Project potential: Study how effective humans are at finding reality vs deep fakes

 A recent study found that AI algorithms identify Deep Fake images quite a bit better than humans. Humans identified them basically at 50%, which is just chance level, while an AI correctly distinguished between the images as high as 97% accuracy. However, humans still identify Deep Fake videos better than the AIs that were used in the study - the AIs were only around chance level, but humans were correct nearly two-thirds of the time. 

If one is into computer science, psychology, media science, or some other fields, something along these lines could be a research project. Perhaps a study of identifying real images from deep fakes as a function of age, or gender. Or as a function of race or where one lives (city vs suburb vs rural). Are there any significant differences between who can identify them better, or is it chance level for all groups? Are certain types of images easier to distinguish than others? If one can develop a good sample of real and faked images, perhaps different studies could be developed by students - be creative! 

With this being an election year, everyone is anticipating the use of Deep Fakes will spike. We should be working in unison with AI algorithms to hopefully weed-out as many of these as possible, as it will be an ongoing battle as long as these technologies exist. Let's try to maintain as much reality in our lives as possible, as we continue on into the age of mis- and dis-information. It is becoming more challenging to pick out real from fake, to be sure. 

Which image below (of a NU professor) is real, which is fake??? 

Left is real...he put himself in battle gear on the right. Here is an article about his work. 

MIT algorithm shows surprising result - it takes less data to find optimal solutions to certain problems

 For more complex types of problem solving, such as different types of operations type problems like supply chain management, or electrical distribution on grid networks, usually require large amounts of data, which can be expensive and time consuming. But a MIT group found an algorithm that optimizes the solution to the problem, using far less data than researchers ever imagined. Read about what they did!

Computational science - Example of how AI is having an impact

 Advanced AI's have been used in high-level science research for some time, long before it became a thing with the public. If you want to see an example of how it impacts mathematics and computational science, check out this Quantum article about its impact on trying to solve Navier-Stokes equations. The N-S equations are the classic equations that are used to model fluid dynamics, and are seriously challenging as far as the math. A new solution, one that the best mathematicians have been trying to find for decades, has been done with AI. 

Time for Top 10 lists of Science stories

 I'll only list one here, but take your favorite science site or magazine, and it's bound to have the editors' picks for the top 10 science stories of 2025. These are fun to check out, both for their importance to society and their respective fields of study as well as getting a sense of where cutting-edge research is at and what is interesting in a wide spectrum of fields of science. Here is the list from the Smithsonian's science magazine. 

HAPPY 2026, and may it be a year of the joy of discovery for you!