Elon Musk is reportedly looking at a potential investment deal between Neuralink and brain-computer startup Synchron that successfully implanted a chip into a severely paralyzed ALS patient in July. Four people who work or have worked at Neuralink told Reuters that Musk has expressed disappointment at the slow pace of progress on the company's brain implant device and recently approached the CEO of Synchron about a possible deal. Brooklyn-based Synchron made history when it implanted a 1.5-inch long brain-computer interface (BCI) called a Stentrode into a patient's brain without the need for cutting into their skull - by accessing the brain via blood vessels. In contrast, Neuralink's device, which is being tested on monkeys, requires surgery to make a small incision to implant it. Four people who work or have worked at Neuralink told Reuters that Musk has expressed disappointment at the slow pace of progress on the company's brain implant device, called the Link (seen above) Neuralink's device, which is being tested on monkeys, requires surgery to make a small incision to implant it, but Synchron's device does not require surgery.

ExLattice, Inc, a manufacturing AI company, has received a Phase I award from the National Science Foundation (NSF) Small Business Innovation Research (SBIR) Program for developing its accelerated simulation engine dedicated to additive manufacturing. The Phase I SBIR grant, valued at over $250,000, will be used to develop and validate ultrafast manufacturing simulation solutions in collaboration with multiple universities. The goal is to cut the time-consuming steps in computation and deliver real-time engineering solutions for users to understand, control and improve additive manufacturing systems and outcomes. "Receiving the SBIR award from NSF is another proof of our vision in engineering software for digital manufacturing," says Dr. Runze Huang, CEO, ExLattice. "The NSF SBIR grant not only provides us the resources, but a platform to collaborate with leading experts in academia and great business partners in bringing AI to manufacturing."

A new generation of cars keeps an eye on you… to make sure you keep an eye on the road. A tiny camera on the dashboard monitors every blink of the driver's eyes to make sure they're not drowsy or distracted. It tracks the exact position and tilt of their face, the direction of gaze, eyelid activity, the rate and duration of every blink, how dilated their pupils are, how open their eyes are, whether their mouth is open, and more. Using AI and computer vision, it is constantly watching out for signs of cell phone usage, seatbelt-wearing and smoking, and checking that the driver is actually focused on the road. If they're not, it calls them out on it.

As per Section 6 of The Patents Act, an application for a patent can be made by any person claiming to be the true and first inventor of the invention. Further Section 2(1)(s) shows how a natural person is set out from others such as the Government under the meaning of'person'. Thus, only a natural person who is true and first to invent, who contributes his originality, technical knowledge or skill to the invention would qualify to be recognized as an inventor in India. However, this was put to test in the case of the Device for Autonomous Bootstrapping of Unified Sentience ("DABUS"), an Artificial Intelligence ("AI") system created by Dr Stephen Thaler. DABUS is trained to substitute aspects of human brain function.

Earlier this month, Meta (the corporation formerly known as Facebook) released an AI chatbot with the innocuous name Blenderbot that anyone in the US can talk with. Immediately, users all over the country started posting the AI's takes condemning Facebook, while pointing out that, as has often been the case with language models like this one, it's really easy to get the AI to spread racist stereotypes and conspiracy theories. When I played with Blenderbot, I definitely saw my share of bizarre AI-generated conspiracy theories, like one about how big government is suppressing the true Bible, plus plenty of horrifying moral claims. But that wasn't what surprised me. We know language models, even advanced ones, still struggle with bias and truthfulness.

We are entering a new era of patient treatment options thanks to cutting-edge technologies that are changing the way life science companies approach and execute pharmaceutical research. One of the more significant solutions that support the faster and more efficient development of new pharmaceutical products – such as the COVID-19 vaccine, which was developed faster than any other vaccine in history – is artificial intelligence (AI)-driven data analysis. Thanks to modern life science technology solutions that employ AI for data analysis, new treatments for various illnesses can be made safer, faster and more focused on specific conditions. It can be difficult to develop treatments for patients dealing with rare illnesses, as it is often difficult to find enough patients to conduct thorough clinical research. Further, because the condition is rare, there may be sparse literature on the illness and even fewer specialists to consult.

The maximum error in scour trough and filling peak forecasts are provided in Table 3 and graphically shown in Figure 22. The maximum error based on the mean of predictions varies between 0.5m to 0.7m for scour troughs and 0.4m to 1.7m for filling peaks. The lower bound (LB) and upper bound (UB) errors show a reasonable degree of variability in the LSTM predictions, varying between 0.2m to 0.9m for scour, and 0m to 1.4m for filling. Impact of Flow Velocity (Discharge) In order to explore whether velocity is a critical feature in presence of stage timeseries, we incorporated the discharge measurements (discharge), obtained from the USGS website, into the LSTM models for bridge 742 as an input feature and compared the performance among three different feature combinations: ssd:[sonar, stage, discharge], sd:[sonar, discharge], and ss:[sonar, stage]. Discharge is computed based on gage-height records (flow velocity) multiplied the river cross-section area. Gage-height records are obtained by systematic observation of a non-recording gage, or with automatic water level sensors relayed by remote gagging stations (Sauer and Turnipseed 2010). Figure 23 provides histograms of the discharge time-series for bridge 742 and its cross-correlation with sonar and stage. Stage and discharge show a large positive correlation as observed both in Figure 23 and Figure 24.

We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains. Unlike classical SCSS approaches, we consider a setting where only examples of the sources are available rather than their models, inspiring a data-driven approach. For source models with underlying cyclostationary Gaussian constituents, we establish a lower bound on the attainable mean squared error (MSE) for any separation method, model-based or data-driven. Our analysis further reveals the operation for optimal separation and the associated implementation challenges. As a computationally attractive alternative, we propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator. We demonstrate in simulation that, with suitable domain-informed architectural choices, our U-Net method can approach the optimal performance with substantially reduced computational burden.

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60-Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states via classification but also individual load operating power levels via regression. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy and, good scaling and generalization properties. Furthermore, the method is shown to have sufficient response time (within 160ms, corresponding to 10 ac cycles) to support building grid-interactive control at fast timescales relevant to the provision of grid frequency support services.

We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of classical machine learning models that were built with hundreds of features at much higher computational cost. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.