Tech giant Google has partnered with NITI Aayog to provide an artificial intelligence (AI)-based skill training opportunity to Indian startups as well as to budding entrepreneurs. The two parties have signed a statement of intent (SoI) to train and incubate Indian startups specialising in AI-based technology. The beneficiaries of the programme will include startups and students (graduates and engineers) across universities and colleges. The training will be imparted through online and developer-run courses in the form of study groups, NITI Aayog said in a media statement. NITI Aayog CEO Amitabh Kant said, "NITI's partnership with Google will unlock massive training initiatives, support startups, and encourage AI research through PhD scholarships, all of which contributes to the larger idea of a technologically empowered New India."

The Boston Consulting Group report has projected that digital payments in India will reach $500 billion by 2020 and more than 50% of all internet users will be actively using digital payments. Another area that is slated to see just as rapid growth is Artificial Intelligence. In fact, Ray Kurzweil from Google has also predicted that AI will overtake human intelligence by 2019. So, is there a coming together of these two technology-driven trends that we can look forward to? Sure enough, there are already signs that the payments industry in India is trying to make the most of technologies such as AI by utilizing them in providing innovative solutions.

Malong, which is based in Shenzhen, China, is building a system that can analyze digital photos and learn to recognize objects. Doing so requires an enormous number of photos, and analyzing all these photos depends on the G.P.U. When the company recently ordered new hardware from a supplier in China, the shipment was delayed by four weeks. And the price of the chips was about 15 percent higher than it had been six months earlier. "We need the latest G.P.U.s to stay competitive," Mr. Scott said.

The promise of oceanic discovery has intrigued scientists and explorers, whether to study underwater ecology and climate change, or to uncover natural resources and historic secrets buried deep at archaeological sites. To meet the challenge of accessing oceanic depths, Stanford University, working with KAUST's Red Sea Research Center and MEKA Robotics, developed Ocean One, a bimanual force-controlled humanoid robot that affords immediate and intuitive haptic interaction in oceanic environments.

By the end of April 2018, blockchain-oriented projects have already collected a total of $6.1 billion year-to-date via initial coin offerings (ICOs), according to CoinSchedule data. This is a new record, as we saw $5.6 billion raised during the last year. Almost 60% of all the projects cover three markets -- communications, finance, and trading & investing -- with machine learning & AI getting only 2.7%. So we have an unexplored market here, with the artificial intelligence (AI) being so much ignored by the general public despite it being one of the revolutionary technologies along with blockchain. AI Crypto, an artificial intelligence (AI) and blockchain-oriented company headquartered in Singapore, wants to combine both of the transformative technologies and bring a game-changing product to the market.

Everyone is talking about artificial intelligence (AI), and with good reason. AI now does simple tasks like playing games. But it also helps pilots fly planes, which is a big reason why we haven't seen a growth in the number of commercial airplane tragedies despite an increasing number of flights. As AI does more complex tasks, it will transform economies, industries and our everyday lives. It will also raise questions about its impact on our economy and jobs.

While enterprises are building businesses around Artificial Intelligence, Machine Learning and even Robotics at an alarming rate, there is speculation among masses about the risks involved. Several headlines abound about how technology and automation are snatching jobs from humans. At the same time, we do not highlight facts that so many startup companies working on these niche technologies are creating new jobs. Technology Leaders like Microsoft, Amazon, Google and IBM are investing heavily as well as hiring at a fast pace from top educational institutes. The fears are spreading beyond concern over blue-collar jobs.

Picture this: US-based banking and financial services major Wells Fargo started piloting an AI-driven Facebook chatbot early last year. It responds to queries from customers, like the current balance in their accounts, and even helps them locate the nearest bank ATM – all through Facebook Messenger. AI has emerged as a powerful disruptor in the Financial Services industry. And most players have already hopped on to the AI bandwagon. In another couple of years, widespread adoption of cognitive systems and AI is expected to boost worldwide revenues.

LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. We present an alternative view to explain the success of LSTMs: the gates themselves are versatile recurrent models that provide more representational power than previously appreciated. We do this by decoupling the LSTM's gates from the embedded simple RNN, producing a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. Ablations on a range of problems demonstrate that the gating mechanism alone performs as well as an LSTM in most settings, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

Decision making is a vital function in this age of machine learning and artificial intelligence, yet its physical realization and theoretical fundamentals are still not completely understood. In our former study, we demonstrated that single-photons can be used to make decisions in uncertain, dynamically changing environments. The two-armed bandit problem was successfully solved using the dual probabilistic and particle attributes of single photons. In this study, we present a category theoretic modeling and analysis of single-photon-based decision making, including a quantitative analysis that is in agreement with the experimental results. A category theoretic model reveals the complex interdependencies of subject matter entities in a simplified manner, even in dynamically changing environments. In particular, the octahedral and braid structures in triangulated categories provide a better understanding and quantitative metrics of the underlying mechanisms of a single-photon decision maker. This study provides both insight and a foundation for analyzing more complex and uncertain problems, to further machine learning and artificial intelligence.