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) …
The latest proprietary Power servers from IBM, armed by the long-awaited IBM Power9 processors, look for relevance among next-generation enterprise workloads, but the company will need some help from its friends to take on its biggest market challenger. IBM emphasizes increased speed and bandwidth with its AC922 Power Systems to better take on high-performance computing tasks, such as building models for AI and machine learning training. The company said it plans to pursue mainstream commercial applications, such as building supply chains and medical diagnostics, but those broader-based opportunities may take longer to materialize. "Most big enterprises are doing research and development on machine learning, with some even deploying such projects in niche areas," said Patrick Moorhead, president and principal analyst at Moor Insights & Strategy. "But it will be 12 to 18 months before enterprises can even start driving serious volume in that space."
Companies running AI applications often need as much computing muscle as researchers who use supercomputers do. IBM's latest system is aimed at both audiences. The company last week introduced its first server powered by the new Power9 processor designed for AI and high-performance computing. The powerful technologies inside have already attracted the likes of Google and the US Department of Energy as customers. The new IBM Power System AC922 is equipped with two Power9 CPUs and from two to six NVIDIA Tesla V100 GPUs.
Nvidia CEO Jensen Huang showed up at a gathering of artificial intelligence researchers in Long Beach, Calif. One was an orchestral piece inspired by music from the Star Wars movies, but composed by an AI program from Belgian startup AIVA that--of course--relies on Nvidia chips. The music went over big with the crowd of AI geeks attending the Neural Information Processing Systems Conference, known as NIPS, including some giants in the field like Nicholas Pinto, head of deep learning at Apple, and Yann LeCun, director of AI Research at Facebook. LeCun was quoted saying the Star Wars bit was "a nice surprise." Huang's other surprise was a bit more practical, and showed just how competitive the AI chip market niche has become.
Nvidia launched a new desktop GPU today that's designed to bring massive amounts of power to people who are working on machine learning applications. The new Titan V card will provide customers with a Nvidia Volta chip that they can plug into a desktop computer. According to a press release, the Titan V promises increased performance over its predecessor, the Pascal-based Titan X, while maintaining the same power requirements. The Titan V sports 110 teraflops of raw computing capability, which is 9X that of its predecessor. It's a chip that's meant for machine learning researchers, developers, and data scientists who want to be able to build and test machine learning systems on desktop computers.
Nvidia cards are the de facto standard for running machine learning workloads and today, the company added yet another high-end compute-centric card to its line-up: the Titan V. This card, which is based on Nvidia's Volta architecture, features 21.1 billion transistors on a 815 mm2 chip that can produce a full 110 teraflops of compute power. All of that power comes at a price, though. The card, which features 12GB of HBM2 memory, will retail for $2,999. For that, though, users will see a 9x increase in raw power compared to the Titan Xp, the card's predecessor, which retailed for "only" $1,299.
IBM is ready to start shipping the first commercial server systems built around its recently released Power9 processor. Dubbed the AC922 Power Systems, these servers will ship by the end of December, and are specifically designed for artificial intelligence (AI) workloads, reports Enterprise Cloud News (Banking Technology's sister publication). The AC922 is the commercial version of the same severs that IBM, along with Nvidia and Mellanox Technologies is using to build two new supercomputers for the US Department of Energy. The "Summit" and "Sierra" supercomputers are expected to go online in 2018, and could reinvigorate the US's standing in the world of high-performance computing. At the heart of the AC922 is IBM's recently released Power9 processor.
INSTANCES of artificial intelligence (AI), machine learning (ML), or deep learning are appearing across all sorts of enterprise service offerings. While there's a certain amount of bandwagon-jumping and overuse of the terms to grab headlines, machine-learning (et al) implementations are becoming quite the norm. Combined with a rise in the numbers of massive public networks of computing power (hyperscale data centers) offering everything-as-a-service (XaaS) from the cloud, it's no surprise that the big enterprise-level server vendors are responding with AI-centric technologies. The first into the fray is IBM, which has announced a new microprocessing chip and a server powered by it, the Power9 and the AC922 respectively. The chip is optimized for the particular demands of AI computation: in tests, it runs workloads on common AI frameworks such as Chainer and TensorFlow at four times the speed of existing systems.
In a world that requires increasing amounts of compute power to handle the resource-intensive demands of workloads like artificial intelligence and machine learning, IBM enters the fray with its latest generation Power chip, the Power9. The company intends to sell the chips to third-party manufacturers and to cloud vendors including Google. Meanwhile, it's releasing a new computer powered by the Power9 chip, the AC922 and it intends to offer the chips in a service on the IBM cloud. "We generally take our technology to market as a complete solution," Brad McCredie, IBM fellow and vice president of cognitive systems explained. The company has designed the new chip specifically to improve performance on common AI frameworks like Chainer, TensorFlow and Caffe, and claims an increase for workloads running on these frameworks by up to almost 4x.
IBM launched its first systems based on its Power9 processor and optimized for artificial intelligence workloads. Big Blue's Power Systems Servers can improve training times of deep learning frameworks by 4x, according to IBM. The Power9 processors and systems built on them are partly the product of collaboration in the OpenPower Foundation, which includes IBM, Google, Mellanox, Nvidia and a bevy of other players. Those technologies are designed to boost bandwidth and throughput in data movement. That movement is what boosts model training time.
As Nvidia Corp. (NVDA) looks to markets beyond gaming computers for its powerful graphic processing units, the company is putting its cash to work in a range of artificial intelligence startups. The chipmaker's Nvidia GPU Ventures unit announced investments in data-crunching technology developer BlazingDB, data visualization company Graphistry and artificial intelligence platform developer H2O.ai, on Thursday. "It makes sense for Nvidia to invest in all of these companies, as they will advance the necessary ecosystem surrounding GPUs and drive their adoption in both on-premise, high-performance development systems, in the cloud as workload accelerators and ultimately also at the edge for AI inference tasks," said 451 Research founder John Abbott in an email. While Nvidia's GPUs are known for powering gamers' computers, the company has been successfully growing its products geared for data centers and other markets. The company's GPUs can process parallel workloads, or in other words perform multiple tasks simultaneously, which powers the units in more demanding environments.