Committee co-chair Sen. Julian Bradley noted that many of the issues discussed will need continued discussion from lawmakers and members of the public in the future as AI continues to develop and become a bigger part of Wisconsinites’ lives. (Screenshot via WisEye)

A Wisconsin study committee met Thursday to discuss regulation of artificial intelligence as well as investments in AI that it will recommend to the state Legislature. 

The committee includes four lawmakers — Sen. Julian Bradley (R-New Berlin), Rep. David Armstrong (R-Rice Lake), Sen. Tim Carpenter (D-Milwaukee) and Rep. Clinton Anderson (D-Beloit) — as well as eight members of the public. Members have been meeting monthly since July to hear from an array of stakeholders about the ways that AI is currently being used in the state, issues that have or could arise from the technology and ways to address those and how the state could capitalize on potential benefits.

The study committee is the latest effort as Wisconsin government and business leaders attempt to confront increased use of and ongoing advancements of AI technology. Gov. Tony Evers and Assembly Speaker Robin Vos both organized task forces in the last year to address the issue.

Bradley, the committee’s co-chair, noted at the end of the meeting that many of the issues will need continued discussion from lawmakers and members of the public as AI develops and becomes a bigger part of Wisconsinites’ lives. The committee is considering recommending an ongoing and more permanent body that will continue looking into AI after members end their work this year.

“I believe that a lot of these start with an ongoing committee. There’s a lot of things that say we need to continue to explore … because the technology is changing,” Bradley said. “Things are changing constantly, so we want to be able to have public members with the Legislature come together and say, ‘Hey, maybe not yet,’ or ‘OK, it’s time now.’ ” 

A final report with recommendations for lawmakers will be put together in coming weeks. 

Potential regulations and state uses 

The committee considered a number of proposals during the meeting for regulating the use of artificial intelligence, protecting consumers and exploring how state government will interact with the existence of AI. 

One of the first proposed concepts was to apply existing state laws to AI models in the same way the laws apply to humans, including when it comes to disinformation, impersonation and creating and distributing fake imagery.

“Anything that’s illegal for a human, should be illegal for AI — as a general principle,” CEO of the MKE Tech Hub Coalition Kathy Henrich said during the meeting. 

Wisconsin has already enacted two laws that regulate the use the AI: 2023 Wisconsin Act 123, which requires disclosure of the use of generative AI in political ads, and 2023 Wisconsin Act 224, which made it a crime to possess virtual child pornography and obscene material, including that created with generative AI.

Henrich acknowledged that this could be a complicated issue because any law would need to be able to determine a liable party. 

“Is it the developer? The deployer? The consumer?” Henrich said. “The devil will be in the details of making sure we’ve really thought through how you hold people accountable and where that accountability lies.” 

Sarah Alt, the chief process and AI officer for Michael Best, said the question of accountability for new technology is not a new dilemma, but it is complex because of how quickly AI has developed. 

Aly also proposed that the Legislature enact a couple of laws related to data privacy. 

Wisconsin considered a bill, 2023 Assembly Bill 466, that would have established certain requirements for “controllers and processors” of consumers’ personal data, including confirming what information they have, obtaining that data and requesting the deletion of data. It also included financial penalties of a forfeiture of up to $7,500 per violation. Three states, including Colorado, Connecticut and Utah, enacted similar data privacy laws in 2023.

Alt said that data is a “crucial raw material for artificial intelligence.” 

“If we’re not going to be able to legislate every possible use case … I do believe data is certainly one of the places where we would declare that to be no different than other raw materials that are regulated,” Alt said. She compared data to asbestos, saying, “there are safe ways that you can use asbestos and there are also very harmful ways that you can use asbestos.”

Jay Hill, Vice President of Advanced Technology for GE Healthcare, agreed that this type of regulation could help keep people accountable for how data is used.

“If it’s free, then people could do all sorts of crazy and exploitative things, but if there’s some cost to it, I think it elevates the level of responsibility for people who will use those data,” Hill said. 

Alt also said that lawmakers should look at resurrecting a bill similar to 2023 Wisconsin AB 824, which would have defined various roles and laid out a set of responsibilities for people or groups that own, control and share personal data. 

Members also discussed three other issues that lawmakers could explore including requiring the labeling of communications that use AI, possible bias embedded in the algorithms of large language models and requiring companies to publish clear AI guidelines and principles.

Anderson said that he thought that requiring disclaimers of AI when communicating with the public would be best practice. 

“I think I’m pretty good at discerning when AI is being used or not, but I think of my dad, who’s definitely not capable of determining what’s AI and what’s not. … So, just making sure we’re protecting the public, so they’re aware,” Anderson said. 

Henrich cautioned that legislation such as that could potentially set a bad precedent by discouraging people from using their own discernment skills to determine whether AI was used. 

“If you legislate that you mark things as AI, people will get trained to believe that anything that’s marked as AI is AI, and anything that isn’t is human,” Henrich said. “If I’m a bad actor, I’m more likely to not mark it and therefore I’m more likely to fall for bad actors who may be using AI in the wrong ways.” 

Henrich said that lawmakers would also need to think about how that kind of legislation would be enforced.

Potential Investments in AI

The committee also considered potential areas where the state could invest to help support universities, technical colleges, businesses and other stakeholders in the ongoing education, innovation and integration related to AI.

One suggestion the committee could make is to expand Fast Forward, a program administered by the Wisconsin Department of Workforce Development that provides grants to employers to provide training to workers, and specifically dedicating funding to learning how to work with AI and requiring courses on computer science and statistics for students. 

Henrich said an investment in teaching people skills related to AI could be essential as the technology may affect the job market and could come through the Fast Forward program and through technical colleges and universities. 

“I’ve seen market dynamics in play, and as much as people want to say everyone will be employable long-term with AI, I am a firm believer that there’s disruption in the middle, that as AI is implemented normal market dynamics will go to saving costs and may result in people losing roles as a result of that,” Henrich said. “Investing up front and people understanding AI so that they maintain employable skill is going to be very critical.”

Henrich noted that there might need to be changes to the Fast Forward legislation to make it applicable to AI. 

Two potential suggestions the committee discussed involve incentives for businesses, including tax breaks and a grant or revolving loan program. 

Armstrong suggested that lawmakers could explore creating a program that would give small manufacturers an incentive to adopt automation and AI through a revolving loan fund or a small grant. He said he did a survey of small manufacturers in his part of the state and found that many aren’t really exploring the use of AI currently. 

“Only 4% of them were even looking at AI right now,” Armstrong said. “A lot of them, when I started talking to them [said] we don’t even know where to start at this point. … They’re looking for somebody within a company to develop a curiosity about, what could AI do for us, and then I think you’d probably have a champion within that company that can run with it. This is more of a way to maybe jump-start that.”

Alt said she liked the concept of a revolving loan because of how quickly technology moves and the fact that a company would have to keep working on improvements. 

“If your grant is one and done, then even if you do get what you needed that first time that you implemented it, there is still the constant tuning and the constant investing and the constant improving, and because the technology moves so quickly your idea is already old within months, if not a couple of years,” Alt said. 

The full list of potential recommendations to lawmakers can be found here. 

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Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

© Credit: Courtesy of Mohamed Elrefaie

The New York Times referred to the major IT outage in July involving Microsoft and CrowdStrike as the “glitch felt around the world.” In today’s digital age amid an increased presence of artificial intelligence tools, it’s no surprise that keeping sensitive data safe is a rising concern for the student transportation industry.

During his STN EXPO West keynote presentation in July, Keith Krueger, the chief executive officer for the Consortium for School Networking (CoSN), discussed the results of an annual survey of school IT leaders that indicated a shift in the top five technology priorities. The number one challenge for the past three years was cybersecurity. Data Privacy & Security, which had been sitting at No. 5 in 2022, moved up to No. 2. Network Infrastructure was third followed by the up-and-coming challenges of Determining AI Strategy and IT Crisis Preparedness.

Transportation departments are handling large amounts of data, including but not limited to onboard video camera footage, student ridership verification, telematics, and transportation employee information.

With these large amounts of data, it’s also important that school districts and vehicle contractors are equipped to effectively use and analyze the data, which could increasingly incorporate the application of AI.

Different facets of data and information security were discussed at the 2024 STN EXPO West conference in Reno, Nevada, in July. During these sessions, speakers and vendors discussed how increased technology offerings also require due diligence in protecting the data that is run through and stored in a given solution.

In one of the STN EXPO West sessions, representatives from Geotab and Tyler Technologies led a discussion titled “What Do I Do with All of this Data? Using Artificial Intelligence and Business Intelligence tools in Student Transportation.” Craig Berndt, the segment manager for student transportation at Geotab, noted that he is expecting AI to be a rising trend.

“Machine learning is like teaching your dog to fetch, except your dog is a computer and fetching is recognizing patterns in data,” he explained. Some of the applications using AI to track data discussed in the sessions included risk management, predictive maintenance, driver training, tracking student attendance, and continuous learning that can assist in effective routing planning.

Berndt noted that historically there has been much conjecture surrounding AI, and a lot of that is hyperbolic. Geotab displayed its new AI assistant software Geotab Ace at the STN EXPO West Trade Show. Berndt added that Geotab protects transportation data by keeping it on a private, secure server. He explained it’s important to know how your data tools work and exactly where the data is landing.

“No one here would put your student data into ChatGPT. Our goal with generative AI is to get away from the staff having to analyze reporting. Would you like to be told what trends are from a reliable source or have to go through the data yourself?” he noted.

Berndt said that it’s important to stay on top of trends in AI, data security and analysis, commenting that “Artificial intelligence isn’t going to take away your job. People who know how to use artificial intelligence are going to take your job.”

Protecting sensitive student data was the topic of a panel discussion moderated by Rick Hays, deputy chief information officer at the Nevada Department of Transportation. Hays holds a doctorate in cybersecurity, served in the U.S. Air Force, and worked for the Cybersecurity and Infrastructure Agency, an arm of Homeland Security. He has extensively worked on military and government levels to further cybersecurity safety practices.

Panelist Jennifer Vobis, who has since retired as executive director of transportation for Clark County School District in Nevada, spoke about a 2020 security breach that affected 40,000 district employees. It wasn’t until three years later that the district discovered information had been sold on the dark web. Vobis said that while her department assumed IT had the data security covered, it’s important to fully understand how those imperfect safeguards affect transportation operations.

Hays noted that many ransomware attacks begin with an email, an easy-to-overlook threat. His advice was to take a moment to analyze the message and sender, and “think before you click.”

“Balance the drive to get tasks done with making sure we know what it is we’re doing,” added panelist Lam Nguyen-Bull, a consultant at Edulog and an attorney, explaining that it’s everyday behavior that creates the most risk.

She continued that understanding and managing data flow and security starts with understanding that “data is just information,” whether physical or digital. Just as Berndt noted, Hays also emphasized the importance of knowing exactly where data is at all times. When it’s being used, when it’s being stored and when it’s in transit. Encryption must be present at all these levels, he explained.

Nguyen-Bull continued that data in storage is the easiest stage to protect it. When data is in transit across the web, it is generally protected by a Secure Sockets Layer (SSL) or Hypertext Transfer Protocol Secure (HTTPS). When it’s being used it is protected by a firewall in a closed environment.

“What makes it vulnerable is when it’s between stages,” she said. “When it’s not being managed by a system.”

Nguyen-Bull used the example of a parent portal app, which she referred to as “a perfectly safe product if you use it right.” Ensuring that only the relevant parties can view data or a particular school bus location, or that a tablet onboard the bus is locked is the responsibility of the owners of the data. “Know what your responsibilities
are regarding the data you handle, you need to know the policies,” said Hays.

The human element of safely managing and effectively protecting data is a team effort, said Vobis, but it may be a teaching moment if all the staff is not up to date on technological education and cybersecurity training. Even though some of these practices may be considered common sense, the panel stressed the importance of covering all your bases and making sure each member of the team understands the implications of data breaches.

When things go wrong, and Nguyen-Bull noted that they will, it’s crucial to have an action plan in place to not only get the issue under control but to understand what happened and how it can be prevented in the future.

During a security audit situation, like one a “white hat” firm performed on Edulog last year, “We don’t usually like to answer questions, but understand we’re not being attacked. [Auditors] are just trying to understand,” she explained. “Be collaborative, learn from other people’s experiences. Despite best efforts to lock things down, there is always a high risk.”

We always think it’ll never happen to us,” said Vobis. Even after the situation at Clark County was resolved, she said there was an impact on how information was shared. Vobis cited an example of improper information sharing via Google Suites, where security privacy settings weren’t on. Nguyen-Bull referenced receiving an email with an attached unencrypted spreadsheet containing detailed data on student riders.

“Practice doesn’t make perfect, but practice does make it better,” said Nguyen-Bull, recommending that districts run tabletop exercises to prepare for when the “unthinkable does happen.”

All the panelists advised that student transporters take time to find out their organization’s cyber policies and security protocols.

Hays spoke to the widespread variety and type of ransomware and cyberattacks, noting that they can happen to very small and extremely large organizations, alike. He advised that transportation departments should have software in place to scan incoming files for possible attacks and that transportation should coordinate with the district to ensure security protocols for transferring or receiving data is being upheld throughout all operations.

Nguyen-Bull noted that even though it may seem like data is spread out between multiple people or databases, it can be easy for that information to get centralized somewhere within the district. She continued that predictive computation could use any amount or type of data to create complete pictures.

“Data is permanent, in all forms,” said Hays. “It can come back to bite you, no matter what stage it’s in.”

Both he and Nguyen-Bull advised being cautious with “new and improved AI” technology that is being created to meet the demand of ever-increasing data. Hays referenced the addage “Trust but verify,” which he said is applicable to all of us, in our personal and professional lives.

In a continually evolving digital landscape, Nguyen-Bull said that while she does work for a software company, she makes sure to prioritize people with face-to-face and voice interactions.

“Don’t reduce everything to digital.”

Editor’s Note: As reprinted in the November 2024 issue of School Transportation News.

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DALLAS, Texas – Zonar, the leader in commercial vehicle fleet health and safety, convened fleet professionals from across North America at its redesigned Zonar Together User Conference. Customers, partners, and employees discussed the latest developments in transportation and strategies for improving operational efficiency and safety across vocational, pupil, mass transit, state and local municipalities, and commercial trucking industries.

In 25 sessions over two days, the conference featured keynote presentations from the Lutzie 43 Foundation and Google AI, and industry updates from the Federal Motor Carrier Safety Administration (FMCSA) and the Commercial Vehicle Safety Alliance (CVSA). Additionally, the conference featured Zonar fleet management software trainings, customer-led sessions, and a panel on cross-industry challenges and successes.

“Zonar Together reimagined the user conference experience, offering a forum for collaboration and discussion around our shared goals to improve fleet health and safety,” said Mike Gould, president and CEO at Zonar. “We were excited to showcase what we’ve done, where we’re going, and how our customers will benefit from improved data analytics and AI innovations all to unlock new potential. Together, we’ll shape the future of transportation.”

Today, fleets are forced to manage more vehicles and assets with fewer resources, make informed decisions from vast amounts of data, and keep vehicles moving amidst increased pressure for safety and efficiency gains. These challenges are further exacerbated by talent shortages and changing government funding, regulations, and compliance mandates. To save critical time, money, and resources, fleets need a modern platform to manage priorities all in one place.

At the conference, Zonar unveiled its next generation Zonar Ground Traffic Control and an expanded 360-degree view camera system for Zonar Coach. With new features and reporting capabilities in the pipeline, fleets can better prioritize maintenance, optimize fuel use and reduce idle time, improve trip analytics for performance and efficiency, capture insights on driver behavior and speed management, track ridership analytics for compliance, and better understand planned versus actual routes for dispatch.

“Our new data model is the underpinning of our next generation Ground Traffic Control, allowing fleets to aggregate actionable insights into a consolidated view for faster decision-making,” said Pablo Supkay, vice president of product management at Zonar. “As we move to a world where innovation becomes standard, we aim to help fleets move from reactive to predictive approaches, transition from manual to automated actions, and more seamlessly translate insights into fleet management and maintenance recommendations to meet the needs of today and tomorrow.”

“At the conference, Zonar demonstrated a commitment to consistently exceeding industry standards and elevating solutions to the next level,” said Tony Cruz, desert southwest regional fleet manager at MasTec Communications Group. “This is essential because a company committed to excellence will succeed when its products are designed for long-term success.”

About Zonar:
Founded in 2001, Zonar has pioneered smart mobility solutions throughout vocational, pupil, mass transit, state and local municipalities, and commercial trucking industries. The Zonar mission is to enhance the safety, performance, and success of our customers by transforming the delivery of innovative insights for commercial fleets. Zonar achieves this by helping fleets of all sizes maximize the use of their assets with solutions dedicated to improving compliance, efficiency, maintenance, ridership visibility, safety, and tracking. Cloud-based services with open APIs drive Zonar smart mobility solutions by making it easy for fleet owners and managers to stay connected to their fleets and drivers and operators to dispatch. Headquartered in Seattle and part of the Continental family, Zonar also has a distribution center in West Chicago.

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