What if researchers could run thousands of experiments all at the same time to solve some of the world's greatest problems -- like sickle cell anemia, Alzheimer’s or cancer? We take you inside what's next for scientific research -- automated science.

Automated science is where AI and hands-on robotic experiments power automated research and self-driving labs, while pushing the boundaries of science altogether. Automated science represents a new way to speed up the scientific research that could lead to the discovery of new therapeutic drugs and find answers to questions about disease and DNA.

By taking on the tedious task of replicating and sequencing experiments while working 24/7, automated science and self-driving labs allow researchers to spend more time on the important stuff – analyzing conclusions, searching for breakthroughs and designing new experiments. These self-driving labs can be operated remotely by researchers around the country or around the world. They allow for more collaboration and data-sharing among researchers, while providing access to sophisticated lab equipment and processes for those scientists who may have limited resources.

However, automated science doesn’t come without risks. With data and experiments being stored and run in the cloud, there are dangers of hacking and bad actors hijacking or corrupting self-driving labs. And there are ethical questions as to whom should have access to automated science, and which kinds of experiments should be allowed in the cloud.

Join host Jeanine Herbst and her guests Subha Das, an associate professor in chemistry, and Andreas Pfenning, an associate professor in the Ray and Stephanie Lane Department of Computational Biology, both at Carnegie Mellon University.

What if a self-driving robotic vehicle or drone delivered your next package? Artificial intelligence is transforming how companies deliver packages the “last mile” to your home, the most expensive and time-consuming part of the supply chain.

Companies are using AI to reduce costs and improve sustainability in delivering products to customers. Robotic delivery vehicles, like Refraction AI's Rev One robot, are being used to deliver food from restaurants and grocery stores. The use of AI in last mile delivery has the potential to create new jobs that are sustainable and offer greater economic opportunities. Safety is a top priority, with measures in place to ensure the safe operation of robotic vehicles. Drones may play a role in last mile delivery especially in rural areas, but face challenges such as weight limitations and landing points. The future of AI in last mile delivery is expected to be transformative and exciting.

Join host Jeanine Herbst and her guest Matthew Johnson-Roberson, Director of the Robotics Institute and a professor in the School of Computer Science at Carnegie Mellon University. He's also the co-founder of Refraction AI, an AI-powered last mile delivery company.

To download CMU’s “A Responsible Voter’s Guide to Generative AI in Political Campaigning,” please go to this link: https://cmu.app.box.com/s/uyuzcrwx8qpuh8ep3bzdej5ydk99fg38

What if AI-generated deep fakes could be used to influence our upcoming elections? Generative AI (GenAI) allows users to create realistic images, videos, audio and text, rapidly, cheaply and at scale. These capabilities can be useful, but during elections, they also could be misused to manipulate and deceive voters.

Deep fakes have already started to appear in this election season. There have been deep fake videos on social platforms, fake images on news outlets, and fake robo calls for candidates from small to big races. They can sound or look like well-known politicians starting at the top with President Biden, former President Trump or former President Obama, or even your favorite singer encouraging you to vote or not vote a certain way.

The accessibility, affordability and ease of AI technology have contributed to the proliferation of deep fakes and disinformation. But who’s doing them, and what’s at stake? A fair election process, and perhaps democracy itself.

What can be done? Watermarking AI-generated content is one piece of the puzzle in identifying deep fakes but it will require collaboration among industry, academia, and governments – as well as citizens themselves – to take action against deep fakes.

Join host Jeanine Herbst and her guests Matt Brown, National Race and Politics reporter for the Associated Press; Kathleen Carley, Director, Computational Analysis of Social and Organizational Systems, Carnegie Mellon University; and Ramayya Krishnan, Dean, Heinz College of Information Systems and Public Policy and the Faculty Director of the Block Center for Technology and Society, Carnegie Mellon University.

To download CMU’s “A Responsible Voter’s Guide to Generative AI in Political Campaigning,” please go to this link: https://cmu.app.box.com/s/uyuzcrwx8qpuh8ep3bzdej5ydk99fg38