A report by the New York Times details two parents who say photos intended to help diagnose infections in their children were flagged by AI as potential CSAM. Their accounts were locked, and the police were alerted.
A new AI-enabled, optical fiber sensor device developed at Imperial College London can measure key biomarkers of traumatic brain injury simultaneously.
The “promising” results from tests on animal brain tissues suggest it could help clinicians to better monitor both disease progression and patients’ response to treatment than is currently possible, which indicate the high potential for future diagnostic trials in humans.
People who experience a serious blow to the head, such as during road traffic accidents, can suffer traumatic brain injury (TBI)—a leading cause of death and disability worldwide that can result in long-term difficulties with memory, concentration and solving problems.
Constructing a tiny robot out of DNA
DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).
I have been invited to participate in a quite large event in which some experts and I (allow me to not consider myself one) will discuss about Artificial Intelligence, and, in particular, about the concept of Super Intelligence.
It turns out I recently found out this really interesting TED talk by Grady Booch, just in perfect timing to prepare my talk.
No matter if you agree or disagree with Mr. Booch’s point of view, it is clear that today we are still living in the era of weak or narrow AI, very far from general AI, and even more from a potential Super Intelligence. Still, Machine Learning bring us with a great opportunity as of today. The opportunity to put algorithms to work together with humans to solve some of our biggest challenges: climate change, poverty, health and well being, etc.
Near-term quantum computers, quantum computers developed today or in the near future, could help to tackle some problems more effectively than classical computers. One potential application for these computers could be in physics, chemistry and materials science, to perform quantum simulations and determine the ground states of quantum systems.
Some quantum computers developed over the past few years have proved to be fairly effective at running quantum simulations. However, near-term quantum computing approaches are still limited by existing hardware components and by the adverse effects of background noise.
Researchers at 1QB Information Technologies (1QBit), University of Waterloo and the Perimeter Institute for Theoretical Physics have recently developed neural error mitigation, a new strategy that could improve ground state estimates attained using quantum simulations. This strategy, introduced in a paper published in Nature Machine Intelligence, is based on machine-learning algorithms.
People have been dreaming of robot butlers for decades, but one of the biggest barriers has been getting machines to understand our instructions. Google has started to close the gap by marrying the latest language AI with state-of-the-art robots.
Human language is often ambiguous. How we talk about things is highly context-dependent, and it typically requires an innate understanding of how the world works to decipher what we’re talking about. So while robots can be trained to carry out actions on our behalf, conveying our intentions to them can be tricky.
If they have any ability to understand language at all, robots are typically designed to respond to short, specific instructions. More opaque directions like “I need something to wash these chips down” are likely to go over their heads, as are complicated multi-step requests like “Can you put this apple back in the fridge and fetch the chocolate?”
Tesla Autopilot is now enabling the car to perceive the space around it thanks to the development of its Occupancy Networks.
❤️ Check out Runway and try it for free here: https://runwayml.com/papers/
📝 The paper “3D Face Reconstruction with Dense Landmarks” is available here:
https://microsoft.github.io/DenseLandmarks/
Continue reading “Microsoft’s New AI: Virtual Humans Became Real! 🤯” »
However, AI functionalities on these tiny edge devices are limited by the energy provided by a battery. Therefore, improving energy efficiency is crucial. In today’s AI chips, data processing and data storage happen at separate places – a compute unit and a memory unit. The frequent data movement between these units consumes most of the energy during AI processing, so reducing the data movement is the key to addressing the energy issue.
Stanford University engineers have come up with a potential solution: a novel resistive random-access memory (RRAM) chip that does the AI processing within the memory itself, thereby eliminating the separation between the compute and memory units. Their “compute-in-memory” (CIM) chip, called NeuRRAM, is about the size of a fingertip and does more work with limited battery power than what current chips can do.
“Having those calculations done on the chip instead of sending information to and from the cloud could enable faster, more secure, cheaper, and more scalable AI going into the future, and give more people access to AI power,” said H.-S Philip Wong, the Willard R. and Inez Kerr Bell Professor in the School of Engineering.
The device uses magnetic fields to carry out computation rather than shuttling electricity around, so it consumes far less power.
