Lifeboat Foundation

The synthesis of plastic precursors, such as polymers, involves specialized catalysts. However, the traditional batch-based method of finding and screening the right ones for a given result consumes liters of solvent, generates large quantities of chemical waste, and is an expensive, time-consuming process involving multiple trials.

Ryan Hartman, professor of chemical and biomolecular engineering at the NYU Tandon School of Engineering, and his laboratory developed a lab-based “intelligent microsystem” employing machine learning, for modeling chemical reactions that shows promise for eliminating this costly process and minimizing environmental harm.

In their research, “Combining automated microfluidic experimentation with machine learning for efficient polymerization design,” published in Nature Machine Intelligence, the collaborators, including doctoral student Benjamin Rizkin, employed a custom-designed, rapidly prototyped microreactor in conjunction with automation and in situ infrared thermography to study exothermic (heat generating) polymerization—reactions that are notoriously difficult to control when limited experimental kinetic data are available. By pairing efficient microfluidic technology with machine learning algorithms to obtain high-fidelity datasets based on minimal iterations, they were able to reduce chemical waste by two orders of magnitude and catalytic discovery from weeks to hours.

https://www.youtube.com/watch?v=r-vbh3t7WVI

Your Neuralink device would be implanted using traditional neurosurgery methods safely and seamlessly with a robot surgeon. As mentioned in the Neuralink published paper, “We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for the avoidance of surface vasculature and targeting specific brain regions.”

Continue reading “Downloading the Human Brain to a Computer: Elon Musk’s Neuralink” »

How to build an entire ML pipeline, including data transformation and model training, without code.

Most of the pieces of Elon Musk’s Master Plan, Part Deux are already in place. Tesla’s mass-market cars, the Model 3 and Model Y, have already been released. The Solar Roof is finally seeing a ramp. And the release of a feature-complete version of the company’s Full Self-Driving suite seems to be drawing closer.

For Tesla’s Full Self-Driving suite to be feature-complete, the electric car maker would need to master inner-city driving. FSD already works for highway driving with Navigate on Autopilot with automatic lane changes. But when it comes to inner-city streets, Full Self-Driving still has some ways to go. Fortunately, if Tesla’s v10.2 2020.12.5 release is any indication, it appears that more and more aspects of city driving are becoming recognized by the company’s neural networks.

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

The Ancient History of AI:

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Continue reading “The Ancient History of Artificial Intelligence” »

Researchers at the Hebrew University of Jerusalem announced on Sunday that they have developed a new method of testing for COVID-19 which is not only 4–10 times faster than the tests most commonly used today, but also significantly cheaper, while supplying the same level of accuracy. Moreover, most of the materials required to perform the new test are already available in Israel, easing significantly both the country’s dire shortage of testing materials and its heavy economic dependence on foreign commercial markets. The method was developed in the labs of Prof. Nir Friedman of the Institute of Life Sciences and the School of Engineering and Computer Sciences and Dr. Naomi Haviv of Hebrew University’s Neuroscience Research Center, and is based on materials which are not affected by global shortages and can be recycled for repeated used on future tests. The method commonly used today for COVID-19 testing involves extracting RNA molecules from a patient’s sample to determine if the molecules produced have viral RNA within them, which confirms the presence of the coronavirus. The new test developed by the researchers performs the same action, but is made from more commonly attainable materials, that produce results at a much higher speed. Dr. Naomi Haviv said that “We have an efficient RNA extraction method, 4–10 times faster than the current method. It is based on magnetic beads and can be performed both robotically and manually.”

Other than the magnetic beads, all of the other materials needed to perform the tests are available for purchase in Israel. The beads themselves are recyclable and can be reused to perform future tests. “The robotic method has already undergone a series of tests at Hadassah Hospital, using hundreds of samples from patients — and is now becoming operational.”

Continue reading “Israeli researchers at Hebrew U develop faster, cheaper COVID-19 test” »

Note: This article is for people who already have a basic idea about working with Unity and are interested in Neural Networks and Reinforcement Learning. No prior experience with neural networks and/or PhD required! This article provides all needed to obtain the following…

The real future may be more Amazon and less androids, but the dystopian power dynamics are hard to miss.