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Killexams : Intel Certification testing - BingNews https://killexams.com/pass4sure/exam-detail/ISS-003 Search results Killexams : Intel Certification testing - BingNews https://killexams.com/pass4sure/exam-detail/ISS-003 https://killexams.com/exam_list/Intel Killexams : We'll get you that Wi-Fi 7 laptop by 2024, Intel says No result found, try new keyword!McLaughlin reportedly said Intel was seeking Wi-Fi Alliance certification of its IEEE 802.11be, aka Wi-Fi 7, products – once that certification is available. " [Wi-Fi 7] will be installed in PC ... Tue, 02 Aug 2022 20:38:23 -0500 en-us text/html https://www.msn.com/en-us/lifestyle/shopping/well-get-you-that-wi-fi-7-laptop-by-2024-intel-says/ar-AA10fMwJ Killexams : Intel accelerating the next era of wireless with Wi-Fi 7
Wi-Fi 7

(Source – Shutterstock)

  • Intel intends to introduce Wi-Fi 7-certified products to the market in alignment with the Wi-Fi Alliance certification timeline (2023–2024)
  • The characteristics of Wi-Fi 7 will Strengthen upon and advance preceding Wi-Fi versions.

The wireless technology industry is always evolving, delivering improvements for network connectivity. In 2022, the Wi-Fi org announced that Wi-Fi certified 6 added enhancements to best deliver advanced use cases. Wi-Fi 6 and Wi-Fi 6E are optimally equipped to meet today’s connectivity demands and allow a steady rate of new product and service innovation.

Soon after that, Wi-Fi became the next possibility. In May 2022,Qualcomm Technologies sampled the world’s most scalable Wi-Fi 7 networking platform portfolio commercially available, with offerings ranging from 6 to 16 streams, for next-generation enterprise access points, high performance routers, and carrier gateways.

Now, Intel is also joining the next era of wireless technology. In a accurate “State of Wireless Connectivity Briefing” on the future of wireless connectivity and what it means for the industry, Carlos Cordeiro, Intel Fellow & Wireless CTO, Client Computing Group, claims that the world will soon have technologies that will maximize the user experience.

According to Cordeiro, “unless we can really understand what the major trends driving that future, we cannot really develop the right technology, feature, capability, and solutions from a core technology point of view, all the way to the software and services.”

Before going into Wi-Fi 7, Intel undertook an exercise to determine the key trends in wireless technology for the upcoming decade, and they have identified:

  • User access to massive data/compute power with ultra-fast speed/low latency.
  • Context aware/predictive devices and networks (AI/SDN/Distributed NW)
  • New human-machine interfaces (HMI) expansion. More video, gestures, and voice interaction.
  • Large growth of cellular PWN + Wi-Fi delivers enterprise everywhere.
  • New types of connected devices (e.g., smart glasses) become mainstream/essential.

Looking at their products and the entire industry, how does Intel help drive all this forward?

The goal of Intel’s wireless strategy is to provide the best PC platform connectivity available today. Additionally, Intel holds significant leadership positions in Wi-Fi standards and certification bodies, driving the larger ecosystem with their cutting-edge solutions and influencing Wi-Fi 7 and beyond.

“As we move to Wi-Fi 7, which builds upon 6E, you get an improved use of the 6 GHz spectrum, higher and better reliability, much lower latency, and we’re going to see a 5 Gbps or better in the solutions that are going to come into the market,” said Eric McLaughlin, VP, Client Computing Group & GM, Wireless Solutions Group at Intel.

Wi-Fi 7 is the next era in wireless

What’s new and what’s driving this Wi-Fi innovation?

  • 320 MHz channels – There is less signal interference because the most common wireless transmission protocol bands are avoided.
  • 4K-QAM – 4K QAM (Quadrature Amplitude Modulation) allows each signal to more densely incorporate larger amounts of data.
  • Multi-Link Operation – Ensures high priority data get transmitted without delay.
  • Multi-RU Puncturing – Wi-Fi 7 devices will be able to access other, unused portions of the same high-speed channel to enable very big channels (even if they are less than the maximum 320 MHz size.)
  • Deterministic latency – Shares redundant or unique data to increase reliability with incredibly fast latencies.

According to Eric, these innovations not only contribute to better connectivity but also some of the previously listed megatrends. Take proximity detection and context awareness as examples.

“With 320 MHz channels, we’re likely to see new uses for Wi-Fi where you can do things like figure out if someone is in the room, how many people are there, and whether or not they’re moving or static. With the right technology, you can even determine whether or not someone is breathing,” explained Mclaughlin.

(Source – Intel)

As with all prior Wi-Fi generations, Intel will continue to offer customers and enterprises market-leading connectivity solutions. Intel Wi-Fi 7 product development is on track and healthy. Intel is in line with the participation in all WFA Wi-Fi 7 plug-fest events. They also do private Wi-Fi 7 interop testing with key ecosystem partners.

Intel intends to introduce Wi-Fi 7-certified products to the market in alignment with the Wi-Fi Alliance certification timeline (2023–2024).

What this means for the “less mature” nations

Regardless of how inventive and exciting, this announcement is, the question is whether every country in the APAC region could relate to or experience this innovation given that some of these nations are “less mature” in this conversation.

According to Mclaughlin, a spectrum policy is needed in order to provide these kinds of solutions to these countries. “Whether they’re opening up for Wi-Fi or spectrum for cellular, that’s the foundation for any of these technologies to work. We could put a lot of products out there, but without those spectrum allocations, they can’t operate them,” he continued.

McLaughlin went on to say that he anticipates that some of these nations, which are still deciding on their spectrum policy, will continue to make these technologies available. “It may start in education, for instance, where you get the right technology in, Chromebooks in, wireless in, and you provide these children and households the ability to have access to the education and information that they need,” he said in his conclusion.







Wed, 27 Jul 2022 12:30:00 -0500 Muhammad Zulhusni en-US text/html https://techwireasia.com/2022/07/intel-accelerating-the-next-era-of-wireless-with-wi-fi-7/
Killexams : China’s military exercises are an intel bonanza — for all sides No result found, try new keyword!China’s massing of ships, aircraft and missiles near Taiwan is giving the U.S. a never-before-seen glimpse of how Beijing might launch a military campaign against the island. But China is also ... Fri, 05 Aug 2022 06:55:35 -0500 en-us text/html https://www.msn.com/en-us/news/world/china-e2-80-99s-military-exercises-are-an-intel-bonanza-e2-80-94-for-all-sides/ar-AA10mejp Killexams : IBM Research Albany Nanotech Center Is A Model To Emulate For CHIPS Act

With the passage of the CHIPS+ Act by Congress and its imminent signing by the President of the United States, a lot of attention has been paid to the construction of new semiconductor manufacturing megasites by Intel, TSMC, and Samsung. But beyond the manufacturing side of the semiconductor business, there is a significant need to invest in related areas such as research, talent training, small and medium business development, and academic cooperation. I recently had the opportunity to tour a prime example of such a facility that integrates all these other aspects of chip manufacturing into a tight industry, government, and academic partnership. That partnership has been going on for over 20 years in Albany New York where IBM Research has a nanotechnology center that is located adjacent to the State University of New York (SUNY) Albany campus. With significant investment by New York State through the New York Creates NY CREATES development agency, IBM in close partnership with several universities and industry partners is developing state-of-the-art semiconductor process technologies in working labs for the next generation of computer chips.

The center provides a unique facility for semiconductor research – its open environment facilitates collaboration between leading equipment and materials suppliers, researchers, engineers, academics, and EDA vendors. Presently, IBM has a manufacturing and research partnership with Samsung Electronics and a research partnership was announced with Intel last year. Key chipmaking suppliers such as ASML, KLA, and Tokyo Electric (TEL) have equipment installed, and are working actively with IBM developing advanced processes and metrology for leading edge technologies.

These facilities do not come cheap. It takes billions of dollars of investment and many years of research to achieve each new breakthrough. For example, the High-k metal gate took 15 years to go into products; the FinFET transistor, essential today, took 13 years; and the next generation transistor, the gate-all-around/nano sheet, which Samsung is putting into production now, was in development for 14 years. In addition, the cost to manufacture chips at each new process node is increasing 20-30% and the R&D costs are doubling for each node’s development. To continue supporting this strategic development, there needs to be a partnership between industry, academia, and government.

IBM Makes The Investment

You might ask why IBM, which sold off its semiconductor manufacturing facilities over the years, is so involved in this deep and expensive research. Well, for one, IBM is very, very good at semiconductor process development. The company pioneered several critical semiconductor technologies over the decades. But being good at a technology does not pay the bills, so IBM’s second motivation is that the company needs the best technology for its own Power and Z computers. To that end, IBM is primarily focused on developments that support high-performance computing and AI processing.

Additional strategic suppliers and partners help to scale these innovations beyond just IBM’s contribution. The best equipment from the world-class equipment suppliers provides a testbed for partners to experiment and advance the state-of-the-art technology. IBM along with its equipment partners have built specialized equipment where needed to experiment beyond the capabilities of standard equipment.

But IBM only succeeds if it can transfer the technology from the labs into production. To do so, IBM and Samsung have been working closely on process developments and the technology transfer.

MORE FROM FORBESIBM Goes Vertical To Scale Transistors

The NanoTech Center dovetails with the CHIPS Act in that it will allow the United States to develop leadership in manufacturing technologies. It can also allow smaller companies to test innovative technologies in this facility. The present fab building is running 24/7/365 and is highly utilized, but there’s space to build another building that can double significantly expand the clean room space. There’s also a plan for a building that will be able to support the next generation of ASML EUV equipment called high NA EUV.

The Future is Vertical

The Albany site also is a center for chiplet technology research. As semiconductor scaling slows, unique packaging solutions for multi-die chips will become the norm for high-performance and power-efficient computing. IBM Research has an active program of developing unique 2.5D and 3D die-stacking technologies. Today the preferred substrate for building these multi-die chips is still made from silicon, based on the availability of tools and manufacturing knowledge. There are still unique process steps that must be developed to handle the specialized processing, including laser debonding techniques.

IBM also works with test equipment manufacturers because building 3D structures with chiplets presents some unique testing challenges. Third party EDA vendors also need to be part of the development process, because the ultimate goal of chiplet-based design is to be able to combine chips from different process nodes and different foundries.

Today chiplet technology is embryonic, but the future will absolutely need this technology to build the next generation of data center hardware. The is a situation where the economics and technology are coming together at the right time.

Summary

The Albany NanoTech Center is a model for the semiconductor industry and demonstrates one way to bring researchers from various disciplines and various organizations together to advance the state-of-the-art semiconductor technology. But this model also needs to scale up and be replicated throughout North America. With more funding and more scale, there also needs to be an appropriately skilled workforce. Here is where the US needs to make investments in STEM education on par with the late 1950s Space Race and sites like Albany that offer R&D on leading-edge process development that should inspire more students to go into physics, chemistry, and electrical engineering and not into building the next crypto currency startup.

Tirias Research tracks and consults for companies throughout the electronics ecosystem from semiconductors to systems and sensors to the cloud. Members of the Tirias Research team have consulted for IBM, Intel, GlobalFoundries, Samsung, and other foundries.

Mon, 08 Aug 2022 11:08:00 -0500 Kevin Krewell en text/html https://www.forbes.com/sites/tiriasresearch/2022/08/08/ibm-research-albany-nanotech-center-is-a-model-to-emulate-for-chips-act/
Killexams : Halo Security Launches Full Attack Surface Management Platform Led By Veterans of Intel and McAfee

LAS VEGAS, August 09, 2022--(BUSINESS WIRE)--TrustedSite, a leading provider of vulnerability scanning and certification, officially launched Halo Security at Black Hat USA. The company’s attack surface management platform combines external asset risk and vulnerability assessment, and penetration testing services to provide organizations complete visibility into the risk posture of their internet-exposed assets on an on-going basis.

Led by experienced penetration testers, scanning leaders and reformed hackers, Halo Security brings the attacker’s perspective to the modern organization with a mission to help organizations protect data from external attackers and build trust with their customers.

Halo Security comes out of stealth having already helped thousands of organizations identify and monitor all their internet-facing assets across clouds and service providers; detect risks and security posture improvements by way of vulnerability scanning, application testing, and manual penetration testing; and organize their data for smarter remediation with Halo Security’s easy-to-use, all-in-one solution for attack surface management. You can’t protect what you don’t see, and Halo Security’s agentless and recursive discovery engine discovers the assets you’re not aware of, so you can prioritize your efforts from a single pane of glass.

Halo Security was founded by veterans of industry leaders, like Intel and McAfee, who set out on a mission to help organizations understand and reduce digital risk. In 2002, they created one of the world’s first commercial website and web application vulnerability scanners, ScanAlert. Halo Security has provided security services to over 8,000 clients since it began in 2013, working under the McAfee umbrella until 2021.

"There’s a reason Gartner and others are sounding the alarm about the need for attack surface management tools," said Halo Security founder and CEO, Tim Dowling. "Existing cybersecurity technologies on the market leave blind spots and thousands of organizations are suffering from a lack of visibility when it comes to their total attack surface, leaving the door open for malicious actors to compromise data and demand ransoms. Halo Security combines advanced asset discovery and monitoring technology with best-in-class manual and automated security testing capabilities to provide organizations a complete view of their external risk."

Halo Security’s penetration testers boast OSCP (Offensive Security Certified Professional) and OSCE (Offensive Security Certified Expert) certifications and have provided testing for some of the largest organizations in the world. Halo Security is an Approved Scanning Vendor authorized by the PCI Security Standards Council, and the company’s founders hold patents for multiple security scanning technologies.

Halo Security is rolling out a new free tool to audit the security controls of any website. The new Halo Security Site Scan service audits the certificates, headers, scripts, forms and technologies in use on any website and provides best practice recommendations for proactively improving its security posture.

See how Halo Security works for your organization with a free trial today.

About Halo Security
Halo Security is a complete attack surface management platform, offering asset discovery, risk and vulnerability assessment, and penetration testing services in a unified, easy-to-use dashboard. Founded by experienced and trusted penetration testers, scanning leaders, and reformed hackers, Halo Security brings the attacker’s perspective to the modern organization. Halo Security’s leadership team has held key roles at McAfee, Intel, Kenna Security, OneLogin, and WhiteHat Security. Learn more at halosecurity.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220805005484/en/

Contacts

Media:
Charity Lacey
Gregory FCA on behalf of Halo Security
619.368.4373
clacey@gregoryfca.com

Tue, 09 Aug 2022 01:00:00 -0500 en-NZ text/html https://nz.finance.yahoo.com/news/halo-security-launches-full-attack-130000938.html
Killexams : Intel Arc Tech Talk Focuses on VRR, HDR, and HDMI 2.1
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Intel’s PR rep for its Arc discrete graphics has sat down for another “Between Two Monitors” tech chat. The company has promised to continually offer information about Arc prior to its upcoming launch. The rationale is it will allow gamers to have all the info they need prior to purchase. To do that it will be releasing short videos tackling questions from gamers. This week it released a new briefing discussing how Arc would handle Variable Refresh Rates (VRR) and HDR, and there’s talk of support for HDMI 2.1.

Starting with VRR, the GPU world is currently bifurcated between Nvidia’s G-Sync and AMD’s FreeSync. However, in May the Video Electronics Standard Association (VESA) formally announced its own open standard based on Adaptive Sync. This is an attempt to offer an open standard that quantifies a display’s ability to offer variable refresh rates with DisplayPort using various performance tests. This effort was designed to override the competing standards, and hopefully lessen confusion among customers. After all, the average gamer may not be able to parse the various G-Sync and FreeSync versions available, especially with ratings like “G-Sync compatible” and “FreeSync Premium Pro” making things a bit muddier.

To demonstrate Arc’s capabilities in this arena, Ryan Shrout fired up Death Stranding on a 4K, 120Hz Acer monitor using an Arc A750 GPU. He didn’t state which certification the monitor has, only that it supports variable refresh rates. He shows the monitor syncing the refresh rate with the game’s frame rate, which is around 100 FPS/Hz. It’s running at 1440p, so overall the GPU is performing quite well in this game. He states Arc is “fully supporting DisplayPort VRR standards.” He summarizes by stating Arc will support “any and all” VESA Adaptive Sync standard. In addition, the company will also be validating Arc on the over 100 of the most popular VRR displays to ensure a smooth experience at launch. As an aside, it’s not clear by Ryan is wearing two watches in this video; easter egg of some kind?

Moving on to HDR, he says Arc will support it on compliant monitors. However, there are many different certifications for HDR that vary depending on a monitor’s brightness capabilities. Those include Display HDR400, DisplayHDR 600, etc. From there, he mentions that HDR is difficult to show on a video since you’d need an HDR display of your own to see it. Therefore, as proof it’s working he jokingly says Intel spares no expense and has a highly-accurate external testing device. That “device” is a person named Ken, who looks at the monitor and agrees it’s working.

Previous benchmarks released by Intel for its A750 GPU.

Finally, he says the the lower-end Arc GPUs will support HDMI 2.0 natively. That includes the A310, A380, and A580. However, they can be modified with a PCON chip to support HDMI 2.1. That decision will be left up to the partners making the GPUs though. The higher-end GPUs, which includes the Arc A750 and the A770, will support HDMI 2.1 natively. A chyron on the screen also states all Arc GPUs will support DisplayPort 2.0 as well. The PCON chip he mentions performs a protocol conversion from DisplayPort 2.0 to HDMI 2.1.

The company recently stated Arc’s launch is “now in sight” but it’s still not clear when that will happen. The big question is whether Intel will be able to launch before AMD and Nvidia’s next-gen GPUs arrive, which might be around September. Intel is probably hoping it can pull it off, as its competition’s GPUs are rumored to be quite powerful. However, that power might come at a great cost, with a lot of heat as well. Therefore, it’s possible Intel is hoping to undercut them on price-to-performance and efficiency.

Now Read:

Thu, 28 Jul 2022 22:16:00 -0500 Josh Norem en-US text/html https://www.extremetech.com/gaming/338376-intel-arc-tech-talk-focuses-on-vrr-hdr-and-hdmi-2-1
Killexams : Accelerating Azure Databricks Runtime for Machine Learning

Getting the best possible performance out of an application always presents challenges. This fact is especially true when developing machine learning (ML) and artificial intelligence (AI) applications. Over the years, Intel has worked closely with the ecosystem to optimize a broad range of frameworks and libraries for better performance.

This brief discusses the performance benefits derived from incorporating Intel-optimized ML libraries into Databricks Runtime for Machine Learning on 2nd Generation Intel® Xeon® Platinum processors. The paper focuses on two of the most popular frameworks used in ML and deep learning (DL): scikit-learn and TensorFlow.

Intel-optimized ML libraries

The Intel oneAPI AI Analytics Toolkit gives data scientists, AI developers, and researchers familiar Python tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel architecture. The components are built using oneAPI libraries for low-level compute optimizations. This toolkit improves performance from preprocessing through ML, and it provides interoperability for efficient model development. Two popular ML and DL frameworks are scikit-learn and TensorFlow.

Scikit-learn

Scikit-learn is a popular open source ML library for the Python programming language. It features various classification, regression, and clustering algorithms, including support for:

  • Vector machines
  • Random forests
  • Gradient boosting
  • k-means
  • DBSCAN

This ML library is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

The Intel Extension for Scikit-learn, available through the Intel oneAPI AI Analytics Toolkit, can help boost ML performance and provide data scientists more time to focus on their models. Intel has invested in optimizing the performance of Python itself with the Intel Distribution for Python, and it has optimized key data science libraries used with scikit-learn, such as XGBoost, NumPy, and SciPy. For more information on using these extensions, read the following article: https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-lear....

TensorFlow

TensorFlow is another popular open source framework for developing end-to-end ML and DL applications. It has a comprehensive, flexible ecosystem of tools and libraries that lets researchers easily build and deploy applications.

To take full advantage of the performance available in Intel processors, TensorFlow has been optimized using Intel oneAPI Deep Neural Network Library (oneDNN) primitives. For more information on these optimizations, in addition to performance data, refer to the article, “TensorFlow Optimizations on Modern Intel Architecture,” available here: https://software.intel.com/content/www/us/en/develop/articles/tensorflow-optimizations-on-modern-int….

Databricks runtime for machine learning

Databricks is a unified data-analytics platform for data engineering, ML, and collaborative data science. It offers comprehensive environments for developing data-intensive applications.

Databricks Runtime for Machine Learning is an integrated end-to-end environment that incorporates:

  • Managed services for experiment tracking
  • Model training
  • Feature development and management
  • Feature and model serving.

It includes the most popular ML/DL libraries, such as TensorFlow, PyTorch, Keras, and XGBoost, and it also includes libraries required for distributed training, such as Horovod. For more information, visit the Databricks web page at https://docs.databricks.com/runtime/mlruntime.html.

Databricks has been integrated with Microsoft Azure. This integration brings great convenience to managing production infrastructure and running production workloads. Though cloud services aren’t free, there are opportunities to reduce the cost of ownership by using optimized libraries. This article uses Databricks on Azure to demonstrate the solution and the performance results achieved in Intel testing.

Intel-optimized ML libraries on Azure Databricks

Databricks Runtime for Machine Learning includes the stock versions of scikit-learn and TensorFlow. To boost performance, however, Intel engineers replaced these versions with Intel-optimized versions and tested the results. Databricks provides initialization scripts to facilitate customization at https://docs.databricks.com/clusters/init-scripts.html.

These scripts run during the startup of each cluster node. Intel also developed two initialization scripts to incorporate the Intel-optimized versions of scikit-learn and TensorFlow. The right script for your needs depends on whether you want the statically patched version or not:

The following instructions describe how to create a cluster using either script. First, copy the initialization script to Databricks File System (DBFS) by completing the following steps:

  1. Download either init_intel_optimized_ml.sh or init_intel_optimized_ml_ex.sh to a local folder.
  2. In the left sidebar, click the Data icon.
  3. Click the DBFS button, and then click Upload at the top. If the DBFS button isn’t shown, follow the guidance provided in “Manage the DBFS file browser” to enable it.
  4. In the Upload Data to DBFS dialog, select a target directory (for example, FileStore).
  5. Browse to the local file that you downloaded to the local folder, and then upload it in the Files box.
picture1 Intel

Next, launch the Databricks cluster using the uploaded initialization script:

  1. On the Cluster Configuration page, click the Advanced Options toggle.
  2. At the bottom-right, click the Init Scripts tab.
  3. In the Destination drop-down menu, select the DBFS destination type.
  4. Specify the path to the previously uploaded initialization script (that is, dbfs:/FileStore/init_intel_optimized_ml.sh or dbfs:/FileStore/init_intel_optimized_ml_ex.sh).
  5. Click Add.
picture2 intel
Intel

Refer to the Intel optimized ML for Databricks guidance at https://github.com/oap-project/oap-tools/tree/master/integrations/ml/databricks for more detailed information.

Performance measurements

The Intel engineers compared scikit-learn and TensorFlow performance for both training and prediction between two Databricks clusters. The baseline cluster was created using default libraries without the initialization script. The other cluster was created using Intel-optimized ML libraries through specifying the initialization script discussed previously.

Scikit-learn training and prediction performance

Intel used Databricks Runtime 9.0 for Machine Learning with the following benchmarks for this testing. The Intel engineers used scikit-learn_bench (https://github.com/IntelPython/scikit-learn_bench) to compare the performance of common scikit-learn algorithms with and without the Intel optimizations. The benchmark_sklearn.ipynb notebook (https://github.com/oap-project/oap-tools/blob/master/integrations/ml/databricks/benchmark/benchmark_...) is provided for convenience to run scikit-learn_bench on a Databricks cluster.

Intel compared training and prediction performance for the libraries by creating one single-node Databricks cluster with the stock library and another using the Intel-optimized version. Both clusters used the Standard_F16s_v2 Azure instance type.

The benchmark notebook was run on both clusters. The Intel engineers set multiple configurations for each algorithm to get accurate training and prediction performance data (for details, see https://github.com/oap-project/oap-tools/blob/master/integrations/ml/databricks/benchmark/skl_config...). Intel tested multiple configurations for each algorithm. Table 1 shows the performance data of one configuration for each algorithm.

Table 1. Comparing the training and prediction performance of stock libraries and Intel-optimized libraries (all times in seconds)

Algorithm

Input configuration

Training time (seconds)

Prediction time (seconds)

Stock scikit-learn (baseline)

Intel Extension for Scikit-learn

Stock scikit-learn (baseline)

Intel Extension for Scikit-learn

kmeans

config1

17.91

17.87

3.76

0.39

ridge_regression

config1

1.47

0.10

0.07

0.06

linear_regression

config1

5.03

0.10

0.07

0.06

logistic_regression

config3

74.82

6.63

0.62

0.08

svm

config2

173.81

10.61

49.90

0.46

The Intel-optimized version of scikit-learn greatly improved training and prediction performance for each algorithm. For some algorithms, like svm and brute_knn, the Intel-optimized version of the scikit-learn library achieved an order of magnitude leap in performance. See Figures 1 and 2 for the training and prediction performance results, respectively.

picture3 Intel

Figure 1. Training performance of the Intel-optimized scikit-learn library over the stock version

picture4 Intel

Figure 2. Prediction performance of the Intel-optimized scikit-learn library over the stock version

TensorFlow training and prediction performance

Bidirectional Encoder Representations from Transformers, or BERT (https://github.com/google-research/bert), is a new method of pre-training language representations. This method obtains state-of-the-art results on a wide range of natural language processing (NLP) tasks. Model Zoo (https://github.com/IntelAI/models) contains links to pretrained models, trial scripts, and step-by-step tutorials for many popular open-source ML models optimized to run on Intel Xeon Scalable processors.

The Intel engineers used Model Zoo to run the BERT Large (https://github.com/IntelAI/models/tree/v1.8.1/benchmarks/language_modeling/tensorflow/bert_large/REA...) model on SQuADv1.1 datasets to compare the performance of TensorFlow with and without Intel’s optimizations. Once again, the team provides a notebook (benchmark_tensorflow_bertlarge.ipynb) to run the benchmark on the Databricks cluster. For more details, refer to “Run Performance Comparison Benchmarks” at https://github.com/oap-project/oap-tools/tree/master/integrations/ml/databricks/benchmark.

The Intel engineers used a single-node Databricks cluster with Standard_F32s_v2, Standard_F64s_v2, and Standard_F72s_v2 instance types for the TensorFlow performance evaluation. For each instance type, the Intel engineers compared the inference and training performance between the stock TensorFlow and Intel-optimized TensorFlow libraries.

The testing found that the latter delivers 1.92x, 2.12x, and 2.24x inference performance on Databricks Runtime for Machine Learning with Standard_F32s_v2, Standard_F64s_v2, and Standard_F72s_v2 instances, respectively (see Figure 3). For training, the Intel-optimized TensorFlow library delivers 1.93x, 1.76x, and 1.84x training performance on Standard_F32s_v2, Standard_F64s_v2, and Standard_F72s_v2 instances, respectively (see Figure 4).

picture5 Intel

Figure 3. Inference speedup of the Intel-optimized TensorFlow library over the stock version; performance varies by use, configurations, and other factors[i]

picture5 Intel

Figure 4. Training speedup for the Intel-optimized TensorFlow library over the stock version; performance varies by use, configurations, and other factors[i]

Concluding remarks

The Intel-optimized versions of the scikit-learn and TensorFlow libraries deliver significant improvements in training and inference performance on Intel XPUs. Intel has demonstrated that organizations can Strengthen performance and reduce costs by replacing the stock scikit-learn and TensorFlow libraries included in Databricks Runtime for Machine Learning with the Intel-optimized versions.

When Databricks adds support for Databricks Container Services (https://docs.databricks.com/clusters/custom-containers.html) to the Databricks Runtime for Machine Learning, Intel will explore incorporating these optimized libraries through Docker images. This approach could make it easier to integrate with your continuous integration/continuous deployment (CI/CD) pipelines as part of your MLOps environment.

For more information, visit: https://github.com/oap-project/oap-tools/tree/master/integrations/ml/databricks

Please note:

  • Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.
  • Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. No product or component can be absolutely secure.
  • Your costs and results may vary.
  • Intel technologies may require enabled hardware, software or service activation.

Learn about Intel OneAPI Analytics and AI-Toolkit, here.

Thu, 04 Aug 2022 07:15:00 -0500 en text/html https://www.infoworld.com/article/3669311/accelerating-azure-databricks-runtime-for-machine-learning.html
Killexams : EDITORIAL: Military intel

Hear that? Sabers are rattling across the globe, from the South China Sea to North Korea to Ukraine and back again. Tehran hasn't stopped efforts to become a nuclear power. And those are just the big ones.

So it was with concern that we read the Heritage Foundation's 2022 index of U.S. military strength, which concludes that the American military is "only marginally able" to defend the country's vital interests. And as far as vital interests, the United States' are more vital than others. We keep the shipping lanes open. But the Heritage Foundation also looks at our ability to defend the homeland, our ability to successfully conclude a major war, and, yes, the "preservation of freedom of movement within the global commons."

It further assesses the American military as unable to successfully manage major conflicts on two fronts, something of a built-in requirement for this nation.

It's plausible that such reports are tailored to rankle Congress and help generate more defense funding. Nevertheless, it paints a troubling picture. Potential enemies are closing the gap in terms of technology and manpower, while U.S. leadership seems to focus instead on diversity, equality and inclusion (aka DEI).

The U.S. still outspends every other country, but military expenditures dropped 1.4 percent in 2021 to $801 billion, 3.5 percent of the GDP. Meanwhile, Red China's military spending topped $293 billion last year, a 6.8 percent increase, and rose for the 27th straight year, according to VOA. In 2022, its military spending is expected to grow another 7.1 percent. Those man-made islands and submarine bases in the South China Sea can get expensive.

And now, an analysis from retired U.S. Army Lt. Gen. Thomas Spoehr suggests that the U.S. military's emphasis on "wokeness" is weakening not just the nation's ability to defend its interests but may be driving away recruits.

Gen. Spoehr served in several senior leaderships roles at the Pentagon and led the Army's Chemical, Biological, Radiological and Nuclear School at Fort Leonard Wood. He served with the 82nd Airborne and 1st Armored divisions. We assume he knows his stuff.

He believes the U.S. military has been let down by its leadership, which seems to fear the wrath of a woke mob as much as any advancing army.

"Wokeness in the military is being imposed by elected and appointed leaders in the White House, Congress, and the Pentagon who have little understanding of the purpose, character, traditions, and requirements of the institution they are trying to change ... Wokeness in the military has become ingrained. And unless the policies that flow from it are illegal or directly jeopardize readiness, senior military leaders have little alternative but to comply."

With defense spending currently not even keeping pace with inflation, it's worrisome to read that the Pentagon has set aside $3 billion for climate initiatives in 2023. Climate initiatives? Do we plan on lobbing EVs at Chinese carriers? And these days, war college is more concerned with critical race theory, now added to its curriculum, than ever. Gen. Spoehr says the military's current training tract amounts to indoctrination.

Meanwhile, the Red Chinese are outspending the U.S. on things like hypersonic weapons, quantum computing and other technologies. (Dare we suggest the Chinese are winning the space race?)

Since 2015, the military has lowered physical fitness, combat and marksmanship standards to meet DEI demands. The general notes that there is now no required combat fitness test.

Which may help explain why, over the past two years, military recruiting has plummeted. In late June, the U.S. Army revealed that it had met just 40 percent of its recruiting goal for the fiscal year ending Sept. 30. Last month, The New York Times featured the military's increasingly hapless effort to attract new recruits. The Army has been forced to offer enlistment bonuses as high as $50,000.

The Heritage Foundation's 2022 index assesses the country's overall military strength in categories ranging from very weak to very strong. The U.S. military received an overall grade of marginal, just as it did in '21. (Basically, a grade of C.)

The U.S. military is "likely capable of meeting the demands of a single major regional conflict," the index found, but it would be "hard-pressed" to do more and is "certainly ill-equipped" to handle two such conflicts.

The military faces "worrisome trends" in force readiness, declining strength in key areas like trained pilots and continued uncertainty regarding the defense budget, which is having negative effects on major acquisition programs and installation-level repair capabilities.

Find almost any retired veteran and ask what he or she thinks of the military's current state of readiness. We spoke with two retired Army officers, both Arkansas natives, who we won't name here. Their consensus is that America could not defeat both Red China and Russia in such a scenario. America could not defend Taiwan. And on its own could not prevent Russia from taking Ukraine. The military has been devastated by politics and poor leadership at the Pentagon, they say. Soldiers today are soft and poorly led. A two-front war? "Not a chance."

Furthermore, "we are good at targeted killing and overwhelming poor countries for a short time."

Here's hoping that one day all armies are soft from lack of use and neglect. But that day, if it ever comes--we don't think it will--remains an unrealistic for today's world. Meanwhile, America's focus on DEI may one day translate into a military that's DOA.

In 1956, Soviet leader Nikita Khrushchev reportedly suggested America would be taken without firing a shot. It would be destroyed from within, he said.

Chilling words, some 66 years later.

Sun, 07 Aug 2022 21:24:00 -0500 en text/html https://www.arkansasonline.com/news/2022/aug/08/editorial-military-intel/
Killexams : TruEra joins Intel Disruptor program to advance AI model quality

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Organizations building artificial intelligence (AI) models face no shortage of quality challenges, most notably the need for explainable AI that minimizes the risk of bias.

For Redwood City, California-based startup TruEra, the path to explainable AI is paved with technologies that provide AI quality for models. Founded in 2019, TruEra has raised over $45 million in funding, including a recent round of investment that included the participation of Hewlett Packard Enterprise (HPE). 

This week, TruEra announced the latest milestone in its growth, revealing that it has been selected to be part of the Intel Disrupter Initiative, which brings technical partnership and go-to-market support for participants.

“The big picture here is that as machine learning is increasingly adopted in the enterprise, there’s a greater need to explain, test and monitor these models, because they’re used in higher-stakes use cases,” Will Uppington, cofounder and CEO of TruEra, told VentureBeat.

TruEra takes on the challenges of explainable AI

As the use of AI matures, there are emerging regulations around the world for its responsible usage.

The responsible use of AI is multifaceted, including prioritizing data privacy and providing mechanisms to enable the explainability of the methods used in models, to help encourage fairness and avoid bias.

Uppington noted that aside from regulations, the performance of AI systems — which require both speed and accuracy — needs to be monitored and measured. In Uppington’s view, anytime software undergoes a new paradigm shift, a new monitoring infrastructure is needed. He argued, however, that the monitoring infrastructure for machine learning is different from other types of software systems that already exist.

Machine learning systems are fundamentally data-driven analytical entities, where models are being iterated at a much more rapid rate than other types of software, he explained.

“The data that you’re seeing in production, becomes the training data for your next iteration,” he said. “So today’s operational data is tomorrow’s training data that’s used to directly Strengthen your product.”

As such, Uppington contends that in order to provide explainable AI, organizations first really need to have the right AI model monitoring in place. The things that a data scientist does to explain and analyze a model during development should be monitored throughout the lifecycle of the model. With that approach, Uppington said that the organization can learn from that operational data and bring it back into the next iteration of the model.

Disrupting the AI market with Intel

The issue of AI quality, or lack thereof, is often seen as a barrier to adoption.

“AI quality and explainability have emerged as huge hurdles for enterprises, ones that often prevent them from achieving a return on their AI investments,” stated Arijit Bandyopadhyay, CTO of enterprise analytics and AI at Intel Corporation, in a media advisory. “In teaming with TruEra, Intel is helping to remove those hurdles by enabling enterprises to access AI evaluation, testing and monitoring capabilities that can help them leverage AI for measurable business impact.”

Uppington noted that as part of his company’s engagement with Intel, it is integrating with cnvrg.io, an Intel company that is building out machine learning training services and software. The goal with the integration is to help make it easier for organizations to build, deploy and monitor AI quality, using the convrg.io platform.

The integration with Intel is not the first, or the only silicon vendor that TruEra has partnered with. Barbara Lewis, chief marketing officer at TruEra, said her company already has a partnership with Nvidia, though she noted that partnership is not as deep as the new Intel Disrupter Initiative.

Looking forward, Uppington said that TruEra will continue to iterate its own technology to further help organizations Strengthen AI quality and accuracy.

“We’re gonna be talking a lot more about just making it easier to systematically test and then do the root-cause analysis of your machine learning systems,” he said.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.

Wed, 03 Aug 2022 08:16:00 -0500 Sean Michael Kerner en-US text/html https://venturebeat.com/business/truera-joins-intel-disruptor-program-to-advance-ai-model-quality/
Killexams : NW Natural Testing Natural Gas in Oregon Turquoise Hydrogen Pilot

Portland, OR-based natural gas utility NW Natural is preparing for carbon neutrality by 2050 with plans to inject hydrogen into its natural gas distribution system. 

NW Natural expects to begin a pilot at its Central Portland facility in early 2023 with technology installations from sustainable heat and power technology company Modern Electron. The pilot would produce turquoise hydrogen, which is created by splitting natural gas through methane pyrolysis into hydrogen and solid carbon. 

“This technology could provide an incredibly elegant and flexible way of producing clean hydrogen – and potentially at a very low cost whenever and wherever we need it on our system to help decarbonize,” said NW Natural’s Kim Heiting, senior vice president of Operations.

The method of methane pyrolysis to be used does not require any electricity, water or other catalyst, according to the utility. 

“The fastest and most economical way to reduce carbon dioxide emissions at a national scale is to effectively leverage existing infrastructure,” said Modern Electron’s Mothusi Pahl, vice president of business development and government affairs. “By decarbonizing natural gas at the point of use, our Modern Hydrogen products help businesses reduce their carbon footprints without the cost and complexity of changing their processes.”

The solid carbon byproduct of the turquoise hydrogen process could be used to create asphalt, construction materials, vehicle tires or soil amendments, NW Natural said.

The utility is aiming to reduce its carbon emissions to net-zero levels by 2050 based on 2015 data, using three scenarios as a guide. Each scenario relies on decarbonizing fossil fuels from NW Natural’s supply mix using carbon capture and sequestration and other technologies. 

In supplanting fossil fuels, NW Natural projects renewable natural gas (RNG) could comprise more than 50% of its supply mix – except in its RNG-constrained scenario, in which RNG supply is capped at about 14 million Dth/year. 

In the RNG-constrained scenario, NW Natural expects hydrogen to comprise about 21.2 million Dth/year of its 70.4 million Dth/year supply mix. In its other two scenarios, NW Natural projects the utility would need 13.2-18 million Dth/year of hydrogen. 

With one of the newest pipeline networks in the country, studies have suggested NW Natural’s “existing pipeline infrastructure could be modified to handle hydrogen blends,” a spokesperson told NGI. “At our Sherwood Operations and Training Center, we’re testing how different blends of hydrogen and natural gas work in our equipment and various types of appliances.”

NW Natural provides natural gas services to about 2.5 million people via 780,000-plus meters in Oregon and southwestern Washington.

Fri, 29 Jul 2022 10:22:00 -0500 en-US text/html https://www.naturalgasintel.com/nw-natural-testing-natural-gas-in-oregon-turquoise-hydrogen-pilot/
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