When life calls on us to project confidence, whether you're going in for a major job interview, pitching investors or networking at a big conference, the old adage is just to fake it until you make it. But a new study from New York's Cold Spring Harbor Laboratory released this week looks at whether the human feeling of confidence can be broken down into objective mathematical calculation. Choices that are big, such as launching a new venture, or small, such as deciding when to merge onto the highway, all require a careful weighing of the risks involved. Of course, some of these judgement calls have to occur faster than others. The study's lead author, associate professor of neuroscience Adam Kepecs, explained the driving question behind the research.

"Conversation is the new interface." The tech community's use of this expression does not mean that we are going to start talking to each other more on a human-to-human, face-to-face level. It means rather that we will be talking more to robots, specifically those artificial intelligence programs that pop up on the likes of Facebook Messenger, Twitter, Slack and so on, to help you with tasks from scheduling to shopping. Rather than going to a website to find information or downloading yet another app, we will summon these artificial intelligence assistants to do our bidding. Bark "find me a flight to Chicago on Saturday" into your phone and a "bot", which understands your location and the fact that you mean this Saturday, will return with some choices.

Financial services incumbents along with early-stage startups and large tech companies are all competing in fintech. CB Insights CEO Anand Sanwal gave a 110-slide presentation at our fintech conference in New York. The full deck covered broad VC funding trends for fintech and the developments in large categories such as wealth management, blockchain, remittance tech, and insurance tech. On robo-advisors, Sanwal noted that market volatility has not necessarily been a boon to the category. "It's not all roses," he said. "There are headwinds … When market volatility picks up, people want to talk to an advisor." At the end Sanwal highlighted 9 trends we're watching that are generating excitement. The slides for those trends are included below. If you have questions, please use the hashtag. Use it on Twitter or on the mobile app, and I'll try to get to them. Please ask me easy questions only. If you like something about the presentation, please tweet it out. If you do not like something about the presentation, please just keep it to yourself. Let me jump in and we'll go through this pretty quickly, I think. So this is what we do, take lots of data and try to predict technology trends. You've seen this join the conversation piece. We have lots of great customers, many of whom are in the room.

Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says. Machine learning is a type of artificial intelligence that allows computer programmes to learn when exposed to new data without being programmed. "With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," Meije Wink explained.

We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 50K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model.

PyGame Learning Environment (PLE) is a learning environment, mimicking the Arcade Learning Environment interface, allowing a quick start to Reinforcement Learning in Python. The goal of PLE is allow practitioners to focus design of models and experiments instead of environment design. PLE hopes to eventually build an expansive library of games. Docs for the project can be found here. A PLE instance requires a game exposing a set of control methods.

Bots (and articles about bots – oops!) are popping up everywhere these days. Indeed, very few concepts have shot up the hype curve so fast, especially considering that the idea of conversational UI has been with us for a long time in various forms, and under different denominations. It looks like a jungle, but all bots are essentially doing the same thing: adding conversational capabilities to FAQ and search. You can read a good evolution and state of the art here and here. Personally, I had the pleasure of working for two companies that developed their own virtual assistant (basically a chatbot with a cute face).

I've just run my second briefing on intelligent assistance. Much happened in the few months between sessions. This time, the second half of the day was dominated with stories about bots and their use cases on messaging platforms. It also included the amazing things now possible via automated voice which Amazon's Alexa Challenge exemplifies. Meanwhile IBM's Watson continues its conquest of carbon life forms with the trashing of an extraordinarily talented Go grand master.

As financial services organisations predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond, says Gideon Hyde from design consultancy Market Gravity. The co-founder shares his thoughts on the emerging technology and explains how businesses can embrace AI to enhance their offerings, meet consumer demand for speed, personalisation and convenience, and launch new products and services to stand out in the competitive marketplace. AI is already around us and used every day within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. It can process'big data' far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour, for example to detect fraud as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the human interaction for consumers.

Artificial intelligence… Two words which together conjure up so much wonder and awe in the imagination of programmers, sci-fi fans and perhaps just about anyone with an interest in the fate of the world! Thanks to man's best friend the dog R2-D2, the evil Skynet, the fantastical 2001: A Space Odyssey, post-apocalyptical androids dreaming of electric sheep, and maybe also Gary Numan, everyone is pretty well familiar with the concept of artificial intelligence (AI). Yep, books, the big screen, comics, er… mashed potato advertisements – AI is in all of them in a big way. It also features heavily in the marketing materials of recently-appearing and exceptionally-ambitious cybersecurity companies. In fact, there's probably only one place today where you can't find it.