For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy. Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities.

Dummy data is randomly generated data that can be substituted for live data. Whether you are a Developer, Software Engineer, or Data Scientist, sometimes you need dummy data to test what you have built, it can be a web app, mobile app, or machine learning model. If you are using python language, you can use a faker python package to create dummy data of any type, for example, dates, transactions, names, texts, time, and others. Faker is a simple python package that generates fake data with different data types. Faker package is heavily inspired by PHP Faker, Perl Faker, and by Ruby Faker.

Now the threat is artificial intelligence. Or is it really a hope? Only three years ago, computer scientist Kai-Fu Lee published "AI Superpowers," a bestselling guide to the subject. But three years is a long time. In "AI 2041: Ten Visions for Our Future," Mr. Lee has teamed up with sci-fi writer Chen Qiufan to tell us what's going to happen next.

At least 10 people have been reported injured in an attack on an airport in Saudi Arabia's city of Jazan, near the border with Yemen. The Saudi Press Agency (SPA) said the attack on Friday evening targeted the King Abdullah Airport. Citing a Saudi-led coalition spokesman, SPA said that the projectile was fired from a drone, shattering the airport's facade windows and causing injuries. Six Saudis, three Bangladeshi nationals and one Sudanese were among those who were injured, according to the Reuters news agency. At least five of the victims suffered only minor injuries, while the conditions of the five others were not immediately known.

False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid.

Deep Convolutional Neural Networks (CNN) models are one of the most popular networks in deep learning. With their large fields of application in different areas, they are extensively used in both academia and industry. CNN-based models include several exciting implementations such as early breast cancer detection or detecting developmental delays in children (e.g., autism, speech disorders, etc.). However, previous studies demonstrate that these models are subject to various adversarial attacks. Interestingly, some adversarial examples could potentially still be effective against different unknown models. This particular property is known as adversarial transferability, and prior works slightly analyzed this characteristic in a very limited application domain. In this paper, we aim to demystify the transferability threats in computer networks by studying the possibility of transferring adversarial examples. In particular, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. In our experiments, we consider five different attacks: (1) the Iterative Fast Gradient Method (I-FGSM), (2) the Jacobian-based Saliency Map attack (JSMA), (3) the L-BFGS attack, (4) the Projected Gradient Descent attack (PGD), and (5) the DeepFool attack. These attacks are performed against two well-known datasets: the N-BaIoT dataset and the Domain Generating Algorithms (DGA) dataset. Our results show that the transferability happens in specific use cases where the adversary can easily compromise the victim's network with very few knowledge of the targeted model.

Artificial intelligence outperforms human intelligence. It can digest data at breakneck speed and stay concentrated on a single job without being distracted. AI can forecast events in the future and utilize sensors to look around physical and virtual corners. So, why does AI get it so wrong all of the time? The algorithms that describe how artificial intelligence works are created by humans, and the processed data represents an imperfect reality.

OceanX, a team of marine biologists, media and filmmakers, embarked on a quest in 2020 to explore the depths of the Red Sea where they not only found a giant shipwreck, but a massive creature that appeared to be larger than a human. While investigating the'Pella,' which sank in November 2011, at a depth of 2,800 feet, the group spotted what they thought could be'The Giant Squid.' 'I will never forget what happened next for as long as I live,' said OceanX science program lead Mattie Rodrigue in a video taken of the discovery. 'All of a sudden, as we're looking at the bow of the shipwreck, this massive creature comes into view, takes a look at the ROV [remotely operated vehicle] and curls its entire body around the bow of the wreck.' It was not until September 2021 did the team learn that the mysterious creature was'the giant form' of the purpleback flying squid, which typically grow up to two feet long. The OceanX team traveled to the Red Sea aboard the OceanXplorer, a research vessel with a 40-ton crane to launch submersibles, towed sonar arrays and other heavy equipment down into the depths.

Elon Musk has said he is "dying" to expand beyond cars and trucks with Tesla and build an electric supersonic jet. The planes would use vertical take-off and landing (VTOL) technology to rise to a high altitude, before using battery-powered propulsion to reach speeds in excess of 1,236km/h (768mph). The polymath billionaire said the only thing stopping him from developing the next-generation aircraft is his current workload. Mr Musk currently heads two multi-billion dollar companies – SpaceX and Tesla – as well as neurotech startup Neuralink and tunnel-digging venture The Boring Company. He is also the co-founder of the artificial intelligence research laboratory OpenAI and the father of six children.

For our latest expert interview on our blog, we've welcomed Dhonam Pemba to share his thoughts on the topic of artificial intelligence (AI) and his journey behind founding KidX AI. Dhonam is a neural engineer by PhD, a former rocket scientist and a serial AI entrepreneur with one exit. He was CTO of the exited company, Kadho which was acquired by Roybi for its Voice AI technology. At Kadho Sports he was their Chief Scientist which had clients in MLB, USA Volleyball, NFL, NHL, NBA, and NCAA. His latest company, KidX, is in the AI edtech space, where he has built NLP and Voice assessment to serve China's leading robotics company with 4M users.