Category Archives: Artificial Intelligence

Artificial intelligence is now used in virtually all aspects of our lives. Yet unchecked biases within existing algorithmic systems, especially those used in sensitive use cases like financial services, hiring, policing, and housing, have worsened existing societal biases, resulting in the continued systemic discrimination of historically marginalized groups. As banks increase AI usage in loan and appraisal decisions, these populations are subjected to an even greater precision in denials, eroding protections provided by civil rights laws in housing. Meanwhile, the use of facial recognition technologies among law enforcement has resulted in the wrongful arrests of innocent men and women of color through poor data quality and misidentification. These online biases are intrinsically connected to the historical legacies that predate existing and emerging technologies and stand to challenge the policies created to protect historically disadvantaged populations. Can civil rights and algorithmic systems coexist? And, if so, what roles do government agencies and industries play in ensuring fairness, diversity, and inclusion?

On TechTank, Nicol Turner Lee is joined by Renee Cummings, data activist in residence and criminologist at the University of Virginias School of Data Science, and Lisa Rice, president and CEO of the National Fair Housing Alliance. Together, they conduct a deep dive into these difficult questions and offer insight on remedies to this pressing question of equitable AI.

You can listen to the episode and subscribe to theTechTank podcastonApple,Spotify, orAcast.

TechTank is a biweekly podcast from The Brookings Institution exploring the most consequential technology issues of our time. From artificial intelligence and racial bias in algorithms, to Big Tech, the future of work, and the digital divide, TechTank takes abstract ideas and makes them accessible. Moderators Dr. Nicol Turner Lee and Darrell West speak with leading technology experts and policymakers to share new data, ideas, and policy solutions to address the challenges of our new digital world.

All Eye Overlordby Aswin Behera is licensed underCC BY 4.0

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TechTank Podcast Episode 39: Civil rights and artificial intelligence: Can the two concepts coexist? - Brookings Institution

Research and innovation in artificial intelligence (AI) in the power industry operations and technologies sector has declined in the last year. The most recent figures show that the number of AI-related patent applications in the industry stood at 84 in the three months ending January down from 191 over the same period in 2020.

Figures for patent grants related to AI followed a similar pattern to filings shrinking from 64 in the three months ending January 2020 to 10 in the same period in 2021.

The figures are compiled by GlobalData, which tracks patent filings and grants from official offices around the world. Using textual analysis, as well as official patent classifications, these patents are grouped into key thematic areas, and linked to key companies across various industries.

AI is one of the key areas tracked by GlobalData. It has been identified as being a key disruptive force facing companies in the coming years, and is one of the areas that companies investing resources in now are expected to reap rewards from.The figures also provide an insight into the largest innovators in the sector.

Siemens was the top AI innovator in the power industry operations and technologies sector in the latest quarter. The company, which has its headquarters in Germany, filed 51 AI-related patents in the three months ending January. That was down from 125 over the same period in 2020.

It was followed by the US-based Honeywell International with 21 AI patent applications, South Korea-based Korea Electric Power (19 applications), and the US-based 3M (10 applications).

Korea Electric Power has recently ramped up R&D in AI. It saw growth of 68.4% in related patent applications in the three months ending January compared to the same period in 2020 the highest percentage growth out of all companies tracked, with more than 10 quarterly patents in the power industry operations and technologies sector.

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Artificial intelligence innovation among power industry companies has dropped off in the last year - Power Technology

An analytic predictive study for the future of artificial intelligence and metaverse in 2030

With the pace artificial intelligence is intertwining within our lives, there is no doubt that it will not end anytime soon. Rather, the future looks like a society that would breathe and thrive through artificial intelligence only. Experts believe that specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life. On the other hand, metaverse already has us wrapped in its not so little fingers. From Facebook to Instagram, virtual reality, Whatsapp, and many more, it is quite predictable that by 2030, its empire would only grow further.

A report published from Harvard University presents the eight areas of human activity in which Artificial intelligence technologies are already affecting urban life and will be even more pervasive by 2030: transportation, home/service robots, health care, education, entertainment, low-resource communities, public safety and security, employment, and the workplace will be fully AI-enabled spaces. Some of the biggest challenges in the next 15 years will be creating safe and reliable hardware for autonomous cars and healthcare robots; gaining public trust for Artificial intelligence systems, especially in low-resource communities; and overcoming fears that the technology will marginalize humans in the workplace.

Weve seen a lot of breakthroughs in data analytics. The example of Watson which is an IBM set of algorithms has been very impressive in terms of managing large amounts of data, and ways of structuring the data so that you can see patterns that may have not emerged otherwise. That has been an important leap. But oftentimes, people confuse that leap with machine intelligence and the way that we think about intelligence for humans and its simply not true. So the big leaps that we have had recently in data analytics are important but it also leaves a lot of room for humans to assist these systems. So, it can be said that the wave of the future is the collaboration of humans and these artificial intelligence technologies.

In its fully realized form, the metaverse promises to offer true-to-life sights, sounds, and even smells, whether a tour of ancient Greece or a visit to a Seoul caf can happen from your home. Decked out with full-spectrum VR headsets, smart clothing, and tactile-responsive haptic gloves, the at-home traveler can touch the Parthenon in Athens or taste the rich foam of a Korean dalgona coffee. You wouldnt even have to be you. Members of the metaverse could prowl the Brazilian rainforest as a jaguar or take the court at Madison Square Garden as LeBron James. The only limits are your imagination. It is also expected that using a blend of physical and behavioral biometrics, emotion recognition, sentiment analysis, and personal data, the metaverse will be able to create a customized and enhanced reality for each person.

While the metaverse industry is growing fast, fueled by the pandemic keeping people at home, its an open question as to whether one company will eventually emerge as the dominant force, such as Google, which now has a near-monopoly among search engines. One positive side of this trend is that since it is a virtual platform, the chances of people actually getting physically hurt will lessen, and also it will encourage them to get out of their comfort zone to try new things. The only wondering question left to ask on this matter will be the legal implications of the metaverse. For example, whether a marriage in the metaverse will be legal or if someone is assaulted in the metaverse, how the convict will be penalized. With the virtual avatar trend, there are huge chances of false identity or theft of identity, so recognizing the right person and their physical address can be a difficult job. This should be a major concern for all of the countries and their legislative and crime division.

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Looking at 2030: The Future of Artificial Intelligence and Metaverse - Analytics Insight

MILAN, March 10, 2022 /PRNewswire/ --Dedalus Group("Dedalus"), the leading healthcare and diagnostic software solutions provider in Europe, and Ibex Medical Analytics("Ibex"), the market leader in artificial intelligence (AI)-powered cancer diagnostics, today announced a strategic partnership to bring the power of artificial intelligence to digital pathology.

The partnership will bring the power of Ibex's clinical-grade AI algorithms into Dedalus' end-to-end digital pathology solution. This will benefit pathologists and patients through enhanced quality of diagnosis, at speed.

The increasing demand for pathology services posed by the growing number of cancer patients and global shortage of trained pathologists, leads pathology laboratories to actively seek efficiency-enhancing solutions that enable them to maintain high accuracy levels and reduce time to diagnosis.

Dedalus' end-to-end digital pathology solution addresses the needs of anatomic pathology labs and ensures interoperability with existing multi-vendor solutions, enabling a seamless, gradual evolution towards full digitization to meet the increasing demands of the healthcare system.

With over 30 years' experience in laboratory solutions, Dedalus has deployments in over 5700 laboratories globally, and has been instrumental in successfully bringing cutting-edge technologies into the laboratories to make the systems as agile, efficient, and accurate as possible.

Ibex transforms cancer diagnosis by harnessing AI and machine learning technologies at an unprecedented scale. The company's Galen platform has demonstrated outstanding outcomes in multiple clinical studies on various tissue types and clinical workflows. It is deployed in labs worldwide where it is used as part of everyday clinical practice.

Ibex's clinical-grade AI algorithms will seamlessly integrate into Dedalus end-to end digital pathology solution, enabling smooth workflows from a single application. The joint solution will analyse cases prior to human pathologists' review, providing decision support tools that will enable increased focus on cancerous slides and areas of interest, streamline reporting, improve laboratory efficiency, and increase diagnostic confidence.

"Dedalus is the leading healthcare and diagnostic software provider in Europe. As part of our commitment to accelerate the digital transformation in healthcare, we strongly believe in the value of artificial intelligence, specifically in pathology and cancer diagnoses. Therefore, we are proud to partner with Ibex Medical Analytics, which is a global leader in AI-powered solutions for pathology labs and cancer diagnostics," said Marlen Suller, Head of In Vitro Diagnostics Business Unit at Dedalus.

"Our AI solutions transform pathology and help physicians around the world provide on-time, quality diagnosis to patients," said Joseph Mossel, CEO and Co-founder of Ibex Medical Analytics. "Yet to unlock the full potential of artificial intelligence, pathologists and health systems need AI-enabled workflows and integrated cancer pathways. We are excited to partner with market leaders Dedalus and to deliver end-to-end diagnostic modalities that improve the way laboratories work and support better cancer care."

ABOUT DEDALUS

Dedalus Group is the leading healthcare and diagnostic software provider in Europe, supporting the digital transformation of 6300 hospitals and 5700 Laboratories worldwide, processing its solutions for more than 540 millions of population worldwide. Dedalus supports the whole continuum of care, offering open standards-based solutions serving each actor of the Healthcare Ecosystem to provide better care in a healthier planet.

For more information, visitwww.dedalus.com

ABOUT IBEX MEDICAL ANALYTICS

Ibex pioneers AI-powered cancer diagnostics in pathology. We empower physicians to provide every patient with an accurate, timely and personalized cancer diagnosis by developing clinical-grade AI algorithms and digital workflows that help detect and grade cancer in biopsies. Our Galen platform is the first-ever AI-powered integrated diagnostics solution in pathology and used in routine clinical practice worldwide, supporting pathologists and providers in improving the quality and accuracy of diagnosis, implementing comprehensive quality control, reducing turnaround times and boosting productivity with more efficient workflows.

For more information, visitwww.ibex-ai.com

CONTACT

SOURCE Ibex Medical Analytics; Dedalus Group

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Dedalus and Ibex announce a strategic partnership to bring the power of artificial intelligence to digital pathology - PR Newswire

"Global optical communications leaders united once again to exchange information and demonstrate ground-breaking interoperability including the newly launched OFCnet, catapulting optical technologies and network advancements into the reality of tomorrow," said OFC General Chairs Shinji Matsuo, David Plant and Jun Shan Wey. "Over the past two years, many of the best and brightest companies have been hard at work and were eager to show the world just how far optical advancements have come."

"Seeing some of the products live on the show floor and networking face-to-face with industry peers after a two-year hiatus from OFC helped make some of the latest optical innovations more concrete," said Woo Jin Ho, Senior Networking and Semiconductor Analyst, Bloomberg Intelligence. "For example, OIF's demonstration of Near-Packaged Optics and Co-Packaged Optics helped me better conceptualize some of the technologies that hyperscale clouds could be adopting into their data centers over the next five to ten years."

Plenary Session:OFC's impressive line-up of plenary visionaries showcased technologies shaping our world and driving innovation for a better future. They demonstrated the impact that the latest developments in optics and photonics will have on the industry moving forward. The plenary line-up included John Bowers, Director, Institute of Energy Efficiency, University of California, Santa Barbara, USA; James Green, Scientist and Senior Advisor, NASA, USA; and Elise Neel, Senior Vice President, New Business Incubation, Verizon, USA.

Professor Bowers, a pioneer in silicon photonics, presented exciting recent developments: from small wafers on indium phosphide to the making of photonic devices on silicon. Elise Neel demonstrated the wonders of the digital transformation enabled by 5G and Verizon's vision of the fourth industrial revolution, or Industry 4.0. James Green took attendees on a journey to deep space, the Moon and Mars, and explained how critical optical communications will support upcoming missions.

Exhibits:Hundreds of companies unveiled new products and innovations at OFC. They reconnected with customers and conducted the business of innovation. Participants included AC Photonics; Advanced Microoptic Systems GmbH; Aerotech Inc.; Aragon Photonics Labs; Broadcom, Inc.; CIENA Corporation; Cisco Systems, Inc.; Corning Incorporated; East Photonics, Inc.; Go!Foton; Infinera; Intel; LIGENTEC; Keysight Technologies; Lumentum; Murata; Nokia; NTT Advanced Technology Corporation; NTT Electronics Corporation; Santec USA; Semtech Corporation; Tektronix, Inc.; Viavi Solutions and VPIphotonics.

"We had a great week here at OFC and are glad to have the industry back together in person," said Eve Griliches, Senior Product Marketing, Cisco. "It was clear the right customers were all here to see the innovation and new technologies that have come to market over the past few years."

"We are excited to be back in person participating in OFC, the premier optical networking event in the industry," said Rob Shore, senior vice president of marketing at Infinera. "It has been an invaluable opportunity to showcase our latest coherent optical innovations, including ICE-XR, ICE6 Turbo and our Open Optical Toolkit designed to help network operators overcome the challenges associated with the relentless growth in bandwidth in the edge and core of the network."

OFCnet:In collaboration with the operator of California's research and education network CENIC, OFC launched OFCnet, a live high-speed fiber path connecting CENIC's facility in San Diego to the San Diego Convention Center, designed to accelerate the development and deployment of cutting-edge networking technology. CENIC's high speed network is built on the Lumen optical fiber and colocation infrastructure. These ultra-fast connections are designed to increase scientific collaboration among diverse organizations across the U.S. OFCnet is the result of collaboration with CENIC, Ciena, EXFO, Lumen, San Diego Convention Center, Smart City Networks, the University of California San Diego's Qualcomm Institute and Supercomputer Center and Viavi.

"Ciena is proud to be the first to light OFCnet using our Wavelogic 5 technology to provide multi-Tbps, ultra-low latency capacity between the OFC exhibit and the CENIC network," said Steve Alexander, Ciena CTO and Chair of OFC's Long Range Planning Committee. "This ongoing collaboration will provide future OFC conference and exhibitions with a unique platform to showcase the latest advancements in optical technologies, systems, and networks, all operating in a real-world environment."

Online Access to Content:On-demand access to paid conference sessions will be available for paid attendees, and additional content is available to all registrants, including Exhibits Pass Plus.

Health and Safety:In preparation for OFC 2022, show management followed all global, U.S. federal, California state, and local San Diego health guidelines. All conference attendees, exhibitors, vendors and staff had to be fully vaccinated and show proof of vaccination with photo ID, and wore masks in the San Diego Convention Center at all times except when actively eating and drinking.

OFC 2023:Mark your calendar for OFC 2023, 05-09 March at the San Diego Convention Center.

About OFCThe 2022 Optical Fiber Communication Conference and Exhibition (OFC) is the premier conference and exhibition for optical communications and networking professionals. For more than 45 years, OFC has drawn attendees from all corners of the globe to meet and greet, teach and learn, make connections, and move businesses forward.

OFC includes dynamic business programming, an exhibition of global companies and high impact peer-reviewed research that, combined, showcase the trends that are shaping the entire optical networking and communications industry. OFC is co-sponsored by the IEEE Communications Society (IEEE/ComSoc) and the IEEE Photonics Society and co-sponsored and managed by Optica (formerly OSA). OFC in 2022 will be presented in a hybrid format with in-person and virtual components and will take place 06-10 March 2022 at the San Diego Convention Center in San Diego, California, USA. Follow OFC on Twitter @OFCConference, learn more at OFC Community LinkedIn, and watch highlights on OFC YouTube.

Media Contact: [emailprotected]

SOURCE The Optical Fiber Communication Conference and Exhibition (OFC)

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The OFC Conference and Exhibition in San Diego Concludes Showcasing Breakthrough Innovations in 5G, Artificial Intelligence, Co-Packaging, Data Center...

DUBLIN--(BUSINESS WIRE)--The "Growth Opportunities in API, Analytics, Cloud, O-RAN, and Artificial Intelligence" report has been added to ResearchAndMarkets.com's offering.

This report provides a snapshot of the emerging ICT led innovations in API, analytics, cloud, open RAN (O-RAN), and artificial Intelligence. This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas, such as telecom, retail, supply chain, and sports.

Companies Mentioned

ITCC TOE's mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more.

The Information & Communication Technology cluster provides global industry analysis, technology competitive analysis, and insights into game-changing technologies in the wireless communication and computing space. Innovations in ICT have deeply permeated various applications and markets.

These innovations have profound impact on a range of business functions for computing, communications, business intelligence, data processing, information security, workflow automation, quality of service (QoS) measurements, simulations, customer relationship management, knowledge management functions and many more. Our global teams of industry experts continuously monitor technology areas such as Big Data, cloud computing, communication services, mobile and wireless communication space, IT applications & services, network security, and unified communications markets. In addition, we also closely look at vertical markets and connected industries to provide a holistic view of the ICT Industry.

Key Topics Covered:

Innovations In API, Analytics, Cloud, Oran And Artificial Intelligence

Key Contacts

For more information about this report visit https://www.researchandmarkets.com/r/a88ji7

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Growth Opportunities in API, Analytics, Cloud, O-RAN, and Artificial Intelligence 2021 - AI-Powered Video Security Platform to Offer Human-Like...

SUNNYVALE, Calif.--(BUSINESS WIRE)--Juniper Networks (NYSE: JNPR), a leader in secure, AI-driven networks, today announced a university funding initiative to fuel strategic research to advance network technologies for the next decade. Junipers goal is to enable universities, including Dartmouth, Purdue, Stanford and the University of Arizona, to explore next-generation network solutions in the fields of artificial intelligence (AI) and machine learning (ML), intelligent multipath routing and quantum communications.

Investing now in these technologies, as organizations encounter new levels of complexity across enterprise, cloud and 5G networks, is critical to replace tedious, manual operations as networks become mission critical for nearly every business. This can be done through automated, closed-loop workflows that use AI and ML-driven operations to scale and cope with the exponential growth of new cloud-based services and applications.

The universities Juniper selected in support of this initiative are now beginning the research that, once completed, will be shared with the networking community. In addition, Juniper joined the Center for Quantum Networks Industrial Partners Program to fund industry research being spearheaded by the University of Arizona.

Supporting Quotes:

Cloud services will continue to proliferate in the coming years, increasing network traffic and requiring the industry to push forward on innovation to manage the required scale out architectures. Junipers commitment to deliver better, simpler networks requires us to engage and get ahead of these shifts and work with experts in all areas in order to trailblaze. I look forward to collaborating with these leading universities to reach new milestones for the network of the future.

- Raj Yavatkar, CTO, Juniper Networks

With internet traffic continuing to grow and evolve, we must find new ways to ensure the scalability and reliability of networks. We look forward to exploring next-generation traffic engineering approaches with Juniper to meet these challenges.

- Sonia Fahmy, Professor of Computer Science, Purdue University

It is an exciting opportunity to work with a world-class partner like Juniper on cutting edge approaches to next-generation, intelligent multipath routing. Dartmouth's close collaboration with Juniper will combine world-class skills and technologies to advance multipath routing performance.

- George Cybenko, Professor of Engineering, Dartmouth University

As network technology continues to evolve, so do operational complexities. The ability to utilize AI and machine learning will be critical in keeping up with future demands. We look forward to partnering with Juniper on this research initiative and finding new ways to drive AI forward to make the network experience better for end users and network operators.

- Jure Leskovec, Associate Professor of Computer Science, Stanford University

The internet of today will be transformed through quantum technology which will enable new industries to sprout and create new innovative ecosystems of quantum devices, service providers and applications. With Juniper's strong reputation and its commitment to open networking, this makes them a terrific addition to building this future as part of the Center for Quantum Networks family.

- Saikat Guha, Director, NSF Center for Quantum Networks, University of Arizona

About Juniper Networks

Juniper Networks is dedicated to dramatically simplifying network operations and driving superior experiences for end users. Our solutions deliver industry-leading insight, automation, security and AI to drive real business results. We believe that powering connections will bring us closer together while empowering us all to solve the worlds greatest challenges of well-being, sustainability and equality. Additional information can be found at Juniper Networks (www.juniper.net) or connect with Juniper on Twitter, LinkedIn and Facebook.

Juniper Networks, the Juniper Networks logo, Juniper, Junos, and other trademarks listed here are registered trademarks of Juniper Networks, Inc. and/or its affiliates in the United States and other countries. Other names may be trademarks of their respective owners.

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Juniper Networks Announces University Research Funding Initiative to Advance Artificial Intelligence and Network Innovation - Business Wire

NEW YORK, March 10, 2022 /PRNewswire-PRWeb/ --Behavidence, a leading startup company that monitors psychiatric and neurological disorders using artificial intelligence, announced a $4.3 million seed round led by Welltech Ventures, featuring Arc Impact and Longevity Ventures.

Founded in 2020 by Roy Cohen (CEO), Dr. Girish Srinivasan, and Dr. Janine Ellenberger, Behavidence tracks and monitors the mental and emotional state of its users based on their digital usage patterns. Through its unique technology and algorithms, Behavidence can delineate the changes in its users' state of mind without needing identifiable information or monitoring private content or information.

"Today's funding announcement demonstrates our investors' support, commitment, and optimism for the future of Behavidence," said Cohen. "We see Behavidence's technology being deployed as a screening and remote monitoring tool, and are thrilled to see the adoption of our technology on so many devices across the globe."

Behavidence has secured more than $1 million in contracts with customers across the United States, Europe, Africa and Asia. Its clients include the Department of Veterans Affairs, Discovery Health Insurance and Essen Health Care.

Behavidence's product is a remote monitoring tool that helps health organizations, medical practitioners, and insurance companies identify potential diagnoses. According to recent robust clinical studies conducted by Behavidence, the company's models score up to 80% accuracy in detecting clinical depression, anxiety, and attention deficit disorders (ADHD) in patients. The company is continuing to develop its machine learning-based tools to detect and assist with remote monitoring and management of mental health conditions through passive digital biomarkers, adding no respondent burden.

"We continue to demonstrate our value to large healthcare organizations and private users by helping them assess the mental and emotional state of their clients and customers," Cohen said. "There is an ease of use to our technology that we are very proud of. The lack of respondent burden for the user makes it easy to implement for both the insurance company and the user. We are seeing metrics from insurance partners that indicate our technology can reduce the cost of a clinical case by up to 50%"

"We are not ruling out the potential of exploring a clinical track either as a medical aid or as the official diagnostic index itself in the future," explains Cohen. "But for now, we are celebrating our next round of seed funding and focused on delivering an intuitive product that will help serve the mental health needs of individuals and organizations throughout the world."

###

About Behavidence:

Behavidence develops machine learning based tools to detect and assist with remote monitoring and management of mental health conditions through passive digital biomarkers. The company was founded by a neuroscientist, neuropsychologist, physician and bioengineer with the passion to improve the lives of millions suffering from psychiatric and neurological disorders. Founded in 2020, Behavidence now offers multiple digital phenotyping models that can predict disorders such as depression, anxiety, ADHD and more. These digital phenotyping solutions have been adopted by health organizations, tech, commercial and government entities. The Behavidence products can be used as a measurement based outcome to monitor employee burnout, stress, predict relapse of conditions, screening, and remote monitoring for clinical interventions and comorbid conditions.

This disruptive solution offers absolute user privacy, zero respondent burden and real time feedback.

Meet the Behavidence Founders Team:

Roy Cohen, MNeuroSci - CEO, Co-founder (Ireland/UK based)

Dr. Girish Srinivasan - Chief Technology Officer (Chicago, IL US based)

Dr. Janine Ellenberger - Chief Medical Officer (Bryn Mawr, PA US based)

Media Contact

Anna Marie Imbordino, Zen Media, +1 630-550-7510, annamarie@zenmedia.com

SOURCE Behavidence

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Behavidence Closes $4.3 Million Seed Round to Amplify its Artificial Intelligence Technology used to Detect, Screen and Monitor Mental Illness -...

Time for AI proponents to step up..

Theres strong support for analytics and data science and the capabilities it offers organizations. However, the people charged with developing analytics and artificial intelligence feel resistance from business executives in getting fully on board with data-driven practices. In addition, efforts to ensure fairness in AI are lagging.

Thats the word from a recent study of 277 data managers and scientists out of SAS, which finds that overall, more than two-thirds were satisfied with the outcomes from their analytical projects. At the same time, 42% say data science results are not used by business decision makers, making it one of the main barriers faced.

A lack of support from above is cited as the leading challenge to getting data analytics initiatives off the ground, the survey shows. Data quality issues ranked second, followed by lack of adoption of the results by decision-makers. Its interesting that explaining data science to others is also seen as a challenge, suggesting that a big part of managers jobs needs to be evangelizing and educating their business counterparts on the benefits data analytics can deliver to their organizations, and how to do it right.

Here are the leading challenges to becoming more data-driven in todays environments:

Managers are generally more satisfied with the companys use of analytics compared to individuals; however, individuals seem more satisfied with the outcome of analytics projects, the studys authors state. That difference echoes the possible difference between satisfaction with their own projects outcomes versus how theyre deployed, and that data science as a whole is more than siloed, individual efforts.

The study also tackled the question of delivery of ethical and unbiased AI. A substantial segment of companies in the survey, 43% do not conduct specific reviews of their analytical processes with respect to bias and discrimination. And only 26% of respondents indicated that unfair bias is used as a measure of model success in their organization. The top two roadblocks were a lack of communication between those who collect the data and those who analyze it, and difficulty in collecting data about groups that may be unfairly targeted.

The studys authors recommend working to make data science a team sport in organizations, and to make an effort to provide data managers and scientists a more active role in the organization. They also urge managers and executives to get more proactive about responsible AI. Get started with your own project and find ways to add in the means to detect and measure bias, they advise. Document your work and present it to management. Sometimes a working example of success is whats needed to get started theres no reason it cant be your work that sparks the beginning.

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Responsible Artificial Intelligence Is Still Out Of Reach For Many Organizations - Forbes

Identifying a malfunction in the nations power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second.

Researchers at the MIT-IBM Watson AI Lab have devised a computationally efficient method that can automatically pinpoint anomalies in those data streams in real time. They demonstrated that their artificial intelligence method, which learns to model the interconnectedness of the power grid, is much better at detecting these glitches than some other popular techniques.

Because the machine-learning model they developed does not require annotated data on power grid anomalies for training, it would be easier to apply in real-world situations where high-quality, labeled datasets are often hard to come by. The model is also flexible and can be applied to other situations where a vast number of interconnected sensors collect and report data, like traffic monitoring systems. It could, for example, identify traffic bottlenecks or reveal how traffic jams cascade.

In the case of a power grid, people have tried to capture the data using statistics and then define detection rules with domain knowledge to say that, for example, if the voltage surges by a certain percentage, then the grid operator should be alerted. Such rule-based systems, even empowered by statistical data analysis, require a lot of labor and expertise. We show that we can automate this process and also learn patterns from the data using advanced machine-learning techniques, says senior author Jie Chen, a research staff member and manager of the MIT-IBM Watson AI Lab.

The co-author is Enyan Dai, an MIT-IBM Watson AI Lab intern and graduate student at the Pennsylvania State University. This research will be presented at the International Conference on Learning Representations.

Probing probabilities

The researchers began by defining an anomaly as an event that has a low probability of occurring, like a sudden spike in voltage. They treat the power grid data as a probability distribution, so if they can estimate the probability densities, they can identify the low-density values in the dataset. Those data points which are least likely to occur correspond to anomalies.

Estimating those probabilities is no easy task, especially since each sample captures multiple time series, and each time series is a set of multidimensional data points recorded over time. Plus, the sensors that capture all that data are conditional on one another, meaning they are connected in a certain configuration and one sensor can sometimes impact others.

To learn the complex conditional probability distribution of the data, the researchers used a special type of deep-learning model called a normalizing flow, which is particularly effective at estimating the probability density of a sample.

They augmented that normalizing flow model using a type of graph, known as a Bayesian network, which can learn the complex, causal relationship structure between different sensors. This graph structure enables the researchers to see patterns in the data and estimate anomalies more accurately, Chen explains.

The sensors are interacting with each other, and they have causal relationships and depend on each other. So, we have to be able to inject this dependency information into the way that we compute the probabilities, he says.

This Bayesian network factorizes, or breaks down, the joint probability of the multiple time series data into less complex, conditional probabilities that are much easier to parameterize, learn, and evaluate. This allows the researchers to estimate the likelihood of observing certain sensor readings, and to identify those readings that have a low probability of occurring, meaning they are anomalies.

Their method is especially powerful because this complex graph structure does not need to be defined in advance the model can learn the graph on its own, in an unsupervised manner.

A powerful technique

They tested this framework by seeing how well it could identify anomalies in power grid data, traffic data, and water system data. The datasets they used for testing contained anomalies that had been identified by humans, so the researchers were able to compare the anomalies their model identified with real glitches in each system.

Their model outperformed all the baselines by detecting a higher percentage of true anomalies in each dataset.

For the baselines, a lot of them dont incorporate graph structure. That perfectly corroborates our hypothesis. Figuring out the dependency relationships between the different nodes in the graph is definitely helping us, Chen says.

Their methodology is also flexible. Armed with a large, unlabeled dataset, they can tune the model to make effective anomaly predictions in other situations, like traffic patterns.

Once the model is deployed, it would continue to learn from a steady stream of new sensor data, adapting to possible drift of the data distribution and maintaining accuracy over time, says Chen.

Though this particular project is close to its end, he looks forward to applying the lessons he learned to other areas of deep-learning research, particularly on graphs.

Chen and his colleagues could use this approach to develop models that map other complex, conditional relationships. They also want to explore how they can efficiently learn these models when the graphs become enormous, perhaps with millions or billions of interconnected nodes. And rather than finding anomalies, they could also use this approach to improve the accuracy of forecasts based on datasets or streamline other classification techniques.

This work was funded by the MIT-IBM Watson AI Lab and the U.S. Department of Energy.

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Using artificial intelligence to find anomalies hiding in massive datasets - MIT News