Fox News Flash top headlines for Nov. 27 are here. Check out what's clicking on Foxnews.com A Georgia man is accused of scamming a Virginia woman out of more than $6.5 million after wooing her into a romantic relationship through an online dating site. Nnamdi Marcellus MgBodile, 35, from Marietta, Georgia, was charged with 20 counts of bank fraud, money laundering and conspiracy to commit bank fraud, according to a Justice Department press release on Wednesday. MgBodile and others also allegedly tried to scam a company out of $350,000 using an email scheme, according to the DOJ.

Nightfall is using machine learning and natural language processing to help organizations discover and protect their most sensitive information with the startup's cloud-native data loss protection platform. The San Francisco-based startup launched out of stealth mode in November with $20.3 million in funding led by Bain Capital Ventures and Venrock, with participation from Atlassian CTO Sri Viswanath and New York Jets offensive tackle Kelvin Beachum, among other investors. The startup's platform supports integrations with Slack to protect sensitive data shared in chat and with GitHub to protect sensitive keys and credentials in code.

I think everyone can agree the new TensorFlow 2.0 is a revolution rather than evolution. It has greatly simplified almost every aspect of the clunky TF1. And while the TensorFlow programmers made it easier to transition to the new Framework by creating the TF2 Upgrade Script, they have undeniably complicated things a bit for newcomers. We now live in a world of billion samples and pieces of StackOverflow snippets and information that at least in the beginning are hard to navigate. You never know what is TensorFlow 1 or 2 or in-between as there was an in-between phase too to make things worse.

The U.S. Department of Energy's Argonne National Laboratory is working to use machine learning and artificial intelligence to build a better battery. Should the DOE's efforts prove fruitful, it could be a positive development for the medical device industry, where batteries have proven to be a technological stumbling block when it comes to device miniaturization. Argonne researchers created a database of approximately 133,000 small organic molecules that could form the basis of battery electrolytes with a computationally intensive model called G4MP2, which represents 166 billion larger molecules that scientists wanted to probe for electrolyte candidates, according to a news release. The researchers applied a machine-learning algorithm to relate the known structures from the small data set to more coarsely modeled structures from the larger set, using a less computationally taxing modeling framework based on density functional theory. It is less accurate than G4MP2, but density functional theory provides a good approximation, according to the DOE.

Pioneer Update: Meeting at Centre for Addiction and Mental Health, Toronto to learn more about mental health and using multimodal techniques to detect issues. On 28 September 2019, we were invited to speak on Artificial Intelligence and Human Interaction at the Google DevFest 2019 held at George Brown College, Toronto. There was a great line up of speakers from Google, IBM, Taiga Robotics, Applied Brain Research, and the Ontario Government. It was an exciting event with over 200 people in the audience – researchers, academia, students and those from government and tech companies. We thank the organizers for giving us this opportunity to speak at the event.

We seem flooded with white papers, summits, meetups, conferences and blogs on Artificial Intelligence (AI) …is all this an indicator that AI has finally reached a maturity stage which supports broader commercialization? The answer is "yes" and the aim of this post is to share some key lessons learned from the front line of implementing AI solutions. The borders between automation, machine learning and AI are sometimes blurred requiring some clarification. AI is the simulation in machines (i.e. AI has also become imbedded in our daily lives from Google Translate to autonomous vehicles to chatbots and there is no turning back.

It is adopted by thousands of companies and becoming more and more popular. There are two concepts which everyone gets confused while understanding a chatbot platform, and that common confusion is between chatbot development platform and publishing platform. We will consider an example to know what a chatbot publishing platform is. Let's consider a scenario, you want to buy some clothes, the first thought that would come to your mind is to go to a mall and get it. Now if there wasn't any mall then you would have to search the city for clothing shops which will be a hassle.

A master player of the Chinese strategy game Go has decided to retire, due to the rise of artificial intelligence that "cannot be defeated". Lee Se-dol is the only human to ever beat the AlphaGo software developed by Google's sister company Deepmind. In 2016, he took part in a five-match showdown against AlphaGo, losing four times but beating the computer once. The South Korean said he had decided to retire after realising: "I'm not at the top even if I become the number one." "There is an entity that cannot be defeated," the 18-time world Go champion told South Korea's Yonhap news agency.

In 2018, Streamr announced a partnership with Daisy AI, Japan, an AI platform using blockchain for deep learning, based in Japan. Streamr intended to become Daisy AI's official data provider to exclusively sell data from Streamr's decentralized data Marketplace. Daisy AI planned to purchase data for a wide range of purposes, including forecasting stock and cryptocurrency prices, economy insights, footfall and traffic. Shohei Ohsawa, representative director of Daisy AI and associate professor at the University of Tokyo, recently made a series of racist and offensive statements on Twitter. He wrote that "Daisy does not hire Chinese people."

CLEVELAND–Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy. And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both inside–and outside–the tumor, a signature of the lab's recent research. "This is no flash in the pan–this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.