From self-driving vehicles and autonomous drones, to virtual doctors and automated personal assistants, AI is expected to fundamentally disrupt the way that people live, work and interact with each other. AI is increasingly the key to significant innovations across almost all segments of society, manifesting itself in vastly different applications. There are vast opportunities for businesses operating in industries where AI has become more prevalent. However, with these opportunities come significant challenges. Ashurst's series of articles on AI will consider these issues from an IP perspective.

By the end of this decade, a milestone is reached in artificial intelligence, with computers now routinely passing the Turing Test.** This test is conducted by a human judge who is made to engage in a natural language conversation with one human and one machine, each of which tries to appear human. Participants are placed in isolated locations. For several decades, information technology had seen exponential growth – leading to vast improvements in computer processing power, memory, bandwidth, voice recognition, image recognition, deep learning and other software algorithms. By the end of the 2020s, it has reached the stage where an independent judge is literally unable to tell which is the real human and which is not.* Answers to certain "obscure" questions posed by the judge may appear childlike from the AI – but they are humanlike nonetheless.*

Generative Adversarial Networks (GAN) training process, in most cases, apply uniform and Gaussian sampling methods in latent space, which probably spends most of the computation on examples that can be properly handled and easy to generate. Theoretically, importance sampling speeds up stochastic gradient algorithms for supervised learning by prioritizing training examples. In this paper, we explore the possibility for adapting importance sampling into adversarial learning. We use importance sampling to replace uniform and Gaussian sampling methods in latent space and combine normalizing flow with importance sampling to approximate latent space posterior distribution by density estimation. Empirically, results on MNIST and Fashion-MNIST demonstrate that our method significantly accelerates the convergence of generative process while retaining visual fidelity in generated samples.

Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at the fragment level with eighteen propaganda techniques and we propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.

Ask Google or Siri: "What is Taiwan?" "A state", they will answer, "in East Asia". But earlier in September, it would have been a "province in the People's Republic of China". And Wikipedia had suddenly changed. The edit was reversed, but soon made again. It became an editorial tug of war that - as far as the encyclopedia was concerned - caused the state of Taiwan to constantly blink in and out of existence over the course of a single day.

Accenture Chief Technology and Innovation Officer Paul Daugherty on how artificial intelligence will impact the workforce in America in the future. Artificial intelligence is devouring the restaurant industry. Delivery sales are projected to grow at more than three times the rate of revenue from customers dining in at restaurants, according to a report from L.E.K. consulting. And more than half of customers are ordering food directly from a restaurant's app or website, according to the same report. Here's how the restaurant industry is leveraging artificial intelligence to boost sales and reach more customers.

New Delhi, October 4,2019:Tech Mahindra, a leading provider of digital transformation, consulting and business reengineering services and solutions, has signed a Memorandum of Understanding(MoU) with Startup Bangladesh to foster the growth of digital startup ecosystem in Bangladesh, by providing guidance and mentoring to the budding entrepreneurs.The MoU was signed in presence of H.E. Sheikh Hasina, Prime Minister of Bangladesh and Shri Piyush Goyal - Minister of Railways and Commerce & Industry, Government of India. As part of the comprehensive growth framework outlined within the MoU, Tech Mahindra will be assisting new-age technology startups in the country, focusing on future technologies like Artificial Intelligence, 5G, Big Data, Cybersecurity, Blockchain, Internet of Things (IoT) and Machine Learning, to leverage digital growth opportunities across its global network. Startup Bangladeshis a concrete initiative by the Government of Bangladesh to create new opportunities, develop technical skills and help realize the vision of Digital Bangladesh. As part of the MoU, Tech Mahindra will extend collaboration opportunities to the innovators of Startup Bangladesh to engage with its research and development arm Makers Lab, which has global footprint including India, US, Europe and Australia. This collaboration will take up initiatives like Ideathons and Hackathons across educational institutions in Bangladesh.

The next extension of machine learning is on its way. Machine teaching promises to bring the power of AI to those unskilled in data science. What could possibly go wrong? Prefer to listen to this story? Here it is in audio format.

We derive an efficient method to perform clustering of nodes in Gaussian graphical models directly from sample data. Nodes are clustered based on the similarity of their network neighborhoods, with edge weights defined by partial correlations. In the limited-data scenario, where the covariance matrix would be rank-deficient, we are able to make use of matrix factors, and never need to estimate the actual covariance or precision matrix. We demonstrate the method on functional MRI data from the Human Connectome Project. A matlab implementation of the algorithm is provided.

Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and health care monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resulting in a complex, high dimensional mathematical object. Spectral embedding methods provide an effective way to transform a graph to a lower dimensional latent Euclidean space that preserves the underlying structure of the network. Although change detection methods that use spectral embedding are available, they do not address sparsity and degree heterogeneity that usually occur in noisy real-world graphs and a majority of these methods focus on changes in the behaviour of the overall network. In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes in entity behaviour. Our spectral embedding approach not only addresses sparsity and degree heterogeneity issues, but also obtains an estimate of the appropriate embedding dimension. We call this method CDP (change detection using Procrustes analysis). We demonstrate the performance of CDP through extensive simulation experiments and a real-world application. CDP successfully detects various types of vertex-based changes including (i) changes in vertex degree, (ii) changes in community membership of vertices, and (iii) unusual increase or decrease in edge weight between vertices. The change detection performance of CDP is compared with two other baseline methods that employ alternative spectral embedding approaches. In both cases, CDP generally shows superior performance.