Tag Archives: Machine deep learning

The Sophos Intercept X with Predictive Protection Briefing

Sophos Intercept X with Predictive Protection Explained!

Sophos today announced the availability of Intercept X with malware detection powered by advanced deep learning neural networks. Join us for a briefing by Sumit Bansal, Sophos Managing Director for ASEAN and Korea!

 

Sophos Intercept X with Predictive Protection

Combined with new active-hacker mitigation, advanced application lockdown, and enhanced ransomware protection, this latest release of the Sophos Intercept X endpoint protection delivers previously unseen levels of detection and prevention.

Deep learning is the latest evolution of machine learning. It delivers a massively scalable detection model that is able to learn the entire observable threat landscape. With the ability to process hundreds of millions of samples, deep learning can make more accurate predictions at a faster rate with far fewer false-positives when compared to traditional machine learning.

This new version of Sophos Intercept X also includes innovations in anti-ransomware and exploit prevention, and active-hacker mitigations such as credential theft protection. As anti-malware has improved, attacks have increasingly focused on stealing credentials in order to move around systems and networks as a legitimate user, and Intercept X detects and prevents this behavior.

Deployed through the cloud-based management platform Sophos Central, Intercept X can be installed alongside existing endpoint security software from any vendor, immediately boosting endpoint protection. When used with the Sophos XG Firewall, Intercept X can introduce synchronized security capabilities to further enhance protection.

 

New Sophos Intercept X Features

Deep Learning Malware Detection

  • Deep learning model detects known and unknown malware and potentially unwanted applications (PUAs) before they execute, without relying on signatures
  • The model is less than 20 MB and requires infrequent updates

Active Adversary Mitigations

  • Credential theft protection – Preventing theft of authentication passwords and hash information from memory, registry, and persistent storage, as leveraged by such attacks as Mimikatz
  • Code cave utilization – Detects the presence of code deployed into another application, often used for persistence and antivirus avoidance
  • APC protection – Detects abuse of Application Procedure Calls (APC) often used as part of the AtomBombing code injection technique and more recently used as the method of spreading the WannaCry worm and NotPetya wiper via EternalBlue and DoublePulsar (adversaries abuse these calls to get another process to execute malicious code)

New and Enhanced Exploit Prevention Techniques

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  • Malicious process migration – Detects remote reflective DLL injection used by adversaries to move between processes running on the system
  • Process privilege escalation – Prevents a low-privilege process from being escalated to a higher privilege, a tactic used to gain elevated system access

Enhanced Application Lockdown

  • Browser behavior lockdown – Intercept X prevents the malicious use of PowerShell from browsers as a basic behavior lockdown
  • HTA application lockdown – HTML applications loaded by the browser will have the lockdown mitigations applied as if they were a browser

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The AWS Masterclass on Artificial Intelligence by Olivier Klein

Just before we flew to Computex 2017, we attended the AWS Masterclass on Artificial Intelligence. It offered us an in-depth look at AI concepts like machine learning, deep learning and neural networks. We also saw how Amazon Web Services (AWS) uses all that to create easy-to-use tools for developers to create their own AI applications at low cost and virtually no capital outlay.

 

The AWS Masterclass on Artificial Intelligence

AWS Malaysia flew in Olivier Klein, the AWS Asia Pacific Solutions Architect, to conduct the AWS Masterclass. During the two-hour session, he conveyed the ease by which the various AWS services and tools allow virtually anyone to create their own AI applications at lower cost and virtually no capital outlay.

The topic on artificial intelligence is rather wide-ranging, covering from the basic AI concepts all the way to demonstrations on how to use AWS services like Amazon Polly and Amazon Rekognition to easily and quickly create AI applications. We present to you – the complete AWS Masterclass on Artificial Intelligence!

The AWS Masterclass on AI is actually made up of 5 main topics. Here is a summary of those topics :

Topic Duration Remark
AWS Cloud and An Introduction to Artificial Intelligence, Machine Learning, Deep Learning 15 minutes An overview on Amazon Web Services and the latest innovation in the data analytics, machine learning, deep learning and AI space.
The Road to Artificial Intelligence 20 minutes Demystifying AI concepts and related terminologies, as well as the underlying technologies.

Let’s dive deeper into the concepts of machine learning, deep learning models, such as the neural networks, and how this leads to artificial intelligence.

Connecting Things and Sensing the Real World 30 minutes As part of an AI that aligns with our physical world, we need to understand how Internet-of-Things (IoT) space helps to create natural interaction channels.

We will walk through real world examples and demonstration that include interactions with voice through Amazon Lex, Amazon Polly and the Alexa Voice Services, as well as understand visual recognitions with services such as Amazon Rekognition.

We will also bridge this with real-time data that is sensed from the physical world via AWS IoT.

Retrospective and Real-Time Data Analytics 30 minutes Every AI must continuously “learn” and be “trained”” through past performance and feedback data. Retrospective and real-time data analytics are crucial to building intelligence model.

We will dive into some of the new trends and concepts, which our customers are using to perform fast and cost-effective analytics on AWS.

In the next two pages, we will dissect the video and share with you the key points from each segment of this AWS Masterclass.

Next Page > Introduction To AWS Cloud & Artificial Intelligence, The Road To AI

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The AWS Masterclass on AI Key Points (Part 1)

Here is an exhaustive list of key takeaway points from the AWS Masterclass on Artificial Intelligence, with their individual timestamps in the video :

Introduction To AWS Cloud

  • AWS has 16 regions around the world (0:51), with two or more availability zones per region (1:37), and 76 edge locations (1:56) to accelerate end connectivity to AWS services.
  • AWS offers 90+ cloud services (3:45), all of which use the On-Demand Model (4:38) – you pay only for what you use, whether that’s a GB of storage or transfer, or execution time for a computational process.
  • You don’t even need to plan for your requirements or inform AWS how much capacity you need (5:05). Just use and pay what you need.
  • AWS has a practice of passing their cost savings to their customers (5:59), cutting prices 61 times since 2006.
  • AWS keeps adding new services over the years (6:19), with over a thousand new services introduced in 2016 (7:03).
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Introduction to Artificial Intelligence, Machine Learning, Deep Learning

  • Artificial intelligence is based on unsupervised machine learning (7:45), specifically deep learning models.
  • Insurance companies like AON use it for actuarial calculations (7:59), and services like Netflix use it to generate recommendations (8:04).
  • A lot of AI models have been built specifically around natural language understanding, and using vision to interact with customers, as well as predicting and understanding customer behaviour (9:23).
  • Here is a quick look at what the AWS services management console looks like (9:58).
  • This is how you launch 10 compute instances (virtual servers) in AWS (11:40).
  • The ability to access multiple instances quickly is very useful for AI training (12:40), because it gives the user access to large amounts of computational power, which can be quickly terminated (13:10).
  • Machine learning, or specifically artificial intelligence, is not new to Amazon.com, the parent company of AWS (14:14).
  • Amazon.com uses a lot of AI models (14:34) for recommendations and demand forecasting.
  • The visual search feature in Amazon app uses visual recognition and AI models to identify a picture you take (15:33).
  • Olivier introduces Amazon Go (16:07), a prototype grocery store in Seattle.
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The Road to Artificial Intelligence

  • The first component of any artificial intelligence is the “ability to sense the real world” (18:46), connecting everything together.
  • Cheaper bandwidth (19:26) now allows more devices to be connected to the cloud, allowing more data to be collected for the purpose of training AI models.
  • Cloud computing platforms like AWS allow the storage and processing of all that sensor data in real time (19:53).
  • All of that information can be used in deep learning models (20:14) to create an artificial intelligence that understands, in a natural way, what we are doing, and what we want or need.
  • Olivier shows how machine learning can quickly solve a Rubik’s cube (20:47), which has 43 quintillion unique combinations.
  • You can even build a Raspberry Pi-powered machine (24:33) that can solve a Rubik’s cube puzzle in 0.9 seconds.
  • Some of these deep learning models are available on Amazon AI (25:11), which is a combination of different services (25:44).
  • Olivier shows what it means to “train a deep learning model” (28:19) using a neural network (29:15).
  • Deep learning is computationally-intensive (30:39), but once it derives a model that works well, the predictive aspect is not computationally-intensive (30:52).
  • A pre-trained AI model can be loaded into a low-powered device (31:02), allowing it to perform AI functions without requiring large amounts of bandwidth or computational power.
  • Olivier demonstrates the YOLO (You Only Look Once) project, which pre-trained an AI model with pictures of objects (31:58), which allows it to detect objects in any video.
  • The identification of objects is the baseline for autonomous driving systems (34:19), as used by Tu Simple.
  • Tu Simple also used a similar model to train a drone to detect and follow a person (35:28).

Next Page > Sensing The Real World, Retrospective & Real-Time Analysis

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The AWS Masterclass on AI Key Points (Part 2)

Connecting Things and Sensing the Real World

  • Cloud services like AWS IoT (37:35) allow you to securely connect billions of IoT (Internet of Things) devices.
  • Olivier prefers to think of IoT as Intelligent Orchestrated Technology (37:52).
  • Olivier demonstrates how the combination of multiple data sources (maps, vehicle GPS, real-time weather reports) in Bangkok can be used to predict traffic as well as road conditions to create optimal routes (39:07), reducing traffic congestion by 30%.
  • The PetaBencana service in Jakarta uses picture recognition and IoT sensors to identify flooded roads (42:21) for better emergency response and disaster management.
  • Olivier demonstrates how easy it is to connect an IoT devices to the AWS IoT service (43:46), and use them to sense the environment and interact with.
  • Olivier shows how the capabilities of the Amazon Echo can be extended by creating an Alexa Skill using the AWS Lambda function (59:07).
  • Developers can create and publish Alexa Skills for sale in the Amazon marketplace (1:03:30).
  • Amazon Polly (1:04:10) renders life-like speech, while the Amazon Lex conversational engine (1:04:17) has natural language understanding and automatic speech recognition. Amazon Rekognition (1:04:29) performs image analysis.
  • Amazon Polly (1:04:50) turns text into life-like speech using deep learning to change the pitch and intonation according to the context. Olivier demonstrates Amazon Polly’s capabilities at 1:06:25.
  • Amazon Lex (1:11:06) is a web service that allows you to build conversational interfaces using natural language understanding (NLU) and automatic speech recognition (ASR) models like Alexa.
  • Amazon Lex does not just support spoken natural language understanding, it also recognises text (1:12:09), which makes it useful for chatbots.
  • Olivier demonstrates that text recognition capabilities in a chatbot demo (1:13:50) of a customer applying for a credit card through Facebook.
  • Amazon Rekognition (1:21:37) is an image recognition and analysis service, which uses deep learning to identify objects in pictures.
  • Amazon Rekognition can even detect facial landmarks and sentiments (1:22:41), as well as image quality and other attributes.
  • You can actually try Amazon Rekognition out (1:23:24) by uploading photos at CodeFor.Cloud/image.
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Retrospective and Real-Time Data Analytics

  • AI is a combination of 3 types of data analytics (1:28:10) – retrospective analysis and reporting + real-time processing + predictions to enable smart apps.
  • Cloud computing is extremely useful for machine learning (1:29:57) because it allows you to decouple storage and compute requirements for much lower costs.
  • Amazon Athena (1:31:56) allows you to query data stored in Amazon S3, without creating a compute instance to do it. You only pay for the TB of data that is processed by that query.
  • Best of all, you will get the same fast results even if your data set grows (1:32:31), because Amazon Athena will automatically parallelise your queries across your data set internally.
  • Olivier demonstrates (1:33:14) how Amazon Athena can be used to run queries on data stored in Amazon S3, as well as generate reports using Amazon QuickSight.
  • When it comes to data analytics, cloud computing allows you to quickly bring massive computing power to bear, achieving much faster results without additional cost (1:41:40).
  • The insurance company AON used this ability (1:42:44) to reduce an actuarial simulation that would normally take 10 days, to just 10 minutes.
  • Amazon Kinesis and Amazon Kinesis Analytics (1:45:10) allows the processing of real-time data.
  • A company called Dash is using this capability to analyse OBD data in real-time (1:47:23) to help improve fuel efficiency and predict potential breakdowns. It also notifies emergency services in case of a crash.

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The NVIDIA Jetson TX2 (Pascal) Tech Report

NVIDIA just announced the Jetson TX2 embedded AI supercomputer, based on the latest NVIDIA Pascal microarchitecture. It promises to offer twice the performance of the previous-generation Jetson TX1, in the same package. In this tech report, we will share with you the full details of the new Pascal-based NVIDIA Jetson TX2!

 

GPUs In Artificial Intelligence

Artificial intelligence is the new frontier in GPU compute technology. Whether they are used to power training or inference engines, AI research has benefited greatly from the massive amounts of compute power in modern GPUs.

The market is led by NVIDIA with their Tesla accelerators that run on their proprietary CUDA platform. AMD, on the other hand, is a relative newcomer with their Radeon Instinct accelerators designed to run on the open-source ROCm (Radeon Open Compute) platform.

 

The NVIDIA Jetson

GPUs today offer so much compute performance that NVIDIA has been able to create the NVIDIA Jetson family of embedded AI supercomputers. They differ from their Tesla big brother in their size, power efficiency and purpose. The NVIDIA Jetson modules are specifically built for “inference at the edge” or “AI at the edge“.

 

Unlike AI processing in the datacenters or in the cloud, AI in the edge refers to autonomous artificial intelligence processing, where there is poor or no Internet access or access must be restricted for privacy or security reasons. Therefore, the processor must be powerful enough for the AI application to run autonomously.

Whether it’s to automate robots in a factory, or to tackle industrial accidents like at the Fukushima Daiichi nuclear plant, AI at the edge is meant to allow for at least some autonomous capability right in the field. The AI in the edge processors must also be frugal in using power, as power or battery life is often limited.

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Hence, processors designed for AI on the edge applications must be small, power-efficient and yet, fast enough to run AI inference in real time. The NVIDIA Jetson family of embedded AI supercomputers promises to tick all of those boxes. Let’s take a look :

Next Page > The NVIDIA Jetson TX2, Specification Comparison, Price & Availability

 

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The NVIDIA Jetson TX2

The NVIDIA Jetson TX2 is the second-generation Jetson embedded AI module, based on the latest NVIDIA Pascal microarchitecture. It supersedes (but not replace) the previous-generation Jetson TX1, which was built on the NVIDIA Maxwell microarchitecture and released in November 2015.

Thanks to the faster and more power-efficient Pascal microarchitecture, the NVIDIA Jetson TX2 promises to be twice as energy-efficient as the Jetson TX1.

This means the developers switching to the Jetson TX2 can now opt to maximise power efficiency, or to maximise performance. In the Max-Q mode, the Jetson TX2 will use less than 7.5 W, and offer Jetson TX1-equivalent performance. In the Max-P mode, the Jetson TX2 will use less than 15 W, and offer up to twice the performance of the Jetson TX1.

 

NVIDIA Jetson Specification Comparison

The NVIDIA Jetson modules are actually built around the NVIDIA Tegra SoCs, instead of their GeForce GPUs. The Tegra SoC is a System On A Chip, which integrates an ARM CPU, an NVIDIA GPU, a chipset and a memory controller on a single package.

The Tegra SoC and the other components on a 50 x 87 mm board are what constitutes the NVIDIA Jetson module. The Jetson TX1 uses the Tegra X1 SoC, while the new Jetson TX2 uses the Tegra P1 SoC.

For those who have been following our coverage of the AMD Radeon Instinct, and its support for packed math, the NVIDIA Jetson TX2 and TX1 modules support FP16 operations too.

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NVIDIA Jetson TX2 Price & Availability

The NVIDIA Jetson TX2 Developer Kit is available for pre-order in the US and Europe right now, with a US$ 599 retail price and a US$ 299 education price. Shipping will start on March 14, 2017. This developer’s kit will be made available in APAC and other regions in April 2017.

The NVIDIA Jetson TX2 module itself will only be made available in the second quarter of 2017. It will be priced at US$ 399 per module, in quantities of 1,000 modules or more.

Note that the Jetson TX2 modules are exactly the same size and uses the same 400-pin connector. They are drop-in compatible replacements for the Jetson TX1 modules.

Next Page > NVIDIA Jetson TX1 Price Adjustments, NVIDIA Jetpack 3.0, The Slides

 

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NVIDIA Jetson TX1 Price Adjustments

With the launch of the Jetson TX2, NVIDIA is adjusting the price of the Jetson TX1. The Jetson TX1 will continue to sell alongside the new Jetson TX2.

The NVIDIA Jetson TX1 Developer Kit has been reduced to US$ 499, down from US$ 599.

The NVIDIA Jetson TX1 production has been reduced to US$ 299, down from US$ 399. Again, this is in quantities of 1,000 modules or more.

 

NVIDIA Jetpack 3.0

The NVIDIA Jetson is more than just a processor module. It is a platform that is made up of developer tools and codes, as well as APIs. Like AMD offers their MIOpen deep learning library, NVIDIA offers Jetpack.

In conjunction with the launch of the Jetson TX2, NVIDIA also announced the NVIDIA Jetpack 3.0. It promises to offer twice the system performance of Jetpack 2.3.

Jetpack 3.0 is not just for the new Jetson TX2. It will offer a nice boost in performance for existing Jetson TX1 users and applications.

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The Presentation Slides

For those who want the full set of NVIDIA Jetson TX2 slides, here they are :

 

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The Complete AMD Radeon Instinct Tech Briefing Rev. 3.0

The AMD Tech Summit held in Sonoma, California from December 7-9, 2016 was not only very exclusive, it was highly secretive. The first major announcement we have been allowed to reveal is the new AMD Radeon Instinct heterogenous computing platform.

In this article, you will hear from AMD what the Radeon Instinct platform is all about. As usual, we have a ton of videos from the event, so it will be as if you were there with us. Enjoy! 🙂

Originally published @ 2016-12-12

Updated @ 2017-01-11 : Two of the videos were edited to comply with the NDA. Now that the NDA on AMD Vega has been lifted, we replaced the two videos with their full, unedited versions. We also made other changes, including adding links to the other AMD Tech Summit articles.

Updated @ 2017-01-20 : Replaced an incorrect slide, and a video featuring that slide. Made other small updates to the article.

 

The AMD Radeon Instinct Platform Summarised

For those who want the quick low-down on AMD Radeon Instinct, here are the key takeaway points :

  • The AMD Radeon Instinct platform is made up of two components – hardware and software.
  • The hardware components are the AMD Radeon Instinct accelerators built around the current Polaris and the upcoming Vega GPUs.
  • The software component is the AMD Radeon Open Compute (ROCm) platform, which includes the new MIOpen open-source deep learning library.
  • The first three Radeon Instinct accelerator cards are the MI6, MI8 and MI25 Vega with NCU.
  • The AMD Radeon Instinct MI6 is a passively-cooled inference accelerator with 5.7 TFLOPS of FP16 processing power, 224 GB/s of memory bandwidth, and a TDP of <150 W. It will come with 16 GB of GDDR5 memory.
  • The AMD Radeon Instinct MI8 is a small form-factor (SFF) accelerator with 8.2 TFLOPS of processing power, 512 GB/s of memory bandwidth, and a TDP of <175 W. It will come with 4 GB of HBM memory.
  • The AMD Radeon Instinct MI25 Vega with NCU is a passively-cooled training accelerator with 25 TFLOPS of processing power, support for 2X packed math, a High Bandwidth Cache and Controller, and a TDP of <300 W.
  • The Radeon Instinct accelerators will all be built exclusively by AMD.
  • The Radeon Instinct accelerators will all support MxGPU SRIOV hardware virtualisation.
  • The Radeon Instinct accelerators are all passively cooled.
  • The Radeon Instinct accelerators will all have large BAR (Base Address Register) support for multiple GPUs.
  • The upcoming AMD Zen “Naples” server platform is designed to supported multiple Radeon Instinct accelerators through a high-speed network fabric.
  • The ROCm platform is not only open source, it will support a multitude of standards in addition to MIOpen.
  • The MIOpen deep learning library is open source, and will be available in Q1 2017.
  • The MIOpen deep learning library is optimised for Radeon Instinct, allowing for 3X better performance in machine learning.
  • AMD Radeon Instinct accelerators will be significantly faster than NVIDIA Titan X GPUs based on the Maxwell and Pascal architectures.

In the subsequent pages, we will give you the full low-down on the Radeon Instinct platform, with the following presentations by AMD :

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We also prepared the complete video and slides of the Radeon Instinct tech briefing for your perusal :

Next Page > Heterogenous Computing, The Radeon Instinct Accelerators, MIOpen, Performance

 

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Why Is Heterogenous Computing Important?

Dr. Lisa Su, kicked things off with an inside look at her two-year long journey as AMD President and CEO. Then she revealed why Heterogenous Computing is an important part of AMD’s future going forward. She also mentioned the success of the recently-released Radeon Software Crimson ReLive Edition.

 

Here Are The New AMD Radeon Instinct Accelerators!

Next, Raja Koduri, Senior Vice President and Chief Architect of the Radeon Technologies Group, officially revealed the new AMD Radeon Instinct accelerators.

 

The MIOpen Deep Learning Library For Radeon Instinct

MIOpen is a new deep learning library optimised for Radeon Instinct. It is open source and will become part of the Radeon Open Compute (ROCm) platform. It will be available in Q1 2017.

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The Performance Advantage Of Radeon Instinct & MIOpen

MIOpen is optimised for Radeon Instinct, offering 3X better performance in machine learning. It allows the Radeon Instinct accelerators to be significantly faster than NVIDIA Titan X GPUs based on the Maxwell and Pascal architectures.

Next Page > Radeon Instinct MI25 & MI8 Demos, Zen “Naples” Platform, The First Servers, ROCm Discussion

 

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The Radeon Instinct MI25 Training Demonstration

Raja Koduri roped in Ben Sander, Senior Fellow at AMD, to show off the Radeon Instinct MI25 running a training demo.

 

The Radeon Instinct MI8 Visual Inference Demonstration

The visual inference demo is probably much easier to grasp, as it is visual in nature. AMD used the Radeon Instinct MI8 in this example.

 

The Radeon Instinct On The Zen “Naples” Platform

The upcoming AMD Zen “Naples” server platform is designed to supported multiple AMD Radeon Instinct accelerators through a high-speed network fabric.

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The First Radeon Instinct Servers

This is not a vapourware launch. Raja Koduri revealed the first slew of Radeon Instinct servers that will hit the market in H1 2017.

 

The Radeon Open Compute (ROCm) Platform Discussion

To illustrate the importance of heterogenous computing on Radeon Instinct, Greg Stoner (ROCm Senior Director at AMD), hosted a panel of AMD partners and early adopters in using the Radeon Open Compute (ROCm) platform.

Next Page > Closing Remarks On Radeon Instinct, The Complete Radeon Instinct Tech Briefing Video & Slides

 

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Closing Remarks On Radeon Instinct

Finally, Raja Koduri concluded the launch of the Radeon Instinct Initiative with some closing remarks on the recent Radeon Software Crimson ReLive Edition.

 

The Complete AMD Radeon Instinct Tech Briefing

This is the complete AMD Radeon Instinct tech briefing. Our earlier video was edited to comply with the AMD Vega NDA (which has now expired).

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The Complete AMD Radeon Instinct Tech Briefing Slides

Here are the Radeon Instinct presentation slides for your perusal.

 

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SMU Deploys NVIDIA DGX-1 Supercomputer For AI Research

Singapore, 30 November 2016NVIDIA today announced that Singapore Management University (SMU) is the first organisation in Singapore and Southeast Asiato deploy an NVIDIA DGX-1 deep learning supercomputer.

Deployed at the SMU Living Analytics Research Center (LARC), the supercomputer will further research on applying artificial intelligence (AI) for Singapore’s Smart Nation project. Established in 2011, LARC aims to innovate technologies and software platforms that are relevant to Singapore’s Smart Nation efforts. LARC is supported and funded by the National Research Foundation (NRF).

 

NVIDIA DGX-1

The NVIDIA DGX-1 is the world’s first deep learning supercomputer to meet the computing demands of AI. It enables researchers and data scientists to easily harness the power of GPU-accelerated computing to create a new class of computers that learn, see and perceive the world as humans do.

Providing through put equivalent to 250 conventional servers in a single box, the supercomputer delivers the highest levels of computing power to drive next-generation AI applications, allowing researchers to dramatically reduce the time to train larger, more sophisticated deep neural networks.

Built on NVIDIA Tesla P100 GPUs that use the latest Pascal GPU architecture, the DGX-1 supercomputer will enable SMU to conduct a range of AI research projects for Smart Nation. One of the featured projects is a food AI application to achieve smart food consumption and healthy lifestyle, which requires the analysis of a large number of food photos.

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“This project involves the processing of large amounts of unstructured and visual data. Food photo recognition is not possible without the DGX-1 solution, which applies cutting-edge deep learning technologies and yields excellent recognition accuracy,” said Professor Steven Hoi, School of Information Systems, SMU.

The first phase of the food AI project is able to recognise 100 of the most popular local dishes in Singapore. The next phase is to expand the current food database to about 1,000 popular food dishes in Singapore. In addition to the recognition of food photos, the team will also analyse food data in supermarkets to help with the recommendation of healthy food options.Once developed, the food AI solution will be made available to developers through an API for them to build smart food consumption solutions.

“SMU has been an NVIDIA GPU Research Center using Tesla GPUs for several years. The NVIDIA DGX-1 will give SMU researchers the performance and deep learning capabilities needed to work on their Smart Nation projects, which will further advance Singapore’s aspirations,” said Raymond Teh, vice president of sales and marketing for Asia Pacific, NVIDIA.

 

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Becoming A Data Ninja With Microsoft SQL Server 2016

Malaysia is at the cusp of a data revolution. Cloud computing as well as big data analytics and intelligence will play a central role in this data revolution. Data science will transform entire industries in a new world in which data is the new currency. To support the transition to a data-centric world, the Microsoft SQL Server has evolved beyond its database roots, into a full-fledged business analytics and data management platform.

On the 8th of April, 2016, Microsoft Malaysia officially launched the new Microsoft SQL Server 2016 to support this transition to a data-centric world. Four key figures from Microsoft and the Multimedia Development Corporation came together to address the expansion of data science in Malaysia, and how SQL Server 2016 will help companies achieve their goals faster and easier than ever before.

 

Microsoft Malaysia On The Launch Of SQL Server 2016

In this video, Michal Golebiewski, Chief Marketing & Operations Officer, Microsoft Malaysia explains Microsoft’s mission and how they aim to help companies transform their operations to a mobile-first, data-first environment with Microsoft SQL Server 2016.

 

Dr. Karl Ng (MDeC) On Data Science In Malaysia

Ir Dr. Karl Ng Kah Hou, Director, Innovation Capital Division, The Multimedia Development Corporation (MDeC) then spoke on the push for data science in Malaysia. He also addressed questions about MDeC’s initiatives on data science in Malaysia. Check it out :

 

How To Become A Data Scientist

Microsoft even brought in a real-life data scientist – Julian Lee. An Advance Analytics Technical Solutions Professional in Microsoft Asia, he gave us a riveting account of his experience as a data scientist in Malaysia. A must-watch for those who are thinking of taking this exciting new career path.

 

Microsoft SQL Server 2016’s Key Features

Ending this exclusive event was Darmadi Komo, the Director of Microsoft’s Data Group Marketing. He flew in all the way from the Microsoft campus at Redmond to share with us the key features of the new Microsoft SQL Server 2016.

He also shared with us the fabulous offer from Microsoft – free SQL Server 2016 licences for companies that want to migrate from Oracle. The offer’s good up to June 2016, so contact Microsoft Malaysia ASAP if you are interested!

The new Microsoft SQL Server 2016 supports hybrid transactional / analytical processing, advanced analytics and machine learning, mobile Business Intelligence, data integration, always encrypted query processing capabilities and in-memory transactions with persistence.

  • Stretch Database technology is an industry first and allows customers to dynamically stretch warm and cold transactional data to Azure so operational data is always accessible. This also enables customers to have a cost-effective data strategy while protecting their customer’s sensitive data.
  • Advanced analytics using our new R support that enables customers to do real-time predictive analytics on both operational and analytic data.
  • Ground-breaking security encryption capabilities that enable data to always be encrypted at rest, in motion and in-memory to deliver maximum security protection.
  • In-memory database support for every workload with performance increases up to 30-100x.
  • Business Intelligence for every employee on every device – including new mobile BI support for iOS, Android and Windows Phone devices.
  • Available on Linux in private preview, making SQL Server 2016 more accessible to a broader set of users
  • Unique cloud capabilities that enable customers to deploy hybrid architectures that partition data workloads across on-premises and cloud based systems to save costs and increase agility
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