A coalition of media organizations obtained Chinese internal documents describing the inner workings of China's detention camps where Uighurs are being held. Among the details from the leak is a description of how China is tapping masses of surveillance data and artificial intelligence to come up with lists of names for authorities to target. The revelations are likely to increase calls for...

The following is the Nov. 21, 2019 Congressional Research Service report, Artificial Intelligence and National Security. Artificial intelligence (AI) is a rapidly growing field of technology with potentially significant implications for national security. As such, the U.S. Department of Defense (DOD) and other nations are developing AI applications for a range of military functions. AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles. Already, AI has been incorporated into military operations in Iraq and Syria.

The bot relies on machine–learning algorithms to map the best routes, as well as navigate and avoid obstacles -- but it's connected to a remote tower …

Scientists say they've developed a framework to make computer algorithms "safer" to use without creating bias based on race, gender or other factors. The trick, they say, is to make it possible for users to tell the algorithm what kinds of pitfalls to avoid – without having to know a lot about statistics or artificial intelligence. With this safeguard in place, hospitals, companies and other potential users who may be wary of putting machine learning to use could find it a more palatable tool for helping them solve problems, according to a report in this week's edition of the journal Science. Computer algorithms are used to make decisions in a range of settings, from courtrooms to schools to online shopping sites. The programs sort through huge amounts of data in search of useful patterns that can be applied to future decisions.

Funding of artificial intelligence-focused companies reached approximately $9.3 billion in the U.S. in 2018, an amount that will continue to rise as the transformative impact of AI is realized. That said, not every AI startup has what it takes to secure an investment and scale to success. So, what do venture capitalists look for when considering an investment in an AI company? Some fundamentals are important in any of our investments, AI or otherwise. First, entrepreneurs need to articulate that they are solving a large and important problem.

Python codes for the above analysis are available on my GitHub, do feel free to refer to them.

Dr Catriona Wallace, an entrepreneur in the Artificial Intelligence field, and Founder & Executive Director of Flamingo AI dropped into Speakers Corner towers to share her expertise on Artificial Intelligence, Ethics & Human Rights in technology and Women in Leadership. Needless to say, we were blown away by her visit and decided to learn more. Find out how Catriona became the second female-led business on the ASX, the importance of neurodiversity in the workplace, and what the future of AI has in store for ethics and the world at large. I only ever wanted to be a farmer! After a couple of years studying agriculture at University, I realised most of my peers were becoming investment bankers and not farmers.

The recent progress in neural architectures search (NAS) has allowed scaling the automated design of neural architectures to real-world domains such as object detection and semantic segmentation. However, one prerequisite for the application of NAS are large amounts of labeled data and compute resources. This renders its application challenging in few-shot learning scenarios, where many related tasks need to be learned, each with limited amounts of data and compute time. Thus, few-shot learning is typically done with a fixed neural architecture. To improve upon this, we propose MetaNAS, the first method which fully integrates NAS with gradient-based meta-learning. MetaNAS optimizes a meta-architecture along with the meta-weights during meta-training. During meta-testing, architectures can be adapted to a novel task with a few steps of the task optimizer, that is: task adaptation becomes computationally cheap and requires only little data per task. Moreover, MetaNAS is agnostic in that it can be used with arbitrary model-agnostic meta-learning algorithms and arbitrary gradient-based NAS methods. Empirical results on standard few-shot classification benchmarks show that MetaNAS with a combination of DARTS and REPTILE yields state-of-the-art results.

Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational hybrid quantum-classical algorithm for approximately solving combinatorial optimization problems. The quality of the solution obtained by QAOA for a given problem instance depends on the performance of the classical optimizer used to optimize the variational parameters. In this paper, we formulate the problem of finding optimal QAOA parameters as a learning task in which the knowledge gained from solving training instances can be leveraged to find high-quality solutions for unseen test instances. To this end, we develop two machine-learning-based approaches. Our first approach adopts a reinforcement learning (RL) framework to learn a policy network to optimize QAOA circuits. Our second approach adopts a kernel density estimation (KDE) technique to learn a generative model of optimal QAOA parameters. In both approaches, the training procedure is performed on small-sized problem instances that can be simulated on a classical computer; yet the learned RL policy and the generative model can be used to efficiently solve larger problems. Extensive simulations using the IBM Qiskit Aer quantum circuit simulator demonstrate that our proposed RL- and KDE-based approaches reduce the optimality gap by factors up to 30.15 when compared with other commonly used off-the-shelf optimizers.