The ancient Chinese game Go, which has a very high number of possible moves, was considered almost impossible for a computer to beat humans two years ago. Last year Alpha Go (a Go programme designed by two Go players) beat the best professional Go Player Lee Sedol in a five game match. Machine learning had breached even the bastion of strategic thought. Impossible Foods, a fourth industrial revolution technology company, makes a plant based food that smells, tastes, looks like real meat. It threatens the future of the $90 billion meat industry.

One answer might come from an artificial intelligence innovation. By combining machine learning analytics with robotic process automation, a new solution almost eliminates the manual search for third party data. The artificial intelligence breakthrough will reduce investigation time for a single alert by up to 70 percent according to an announcement from U.S. firm Nice Actimize. As the fastest growing wealth region in the world, money laundering and terrorism financing is a growing area of concern for Asia. Recognising the threat the Monetary Authority of Singapore launched a government-industry effort to strengthen Singapore's capabilities in identifying and fighting the threats last year.

As the scope for cyber breaches continues to expand with the increase in encrypted web traffic volume and supply chain attacks, security professionals in India will spend more on tools that use Artificial Intelligence (AI) and Machine Learning (ML) to fight malware attacks, said a Cisco report on Wednesday. Applying these tools can help enhance network security defences and, over time, "learn" how to automatically detect unusual patterns in encrypted web traffic, Cloud and Internet of Things (IoT) environments. The "Cisco 2018 Annual Cybersecurity Report" showed that more than half of the organisations surveyed in India are reliant on automation, ML and AI. "Attackers are exploiting undefended gaps in security, many caused by the expanding Internet of Things (IoT) and use of Cloud services. Defenders often pay scant attention to the security of these systems. Unpatched and unmonitored IoT devices present attackers with opportunities to infiltrate networks," Vishak Raman, Director, Security Business, Cisco India & Saarc, told IANS.

The ancient Chinese game Go, which has a very high number of possible moves, was considered almost impossible for a computer to beat humans two years ago. Last year Alpha Go (a Go programme designed by two Go players) beat the best professional Go Player Lee Sedol in a five game match. Machine learning had breached even the bastion of strategic thought. Impossible Foods, a fourth industrial revolution technology company, makes a plant based food that smells, tastes, looks like real meat. It threatens the future of the $90 billion meat industry.

Indian software developers have varied and rich experience that ensure developing software solutions that provide business an edge over the competition. They serve customers all over the world. Outsourcing development India provides high quality and effective services to customers anywhere in the world. The emergence of AI or artificial intelligence heralded a shift in how businesses and consumers engage with technology. Deeply embedded in people's lives already, the transformative potential of artificial intelligence could bring exciting market opportunities to businesses that are quick to embrace it.

For small businesses (SMBs) the margins between success and failure are tiny. So, it's vital to do everything possible to maximise employee performance and minimise costs, and this is where the company's choice of business technology plays a pivotal role. Yet research has continually shown that many SMBs are slow to move to modern PCs, even though such a PC Refresh – the act of regularly upgrading computers being used in the organisation -- enables them to cut bottom-line costs and improve productivity. A 2014 study by Techaisle[1] found that 36% of SMBs had PCs that were four years or older, which resulted in application and system performance issues, malware attacks and connectivity issues for many of the respondents. This, in turn, had a direct impact on diminishing productivity and employee satisfaction.

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

We study sampling as optimization in the space of measures. We focus on gradient flow-based optimization with the Langevin dynamics as a case study. We investigate the source of the bias of the unadjusted Langevin algorithm (ULA) in discrete time, and consider how to remove or reduce the bias. We point out the difficulty is that the heat flow is exactly solvable, but neither its forward nor backward method is implementable in general, except for Gaussian data. We propose the symmetrized Langevin algorithm (SLA), which should have a smaller bias than ULA, at the price of implementing a proximal gradient step in space. We show SLA is in fact consistent for Gaussian target measure, whereas ULA is not. We also illustrate various algorithms explicitly for Gaussian target measure, including gradient descent, proximal gradient, and Forward-Backward, and show they are all consistent.

Topic models are one of the most frequently used models in machine learning due to its high interpretability and modular structure. However extending the model to include supervisory signal, incorporate pre-trained word embedding vectors and add nonlinear output function to the model is not an easy task because one has to resort to highly intricate approximate inference procedure. In this paper, we show that topic models could be viewed as performing a neighborhood aggregation algorithm where the messages are passed through a network defined over words. Under the network view of topic models, nodes corresponds to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and add nonlinear output function to the model in a simple manner. Moreover, we describe a simple way to train the model that is well suited in a semi-supervised setting where we only have supervisory signals for some portion of the corpus and the goal is to improve prediction performance in the held-out data. Through careful experiments we show that our approach outperforms state-of-the-art supervised Latent Dirichlet Allocation implementation in both held-out document classification tasks and topic coherence.

Sparse regularization such as $\ell_1$ regularization is a quite powerful and widely used strategy for high dimensional learning problems. The effectiveness of sparse regularization has been supported practically and theoretically by several studies. However, one of the biggest issues in sparse regularization is that its performance is quite sensitive to correlations between features. Ordinary $\ell_1$ regularization can select variables correlated with each other, which results in deterioration of not only its generalization error but also interpretability. In this paper, we propose a new regularization method, "Independently Interpretable Lasso" (IILasso). Our proposed regularizer suppresses selecting correlated variables, and thus each active variable independently affects the objective variable in the model. Hence, we can interpret regression coefficients intuitively and also improve the performance by avoiding overfitting. We analyze theoretical property of IILasso and show that the proposed method is much advantageous for its sign recovery and achieves almost minimax optimal convergence rate. Synthetic and real data analyses also indicate the effectiveness of IILasso.