Making pizza is not rocket science, but for this actual rocket scientist it is now. Benson Tsai is a former SpaceX employee who is now using his skills to launch a new venture: Stellar Pizza, a fully automated, mobile pizza delivery service. When a customer places an order on an app, an algorithm decides when to start making the pizza based on how long it will take to get to the delivery address. Inside Edition Digital’s Mara Montalbano has more.
Category: robotics/AI – Page 1,910
Deepmind’s New AI May Be Better at Distributing Society’s Resources Than Humans Are
How groups of humans working together collaboratively should redistribute the wealth they create is a problem that has plagued philosophers, economists, and political scientists for years. A new study from DeepMind suggests AI may be able to make better decisions than humans.
AI is proving increasingly adept at solving complex challenges in everything from business to biomedicine, so the idea of using it to help design solutions to social problems is an attractive one. But doing so is tricky, because answering these kinds of questions requires relying on highly subjective ideas like fairness, justice, and responsibility.
For an AI solution to work it needs to align with the values of the society it is dealing with, but the diversity of political ideologies that exists today suggests that these are far from uniform. That makes it hard to work out what should be optimized for and introduces the danger of the developers’ values biasing the outcome of the process.
Yann LeCun’s vision for creating autonomous machines
In the midst of the heated debate about AI sentience, conscious machines and artificial general intelligence, Yann LeCun, Chief AI Scientist at Meta, published a blueprint for creating “autonomous machine intelligence.”
LeCun has compiled his ideas in a paper that draws inspiration from progress in machine learning, robotics, neuroscience and cognitive science. He lays out a roadmap for creating AI that can model and understand the world, reason and plan to do tasks on different timescales.
While the paper is not a scholarly document, it provides a very interesting framework for thinking about the different pieces needed to replicate animal and human intelligence. It also shows how the mindset of LeCun, an award-winning pioneer of deep learning, has changed and why he thinks current approaches to AI will not get us to human-level AI.
Harvard Developed AI Identifies the Shortest Path to Human Happiness
Deep Longevity has published a paper in Aging-US outlining a machine learning approach to human psychology in collaboration with Nancy Etcoff, Ph.D., Harvard Medical School, an authority on happiness and beauty.
The authors created two digital models of human psychology based on data from the Midlife in the United States study.
The first model is an ensemble of deep neural networks that predicts respondents’ chronological age and psychological well-being in 10 years using information from a psychological survey. This model depicts the trajectories of the human mind as it ages. It also demonstrates that the capacity to form meaningful connections, as well as mental autonomy and environmental mastery, develops with age. It also suggests that the emphasis on personal progress is constantly declining, but the sense of having a purpose in life only fades after 40–50 years. These results add to the growing body of knowledge on socioemotional selectivity and hedonic adaptation in the context of adult personality development.