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To aid utility success in this dramatically shifting landscape, ESource has launched EV4Sight, a consortium to continuously forecast the utility-specific impacts of electric transportation growth. Using our growing data hub and advanced data science, EV4Sight will also provide consortium members with ongoing insights into electric transportation charging patterns, analysis of factors that influence charging behavior, and impacts of load growth at the national, state, service-territory or distribution-feeder level. EV4Sight will address both consumer vehicles as well as key categories of commercial and fleet vehicles.

Electric vehicles promise to be the primary engine for electricity growth in the coming decades as well as the pathway to a low carbon future. Load growth forecasts have been based on widely varying estimates, often extrapolating from local policy goals instead of building on relevant local datauntil now.

Utilities are facing considerable risk exposure if they either under or over build in anticipation of electric vehicle demand, and regulators and policy-makers want hard data to support investments. We also know that electric load growth will be very uneven, with some regions, and even neighborhoods, having vastly different trajectories of adoption, says Bill LeBlanc, ESources Chief Instigation Agent.

EV4Sight consortium members will have access to continuously updated dashboards powered by forecasting models rooted in advanced data science techniques. The data includes charging load shapes, EV owner data records, and proprietary market insights on every household and business in the U.S. ESource, through its partner Rolling Energy Resources, will enroll 100 consumer vehicles in each consortium members service territory to collect detailed real-time information on charging location and speed of charge, miles traveled, and state of battery charge. Members are encouraged to also add their own data, such as charging load shapes/AMI data, rate designs, and customer program participation, to develop granular and accurate load forecasts. As the EV markets rapidly evolve, the projections will too.

Today, we just dont know what the real influences and levers are of EV adoption. Similarly, we arent aware of what affects charging habits the most. explains Ted Schultz, President of ESource Data Science. For consumer EV adoption, is it gas prices, number of public charging stations, model availability, EV range, or something that is hidden from us? For commercial vehicles, is rate design, range, managed charging, vehicle cost or other factors that are the biggest influencers? Thats where data science will provide consortium members the tools you need in an ongoing basis to help manage the grid of the future.

With dozens of utilities all pooling their data along with ESources proprietary data sets, advanced data science within EV4Sight will enable us to discern which utility programs, rate designs, policies, and activities work best with various target groups of customers under varying driving conditions.

ESources long-standing role of bringing together utilities to solve big problems will allow each EV4Sight consortium member to learn from all the other utilities programs and approaches and EV data without having to conduct those experiments themselves, explains LeBlanc. We are just at the beginning of this EV growth curve, which means markets will be shifting dramatically over the coming decade, necessitating real time analysis of this complex addition to our electric grid throughout the coming decade.

Dont risk unplanned EV load growth! Become an EV4Sight consortium member today by visitingwww.esource.com/ev4sight, call 1-800-ESOURCE, or email[emailprotected].

About ESource:ESource is a leading partner to more than 500 electric, gas, and water utilities and municipalities, and their partners, across theUSand Canada. We provide data science, market research, benchmarking, and consulting services. Our 35 years of technology validation, market assessment, program design, and customer experience expertise help clients make informed, data-driven decisions; plan for tomorrows infrastructure needs; strengthen customer relationships; and meet critical business objectives while becoming more innovative and responsive in the rapidly evolving market.

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E Source launches electric vehicle data science consortium, empowering utilities by using data science to predict the future of electric...

In the race to out-compete other companies artificial intelligence (AI) design is lacking a deep understanding of what data about humans mean and its relation to equity. Two Drexel University sociologists suggest we pay greater attention to the societal impact of AI, as it is appearing more frequently than ever before.

The coronavirus pandemic has sped up the use of AI and automation to replace human workers, as part of the effort to minimize the risks associated with face-to-face interactions, saidKelly Joyce, PhD,a professor in theCollege of Arts and Sciencesand founding director of theCenter for Science, Technology and Societyat Drexel. Increasingly we are seeing examples of algorithms that are intensifying existing inequalities. As institutions such as education, healthcare, warfare, and work adopt these systems, we must remediate this inequity.

In a newly published paper inSocius,Joyce,Susan Bell, PhD, a professor in theCollege of Arts and Sciences,and colleagues raise concerns about the push to rapidly accelerate AI development in the United States without accelerating the training and development practices necessary to make ethical technology. The paper proposes a research agenda for a sociology of AI.

Sociology's understanding of the relationship between human data and long-standing inequalities is needed to make AI systems that promote equality, explained Joyce.

The term AI has been used in many different ways and early interpretations associate the term with software that is able to learn and act on its own. For example, self-driving cars learn and identify routes and obstacles just as robotic vacuums do the perimeter or layout of a home, and smart assistants (Alexa or Google Assistant) identify the tone of voice and preferences of their user.

AI has a fluid definitional scope that helps explain its appeal, said Joyce. Its expansive, yet unspecified meaning enables promoters to make future-oriented, empirically unsubstantiated, promissory claims of its potential positive societal impact.

Joyce, Bell and colleagues explain that in recent years, programming communities have largely focused on developing machine learning (ML) as a form of AI. The term ML is more commonly used among researchers than the term AI, although AI continues to be the public-facing term used by companies, institutes, and initiatives. ML emphasizes the training of computer systems to recognize, sort, and predict outcomes from analysis of existing data sets, explained Joyce.

AI practitioners, computer scientists, data scientists and engineers are training systems to recognize, sort and predict outcomes from analysis of existing data sets. Humans input existing data to help train AI systems to make autonomous decisions. The problem here is that AI practitioners do not typically understand how data about humans is almost always also data about inequality.

AI practitioners may not be aware that data about X (e.g., ZIP codes, health records, location of highways) may also be data about Y (e.g., class, gender or race inequalities, socioeconomic status), said Joyce, who is the lead author on the paper. They may think, for example, that ZIP codes are a neutral piece of data that apply to all people in an equal manner instead of understanding that ZIP codes often also provide information about race and class due to segregation. This lack of understanding has resulted in the acceleration and intensification of inequalities as ML systems are developed and deployed."

Identifying correlations between vulnerable groups and life chances, AI systems accept these correlations as causation, and use them to make decisions about interventions going forward. In this way, AI systems do not create new futures, but rather replicate the durable inequalities that exist in a particular social world, explains Joyce.

There are politics tied to algorithms, data and code. Consider the search engine Google. Although Google search results might appear to be neutral or singular outputs, Googles search engine recreates the sexism and racism found in everyday life.

Search results reflect the decisions that go into making the algorithms and codes, and these reflect the standpoint of Google workers, explains Bell. Specifically, their decisions about what to label as sexist or racist reflect the broader social structures of pervasive racism and sexism. In turn, decisions about what to label as sexist or racist trains an ML system. Although Google blames users for contributing to sexist and racist search results, the source lies in the input.

Bell points out in contrast to the perceived neutrality of Googles search results, societal oppression and inequality are embedded in and amplified by them.

Another example the authors point out are AI systems that use data from patients' electronic health records (EHRs) to make predictions about appropriate treatment recommendations. Although computer scientists and engineers often consider privacy when designing AI systems, understanding the multivalent dimensions of human data is not typically part of their training. Given this, they may assume that EHR data represents objective knowledge about treatment and outcomes, instead of viewing it through a sociological lens that recognizes how EHR data is partial and situated.

"When using a sociological approach," Joyce explains, "You understand that patient outcomes are not neutral or objective these are related to patients socioeconomic status, and often tell us more about class differences, racism and other kinds of inequalities than the effectiveness of particular treatments."

The paper notes examples such asan algorithm that recommended that black patients receive less health care than white patientswith the same conditions and a report showing thatfacial recognition software is less likely to recognize people of color and womenshowed thatAI can intensify existing inequalities.

A sociological understanding of data is important, given that an uncritical use of human data in AI sociotechnical systems will tend to reproduce, and perhaps even exacerbate, preexisting social inequalities, said Bell. Although companies that produce AI systems hide behind the claim that algorithms or platform users create racist, sexist outcomes, sociological scholarship illustrates how human decision making occurs at every step of the coding process.

In the paper, the researchers demonstrate that sociological scholarship can be joined with other critical social science research to avoid some of the pitfalls of AI applications.By examining the design and implementation of AI sociotechnical systems, sociological work brings human labor and social contexts into view, said Joyce.Building on sociologys recognition of the importance of organizational contexts in shaping outcomes, the paper shows that both funding sources and institutional contexts are key drivers of how AI systems are developed and used.

Joyce, Bell and colleagues suggest that, despite well-intentioned efforts to incorporate knowledge about social worlds into sociotechnical systems, AI scientists continue to demonstrate a limited understanding of the social prioritizing that which may be instrumental for the execution of AI engineering tasks, but erasing the complexity and embeddedness of social inequalities.

Sociologys deeply structural approach also stands in contrast to approaches that highlight individual choice, said Joyce. One of the most pervasive tropes of political liberalism is that social change is driven by individual choice. As individuals, the logic goes, we can create more equitable futures by making and choosing better products, practices, and political representatives. The tech world tends to sustain a similarly individualistic perspective when its engineers and ethicists emphasize eliminating individual-level human bias and improving sensitivity training as a way to address inequality in AI systems.

Joyce, Bell and colleagues invite sociologists to use the disciplines theoretical and methodological tools to analyze when and how inequalities are made more durable by AI systems. The researchers emphasize that the creation of AI sociotechnical systems is not simply a question of technological design, but also raises fundamental questions about power and social order.

Sociologists are trained to identify how inequalities are embedded in all aspects of society and to point toward avenues for structural social change. Therefore, sociologists should play a leading role in the imagining and shaping of AI futures, said Joyce.

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The Future of Artificial Intelligence Requires the Guidance of Sociology - DrexelNow - Drexel Now

The American Family Insurance Data Science Institutes first two years have been nothing if not eventful.

Last spring, as the campus and state shut down in response to the COVID-19 pandemic, AFIDSI responded to the need for mathematical modeling to guide policy decisions and public health outreach. Within weeks, the institute had convened a team of leaders and experts from across the U.S. The COVID-19 Research Group met daily in the early weeks of the pandemic, creating models and sharing findings as quickly as possible.

The institute has shown strong leadership during the pandemic, pulling together a team of more than a hundred people to further understanding of COVID-19 and inform campus mitigation efforts, says Amy Wendt, associate vice chancellor for research.

Leadership is the next big project for the institute, which is recruiting a permanent director. Applications are due April 15.

Meanwhile, AFIDSI staff are working with the School of Medicine and Public Health to identify potential hotspots for virus transmission, using natural language processing techniques with testing and contact tracing data. With the Wisconsin State Lab of Hygiene, research group members are supporting efforts to detect and measure COVID-19 variants through wastewater surveillance.

In partnership with AFIDSI, Michael Ferris and Corey Jackson, both with the School of Computer, Data and Information Sciences, are supporting vaccine logistics with the Wisconsin Department of Health Services and the Wisconsin National Guard. To overcome disparities that stem from health, economic and educational inequities, as well as discrimination, Jackson and Ferris have begun work on a recommendation engine to ensure vaccines are allocated fairly.

In addition to campus collaborations, the institute is working closely with American Family Insurance to create the American Family Funding Initiative.

The American Family Funding Initiative is supporting fundamental data science research, including third-wave AI systems and a variety of novel machine learning research projects, with researchers from multiple campus departments, said Whitney Sweeney, AFIDSI assistant director. The initiative also supports applied research. To date, more than $2 million has been awarded through this initiative to 16 data science research teams.

Jon Eckhardt, associate professor in the Wisconsin School of Business, heads one of these teams. With support from the American Family Funding Initiative, his team at the Entrepreneurship Science Lab is using data-based insights to better prepare student entrepreneurs for successful careers that add to the economic prosperity of their communities.

Support from American Family Insurance is crucial to the success of the lab, says Eckhardt. We are excited to be working with Dan Reed, Glenn Fung, and other innovators at AmFam to create opportunities for our students, graduates and communities.

The American Family Funding Initiative is also supporting fundamental data science research, including third-wave AI systems and a variety of novel machine learning research projects, with researchers from multiple campus departments.

The future of data science will be driven by insights from all disciplines and AFIDSI is committed to working across campus. Iain McConnell, a data scientist at the institute, is collaborating with the Institute for Research on Poverty to improve cross-referencing of data from multiple social service programs, so researchers can better understand how people use the services offered through these programs. The ultimate goal of this work is to reduce poverty in Wisconsin.

The American Family Insurance Data Science Institute embodies the Wisconsin Idea, that the impacts of UWMadison must ripple beyond our classrooms and labs into the community, says Steve Ackerman, vice chancellor for research and graduate education. Data science is a high priority for campus because of its potential to move our cutting-edge research toward the goal of solving societys most difficult problems. The institute is actively leading work that brings people together to achieve this goal.

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Off to a busy start, data institute responds to COVID and seeks director - University of Wisconsin - University of Wisconsin-Madison

SAN JOSE, Calif.--(BUSINESS WIRE)--Pavilion Data Systems, the universally unmatched leader in data storage, announced today its partnership with Graphistry to support large-scale, multi-GPU, and NVIDIA Magnum IO GPUDirect technologies to supercharge how data analysts investigate event and entity data.

Pavilion and Graphistry will showcase their joint capabilities at NVIDIA GTC in session # SS33029 - Visually Investigating Patterns in Logs at Scale with Graphistry, RAPIDS, and Pavilion - using the most performant, dense, scalable, and flexible data storage platform in the universe.

Attendees will learn how to make big log volumes (including multi-petabytes) easily explorable by simple low-coding of GPU and graph interfaces. The session will walk attendees through how Graphistry has integrated friendly data UI tools like Jupyter notebooks, Streamlit dashboards, and Graphistry visual analytics with the NVIDIA RAPIDS GPU ecosystem (Dask_cuDF, GPU Direct storage, DGX A100) and with the Pavilion HyperParallel Data Platform. The combined result is attendees will learn how to adapt their existing familiar workflows to a style where they can now see and explore their entire dataset.

The presenters will also provide recommendations to optimize log collection and GPU storage optimization while guiding attendees regarding how they can run and fork the open-source reference architecture providing attendees with new insights that will be shared with the RAPIDS accelerated data science community.

There are just a few visualization tools that take full advantage of NVIDIA GPUs with Magnum IO GPUDirect to deliver real-time graph analytics across petabytes of incoming and stored data. Graphistry's breakthrough software and pipeline technology, combined with NVIDIA GPUs and Magnum IO GPUDirect along with Pavilion's universally unmatched storage, enable the next generation of interactive visualization for incident response, SIEM optimization, and threat hunting across market segments, including financial services, federal agencies, and data science.

Historically, teams have been excited to analyze logs at speed-of-thought by trying GPU acceleration, but bigger-than-memory datasets have always been difficult to connect to, said Leo Meyerovich, CEO and founder of Graphistry. Now that we can connect GPUs to Pavilion's HyperParallel Data Platform, we can automatically keep the GPUs running at high rates on large data sets without waiting on the storage tier, so analysts can focus more on investigating and less on massaging queries.

Just about every enterprise has a vast ocean of data at its fingertips, and most companies need a way to process those volumes of data into insights more quickly so that they take action, said Rob Davis, Vice President of Storage Technology at NVIDIA. Running data science workloads on analytics solutions like Graphistry with multiple NVIDIA GPUs powered by NVIDIA MagnumIO GPUDirect storage enables enterprises to effectively leverage high-performance storage and process massive amounts of data at scale.

The Pavilion HyperParallel Data Platform can hold over 2 petabytes of usable capacity in 4 rack units, making it an ideal fit for data-intensive applications like Graphistry, said Costa Hasapopoulos, Field Chief Technology Officer of Pavilion. With Graphistry bringing automated queries across Splunk, Elasticsearch, Apache Spark, and CSVs, delivering the highest fidelity at scale requires NVIDIA GPUs and Magnum IO GPUDirect to enable analysts to visualize key relationships, patterns, and anomalies in real-time.

To learn more about the solution with Graphistry and Pavilion, see the latest blog.

Pavilion is also presenting three other sessions at GTC21, including with partner OmniSci, as the two companies jointly showcase the power of Accelerated Analytics within a Data Science Platform. It is GTC # SS33294. You can also read the blog - How to respond to rapidly scaling Geospatial-Intelligence with OmniSci and Pavilion.

Attendees looking to understand how to virtualize, share and aggregate GPUs will find this session of particular interest: GTC # SS33026 and associated blog - How High-Performance NVMe-oF Storage Accelerates CPU & GPU-Powered Virtualized Environments Demonstrated by Pavilion.

Attendees wanting to understand the competitive advantage of a multi-controller, parallel architecture will find this session quite informative: GTC # SS33030 and associated blog - How a multi-controller storage architecture shatters expectations for modern applications.

About Pavilion

Pavilion shatters customer expectations and resulting organizational outcomes by revolutionizing data processing for modern AI/ML, HPC, Analytics, Enterprise Edge, and other data-driven applications. The Pavilion HyperParallel Data Platform, powered by Pavilion HyperOS, delivers unmatched performance and density, ultra-low latency, unlimited scalability, and flexibility, providing customers unprecedented choice and control. Learn why Fortune 500 companies and federal government agencies choose Pavilion. Visit http://www.pavilion.io or follow the company on LinkedIn.

About Graphistry Inc.

As a spinout of UC Berkeleys Parallel Computing Lab, Graphistry, Inc. is supercharging how organizations interact with their data. Powered by NVIDIA GPUs, Graphistrys investigation platform creates visual fast paths through complex investigations by transforming diverse data into interactive graphs that visually answer complex questions like correlations, scope, progression, root cause, patterns, and outliers. Ideally suited for investigations in areas like cybersecurity incident response, threat hunting, retail anti-fraud, and anti-money laundering, Graphistry enables analysts to find hidden connections, pivot on the fly easily, and share their findings with other teams. Graphistry, Inc's investors include Bloomberg Beta, Nvidia, In-Q-Tel, Greylock Partners, and angel investors, including Prof. Kurt Keutzer (UC Berkeley, ex-CTO Synopsys), Andy Chou (founder & ex-CTO Coverity), and Patrick OMalley (CFO Seagate). The company's headquarters are in San Francisco, California, with a satellite team in Austin, Texas. More information can be found at http://www.graphistry.com.

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Graphistry and Pavilion Partner to Accelerate Graph Analytics Using RAPIDS and NVIDIA GPUs - Business Wire