SparkCognition and SkyGrid announced a new collaboration to deploy AI-powered cybersecurity directly on drones, protecting them from zero-day attacks during flight. Equipped with SparkCognition's DeepArmor cybersecurity product, SkyGrid is the first airspace management system to enable drone protection powered by AI. This approach provides more advanced airspace security than traditional anti-malware reliant on signatures of known threats. "In the near future, we'll essentially have a network of flying computers in the sky, and just like the computers we use today, drones can be hacked if not secured properly," said Amir Husain, CEO and founder of SparkCognition and SkyGrid. "In this emerging environment, traditional anti-malware technology won't be adequate to detect these never-before-seen attacks. SkyGrid is taking a new, intelligent approach by using AI to more accurately detect and prevent cyberattacks from impacting a drone, a payload, or a ground station."

AI companies are generating synthetic data to train machine learning systems.Why it matters: Using computer-generated data to train AI systems can help address privacy concerns and cut down on bias while meeting the needs of models that operate in highly specific environments.Stay on top of the latest market trends and economic insights with Axios Markets. Subscribe for freeHow it works: A synthetic data set is artificially created, rather than scraped from the real world.For a computer vision system being trained on facial recognition, that might mean a dataset of artificially generated human faces in lieu of online photos of real people pulled off the internet — often without their explicit consent. "This allows you to train systems in a completely virtual domain," says Yashar Behzadi, the CEO of Synthesis AI, which generates synthetic data for computer vision models.Details: Synthetic data has been used for some time in robotics and autonomous vehicles, which need to be trained with highly specific data — like the precise 3D position of an object — that can be expensive or difficult to pull from the real world.But as concerns about AI bias and privacy grow, synthetic data makes it possible to generate data sets that can be molded to specification, allowing AI researchers to counter the bias that can be built into the real world."If we want to be robust against skin color or skin tone or demographics, any element that may not be well-represented, you can just model your distribution to equally representing each of those categories," says Behzadi.Yes, but: The real world contains outliers that synthetic data generators may not think to cover, which could leave models unprepared for certain situations.And it's still up to the generators of synthetic data to ensure that their datasets are fairer than what might be picked up in the real world. The bottom line: Synthetic data can be even better than the real thing, but only if it's designed the right way. Like this article? Get more from Axios and subscribe to Axios Markets for free.

Global Artificial Intelligence In Military market research report provides a thorough analysis of the market status, market size, market growth, share, …

Digital Secretary Oliver Dowden revealed the move as he set out his Ten Tech Priorities to power a golden age of tech in the UK this week. Unleashing the power of AI is a top priority in our plan to be the most pro-tech government ever. The UK is already a world leader in this revolutionary technology and the new AI Strategy will help us seize its full potential - from creating new jobs and improving productivity to tackling climate change and delivering better public services. The Government will build on the UK's strong foundations put in place through the AI Sector Deal to develop and deliver an AI Strategy that is both globally ambitious and socially inclusive. It will consider recommendations from the AI Council, an independent expert committee that advises the government, which published its AI Roadmap in January, alongside input from industry, academia and civil society.

Former Secretary of the Navy J. William Middendorf II, of Little Compton, lays out the threat posed by the Chinese Communist Party in his recent book, "The Great Nightfall." With the emerging priority of artificial intelligence (AI), China is shifting away from a strategy of neutralizing or destroying an enemy's conventional military assets -- its planes, ships and army units. AI strategy is now evolving into dominating what are termed adversaries' "systems-of-systems" -- the combinations of all their intelligence and conventional military assets. What China would attempt first is to disable all of its adversaries' information networks that bind their military systems and assets. It would destroy individual elements of these now-disaggregated forces, probably with missiles and naval strikes.

Throughout history we have experienced four industrial revolutions that profoundly reshaped the way we lived, worked, and educated future generations. We progressively advanced from mechanization, to electrification, automation, globalization, and digitalization. These were all possible due to the design, development and deployment of novel technologies which had transformative impact during each of those eras such as steam engines, electricity, combustion engines, computers, internet and more recently robotics, AI, IoT, blockchain, nanotechnology, 3D printing, bio-implants, genomics, virtual reality, augmented reality, extends reality, etc. So which technology will cause the next major disruption and drive the onset of the 5th industrial revolution? Within a relatively short time period we have enjoyed the benefits of 1G to use the first cell phones, 2G to send text messages, 3G to surf the web and 4G to offer the speeds of today. We are currently in the midst of deploying 5G worldwide which uses high frequency millimeter wavelengths and will allow us incredible speeds, low latency and greater bandwidth.

Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizing the mutual information (MI) between the data and the model parameters. When the analytical expression of the MI is unavailable, e.g., having implicit models with intractable data distributions, a neural network-based lower bound of the MI was recently proposed and a gradient ascent method was used to maximize the lower bound. However, the approach in Kleinegesse et al., 2020 requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models. In this work, we propose a hybrid gradient approach that leverages recent advances in variational MI estimator and evolution strategies (ES) combined with black-box stochastic gradient ascent (SGA) to maximize the MI lower bound. This allows the design process to be achieved through a unified scalable procedure for implicit models without sampling path gradients. Several experiments demonstrate that our approach significantly improves the scalability of BED for implicit models in high-dimensional design space.

Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intractable but sampling is possible, conventional BED methods have difficulties in efficiently estimating the posterior distribution and maximizing the mutual information (MI) between data and parameters. Recent work proposed the use of gradient ascent to maximize a lower bound on MI to deal with these issues. However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models. In this paper, we propose a novel approach that leverages recent advances in stochastic approximate gradient ascent incorporated with a smoothed variational MI estimator for efficient and robust BED. Without the necessity of pathwise gradients, our approach allows the design process to be achieved through a unified procedure with an approximate gradient for implicit models. Several experiments show that our approach outperforms baseline methods, and significantly improves the scalability of BED in high-dimensional problems.

There's a huge opportunity to position India as a global hub for data and artificial intelligence (AI), enabling investment, jobs and innovation, says Anant Maheshwari, president, Microsoft India. How prepared are enterprises today in a pandemic-stricken marketplace? Across every industry and sector, we've seen years' worth of digital transformation happen over the last few months. Organisations, both in private and public sector, are quickly adapting to new ways of working and serving customers. Innovation is being applied as organisations work on transforming products, services and business models to stay relevant.

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold.