If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
We dug into the private market bets made by major computer chip companies, including GPU makers. Our analysis encompasses the venture arms of NVIDIA, Intel, Samsung, AMD, and more. Recent developments in the semiconductor industry have been sending mixed signals. Stories about Moore's Law slowing have grown common, but analysts affirm that the latest crop of chips (specifically Intel's newest 10-nanometer technology) prove Moore's Law is still alive and well. Meanwhile, the vast application of graphics hardware in AI has propelled GPU (graphics processing unit) maker NVIDIA into tech juggernaut status: the company's shares were the best-performing stock over the past year.
As chief architect at Kontiki Labs, I wear two hats - one as a AI researcher looking at new developments in AI and bringing that into the main body of capabilities of our company, as needed. The second role is an AI evangelist / Product Management role where I work with businesses to understand there needs or problems and suggest the right AI powered solutions for them. Needless to say I am constantly toggling between developer and business roles and looking for workflows to optimise my available dev time. During business travel, I tend to use my Sundays for some lock-down research and development around ML or AI. This is the narrative of a typical AI Sunday, where I decided to look at building a sequence to sequence (seq2seq) model based chatbot using some already available sample code and data from the Cornell movie database.
CES showcases the tech trends that will shape the year ahead. See the most important products that will impact businesses and professionals. NVIDIA, as I've written about several times, is the company that started in gaming and graphics but which has rapidly transformed into an organization focused on AI. Nope, NVIDIA is swinging for the fences, leveraging its GPU technology, deep learning, its Volta architecture, its Cuda GPU programming platform and a dizzying array of partnerships to move beyond mere tech and become an industrial powerhouse. CEO and Founder Jensen Huang gave the Sunday night keynote at CES, an prized time slot once dominated by Microsoft.
Last summer, I spent a week at a conference dedicated to graphics-processing units--GPUs. It was presented by GPU big name Nvidia, a brand that is largely associated with gaming hardware. At the conference, however, gaming was a sideshow. For that matter, graphics themselves (excluding VR) were a sideshow, despite being in the actual name. In general, this was a machine learning conference, and, to most of the attendees, of course it was.
The case for learned index structures Kraska et al., arXiv Dec. 2017 Welcome to another year of papers on The Morning Paper. With the rate of progress in our field at the moment, I can't wait to see what 2018 has in store for us! Two years ago, I started 2016 with a series of papers from the'Techniques everyone should know' chapter of the newly revised'Readings in Database Systems.' So much can happen in two years! I hope it doesn't take another ten years for us to reach the sixth edition of the'Red Book,' but if it does, in today's paper choice Kraska et al., are making a strong case for the inclusion of applied machine learning in a future list of essential techniques for database systems.
We explore the basic concepts of artificial intelligence and get started using deeplearn.js. With all the buzz around artificial intelligence (AI), I decided that it was time to learn a bit more about it. Going into it, I was concerned that I would quickly hit a wall and not get too far; luckily this was not the case. I started my AI learning using Google's TensorFlow. Searching for other AI platforms, I found another Google project, deeplearn.js,
We are excited to announce the general availability of Graphic Processing Unit (GPU) and deep learning support on Databricks! This blog post will help users get started via a tutorial with helpful tips and resources, aimed at data scientists and engineers who need to run deep learning applications at scale. Databricks now offers a simple way to leverage GPUs to power image processing, text analysis, and other machine learning tasks. Users can create GPU-enabled clusters with EC2 P2 instance types. Databricks includes pre-installed NVIDIA drivers and libraries, Apache Spark deployments configured for GPUs, and material for getting started with several popular deep learning libraries.
Growing up and playing sports through college, I learned that winning is a team effort. No single player, no matter how spectacular, can carry a team to a championship alone. This analogy resonates as that's what we are now seeing as we embark on the artificial intelligence (AI) era. I believe that the whole is greater than the sum of its parts. This mindset serves particularly well in the "post-CPU-only" era, where chips alone can't deliver a complete solution and the industry can't ever get to zero nanometer silicon.
Docker is the best platform to easily install Tensorflow with a GPU. This tutorial aims demonstrate this and test it on a real-time object recognition application. Docker is a tool which allows us to pull predefined images. The image we will pull contains TensorFlow and nvidia tools as well as OpenCV. The idea is to package all the necessary tools for image processing.
Some of the most interesting new mobile applications from 2017 included machine learning and artificial intelligence in new and exciting ways. And with all the major tech houses such as Google, Microsoft, Adobe, Apple and Tencent all pouring large amounts of money into research and development in this area, 2017 is looking to become the start of a new era of mobile intelligence. This has happened due to a number of reasons, but one of the most important ones is the fact that the hardware in smartphones and tablets have become so powerful that the small devices can run advanced algorithms locally, instead of having to use the internet and request servers to do the heavy lifting in terms of calculations. Another important reason for the large amount of new ML enhanced applications has to do with the impressive number and quality of machine learning tools and frameworks for mobiles that is designed to be lighter and faster than the traditional tools built for fast desktop computers. For now, let's have a look at some of the best frameworks and toolkits for developing machine learning enhanced apps with the current technology we have available.