UK-based artificial intelligence startup PROWLER.io has raised 1.5 million pounds in seed funding. The investors were Passion Capital, Amadeus Capital and Infocomm Investments. Cambridge, UK – 30th September, 2016 – Cambridge-based startup PROWLER.io has secured 1.5 million in seed investment to fund development of its sophisticated decision-making AI engine designed to revolutionise autonomous system design. This 1.5 million investment will enable PROWLER.io to build a prototype version of its platform with select partners. Initially the company will target games developers, with numerous other sectors to follow.

The online world isn't as simple as we've thought it to be. Behind the seemingly quiet and vast space of nothingness, huge amounts of data are uploaded and downloaded in fractions of a second. Data science does not only keeps track of these numbers, but also attempts to analyze and organize them. Algorithms are created to keep tabs on searches in search engines, analyze user data preference, and so on and so forth. The demand for qualified data scientists have become very high. A recent Bloomberg article predicted that there will be a shortage of data scientists in the US by the year 2018.

On weekdays, Daisuke Kuramoto, 36, is just another computer engineer who develops education materials for an e-learning content provider. But once a month, he becomes Qramo, organizer of a computer programming workshop for children. "If you say I am'teaching' programming, that's incorrect," said Kuramoto, who heads the Tokyo-based volunteer group Otomo. "At the workshop, I'm just a participant who loves to play around with programming." Kuramoto started the workshop in 2008 and launched Otomo the following year, recruiting professional programmers, computer science students, parents and others with a knack for the activity.

This post aims to cram in a synopsis of Jeremy Howard's talk at the inaugural Data Science Melbourne MeetUp at Inspire9 in Richmond on 12th May so may be a little disjointed in it's flow. Jeremy freestyled his delivery once he had established from the members pretty quickly with a show of hands what it was he should be talking about. There is a lack of intelligence from computers and data and what is at stake is not the proof of things we already know but knowing about the things we did not think of from data or what we should be questioning. This is where machine learning asks the computer to come up with some of the intelligence for you. Using machine learning to find the interesting insights and adding value is the huge appeal Jeremy finds in machine learning and to explain this, he kicked off with talking about Arthur Samuel who essentially came up with machine learning and invented what appeared to be the world's first self-learning program.

STOCKHOLM – It is serendipity: from America to Viagra, history is full of great discoveries helped along by chance, as more than a century of Nobel prizes can attest. Among the chance discoveries that have been honored with the prestigious prize are X-rays (physics, 1901), penicillin (medicine, 1945), fullerenes that paved the way for nanotechnology (chemistry, 1996), conductive polymers (chemistry, 2000), and the bacteria responsible for ulcers (medicine, 2005). But, as the father of pasteurization Louis Pasteur noted in 1854, "In the fields of observation, chance only favors the prepared mind" -- a remark made in reference to the discovery of the link between electricity and magnetism by Danish scientist Hans Christian Orsted. Orsted happened to notice that a compass needle deflected from magnetic north when an electric current from a battery was switched on and off -- a pioneering discovery in electromagnetism. Like Pasteur, Dutch scientist Pek Van Andel also believes in the unexpected.

Bank of America analysts claim there's a 20-50% chance we currently live in the Matrix. SpaceX founder and Tesla motors co-founder Elon Musk is one of those who is almost certain that there is a chance that our world is being run by artificial intelligence developed by a future civilization. Nick Bostrum's report: http://www.simulation-argument.com/si... Baby Mice Created From Sperm Alone (No Female Needed) https://youtu.be/voSmUk2e7yY HOW TO BECOME BATMAN IN REAL LIFE https://youtu.be/CL62CGI2wQY Top 5 Accidental Deaths (Darwin Awards) https://youtu.be/4XDtbRTWIvg 5 Times Pokemon Go Ended In Murder & Mayhem!

For all their brilliance, our phones still have as much emotional intelligence as glue. Yet, as electronics become ever more important in our lives, it may make sense to start teaching them to be more aware of our feelings. Early glimpses of such efforts were afoot at a gathering of over 700 artificial-intelligence software developers, academics and researchers this week in Manhattan, where several talks focused on finding ways to make our robots, voice assistants and chatbots more, well, emotional. "People are building these very intimate relationships with these companions, but right now these companions have no empathy," Rana el Kaliouby, CEO of emotional-recognition tech firm Affectiva, said onstage Tuesday at the inaugural O'Reilly Artificial Intelligence Conference. Teaching robots about emotion illustrates the promise and the huge challenges in developing AI tools.

With up to 16 NVIDIA Tesla K80 GPUs, P2 instances are the most powerful GPU instances available in the cloud. "The massive parallel floating point performance of Amazon EC2 P2 instances, combined with up to 64 vCPUs and 732 GB host memory, will enable customers to realize results faster and process larger datasets than was previously possible." P2 instances allow customers to build and deploy compute-intensive applications using the CUDA parallel computing platform or the OpenCL framework without up-front capital investments. To offer the best performance for these high performance computing applications, the largest P2 instance offers 16 GPUs with a combined 192 Gigabytes (GB) of video memory, 40,000 parallel processing cores, 70 teraflops of single precision floating point performance, over 23 teraflops of double precision floating point performance, and GPUDirect technology for higher bandwidth and lower latency peer-to-peer communication between GPUs. P2 instances also feature up to 732 GB of host memory, up to 64 vCPUs using custom Intel Xeon E5-2686 v4 (Broadwell) processors, dedicated network capacity for I/O operation, and enhanced networking through the Amazon EC2 Elastic Network Adaptor.

This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. The intensity function of the proposed process is a mixture of intensities, and its complexity grows with the complexity of temporal patterns of data. Moreover, it utilizes a hierarchical dependent nonparametric approach to model marks of events. These capabilities allow the proposed model to adapt its temporal and topical complexity according to the complexity of data, which makes it a suitable candidate for real world scenarios. An online inference algorithm is also proposed that makes the framework applicable to a vast range of applications. The framework is applied to a real world application, modeling the diffusion of contents over networks. Extensive experiments reveal the effectiveness of the proposed framework in comparison with state-of-the-art methods.

Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training methods. In this work we present a novel approach for unsupervised training of Convolutional networks that is based on contrasting between spatial regions within images. This criterion can be employed within conventional neural networks and trained using standard techniques such as SGD and back-propagation, thus complementing supervised methods.