Silicon Integration Initiativehas launched an industry-wide survey to identify planned usage and structural gaps for prioritizing and implementing artificial intelligence and machine learning in semiconductor electronic design automation.

The Si2 platform provides a unique opportunity for semiconductor companies, EDA suppliers and IP providers to voice their needs and focus resources on common solutions, including enabling and leveraging university research.

The survey is organized by a recently formed Si2 Special Interest Group chaired by Joydip Das, senior engineer, Samsung Electronics, and co-chaired by Kerim Kalafala, senior technical staff member, EDA, and master inventor, IBM. The 18-member group will identify where industry collaboration will help eliminate deficiencies caused by a lack of common languages, data models, labels, and access to robust and categorized training data.

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This SIG is open to all Si2 members. Current members include:

Advanced Micro DevicesANSYSCadence Design SystemsHewlett Packard EnterpriseIBMIntelIntento DesignKeysight TechnologiesMentor, a Siemens Business

NC State UniversityPFD Solutions

Qualcomm

Samsung Electronics

Sandia National LaboratoriesSilvaco

SynopsysThrace SystemsTexas Instruments

The survey is open April 15 May 15.

Leigh Anne Clevenger, Si2 senior data scientist, said that the survey results would help prioritize SIG activities and timelines. The SIG will identify and develop requirements for standards that ensure data and software interoperability, enabling the most efficient design flows for production, Clevenger said. The ultimate goal is to remove duplicative work and the need for data model translators, and focus on opening avenues for breakthroughs from suppliers and users alike.

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High manufacturing costs and the growing complexity of chip development are spurring disruptive technologies such as AI and ML, Clevenger explained. The Si2 platform provides a unique opportunity for semiconductor companies, EDA suppliers and IP providers to voice their needs and focus resources on common solutions, including enabling and leveraging university research.

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si2 Launches Survey on Artificial Intelligence and Machine Learning in Eda - AiThority

Machine learning has fundamentally changed how we engage with technology. Today, it's able to curate social media feeds, recognize complex images, drive cars down the interstate, and even diagnose medical conditions, to name a few tasks.

But while machine learning technology can do some things automatically, it still requires a lot of input from human engineers to set it up, and point it in the right direction. Inevitably, that means human biases and limitations are baked into the technology.

So, what if scientists could minimize their influence on the process by creating a system that generates its own machine-learning algorithms? Could it discover new solutions that humans never considered?

To answer these questions, a team of computer scientists at Google developed a project called AutoML-Zero, which is described in a preprint paper published on arXiv.

"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," the paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."

Automatic machine learning (AutoML) is a fast-growing area of deep learning. In simple terms, AutoML seeks to automate the end-to-end process of applying machine learning to real-world problems. Unlike other machine-learning techniques, AutoML requires relatively little human effort, which means companies might soon be able to utilize it without having to hire a team of data scientists.

AutoML-Zero is unique because it uses simple mathematical concepts to generate algorithms "from scratch," as the paper states. Then, it selects the best ones, and mutates them through a process that's similar to Darwinian evolution.

AutoML-Zero first randomly generates 100 candidate algorithms, each of which then performs a task, like recognizing an image. The performance of these algorithms is compared to hand-designed algorithms. AutoML-Zero then selects the top-performing algorithm to be the "parent."

"This parent is then copied and mutated to produce a child algorithm that is added to the population, while the oldest algorithm in the population is removed," the paper states.

The system can create thousands of populations at once, which are mutated through random procedures. Over enough cycles, these self-generated algorithms get better at performing tasks.

"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

If computer scientists can scale up this kind of automated machine-learning to complete more complex tasks, it could usher in a new era of machine learning where systems are designed by machines instead of humans. This would likely make it much cheaper to reap the benefits of deep learning, while also leading to novel solutions to real-world problems.

Still, the recent paper was a small-scale proof of concept, and the researchers note that much more research is needed.

"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.

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New AI improves itself through Darwinian-style evolution - Big Think

The COVID-19 pandemic is raising critical questions regarding the dynamics of the disease, its risk factors, and the best approach to address it in healthcare systems. MIT Sloan School of Management Prof. Dimitris Bertsimas and nearly two dozen doctoral students are using machine learning and optimization to find answers. Their effort is summarized in the COVIDanalytics platform where their models are generating accurate real-time insight into the pandemic. The group is focusing on four main directions; predicting disease progression, optimizing resource allocation, uncovering clinically important insights, and assisting in the development of COVID-19 testing.

The backbone for each of these analytics projects is data, which weve extracted from public registries, clinical Electronic Health Records, as well as over 120 research papers that we compiled in a new database. Were testing our models against incoming data to determine if it makes good predictions, and we continue to add new data and use machine-learning to make the models more accurate, says Bertsimas.

The first project addresses dilemmas at the front line, such as the need for more supplies and equipment. Protective gear must go to healthcare workers and ventilators to critically ill patients. The researchers developed an epidemiological model to track the progression of COVID-19 in a community, so hospitals can predict surges and determine how to allocate resources.

The team quickly realized that the dynamics of the pandemic differ from one state to another, creating opportunities to mitigate shortages by pooling some of the ventilator supply across states. Thus, they employed optimization to see how ventilators could be shared among the states and created an interactive application that can help both the federal and state governments.

Different regions will hit their peak number of cases at different times, meaning their need for supplies will fluctuate over the course of weeks. This model could be helpful in shaping future public policy, notes Bertsimas.

Recently, the researchers connected with long-time collaborators at Hartford HealthCare to deploy the model, helping the network of seven campuses to assess their needs. Coupling county level data with the patient records, they are rethinking the way resources are allocated across the different clinics to minimize potential shortages.

The third project focuses on building a mortality and disease progression calculator to predict whether someone has the virus, and whether they need hospitalization or even more intensive care. He points out that current advice for patients is at best based on age, and perhaps some symptoms. As data about individual patients is limited, their model uses machine learning based on symptoms, demographics, comorbidities, lab test results as well as a simulation model to generate patient data. Data from new studies is continually added to the model as it becomes available.

We started with data published in Wuhan, Italy, and the U.S., including infection and death rate as well as data coming from patients in the ICU and the effects of social isolation. We enriched them with clinical records from a major hospital in Lombardy which was severely impacted by the spread of the virus. Through that process, we created a new model that is quite accurate. Its power comes from its ability to learn from the data, says Bertsimas.

By probing the severity of the disease in a patient, it can actually guide clinicians in congested areas in a much better way, says Bertsimas.

Their fourth project involves creating a convenient test for COVID-19. Using data from about 100 samples from Morocco, the group is using machine-learning to augment a test previously designed at the Mohammed VI Polytechnic University to come up with more precise results. The model can accurately detect the virus in patients around 90% of the time, while false positives are low.

The team is currently working on expanding the epidemiological model to a global scale, creating more accurate and informed clinical risk calculators, and identifying potential ways that would allow us to go back to normality.

We have released all our source code and made the public database available for other people too. We will continue to do our own analysis, but if other people have better ideas, we welcome them, says Bertsimas.

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Research Team Uses Machine Learning to Track Covid-19 Spread in Communities and Predict Patient Outcomes - The Ritz Herald

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

Every day for the past few weeks, charts and graphs plotting the projected apex of Covid-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the Covid-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus.

Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology, explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).

Most models used to predict the spread of a disease follow what is known as the SEIR model, which groups people into susceptible, exposed, infected, and recovered. Dandekar and Barbastathis enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others.

The model finds that in places like South Korea, where there was immediate government intervention in implementing strong quarantine measures, the virus spread plateaued more quickly. In places that were slower to implement government interventions, like Italy and the United States, the effective reproduction number of Covid-19 remains greater than one, meaning the virus has continued to spread exponentially.

The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the United States will arrive somewhere between April 15-20. This prediction is similar to other projections like that of the Institute for Health Metrics and Evaluation.

Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one, says Barbastathis. That corresponds to the point where we can flatten the curve and start seeing fewer infections.

Quantifying the impact of quarantine

In early February, as news of the virus troubling infection rate started dominating headlines, Barbastathis proposed a project to students in class 2.168. At the end of each semester, students in the class are tasked with developing a physical model for a problem in the real world and developing a machine learning algorithm to address it. He proposed that a team of students work on mapping the spread of what was then simply known as the coronavirus.

Students jumped at the opportunity to work on the coronavirus, immediately wanting to tackle a topical problem in typical MIT fashion, adds Barbastathis.

One of those students was Dandekar. The project really interested me because I got to apply this new field of scientific machine learning to a very pressing problem, he says.

As Covid-19 started to spread across the globe, the scope of the project expanded. What had originally started as a project looking just at spread within Wuhan, China grew to also include the spread in Italy, South Korea, and the United States.

The duo started modeling the spread of the virus in each of these four regions after the 500th case was recorded. That milestone marked a clear delineation in how different governments implemented quarantine orders.

Armed with precise data from each of these countries, the research team took the standard SEIR model and augmented it with a neural network that learns how infected individuals under quarantine impact the rate of infection. They trained the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.

Using this model, the research team was able to draw a direct correlation between quarantine measures and a reduction in the effective reproduction number of the virus.

The neural network is learning what we are calling the quarantine control strength function, explains Dandekar. In South Korea, where strong measures were implemented quickly, the quarantine control strength function has been effective in reducing the number of new infections. In the United States, where quarantine measures have been slowly rolled out since mid-March, it has been more difficult to stop the spread of the virus.

Predicting the plateau

As the number of cases in a particular country decreases, the forecasting model transitions from an exponential regime to a linear one. Italy began entering this linear regime in early April, with the U.S. not far behind it.

The machine learning algorithm Dandekar and Barbastathis have developed predictedthat the United States will start to shift from an exponential regime to a linear regime in the first week of April, with a stagnation in the infected case count likely betweenApril 15 and April20. It also suggests that the infection count will reach 600,000 in the United States before the rate of infection starts to stagnate.

This is a really crucial moment of time. If we relax quarantine measures, it could lead to disaster, says Barbastathis.

According to Barbastathis, one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly. While the team didnt study Singapores Covid-19 cases in their research, the second wave of infection this country is currently experiencing reflects their models finding about the correlation between quarantine measures and infection rate.

If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic, Barbastathis adds.

The team plans to share the model with other researchers in the hopes that it can help inform Covid-19 quarantine strategies that can successfully slow the rate of infection.

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Model quantifies the impact of quarantine measures on Covid-19's spread - MIT News

Futuristic Reports, The growth and development of Global Machine Learning as a Service (MLaaS) Market Report 2020 by Players, Regions, Type, and Application, forecast to 2026 provides industry analysis and forecast from 2020-2026. Global Machine Learning as a Service (MLaaS) Market analysis delivers important insights and provides a competitive and useful advantage to the pursuers. Machine Learning as a Service (MLaaS) processes, economic growth is analyzed as well. The data chart is also backed up by using statistical tools.

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Machine Learning as a Service (MLaaS) Market Significant Growth with Increasing Production to 2026 | Broadcom, EMC, GEMALTO - Cole of Duty

The Respond Analyst Simplifies Endpoint Analysis, Delivers Real-Time, Expert Diagnosis of Security Incidents at a Fraction of the Cost of Manual Monitoring and Investigation

Respond Software today announced analysis support of Endpoint Detection and Response (EDR) data from Carbon Black, CrowdStrike and SentinelOneby the Respond Analyst the virtual cybersecurity analyst for security operations. The Respond Analyst provides customers with expert EDR analysis right out of the box, creating immediate business value in security operations for organizations across industries.

The Respond Analyst provides a highly cost-effective and thorough way to analyze security-related alerts and data to free up people and budget from initial monitoring and investigative tasks. The software uses integrated reasoning decision-making that leverages multiple alerting telemetries, contextual sources and threat intelligence to actively monitor and triage security events in near real-time. Respond Software is now applying this unique approach to EDR data to reduce the number of false positives from noisy EDR feeds and turn transactional sensor data into actionable security insights.

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Mike Armistead, CEO and co-founder, Respond Software, said: As security teams increase investment in EDR capabilities, they not only must find and retain endpoint analysis capabilities but also sift through massive amounts of data to separate false positives from real security incidents. The Respond Analyst augments security personnel with our unique Robotic Decision Automation software that delivers thorough, consistent and 24x7x365 analysis of security data from network to endpoint saving budget and time for the security team. It derivesmaximum value from EDR at a level of speed and efficiency unmatched by any other solution today.

Jim Routh,head of enterprise information risk management,MassMutual, said:Data science is the foundation for MassMutuals cybersecurity program.Applying mathematics and machine learning models to security operations functions to improve productivity and analytic capability is an important part of this foundation.

Jon Davis, CEO of SecureNation, said:SecureNation has made a commitment to its customers to deliver the right technology that enables the right security automation at lower operating costs. The EDR skills enabled by the Respond Analyst will make it possible for SecureNation to continue to provide the most comprehensive, responsive managed detection and response service available to support the escalating needs of enterprises today and into the future.

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EDR solutions capture and evaluate a broad spectrum of attacks spanning the MITRE ATT&CK Framework. These products often produce alerts with a high degree of uncertainty, requiring costly triage by skilled security analysts that can take five to 15 minutes on average to complete. A security analyst must pivot to piece together information from various security product consoles, generating multiple manual queries per system, process and account. The analyst must also conduct context and scoping queries. All this analysis requires deep expert system knowledge in order to isolate specific threats.

The Respond Analyst removes the need for multiple console interactions by automating the investigation, scoping and prioritization of alerts into real, actionable incidents. With the addition of EDR analysis, Respond Software broadens the integrated reasoning capabilities of the Respond Analyst to include endpoint system details identifying incidents related to suspect activity from binaries, client apps, PowerShell and other suspicious entities.

Combining EDR analysis with insights from network intrusion detection, web filtering and other network telemetries, the Respond Analyst extends its already comprehensive coverage. This allows security operations centers to increase visibility, efficiency and effectiveness, thereby reducing false positives and increasing the probability of identifying true malicious and actionable activity early in the attack cycle.

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Respond Software Unlocks the Value in EDR Data with Robotic Decision - AiThority