29th Dec.’ 17 – HeadlinesAlok Dhuri
Nvidia’s bye-bye to 32-bit operating systems, New SVM library ThunderSVM, ClustrixDB 9, and Microsoft-Litbit AI partnership on Kubernetes among today’s top stories in data science.
The last remnants of 32-bit machines..
The transition of the PC industry from 32-bit to 64-bit is almost in the final stages of completion. NVIDIA has announced that it will stop developing drivers supporting 32-bit operating systems for any GPU architecture in the near future.
NVIDIA driver version 390 will be the final drivers from the company that will support 32-bit Windows 7/8/8.1/10, Linux or FreeBSD. Whatever version comes after, it will only run on 64-bit versions of OSes. The company will continue to release 32-bit drivers containing security fixes till January 2019, but has no plans to improve the performance or add features to such releases.
SVM is a short for Support Vector Machine, a machine learning technique used typically for classification and regression. SVMs have been used in various applications including spam filtering, document classification, network attack detection etc.
ThunderSVM is a library that has been created to help users apply SVMs to solve problems. It exploits GPUs and multi-core CPUs to achieve high efficiency.
- Support all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs.
- Use same command line options as LibSVM.
- Support Python, R and Matlab interfaces.
- Supported Operating Systems: Linux, Windows and MacOS
- CUDA 7.5 or above | cmake 2.8 or above | gcc 4.8 or above
git clone firstname.lastname@example.org:zeyiwen/thundersvm.git
ThunderSVM uses the same command line options as LibSVM, so existing users of LibSVM can use ThunderSVM quickly. For new users of SVMs, the user guide provided in the LibSVM website can be helpful for training.
So if you are one of the few still holding on to your 32-bit version of Windows (and specially if you’re a gamer) it’s time to upgrade!
Microsoft bets on Kubernetes to push the boundaries of cloud-based artificial intelligence
Microsoft collaborates with AI specialist Litbit on a system that uses Kubernetes to automatically scale unpredictable machine learning workloads
Microsoft unveiled a new auto-scaling system that uses Kubernetes, the popular open-source container orchestration platform, to expand or shrink the amount of cloud-computing resources required for learning training workloads. The system was developed in partnership with Litbit, a California based technology startup that uses Internet of Things data to create “AI Personas” that workplaces can use to augment the capabilities of their employees based on their collective experiences and know-how. For instance, an organization can create and train a persona that helps its field technicians detect and diagnose equipment problems before jumping in a work truck and physically visit machinery that is acting up to save time and expense.
“Some of these training jobs like Spark ML make heavy use of CPUs, while others like TensorFlow make heavy use of GPUs. In the latter case, some jobs retrain a single layer of the neural net and finish very quickly, while others need to train an entire new neural net and can take several hours or even days,” Microsoft representatives said in a blog post.
Newest ClustrixDB Supports Modern Data
ClustrixDB 9 supports modern data features like JSON, Fractional-second Events and Generated Columns without sacrificing performance, scalability and ACID compliance
Clustrix has announced that its most recent generally-available release, ClustrixDB 9.0, handles sophisticated, modern data including semi-structured data, fractional-second events, and generated columns. ClustrixDB 9 will continue to deliver the advantages of performance, scalability without sharding or replication, and ACID compliance.
“Making the application developer’s job easier by putting functionality and logic into the database instead of application code has always been a significant part of the Clustrix mission,” said Mike Azevedo, CEO of Clustrix. “With ClustrixDB 9, developers who want to innovate do not have to choose between an RDBMS that has these features but does not scale well and a NoSQL database that has to relax availability and consistency in order to scale.”
Read more about ClustrixDB here: https://www.clustrix.com/scaleout-database/
The first US-China blockchain conference
Next month’s Blockchain Connect Conference will bring together the two powerhouses in the fast-moving blockchain space
The Blockchain Connect Conference will be held in San Francisco on Jan 26, 2018.
This will bring together over 1000 scientists, entrepreneurs, investors and developers from all over the world for a day of blockchain discussion.
Click here to register for the conference.
The most important use case: AI, Machine Learning, Predictive Analytics for patient’s resque
CLEW Medical unveils AI Predictive Platform, leveraging untapped patient data to provide actionable tracking for patients at risk
Previously known as Intensix, CLEW Medical is launching its artificial intelligence powered predictive analytics platform to prevent life threatening complications in all care settings, using real-time data and machine learning technology. Already proven in the ICU, CLEW’s platform provides medical staff and healthcare administrators with actionable clinical, operational and financial insights to streamline medical care. The average digital footprint of a patient includes 300 unique data elements, some of which are measured every few milliseconds. The amount of data is sometimes too big to analyze in the time that critical decisions need to be made by medical professionals. This is where CLEW’s centralized AI platform offers healthcare providers with predictive insights for the health conditions of all admitted patients in different departments.
“More and more we’re seeing hospitals around the world adapting to the digital age of medical technology, and we’d like to make this digital transformation in healthcare as efficient as possible by leveraging data that’s already available to us,” Founder and CEO Gal Salomon said. “With our advanced clinical ICU-tested algorithms that customizes physiological models and predicts patients deterioration before it happens, our goal is to bring hospitals into the future of medical care, and redefine healthcare delivery.”