"As for why I tell a lot of stories, there's a joke about that. There was once a man who had a computer, and he asked it, 'Do you compute that you will ever be able to think like a human being?' And after assorted grindings and beepings, a slip of paper came out of the computer that said, 'That reminds me of a story . . . "
– from ANGELS FEAR: TOWARDS AN EPISTEMOLOGY OF THE SACRED. Gregory Bateson & Mary Catherine Bateson. (Part III 'Metalogue').
What would our computers tell us if we gave them a voice? We'll soon find out thanks to Natural Language Generation which gives computers a written opinion on virtually anything. For now, we must program their responses, but soon they'll form their own opinions and develop a creative voice. This may seem a long way off, so let's consider their progression as a writer in comparison to a human. A child progresses as a writer by starting with basic creative writing exercises: What did you do over summer break?
People are starting get jumpy about the prospect of AI being used to automate everything and anything. Now that AI has proven its ability to squeeze out both blue-collar jobs (through robotics et al.) and white-collar occupations (through natural language generation et al.), cultural sensitivities surrounding this technology are on the upswing. That may explain why we're starting to see people use near-synonyms and quasi-euphemisms for "automation" when it comes to discussing AI's impact. Some observers prefer to use terms such as "operationalize," "productionize," "augment," and "accelerate" when discussing the encroachment of automation into the development of AI-driven applications. We also see a fair bit of discussion around "self-service" tools for building "repeatable workflows" and the like, which certainly sounds like a next logical step to automating that workflow.
Within AI, there are numerous technologies such as natural language processing (NLP), natural language generation (NLG), machine learning, deep learning, and text analytics. As more businesses and organizations begin to comprehend the transformative potential of AI, the remaining scientists and technicians trained in AI, machine learning and deep learning are being sought after. In the meantime, existing analytics talent is being trained to learn AI. Today we are seeing C-level, Chief Analytics Officer (CAO) and Chief Data Officer (CDO) roles created to maximize the value of data assets, improve decision making and enhance business processes.
In June 2017 Apple released its Core ML API designed to make AI faster on its iPhone, iPad, and Apple Watch products. The API covers all sorts of ML operations such as image and face recognition, object detection, NLP (natural language processing) and NLG (natural language generation). It may be easily integrated into an Xcode development environment and become a part of your iOS app functionality. By making pre-trained ML models available for iOS developers, Apple's Core ML promises to increase the scope of iOS applications with core AI/ML functionality available to users of Apple products.
USAA is using NLG to automatically generate descriptions of metrics surfaced in its BI reporting tool. USAA uses Adobe Analytics to track and report this data. But that information then created more questions, which led to USAA's need for natural language generation software. "The idea is to publish a dashboard that answers questions, [as well as] the questions that it generates," Horgan said.
What is important is identifying the questions machine learning is well-tailored to answer, the questions it struggles with, and perhaps most importantly, how the paradigmatic shift in AI frameworks is impacting the relationship between humans, their data, and the world it describes. While Hofstadter might have contemplated artificial intelligence as a reflection of human intelligence, modern AI architects have no tendency to share the same preoccupation. Machine learning is only as good as the data it is built upon, and when that data is subject to human biases, AI systems inherit these biases. Mike Pham is a technical product manager at Narrative Science, a company that makes advanced natural language generation (Advanced NLG) for the enterprise.
This is where Artificial Intelligence and Natural Language Generation software can help. The software can transform data into simple, accurate, real-time narratives and reports. Artificial Intelligence and Natural Language Generation, however, allows businesses to perform real-time data analysis with one click of a button. This is where Artificial Intelligence and Natural Language Generation software can help.
Natural Language Generation, a field which has made great strides of late, has begun to manifest in many areas of our lives. It is currently being used in customer service to generate reports and market summaries. By providing algorithms, APIs (application programming interface), development and training tools, Big Data, applications and other machines, machine learning platforms are gaining more and more traction every day. Companies focused in machine learning include Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.
Given the nature of our business, we often encounter confusion between Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU). To most folks, NLP is "Computers reading language." I mentioned NLU earlier; NLU stands for Natural Language Understanding, and is a specific type of NLP. The "reading" aspect of NLP is broad and encompasses a variety of applications, including things like: A more advanced application of NLP is NLU, ie.
Content marketing automation currently involves two core technologies, both of which are components of AI. They are natural language processing (NLP) and natural language generation (NLG). Hoping to finish in the top ten after a 14th place finish last season, Leicester City splashed out 26.70 million in the summer transfer period. Current automated content creation tools are event-driven.