May 25th, 2018
Intel’s Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration.
In a Q&A timed with the first Intel AI DevCon event, the
Gadi Singer, vice president and architecture general manager for the Artificial Intelligence Products Group at Intel, uses artificial intelligence to reshape scientific exploration. Before his role with AI, the 35-year Intel veteran helped create the first Pentium processor; led development of the first Xeon processors and the first Atom processor; and oversaw architecture for generations of the Intel Core processors. (Photo Credit: Walden Kirsch/Intel Corporation)
Intel vice president and architecture general manager for its Artificial Intelligence Products Group discussed his role at the intersection of science — computing’s most demanding customer — and AI, how scientists should approach AI and why it is the most dynamic and exciting opportunity he has faced.
Q. How is AI changing science?
Scientific exploration is going through a transition that, in the last 100 years, might only be compared to what happened in the ‘50s and ‘60s, moving to data and large data systems. In the ‘60s, the amount of data being gathered was so large that the frontrunners were not those with the finest instruments, but rather those able to analyze the data that was gathered in any scientific area, whether it was climate, seismology, biology, pharmaceuticals, the exploration of new medicine, and so on.
Today, the data has gone to levels far exceeding the abilities of people to ask particular queries or look for particular insights. The combination of this data deluge with modern computing and deep learning techniques is providing new and many times more disruptive capabilities.
What’s New: Intel collaborates with Novartis* on the use of deep neural networks (DNN) to accelerate high content screening – a key element of early drug discovery. The collaboration team cut time to train image analysis models from 11 hours to 31 minutes – an improvement of greater than 20 times1.
Collaboration team members from Novartis and Intel used eight CPU-based servers, a high-speed fabric interconnect and optimized TensorFlow to achieve the improvement in time needed to process a dataset of 10K images.
Why It’s Important: High content screening of cellular phenotypes is a fundamental tool supporting early drug discovery. The term “high content” signifies the rich set of thousands of pre-defined features (such as size, shape, texture) that are extracted from images using classical image-processing techniques. High content screening allows analysis of microscopic images to study the effects of thousands of genetic or chemical treatments on different cell cultures.
The promise of deep learning is that relevant image features that can distinguish one treatment from another are “automatically” learned from the data. By applying deep neural network acceleration, biologists and data scientists at Intel and Novartis hope to speed up the analysis of high content imaging screens. In this joint work, the team is focusing on whole microscopy images as opposed to using a separate process to identify each cell in an image first. Whole microscopy images can be much larger than those typically found in deep learning datasets. For example, the images used in this evaluation are more than 26 times larger than images typically used from the well-known ImageNet* dataset of animals, objects and scenes.
IC Insights raises its full-year spending growth forecast for this year from 8% to 14%.
IC Insights recently released its May Update
to the 2018 McClean Report.
included a look at the top-25 1Q18 semiconductor suppliers, a discussion of the 1Q18 IC industry market results, and an update of the 2018 capital spending forecast by company.Overall, the capital spending story for 2018 is becoming much more positive as compared with the forecast presented in IC Insights’ March Update
to The McClean Report 2018 (MR18)
. In the March Update
, IC Insights forecast an 8% increase in semiconductor industry capital spending for this year. However, as shown in Figure 1, IC Insights has raised its expectations for 2018 capital spending by six percentage points to a 14% increase. If this increase occurs, it would be the first time that semiconductor industry capital outlays exceeded $100 billion.
The worldwide 2018 capital spending forecast figure is 53% higher than the spending just two years earlier in 2016.
Although Samsung says it still does not have a full-year capital spending forecast for this year it did say it will spend “less” in semiconductor capital outlays in 2018 as compared to 2017, when it spent $24.2 billion. However, as of 1Q18, with regard to its capex, its “foot is still on the gas!” Samsung spent $6.72 billion in capex for its semiconductor division in 1Q18, slightly higher than the average of the previous three quarters. This figure is almost 4x the amount the company spent just two years earlier in 1Q16! Over the past four quarters, Samsung has spent an incredible $26.6 billion in capital outlays for its semiconductor group. Wow!
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