Music has been around in one form or another throughout the world for as long as anyone can remember. Some people listen to music to relax, others to party, others just because. Scientists have now discovered that you can improve brain function by listening to certain types of music, and now a new artificial intelligence has been created specifically for developing that magical blend of tones. Brain.fm was founded by Adam Hewett and Junaid Kalmadi and is currently online inviting users to sign up and try it out for free. By teaming up with neuroscience experts, the team is ensuring that the responses they are hoping to achieve from the music are the ones they are getting.

Where is the IoT market heading for in 2016? Looking back at 2015, we witnessed a strong buzz around the size of the IoT market, veracity of the market being real vs. I do not foresee such assessments and predictions to die down soon. In fact, with more technological interventions seeing light of the day, the billion dollar counter against the IoT market potential will only go one way – upside. In this post, I've highlighted some of the key and realistic IoT trends we are likely to witness in the coming year.

The term deus ex machina means "a god from a machine." "Machine," in this example, pertains to a crane that held a god over a theater stage in ancient Greek drama. Typically, the playwright would introduce an actor portraying a god at the end of his play who, from his elevated perch on the crane, would magically provide a resolution to an impossible dilemma to advance the plot to its end. Over the centuries "deus ex machina" has evolved to mean the intervention of unlikely saviors, devices or surprising events that bring order out of chaos in fast and often remarkable ways. Today, machine learning is acting in much the same way.

UPD (April 20, 2016): Scikit Flow has been merged into TensorFlow since version 0.8 and now called TensorFlow Learn. Google released a machine learning framework called TensorFlow and it's taking the world by storm. Now, but how you to use it for something regular problem Data Scientist may have? A reasonable question, why as a Data Scientist, who already has a number of tools in your toolbox (R, Scikit Learn, etc), you care about yet another framework? Let's start with simple example -- take Titanic dataset from Kaggle.

Sending money online is about to get easier and a lot more social. By using artificial intelligence and machine learning, startup Azimo is creating a Facebook Messenger bot that will let you communicate naturally with it and make transferring money easier. "You would be able to speak to Azimo and ask what the international rates are," company co-founder Marta Krupinska said at the WIRED Money event in London. Krupinksa, a Polish expat, added that artificially intelligent bots which are able to communicate in natural language – as if they were a human – will play a role in money transfers in the future. "We want to get to a point where the experience is like going into a branch," she says.

Last week I bumped into Robert Hoffer, the famed creator of SmarterChild, the automated chatbot that used to sit at the very top of everyone's AIM Buddy List. For many people, it was the first experience conversing with a pre-programmed tool over a traditionally human-to-human channel. While learning more about SmarterChild's childhood from Robert, I was reminded of the time before chatbots were "cool." Long before Messenger, Whatsapp and Telegram came on the scene, SMS was the most intimate way for a brand to reach a customer. And we must give credit to the brands that had the foresight to experiment with a new technology across this personal communication channel.

Artificial intelligence (AI) is not a new concept, but thanks to breakthroughs in deep learning, recent years have seen a rapid resurgence leading to an increasing impact across many industries. Finance in particular is seeing significant disruption by AI, as deep learning tools and techniques become more widespread and accessible, companies are using algorithms or'neural networks' that learn from data, allowing computers to make better predictions and take smart actions in real time. At the RE•WORK Deep Learning in Finance Summit, in London on 23 September, we'll explore how AI is revolutionising the financial sector, through stock market prediction and forecasting, robo-advisors, mobile banking, blockchain technology and more. Speakers in both industry and academia will share insights into recent breakthroughs in technical advancements and fintech applications alongside academics and startups sharing their work from the financial industry. By bringing together key influencers to share cutting-edge research and developments, we can explore how to successfully apply artificially intelligent software to enhance and grow the finance, banking and trading industry.

I visited my local health-insurance office a few months ago. After entering the building, I was welcomed into a long and dark corridor, full of nervous people carrying bloated folders. The atmosphere was gloomy, and it was obvious that no one wanted to be there. After about 30 minutes I realized why: During that time, the line had barely moved, and it took me the better part of the day to reach a clerk. As a result, I was late for two other errands I had planned.

BEIJING – China has issued new regulations demanding that search engines clearly identify paid search results, months after a terminally ill cancer patient complained that he was misled by the giant search engine Baidu. Wei Zexi, a college student who died in April of a rare cancer, had written a long post on a Chinese website detailing how he was led to a Beijing hospital for treatments after searching on Baidu. He said that the treatment turned out to be ineffective and expensive and that later he learned the therapy was yet to be fully approved. Wei accused Baidu of taking money to promote less proven treatments. The Cyberspace Administration of China (CAC) announced on its website Saturday the new regulations, which also ban search engines from showing subversive content and obscene information.

Currently, more than 30 million people in the world have speech impairments and thus to communicate have to use sign language resulting in a language barrier between sign language and non-sign language users. This project explores the development of a sign language to speech translation glove by implementing a Support Vector Machine(SVM) on the Intel Edison to recognize various letters signed by sign language users. The data for the predicted signed gesture is then transmitted to an Android application where it is vocalized. The sign language glove has five flex sensors mounted on each finger to quantify how much a finger is bent. Flex sensors are sensors that change their resistance depending on the amount of bend on the sensor.