"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
What if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000. It's a multibillion-dollar opportunity that can help billions. The worldwide pharmaceutical market, one of the slowest monolithic industries to adapt, surpassed $1.1 trillion in 2016. In 2018, the top 10 pharmaceutical companies alone are projected to generate over $355 billion in revenue.
Many things come in bundles. Amit Ray, author of Mindfulness Meditation for Corporate Leadership and Management says "As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership." A report by Source Media, sponsored by Pure Storage says powerful, advanced computing and storage capacity and capabilities are needed too. Currently, some radiology departments use it effectively to improve workloads. Progress across other clinical activities depends on extra computing and storage power for two activities, training and clinical use.
Drones armed with computer vision software could enable new forms of automated skyborne surveillance to watch for violence below. One glimpse of that future comes from UK and Indian researchers who demonstrated a drone surveillance system that can automatically detect small groups of people fighting each other. The seed idea for researchers to develop such a drone surveillance system was first planted in the wake of the Boston Marathon bombing that killed three and injured hundreds in 2013. It was not until the Manchester Arena bombing that killed 23 and wounded 139--including many children leaving an Ariana Grande concert--when the researchers made some progress. This time, they harnessed a form of the popular artificial intelligence technique known as deep learning.
We take the vast computing power of our brains for granted. But scientists are still trying to get computers to the brain's level. This is how we ended up with artificial intelligence algorithms that learn through virtual neurons -- the neural net. Now a team of engineers has taken another step closer to emulating the computers in our noggins: they've built a physical neural network, with circuits that even more closely resemble neurons. When they tested an AI algorithm on the new type of circuitry, they found that it performed as well as conventional neural nets already in use.
IBM, NVIDIA, and the U.S. Department of Energy (DOE) recently announced that they have completed testing the world's fastest supercomputer, Summit, at the Oak Ridge National Laboratory in Oak Ridge, Tennessee. Capable of over 200 petaflops (200 quadrillion operations per second), Summit consists of 4600 IBM dual socket Power 9 nodes, connected by over 185 miles of fiber optic cabling. Each node is equipped with 6 NVIDIA Volta TensorCore GPUs, delivering total throughput that is 8 times faster than its predecessor, Titan, for double precision tasks, and 100 times faster for reduced precision tasks common in deep learning and AI. China has held the top spot in the Top 500 for the last 5 years, so this brings the virtual HPC crown home to the USA. Figure 1: The Summit Supercomputer at the Department of Energy's Oak Ridge National Labs is now the fastest computer in the world. Some of the specifications are truly amazing; the system exchanges water at the rate of 9 Olympic pools per day for cooling, and as an AI supercomputer, Summit has already achieved (limited) "exascale" status, delivering 3 exaflops of AI precision performance.
HANOVER, GERMANY - APRIL 25: Close up of the digital display while a camera and radar system assists as artificial intelligence takes over driving the car during tests of autonomous car abilities conducted by Continental AG on the A2 highway on April 25, 2018, near Hanover, Germany. Israeli artificial intelligence (AI) startup, Hailo Technologies, has closed a $12.5 million series A from Maniv Mobility, OurCrowd, and NextGear to develop a chip for deep learning on edge devices and processing of high-resolution sensory data in real time. According to a report from Markets and Markets, edge computing will be worth $6.72 billion by 2020, and IC Insights reported that integrated circuits in cars are expected to generate global sales of $42.9 billion in 2021. In 2017, McKinsey reported in the study, Self Driving Car Technology: when will robots hit the road?, that ADAS systems grew to 140 million in 2016 from 90 million units in 2014. "Because of the low latency required for autonomous driving and advanced driving assistance, deep learning with convolutional neural networks, running on in-vehicle hardware, is necessary," offers Tom Coughlin, IEEE Fellow and President at Coughlin Associates.
Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. SE: Where are we with machine learning? What problems still have to be resolved? Aitken: We're in a state where things are changing so rapidly that it's really hard to keep up with where we are at any given instance. We've seen that machine learning has been able to take some of the things we used to think were very complicated and rendered them simple to do.
IBM on Friday unveiled its next-generation Power Systems Servers that includes its new processor that will work for artificial intelligence systems that require heavy computing capability. "Built specifically for compute-intensive AI workloads, the new POWER9 systems are capable of improving the training times of deep learning frameworks by nearly four times, allowing enterprises to build more accurate AI applications, faster," IBM said in a statement. With a focus on AI and machine learning, it can do a lot of tasks with greater efficiency. As a result of using this new, high-powered system, data scientists can build applications faster, ranging from deep learning insights in scientific research, real-time fraud detection and credit risk analysis. "IT infrastructure needs to be re-designed for the AI era, which lets companies analyse data in milliseconds and make decisions driven by data.
Plenty of people around the world got new gadgets Friday, but one in Eastern Tennessee stands out. Summit, a new supercomputer unveiled at Oak Ridge National Lab is, unofficially for now, the most powerful calculating machine on the planet. It was designed in part to scale up the artificial intelligence techniques that power some of the recent tricks in your smartphone. America hasn't possessed the world's most powerful supercomputer since June 2013, when a Chinese machine first claimed the title. Summit is expected to end that run when the official ranking of supercomputers, from an organization called Top500, is updated later this month.