Lifeboat Foundation

“As a child, I wished for a robot that would explain others’ emotions to me” says Sharifa Alghowinem, a research scientist in the Media Lab’s Personal Robots Group (PRG). Growing up in Saudi Arabia, Alghowinem says she dreamed of coming to MIT one day to develop Arabic-based technologies, and of creating a robot that could help herself and others navigate a complex world.

In her early life, Alghowinem faced difficulties with understanding social cues and never scored well on standardized tests, but her dreams carried her through. She earned an undergraduate degree in computing before leaving home to pursue graduate education in Australia. At the Australian National University, she discovered affective computing for the first time and began working to help AI detect human emotions and moods, but it wasn’t until she came to MIT as a postdoc with the Ibn Khaldun Fellowship for Saudi Arabian Women, which is housed in the MIT Department of Mechanical Engineering, that she was finally able to work on a technology with the potential to explain others’ emotions in English and Arabic. Today, she says her work is so fun that she calls the lab “my playground.”

Alghowinem can’t say no to an exciting project. She found one with great potential to make robots more helpful to people by working with Jibo, a friendly robot companion developed by the founder of the Personal Robots Group (PRG) and the social robot startup Jibo Inc., MIT Professor and Dean for Digital Learning Cynthia Breazeal’s research explores the potential for companion robots to go far beyond assistants who obey transactional commands, like requests for the daily weather, adding items to shopping lists, or controlling lighting. At the MIT Media Lab, the PRG team designs Jibo to make him an insightful coach and companion to advance social robotics technologies and research. Visitors to the MIT Museum can experience Jibo’s charming personality.

Astronomers from the University of Maryland and the Michigan Technological University, have inspected a mysterious ultra-high energy gamma-ray source known as LHAASO J2108+5157. Results of the study, published August 31 on the pre-print server arXiv, could help us unveil the true nature of this source.

Sources emitting gamma radiation with photon energies between 100 GeV and 100 TeV are called very-high energy (VHE) gamma-ray sources, while those with photon energies above 0.1 PeV are known as ultra-high energy (UHE) gamma-ray sources. The nature of these sources is still not well understood; therefore, astronomers are constantly searching for new objects of this type to characterize them, which could shed more light on their properties in general.

A team of astronomers led by University of Maryland’s Sajan Kumar decided to take a closer look at one such UHE gamma-ray source designated LHAASO J2108+5157. It is a point-like source with an extension less than 0.39 degrees, known to be associated with the molecular cloud [MML2017]4607—located some 10,700 light years away.

A recent study found that an AI chatbot-powered software company could develop software in under seven minutes for less than $1 in costs, on average. ILYA SEDYKH/Getty.

Computing is at an inflection point. Moore’s Law, which predicts that the number of transistors on an electronic chip will double about every two years, is slowing down due to the physical limits of fitting more transistors on affordable microchips. Increases in computer power are slowing down as the demand grows for high-performance computers that can support increasingly complex artificial intelligence models.

This inconvenience has led engineers to explore new methods for expanding the computational capabilities of their machines, but a solution remains unclear.

Photonic computing is one potential remedy for the growing computational demands of machine-learning models. Instead of using transistors and wires, these systems utilize photons (microscopic light particles) to perform computation operations in the analog domain.

Not everyone uses their bicycle at night, but for those that do, safety is key! You’ve probably already have a bicycle helmet, a bicycle safety light and reflectors, but what about seeing the road/path in front of you. Well, this ingenious invention helps map the terrain changes in front of you while you’re riding. It’s called Lumigrids, and it’s essentially a mini projector that you mount on the front of your bicycle handlebars, and it places a grid of laser lights in front of you, mapping any terrain changes such as bumps, curbs, potholes, and more, to make it easy for you to see and maneuver around them.

The creators of the Lumigrids bicycle grid projection light claims that its an improvement over regular bicycle lights which cast shadows over ridges, bumps, and concaves which make it harder to for the bike rider to react properly to the terrain in front of them. Since the Lumigrids projecting light uses a grid system, it makes it much easier to identify issues with the terrain in front of you whether a spot is concaved, convexed, etc. If lines on the grid don’t line up properly, you’ll know there’s something in front of you.

You can change the settings of the bicycle grid projector to emit a larger or smaller sized grid depending on your needs, including a small grid for single bicycle usage at lower speeds, a higher speed setting for single bicycle usage which emits a larger grid, as well as an extra large grid that measures for use with multiple bikers.

If you thought ChatGPT was impressive, you ain’t seen nothing yet…

DeepMind co-founder Mustafa Suleyman predicts ongoing, exponential progress in LLMs and other generative AI. But the emergence of such powerful technology raises huge ethical and safety concerns.

Continue reading “AI training models will be 1,000x larger in three years” »

Researchers from the University of Warsaw’s Faculty of Physics, in collaboration with experts from the QOT Centre for Quantum Optical Technologies, have pioneered an innovative technique that allows the fractional Fourier Transform of optical pulses to be performed using quantum memory.

This achievement is unique on the global scale, as the team was the first to present an experimental implementation of the said transformation in this type of system. The results of the research were published in the prestigious journal Physical Review Letters.

Physical Review Letters (PRL) is a peer-reviewed scientific journal published by the American Physical Society. It is one of the most prestigious and influential journals in physics, with a high impact factor and a reputation for publishing groundbreaking research in all areas of physics, from particle physics to condensed matter physics and beyond. PRL is known for its rigorous standards and short article format, with a maximum length of four pages, making it an important venue for rapid communication of new findings and ideas in the physics community.

In the study, published today in Science Translational Medicine, the researchers used engineered CAR T cells to target CD45—a surface marker found on nearly all blood cells, including nearly all blood cancer cells. Because CD45 is found on healthy blood cells too, the research team used CRISPR base-editing to develop a method called “epitope editing” to overcome the challenges of an anti-CD45 strategy, which would otherwise result in low blood counts, with potentially life-threating side effects. The early results represent a proof-of-concept for epitope editing, which involves changing a small piece of the target CD45 molecule just enough so that the CAR T cells don’t recognize it, but it… More.

A broad new strategy could hold hope for treating virtually all blood cancers with CAR T cell therapy, which is currently approved for five subtypes of blood cancer. A new preclinical, proof-of-concept study details the “epitope-editing” approach.

If people remember how sampling changed music, watch what this guys does to make AI music. A long time ago when people said AI will replace musicians, I replied AI is just a sampler. If people use a Tupac voice on a song like this guy did, they just pay royalties. Then with samplers arists made sample disks royalty free. They make money when you buy the sample disk. The same with AI, you just upload your sample disk into your AI, whether the music AI is from Meta or Google. Yeah Meta has music AI, you can see it used here.

Welcome to a showcase of sounds sampled through the power of artificial intelligence. Gone are the days of vinyl digging; now, we embrace prompt digging…

Continue reading “A.I. Sampling and how the Music Industry will change forever” »