Breast cancer is one of the leading causes of death in women, worldwide. Statistical data suggests the potential risk of cancer is 1 in every 8 women, just in the U.S. However, this type of cancer incidence rate varies from country to country. Genetic history and family history are also two very important factors that influence this cancer rate. In this technologically driven era, a lot of breast cancer startups are making waves in the healthcare industry by pioneering novel diagnosis and treatment methods to help fight against breast cancer.

As AI becomes increasingly adopted in more industries, its users attempt to achieve the delicate balance of making efficient use of its utility while striving to protect the privacy of its customers. A common best practice of AI is to be transparent about its use and how it reaches certain outcomes. However, there is a good and bad side to this transparency. Here is what you should know about the pros and cons of AI transparency, and possible solutions to achieve this difficult balance. The number of uses of AI has continued to expand over the last several years.

The U.S. government announced on May 27 that it was joining the Global Partnership on AI (GPAI), a group launched by the G7 to provide cooperation between nations on artificial intelligence. This is a sharp change of course for the United States which had held out on joining the group initially out of concern that its European allies would use the forum to push for aggressive regulation of the technology without clear evidence of harms. Instead, the group is now set up to focus on the responsible development of AI, including by developing research agendas, promoting AI workforce development, and spurring AI innovation and commercialization, while ensuring these uses align with shared democratic values.

Later this month, NASA is expected to launch its latest Mars rover, Perseverance, on a first-of-its-kind mission to the Red Planet. Its job is to collect and store geological samples so they can eventually be returned to Earth. Perseverance will spend its days poking the Jezero Crater, an ancient Martian river delta, and the samples it collects may contain the first evidence of extraterrestrial life. But first it has to find them. For that, it needs some damn good computers--at least by Martian standards.

The U.S. economy has been rocked by the coronavirus pandemic, with stock values crashing this year during the period between late February and late March, when states began issuing stay-at-home orders. One intrepid digital company saw its stock surge, however--Zoom Video Communications, Inc., which has more than doubled its individual stock price while the economy around it crashed. But why has Zoom become the go-to platform during the pandemic, when there are dozens of other video conferencing services out there? The answer lies in Zoom's intuitive interface, says Mathias Unberath, an assistant professor of computer science at Johns Hopkins Whiting School of Engineering and a member of the Malone Center for Engineering and Healthcare. "Whether someone is hosting a work meeting or a baby shower, the app is easy to use," he says.

Now is a particularly opportune time to drive towards this goal. As the world moves towards a COVID-19 post-pandemic recovery, the UN has called on governments to heed the "unprecedented wake-up call" and "build back better" by creating more sustainable, resilient and inclusive societies. There are two approaches to Green AI – using AI to solve sustainability challenges and using AI in a more sustainable way. How can AI solve sustainability challenges? Delivering societal and environmental well-being through AI are key strategic considerations of the European Commission, who acknowledge that "AI systems promise to help [tackle] the most pressing concerns, including climate change and environmental degradation".

As we look to the future of cybersecurity, we must consider the recent past and understand what the pandemic has taught us about our security needs. Many cybersecurity platforms proved inadequate when a large percentage of the world's workforce abruptly shifted to remote work in the Spring of 2020. Companies found themselves fighting against the limitations of their own cybersecurity platforms. Modern systems enhanced with self-learning AI capabilities have fared best in the face of the pandemic's impact on networking. For others, immediate, manual interventions were the only thing standing between enterprise security and the bad actors who had been standing by waiting for a global event of this scale.

In the light of the Black Lives Matter protests, AI and facial recognition vendors and users are taking notice of concerns over racial bias and privacy, reports Ron Alalouff. The use of artificial intelligence (AI) has come under the spotlight recently, especially how algorithms can be biased against people of colour or women. And most recently, in the wake of the Black Lives Matter campaigns following the death of George Floyd in May, tech giants such as Amazon and IBM have suspended or withdrawn their facial recognition technologies which are based on AI algorithms. In the United States the issue of bias in AI is most explosive. Miriam Vogel, President and CEO of Equal AI, believes that while racism has its historical roots, "AI now plays a role in creating, exacerbating and hiding these disparities behind the facade of a seemingly neutral, scientific machine".

This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events. While counterfactual reasoning is natural for humans, understanding these expressions is difficult for artificial agents due to a variety of linguistic subtleties. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also explored various other approaches that involved the use of classical methods, other neural architectures and the incorporation of different linguistic features.

Anomaly Detection is one of the most important tasks in unsupervised learning as it aims at detecting anomalous behaviours w.r.t. historical data; in particular, multivariate Anomaly Detection has an important role in many applications thanks to the capability of summarizing the status of a complex system or observed phenomenon with a single indicator (typically called `Anomaly Score') and thanks to the unsupervised nature of the task that does not require human tagging. The Isolation Forest is one of the most commonly adopted algorithms in the field of Anomaly Detection, due to its proven effectiveness and low computational complexity. A major problem affecting Isolation Forest is represented by the lack of interpretability, as it is not possible to grasp the logic behind the model predictions. In this paper we propose effective, yet computationally inexpensive, methods to define feature importance scores at both global and local level for the Isolation Forest. Moreover, we define a procedure to perform unsupervised feature selection for Anomaly Detection problems based on our interpretability method. We provide an extensive analysis of the proposed approaches, including comparisons against state-of-the-art interpretability techniques. We assess the performance on several synthetic and real-world datasets and make the code publicly available to enhance reproducibility and foster research in the field.