Category Archives: Machine Learning

If you are in any way connected to the computer world, you have heard of the term machine learning. It is such an important concept, but it has been used as a buzzword so much that it is starting to lose its effectiveness. That said, machine learning is one of the most important developments in the computing world and if it can be utilized to its full potential, it is set to revolutionize the way we use computers. Because of its versatility and flexibility, machine learning can be used in almost any industry where tasks can be automated. These are industries where machines can learn to think like humans and be able to perform at the same level as or even better than humans. One of these areas is transportation.

When many people think about artificial intelligence (AI) and machine learning as it ties into the automotive industry, they think about driverless cars and fleets of cars communicating with each other in real-time. While this is one part of it, there is so much more. Machine learning can be used to:

When doing their research, scientists and computer programmers are starting to look at machine learning at a higher level and using it to revolutionize the engine and to help decision-makers make the best decisions about transportation systems.

In the past, computer programmers had to write code that told the computer what to do in specific situations. This code would get more complex and unmaintainable as computer programmers tried to plan for and provide code for every case their program would encounter. Now, programmers can write the base code and use neural networks to train computers on what to do in all these different scenarios. Because computers are able to crunch data faster than we could, they are able to discover cases we never could.

Now, computer programmers feed machine learning algorithms using:

The machines are then asked to find a relationship between the two. Once it is done, the data produced is used to create modes that are used to make predictions.

Researchers are using machine learning to explore how transportation systems are designed. This helps them understand what issues are contained therein and how they affect entire transportation systems.

Their research will help transportation departments:

Understanding the complexity of transportation systems is almost impossible unless researchers comb through a huge amount of data. Machine learning can help them not only decipher this data, but also help them find trends and relationships and see how both of these affect transportation systems.

The insights that come out of such explorations will help:

These insights also help with decision making as they can help people and autonomous vehicles make better decisions, help coordinate emergency responses and help planners minimize the impact of the disruption of a transportation system in a given area.

Machine learning is also being used to optimize engine designs and the processes used to produce these engines. For example, researchers have been able to develop new combustion models using machine learning. These models have reduced the amount of time it takes to complete engine combustion simulations.

Using neural networks, researchers have also been able to model complex properties that were previously not available. Now, scientists can create complex reaction pathways to see how combustion happens inside new engine models. Because of this, researchers and automotive manufacturers are able to better optimize their engines.

In the past, researchers were forced to reduce the complexity of their combustion models. This is because they did not have powerful tools to help them carry out complex simulations. This led to data that was not as accurate as it should have been. All this has now changed with the advancement of machine learning, deep learning, AI and neural networks.

Computers that run machine learning models are very good at making predictions using past data. This data can be used to optimize route planning for both drivers and fleet managers. Machine learning can help these parties understand:

Once drivers and fleet operators understand all these things, they can choose cars and routes that save fuel while saving time and maximizing transportation efficiency.

The only way to understand what is going to happen in the future is to make predictions that are as accurate as possible. This has been enabled by the use of machine learning. Machine learning is being used to predict how transportation systems will look in the future. Researchers are doing this with the aim of predicting the impact of the transportation system on the world around us as it continues to grow and how this growing transportation system will impact energy needs.

Researchers are forced to model their predictions using:

Using these predictions, researchers can see how different technologies will impact transportation systems of the future. This allows them to focus on the technologies that will have the most impact.

Machine learning has brought us new modes of transport. These include autonomous cars, driverless shuttles and more. You can click here to learn more about how future transportation is likely to look. Perhaps the most common mode of transportation impacted by machine learning is autonomous cars.

Autonomous cars are fitted with computers that run different scenarios as they drive or are driven around. This computer makes it possible for this car to identify:

All this data is used to identify the safest route to follow to avoid collisions and keep transportation systems as safe as possible. As it stands, these cars need a human to always be behind the wheel in case of an emergency. As this technology matures and the computers in autonomous cars become more powerful, we will have cars that can drive themselves. The possibilities are both exciting and endless.

Perhaps we will have other autonomous modes of transportation like driverless trucks and autonomous airplanes. At this point, we can only speculate.

It is almost impossible to talk about the future of transportation without talking about 5G technology. 5G is the fifth generation of mobile communication and it comes with so many advantages. The most important of these are:

As we look into the future of driverless fleets of cars, it becomes clear that these cars need some way to communicate with each other. This is for purposes like overtaking, turning at junctions, giving right of way and more.

Ideally, we want these cars to communicate in real-time or as close to it as we can get. With low latency times and fast speeds, 5G stands as the best option for this purpose. Of course, communication technology will continue to evolve and we might see better speeds and lower latency in the future. That said, we already have something we can use to enable fleets of driverless cars.

Machine learning is a very complex topic, with both upsides and downsides, because we are just starting to see its potential. That said, there are some upsides that we are already seeing:

Machine learning has some downsides too. One of the biggest ones is job loss. As machine learning creates jobs in some sectors, it will lead to massive job losses in the transportation sector. Just think about all the drivers who will be left without a job if we switch to driverless cars. All these taxi and long-haul drivers will have to find new jobs.

There is no denying that machine learning is here and it will revolutionize the transportation sector. Its impacts on the reduction of fuel and the time it takes to get from one place to another are touted as its biggest achievements, as is the development of fuel-efficient engines, something that will have a massive positive impact on the environment.

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Machine Learning and How it Is Transforming Transportation - IT Business Net

Image: The Respiratory Epithelium (Photo courtesy of Wikimedia Commons)

In the study, the scientists identified ciliated cells as the major target of SARS-CoV-2 infection. The bronchial epithelium acts as a protective barrier against allergens and pathogens. Cilia removes mucus and other particles from the respiratory tract. Their findings offer insight into how the virus causes disease. The scientists infected HBECs in an air-liquid interface with SARS-CoV-2. Over a period of three days, they used single-cell RNA sequencing to identify signatures of infection dynamics such as the number of infected cells across cell types, and whether SARS-CoV-2 activated an immune response in infected cells.

The scientists utilized advanced algorithms to develop working hypotheses and used electron microscopy to learn about the structural basis of the virus and target cells. These observations provide insights about host-virus interaction to measure SARS-CoV-2 cell tropism, or the ability of the virus to infect different cell types, as identified by the algorithms. After three days, thousands of cultured cells became infected. The scientists analyzed data from the infected cells along with neighboring bystander cells. They observed ciliated cells were 83% of the infected cells. These cells were the first and primary source of infection throughout the study. The virus also targeted other epithelial cell types including basal and club cells. The goblet, neuroendocrine, tuft cells, and ionocytes were less likely to become infected.

The gene signatures revealed an innate immune response associated with a protein called Interleukin 6 (IL-6). The analysis also showed a shift in the polyadenylated viral transcripts. Lastly, the (uninfected) bystander cells also showed an immune response, likely due to signals from the infected cells. Pulling from tens of thousands of genes, the algorithms locate the genetic differences between infected and non-infected cells. In the next phase of this study, the scientists will examine the severity of SARS-CoV-2 compared to other types of coronaviruses, and conduct tests in animal models.

Machine learning allows us to generate hypotheses. Its a different way of doing science. We go in with as few hypotheses as possible. Measure everything we can measure, and the algorithms present the hypothesis to us, said senior author David van Dijk, PhD, an assistant professor of medicine in the Section of Cardiovascular Medicine and Computer Science.

Related Links:Yale School of Medicine

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Yale Researchers Use Single-Cell Analysis and Machine Learning to Identify Major COVID-19 Target - HospiMedica

Reports published inMarket Research Incfor the Machine Learning as a Service market are spread out over several pages and provide the latest industry data, market future trends, enabling products and end users to drive revenue growth and profitability. Industry reports list and study key competitors and provide strategic industry analysis of key factors affecting market dynamics. This report begins with an overview of the Machine Learning as a Service market and is available throughout development. It provides a comprehensive analysis of all regional and major player segments that provide insight into current market conditions and future market opportunities along with drivers, trend segments, consumer behavior, price factors and market performance and estimates over the forecast period.

Request a pdf copy of this report athttps://www.marketresearchinc.com/request-sample.php?id=16701

Key Strategic Manufacturers

:Microsoft (Washington,US), Amazon Web Services (Washington, US), Hewlett Packard Enterprises (California, US), Google, Inc

The report gives a complete insight of this industry consisting the qualitative and quantitative analysis provided for this market industry along with prime development trends, competitive analysis, and vital factors that are predominant in the Machine Learning as a Service Market.

The report also targets local markets and key players who have adopted important strategies for business development. The data in the report is presented in statistical form to help you understand the mechanics. The Machine Learning as a Service market report gathers thorough information from proven research methodologies and dedicated sources in many industries.

Avail 40% Discount on this report athttps://www.marketresearchinc.com/ask-for-discount.php?id=16701

Key Objectives of Machine Learning as a Service Market Report: Study of the annual revenues and market developments of the major players that supply Machine Learning as a Service Analysis of the demand for Machine Learning as a Service by component Assessment of future trends and growth of architecture in the Machine Learning as a Service market Assessment of the Machine Learning as a Service market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Machine Learning as a Service market Study of contracts and developments related to the Machine Learning as a Service market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Machine Learning as a Service across the globe.

Furthermore, the years considered for the study are as follows:

Historical year 2015-2019

Base year 2019

Forecast period 2020 to 2026

Table of Content:

Machine Learning as a Service Market Research ReportChapter 1: Industry OverviewChapter 2: Analysis of Revenue by ClassificationsChapter 3: Analysis of Revenue by Regions and ApplicationsChapter 6: Analysis of Market Revenue Market Status.Chapter 4: Analysis of Industry Key ManufacturersChapter 5: Marketing Trader or Distributor Analysis of Market.Chapter 6: Development Trend of Machine Learning as a Service market

Continue for TOC

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Machine Learning as a Service Market Benefits, Forthcoming Developments, Business Opportunities & Future Investments to 2027 - 3rd Watch News

New techniques based on intensive computing and massive amounts of distributed memory.

The pace of deep machine learning and artificial intelligence (AI) is changing the world of computing at all levels of hardware architecture, software, chip manufacturing, and system packaging. Two major developments have opened the doors to implementing new techniques in machine learning. First, vast amounts of data, i.e., Big Data, are available for systemsto process. Second, advanced GPU architectures now support distributed computing parallelization. With these two developments, designers can take advantage of new techniques that rely on intensive computing and massive amounts of distributed memory to offer new, powerful compute capabilities.

Neuromorphic computing-based machine learning utilizes techniques of Spiking Neural Networks (SNN), Deep Neural Networks (DNN) and Restricted Boltzmann Machines (RBM). Combined with Big Data, Big Compute is utilizing statistically based High-Dimensional Computing (HDC) that operates on patterns, supports reasoning built on associative memory and on continuous learning to mimic human memory learning and retention sequences.

Emerging memories range from Compute-In-memory SRAMs (CIM), STT-MRAMs, SOT-MRAMs, ReRAMs, CB-RAMs, and PCMs. The development of each type is simultaneously trying to enable a transformation in computation for AI. Together, they are advancing the scale of computational capabilities, energy efficiency, density, and cost.

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Neuromorphic Computing Drives The Landscape Of Emerging Memories For Artificial Intelligence SoCs - SemiEngineering

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.

To learn more about the demonstration of the worlds first cloud gaming solution with AI-enabled user interface, please click here.

About InterDigital

InterDigital develops mobile and video technologies that are at the core of devices, networks, and services worldwide. We solve many of the industrys most critical and complex technical challenges, inventing solutions for more efficient broadband networks, better video delivery, and richer multimedia experiences years ahead of market deployment. InterDigital has licenses and strategic relationships with many of the worlds leading technology companies. Founded in 1972, InterDigital is listed on NASDAQ and is included in the S&P MidCap 400 index.

InterDigital is a registered trademark of InterDigital, Inc.

For more information, visit: http://www.interdigital.com.

About Blacknut

Blacknut was founded in 2016 by Olivier Avaro (CEO) and is headquartered in Rennes, France, with offices in Paris and San Francisco. Blacknut designs, develops and commercializes a cloud gaming service. Blacknut first launched in France in 2018, for PC, Mac, and Linux. The service allows to play more than 400 premium games for a monthly subscription fee. Blacknut is now available across Europe & North America on a wider range of devices, including mobiles, set-top-boxes and Smart TVs. Blacknut is also distributed through major ISPs, device manufacturers, OTT services & Media companies.

For more information, visit: http://www.blacknut.com

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InterDigital, Blacknut, and Nvidia Unveil World's First Cloud Gaming Solution With AI-Enabled User Interface - GlobeNewswire

27th May 20209:41 am27th May 20209:41 am

IEDs and so-called butterfly landminescould be detected over wide areas using drones and advanced machine learning, according to research from Binghamton University, State University at New York.

The team had previously developed a method that allowed for the accurate detection of butterfly landmines using low-cost commercial drones equipped with infrared cameras.

EPSRC-funded project takes dual approach to clearing landmines

Their new research focuses on automated detection of landmines using convolutional neural networks (CNN), which they say is the standard machine learning method for object detection and classification in the field of remote sensing. This method is a game-changer in the field, said Alek Nikulin, assistant professor of energy geophysics at Binghamton University.

All our previous efforts relied on human-eye scanning of the dataset, Nikulin said in a statement.Rapid drone-assisted mapping and automated detection of scatterable mine fields would assist in addressing the deadly legacy of widespread use of small scatterable landmines in recent armed conflicts and allow to develop a functional framework to effectively address their possible future use.

There are at least 100 million military munitions and explosives of concern devices in the world, of various size, shape and composition. Furthermore,an estimated twenty landmines are placed for every landmine removed in conflict regions

Millions of these are surface plastic landmines with low-pressure triggers, such as the mass-produced Soviet PFM-1 butterfly landmine. Nicknamed for their small size and butterfly-like shape, these mines are extremely difficult to locate and clear due to their small size, low trigger mass and a design that mostly excluded metal components, making them virtually invisible to metal detectors.

The design of the mine combined with a low triggering weight have earned it notoriety as the toy mine, due to a high casualty rate among small children who find these devices while playing and who are the primary victims of the PFM-1 in post-conflict nations, like Afghanistan.

The researchers believe that these detection and mapping techniques are generalisable and transferable to other munitions and explosives. They could be adapted to detect and map disturbed soil for improvised explosive devices (IEDs).

The use of Convolutional Neural Network-based approaches to automate the detection and mapping of landmines is important for several reasons, the researchers said in a paper published inRemote Sensing. One, it is much faster than manually counting landmines from an orthoimage (i.e. an aerial image that has been geometrically corrected). Two, it is quantitative and reproducible, unlike subjective human error-prone ocular detection. And three, CNN-based methods are easily generalisable to detect and map any objects with distinct sizes and shapes from any remotely sensed raster images.

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Butterfly landmines mapped by drones and machine learning - The Engineer