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Google’s Firebase Predictions, QuickPivot’s machine learning suite Ada, Tensor algebra software Taco, and more in today’s data science news.

Google Firebase in data science news

Google applies machine learning expertise to create Firebase Predictions for user segmentation

At the ongoing 2017 Firebase Dev Summit at Amsterdam, Google has unveiled Firebase Predictions, which can help “predict what users are going to do, before they actually do it.” Firebase Predictions uses machine learning on the analytics data to create dynamic user groups based on users’ predicted behavior. These predictions are automatically available for use with Firebase Remote Config, the Notifications composer, and A/B testing. Google said that with Remote Config, users can boost conversions with a custom experience based on each user’s predicted behavior. And while Notifications composer will deliver the right message to the right user groups, A/B testing can help evaluate the effectiveness of prediction-based strategies.

NVIDIA in News

NVIDIA previews NVDLA deep learning processor it open sourced for deep neural network inference

Recently, NVIDIA had open sourced the NVDLA deep learning processor that was based on the architecture of its “Xavier” automotive processor. A short for “NVIDIA Deep Learning Accelerator,” the NVDLA was created to promote a standard way to design deep learning inference accelerators. Now, at a recent briefing, NVIDIA’s Vice President and General Manager of Autonomous Machines Deepu Talla has explained that the company’s open-sourcing decision was taken into consideration thinking it could expand demand for cloud-based training of deep learning models. Currently, NVDLA is compatible with Linux while it could be ported to other operating systems. The modular NVDLA accelerator architecture includes a convolution core, single data processor, planar data processor, channel data processor, dedicated memory and data reshape engine.

NVIDIA initiates new AI partnerships, training courses for Deep Learning Institute

Expanding the scope of its Deep Learning Institute (DLI), NVIDIA said it is entering into new partnerships with Booz Allen Hamilton and deeplearning. ai to further broaden the range of its training content on artificial intelligence for thousands of students, developers and government specialists. The company has incorporated new University Ambassador Program under which instructors worldwide including professors from Arizona State, Harvard, Hong Kong University of Science and Technology and UCLA, will teach students critical job skills and practical applications of AI at no cost. The new courses will impart domain-specific applications of deep learning for finance, natural language processing, robotics, video analytics and self-driving cars. DLI is also bringing free AI training to young people through the nonprofit organization AI4ALL.


Machine Learning suite Ada in News

QuickPivot incorporates predictive models into marketing campaigns with machine learning suite Ada

To uncover insights that could drive revenue growth, QuickPivot has launched Ada, a machine learning suite of three predictive marketing models. The three models are named Churn, Basket, and Cluster. Churn applies machine learning to calculate whether a customer will churn in 30, 60 or 90 days and understand how to best engage them before it’s too late. Basket increases average customer spend by understanding which of your products are often purchased together. Cluster predicts which purchase behaviors apply to certain demographics, finding both trends and anomalies.

Tensor algebra compiler in News

Taco: ‘Tensor algebra’ software speeds computations involving ‘sparse tensors’ 100-fold

A team of researchers from MIT, French Alternative Energies and Atomic Energy Commission, and Adobe Research have created a new system called “Taco” that automatically produces code optimized for sparse data. Taco stands for tensor algebra compiler, and it speeds up computations 100-fold against the existing software packages. “Sparse representations have been there for more than 60 years,” says Saman Amarasinghe, MIT professor who worked as senior author on the paper. “But nobody knew how to generate code for them automatically. People figured out a few very specific operations—sparse matrix-vector multiply, sparse matrix-vector multiply plus a vector, sparse matrix-matrix multiply, sparse matrix-matrix-matrix multiply. The biggest contribution we make is the ability to generate code for any tensor-algebra expression when the matrices are sparse.”

Other data science news

Pyramid Analytics unveils platform-agnostic analytics OS “Pyramid 2018”

Pyramid Analytics has announced the launch of Pyramid 2018, a server-based, multi-user analytics OS which helps conduct advanced self-service analytics without IT help. Using Pyramid 2018, business users can manage data strategies across any environment (on-premises, in the cloud, or across hybrid deployments), irrespective of the technology (like Oracle, SAP, Microsoft, Big Data, etc.). Pyramid 2018 also offers multiple AI engines and language support such as R, Python, TensorFlow, Weka, MLIB, SAS runtime and others, enabling organizations to integrate machine learning algorithms into their data activities.

IBM launches machine learning tool “Trusteer New Account Fraud” to prevent bank fraud

To help stop bank fraud, IBM has launched a new security tool named “IBM Trusteer New Account Fraud” that will apply machine learning and analytics to identify and stop cyber criminals from opening fraudulent bank accounts. The new tool, which will be added onto the Trusteer Pinpoint Detect portfolio, will bring together the device and network information used to open a new account, specifically looking into both the positive information as well as the negative indicators in the transaction process. The tool also uses behavioural analytics to verify fraud patterns.


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