AI and the Future of Work: Preparing the Workforce for an AI-Driven Economy

By Brent Orrell, U.S. Chamber of Commerce

To paraphrase Mark Twain, reports of the “end of work” have been greatly exaggerated – more than once. Throughout history, the arrival of new technology has been regarded as a threat to human work and, in every instance, new technology has been integral to unlocking new work, new value, and rising incomes.

This hopeful view is not the same thing, however, as saying that new technology, like artificial intelligence, will be all upside for every worker, all the time, everywhere. The recent report from the U.S. Chamber’s Commission on Artificial Intelligence Competition, Inclusion, and Innovation acknowledges that the effects of AI on employment will be both uneven and hard to predict. The report emphasizes that, at its core, AI tools are informing and expanding, not replacing, human labor and, “if developed and deployed ethically, [AI] has the ability to augment human capabilities and empower people to do much more.”

How Workers and Businesses Can Prepare for the AI Economy of the Future

By its nature, technological innovation requires businesses and workers to learn and adapt—and learning and adaptation can be hard. Sometimes, it means upskilling within an existing job and at other times finding a whole new job in a different sector.

This learning and adaptation process is likely to be particularly demanding when it comes to AI. A recent University of Pennsylvania study found that 80 percent of American jobs are likely to see at least 10 percent of their tasks altered by AI while almost 20 percent of jobs will see at least 50 percent of their tasks altered. Another study by Goldman Sachs largely echoed these findings estimating that 18 percent of jobs globally could be computerized with “knowledge” and “information” tasks especially exposed.

During one of the AI Commission’s field hearings, Cheryl Oldham, vice president of education policy at the U.S. Chamber, emphasized that if we’re going to minimize any labor market disruptions and build new and effective pathways that lead to AI-related jobs, “we need to proactively lean into workforce development.”

To do so, the report recommends:

Training and Reskilling: The creation of new programs that can help ease worker transitions find and improve incentives for businesses to invest in retraining as necessary.

Educating the Future Workforce: Urging students and workers to prepare early and to continuously upgrade their knowledge, skills, and abilities.

Economic Policies: Encouraging Congress to adopt tax policies that support “human labor augmentation” within firms rather than ones that incentivize the substitution of technology for human labor and skill.

AI is neither the end of work nor a future delivered on a golden platter. Rather, it is a new tool that, just like new tools of the past, will take time, effort, and practice to master.

How Technology Is Driving The Transportation Industry Toward A Sustainable Future

By Daragh Mahon, Forbes

Every industry contributes to the impact of greenhouse gas emissions, and trucking is no exception. For decades, we have worked to improve sustainability by increasing fuel efficiency and reducing carbon impact.

Fortunately, the world has reached a technical maturity where we can—and must—start taking steps toward a more sustainable future. Ideas that have been around for years, such as alternative fuels and autonomous vehicles, are now within reach. If we act fast enough and invest in the necessary resources, the transportation industry can harness technology in never-before-seen ways.

Logistics And Maintenance

Technology is driving the most sustainable impact through supply chain optimization. For example, empty miles, or when a truck drives with no freight, is an issue the transportation and logistics industry has been improving upon for years. Now, developments in machine learning and AI represent an opportunity to make even larger reductions. Continued improvements to route optimization, including incorporating real-time data for weather and accidents, help reduce idle time and increase route efficiency.

Maintenance efficiency is another area where technology is making an impact. Predictive maintenance systems use IoT devices and onboard sensors to monitor vehicle equipment and alert drivers when there is a potential mechanical problem or the truck is due for routine maintenance. This keeps vehicles operating at peak fuel efficiency and reduces the likelihood of a roadside breakdown, saving the additional emissions from towing.

Alternative Fuels

Vehicle emissions have improved tremendously over the past few decades, especially diesel. Exhaust technology and fuel-refining processes mean fossil fuels are burning cleaner than ever. But as usage only continues to grow, it’s clear that we must diversify our fuel sources to meet future demand.

The idea of alternative fuels has been around for decades, but now it’s time to act and get honest about the viability of each. Transportation companies should have conversations with startups, emerging brands and partner brands to help find viable, alternative solutions that support the trucking ecosystem.

Though we feel that a long-term solution has not yet been identified, there are a few fuel alternatives we have been keeping a close eye on as they develop—electricity, hydrogen and natural gas.

Electric vehicles (EVs) run on a renewable resource and produce no tailpipe emissions; however, this is one energy source that we need to get real about. For trucks alone, three unique challenges need to be solved.

  • The U.S. needs the electrical grid to deliver or produce the electricity required to support an EV-driven nation. An American Transportation Research Institute study finds that the national demand for an all-EV U.S. vehicle fleet would require over 40% of the power currently generated.
  • Currently, there is no battery that can withstand long, over-the-road distances and has a weight that can work on trucks and trailers. Most importantly, the millions of tons of raw materials needed to produce these batteries require extraction from the ground. The environmental damage is not fully understood, but we know that mining and processing these materials produces considerable CO2 and causes pollution issues. Coupled with other problems like water usage and labor exploitation, we should rethink if this is a viable alternative.
  • The charging infrastructure is problematic—what will the cost be to create it and for the trucker to use it? Where do we add charging stations? How will we develop long-term parking to accommodate 8–12 hours of charging?

Hydrogen is a flexible fuel that can be used in both fuel-cell technology and internal combustion engines. Currently, hydrogen engines burn more energy than they create, making them unviable for implementation across a large fleet. Evidence suggests that this can and will change, but it’s far from being a realistic alternative.

Natural gas is an abundant resource that burns cleaner than gas and is more affordable. It is a viable option to reduce emissions as more infrastructure supports the country’s transportation needs. However, testing and resources are required to make this attainable for the industry.

These issues do not mean a future powered by alternative fuels is impossible. Their use has been prominent in progressing toward carbon reduction goals; however, we need to recognize the issue’s complexity to plan for our energy future appropriately. Transportation companies are responsible for testing and piloting new options, as we have a front seat to help drive innovation.

Autonomous Vehicles

Beyond alternative fuels and supply chain optimization, autonomous vehicles (AVs) could support the industry’s impact on climate change. Advanced AI models can calculate operations for maximum efficiency and optimize routes continuously using real-time navigation data, keeping fuel consumption at the lowest levels.

From a technical perspective, AVs are entirely possible. However, perception issues around safety and liability need to be addressed before wide-scale adoption can occur.

Realistically, we are likely looking at a hybrid transportation model with a mix of human and machine drivers. Features like breaking assist and parking assistance, known as advanced driver assistance systems (ADAS), are already used in vehicles today. We will continue to build upon these types of systems, slowly shifting responsibility over to the computer while keeping human drivers present to monitor and ensure all technology is working as intended.

Further down the line, we could have driverless vehicles in limited circumstances. Long-haul routes could become autonomous, traveling between a national network of transportation hubs built outside large population areas. There, loads could be transferred to human drivers for shorter routes that require more skillful driving. A model like this would allow drivers to return home most nights while utilizing the carbon-reducing efforts of autonomous vehicles for the long haul.

Investors In Change And Industry-Wide Buy-In

The industry is at a turning point. We can see a sustainable future on the horizon, but there is still work to do. In the meantime, companies can implement existing technologies that help optimize their operations and maintain equipment efficiency. Future tech requires the investment of industry leaders to fast-track innovation and reduce the cost of industry-wide adoption.

New Company Uses AI to Train Autonomous Trucks

Deborah Lockridge, HDT Talks Trucking Linked Interview

It’s recently appeared that self-driving trucks are not going to be hauling freight down the road without a driver as quickly as some developers and investors had expected. Startup Waabi says its AI-focused approach will allow it to commercialize the technology faster.

HDT Editor in Chief Deborah Lockridge talks to Waabi’s Dustin Koehl, a former fleet manager, about how the company’s approach differs and why they call themselves the next generation of autonomous trucks. In the interview, Koehl hints of a big OEM announcement. Since our interview, Volvo Group Venture Capital announced an investment in Waabi Innovation Inc.

In This Episode:

  • Has autonomous-truck development hit a wall?
  • Using AI to train Waabi Driver
  • Where is Waabi in the development process?
  • Working with truck makers
  • Will autonomous trucks put drivers out of work?
  • Path to commercialization
  • What needs to happen for self-driving trucks to become an everyday reality in logistics?

National Science Foundation Spearheads New Funding to Improve Diversity in AI Workforce

By Alexandra Kelley, Nextgov

Several federal research bodies are collaborating to launch a new inclusivity program that aims to help bring minority-serving educational institutions into the artificial intelligence field, as more technologies incorporate AI and machine learning software.

The U.S. National Science Foundation, in conjunction with other agencies including the Department of Homeland Security, Science and Technology Directorate; U.S Department of Agriculture, National Institute of Food and Agriculture, and National Institute of Standards and Technology, established the ExpandAI program to cultivate a more diverse AI/ML workforce.

“In close collaboration with our federal partners and with the AI Institutes program, NSF Is launching ExpandAI in order to enable an even broader community of researchers to advance the Nation’s AI capacity in scientific power and workforce,” said Margaret Martonosi, the NSF assistant director for Computer and Information Science and Engineering, in a statement.

The program, adhering to the guidance outlined in the earlier in the National AI Strategic Plan published in 2019, will direct more federal funding to AI research and development education, specifically within institutions that serve a diverse student population and specify in AI education.

The key feature of ExpandAI is providing federal funding for development projects and partnerships among the participating National AI Research Institutes and incorporating more diverse student teams. Capacity development projects will specifically work to establish new AI education centers within minority serving colleges and universities that do not currently offer AI/ML curriculs and have a large population of African Americans/Black American, Hispanic American, American Indian, Alaska Native, Native Hawaiian, and Pacific Islander students.

Some of the schools that already offer strong AI/ML education tracks that have partnered with NSF include Ohio State University, the University of California San Diego, Georgia Tech, and Duke University.

“We hope to see a more diverse, more inclusive participation of talented innovators from across our nation, driving AI research and innovation that continues to build our country’s AI leading capabilities and workforce development,” Martonosi said.

Each institution looking to qualify for capacity building funding may receive a grant of up to $400,000 dispersed over the course of two years. By contrast, institutions that already offer advanced AI/ML courses can receive between $300,000 to $700,000 over the course of up to four years.

Some of the previous projects funded by ExpandAI have focused on advancing research in rural health, molecular biology research, environmental science, and industry optimization.

Increasing diversity in the programming workforce behind AI/ML technologies has been a priority area for the Biden administration and various private industry leaders as AI algorithms have proven to discriminate against people of color and other historically vulnerable groups.