Recent Progress with Intel-Powered Solutions Shows Artificial
Intelligence Is Good Medicine.
The following is an opinion editorial provided by Navin Shenoy,
executive vice president and general manager of the Data Center
Group at Intel Corporation.
This press release features multimedia. View
the full release here:
https://www.businesswire.com/news/home/20180321006119/en/
Navin Shenoy, executive vice president
and general manager of the Data Center Group at Intel Corporation,
speaks Wednesday, March 21, 2018, at the University of California,
San Francisco. Intel invited leading healthcare organizations to
address the most pressing topics and challenges in healthcare.
(Credit: Intel Corporation)
In the wide world of big data, artificial intelligence (AI)
holds transformational promise. Everything from manufacturing to
transportation to retail to education will be improved through its
application. But nowhere is that potential more profound than in
healthcare, where every one of us has a stake.
What if we could predict the next big disease epidemic, and stop
it before it kills? What if we could look at zettabytes of data to
find those at greatest risk of becoming sick, then quickly and
precisely prevent that from happening? What if the treatment and
management of chronic disease could be so personalized that no two
individuals get the same medicine, but equally enjoy the best
possible outcome? What if we could drastically reduce the time and
cost to discover new drugs and bring them on the market? What if we
could do all of that now?
Thanks to artificial intelligence and the work of Intel and its
partners, we can.
Real Impact Today
There’s a common myth that AI in healthcare is the stuff of
science fiction – think machines diagnosing illness and prescribing
treatment without a doctor involved. But that is not only highly
unlikely, it’s not even close to the best examples of how AI is
emerging in healthcare today.
Intel and partners throughout the healthcare industry –
including GE Healthcare, Siemens, Sharp Healthcare, the Broad
Institute, UCSF and the Mayo Clinic – are successfully applying AI
solutions today, from the back office to the doctor’s office, from
the emergency room to the living room. A few customers that we’re
working closely with include:
Montefiore Medical System: using prescriptive
models to identify patients at risk for respiratory failure, so
healthcare workers can act on alerts that lead to timely
interventions that save lives and resources.
Stanford Medical: using AI to augment MRI image
reconstruction so that a complete image can be delivered in about a
minute versus what normally would take about an hour – eliminating
risky intubation and sedation in pediatric patients during imaging
exams.
ICON plc: Instead of only relying on burdensome clinic
visits and paper diaries, using clinical data from sensors and
wearable devices to more quickly assess the impact of new therapies
in clinical trials.
AccuHealth: Using home monitoring along with data mining
and predictive modeling to identify changes of concern among
chronic disease patients to enable intervention before conditions
escalate and become acute.
Better Health Tomorrow
But the triumph of artificial intelligence in healthcare isn’t
inevitable. Right now, the average hospital generates 665 terabytes
of data annually,1 but most of that data isn’t useful. At least 80
percent of hospital data is unstructured,2 such as clinical notes,
video and images. Electronic medical records (EMRs) are a mandated
system of record, but they aren’t as actionable as they could be.
Only with AI can we leverage healthcare data to create a system of
insights.
Getting healthcare systems to provide greater access to their
data would help. Government also has a role to play by providing
appropriate incentives and regulatory clarity for sharing data. We
agree with the recent White House proposal to give patients control
and ownership of all their health data, bringing it with them
wherever they go rather than residing in various doctor’s offices,
clinics and hospitals.
New technology can help as well. One example: Intel researchers
are making great strides toward practical methods for homomorphic
encryption, a method that will allow computer systems to perform
calculations on encrypted information without decrypting it first.
Such encryption would enable researchers to operate on data in a
secure and private way, while still delivering insightful
results.
Indeed, much work is ahead, and Intel is uniquely positioned to
help healthcare organizations succeed. Emerging healthcare data is
massive data – images, a growing list of ‘omics (i.e. genomics,
proteomics), video – and will require a storage plan and a network
that addresses speed, latency and reliability. We have been
investing with our partners to build the right systems - data,
storage, network, full infrastructure – all the way from the edge
to the network to the cloud, and everywhere in between. With the
advancements in our hardware and optimizations of popular deep
learning frameworks, the Intel Xeon Scalable processor has 198x
better inference performance and 127x better training performance
than prior generations3. As a result, the Xeon platform is at the
center or many AI workloads that are real today because it is well
suited for many machine and deep learning applications across
industries like healthcare.
But hardware, storage and network alone are not enough. We need
to leverage the unparalleled expertise from data scientists,
software developers, industry experts, and ecosystem partners ––to
address AI in healthcare end to end. As part of the effort to
expand expertise across AI, we launched the Intel AI Academy, a
place that offers learning materials, community tools and
technology to boost AI developments. With more than 250K monthly
participants, I invite you to join for free as well.
I feel very fortunate to work for a company like Intel that is
committed to powering AI solutions that will tackle some of the
biggest challenges of our time, including healthcare. I’m also
proud to be leading the team that will deliver that vision.
____________________
1 Source: http://www.netapp.com/us/media/wp-7169.pdf
2 Source:
http://www.zdnet.com/news/unstructured-data-challenge-or-asset/6356681
3 Source: Configuration: AI Performance – Software + Hardware
(see below)
- INFERENCE using FP32 Batch Size Caffe
GoogleNet v1 256 AlexNet 256.
- Performance estimates were obtained
prior to implementation of recent software patches and firmware
updates intended to address exploits referred to as "Spectre" and
"Meltdown." Implementation of these updates may make these results
inapplicable to your device or system. Software and workloads used
in performance tests may have been optimized for performance only
on Intel microprocessors. Performance tests, such as SYSmark and
MobileMark, are measured using specific computer systems,
components, software, operations and functions. Any change to any
of those factors may cause the results to vary. You should consult
other information and performance tests to assist you in fully
evaluating your contemplated purchases, including the performance
of that product when combined with other products. For more
complete information visit http://www.intel.com/performance Source:
Intel measured as of June 2017 Optimization Notice: Intel's
compilers may or may not optimize to the same degree for non-Intel
microprocessors for optimizations that are not unique to Intel
microprocessors. These optimizations include SSE2, SSE3, and SSSE3
instruction sets and other optimizations. Intel does not guarantee
the availability, functionality, or effectiveness of any
optimization on microprocessors not manufactured by Intel.
Microprocessor-dependent optimizations in this product are intended
for use with Intel microprocessors. Certain optimizations not
specific to Intel microarchitecture are reserved for Intel
microprocessors. Please refer to the applicable product User and
Reference Guides for more information regarding the specific
instruction sets covered by this notice.
- Configurations for Inference
throughput
- Processor :2 socket Intel(R) Xeon(R)
Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total
Memory 376.46GB (12slots / 32 GB / 2666 MHz).CentOS
Linux-7.3.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD
1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework caffe
version: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology
:googlenet_v1 BIOS:SE5C620.86B.00.01.0004.071220170215 MKLDNN:
version: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer.
Measured: 1190 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3
@ 2.30GHz (18 cores), HT enabled, turbo disabled, scaling governor
set to “performance” via intel_pstate driver, 256GB DDR4-2133 ECC
RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel
3.10.0-514.el7.x86_64. OS drive: Seagate* Enterprise ST2000NX0253 2
TB 2.5" Internal Hard Drive. Performance measured with: Environment
variables: KMP_AFFINITY='granularity=fine, compact,1,0‘,
OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d
2.3G -u 2.3G -g performance. Deep Learning Frameworks: Intel Caffe:
(http://github.com/intel/caffe/), revision
b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with
“caffe time --forward_only” command, training measured with “caffe
time” command. For “ConvNet” topologies, dummy dataset was used.
For other topologies, data was stored on local storage and cached
in memory before training. Topology specs from
https://github.com/intel/caffe/tree/master/models/intel_optimized_models
(GoogLeNet, AlexNet, and ResNet-50),
https://github.com/intel/caffe/tree/master/models/default_vgg_19
(VGG-19), and
https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners
(ConvNet benchmarks; files were updated to use newer Caffe prototxt
format but are functionally equivalent). GCC 4.8.5, MKLML version
2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe,
Inference & Training measured with “caffe time” command. For
“ConvNet” topologies, dummy dataset was used. For other topologies,
data was st ored on local storage and cached in memory before
training BVLC Caffe (http://github.com/BVLC/caffe), revision
91b09280f5233cafc62954c98ce8bc4c204e7475 (commit date 5/14/2017).
BLAS: atlas ver. 3.10.1.
- Configuration for training
throughput:
- Processor :2 socket Intel(R) Xeon(R)
Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total
Memory 376.28GB (12slots / 32 GB / 2666 MHz).CentOS
Linux-7.3.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD
1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework caffe
version: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology
:alexnet BIOS:SE5C620.86B.00.01.0009.101920170742 MKLDNN:
version: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer.
Measured: 1023 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3
@ 2.30GHz (18 cores), HT enabled, turbo disabled, scaling governor
set to “performance” via intel_pstate driver, 256GB DDR4-2133 ECC
RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel
3.10.0-514.el7.x86_64. OS drive: Seagate* Enterprise ST2000NX0253 2
TB 2.5" Internal Hard Drive. Performance measured with: Environment
variables: KMP_AFFINITY='granularity=fine, compact,1,0‘,
OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d
2.3G -u 2.3G -g performance. Deep Learning Frameworks: Intel Caffe:
(http://github.com/intel/caffe/), revision
b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with
“caffe time --forward_only” command, training measured with “caffe
time” command. For “ConvNet” topologies, dummy dataset was used.
For other topologies, data was stored on local storage and cached
in memory before training. Topology specs from
https://github.com/intel/caffe/tree/master/models/intel_optimized_models
(GoogLeNet, AlexNet, and ResNet-50),
https://github.com/intel/caffe/tree/master/models/default_vgg_19
(VGG-19), and
https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners
(ConvNet benchmarks; files were updated to use newer Caffe prototxt
format but are functionally equivalent). GCC 4.8.5, MKLML version
2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe,
Inference & Training measured with “caffe time” command. For
“ConvNet” topologies, dummy dataset was used. For other topologies,
data was st ored on local storage and cached in memory before
training BVLC Caffe (http://github.com/BVLC/caffe), revision
91b09280f5233cafc62954c98ce8bc4c204e7475 (commit date 5/14/2017).
BLAS: atlas ver. 3.10.1.)
*Other names and brands may be claimed as the property of
others.
View source
version on businesswire.com: https://www.businesswire.com/news/home/20180321006119/en/
Intel CorporationRobin HoltRobin.holt@intel.com
Intel (NASDAQ:INTC)
Historical Stock Chart
From Mar 2024 to Apr 2024
Intel (NASDAQ:INTC)
Historical Stock Chart
From Apr 2023 to Apr 2024