Why the AI hype cycle won't end anytime soon


Increasingly affordable AI maintenance and the increased speed of calculations thanks to GPU are significant factors in the unbridled growth of AI. The astonishing results that were achieved on training a neural network on GPU cards made Nvidia a key player, with 70 percent of the market share that Intel failed to gain. Compared with the results from the analog algorithms, and thanks to the combination of machine learning and big data, previously "unsolvable" problems are now being solved. Machine learning algorithms can directly analyze thousands of previous cases of different types of diseases and make their own conclusions as to what constitutes a sick individual versus a healthy individual, and consequently help diagnose dangerous conditions including cancer.

Qualcomm Dives Into The Connected Car Market With Its Drive Data Platform


At CES recently, Qualcomm introduced its Drive Data Platform, which the company claims is designed to intelligently collect and analyze information from vehicle's sensors. In addition, the Drive Data Platform can assist smart cars in accurately detecting location, monitoring and learning driving patterns, perceiving surroundings, and capturing and sharing this data. Clearly, the Drive Data Platform should help Qualcomm to tap into connected and autonomous car market, which has a huge growth potential. According to a research by Boston Consulting Group, the autonomous car market could be a $42 billion market by 2025, which could be around 12-13% of the total auto market. As more and more customers look for improved digital experience in the car, in addition to other parameters, car companies are banking on technological advancements in their interiors to compete with each other.

AI in self-driving cars - NVIDIA and Bosch collaboration


Health care doesn;t have a big data problem. It has a big data opportunity, thanks to artificial intelligence. Think about the number of inefficiencies in your daily life -- long lines, traffic jams, a reliance on;snail mail; for certain bills or communications. Those inefficiencies are inconvenient and annoying, yes, but they are usually not a matter of life and death. The need for productivity in health care is different.

Intel shares artificial intelligence strategy


Intel announced a slew of products, technologies and investment in an effort to fix its position in the field of artificial intelligence. In the new move, Intel has assembled a set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars. Intel is increasing its focus on AI as it believes it can power the AI products released recently by companies like Facebook and Google. In a blog Intel CEO Brian Krzanich had said, "Intel is uniquely capable of enabling and accelerating the promise of AI. Intel is committed to AI and is making major investments in technology and developer resources to advance AI for business and society."

Four big data and AI trends to keep an eye on


I interpret AI as a larger, encompassing umbrella that includes machine learning -- which in turn includes deep learning methods -- but also implies thought. Platforms such as Spark are providing more accessible big data access through higher-level programming languages such as Python and R. We can see even easier approaches emerging with new point-and-click, drag-and-drop big data analytics products from companies such as Dataiku or Cask. For example, enterprise data lakes and end-to-end production big data flows need professional data monitoring, managing, troubleshooting, planning and architecting. Much of this may seem familiar to longtime IT experts But this is a new world, and providing big data and big data flows with their own systems management focus has real merit as data grows larger and faster.

[session] Bert Loomis and AI in the Cloud By @IBMCloud @CloudExpo #AI #Cloud #DigitalTransformation


Bert Loomis was a visionary. This general session will highlight how Bert Loomis and people like him inspire us to build great things with small inventions. In their general session at 19th Cloud Expo, Harold Hannon, Architect at IBM Bluemix, and Michael O'Neill, Strategic Business Development at Nvidia, will discuss the accelerating pace of AI development and how IBM Cloud and NVIDIA are partnering to bring AI capabilities to "every day," on-demand. They will also review two "free infrastructure" programs available to startups and innovators. Speaker Bios Harold Hannon has worked in the field of software development as both an architect and developer for more than 15 years, with a focus on workflow, integration, and distributed systems.

Hitting it Out of the Park with Deep Learning NVIDIA Blog


He's now using GPU-accelerated deep learning to reveal minute details of player behavior and game patterns, which has the potential to revolutionize how coaches manage players and plan strategy. Statcast added big data and machine learning, making it possible to track things that weren't measurable before, especially the performance of outfielders. Instead, the NYU research team aims to use deep learning technology by coupling the Statcast data with detailed human movements acquired with motion-capture systems. The team is using our DGX-1 AI supercomputer -- recently acquired by NYU's Center for Data Science -- which provides the deep learning computing performance equivalent to 250 conventional servers.

Q&A: Artificial intelligence, advancements and applications


Is there a difference between artificial intelligence, machine learning and deep learning? Deep learning builds layers of nodes or neurons, called deep neural networks, to solve problems. In computing terms, gaming is a big data problem, requiring massive amounts of data to be processed in parallel to generate realistic scenes. If you're interested in finding out more about applications of AI, check out the GPU Technology Conference Europe, taking place in Amsterdam on September 28th and 29th.

Partnerships Will Drive NVIDIA Corporation (NASDAQ:NVDA) Stock higher


International Business Machines (NYSE:IBM) announced three new servers built for cognitive computing, Artificial Intelligence (AI) and Machine Learning (ML). One of the new servers, the Power System S822LC for High-Performance Computing, leverages the power of NVIDIA's (NSDQ:NVDA) Tesla P100 Graphical Processing Units (GPUs) and NVLink to deliver high-performance analytics and enable deep learning applications for Big Data. The tight coupling of IBM and NVIDIA technology enables five times faster internal data flow, accelerating critical applications such as advanced analytics, deep learning and AI. In fact, while large tech companies like IBM compete to deliver faster servers and AI applications for Big Data, NVIDIA is pursuing a pick-and-shovel strategy - selling key components to all the participants in the race - in the niche of specialized hardware accelerators for cognitive computing and AI, where it is the leader and has only a few competitors.

IBM introduces new servers for AI workloads


The devices, designed to give a significant boost to artificial intelligence, deep learning and advanced data analytics, were picked up by the Chinese telecommunications company Tencent, and IBM claims the results are basically out of this world. The first of the three, and obviously the flagship server, is the IBM Power System S822LC for High Performance Computing. "The user insights and the business value you can deliver with advanced analytics, machine learning and artificial intelligence is increasingly gated by performance. All three servers, Power System S822LC for High Performance Computing, IBM Power System S821LC and the IBM Power System S822LC for Big Data are available today, with the starting price of 5,999 ( 4,513).