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. In this paper, an attempt has been made to reconcile classical understanding and modern practice within a unified performance curve.

Cleveland Clinic is a non-profit academic medical center. Advertising on our site helps support our mission. For over a century, malignant brain tumors such as glioblastoma (GBM) have carried a dismal prognosis. The most recent substantial advance has been provided by surgical resection and chemoradiation followed by adjuvant temozolomide therapy. Yet a problem during the requisite post-treatment surveillance imaging is that the brain's reaction to heavy doses of radiation can mimic the appearance of true tumor progression on MRI (Figure 1).

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.

American localization specialist Lionbridge Technologies has been employing machine translation tools for many years. Eventually, its customers started asking for multilingual training data. Today, Lionbridge has a separate division entirely dedicated to AI, doing everything from collection of chatbot training data to image annotation, audio transcription and even multilingual content moderation services. To find out more about the work of the division, AI Business talked to Aristotelis Kostopoulos, vice president of product solutions, artificial intelligence at Lionbridge. Q: The AI division at Lionbridge grew out of the machine translation business, but today it does so much more.

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.

Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

Organisations are becoming so overwhelmed with data relating to cybersecurity that they are having to turn to artificial intelligence (AI) in order to keep abreast of it all. More than half of them reported that they were using or looking to use AI because their organisations had too much data to deal with. The machine-learning systems can help by processing huge volumes of data in a way that would be impossible for human analysts. Some cyber-attacks can be identified and blocked automatically. The AI can also alert human analysts to areas of data that they should be paying particular attention to, allowing them to respond to threats more effectively.

Automated decision making comes of age.

Consider a world where human decision-making and thought processes play less of a role in the day-to-day functioning of society. Think now of the implications this would have for the security and defence sector. Over the next few decades, it is likely that Artificial Intelligence (AI) will not only have major implications for most areas of society such as healthcare, communications and transport, but also for security and defence. AI can be broadly defined as systems that display intelligent behaviour and perform cognitive tasks by analysing their environment, taking actions and even sometimes learning from experience. The complex attributes of the human mind are well known, but to replicate most of these abilities in machine or algorithmic form has given policymakers and scholars pause for thought.