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Alibaba’s 11-qubit quantum computer, InfluxDB’s support for ephemeral data, regres releases on CRAN, OpenAI’s MADDPG, and more in today’s top stories around machine learning, blockchain, and data science news.

1. Alibaba launches its 11-qubit quantum computing service

Alibaba has progressed towards quantum computing with the launch of its 11-qubit quantum computing service. This is a joint venture between Alibaba’s cloud service subsidiary Aliyun and the Chinese Academy of Sciences. This service is available to the public on the Quantum Computing Cloud Platform. Alibaba Quantum Lab (AQL) has also released an ambitious 15-year roadmap. By 2025, it expects to have built quantum computers that will be the world’s fastest by today’s measure. By 2030, AQL hopes to achieve a general quantum computing prototype with 50–100 qubits. Aliyun is also offering a new 32-qubit quantum computer simulation service. By comparing simulated experiment results with real results on quantum computers, users can measure the latter’s performance, verify correctness, etc.

2. InfluxDB adds support for ephemeral data to its databases

InfluxData Inc. have updated their Time-series database platforms with support for Ephemeral data. Ephemeral data refers to data that only exists for a very short period of time. It is increasingly being generated by new technology deployments such as software containers, Kubernetes and IoT sensors. The nature of this data makes it troublesome for existing database solutions to keep up with the influx of this temporary data. To counter this issue, InfluxData has built two time-series databases called InfluxDB and InfluxEnterprises, which are designed to query time-stamped metrics, events and measurements more efficiently than traditional relational databases. InfluxDB boasts significant number of users, including IBM Corp. which uses the platform to analyze operational information in real time.

3. regres releases on CRAN

regres is now released in CRAN. reqres is a new (in R context) approach to working with HTTP messages, that is, the requests send to a server and the response it returns. There are two main objects in reqres, the Request class and the Response class. Both of these are built on R6 and heavily inspired by the request and response classes in Express.js (a web server framework for Node.js). With regres launched in CRAN, working directly with HTTP messages will be simplified as reqres takes care of the minimum requirements letting the developers focus on the server logic instead.

4. Open AI releases MADDPG, an algorithm for multi-agent reinforcement learning

Open AI researchers have developed a new algorithm for centralized learning and decentralized execution in multiagent environments. Called the MADDPG, this algorithm allows agents to learn to collaborate and compete with each other. MADDPG extends a reinforcement learning algorithm called DDPG, taking inspiration from actor-critic reinforcement learning techniques. They treat each agent as an “actor”, and each actor gets advice from a “critic” that helps the actor decide what actions to reinforce during training. More information is available at the OpenAI blog.

5. ServiceNow launches Agent Intelligence to make machine learning more accessible to organizations

ServiceNow have added machine-learning capabilities directly into the Now Platform, making it accessible to all their cloud services and other applications built on ServiceNow. Their Agent Intelligence ML solution will automate the categorization, prioritization and assignment of work to reduce resolution times, minimize human error and improve customer satisfaction. It can also quickly classify and route requests with fewer errors, increasing agent productivity. Agent Intelligence will initially be applied to improving the speed and quality of IT and customer-service processes.


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