Category Archives: Machine Learning

London, June 03, 2020 (GLOBE NEWSWIRE) -- Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

Theartificial intelligence in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to 2027 to reach $21.8 billion by 2027. The growth in this market is mainly driven by rising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. In addition, consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain complementing growing industrial automation are further offering opportunities for vendors providing AI solutions in the supply chain industry. However, high deployment and operating costs and lack of infrastructure hinder the growth of the artificial intelligence in supply chain market.

In this study, the globalAI in supply chain market is segmented on the basis of component, application, technology, end user, and geography.

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Based on component, AI in supply chain market is broadly segmented into hardware, software, and services. The software segment commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increasing demand for AI-based platforms and solutions, as they offer supply chain visibility through software, which include inventory control, warehouse management, order procurement, and reverse logistics & tracking.

Based on technology, AI in supply chain market is broadly segmented into machine learning, computer vision, natural language processing, and context-aware computing. In 2019, the machine learning segment commanded the largest share of the overall AI in supply chain market. This growth can be attributed to the growing demand for AI-based intelligent solutions; increasing government initiatives; and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies

Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

Based on end user, artificial intelligence in supply chain market is broadly segmented into manufacturing, food & beverage, healthcare, automotive, aerospace, retail, and consumer packaged goods sectors. The retail sector commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increase in demand for consumer retail products.

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Based on geography, the global artificial intelligence in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In 2019, North America commanded for the largest share of the global artificial intelligence in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

On the other hand, the Asia-Pacific region is projected to grow at the fastest CAGR during the forecast period. The high growth rate is attributed to rapidly developing economies in the region; presence of young and tech-savvy population in this region; and growing proliferation of internet of things (IoT); rising disposable income; increasing acceptance of modern technologies across several industries including automotive, manufacturing, and retail; and broadening implementation of computer vision technology in numerous applications. Furthermore, the growing adoption of AI-based solutions and services among supply chain operations, increasing digitalization in the region, and improving connectivity infrastructure are also playing a significant role in the growth of this market in the region.

The globalAI in supply chain market is fragmented in nature and is characterized by the presence of several companies competing for the market share. Some of the leading companies in the artificial intelligence in supply chain market are from the core technology background. These include IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google LLC (U.S.), and Amazon.com, Inc. (U.S.). These companies are leading the market owing to their strong brand recognition, diverse product portfolio, strong distribution & sales network, and strong organic & inorganic growth strategies. The other key players in the global artificial intelligence in supply chain market are Intel Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Samsung (South Korea), LLamasoft, Inc. (U.S.), SAP SE (Germany), General Electric (U.S.), Deutsche Post DHL Group (Germany), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), FedEx Corporation (U.S.), ClearMetal, Inc. (U.S.), Dassault Systmes (France), and JDA Software Group, Inc. (U.S.), among others.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous...

Special Operations Commands Gen. Richard Clarke with students at the Special Forces Qualification Course.

WASHINGTON: Special Operations Command is in a war for influence with adversaires from non-state groups to state-funded information operations, the commands top general said recently, and is rushing to fund artificial intelligence and machine learning programs to find an edge.

Were going to have to have artificial intelligence and machine learning tools, specifically for information ops that hit a very broad portfolio, SOCOM commander Gen. Richard Clarke said recently, because were going to have to understand how the adversary is thinking, how the population is thinking, and work in these spaces.

Special Operations have cultivated an image in popular culture over two decades of constant war in the Middle East as almost superhuman door kickers dropping from the sky to blast their way quickly through an objective, disappearing as quickly as they had arrived. That view has in part led policymakers and the public to look to these troops as a solution to almost any problem, placing an enormous burden on a force of about 70,000 troops.

Clarke said that kinetic mission wont change any time soon, but other missions the various tribes of SOCOM and SOF have always performed intelligence gathering, training and advising, and influence operations need to be reprioritized.

We need coders, he told the virtual Special Operations Forces Industry Conference last month. Weve been having discussions internally that the most important person on the mission is no longer the operator kicking down the door, but the cyber operator who the team has to actually get to the environment so he or she can work their cyber tools into the fight.

SOCOM has started using AI in developing information operations in places like Afghanistan, but the commands interest is hardly limited to that space.

Acquisition chief Jim Smith told the conference his team is looking at a wide range of applications for employing AI, including intel gathering and fusion, surveillance and reconnaissance, precision fires, and health and training efforts. All of these functions are time and manpower-intensive, requiring long hours and entire teams to collect, understand, analyze, and move data, sometimes forcing troops to react as opposed to seizing initiative.

Those tasks are becoming more critical as defense budgets tighten and adversaries catch up and even surpass US capabilities across a wide range of technologies and capabilities.

So how do we use artificial intelligence and machine learning to get those sensors to interoperate autonomously and provide feedback to a single operator to enable that force to maneuver on the objective? Smith asked, noting that this is one of the biggest issues his office is coping with/.

Think of those small UAVs or your small ground vehicles and give them enough artificial intelligence and machine learning to be able to be autonomous, so that they can clear a building or they can clear a tunnel, which then allows the maneuver force to focus on other tasks.

These technologies could also help operators in the field launch countermeasures to intercept and disrupt enemy communications, which right now can be a slow process.

Today the way we do that is we have a library of threat radar signatures Smith said, and if you see one of those threat radars in our library we counter it. So SOCOM is looking for ways to use machine learning to identify anomalies in this space so it wasnt just the threat radars we had loaded into the library, that were already known, but maybe its a new radar that we havent seen before or a radar that we didnt realize was operating in that theater that we could identify.

Smith said his approach is to bake in AI and machine learning requirements with every program that SOCOM develops from here on out.

What were starting to see is our industry partners coming in on proposals and theyre baking in artificial intelligence and machine learning, he said. Thats exactly where we want to be.

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SOCOM Looking To Bake In AI Requirements On Every New Program - Breaking Defense

WILMINGTON, Del., June 03, 2020 (GLOBE NEWSWIRE) -- InterDigital, Inc. (NASDAQ:IDCC), a mobile and video technology research and development company, today introduced the worlds first cloud gaming solution with an AI and machine learning-enabled user interface, presented in collaborative partnership with cloud gaming trailblazer Blacknut and in cooperation with GPU pioneer Nvidia. The tripartite collaboration represents the first time that an AI and machine learning-driven user interface is utilized, wearable-free, with a live cloud gaming solution. The technology demonstrates the incredible potential of integrating localized and far-Edge enabled AI capabilities into home gaming experiences.

The AI and machine learning-enabled user interface is connected to a cloud gaming solution that operates without joysticks or wearable accessories. The demonstration leverages unique technologies, including real-time video analysis on home and local edge devices, dynamic adaptation to available compute resources, and shared AI models managed through an in-home AI hub, to implement a cutting-edge gaming experience.

In the demonstration, users play a first-person view snowboarding game streamed by Blacknut and displayed on a commercial television. Users do not require a joystick or handheld controller to play the game; instead, their movements and interactions are tracked by AI processing of the live video capture of the users movements. The users presence is detected using an AI model and his or her body movements are matched with the snowboarder in the game, in real time, using InterDigitals low latency Edge AI running on a local AI accelerator. The groundbreaking demo addresses the challenges of ensuring the lowest possible end-to-end latency from gesture capture to game action, while accelerating inference of concurrent AI models serving multiple applications to deliver an interactive and more seamless gaming experience. This demonstration enables AI and machine learning tasks to be completed locally, revolutionizing our current implementation of cloud gaming solutions.

We are so proud of the work of this demonstration, as it displays the real potential of AI and edge computing, highlights the power of industry collaboration, and helps blaze a trail for new cloud gaming capabilities. Of course, such a success would not have been possible without the utmost implication of all the teams from Interdigital, Blacknut, and Nvidia, and I would like to take the opportunity to credit and thank their outstanding work, said Laurent Depersin, Director of the Home Experience Lab at InterDigital.

The far-Edge AI and machine learning technologies put forth by InterDigital bring a plethora of new capabilities to the cloud gaming experience. Far-Edge AI enables low-latency analysis to deliver an interactive and entertaining experience, reduces cloud computing costs by leveraging available computing resources, and saves significant bandwidth by prioritizing up-linking. In addition, far-Edge AI in edge cloud architecture offers an important solution for privacy concerns by localizing computing and supports a variety of new and emerging vertical applications beyond gaming, including smart home and security, remote healthcare, and robotics.

Cloud gaming with far-Edge AI leverages artificial intelligence and localized Edge computing to showcase the ways an interactive television or gaming experience can be enhanced by the localized AI analysis of a cameras video stream. Ongoing research in the real-time processing of user generated data will drive new innovations and vertical applications in the home, from cloud gaming to remote medical care, and those innovations will be enhanced by the ability to execute artificial intelligence models under low latency conditions.

Blacknuts mission is to bring to our customers unlimited hours of gaming fun in the simplest manner, said Pascal Manchon, CTO at Blacknut. Our unique cloud gaming solution allows to free games from dedicated consoles or hardware. Using AI and machine learning to transform the human body itself in a full-fledge game controller was challenging but Blacknuts close collaboration with Interdigital and NVidia led to outstanding performances. And yes, it is addictive and fun to play this way!

Cloud gaming is an exciting industry use case that leverages innovations in network architecture, video streaming and content delivery to shape the future of interactive gaming and entertainment. This worlds first cloud gaming solution, and the broader exploration of AI-enabled cloud solutions, would not be possible without a commitment to collaboration with industry leaders and partners.

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InterDigital, Blacknut, and Nvidia unveil worlds first Cloud gaming solution with AI-enabled user interface - TelecomTV

(Photo by Vitaly NevarTASS via Getty Images)

Wind farms have traditionally made less money for the electricity they produce because they have been unable to predict how windy it will be tomorrow.

The way a lot of power markets work is you have to schedule your assets a day ahead, said Michael Terrell, the head of energy market strategy at Google. And you tend to get compensated higher when you do that than if you sell into the market real-time.

Well, how do variable assets like wind schedule a day ahead when you don't know the wind is going to blow? Terrell asked, and how can you actually reserve your place in line?

We're not getting the full benefit and the full value of that power.

Heres how: Google and the Google-owned Artificial Intelligence firm DeepMind combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central United States. Using machine learning, they have been able to better predict wind production, better predict electricity supply and demand, and as a result, reduce operating costs.

What we've been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that's available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets, Terrell said in a recent seminar hosted by the Stanford Precourt Institute of Energy. Stanford University posted video of the seminar last week.

The result has been a 20 percent increase in revenue for wind farms, Terrell said.

The Department of Energy listed improved wind forecasting as a first priority in its 2015 Wind Vision report, largely to improve reliability: Improve Wind Resource Characterization, the report said at the top of its list of goals. Collect data and develop models to improve wind forecasting at multiple temporal scalese.g., minutes, hours, days, months, years.

Googles goal has been more sweeping: to scrub carbon entirely from its energy portfolio, which consumes as much power as two San Franciscos.

Google achieved an initial milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell said. But the company has not been carbon-free in every location at every hour, which is now its new goalwhat Terrell calls its 24x7 carbon-free goal.

We're really starting to turn our efforts in this direction, and we're finding that it's not something that's easy to do. It's arguably a moon shot, especially in places where the renewable resources of today are not as cost effective as they are in other places.

The scientists at London-based DeepMind have demonstrated that artificial intelligence can help by increasing the market viability of renewables at Google and beyond.

Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide, said DeepMind program manager Sims Witherspoon and Google software engineer Carl Elkin. In a Deepmind blog post, they outline how they boosted profits for Googles wind farms in the Southwest Power Pool, an energy market that stretches across the plains from the Canadian border to north Texas:

Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind-power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance.

The DeepMind system predicts wind-power output 36 hours in advance, allowing power producers to make ... [+] more lucrative advance bids to supply power to the grid.

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Thanks To Renewables And Machine Learning, Google Now Forecasts The Wind - Forbes

Emma Woollacott02 June 2020 at 13:14 UTC Updated: 02 June 2020 at 14:48 UTC

ML security evasion event is based on a similar competition held at DEF CON 27 last summer

The defensive capabilities of machine learning (ML) systems will be stretched to the limit at a Microsoft security event this summer.

Along with various industry partners, the company is sponsoring a Machine Learning Security Evasion Competition involving both ML experts and cybersecurity professionals.

The event is based on a similar competition held at AI Village at DEF CON 27 last summer, where contestants took part in a white-box attack against static malware machine learning models.

Several participants discovered approaches that completely and simultaneously bypassed three different machine learning anti-malware models.

The 2020 Machine Learning Security Evasion Competition is similarly designed to surface countermeasures to adversarial behavior and raise awareness about the variety of ways ML systems may be evaded by malware, in order to better defend against these techniques, says Hyrum Anderson, Microsofts principal architect for enterprise protection and detection.

The competition will consist of two different challenges. A Defender Challenge will run from June 15 through July 23, with the aim of identifying new defenses to counter cyber-attacks.

The winning defensive technique will need to be able to detect real-world malware with moderate false-positive rates, says the team.

Next, an Attacker Challenge running from August 6 through September 18 provides a black-box threat model.

Participants will be given API access to hosted anti-malware models, including those developed in the Defender Challenge.

RECOMMENDED DEF CON 2020: Safe Mode virtual event will be free to attend, organizers confirm

Contestants will attempt to evade defenses using hard-label query results, with samples from final submissions detonated in a sandbox to make sure theyre still functional.

The final ranking will depend on the total number of API queries required by a contestant, as well as evasion rates, says the team.

Each challenge will net the winner $2,500 in Azure credits, with the runner up getting $500 in Azure credits.

To win, researchers must publish their detection or evasion strategies. Individuals or teams can register on the MLSec website.

Companies investing heavily in machine learning are being subjected to various degrees of adversarial behavior, and most organizations are not well-positioned to adapt, says Anderson.

It is our goal that through our internal research and external partnerships and engagements including this competition well collectively begin to change that.

READ MORE Going deep: How advances in machine learning can improve DDoS attack detection

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Microsoft throws weight behind machine learning hacking competition - The Daily Swig

The Machine Learning as a Service Market research report enhanced worldwide Coronavirus COVID19 impact analysis on the market size (Value, Production and Consumption), splits the breakdown (Data Status 2014-2019 and 6 Year Forecast From 2020 to 2026), by region, manufacturers, type and End User/application. This Machine Learning as a Service market report covers the worldwide top manufacturers like (Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, Xeround) which including information such as: Capacity, Production, Price, Sales, Revenue, Shipment, Gross, Gross Profit, Import, Export, Interview Record, Business Distribution etc., these data help the consumer know about the Machine Learning as a Service market competitors better. It covers Regional Segment Analysis, Type, Application, Major Manufactures, Machine Learning as a Service Industry Chain Analysis, Competitive Insights and Macroeconomic Analysis.

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Scope of Machine Learning as a Service Market:Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

On the basis on the end users/applications,this report focuses on the status and outlook for major applications/end users, shipments, revenue (Million USD), price, and market share and growth rate foreach application.

Personal Business

On the basis of product type, this report displays the shipments, revenue (Million USD), price, and market share and growth rate of each type.

Private clouds Public clouds Hybrid cloud

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Important Machine Learning as a Service Market Data Available In This Report:

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Machine Learning as a Service Market ShareYear-Over-Year Growthof Key Players in Promising Regions.

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