a microcontroller Discovery kit.

Developed in conjunction with machine-learning expert and ST Authorized Partner Cartesiam, theFP-AI-NANOEDG1 software packcontains all the necessary drivers, middleware, documentation, and sample code to capture sensor data, integrate, and run Cartesiams NanoEdge libraries. Users without specialist AI skills can quickly create and export custom machine-learning libraries for their applications using Cartesiams NanoEdge AI Studio tool running on a Windows 10 or Ubuntu PC. The function pack simplifies complete prototyping and validation free of charge on STM32 development boards, before deploying on customer hardware where standard Cartesiam fees apply.

The straightforward methodology established with Cartesiam uses industrial-grade sensors on-board a Discovery kit such as theSTM32L562E-DKto capture vibration data from the monitored equipment both in normal operating modes and under induced abnormal conditions. Software to configure and acquire sensor data is included in the function pack. NanoEdge AI Studio analyzes the benchmark data and selects pre-compiled algorithms from over 500 million possible combinations to create optimized libraries for training and inference. The function-pack software provides stubs for the libraries that can be easily replaced for simple embedding in the application. Once deployed, the device can learn the normal pattern of the operating mode locally during the initial installation phase as well as during the lifetime of the equipment, as the function pack permits switching between learning and monitoring modes.

Using the Discovery kit to acquire data, generate, train, and monitor the solution, leveraging free tools and software, and the support of theSTM32 ecosystem, developers can quickly create a proof-of-concept model at low cost and easily port the application to other STM32 microcontrollers. As an intelligent edge device, unlike alternatives that rely on AI in the cloud, the solution allows equipment owners greater control over potentially sensitive information by processing machine data on the local device.

The FP-AI-NANOEDG1 function pack is available now atwww.st.com, free of charge.

The STM32L562E-DK Discovery kit contains anSTM32L562QEI6QUultra-low-power microcontroller, an iNEMO 3D accelerometer and 3D gyroscope, as well as two MEMS microphones, a 240240 color TFT-LCD module, and on-board STLINK-V3E debugger/programmer. The budgetary price for the Discovery kit is $76.00, and it is available fromwww.st.comor distributors.

For further information, visitwww.st.com

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STMicroelectronics Releases STM32 Condition-Monitoring Function Pack Leveraging Tools from Cartesiam for Simplified Machine Learning - ELE Times

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Surprisingly Recent Galaxy Discovered Using Machine Learning May Be the Last Generation Galaxy in the Long Cosmic History – SciTechDaily

HSC J1631+4426 broke the record for the lowest oxygen abundance. Credit: NAOJ/Kojima et al.

Breaking the lowest oxygen abundance record.

New results achieved by combining big data captured by the Subaru Telescope and the power of machine learning have discovered a galaxy with an extremely low oxygen abundance of 1.6% solar abundance, breaking the previous record of the lowest oxygen abundance. The measured oxygen abundance suggests that most of the stars in this galaxy formed very recently.

To understand galaxy evolution, astronomers need to study galaxies in various stages of formation and evolution. Most of the galaxies in the modern Universe are mature galaxies, but standard cosmology predicts that there may still be a few galaxies in the early formation stage in the modern Universe. Because these early-stage galaxies are rare, an international research team searched for them in wide-field imaging data taken with the Subaru Telescope. To find the very faint, rare galaxies, deep, wide-field data taken with the Subaru Telescope was indispensable, emphasizes Dr. Takashi Kojima, the leader of the team.

However, it was difficult to find galaxies in the early stage of galaxy formation from the data because the wide-field data includes as many as 40 million objects. So the research team developed a new machine learning method to find such galaxies from the vast amount of data. They had a computer repeatedly learn the galaxy colors expected from theoretical models, and then let the computer select only galaxies in the early stage of galaxy formation.

The research team then performed follow-up observations to determine the elemental abundance ratios of 4 of the 27 candidates selected by the computer. They have found that one galaxy (HSC J1631+4426), located 430 million light-years away in the constellation Hercules, has an oxygen abundance only 1.6 percent of that of the Sun. This is the lowest values ever reported for a galaxy. The measured oxygen abundance suggests that most of the stars in this galaxy formed very recently. In other words, this galaxy is undergoing an early stage of the galaxy evolution.

What is surprising is that the stellar mass of the HSC J1631+4426 galaxy is very small, 0.8 million solar masses. This stellar mass is only about 1/100,000 of our Milky Way galaxy, and comparable to the mass of a star cluster in our Milky Way, said Prof. Ouchi of the National Astronomical Observatory of Japan and the University of Tokyo. This small mass also supports the primordial nature of the HSC J1631+4426 galaxy.

The research team thinks that there are two interesting indications from this discovery. First, this is the evidence about a galaxy at such an early stage of galaxy evolution existing today. In the framework of the standard cosmology, new galaxies are thought to be born in the present universe. The discovery of the HSC J1631+4426 galaxy backs up the picture of the standard cosmology. Second, we may witness a new-born galaxy at the latest epoch of the cosmic history. The standard cosmology suggests that the matter density of the universe rapidly drops in our universe whose expansion accelerates. In the future universe with the rapid expansion, matter does not assemble by gravity, and new galaxies wont be born. The HSC J1631+4426 galaxy may be the last generation galaxy in the long cosmic history.

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This 13-year-old from Bengaluru is crowd funding to set up refrigerators for the poor – EdexLive

Shelves set up by Mishant to store and help the needy

Don't waste food and share it with the ones who need it. Isn't this taught at our home? The left over or excess food can save people from going to bed hungry every night. And this is what 13-year-old Mishant Jain believes. Recently, he started crowd funding to set up refrigerators and shelves at places like bakeries, hotels, restaurants etc so that excess food can be stored and the poor people take them away without any hesitation.

Mishant who is in class 8 at National Academy for Learning in Bengaluru, says, "I am inspired by my grandfather to do this work. He has been into social work for many years and helps people from different states. One day, he got a call from one of the government schools in Rajasthan asking for a refrigerator. The reason was while the government gave fresh milk to children, there was no place to store it. Since the state experiences hot climate, the milk would often get spoilt and children were not able to drink it. Hence, my grand father along with his friends gifted a refrigerator to this school so that they can store milk for a day or so and children can drink it when ever they want."

Now, Mishant wants to implement the same in Bengaluru but for the poor people. He says, "I have started raising funds on a website called Impact Guru and the project's name is poorti (Sampoorti). While my target is to raise Rs 5 lakh, I have been able to raise Rs 56,320 till now. I have planned to put up these fridges and shelves in 25 locations. I have already a mini shelf nearmy father's office to check how it works. Then, I will implement the same in different locations. Even 1M1B Foundation also supported my intiative in all the ways they can."

Mishant is currently attending online classes and dedicating his free time to this project. "In times of pandemic like this, if poor are benefitted from my project, then there is nothing like it," he concludes.

If you want to support Mishant's initiative, click on this link- http://www.impactguru.com/fundraiser/help-initiative-sampoorti

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ASX set to move into industry-wide testing of blockchain-based settelement system – Finextra

Over 90% of CHESS users can meet the proposed go-live date of April 2022; ASX now reviewing consultation feedback.

ASX is now carefully reviewing the consultation feedback and following up with some CHESS users on points of detail they raised in order to meet the proposed implementation timetable.

Early results show an overwhelming majority of CHESS users can meet the proposed timetable for implementing CHESS. Despite the high number of positive responses, no final decision on the revised schedule has been made. It remains subject to a detailed review of all submissions and any other relevant considerations before being finalised by ASX.

As at Tuesday, 4 August: * 88 submissions have been received, representing 92% of the 96 CHESS users*91% of CHESS users who made submissions can meet the revised go-live date for CHESS replacement of April 2022 *The few exceptions not yet able to confirm readiness have asked for more information on particular issues, which ASX will assist with in the near-term.

CHESS users are those organisations that plan to connect to the new system, including clearing and settlement participants, product issuer settlement participants, approved market operators, back office software developers, payment providers and share registries.

ASX is currently following up with CHESS users that havent responded to ensure as much input as possible is received from those organisations that must accredit their systems and/or attest to their operational readiness prior to go-live. Their feedback is important for the safe and timely transition to the new system.

ASX will publish its response and a summary of the feedback once all submissions have been reviewed. We will also engage with the regulatory agencies on the revised project timetable prior to its public release.

Dominic Stevens, ASX Managing Director and CEO said: We appreciate the input and responses weve received from the market - not just for this consultation but for the CHESS replacement project overall. The project has taken on even greater significance in recent months, with the accelerating need for more innovation, digitisation and straight-through processing of transactions and corporate actions.

The CHESS replacement project has involved the most interaction ASX has ever undertaken with the market. Were grateful that so many CHESS users have responded constructively to this consultation. This provides us with a sound starting point as we now carefully consider all submissions.

Mr Stevens continued: While recognising there is still much for everyone to do, we are excited by the fact we are close to 100% complete on customer functionality and set to move into industry-wide testing in the coming months.

Background

ASX and a broad stakeholder community have been working together since 2016 to successfully deliver the system to replace CHESS. This has involved significant collaboration on business requirements, adoption and mapping of ISO 20022 messaging, solution design for new features, and connectivity to the new system.

At its core, the new system will deliver existing services; new functionality; high availability, reliability and performance; and underpin Australias financial markets for the next decade and beyond.

In developing the consultation paper published on 30 June that set out a proposed 12-month extension, ASX considered several factors. These included the ongoing impact of COVID-19, functionality changes requested by users, and additional time for ASX and CHESS users to complete development and readiness activities.

The project is progressing well, with 90% of the core clearing and settlement functionality used by customers already deployed in the Customer Development Environment.

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Machine Learning Reveals What Makes People Happy In A Relationship – Forbes

Who you are together is more important than who you are alone.

What makes us happy in a romantic relationship? The question might seem too complex to answer, too varied couple to couple. But a new study in the Proceedings of the National Academy of Sciences attempts to answer just that - using machine learning.

Previous studies on romantic satisfaction were limited in size. By using machine learning, however, researchers were able to analyze a massive amount of data, which included over 11,000 different couples from 43 data sets. Individual studies are many times limited - it is difficult and expensive to recruit couples for the studies. Its also exhausting for the participants. Using machine learning to analyze a large amount of data from pre-existing studies bypasses these problems.

The researchers looked at variables that could predict happiness within a relationship. Some of these, such as neuroticism, political orientation, conscientiousness or family history were qualities of the individuals involved. Others, such as appreciation, affection and perceived partner commitment were qualities of the relationship.

Of these, qualities of the relationship, rather than the individuals involved, contributed more to overall satisfaction. The five most important were how much they believed their partner was committed to the relationship, how much they appreciated their partner, sexual satisfaction, how much they believed their partner was happy in the relationship, and not fighting often.

Appreciation and commitment are key for a fulfilling relationship.

Qualities of the individuals contribute too - but not as much. In fact, 45% of the variability in a relationship is due to the qualities of the relationship. 21% were due to the individuals themselves. In addition, once qualities of the relationship were taken into account, the differences due to the individuals were not as important.

Experiencing negative affect, depression, or insecure attachment are surely relationship risk factors. But if people nevertheless manage to establish a relationship characterized by appreciation, sexual satisfaction, and a lack of conflictand they perceive their partner to be committed and responsivethose individual risk factors may matter little, say the authors.

In other words, for a happy relationship, its more important who you are together than who you are apart.

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Machine Learning Reveals What Makes People Happy In A Relationship - Forbes

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Benefits Of AI And Machine Learning | Expert Panel | Security News – SecurityInformed

The real possibility of advancing intelligence through deep learning and other AI-driven technology applied to video is that, in the long term, were not going to be looking at the video until after something has happened. The goal of gathering this high level of intelligence through video has the potential to be automated to the point that security operators will not be required to make the decisions necessary for response. Instead, the intelligence-driven next steps will be automatically communicated to various stakeholders from on-site guards to local police/fire departments. Instead, when security leaders access the video that corresponds to an incident, it will be because they want to see the incident for themselves. And isnt the automation, the ability to streamline response, and the instantaneous response the goal of an overall, data-rich surveillance strategy? For almost any enterprise, the answer is yes.

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Preparing new machine learning models used to take weeks Activeloop teams up with NVIDIA to reduce that time to hours – MENAFN.COM

(MENAFN - EIN Presswire)

Activeloop user interface and toolset work with NVIDIA processing to help InteinAir achieve great ML results

Activeloop.ai logo

Y Combinator alum achieves better aerial data pipelines for IntelinAir in an industry-leading Agriculture Tech solution

MOUNTAIN VIEW, CA, USA, August 4, 2020 / EINPresswire.com / -- In a case study now available online, Activeloop ( [To enable links contact MENAFN] ), a Y Combinator-backed startup, is announcing a major success in helping an early customer, IntelinAir , improve the efficiency of their AI analysis of aerial footage. Activeloop's software builds plug-and-play data pipelines for unstructured data. The software helps data scientists streamline their data aggregation and preparation, and automates and optimizes their training of machine learning models. Together with NVIDIA , Activeloop has achieved a massive reduction in the time-to-value and cost of machine learning / deep learning efforts. The case study documents a breakthrough in the field of aerial imagery with their joint customer IntelinAir, a leading crop intelligence firm.

Activeloop's solution is becoming available just in time for the exploding artificial intelligence and advanced machine learning market, projected to grow up to $281.24 billion by 2026 with CAGR of 37.95%. This coincides with the massive growth of data available to be analyzed by AI. All data generated by the end of 2020 will be about 40 trillion gigabytes (40 zettabytes), with IBM estimating that 90% of it has been created over the past 2 years. As data gets bigger faster than ever, translating it into actionable insights is becoming increasingly difficult and expensive. As a result, the effort needed to set up a new model and get it running efficiently can be beyond the reach of many teams who could otherwise benefit from machine learning. Existing solutions often have large cloud storage and processing costs. These solutions can't be made more efficient without radical changes.

'Unstructured data - including text, images, or videos, comprises about 80-90% of the data people generate today', says Davit Buniatyan, Activeloop Founder and CEO. 'As it comes in different forms, sizes, and even shapes, analyzing and managing it is an extremely difficult and costly task. In fact, data scientists spend about 50 to 80% of their time setting up their unstructured dataset rather than analyzing it via machine or deep learning. We're changing that by creating a fast, simple platform for building and scaling data pipelines for machine learning.'

'We operate in an agile fashion: we want to focus on building high-quality models instead of fighting with data pipelines, infrastructure, and deployment challenges' says Jennifer Hobbs, Director of Machine Learning at IntelinAir. 'Thanks to Activeloop, we've been able to deploy new models in a matter of days instead of weeks. With the help of Activeloop's platform and NVIDIA's powerful GPUs, we were able to increase the inference speed threefold and improve the accuracy of the trained models at half the cost."

You can read more about the success story here: [To enable links contact MENAFN] .

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About Activeloop

Activeloop ( [To enable links contact MENAFN] ), is a startup backed by Y Combinator and prominent Silicon Valley investors. The company has already been featured by major outlets including TechCrunch and is now coming out of stealth mode to make its product available to the machine learning community. Formerly named Snark AI, Activeloop aims to optimize the way machine and deep learning models are trained and streamline the huge amounts of data required for this work. Activeloop is a member of NVIDIA's Inception program for AI/ML development.

About IntelinAir

IntelinAir ( [To enable links contact MENAFN] ) is a full-season and full-spectrum crop intelligence company focused on agriculture that delivers actionable intelligence to help farmers make data-driven decisions to improve operational efficiency, yields, and ultimately their profitability.

Mikayel HarutyunyanActiveloop.ai+1 415-876-5667email us here Visit us on social media:Facebook Twitter LinkedIn

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Preparing new machine learning models used to take weeks Activeloop teams up with NVIDIA to reduce that time to hours - MENAFN.COM

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