Category Archives: Machine Learning

We share relevant third party stories on our website. This release was written and issued by Agxio.

A revolutionary new machine learning platform built entirely by the brilliance of AI could prove to be a vital weapon in the fight against coronavirus.

Apollo is a pioneering system to deliver a fully automated, AI-driven machine learning engine and is already being hailed as a game-changer.

Created by Cambridge and Aberystwyth-based applied AI innovation company, Agxio, Apollo operates beyond-human-scale performance, enabling the robotic platform to evaluate critical data to produce predictive models to solve real world problems. It then optimises these to look for patterns or configurations of parameters that human modellers may not even consider or have the patience to develop. And in a matter of hours.

With the appropriate data, Apollo and the power of machine learning can be used to analyse and predict the efficacy of potential vaccine combinations, outbreak trends, behavioural nudge factors, early warning indicators, medical images against risk indicators, and isolation rate projections, for example. The range of use cases for automated machine learning is however endless.

Importantly, the fully automated AI-driven engine doesnt require the user to be a programming expert or data scientist specialist enabling an expert in a non-data science or machine learning field to be able to study ideas or data that would otherwise take years of experience to be able to apply.

Agxio, which is already backed by the Welsh Government through the Development Bank of Wales, is now offering free use of the platform, together with its technical support team, to all credible researchers, practitioners and government bodies working to defeat COVID-19 for the duration of the pandemic.

Agxio CEO and co-founder, Dr Stephen Christie says: Whats different about Apollo is that this is AI built by AI - artificially intelligent machine learning. Its the machine building the machines, a series of robots building the best brains to answer targeted questions. Apollo is designed to focus on problems that are beyond human scale in dimension or complexity and is, without doubt, the most advanced approach of its kind.

What would take a human literally weeks and months to do, Apollo can generate in minutes and hours. Machine learning is one of the most important tools and defining technologies of our generation, and Apollo is a complete game-changer in terms of accelerating the building of machine learning and solutions.

While humans naturally tend to have biases, Apollo doesnt have any and is additionally data-agnostic. Most importantly, Apollo has speed and accuracy - and, right now, we need both to be really responsive to the situation. Accurate evaluation of data is vital in the governments planning of next-step measures. And I think it is critical for the government to be using the best tools and techniques we have available at this time.

To that end, the Agxio team has additionally created a single COVID-19 data portal for the global community. Coviddata.io is open to any parties for augmentation as cases, data and innovations evolve.

Dr Christie who was awarded Tech CEO of the Year 2019 and 2020 (Innovation & Excellence Awards) and has additionally won Life Sciences Awards (EBA) two years running - explains: If you are going to do anything around research and machine learning, data is critical - as is the sharing and pooling of that data in a properly trusted and curated form, and making the data accessible and available to researchers.

When making projections on isolation rates and strategies, you need real data and an engine that is able to crunch that data in a structured way, which is Apollo. Secondly, you need the data to be carefully curated and comprehensive. If you dont have either of those, youre going to struggle to come up with the correct answer.

Agxio secured investment from the Development Bank of Wales in January 2020. Andrew Critchley is an Investment Executive with the Development Bank of Wales. He adds: As backers of Agxio, we are delighted to see the company offering free use of their Apollo platform and expertise to help with the fight against Covid19.

Weve got to work together to beat this pandemic. Agxios cutting edge technology has the potential to help save lives, the impact could be global.

Apollo was originally developed as an expert system to enable arable farmers to analyse traditional and advanced IoT data to address the growing populations needs for improved yields and disease resistance. However, it has since proved to be a powerful tool for a number of different applications including fraud analytics, disease detection, economic anomalies, and bio-sequencing applications - automating the role of the data scientist to build optimal machine learning models against a target prediction. Data-agnostic, it can operate on numerical, textual and image data, both on and off premises.

Agxio is keen to hear from any data scientists and Python machine learning programmers who would like to volunteer support to researchers projects. If you would like to put your COVID-19 initiative forward for access to the Apollo platform, or volunteer your technical expertise to projects, please contact Covid-19@agxio.com

For more information please visit http://www.agxio.com.

Read more from the original source:
Agxio offers AI-built-by-AI fully-automated machine learning platform free in global fight against COVID-19 - Development Bank of Wales

Parasoft C/C++test is honored for its leading technology to increase software engineer productivity and achieve safety compliance

MONROVIA, Calif., April 7, 2020 /PRNewswire/ --Parasoft, a global software testing automation leader for over 30 years, received the VDC Research Embedded Award for 2020. The technology research and consulting firm yearly recognizes cutting-edge Software and Hardware Technologies in the embedded industry. This year, Parasoft C/C++test, aunified development testing solution forsafety and securityof embedded C and C++ applications, was recognized for its new, innovative approach that expedites the adoption of software code analysis, increasing developer productivity and simplifying compliance with industry standards such as CERT C/C++, MISRA C 2012 and AUTOSAR C++14. To learn more about Parasoft C/C++test, please visit: https://www.parasoft.com/products/ctest.

Parasoft C/C++test is honored for its leading technology to increase software engineer productivity and achieve safety compliance

"Parasoft has continued its investment in the embedded market, adding new products and personnel to boost its market presence. In addition to highlighting expanded partnerships and coding-standard support, the company announced the integration of AI capabilities into its static analysis engine. While defect prioritization systems have been part of static analysis solutions for well over ten years, Parasoft's solution takes the idea a step further. Their solution now effectively learns from past interactions with identified defects and the codebase to better help users triage new findings," states Chris Rommel, EVP, VDC Research Group.

Parasoft's latest innovation applies AI/Machine Learning to the process of reviewing static analysis findings. Static analysis is a foundational part of the quality process, especially in safety-critical development (e.g., ISO26262, IEC61508), and is an effective first step to establish secure development practices. A common challenge when deploying static analysis tools is dealing with the multitude of reported findings. Scans can produce tens of thousands of findings, and teams of highly qualified resources need to go through a time-consuming process of reviewing and identifying high-priority findings. This process leads to finding and reviewing critical issues late in the cycle, delaying the delivery, and worse, allowing insecure/unsafe code to become embedded into the codebase.

Parasoft leaps forwardbeyond the rest of the competitive market by having AI/ML take into account the context of both historical interactions with the code base and prior static analysis findings to predict relevance and prioritize new findings. This innovation helps organizations achieve compliance with industry standards and offers a unique application of AI/ML in helping organizations with the adoption of Static Analysis. This innovative technology builds on Parasoft's previous AI/ML innovations in the areas of Web UI, API, and Unit testing - https://blog.parasoft.com/what-is-artificial-intelligence-in-software-testing.

"We are extremely honored to have received this award, particularly in light of the competition, VDC's expertise and knowledge of the embedded market," said Mark Lambert, VP of Products at Parasoft. "We have always been committed to innovation led by listening to our customers and leveraging capabilities that will help drive them forward. This creativity has always driven Parasoft's development and is something that has been in the company's DNA from its founding."

Story continues

About Parasoft (www.parasoft.com):Parasoft, the global leader in software testing automation, has been reducing the time, effort, and cost of delivering high-quality software to the market for the last 30+ years. Parasoft's tools support the entire software development process, from when the developer writes the first line of code all the way through unit and functional testing, to performance and security testing, leveraging simulated test environments along the way. Parasoft's unique analytics platform aggregates data from across all testing practices, providing insights up and down the testing pyramid to enable organizations to succeed in today's most strategic development initiatives, including Agile/DevOps, Continuous Testing, and the complexities of IoT.

View original content to download multimedia:http://www.prnewswire.com/news-releases/parasoft-wins-2020-vdc-research-embeddy-award-for-its-artificial-intelligence-ai--and-machine-learning-ml-innovation-301036797.html

SOURCE Parasoft

Here is the original post:
Parasoft wins 2020 VDC Research Embeddy Award for Its Artificial Intelligence (AI) and Machine Learning (ML) Innovation - Yahoo Finance

New Jersey, United States: Market Research Intellect has added a new research report titled, AI/Machine Learning Market Professional Survey Report 2020 to its vast collection of research reports. The AI/Machine Learning market is expected to grow positively for the next five years 2020-2026.

The AI/Machine Learning market report studies past factors that helped the market to grow as well as, the ones hampering the market potential. This report also presents facts on historical data from 2011 to 2019 and forecasts until 2026, which makes it a valuable source of information for all the individuals and industries around the world. This report gives relevant market information in readily accessible documents with clearly presented graphs and statistics. This report also includes views of various industry executives, analysts, consultants, and marketing, sales, and product managers.

Market Segment as follows:

The global AI/Machine Learning Market report highly focuses on key industry players to identify the potential growth opportunities, along with the increased marketing activities is projected to accelerate market growth throughout the forecast period. Additionally, the market is expected to grow immensely throughout the forecast period owing to some primary factors fuelling the growth of this global market. Finally, the report provides detailed profile and data information analysis of leading AI/Machine Learning company.

AI/Machine Learning Market by Regional Segments:

The chapter on regional segmentation describes the regional aspects of the AI/Machine Learning market. This chapter explains the regulatory framework that is expected to affect the entire market. It illuminates the political scenario of the market and anticipates its impact on the market for AI/Machine Learning .

The AI/Machine Learning Market research presents a study by combining primary as well as secondary research. The report gives insights on the key factors concerned with generating and limiting AI/Machine Learning market growth. Additionally, the report also studies competitive developments, such as mergers and acquisitions, new partnerships, new contracts, and new product developments in the global AI/Machine Learning market. The past trends and future prospects included in this report makes it highly comprehensible for the analysis of the market. Moreover, The latest trends, product portfolio, demographics, geographical segmentation, and regulatory framework of the AI/Machine Learning market have also been included in the study.

Ask For Discount (Special Offer: Get 25% discount on this report) @ https://www.marketresearchintellect.com/ask-for-discount/?rid=193669&utm_source=SI&utm_medium=888

Table of Content

1 Introduction of AI/Machine Learning Market1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 AI/Machine Learning Market Outlook4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 AI/Machine Learning Market, By Deployment Model5.1 Overview

6 AI/Machine Learning Market, By Solution6.1 Overview

7 AI/Machine Learning Market, By Vertical7.1 Overview

8 AI/Machine Learning Market, By Geography8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 AI/Machine Learning Market Competitive Landscape9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix11.1 Related Research

Complete Report is Available @ https://www.marketresearchintellect.com/product/global-ai-machine-learning-market-size-and-forecast/?utm_source=SI&utm_medium=888

We also offer customization on reports based on specific client requirement:

1-Freecountry level analysis forany 5 countriesof your choice.

2-FreeCompetitive analysis of any market players.

3-Free 40 analyst hoursto cover any other data points

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage and more. These reports deliver an in-depth study of the market with industry analysis, market value for regions and countries and trends that are pertinent to the industry.

Contact Us:

Mr. Steven FernandesMarket Research IntellectNew Jersey ( USA )Tel: +1-650-781-4080

Email: [emailprotected]

Get Our Trending Report

https://www.marketresearchblogs.com/

https://www.marktforschungsblogs.com/

Tags: AI/Machine Learning Market Size, AI/Machine Learning Market Growth, AI/Machine Learning Market Forecast, AI/Machine Learning Market Analysis, AI/Machine Learning Market Trends, AI/Machine Learning Market

Go here to read the rest:
AI/Machine Learning Market Size Analysis, Top Manufacturers, Shares, Growth Opportunities and Forecast to 2026 - Science In Me

New Jersey, United States: Market Research Intellect has added a new research report titled, Machine Learning as a Service Market Professional Survey Report 2020 to its vast collection of research reports. The Machine Learning as a Service market is expected to grow positively for the next five years 2020-2026.

The Machine Learning as a Service market report studies past factors that helped the market to grow as well as, the ones hampering the market potential. This report also presents facts on historical data from 2011 to 2019 and forecasts until 2026, which makes it a valuable source of information for all the individuals and industries around the world. This report gives relevant market information in readily accessible documents with clearly presented graphs and statistics. This report also includes views of various industry executives, analysts, consultants, and marketing, sales, and product managers.

Key Players Mentioned in the Machine Learning as a Service Market Research Report:

Market Segment as follows:

The global Machine Learning as a Service Market report highly focuses on key industry players to identify the potential growth opportunities, along with the increased marketing activities is projected to accelerate market growth throughout the forecast period. Additionally, the market is expected to grow immensely throughout the forecast period owing to some primary factors fuelling the growth of this global market. Finally, the report provides detailed profile and data information analysis of leading Machine Learning as a Service company.

Machine Learning as a Service Market by Regional Segments:

The chapter on regional segmentation describes the regional aspects of the Machine Learning as a Service market. This chapter explains the regulatory framework that is expected to affect the entire market. It illuminates the political scenario of the market and anticipates its impact on the market for Machine Learning as a Service .

The Machine Learning as a Service Market research presents a study by combining primary as well as secondary research. The report gives insights on the key factors concerned with generating and limiting Machine Learning as a Service market growth. Additionally, the report also studies competitive developments, such as mergers and acquisitions, new partnerships, new contracts, and new product developments in the global Machine Learning as a Service market. The past trends and future prospects included in this report makes it highly comprehensible for the analysis of the market. Moreover, The latest trends, product portfolio, demographics, geographical segmentation, and regulatory framework of the Machine Learning as a Service market have also been included in the study.

Ask For Discount (Special Offer: Get 25% discount on this report) @ https://www.marketresearchintellect.com/ask-for-discount/?rid=195381&utm_source=SI&utm_medium=888

Table of Content

1 Introduction of Machine Learning as a Service Market1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Machine Learning as a Service Market Outlook4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Machine Learning as a Service Market, By Deployment Model5.1 Overview

6 Machine Learning as a Service Market, By Solution6.1 Overview

7 Machine Learning as a Service Market, By Vertical7.1 Overview

8 Machine Learning as a Service Market, By Geography8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Machine Learning as a Service Market Competitive Landscape9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix11.1 Related Research

Complete Report is Available @ https://www.marketresearchintellect.com/product/global-machine-learning-as-a-service-market-size-and-forecast/?utm_source=SI&utm_medium=888

We also offer customization on reports based on specific client requirement:

1-Freecountry level analysis forany 5 countriesof your choice.

2-FreeCompetitive analysis of any market players.

3-Free 40 analyst hoursto cover any other data points

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage and more. These reports deliver an in-depth study of the market with industry analysis, market value for regions and countries and trends that are pertinent to the industry.

Contact Us:

Mr. Steven FernandesMarket Research IntellectNew Jersey ( USA )Tel: +1-650-781-4080

Email: [emailprotected]

Get Our Trending Report

https://www.marketresearchblogs.com/

https://www.marktforschungsblogs.com/

Tags: Machine Learning as a Service Market Size, Machine Learning as a Service Market Growth, Machine Learning as a Service Market Forecast, Machine Learning as a Service Market Analysis, Machine Learning as a Service Market Trends, Machine Learning as a Service Market

Read more:
Machine Learning as a Service Market Size Analysis, Top Manufacturers, Shares, Growth Opportunities and Forecast to 2026 - Science In Me

Awarded to recognize Google Cloud partners who have made a positive impact on the world

Quantiphi, an award-winning applied artificial intelligence and data science software and services company, announced today that it has been named 2019 Social Impact Partner of the Year by Google Cloud. Quantiphi was recognized for its achievements for working with nonprofits, research institutions, and healthcare providers, to leverage AI for Social Good.

We are believers in the power of human acumen and technology to solve the worlds toughest challenges. This award is a recognition of our mission driven culture and our passion to apply AI for social good, said Asif Hasan, Co-founder, Quantiphi. Partnering with Google Cloud has given us the opportunity to work with the worlds leading nonprofit, healthcare and research institutions and we are truly humbled by this recognition.

Recommended AI News:Opinion: Young Jamaicans Invention Could Help Tackle Spread of Viruses Like COVID-19

Were delighted to recognize Quantiphis commitment to social impact, said Carolee Gearhart, Vice President, Worldwide Channel Sales at Google Cloud. By applying its capabilities in AI and ML to important causes, Quantiphi has demonstrated how Google Cloud partners are contributing to positive change in the world.

A few initiatives that helped Quantiphi earn this recognition:

Recommended AI News:Automation Provides A Content Lifeline For Remote Work

Quantiphi previously earned the Google Cloud Machine Learning Partner of the Year twice in a row for 2018 and 2017 and is a premier partner for Google Cloud and holds Specializations in machine learning, data analytics and marketing analytics.

Recommended AI News:Identity Theft is Booming; Your SSN Sells for Less than $4 on Darknet

Visit link:
Quantiphi Wins Google Cloud Social Impact Partner of the Year Award - AiThority

When Machines Design: Artificial Intelligence and the Future of Aesthetics

Facebook

Twitter

Pinterest

Whatsapp

Mail

Or

Are machines capable of design? Though a persistent question, it is one that increasingly accompanies discussions on architecture and the future of artificial intelligence. But what exactly is AI today? As we discover more about machine learning and generative design, we begin to see that these forms of "intelligence" extend beyond repetitive tasks and simulated operations. They've come to encompass cultural production, and in turn, design itself.

+ 8

When artificial intelligence was envisioned during thethe 1950s-60s, thegoal was to teach a computer to perform a range of cognitive tasks and operations, similar to a human mind. Fast forward half a century, andAIis shaping our aesthetic choices, with automated algorithms suggesting what we should see, read, and listen to. It helps us make aesthetic decisions when we create media, from movie trailers and music albums to product and web designs. We have already felt some of the cultural effects of AI adoption, even if we aren't aware of it.

As educator and theorist Lev Manovich has explained, computers perform endless intelligent operations. "Your smartphones keyboard gradually adapts to your typing style. Your phone may also monitor your usage of apps and adjust their work in the background to save battery. Your map app automatically calculates the fastest route, taking into account traffic conditions. There are thousands of intelligent, but not very glamorous, operations at work in phones, computers, web servers, and other parts of the IT universe."More broadly, it's useful to turn the discussion towards aesthetics and how these advancements relate to art, beauty and taste.

Usually defined as a set of "principles concerned with the nature and appreciation of beauty, aesthetics depend on who you are talking to. In 2018, Marcus Endicott described how, from the perspective of engineering, the traditional definition of aesthetics in computing could be termed "structural, such as an elegant proof, or beautiful diagram." A broader definition may include more abstract qualities of form and symmetry that "enhance pleasure and creative expression." In turn, as machine learning is gradually becoming more widely adopted, it is leading to what Marcus Endicott termed a neural aesthetic. This can be seen in recent artistic hacks, such as Deepdream, NeuralTalk, and Stylenet.

Beyond these adaptive processes, there are other ways AI shapes cultural creation. Artificial intelligence hasrecently made rapid advances in the computation of art, music, poetry, and lifestyle. Manovich explains that AIhas given us the option to automate our aesthetic choices (via recommendation engines), as well as assist in certain areas of aesthetic production such as consumer photography and automate experiences like the ads we see online. "Its use of helping to design fashion items, logos, music, TV commercials, and works in other areas of culture is already growing." But, as he concludes, human experts usually make the final decisions based on ideas and media generated by AI. And yes, the human vs. robot debate rages on.

According to The Economist, 47% of the work done by humans will have been replaced by robots by 2037, even those traditionally associated with university education. The World Economic Forum estimated that between 2015 and 2020, 7.1 million jobs will be lost around the world, as "artificial intelligence, robotics, nanotechnology and other socio-economic factors replace the need for human employees." Artificial intelligence is already changing the way architecture is practiced, whether or not we believe it may replace us. As AI is augmenting design, architects are working to explore the future of aesthetics and how we can improve the design process.

In a tech report on artificial intelligence, Building Design + Construction explored how Arup had applied a neural network to a light rail design and reduced the number of utility clashes by over 90%, saving nearly 800 hours of engineering. In the same vein, the areas of site and social research that utilize artificial intelligence have been extensively covered, and examples are generated almost daily. We know that machine-driven procedures can dramatically improve the efficiency of construction and operations, like by increasing energy performance and decreasing fabrication time and costs. The neural network application from Arup extends to this design decision-making. But the central question comes back to aesthetics and style.

Designer and Fulbright fellow Stanislas Chaillou recently created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans.

As Chaillou summarizes, architectural styles carry implicit mechanics of space, and there are spatial consequences to choosing a given style over another. In his words, style is not an ancillary, superficial or decorative addendum; it is at the core of the composition.

Artificial intelligence and machine learningare becomingincreasingly more important as they shape our future. If machines can begin to understand and affect our perceptions of beauty, we should work to find better ways to implement these tools and processes in the design process.

Architect and researcher Valentin Soana once stated that the digital in architectural design enables new systems where architectural processes can emerge through "close collaboration between humans and machines; where technologies are used to extend capabilities and augment design and construction processes." As machines learn to design, we should work with AI to enrich our practices through aesthetic and creative ideation.More than productivity gains, we can rethink the way we live, and in turn, how to shape the built environment.

See the original post:
When Machines Design: Artificial Intelligence and the Future of Aesthetics - ArchDaily