Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
Alibaba Cloud (Alibaba) has released the source code its Alink machine learning platform on GitHub. Developed by Alibaba, Alink offers a broad range of algorithm libraries that support both batch and stream processing, vital for machine learning tasks such as online product recommendation and intelligent customer services. According to Alibaba, Alink was developed based on Flink, a unified distributed computing engine. With seamless unification of batch and stream processing, Alibaba says Alink offers a more effective platform for developers to perform data analytics and machine learning tasks. The platform supports open-source data storage such as Kafka, HDFS and HBase, as well as Alibaba's proprietary data storage format.
Neural networks are trained to exactly fit the data. Such models usually would be considered as over-fitting, and yet they have managed to obtain high accuracy on test data. It is counter-intuitive -- but it works. This has raised many eyebrows, especially regarding the mathematical foundations of machine learning and their relevance to practitioners. In order to address these contradictions, researchers at OpenAI, in their recent work, double down on this widely believed grand illusion of bigger is better.
The struggle is real, as they say, when it comes to getting machine learning into production. That was one of the big messages of 2019 as enterprises completed successful machine learning pilots but found it much more difficult to put their efforts into production let alone scale them across the whole organization. Even though everyone seems to be working on it, machine learning deployed in production grew at a slower rate between 2018 and 2019, according to Gartner's annual CIO survey. Gartner VP analyst and fellow Rita Sallam is forecasting that enterprises that may have experimented with open source technologies in their pilot efforts will likely turn to commercial artificial intelligence and machine learning platforms to pull together those open source efforts into their enterprise deployment efforts. What's more, enterprises are likely to turn to the AI and ML platforms offered by public cloud providers such as Amazon AWS, Google, and Microsoft Azure.
Responsible Operations is intended to help chart library community engagement with data science, machine learning, and artificial intelligence (AI) and was developed in partnership with an advisory group and a landscape group comprised of more than 70 librarians and professionals from universities, libraries, museums, archives, and other organizations. This research agenda presents an interdependent set of technical, organizational, and social challenges to be addressed en route to library operationalization of data science, machine learning, and AI. Organizations can use Responsible Operations to make a case for addressing challenges, and the recommendations provide an excellent starting place for discussion and action.
Teams that work with open data may feel like they face an explosion of information these days, but there are resources being brought to bear to process such data and stem the tide. Last week's FICO World conference in New York revealed some of the varied ways the credit niche of the financial world tries to apply big data analytics and so-called decision technology. The conference was largely a showcase for data analytics company FICO, but some presentations spoke to a broader context -- using machine learning and other resources to process vast amounts of data. Peter Maynard, senior vice president of data and analytics for strategic client and partner engagement at Equifax spoke about a partnership between his consumer credit reporting agency and FICO. He was joined by Tom Johnson, senior director with FICO, to discuss their joint effort combining data in a platform for decision making.
Machine learning solutions and workflows are meant to save time and vastly improve operational efficiency, but you still need the right human team to ensure every aspect is optimized and running on all cylinders. Before getting started with finding the right people, you should take stock of the business problem at hand. The goal of an ML initiative may be to optimize rote business processes (e.g. No matter the case, it is imperative to first establish how the ML model fits within the greater workflow. Once your organization understands the implications of ML on the business, then it can begin to assemble the optimal team.
Continual learning to build and automate ML pipelines from research to production, automatically retraining models in production with incoming data and advanced monitoring capabilities to ensure that models are accurate, healthy and performing well. Machine learning management that standardizes the full ML process in a collaborative environment, which supports management of models, experiments, data and research for "100% reproducible data science". An open platform that works with any framework or programming language. The platform's advanced connectivity to any compute resources (cloud/on premis) lets companies utilize on-premise infrastructure, including Kubernetes, Data Lakes, Hadoop, and more – as well as scale to any cloud service. Continual learning to build and automate ML pipelines from research to production, automatically retraining models in production with incoming data and advanced monitoring capabilities to ensure that models are accurate, healthy and performing well.
Despite 60% of Marketers Demanding Control of the'Digital Experience', Many Do Not Understand Common Digital Terms Despite 60% of marketers wanting to'own' the digital experience, many admit that they don't fully understand digital terminology such as API, big data and machine learning. The research, which surveyed over 200 IT professionals and 200 marketers, explores the growing disconnect between each group as they struggle to decide who should'own' the emerging digital experience sector. Magnolia found that 24% of marketers don't understand what'machine learning' is, and 23% say they don't know what the term'big data' means. A third of marketers also confess to not know what API stands for. IT teams are also suffering from a similar disconnect, with 77% saying they don't understand the buzzwords marketers use.
India has commenced the project to map the country digitally with a resolution of 10cm via drones and disruptive technologies including AI and big data. The massive task was taken up by the Survey of India a few months ago. The Survey of India is a part of the Department of Science and Technology and has planned to complete the project in two years as stated by Prof. Ashutosh Sharma, Department's Secretary. He also revealed that the Survey of India has been equipped with the latest technologies like drones, AI, big data analytics, image processing, and continuously operating reference system. After the completion of the project, the data will be made available to citizens and Gram Panchayats/local bodies.
Berkeley Lab researchers (from left) Vahe Tshitoyan, Anubhav Jain, Leigh Weston, and John Dagdelen used machine learning to analyze 3.3 million abstracts from materials science papers. Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials. "Without telling it anything about materials science, it learned concepts like the periodic table and the crystal structure of metals," says Jain. "That hinted at the potential of the technique. But probably the most interesting thing we figured out is, you can use this algorithm to address gaps in materials research, things that people should study but haven't studied so far."