OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning, computational game theory, and search.

It is becoming increasingly clear that the big tech giants such as Google, Facebook, and Microsoft are extremely generous with their latest machine learning algorithms and packages (they give those away freely) because the entry barrier to the world of algorithms is pretty low right now. The open source community and tools (such as scikit-earn) have come a long way, and plenty of open source initiatives are propelling the vehicles of data science, digital analytics, and machine learning. Standing in 2019, we can safely say that algorithms, programming frameworks, and machine learning packages (or even tutorials and courses how to learn these techniques) are not the scarce resource but high-quality data is. This often becomes a thorny issue on the side of the practitioners in data science (DS) and machine learning (ML) when it comes to tweaking and fine-tuning those algorithms. It will also be wise to point out, at the very beginning, that the current article pertains to the scarcity of data for algorithmic investigation, pedagogical learning, and model prototyping.

Today HPE announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments. The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days. Only operational machine learning models deliver business value," said Kumar Sreekanti, SVP and CTO, Hybrid IT at HPE. "And with HPE ML Ops, we provide the only enterprise-class solution to operationalize the end-to-end machine learning lifecycle for on-premises and hybrid cloud deployments. The new HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE's Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments.

BENGALURU: The Central Board of Secondary Education (CBSE) had earlier announced the introduction of Artificial Intelligence (AI) as an elective for students in classes 9 to 12. The curriculum for the subject is now being developed from scratch by a team from IBM India along with members of its global team and other subject experts. To begin with, IBM will conduct a pilot project in 1,000 schools in Bengaluru, Delhi, Kolkata, Bhubaneswar, Hyderabad and Chennai, before finalising the curriculum and embedding it in the CBSE curriculum from the next academic year. The pilot is being launched in Delhi on Wednesday. The project will start with creating awareness for school principals, followed by a two-and-a half day training of teachers on the foundational skills for the subject.

RV-SKILLS focuses on training and research, with special emphasis on emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and Automotive Electronics (AE). Limited, RV-SKILLS is set to emerge as the one-stop destination for people pursuing excellence in emerging fields like AI, ML, AE and VLSI design under the tutelage of industry experts. Venkatesh Prasad, Group CEO-RV SKILLS, said, "Technology is advancing at a rapid pace and it is becoming increasingly difficult for people to stay ahead of the learning curve. Today, beyond the realm of engineering education, AI, Analytics, Big Data, ML and AE, are making a big impact on several core sectors. It becomes imperative for us to keep pace with emerging technologies if we have to remain innovative and competitive."

While students may have a career in mind, seldom if ever do they understand the nuances of the path they will need to take to be successful, nor do they have an understanding of the options available to them. On the other hand, academic organizations might lack the input to optimize their programs based on goals, objectives and popular competencies.

Over 120 million workers will have to be retrained within the next three years due to mass disruption caused by AI and automation. That's according to a new study by the IBM Institute for Business Value, based on input from 5,670 global executives in 48 countries. The issue is not just one of jobs being lost, but the lack of a strong skills base to underpin the emerging technologies, with the institute warning that the time it takes to close a skills gap through training is growing. Interestingly, the report finds that executives see technical core STEM capabilities – along with basic computer and software skills – as significantly less important than they did three years ago. According to the global research, the time it takes to close a skills gap through training has increased ten-fold in just four years. In 2014, it took three days on average to close a capability gap through training in the enterprise; in 2018, it took 36 days.

This story is part of a series on how we learn--from augmented reality to music-training devices. By now you must have heard the good news about our savior, artificial intelligence. It makes you look better in selfies, prevents blindness, and can even turn water into tastier beer. Tech giants and governments say we're living in a golden age of AI. Truth is, most times you hear the term artificial intelligence, the specific technology at work is called machine learning.

So, a few months ago, I was walking down the street in Shenzhen, in the Guangdong Province of southeastern China. I was hungry and looking for lunch. Armed with my credit card and plenty of the local currency, I strode out of my hotel to check out the many street vendors selling delicious-smelling food. Using Google Translate, I was able to order a fried fish dish, but when I went to pay, the vendor refused my credit card. Undaunted I pulled out cash, but that too was refused. The guy pointed me to a large QR code and asked me to pay using the WeChat app. As this was my first day in China, I hadn't yet set the app to pay for things, so I walked away, a little embarrassed. Still hungry, I came to a large junction and saw a promising looking restaurant across a busy street.