These are the most popular AI & machine learning books on Amazon. Have a look... you may find something of interest here.

This is an introductory tutorial on using Theano, the Python library. I'm going to start from scratch and assume no previous knowledge of Theano. However, understanding how neural networks work will be useful when getting to the code examples towards the end. I recently gave this tutorial as a talk in University of Cambridge and it turned out to be way more popular than expected. In order to give more people access to the material, I'm now writing it up as a blog post. I do not claim to know everything about Theano, and I constantly learn new things myself.

Last month I posted about the great improvements that we were likely to achieve thanks to the implementation of machine learning mining software (pKantuML) that used an OpenCL/C hybrid process for the mining of machine learning trading strategies. After spending a lot of time building and testing the software plus doing all the server side implementations for the cloud mining and the processing of cloud mining results today I am glad to say that we now have a fully functioning machine learning cloud mining operation using the power of mixed OpenCL/C calculations. On today's blog post I want to talk a bit about what we have achieved, how this will evolve and why this will bring a significant level of diversification to our current trading operations. The idea with pKantuML is to take advantage of GPU simulations to mine machine learning trading strategies, however since the ML part cannot be easily translated into OpenCL code -especially in a way in which results are the same between OpenCL and C/C for live trading – we decided to generate all the needed ML predictions within our C/C tester and then use these generated prediction files to perform massive amounts of simulations in OpenCL code varying things that do not depend on the machine learning code (such as the filtering hours, stoploss values, trailing stop types, etc). The idea is to do a small amount of computationally intensive work in C/C that can then be expanded greatly by using OpenCL.

So, you don't work at Google Deep Mind and you don't have access to Nature. You've come to the right place. BetaGo lets you run your own Go engine. It downloads Go games for you, preprocesses them, trains a model on data, for instance a neural network using keras, and serves the trained model to an HTML front end, which you can use to play against your own Go bot. It should start a playable demo in your browser!

MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), together with researchers from security firm PatternEx, has revealed a new AI (Artificial Intelligence) system called AI2, which can detect 85 percent of cyber-attacks, with false positives rates five times smaller than existing solutions. The new system doesn't rely entirely on artificial intelligence (AI), but also on user input, something that researchers call analyst intuition (AI), hence its name of AI2. Researchers said they fed AI2 with over 3.6 billion lines of log files, allowing the system to scan the content with unsupervised machine-learning techniques. At the end of each day, the system presents its findings to a human operator, who then confirms or dismisses security alerts. This human feedback is then incorporated into AI2's learning system and used the next day for analyzing new logs. After their tests had concluded, MIT and PatternEx researchers said AI2 achieved an 85 percent accuracy rate in detecting cyber-attacks, which is 2.92 times better than similar automated cyber-attack detection systems used today.

Photographs used in Tsvetkov's study. You may think you do, but a recent experiment by a Russian photographer suggests otherwise. In a project entitled, "Your face is big data," Rodchenko Art School student, Egor Tsvetkov, began by photographing about 100 people who happened to sit across from him on the subway at some point. He then used FindFace, a facial-recognition app that taps neural-network technology, to try to track them down on Russian social media site VK. It was ridiculously easy to find 60 to 70 per cent of the subjects aged between 18 and 35 or so, he found, although for older people it was more difficult.

As part of the agreement, BioClinica becomes ArisGlobal's preferred partner in business process consulting and change management as sponsors implement or upgrade ARISg, ArisGlobal's pharmacovigilance and clinical safety system. The cloud-based solution helps facilitate drug development and regulatory compliance throughout the entire product life cycle. "We decided to partner with ArisGlobal because we saw the opportunity to offer a continuum of pharmacovigilance services that can fit the needs of sponsors and CROs as those needs change, whether sponsors are growing, merging, experiencing a surge or a drop in safety events," Mukhtar Ahmed, BioClinica President eHealth Solutions, told Outsourcing-Pharma.com. "In the same way that therapies are moving from a'one size fits all' model, so is our offering model." According to the company, the partners are currently working to develop ArisGlobal's next generation of products, which will combine medical knowledge with technologies derived from such areas as artificial intelligence, machine learning, and natural language processing.

If you've been dying to try out Skype bots but don't have a Windows PC, Android, or iOS device to chat on, you're finally in luck. Skype recently rolled out bot integration to the Mac client and the service's beta web app. There are only six bots to try out right now, which is to be expected since this is still a preview feature. Despite the small numbers, some of the automated services are fairly useful. Give Bing News some keywords such as "Presidential election" and you get three recent headlines for that topic. Skype's current bot selection shown on the web app.

ONE Saturday night last year, 11 people went looking for love. Like countless speed daters before them, they met in a room draped with curtains, the lights on low. In one hand they held traditional glasses of bubbly, but in the other were sheets of paper they had filled with their personal data. This twist on speed-dating was part of an experiment run by a team at Newcastle University in the UK. They wanted to know what would happen in a world where instead of vetting potential dates by their artfully posed selfies or carefully crafted dating-site profiles, we looked at data gathered by their computers and phones.

An AI created by scientists at the Massachusetts Institute of Technology (MIT) uses machine learning to detect suspicious activity - getting it right 85% of the time. The system uses an algorithm called "AI2", that detects anomalies, in conjunction with a human expert, because AI2 on its own can lead to false positives, according to MIT News. "The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions," said one of the researchers behind the project, Kalyan Veeramachaneni. "That human-machine interaction creates a beautiful, cascading effect." The merging of artificial intelligence and what researchers call "analyst intuition" has allowed for this new system to be successful in its early development, Veeramachaneni and fellow scientist Ignacio Arnaldo said.