I have a pretty awesome backlog of blog posts from Udacity Self-Driving Car students, partly because they're doing awesome things and partly because I fell behind on reviewing them for a bit. Here are five that look pretty neat. This is a great blog post if you're looking to get started with point cloud files. The most popular laptop among Silicon Valley software developers is the Macbook Pro. The current version of the Macbook Pro, however, does not include an NVIDIA GPU, which restricts its ability to use CUDA and cuDNN, NVIDIA's tools for accelerating deep learning.
Welcome back to Mind Over Money. I'm Kevin Cook, your field guide and story teller for the fascinating arena of Behavioral Economics. Since I am an investor in an exciting technology company you may have heard of called NVIDIA (NVDA), I often find myself in the position of having to explain to my followers and fellow investors "what exactly is AI" in a practical, right-now sense, and not some science fiction sense. NVDA's type of computer chip, the GPU, is at the heart of modern AI R&D and they sell a lot of them not just for advanced gaming graphics but also to industry for applications in autonomous driving where Tesla (TSLA), Toyota and Mercedes are customers. NVDA also has a bigger business selling their processors to big cloud companies like Amazon, Google (GOOGL), Microsoft, IBM (IBM) and Alibaba (BABA).
Intel CEO Brian Krzanich speaks at a 2016 AI event. Intel might be an old-school computing company, but the chipmaker thinks the latest trends in artificial intelligence will keep it an important part of your high-tech life. AI technology called machine learning today is instrumental to taking good photos, translating languages, recognizing your friends on Facebook, delivering search results, screening out spam and many other chores. It usually uses an approach called neural networks that works something like a human brain, not a sequence of if-this-then-that steps as in traditional computing. Lots of companies, including Apple, Google, Qualcomm and Nvidia, are designing chips to accelerate this sort of work.
Leading stock photo company Shutterstock unveiled a new deep learning-based tool that lets users search photos by their composition. "Built on our next generation visual similarity model, this tool helps you find the exact image you need by placing keywords on a canvas and moving them around where you want subject matter to appear in the image," mentioned Kevin Lester, VP of Engineering at Shutterstock in a related blog. "The patent-pending spatially aware technology will find strong matches based not only on your search terms, but also on the placement of your search terms." Using TITAN X GPUs and the cuDNN-accelerated Torch deep learning framework, the researchers trained their visual model on their own internal image dataset and the language model to match a textual query to the embedding of a corresponding image. Once trained, they leverage Tesla GPUs on the Amazon cloud to give users total control over the image composition on any project – such as being able to use search terms like "wine" and "cheese" and being able to drag it around so photos of "wine" are on the left and "cheese" on the right.
Video is the world's largest generator of data, created every day by over 500 million cameras worldwide. That number is slated to double by 2020. The potential there, if we could actually analyze the data, is off the charts. It's data from government property and public transit, commercial buildings, roadways, traffic stops, retail locations, and more. The result would be what NVIDIA calls AI Cities, a thinking robot, with billions of eyes trained on residents and programmed to help keep people safe.
When the AI boom came a-knocking, Intel wasn't around to answer the call. Now, the company is attempting to reassert its authority in the silicon business by unveiling a new family of chips designed especially for artificial intelligence: the Intel Nervana Neural Network Processor family, or NNP for short. The NNP family is meant as a response to the needs of machine learning, and is destined for the data center, not your PC. Intel's CPUs may still be a stalwart of server stacks (by some estimates, it has a 96 percent market share in data centers), but the workloads of contemporary AI are much better served by the graphical processors or GPUs coming from firms like Nvidia and ARM. Consequently, demand for these companies' chips has skyrocketed.
Intel enlisted one of the most enthusiastic users of deep learning and artificial intelligence to help out with the chip design. "We are thrilled to have Facebook in close collaboration sharing their technical insights as we bring this new generation of AI hardware to market," said Intel CEO Brian Krzanich in a statement. On top of social media, Intel is targeting healthcare, automotive and weather, among other applications. Unlike its PC chips, the Nervana NNP is an application-specific integrated circuit (ASIC) that's specially made for both training and executing deep learning algorithms. "The speed and computational efficiency of deep learning can be greatly advanced by ASICs that are customized for ... this workload," writes Intel's VP of AI, Naveen Rao.
AI has become a hot topic among tech corporations, startups, investors, the media, and the public. That's only because machine learning platforms have already been doing hard work for years now. Last month, NVIDIA announced the addition of Huawei and Alibaba as adopters of its system "Metropolis", an AI-platform for smart cities. More than 50 organizations are already using Metropolis and, by 2020, according to NVIDIA, there will be 1 billion video cameras worldwide that could be connected to AI platforms to make cities smarter. When connected to AI, cameras can be used to recognize shapes, faces and even the emotions of individuals, which has varied applications: autonomous cars, video surveillance (traffic flow, crime monitoring), and consumer behavior analysis (reaction to ads for example).
Yesterday I had the chance to meet Andreas Liebl, partner at UnternehmerTUM, who launched a programme Applied.AI, wherein people from across the world are invited to apply. If you are an engineer, or a professional, and wish to launch an application or gain expertise in the field of AI, this is the programme for you. The institute was founded by entrepreneur Susanne Klatten in 2002 and is supported by several corporations such as Nvidia, Mercedes Benz and SAP, to name a few. What I found interesting about the institute is that not only do they train you, but can also support you, should you wish to start up. The institute has a venture capital arm that can facilitate investments towards startups with promising ideas.
Product design and development firm Cambridge Consultants developed a deep learning-based system that turns human sketches into paintings that resemble Van Gogh, Cézanne and Picasso. "What we've built would have been unthinkable to the original deep learning pioneers," said Monty Barlow, director machine learning at Cambridge Consultants in reference to their interactive system that call Vincent. "By successfully combining different machine learning approaches, such as adversarial training, perceptual loss, and end-to-end training of stacked networks, we've created something hugely interactive, taking the germ of a sketched idea and allowing the history of human art to run with it." Once trained on nearly 200 million parameters, Vincent is able to understand the important edges in paintings and uses this understanding to produce a complete picture.