Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. What is the technology stack you need to create fully autonomous vehicles? Companies and researchers are divided on the answer to that question. Approaches to autonomous driving range from just cameras and computer vision to a combination of computer vision and advanced sensors.

Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study in Radiology: Artificial Intelligence. Amnestic mild cognitive impairment (aMCI) is a transition phase from normal aging to Alzheimer's disease (AD). People with aMCI have memory deficits that are more serious than normal for their age and education, but not severe enough to affect daily function. For the study, Ni Shu, PhD, from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, and colleagues used a machine learning approach to train a brain age prediction model based on the T1-weighted MR images of 974 healthy adults aged from 49.3 to 95.4 years. The trained model was applied to estimate the predicted age difference (predicted age vs. actual age) of aMCI patients in the Beijing Aging Brain Rejuvenation Initiative (616 healthy controls and 80 aMCI patients) and the Alzheimer's Disease Neuroimaging Initiative (589 healthy controls and 144 aMCI patients) datasets.

Maine has just passed the nation's toughest law restricting the use of facial recognition technology. LD 1585 was unanimously approved by the Maine House and Senate on June 16 and 17, respectively, and became law without the signature of Gov. Janet Mills. The bill's sponsor, Rep. Grayson Lookner, D-Portland, hopes that Maine's new law--which goes into effect Oct. 1--will "provide an example to other states that want to rein in the government's ability to use facial recognition and other invasive biometric technologies." The country's only other statewide law regulating facial recognition was passed in Washington in 2020, and it authorized state police to use facial recognition technology for "mass surveillance of people's public movements, habits, and associations." The Washington law--written by state Sen. and Microsoft employee Joe Nguyen-- was opposed by the ACLU.

Summary: New AI technology can detect a patient's stroke depression type, and improve treatment options. An AI developed by Japanese researchers might soon help stroke survivors get the right treatment by detecting a patient's post-stroke depression (PSD) type, a frequently seen but often overlooked neuropsychiatric manifestation after a stroke that could impair functional recovery. The AI was developed by Hiroshima University (HU) researchers using a probabilistic artificial neural network called log-linearized Gaussian mixture network. The neural network was trained to distinguish between depression, apathy, or anxiety based on 36 evaluation indices obtained from functional, physical, and cognitive tests on 274 patients. Details about their research that analyzed the relationship between PSD and activities of daily living independence, degree of paralysis, stress awareness, and higher brain function using machine learning are published in Scientific Reports.

The first episode of Sesame Street in 1969 included a segment called "One of These Things Is Not Like the Other." Viewers were asked to consider a poster that displayed three 2s and one W, and to decide--while singing along to the game's eponymous jingle--which symbol didn't belong. Dozens of episodes of Sesame Street repeated the game, comparing everything from abstract patterns to plates of vegetables. Kids never had to relearn the rules. Understanding the distinction between "same" and "different" was enough.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. In 2019, Facebook open-sourced AI Habitat, a simulator that can train AI systems embodying things like a home robot to operate in environments meant to mimic real-world settings, like apartments and offices. Today Facebook announced that it's extended the capabilities of Habitat to make it "orders of magnitude" faster than other 3D simulators available, allowing researchers to perform more complex tasks in simulation, like setting the table and stocking the fridge. Coinciding with this, Facebook collaborated with 3D space capture company Matterport to open-source what it claims is the largest dataset of indoor 3D scans to date.

Explaining Traditional Engineering Models It is a well-known fact that models of physical phenomena that are generated through mathematical equations can be explained. This is one of the main reasons behind the expectation of engineers and scientists that any potential model of the physical phenomena should be explainable. Explainability of the traditional models of physical phenomena is achieved through the solutions of the mathematical equations that are used to build the models. Explanations of such models are achieved through analytical solutions (for reasonably simple mathematical equations) or numerical solutions (for complex mathematical equations) of the mathematical equations. Solutions of the mathematical equations provide the opportunities to get answers to almost any question that might be asked from the model of the physical phenomena. Solutions of the mathematical equations are used to explain why and how certain results are generated by the model. It allows examination and explanation of the influence and effect of all the involved parameters (variables) on one another and on the model's results (output parameters).

Rescuers deployed sonar and camera equipment early on as officials scoured the rubble for survivors. Heavy machinery was brought in to remove some bits of the pancaked building materials. Yet, nearly 150 people remain unaccounted for. And officials still have a tedious mission ahead as teams try to avoid falling debris and other unforeseen obstacles.

New machine learning algorithms could soon help firefighters forecast dangerous flashover ignition events using sensor data from burning buildings. Called P-Flash, the system was developed by Thomas Cleary and colleagues at the National Institute of Standards and Technology (NIST) in the US and Hong Kong Polytechnic University. Trained using data from thousands of simulated fires, the model can predict some flashovers in housefires up to 30 s before they occur. Flashovers are among the most hazardous threats faced by firefighters. At high temperatures, all exposed combustible material in a room can be ignited simultaneously, releasing a huge amount of energy. To avoid danger, while maximizing the amount of time spent searching a fire for victims, it is critical for firefighters to predict these events as far in advance as possible.