A subcategory of machine learning, deep learning uses multi-layered neural networks to automate historically difficult machine tasks--such as image recognition, natural language processing (NLP), and machine translation--at scale. TensorFlow, which emerged out of Google in 2015, has been the most popular open source deep learning framework for both research and business. But PyTorch, which emerged out of Facebook in 2016, has quickly caught up, thanks to community-driven improvements in ease of use and deployment for a widening range of use cases. PyTorch is seeing particularly strong adoption in the automotive industry--where it can be applied to pilot autonomous driving systems from the likes of Tesla and Lyft Level 5. The framework also is being used for content classification and recommendation in media companies and to help support robots in industrial applications.

The graph represents a network of 1,962 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 31 August 2020 at 11:18 UTC. The requested start date was Monday, 31 August 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 9-day, 5-hour, 22-minute period from Friday, 21 August 2020 at 06:23 UTC to Sunday, 30 August 2020 at 11:46 UTC.

By now most of us understand that, in our current era, artificial intelligence (AI) and its subset machine learning (ML) have little to do with human intelligence. AI/ML is all about recognizing patterns in data and automating discrete tasks, from algorithms that flag fraudulent financial transactions to chatbots that answer customer questions. IT leaders appreciate the enormous potential. According to a CIO Tech Poll of IT leaders published in February, AI/ML was considered the most disruptive technology by 62 percent of respondents and the technology with the greatest impact by 42 percent – in both cases double the percentage of AI/ML's nearest rival, big data analytics. An impressive 18 percent already had an AI/ML solution in production.

The graph represents a network of 1,554 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 13 July 2020 at 10:38 UTC. The requested start date was Monday, 13 July 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 22-hour, 34-minute period from Saturday, 11 July 2020 at 01:18 UTC to Sunday, 12 July 2020 at 23:53 UTC.

H2O WORLD SAN FRANCISCO – H2O.ai, the open source leader in AI and ML, today announced new and innovative capabilities for its data science and machine learning platforms, H2O, AutoML and H2O Driverless AI, to address the critical scalability and performance needs of all organizations. As part of these new capabilities, and to further the company's mission to democratize AI, H2O.ai has added several new algorithms that address common use cases that customers need today. In addition, H2O Driverless AI is a winner of InfoWorld's 2019 Technology of the Year for the second year in a row. The award honors and recognizes the best in software development, cloud computing, big data analytics, and machine learning tools. This year's judging panel recognized H2O Driverless AI for outpacing all other vendors with "automated simplicity" of its algorithms that do the heavy lifting of feature engineering, model selection, training and optimization – enabling even non-AI experts to uncover hidden patterns using both supervised and unsupervised machine learning.

The graph represents a network of 933 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 18 March 2018 at 23:55 UTC. The requested start date was Sunday, 18 March 2018 at 16:42 UTC and the maximum number of tweets (going backward in time) was 10,000. The tweets in the network were tweeted over the 2-day, 0-hour, 23-minute period from Friday, 16 March 2018 at 16:17 UTC to Sunday, 18 March 2018 at 16:41 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

After spending decades in the shadows as a specialty discipline, machine learning is suddenly front and center as a business tool. The hard part, though, is making it useful, especially to the developers and budding data scientists who are being tasked with the job. To that end, we rounded up some of the most common and useful open source machine learning tools we've spotted in the wild. For Python: Data scientists have jumped on Python as a more open-ended alternative to analytical languages like R, and many employers looking to add big-data expertise to their rosters are listing Python as a desired skill. As a result, plenty of machine learning libraries have shown up in Python's ever-expanding software roster.

When quantity reaches a certain level, it makes a qualitative difference. "When you have enough memory and compute, a funny thing happens. Nguyen, former engineering director for Google Apps, was referring to a slice of the technology behind his startup, Adatao, which just received $13 million in funding from Andreessen Horowitz. Adatao's value proposition comes in two parts: pInsights, a document-based visualization layer that provides end-users with simple, real-time querying of vast data sets; and pAnalytics, a monster data processing engine built on Hadoop and Apache Spark. All of this, including the ANN (artificial neural network) component, is made possible by the huge memory and processing power that, today, has become a commodity. It depends on who you ask.

Google Translate is currently best known for being a quick and dirty way to render Web pages or short text snippets in another language. But according to Der Spiegel, the next step for the core technology behind that service is a device that amounts to the universal translator from "Star Trek." Apparently everyone from Facebook to Microsoft is ramping up similar ambitions: to create services that eradicate language barriers as we currently know them. Machine translation has been around in one form or another for decades, but has always lagged far behind translations produced by human hands. Much of the software written to perform machine translation involved defining different languages' grammars and dictionaries, a difficult and inflexible process.

"Mahout" is a Hindi term for a person who rides an elephant. The elephant, in this case, is Hadoop -- and Mahout is one of the many projects that can sit on top of Hadoop, although you do not always need MapReduce to run it. Mahout puts powerful mathematical tools in the hands of the mere mortal developers who write the InterWebs. It's a package of implementations of the most popular and important machine-learning algorithms, with the majority of the implementations designed specifically to use Hadoop to enable scalable processing of huge data sets. Some algorithms are available only in a nonparallelizable "serial" form due to the nature of the algorithm, but all can take advantage of HDFS for convenient access to data in your Hadoop processing pipeline.