Logojoy is a new kind of online logo maker. By using artificial intelligence, genetic algorithms, and a few other fancy technologies, we're empowering everyone to design their own logo. You don't need graphic design skills or a huge budget to create a logo – it's easy and affordable! Logojoy was created by designers on a mission to make it a delight for anyone to create an amazing logo - whether it's for their business, blog, or even club.

When you upload photos to Facebook, have you noticed that the website already seems to know who's in them? It's remarkable, and you can give the credit to big data. Face recognition software, like fraud detection and ad matching algorithms, draws on deep libraries of content in order to deliver the correct results. And these data collections are hard at work across the web and in many of your favorite apps. It comes as no surprise that developers have been hard at work on face recognition software since it's an integral part of security programs.

The Mill created a massive real-time data art installation built from the computation analysis of pop music, social media and news media by IBM's Watson. Natural language and musical compositions were assigned emotional values by Watson which we then translated into immersive visualizations that could be navigated by time, emotion and genre. Intricate color coding of the visuals was based on a five color palette, one each for joy, anger, disgust, sadness, and fear. Follow @Millchannel on Twitter, Facebook & Instagram for more updates.

If you asked the average person what they know about artificial intelligence (AI), they would probably launch into stories about intelligent computers taking over the world and rebellious robots running amok. While the misconception the movies have created may be wildly wide of the mark, AI is an area of technological development having a massive impact in all corners of our lives for generations to come. That's why Stanford University has launched a long-term project to study the impact of AI on society. A study that's not necessarily going to offer solutions, but will promote a dialogue about AI to guide us through the ethical, legal, and technological challenges machine intelligence might bring. I think that's a pretty cool undertaking.

Automation and artificial intelligence are no longer hype, but reality – the Fourth Industrial Revolution has begun! Over the next 3-5 years, combining advances in simple, easily configurable RPA technology with cognitive capabilities will lead to the cost reductions, improved performance and enhanced real-time decision making that all adds up to massive competitive advantage. RPA and Artificial Intelligence for Enterprise unites the needs of the 250,000-strong SSON and PEX Network communities to bring together those furthest along the maturity curve in automated and intelligent service innovation, like Vodafone, Barclays, ENGIE and even NHS Wales Shared Services, with those who are just starting out, like SAB Miller, LV and National Grid, for a frank and open discussion surrounding the best ways to compete in the digital business era. Combining scores of practical end-user case studies, multiple conference streams surrounding human workforce augmentation across the front and back-offices and over 15 hours of interactive sessions and networking, this is your one-stop shop for ensuring you build the value-adding, scalable, intelligent processes that meet the business needs of the future.

When you think about artificial intelligence (AI), robots, androids and other futuristic technologies may come to mind. And while some of the most successful tech companies like Facebook, Amazon and Google have built their success on industry-changing applications of artificial intelligence, the concept is still fairly new to content marketing teams. In the most basic sense "artificial intelligence" broadly refers to the processes and technologies that are created to teach machines to perform intelligent tasks. For content marketers, "intelligent tasks" typically refer to algorithms designed to process data. This goes far beyond automation, which is where the majority of marketing technology supports marketing teams today.

There is an ongoing race among semiconductor companies, including the established market heavyweights and startups alike, to define the hardware platform that will run compute-intensive deep learning algorithms quickly and efficiently. Until now, NVIDIA has dominated the deep learning market with its graphics processor unit (GPU) chips, which bring massive parallelization, however field programmable gate arrays (FPGAs) and digital signal processors (DSPs) are starting to catch up. Deep learning is largely characterized by deep neural networks (DNNs) and convolutional neural networks (CNNs), which can become massively complex. Google's cat recognition neural network back had 1 billion connections using 16,000 processors. GPUs are known to achieve the best speed and throughput, around 100x faster compared to an FPGA, while FPGAs are known to have better power efficiency, around 50x better compared to a GPU.

Everyone is talking about artificial intelligence (AI) and machine learning these days. This is not just of strategic relevance for companies the likes of Google, Apple, Amazon, Facebook or Salesforce.com. AI is now a term that all companies should be familiarizing themselves with (if they're not already) because it will have a profound impact on their business in the near future. We have already witnessed vehicles operating autonomously and a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks, which has all given rise to a flurry of people claiming that the AI revolution is upon us. What is Driving This Next Wave of Change?

Google's artificial intelligence can play the ancient game of Go better than any human. It can identify faces, recognize spoken words, and pull answers to your questions from the web. But the promise is that this same kind of technology will soon handle far more serious work than playing games and feeding smartphone apps. One day, it could help care for the human body. Demonstrating this promise, Google researchers have worked with doctors to develop an AI that can automatically identify diabetic retinopathy, a leading cause blindness among adults. Using deep learning--the same breed of AI that identifies faces, animals, and objects in pictures uploaded to Google's online services--the system detects the condition by examining retinal photos.

This article was posted by Adrian Rosebrock on Pyimagesearch. Adrian is an entrepreneur and Ph.D who has launched two successful image search engines, ID My Pill and Chic Engine. This list is by no means exhaustive, it's simply a list of libraries that he has used in his computer vision career and found particular useful at one time or another. The goal of this blog post is to introduce you to these libraries. He encourages you to read up on each them individually to determine which one will work best for you in your particular situation.