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) …
NVIDIA (NASDAQ:NVDA) is primarily known as the company that revolutionized computer gaming. The debut of the Graphics Processing Unit (GPU) in 1999 provided gamers with faster, clearer, and more lifelike images. The GPU was designed to quickly perform complex mathematical calculations that were necessary to accelerate the creation of realistic graphics. It achieved this feat by performing many functions at the same time, known as parallel computing. This resulted in faster, smoother motion in game graphics and a revolution in modern gaming.
While AI (artificial intelligence) has been around since the 50's, IBM was the pioneer in the latest AI cycle with their own custom solution dubbed Watson. Ever since the introduction of Watson and its ability to beat Jeopardy Champion Ken Jennings, the company has been increasing its investment in the space. IBM Watson is now an entire division of the company which indicates the importance they put on the future of AI. Watson is only one part of IBM's AI investment which I consider the "easy button" for those enterprise who don't want to create everything from scratch. IBM also has DIY (do it yourself) infrastructure for cloud providers through POWER8, OpenPOWER, OpenCAPI, designed for cloud giant rolls their own AI software. But what about enterprises who are in the middle, those who want solid infrastructure and want to invest in the latest deep neural network frameworks?
Over the last year in particular, we have documented the merger between high performance computing and deep learning and its various shared hardware and software ties. This next year promises far more on both horizons and while GPU maker Nvidia might not have seen it coming to this extent when it was outfitting its first GPUs on the former top "Titan" supercomputer, the company sensed a mesh on the horizon when the first hyperscale deep learning shops were deploying CUDA and GPUs to train neural networks. All of this portends an exciting year ahead and for once, the mighty CPU is not the subject of the keenest interest. Instead, the action is unfolding around the CPU's role alongside accelerators; everything from Intel's approach to integrating the Nervana deep learning chips with Xeons, to Pascal and future Volta GPUs, and other novel architectures that have made waves. While Moore's Law for traditional CPU-based computing is on the decline, Jen-Hsun Huang, CEO of GPU maker, Nvidia told The Next Platform at SC16 that we are just on the precipice of a new Moore's Law-like curve of innovation--one that is driven by traditional CPUs with accelerator kickers, mixed precision capabilities, new distributed frameworks for managing both AI and supercomputing applications, and an unprecedented level of data for training.
Just over five years ago, IBM's Watson supercomputer crushed opponents in the televised quiz show Jeopardy. It was hard to foresee then, but artificial intelligence is now permeating our daily lives. Since then, IBM has expanded the Watson brand to a cognitive computing package with hardware and software used to diagnose diseases, explore for oil and gas, run scientific computing models, and allow cars to drive autonomously. The company has now announced new AI hardware and software packages. The original Watson used advanced algorithms and natural language interfaces to find and narrate answers.
DeepBench is available online along with first results from Intel and Nvidia processors running it. The benchmark tests low-level operations such as matrix multiplication, convolutions, handing recurrent layers and the time it takes for data to be shared with all processors in a cluster. Machine learning has emerged as a critical workload for Web giants such as Baidu, Google, Facebook and others. The workloads come in many flavors serving applications such as speech, object and video recognition and automatic language translation. Today the job of training machine learning models "is limited by compute, if we had faster processors we'd run bigger models…in practice we train on a reasonable subset of data that can finish in a matter of months," said Greg Diamos, a senior researcher at Baidu's Silicon Valley AI Lab.
After the second day of Apple Inc. (NASDAQ: AAPL)'s 2016 Worldwide Developers Conference (WWDC), Global Equities Research's Trip Chowdhry shared some insights into the company's major Machine Learning and Deep Learning initiatives and architectures. Moreover, it should be noted that this process is continuous, "as the Neural Network model continues to refine itself based on various parameters," he added. Moving on to Apple's TF-IDF (Term Frequency - Inverse Document Frequency) algorithm, Chowdhry explicated that it is implemented in Caffe Deep Learning Framework. "Apple is also opening Machine Learning/ Deep Learning based services to developers (the complexity is however hidden from the App Developer)," the note concluded.
Speaking at the GPUTech conference in San Jose, California, Huang noted that 5 billion was invested last year in A.I. startups, and there are probably a thousand companies working on the technology for applications ranging from face recognition to self-driving cars. Now the algorithm can be used with massive amounts of data and huge amounts of processing power to yield big benefits in things like computer vision. "Industry after industry is taking advantage of deep learning," Huang said.
Outwardly, it sounds like the usual goodwill gestures employed by proprietary companies trying to build bridges with modern, open source-driven development communities. Many modern machine learning frameworks are designed to run on either kind of hardware, but they have to address CPUs and GPUs separately, with GPUs typically programmed with the Nvidia CUDA framework. AMD's plan is to allow GPU and CPU applications to be written using a single C11 or C 11/14 set of libraries using a specially designed compiler (the Heterogenous Compute Compiler, or HCC). However, AMD is not yet making any actual GPU hardware designs open source, likely due to the massive legal tangle involved in such a project, in much the same way Java was only open-sourced after great and laborious effort.