I am currently building a genetic algorithm to tune n parameters where n will probably be in the range of 3 n 8 but could be up to 15. I would like my initial population N (let's say N 1000) to be evenly dispersed across the input space. When calculating the next generation I surmised that the most effective way to combine parents would be to calculate the centroid, on the surface of the hypersphere, between some m nearest-neighbour parents. The larger m is, the fewer new points we would add. The rest being calculated in a similar fashion but from random parents.

"The thing I want to say to everybody in the room is: We ought to care about women being in computer science," Gates said at a recent ReCode conference, according to Bloomberg Technology. "You want women participating in all of these things because you want a diverse environment creating AI and tech tools and everything we're going to use." Related: Women's computer code is preferred, only if their gender is unknown Melinda Gates' comment came after her husband Bill extolled the virtues of artificial intelligence. "Certainly, it's the most exciting thing going on," he said. It's the big dream that anybody who's ever been in computer science has been thinking about." Pointing out that currently only 17 percent of computer science graduates are women, compared to a previous high of 37 percent, Melinda Gates was making a statement not only about computer science in general, but also how it could matter in the field of artificial intelligence, where rules-based behavior could be shaped predominately by men.

While this narrative is a vast simplification of modern economic history, it helps to make sense of how people think about technology. Average productivity growth since the global economic crisis is down to just over 1%, lower than after the Nixon Shock. It's no wonder that our dreams have taken a darker turn. We believe in innovation, but have given up on progress, and the possibility of moral and social improvement. The defining feature of our days is that we feel like we live in an era of incredibly innovation, mostly thanks to staggering breakthroughs in science and technology; but, at the same time, we feel like there are insurmountable limits in the form of economic, political and environmental risks.

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.