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.
The issue lies with a prevalent tactic in AI development called "back propagation". Geoffrey Hinton has been called the "Godfather of Deep Learning". It relates directly to how AIs learn and store information. Since its conception, back propagation algorithms have become the "workhorses" of the majority of AI projects.
It's been nearly a year since I published my first Special Report on artificial intelligence and urged readers to buy the processor maker NVIDIA (NVDA) at $68.80. With US annual auto production at 17 million, and global car and commercial vehicle production at a record 94.64 million, that is a lot of processors. All new AI startups comprise small teams of experts from private labs and universities financed by big venture capital firms like Sequoia Capital, Kleiner Perkins, and Andreeson Horowitz. The global artificial intelligence market is expected to grow at an annual rate of 44.3% a year to $23.5 billion by 2025.
Instead of preprogramming software to complete a specific task, as narrow AI does, machine learning uses algorithms that allow a computer to learn from the vast amounts of data it receives so it can complete a task on its own. International Business Machines uses deep learning powered by NVIDIA's graphics processing units (GPUs) to comb through medical images to find cancer cells. The company makes the graphics processors that are integral in AI, machine learning, and deep learning, and lots of companies already look to NVIDIA's hardware to make their AI software a reality. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon, Facebook, and Nvidia.
Artificial intelligence is becoming a defining characteristic in the smartphone market, powering personalization, virtual assistants, and even battery life. To make up for that, companies like Apple and Huawei are adding additional chips into smartphones to handle such tasks. Apple's Neural Engine will process tasks like its new FaceID facial recognition, understanding voice commands for Siri, and image-processing. Google built an AI chip called a Tensor Processing Unit that will soon be available to users of its cloud business.
With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. Later I ventured further down the road and I developed a new 8-bit compression technique which enables you to parallelize dense or fully connected layers much more efficiently with model parallelism compared to 32-bit methods. For example if you have differently sized fully connected layers, or dropout layers the Xeon Phi is slower than the CPU. GPUs excel at problems that involve large amounts of memory due to their memory bandwidth.
Technologies like artificial intelligence, machine learning, big data, Internet of things (IOT), and deep learning will come together to help realize Industry 4.0. Similarly, PTC plans this year to link its Creo computer-aided design system, to the company's ThingWorx IoT development platform. Fusion Connect Internet of Things software from Autodesk can help connect factory applications across a number of industrial machines and make sense of information returned from the connected machines. Introduced last summer, Autodesk's Design Graph is another machine learning system that helps users manage 3D content, offering Google search-like functionality for 3D models, says Mike Haley, who leads the machine intelligence group at Autodesk.
The research team at ICL combined image analysis and machine learning algorithms to model heart contractions -- you can see the mesmerizing work in the video below. O'Regan is a senior clinician scientist and consultant radiologist at the MRC London Institute of Medical Sciences (LMS) who heads a research program using machine learning to predict outcomes in heart failure. "The computer performs the analysis in seconds and simultaneously interprets data from imaging, blood tests and other investigations without any human intervention," said co-author Dr. Tim Dawes, of the London Institute of Medical Sciences, who developed the algorithms that underpinned the software. While using machine learning to measure the size and function of the heart can improve diagnostic efficiency, O'Regan believes the research would take another major step forward if the team could integrate imaging results, genetics tests, blood pressure results and the like into the process.
For example, when Google DeepMind's AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. Another algorithmic approach from the early machine-learning crowd, Artificial Neural Networks, came and mostly went over the decades. Today, image recognition by machines trained via deep learning in some scenarios is better than humans, and that ranges from cats to identifying indicators for cancer in blood and tumors in MRI scans. Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI.
Imagine Wolfgang Amadeus Mozart as an algorithm. At our annual GTC Technology Conference in May, our video from the keynote, titled "I Am AI," featured music that was composed by AI itself. To accomplish this, we enlisted the help of Pierre Barreau and his startup, Aiva Technologies, which uses deep learning to create music. Barreau credits growing up in a "family of artists" as his reason for wanting to bring AI into music. "I'm a self-taught pianist and I also studied computer science at university," Barreau said in conversation with AI Podcast host Michael Copeland. "So basically, I got this idea of using my technical background and my musical background and bringing them together to build this artificial intelligence." How AI Makes Music The process for using AI in music composition is as follows: The algorithm will compose themes, which may or may not be curated, depending on the client's briefing. The algorithm can also be trained to create different themes if the client wants something different. This quick turnaround is made possible because of how fast the system can compose themes. An algorithm can create a theme in four minutes, Barreau said. However even music composed by an AI system faces some of the same barriers faced by human composers, because it must be played by humans. By listening to great music, however, the AI has learned how to work within our human limitations. "Essentially, you could create a lot of different compositions where human players wouldn't be able to stretch their hands," Barreau said. "Indirectly, the algorithm learns those features because the compositions that it learns from were created by humans that have these constraints." Beyond scoring videos, Barreau hopes Aiva can solve "use-cases that humans alone cannot solve."