You can help prevent others from falling victim to the same romance scam and remember if something seems too good to be true. Get ready for this quick heartbreaking story about love gone wrong from a crafty and callous global dating scam artist. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER I recently received an email from Linda, who is concerned and wondering if she should worry about falling for a scam from a person she's been talking to online. Here's what she had to say: "I have been in contact with a man who is a Structural Engineer that says he lives and has his office in Wisconsin, but currently is in Dubai overseeing the construction of buildings that he was awarded a contract to build, we talk on the phone all the time and text all the time. He has shared everything that I have asked.

Imperial privacy experts have created an AI algorithm that automatically tests privacy-preserving systems for potential data leaks. This is the first time AI has been used to automatically discover vulnerabilities in this type of system, examples of which are used by Google Maps and Facebook. The experts, from Imperial's Computational Privacy Group, looked at attacks on query-based systems (QBS)--controlled interfaces through which analysts can query data to extract useful aggregate information about the world. They then developed a new AI-enabled method called QuerySnout to detect attacks on QBS. QBS give analysts access to collections of statistics gathered from individual-level data like location and demographics.

Using arrest and victimization records from the Chicago PD, a machine learning model can predict the risk of being shot in the next 18 months. UMD College of Information Studies Assistant Professor Zubin Jelveh--alongside co-authors Sara B. Heller of the University of Michigan, Benjamin Jakubowski of the Courant Institute of Mathematical Sciences, and Max Kapustin of the Brooks School of Public Policy--recently published a paper on research that supports that shootings are predictable enough to be preventable. Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, the team trained a machine learning model to predict the risk of being shot in the next 18 months. They addressed central concerns about police data and algorithmic bias by predicting shooting victimization rather than arrest, which accurately captures risk differences across demographic groups despite bias in the predictors. Out-of-sample accuracy is strikingly high: of the 500 people with the highest predicted risk, 13 percent are shot within 18 months, a rate 130 times higher than the average Chicagoan.

The history of deep fake technology is surprisingly long. Researchers at academic institutions have been developing deep fake tech since the early 1990s. The idea is even older, as popular science fiction--like the 1987 film The Running Man--can attest. But deep fakes are no longer relegated to the realm of sci-fi; they are, in fact, more present in our daily lives than you might realize. It's easy to think of deep fakes as some sort of advanced CGI used to create highly realistic animated films or to replace established actors in a film or television series, especially in cases where actors pass away unexpectedly before filming is complete.

Data science and artificial intelligence can help prevent outbreaks COVID-19?

Most artificial intelligence systems strive for 95% accuracy of results when benchmarked against the traditional methods of determining outcomes. But how can organizations safeguard against systems so the AI doesn't inadvertently inject bias that affects the accuracy of results? Bias can be injected into AI by faulty algorithms, by lack of complete data on which the algorithms operate or even by machine learning that operates on certain biased assumptions. One example is an Amazon recruiting tool that began with an AI project in 2014. The intent of the AI application was to save recruiters time going through resumes.

Over the past six months the world has been focused on the singular goal of developing treatments, vaccines, and containment strategies but what no one expected was how the tech world would rise to the challenge presented by Covid-19. While front line responders and essential workers put their lives on the line, researchers and scientists turned to artificial intelligence (AI) for answers. In mere months, emerging technologies have been rolled out and leveraged to full effect to vastly boost computing power, dramatically increase access to high-performance computing (HPC), and accelerate research by orders of magnitude. AI and HPC are being leveraged to not only assess and develop treatments but also to create potential vaccines, manage shutdowns and reopenings, analyze and enable access to digital medical records, and even to help develop better face masks. Samsung Semiconductor technology has played a particularly essential role in the fight against Covid-19.

Machine learning experts working at Google Health have published a new study in tandem with the University of California San Francisco's (UCSF) computational health sciences department that describes a machine learning model the researchers built that can anticipate normal physician drug prescribing patterns, using a patient's electronic health records (EHR) as input. That's useful because around 2% of patients who end up hospitalized are affected by preventable mistakes in medication prescriptions, some instances of which can even lead to death. The researchers describe the system as working in a similar manner to automated, machine learning-based fraud detection tools that are commonly used by credit card companies to alert customers of possible fraudulent transactions: They essentially build a baseline of what's normal consumer behavior based on past transactions, and then alert your bank's fraud department or freeze access when they detect a behavior that is not in line with an individual's baseline behavior. Similarly, the model trained by Google and UCSF worked by identifying any prescriptions that "looked abnormal for the patient and their current situation." That's a much more challenging proposition in the case of prescription drugs versus consumer activity -- because courses of medication, their interactions with one another and the specific needs, sensitivities and conditions of any given patient all present an incredibly complex web to untangle.

If you've been keeping up with the news recently, you may have read about the death of the last male northern white rhino. Conservationists view his death as a sign that unsustainable human activities like overpopulation and climate change are driving a new era of mass extinction on a global level. And it isn't just the rhino – flagship species like the cheetah are disappearing, and with them, the biodiversity that supports us all. Currently, there are only about 7,000 cheetahs left in the world – down from 100,000 less than a century ago. WildTrack, a nonprofit conservation group, seeks to prevent these powerful cats (among others) from landing on the extinction list – and we are proud to support them in their efforts.

When we train neural networks with gradient descent, we risk the network falling into local minima, in which the network stops somewhere along the error surface that is not the lowest point on the overall surface. This is because the error surfaces are not inherently convex, so the surface may contain many independent local minima separate from the global minimum. Additionally, while the network may reach a global minimum and converge to a desirable point for the training data, there is no guarantee as to how well it will generalize what it has learned. This means that they are prone to overfitting on the training data. There are several things that we may use in order to help mitigate these issues, although there is no way to definitively prevent them from occurring, as the error surfaces for these networks tend to be quite difficult to traverse, and neural networks as a whole are rather difficult to interpret.