Google, which already dominates India's smartphone, search, and online video market, today launched a learning app for primary school children in the country as part of an effort to cement its grip on the world's fastest-growing internet market. The Android app, called Bolo, aims to help young kids improve their reading comprehension and vocabulary skills in Hindi and English. Bolo (the Hindi word for "speak"), features a range of games and tasks, and it rewards kids as they progress. Bolo, which is powered by Google's speech recognition and text-to-speech technology, first asks kids to read sentences. The app then listens to the efforts and reviews them, and an animated voice assistant -- called Diya -- suggests pronunciation and vocabulary corrections wherever applicable.

Artificial Intelligence (AI) can be an effective tool in anti-corruption work. Its potential for handling big data is unique, its ability to detect anomalies or patterns, for example in financial transaction data, unparalleled. Some of the ways AI is applied in society also raise sceptic voices who fear a society under ever more surveillance where privacy and individual freedom is at risk. The risks and opportunities of new technologies for anti-corruption is up for discussion at the OECD Global Anti-Corruption & Integrity Forum conference in Paris on March 20-21. Ethical dilemmas, perils and promises, in short, AI's potential and pitfalls as a tool in and for anti-corruption in development programming are up for debate.

Today, AI has turned into reality what used to be the stuff of sci-fi novels. For decades, scholars from diverse disciplines have been predicting how AI and robotics are about to change the way we think, work and live. Although, not everyone is on the same page when it comes to AI, there is no denying that it is already demonstrating its positive potential in many industries. One area where AI is expected to play a huge role is education. However, in India, the education sector is still seeking ways to respond to the advent of this technology.

The government said Thursday it plans to ask horse race and pachinko parlor operators to consider using facial recognition systems to restrict admission of gambling addicts. The adoption of facial recognition systems is part of a basic plan to combat gambling addiction that the government drafted to pave the way for opening casinos in the nation. Facial recognition systems can help efficiently identify gambling addicts after restrictions on their entry into racetracks and pachinko parlors are requested by themselves or family members, officials said. The government's plan also includes the removal of automated teller machines from such gambling sites. After seeking views from the public, the government aims to adopt the plan next month.

Please disable your ad blocker for POST to be able to enjoy our free content. The research, which has been published in two reports, "Asia's AI agenda: The ecosystem" and "Asia's AI agenda: AI for business," is based on a multi-industry survey of 871 senior executives drawn from 13 Asia-Pacific markets. From China to Japan, Singapore to India, policymakers across Asia are developing national-level plans for using AI to enhance domestic and regional competitiveness. Business leaders are positive about the region's AI resources, particularly data availability and the quality of external talent. More can be done to bolster the R&D environment, and governments can still provide greater support.

More than a few of us were surprised in 2018 when Google demonstrated how their artificial intelligence personal assistant can now ring unknowing hairdressers and make a booking. It's perhaps a little uncomfortable to think that soon we may not be able to tell the difference between a machine and a person. That, though, is beside the point. Google is the unprecedented world leader of internet search. Each time you type a sentence into Google, and click on one of the links that comes back, you provide the AI system with a data point.

The European Commission has chosen Time Machine as one of the six proposals retained for preparing large-scale research initiatives to be strategically developed in the next decade. Time Machine foresees to design and implement advanced new digitisation and Artificial Intelligence (AI) technologies to mine Europe's vast cultural heritage, providing fair and free access to information that will support future scientific and technological developments in Europe. The Time Machine Project, which involves FAU as well as several other institutions, will create advanced AI technologies to make sense of vast amounts of information from complex historical data sets. This will enable the transformation of fragmented data – with content ranging from medieval manuscripts and historical objects to smartphone and satellite images – into useable knowledge for industry. In essence, a large-scale computing and digitisation infrastructure will map Europe's entire social, cultural and geographical evolution.

We propose estimation methods for unnormalized models with missing data. The key concept is to combine a modern imputation technique with estimators for unnormalized models including noise contrastive estimation and score matching. Further, we derive asymptotic distributions of the proposed estimators and construct the confidence intervals. The application to truncated Gaussian graphical models with missing data shows the validity of the proposed methods.

Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defences for graph data. In this paper, we propose both attack and defence techniques. For attack, we show that the discrete feature problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defence, we propose to partially learn the adjacency matrix to integrate the information of distant nodes so that the prediction of a certain target is supported by more global graph information rather than just few neighbour nodes. This, therefore, makes the attacks harder since one need to perturb more features/edges to make the attacks succeed. Our experiments on a number of datasets show the effectiveness of the proposed methods.