Juniper’s advancement on self-driving networks, Chain’s new compiler, DeepMind’s NarrativeQA, and Microsoft SQL Operation Studio new release among today’s top stories in machine learning, artificial intelligence, and data science news.
An ‘intelligent’ CRM to tap China’s new retail wave
Gridsum announces AI-powered Intelligent CRM Solution
Web analytics company Gridsum Holding said it’s launching an AI-driven, Software as a Service (SaaS), Intelligent CRM Solution for the China market. The Intelligent CRM Solution is a cloud-based, marketing-centric CRM solution tailor-made for both multinational and local companies operating in China. The solution leverages Gridsum’s artificial intelligence engine “Gridsum Prophet” and its marketing automation suite, combining the company’s expertise in big data analytics and AI. The solution will also look to leverage Blockchain technologies in a number of specific areas, the company said.
CEO Guosheng Qi said Gridsum is launching the solution after “substantial research, development and real world operation.” He noted how the Chinese consumer market is undergoing a ‘new retail’ bubble, and that needs to be ably backed with an automated machine learning-driven CRM solution. “By converging a client’s business intelligence data with Gridsum Prophet and our marketing automation suite, we will be able to substantially drive sales efficiency with significant, immediate and quantifiable KPI enhancement for our clients,” he said.
Bringing Self-Driving Network closer to reality—crawl, walk and run to a fully automated environment!
Juniper introduces AI bots to “intent-based” networks
In what could translate intent into automated workflows, Juniper Networks has announced the first set of ‘intelligent’ software bots in its bid towards “self-driving networks” – autonomous enterprise networks that can configure, monitor, and manage themselves automatically, with little human intervention.
The three Juniper Bots are: a Contrail TestBot, which allows operators to test network changes before they are applied; AppFormix HealthBot, which uses machine learning to analyze network health and provide suggestions for improvements; and Contrail PeerBot, which automates network peering.
Contrail PeerBot automates the process of network peering. This makes it easier to manage multiple Border Gateway Protocol (BGP) domains, simplifies policy enforcement and enables on-demand scaling. Contrail TestBot enables network professionals to shift to a DevOps approach for continuous integration/continuous deployment of network resources. The Bots can be used to automate auditing and provisioning modifications of the network. Whereas AppFormix HealthBot uses machine learning to track the fitness and health of the network by leveraging AppFormix to collect real-time network data used to discover new insights.
Simplifying Smart Contracts on Bitcoin Blockchain
Chain launches new open-source compiler and developer environment for writing Bitcoin smart contracts using Ivy
Blockchain tech startup Chain has released an open-source compiler that translates between Ivy, Chain’s own high-level smart contract language, and Bitcoin Script, the low-level programming language of the world’s first and largest blockchain. Breaking the announcement in its official blog post, Chain said Ivy aims to help developers “write custom, SegWit-compatible bitcoin addresses that enforce arbitrary combinations of conditions supported by the bitcoin protocol, including signature checks, hash commitments, and timelocks.” Chain has put a note of caution that the Ivy language is more for educational and research purposes as it’s an untested prototype software as of now. Ivy was introduced in a public demo in December 2016.
Amazon expands geographical scope of AMIs
AWS Deep Learning AMIs now available in 4 new regions across China, Europe and Asia Pacific
Amazon Web Services has announced that its Deep Learning AMIs are now available in four new AWS Regions: Beijing, Frankfurt, Singapore, and Mumbai.
The AMIs, elaborated as Amazon Machine Images, provide machine learning practitioners with the necessary infrastructure and tools to quickly start experimenting with deep learning models. The AMIs come with pre-built packages of popular deep learning frameworks including Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, and Keras. In addition, to expedite development and model training, the AMIs are pre-configured with NVIDIA CUDA and cuDNN drivers, and are optimized for GPU acceleration on Amazon EC2 P2 and P3 instances.
We wrote about AWS AMIs last month.
SQL Operations Studio: First major update
The December Public Preview of SQL Operations Studio is now available
This year at Connect(), SQL Operations Studio was announced for Public Preview, and now Microsoft has announced its December release. SQL Operations Studio is a data management tool that enables users to work with SQL Server, Azure SQL DB and SQL DW from Windows, macOS and Linux. More details about it is available on GitHub.
The December release includes several major repo updates and feature releases, including:
- Migrating SQL Ops Studio Engineering to public GitHub repo
- Azure Integration with Create Firewall Rule
- Windows Setup and Linux DEB/RPM installation packages
- Manage Dashboard visual layout editor
- “Run Current Query with Actual Plan” command
For complete updates, refer to the Release Notes.
DeepMind’s NarrativeQA for complex narratives..
Introducing NarrativeQA: human questions & answers about entire books, plays and movies to help improve understanding of complicated narratives
DeepMind has launched a new dataset that reads comprehensions and solves challenging questions. It is called NarrativeQA. This dataset includes fictional stories from books and movie scripts, with human written questions and answers based solely on human-generated abstract summaries. The NarrativeQA dataset is divided into three parts:
- non-overlapping training
- Validation
- testing
For complete details on the NarrativeQA dataset, please refer to the research paper at arXiv.