The launch of the Texas Artificial Intelligence Center of Excellence was announced this week. The initiative will facilitate the development of AI concepts and standards throughout state and local government.

Texas now has a center for making artificial intelligence part of how government does business.

In a press release, the Texas Department of Information Resources (DIR) revealed the states new Artificial Intelligence Center of Excellence (AI-CoE). AI-CoEs purpose is to assist state and local agenciesand public institutions of higher learning with exploring how AI can be used to enhance service delivery to citizens.

Following the success of DIRs Cloud Center of Excellence, we want the AI-CoE to enable and foster the adoption of AI in state government to increase efficiency, said John Hoffman, who serves as chief technology officer and deputy chief information officer of Texas. This will also ensure the implementation of the states strategic goals for technology as outlined in the 2020-2024 State Strategic Plan for Information Resources Management.

One of the objectives of the 2020-2024 strategic plan is to initiate testing of artificial intelligence (AI) solutions to drive new interaction and services with the public. AI-CoE aims to accomplish this objective by utilizing the expertise of DIR staff and private partners so that IT leaders across agencies can identify areas where AI can speed up processes and reduce costs.

With this initiative, we would conduct training, coaching events and hands-on workshops to help agencies explore AI proof-of-concepts and rapid prototyping in developing standards and best practices, said Krishna Edathil, director of Enterprise Solution Services for DIR and practice lead for AI-CoE.

The release states that all AI technologies will be examined, from machine learning to natural language processing to computer vision.

Looking for the latest gov tech news as it happens? Subscribe to GT newsletters.

Read the rest here:
New Center of Excellence to Infuse AI into Texas Government - Government Technology

Market Overview:Artificial intelligence in the education sector monitors student abilities with the help of technologies such as machine learning and natural learning, helping to improve the learning process based on the student's needs. In education, AI focuses on individual learning and monitoring, helping students understand topics at their own pace.

AI provides interactive custom software tools for students of all grades, integrated with augmented and virtual reality deployed on digital devices such as smartphones, tablets and wearable devices. Digital interactive content accelerates student learning and understanding. Deploying AI for education improves the learning environment with a special focus on implementing experimental and analytical learning, allowing students to clearly understand concepts.

Key Players:The prominent players in the market of AI in education are, IBM Corporation (US), Microsoft Corporation (US), Google (US), Amazon.com, Inc., (US), Cognizant (US), Pearson (UK), Bridge-U (UK), DreamBox Learning (US), Fishtree (US), Jellynote (France), Jenzabar, Inc., (US). Other players in the market include Knewton, Inc., (US), Metacog, Inc., (US), Querium Corporation. (US), Century-Tech Ltd (UK), Blackboard, Inc., (US), Third Space Learning (UK), Quantum Adaptive Learning, LLC (US).

For More Information @ https://www.marketresearchfuture.com/reports/artificial-intelligence-education-market-6365

Market Segmentation:By Technology* Deep Learning and Machine Learning* Natural Language Processing (NLP)By Application* Virtual Facilitators and Learning Environments* Intelligent Tutoring Systems (ITS)* Content Delivery Systems* Fraud and Risk Management* Student-initiated learning* Others (education data management, job recommendation, and training and development)By Component* Solutionso Software toolso Platforms* Serviceso Professional serviceso Managed servicesBy Deployment Mode* Cloud* On-premises

Regional Analysis:Of all regions, North America used AI the most in education solutions during 2014-2019 and is expected to generate the greatest demand for these solutions in the coming years. This includes a highly developed educational infrastructure, growing demand for intelligent education solutions to improve student engagement, increasing demand for personalized learning in the classroom, increasing interest in reducing the burden of teachers, and advocating for EdTech to drive AI in education.

It includes expenses. Because of the increase. US and ultimately local markets. The fastest growing AI demand for education solutions and services is expected to be witnessed in the Asia Pacific region during the forecast period.

Company name: Market Research Future

About Market Research Future:At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research & Consulting Services.MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by Components, Application, Logistics and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions.

Contact:Market Research FutureOffice No. 528, Amanora ChambersMagarpatta Road, Hadapsar,Pune - 411028Maharashtra, India+1 646 845 9312

Go here to read the rest:
Artificial Intelligence in Education Market Share, Emerging Trends, Increasing Demand, Forecast 2025 - DC Velocity

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