The Department of Pathology at the Icahn School of Medicine at Mount Sinai has established the Center for Computational and Systems Pathology to revolutionize pathology practice, using advanced computer science and mathematical techniques coupled with cutting-edge microscope technology and artificial intelligence. The goal of this new academic research facility is to explore efforts to more accurately classify diseases and guide treatment using computer vision and machine learning techniques. The Center for Computational and Systems Pathology will be a hub for the development of new diagnostic, predictive, and prognostic tests and will partner with Mount Sinai-based "Precise Medical Diagnostics" (Precise MDTM), which has been under development for more than three years by a team of physicians, scientists, mathematicians, engineers, and programmers. Carlos Cordon-Cardo, MD, PhD, will oversee the new center, located at Mount Sinai St. Luke's, and will continue his role as Chair of the Department of Pathology at the Mount Sinai Health System and Professor of Pathology, Genetics and Genomic Sciences, and Oncological Sciences at the Icahn School of Medicine. Gerardo Fernandez, MD, Associate Professor of Pathology, and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, will be the Center's Medical Director.

Information contained on this page is provided by an independent third-party content provider. If you are affiliated with this page and would like it removed please contact pressreleases@franklyinc.com BellHawk Systems Corporation announces the availability of a new white paper "The Artificial Intelligence Revolution in Manufacturing Operations Management." This white paper is available for download from the front page News section of www.BellHawk.com. This white paper describes how real-time Artificial Intelligence (AI) techniques originally developed for the USAF and NASA are being applied to manufacturing organizations to enable managers to run their manufacturing plants with less stress and much smaller management teams. It gives examples of how even small manufacturing organizations are able to use these methods to automate their planning and scheduling and for managers to be alerted whenever problems arise.

In addition, IRIS-NoiseSweeper software has also been ported to the Tensilica Vision DSP to provide premium video quality and cleaner still-images. The software is targeted at image noise reduction for high-resolution still images. The technology features an automated noise profile estimation that reduces calibration needs and shortens development time. It operates in a single frame and eliminates any motion-blurring phenomena associated with multi-frame techniques. The Tensilica family of imaging/vision DSPs was designed for the complex algorithms in imaging, video and computer vision applications including innovative multi-frame image capture, video pre- and post-processing, object and face recognition, low-light enhancement and many other complex tasks.

The road to self-driving cars got a little more crowded Tuesday, as Mobileye (MBLY) announced it will partner with General Motors (GM) supplier Delphi Automotive (DLPH) to jointly develop off-the-shelf autonomous driving technology for automakers. The two companies announced they will co-develop "the market's first turnkey Level 4/5 automated driving solution," which carmakers could begin integrating into vehicles starting in 2019. Level 5 is totally self-driving, while Level 4 is close to complete autonomy. All the major automakers and a number of the largest tech companies are working to develop self-driving cars, mostly through joint ventures. This month, General Motors said it was testing self-driving cars in Scottsdale, Ariz.

The world's most prestigious startup school launched 48 companies today at part 2 of its Summer 2016 Demo Day. Nanoparticle analytics and delivery robots were amongst the products revealed in the B2B, biotech, enterprise, edtech, fintech, and hardware verticals. You can check out our write-ups of all 44 startups that launched yesterday, and TechCrunch's picks for the top 7 from the batch. Trying to distill trends from the hodgepodge of startups at Demo day can be futile, because the real winners are the ones ahead of the trends. For example, TechCrunch thought Airware's drone operating system was a little too early in 2013. It turned out to be smartly ahead of the curve. Now you see lots of drone startups in YC, but many are chasing Airware which has gone on to raise 70 million. Y Combinator president Sam Altman explains "The best company at any given Demo Day is not the one that fits the theme of that Demo Day. Altman cites the Alan Kay quote that "the best way to predict the future is to invent it", adding "I think short of that, the future is basically unknowable. What I like about YC is the companies get to invent the future. They don't have to guess." One important development is that 30% of this batch's companies were founded outside the US, a bigger portion than in the past. YC partner Justin Kan credits that to the program being around long enough that it's funded successful companies from tons of countries.

A data entrepreneur, Phillips comes to Magnetic from leading data analytics provider, Causata, which Phillips founded and led. The company was acquired by NICE Systems Ltd. Phillips also founded Touch Clarity, which specialized in personalization and machine learning. The company was acquired by Omniture, eventually becoming a part of Adobe. "Magnetic is unique in having access to both data from the world of advertising and the world of CRM. The future of marketing will not only demand that we understand what people are in-market for right now, but what every touch may mean to the expected lifetime value of a customer. Magnetic's data assets and technology platform position us well to deliver on that promise," says Phillips.

Microsoft announced today that it acquired Genee, an AI-powered scheduling assistant bot that specializes in planning meetings for large groups or when organizers don't have direct access to the calendars of everyone involved. Genee's app is a chatbot accessible via an iPhone app, email, SMS, FB, Twitter or Skype, and it understands natural language input, so you can just text it the kind of event you want to schedule, when you want to happen and who you want to include, and it should theoretically output a proper meeting invite. The standalone service is going to be shut down on September 1, 2016, as a result of the acquisition. It originally debuted in August last year. Genee co-founders Ben Cheung and Charles Lee explained in a blog post announcing the news that easing calendar entries created by the service will still function, but it won't create any new ones or send reminders or agendas related to upcoming events. The team also says they "consider Microsoft to be the leader in personal and enterprise productivity, AI, and virtual assistant technologies," hence their excitement about teaming up with Redmond.

It's been two and a half years since we let Genee out of the bottle. In our drive to deliver large productivity gains through intelligent scheduling coordination and optimization, we often found ourselves on the forefront of technology involving natural language processing, artificial intelligence (AI), and chat bots. We were extremely fortunate to find many who believed in the vision and supported us with their resources, talent, time, and advice along the way, which made Genee possible. Today, we are pleased to announce that Genee has signed an agreement to be acquired by Microsoft. A new beginning means the end of another.

SAN DIEGO, CA--(Marketwired - Aug 15, 2016) - DB Networks, a leader in database cybersecurity, today announced that that it will be exhibiting at the NSA Information Assurance Symposium (IAS) from Aug. 16-18 in Washington, D.C., in booth number 724; and at the CyberTexas Conference from Aug. 23-24 in San Antonio, Texas, in booth number 110. At these upcoming events, DB Networks will hold booth demonstrations of the DBN-6300, an artificial intelligence (AI)-based database security appliance that non-intrusively discovers databases, immediately alerts when databases are under attack and pinpoints credentials that have been compromised. IT security teams are severely understaffed, and presently there's a shortage of more than 200,000 security professionals in the U.S. In addition, security operation centers (SOCs) are deluged with alerts each day and security personnel are able to respond to only a small fraction of the alerts. AI-based security solutions address these issues by being extremely accurate at identifying actual attacks, thus eliminating false positive alerts, and also by alleviating overworked staff from creating and maintaining white lists/black lists. DB Networks is dedicated to protecting mission critical databases through its patented AI technologies that utilize machine learning and behavioral analysis.

Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting ("curse of dimensionality") and also reduce computational costs. Ronald A. Fisher formulated the Linear Discriminant in 1936 (The Use of Multiple Measurements in Taxonomic Problems), and it also has some practical uses as classifier. The original Linear discriminant was described for a 2-class problem, and it was then later generalized as "multi-class Linear Discriminant Analysis" or "Multiple Discriminant Analysis" by C. R. Rao in 1948 (The utilization of multiple measurements in problems of biological classification) The general LDA approach is very similar to a Principal Component Analysis (for more information about the PCA, see the previous article Implementing a Principal Component Analysis (PCA) in Python step by step), but in addition to finding the component axes that maximize the variance of our data (PCA), we are additionally interested in the axes that maximize the separation between multiple classes (LDA). So, in a nutshell, often the goal of an LDA is to project a feature space (a dataset n-dimensional samples) onto a smaller subspace (where) while maintaining the class-discriminatory information.