Artificial intelligence can thank Hollywood for its bad rep. Movies like "Ex Machina" and the "Terminator" series conjure up futuristic doomsday scenarios where intelligent machines wreak havoc on humans. However, in real life, humans are already surrounded by artificial intelligence. Amazon and Netflix recommendation engines suggest books or movies based on previous selections, Google Now reroutes drivers around traffic accidents on the commute home, and smartphone digital assistants like Apple's Siri and Microsoft's Cortana answer questions about the weather and sports scores. Brands are using artificial intelligence to build better customer experiences and they're just getting started.

Mastercard has announcedplans to launch an artificial intelligence (AI) bot platform that allows consumers to transact, manage finances, and shop via messaging platforms. The Mastercard bot for banks will extend Mastercard services to customers on messaging platforms and make financial information and decisions part of consumers' everyday lives. In the pilot phase, Mastercard is partnering with Kasisto, the company that powers branded virtual assistants and smart bots for financial services and is a current participant in the Mastercard Start Path Global program. The Mastercard bot is powered by KAI Banking, a conversational AI platform with deep knowledge in financial services. It can fulfill customer requests and solve problems on messaging platforms such as Facebook Messenger and SMS, enabling financial institutions to create entirely new consumer experiences – ones that are as easy and natural as texting a friend.

"It's not just a normal update," Uber CEO Travis Kalanick said during a press briefing Tuesday at the company's swanky San Francisco headquarters. "This is a radical redesign and rebuild of the app from scratch." Uber's premise is simple: It pairs drivers with passengers via smartphone app. In the past seven years, it has gone from a small startup to one of the largest ride-hailing services on the planet, operating in more than 70 countries. With a valuation of $68 billion, Uber is also the world's highest-valued venture-backed company.

The UK fintech market is experiencing a new wave of innovation. Developments in artificial intelligence and machine learning are giving rise to a new generation of fintech startups. Prop trading houses and hedge funds have been using machine learning techniques for years to beat financial markets. It's interesting to see A.I. making its way into mainstream fintech. Companies are leveraging the technology to make sense of large volumes of data, making financial products and services more accessible for the end user.

BOT TO THE FUTURE: Mastercard wants to make commerce'more conversational' Mastercard has unveiled plans to launch artificial intelligence (AI) bots for its merchant and bank partners, allowing consumers to use chat, messaging and natural language interfaces to shop and manage their finances. Mastercard KAI, the payments giant's bot for banks, will allow consumers to ask the bot questions about their accounts, review purchase history, monitor spending levels and receive contextual offers. Meanwhile, the Mastercard Bot for Merchants will allow consumer to shop and transact on messaging platforms and then check out with the Masterpass global digital payment service. According to research firm Gartner, nearly US$2bn in online sales will be performed exclusively through mobile digital assistants by the end of 2016. Kiki Del Valle, SVP at Mastercard, explained to NFC World the company was aiming to make commerce "more conversational by combining secure digital payments and artificial intelligence technology".

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

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Recent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenia brain. However, most of the studies focused on the correlation value to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered functional connectivity in the schizophrenia brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenia brain relative to healthy brain with various similarity measures using time-frequency domain analysis, while participants performing the working memory task in the MRI scanner. To assess the distinctiveness of the BOLD connectivity in the schizophrenia, recognition performances of pattern classifier machine trained by each similarity measure were compared. Interestingly, classifier machine trained by time-lagging patterns of low-frequency fluctuation (LFF) produced highest classifying accuracy than the machines trained by other measures. Also, classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern. These results indicate that characteristics of altered functional network in the schizophrenia brain can hardly defined with single aspect of relationship across the multiple brain regions.