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MALVERN, PA Four Penn State Great Valley professors will be researching ways to test for risk and vulnerability in Artificial Intelligence at the development stage, so practical problems can be headed off.

Self-driving cars and other Artificial Intelligence-assisted technologies awaiting mainstream use depend on large volumes of collected data. If data is in any way distorted, biased or tampered with, it's suddenly not so awesome and could pose risks to people's lives and to public safety.

A research grant will fund their project, "Managing Risks in AI Systems: Mitigating Vulnerabilities and Threats Using Design Tactics and Patterns." The Great Valley faculty team was one of eight across Penn State campuses to receive the one-year seed grants to fund research on cybersecurity for Artificial Intelligence.

"Every AI project should manage risks in a broad sense." said Youakim Badr, associate professor of data analytics at Penn State Great Valley. He explained, "The research project aims at applying risk management when we design an AI system and continuously monitor its behavior at runtime."

In the near future, many AI applications will be in physical contact with humans and will offer unimagined opportunities in many areas such as driverless trucks, fruit harvesting robots, autonomous boats, and robotic surgery, to mention just a few, said Badr.

Poorly designed, misused or hacked AI systems could mean loss of human control and could compromise the integrity of their own operating.

Badr said AI has become and will be a norm in the future to achieve superhuman performance in cognitive tasks, ranging from text understanding, translation between languages, question answering, to generating novels and artistic works.

AI techniques are also increasingly used to enhance decision-making processes to approve loans, diagnose diseases, predict recidivism and leverage our homeland security and defense.

The increasing dependency on AI systems poses potential risks. Risks stem from various sources, including deliberate cyberattacks from adversaries, biases in training data and machine learning algorithms, events of unpredictable root-cause, and bugs in software development.

AI Risks, if manifested, could expose them to potential threats and misbehavior that their designers would not expect or desire.

Badr and the Great Valley team at Penn State recently received a grant for research on "Managing Risks in AI Systems: Mitigating Vulnerabilities and Threats Using Design Tactics and Patterns." The project's co-principle investigators are Parth Mukherjee, assistant professor of data analytics, Raghu Sangwan, associate professor of software engineering, and Satish Srinivisan assistant professor of information science.

The project also includes Prasenjit Mitra, professor of information sciences and technology, associate dean for research in the College of Information Sciences and Technology, and the director of the Center for Socially Responsible Artificial Intelligence.

The impetus for the project came when Badr noticed significant vulnerabilities in AI systems, like self-driving cars that could be tricked to misread traffic signs or human biases imprinted upon AI algorithms and training datasets that could lead to stereotypes and injustice

Because intelligent systems aren't solely designed from software, the team saw an opportunity to explore how identifying and mitigating risks and vulnerabilities at the development stage could help AI-based systems to become safer and trustworthy.

The Great Valley faculty team was one of eight across Penn State campuses to receive the one-year seed grants to fund research on cybersecurity for Artificial Intelligence.

Risk management in AI systems is just beginning. "The discipline of AI risk management still in its infancy," said Badr.

"Today's AI systems use human reasoning as a model to achieve outperformance in specific tasks, but they are far from building the Artificial General Intelligence (AGI) which aims to understand and perform any cognitive task. AI systems learn by example to automate reasoning and thus solve problems," Badr said.

Intelligent tasks accomplished by AI systems rely on training data to build their capabilities in decision-making and prediction on unforeseen data. Badr explained AI's predictive capabilities mainly come from data collected from real-world or through interactions with AI's environments.

"And that can be the root cause of many risks, like biases and skewness" he said.

"AI systems are not only hungry for data but also thirsty for computational resources," Badr said. This opens the door for several cybersecurity risks and attacks that threaten their underlying infrastructures, communication networks and software applications.

"Adversarial attacks are remarkable cybersecurity threats by which malicious adversaries intentionally provide input (like images or text) designed in a specific way to inject backdoor patterns that may trigger AI systems to make a wrong prediction," Badr said.

For example, Badr explained, adversarial attacks can fool a self-driving vehicle by compromising its speed detector, which basically recognizes the speed limit from road signs images. An attacker could target the speed detector during the training phase by adding poisoned images of road signs with imperceptible perturbations. This can lead the car engine to speed up when the speed detector's camera captures an altered road sign with small stickers that intentionally increase the car's speed.

Risk management in AI systems is one step in a long journey to build trustworthy and safe AI. "By identifying risks at design time and at runtime, we will be able to mitigate them with appropriate treatment and enable controlled behavior with respect to predefined requirements," said Badr.

As AI technologies become more and more pervasive and efficient, every AI project must consider risk management, said Badr. But he expects we are up to the task.

"AI is to our century what electricity was in its time," he said.

Dealing with AI risks implies new complex systems and require us to look at problems from varied perspectives so that abnormalities and malicious behavior are identified but also then analyzed, evaluated and resolved. This takes multiple academic disciplines, he said.

The research of Badr, Mukherjee, Sangwan, and Srinivasan is multidisciplinary.

"It's an excellent opportunity for our campus and faculty to bring together different expertise around AI, cybersecurity and software engineering," Badr said.

Part of the work, he said, is to enable resilience and fault tolerance into AI systems, create methods and tools to test the system operating if and when one or more components are compromised or misbehave.

The team seeks to come up with a systematic approach for people who are interested in developing intelligent systems so they become aware of AI risks and vulnerabilities before they develop their products at a large scale and deploy it into real situations.

The team's diverse research background creates a unique approach to test for vulnerabilities when developing AI systems. Badr will focus on the risk management framework for AI-based systems, Mukherjee on monitoring and evaluating risk propagation when these systems are distributed, Sangwan on developing a software engineering approach to architecting and designing AI systems centering on their testability of their behaviour, and Srinivasan on fault tolerance and predictions.

The grants are funded in concert with the 2020 industryXchange, an annual University-wide event hosted by the College of Engineering.

"We are confident that our research topic will attract industry partners and have a significant impact on the development of trustworthy decentralized AI systems," said Badr.

The Great Valley campus focuses on bridging the gap between industry and academia, both for full-time students preparing to enter the workforce and for students already working in industry full-time. The broad reach of cybersecurity and AI will provide opportunities for graduate students from multiple programs to contribute to the research, also.

"We seek to create the synergy needed to provide the best opportunities between research and academic programs for the students," Badr said.

"We hope that the project's outcomes can be transferred to our classrooms and support our campus mission of providing high-quality, innovative and technologically progressive opportunities to collaborate with companies and industry."

Read more:
Artificial Intelligence Risk Is Topic Of Great Valley Research - Patch.com

According to Trane, data center cooling is "all about the design." (Photo: Rich Miller)

A new white paper from Siemens looks into the details for dynamically match cooling to IT load in real time. The new pape asserts that artificial intelligence is playing a key role in cooling todays data centers.

Amid the global pandemic, societal habits have increased demand for data usage at an unprecedented rate, says the report.

While this may mean more revenue for commercial data centers, the surge in usage is also increasing risks of downtime creating more demand on staff, equipment and energy consumption. These changes give rise to a bigger challenge: how do you scale to meet current demand and plan for future capacity in an age of hyperconnectivity? Siemens

But there may be an answer in artificial intelligence, known as AI, which continues to offer data centers potentialsolutions to improve operations over the long term.

Its true. Incorporating AI into an organizations systems can be challenging. But theres good news, according to the report.

Data centers can easily and successfully implement AI in their operations with new thermal cooling solutions, said Siemens.

The new paper explores AI and its impact on data centers, using white space cooling optimization as an example of how AI can be implemented today.

It starts by taking a look at thechanging data center landscape, and this report provides a glimpse into what the future holds and examines the critical aspects of thermal cooling, specifically thermal optimization.

Some of the challenges facing data centers today include:

Thats according to research from Forbes Insights in early 2020, which attempted to answer the question of whats next for data centers.

One thing is for sure, AIs impact on data centers is going to be swift and definitive.

Artificial Intelligence (AI) is part of the digital transformation and is poised to have a tremendous impact on data center management, productivity and infrastructure, the report states.

And there is one area where AI can immediatelydeliver real benefits is data center cooling and control. As demand for data grows, so does the need to better manage cooling conditions in data centers.

The report contends that thermal optimization can be an answer, which according to Seimens, eliminates the need to manually maintain the optimal, cool and consistent temperatures required to house data center equipment safely.

The report focuses on how a data center can easily begin integrating AI into its processes through whitespace cooling optimization (WSCO) and reviews how a global financial firm is using WSCO as part of its thermal optimization plan, with promising results.

Specifically, the report covers the following topics in detail:

Get the full report, How Artificial Intelligence Is Cooling Data Center Operations, to explore how to integrate AI into your data center usingthermal cooling solutions.

See more here:
The Connection Between Artificial Intelligence and Data Center Cooling - Data Center Frontier

Augsburg / Munich, December 1, 2020 Part 4 of the digital press conference series Join us for a coffee took a look into the future of manufacturing and showed how industrial companies can increase efficiency, product quality and revenue by linking artificial intelligence and the Internet of Things this time with the expertise of KUKA, Device Insight and Sentian.

When it comes tocombining artificial intelligence and IoT, industry has so far clearly focused on predictive maintenance. A mistake, says Dr. Christian Liedtke, Head of Strategic Alliances at KUKA, with conviction. As the expert made clear at the beginning of the virtual discussion round: If companies focus exclusively on predictive maintenance, they can only achieve better availability of a single machine, which shouldnt fail anyway. What end users are really interested in isgenerating more revenue. To achieve this, however, all those involved in the process must work better together and individual processes must interlock seamlessly.

One approach enabling such a holistic optimization of production is the combination of artificial intelligence and the Internet of Things to form an Artificial Intelligence of Things (AIoT), as created by KUKA subsidiaryDevice Insightand AI specialistSentian. Here, the aim is tocontinuously reduce deviations from the optimum within a manufacturing processand to automate improvements. As initial applications of AIoT show, it is precisely the fine adjustments of industrial production that can exploit enormous potential to increase the quality of goods produced and overall yield. According to McKinsey, this will enable anincrease in efficiency of up to 30 percent. The key is therefore to synchronize AI and IoT technologies.

This is why IoT pioneer Device Insight has joined forces with the Swedish AI specialist Sentian. Together, they are now able to accompany companies on the way tointelligent production away from individual solutions and selective improvements, such as those possible with predictive maintenance, and towards aholistically optimized smart factory.

In 10 to 15 years, artificial intelligence will be in every production process, says Martin Rugfelt, CEO of Sentian. In fact,AI is already important for many industrial companies. It can reduce energy consumption in the chemical industry, cut waste in the pharmaceutical industry, handle variation in paper production or optimize production lines in discrete manufacturing. For example, JUMO, a German manufacturer of automation and sensor technology, has been able to increase the proportion of its sensors in the highest quality class by 8 percent.

Link:
How the Combination of Artificial Intelligence and IoT makes the Smart Factory a Reality - MarketScale

Shortly after her inauguration, the new European Commissions president, Ursula von der Leyen, expressed the intent of the new commission to come up with a Digital Strategy addressing Artificial Intelligence regulation within 100 days from beginning of her presidency. Keeping this promise, the European Commission published a first White Paper on Artificial Intelligence A European approach to excellence and trust in February 2020. Statements on such White Paper were collected until 31 May 2020. Together with the White Paper the Commission Report on safety and liability implications of AI, the Internet of Things and Robotics was published, providing more details on the gaps the Commission has identified in existing laws.

What are we talking about?

A definition of Artificial Intelligence was provided by the European Commissions AI High Level Expert Group (AI HLEG) on 8 April 2019, when this group provided Ethics guidelines for trustworthy AI. The Commissions papers are referring to AI HLEG, which defines AI as follows:

Artificial intelligence (AI) systems are software (and possibility also hardware) systems

These defining aspect of AI form the basis of an evaluation of legal gaps and possible requirements for new regulation.

Where are the legal gaps?

The Commission Report on safety and liability implications of AI, the Internet of Things and Robotics identified legal gaps mainly in the following respect:

The European Parliament published a further Report on Intellectual property rights for the development of artificial intelligence technologies, evaluating the status quo and identifying various gaps in IP law. The report found, for example, gaps in respect to the question whether AI is or can be protected by IP, whether IP protected content can be food for AI training and whether someone owns rights to works created by AI.

What kind of regulation do we have to expect?

Upon identification of the various gaps, the EU intends to issue a comprehensive legislative package on AI, which will include new regulations for those who build and deploy AI. First hints on what could be part of such package can be taken from three resolutions the European parliament adopted on 20 October: the Framework of ethical aspects of artificial intelligence, robotics and related technologies; the Civil liability regime for artificial intelligence and the Intellectual property rights for the development of artificial intelligence technologies.

Looking at these Resolution, the following topics might be seen as key to build an ecosystem of trust and enhance the general social acceptance of AI:

When?

A first draft of such new AI legal framework is expected for the first quarter of 2021, whereas some parts could already be reflected in the Digital Services Act, for which a first draft is already expected in December 2020.

Who?

Addressee of such obligations might not always be the software developer, but could be the actor who is best placed to address potential risks. Obligations could therefore also be imposed on the deployer or the service provider. And the obligations will have to be obeyed by all economic operators providing AI-enabled products or services in the EU, regardless of whether they are established in the EU or not.

What to do?

Anyone engaged or interested in AI should monitor closely the developments of the upcoming months, as the new laws will most certainly have impact on AI systems that are currently trained or are even already on the market.

In practice, contractual frameworks for AI systems should therefore already be examined and might include clauses anticipating future developments. Certain upcoming liability risks might also already be taken into account.

Read more:
Regulation of Artificial Intelligence in Europe - What's in the pipeline? - Lexology

RICHMOND HILL, N.Y.--(BUSINESS WIRE)--iHealthScreen CEO Alauddin Bhuiyan, Ph.D. along with collaborators at New York Eye and Ear Infirmary at Mount Sinai (NYEEI), presented a prospective study for early diagnosis of AMD at the annual conference of AAO, 2020. The results presented during the prestigious Original Paper Session demonstrated 88.7% accuracy, compared to ophthalmologist gradings, on detecting referable AMD.

AMD, with no early signs or symptoms, is a leading cause of adult blindness in the developed world. Early detection can enable preventative measures in time and stop AMD incident. Currently, AMD diagnosis has been limited to retinal examination by an ophthalmologist. AMD cases in the U.S. are expected to grow from 2.1 million to 5.4 million in the next ten years, and there is an increasing need for large scale screening and identification of individuals who are at risk of developing late AMD. iHealthScreens AI based tool can facilitate this screening and help prevention of late AMD.

In an interview, Dr. Bhuiyan spoke about the study and the findings: We are encouraged by the results and believe that the new AI-based technology can diagnose early AMD in primary care settings, which enables the timely preventative measures by ophthalmologist and prevent this deterioration of vision. We want to express our sincerest thanks to the participants and professional staff who were involved in this clinical trial and gathered the data.

These results speak to the feasibility of this approach, said Theodore Smith, M.D., Ph.D., Trials Principal-investigator and Professor at Icahn School of Medicine at Mount Sinai. I believe that the ease of use of iHealthScreens AI tool will contribute to its adoption in the wider primary care community.

About iHealthScreen

iHealthScreen is a private, clinical-stage, medical diagnostic/device company. iHealthScreen has developed iPredict, an AI and telemedicine-based HIPAA compliant platform for automated screening and prediction of individuals at risk of developing late age-related AMD, diabetic retinopathy (DR), glaucoma, cardiovascular heart disease and stroke.

Continue reading here:
iHealthScreen's Artificial Intelligence (AI) Based Tool Can Accurately Diagnose Age-Related Macular Degeneration (AMD) and Help Prevent Blindness -...