Machine Storytelling


AI in storytelling: Machines as cocreators

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Computers don't cry during sad stories, but they can tell when we will. Sunspring debuted at the SCI-FI LONDON film festival in 2016. Set in a dystopian world with mass unemployment, the movie attracted many fans, with one viewer describing it as amusing but strange. But the most notable aspect of the film involves its creation: an artificial-intelligence (AI) bot wrote Sunspring's screenplay. "Maybe machines will replace human storytellers, just like self-driving cars could take over the roads."


MIT professor and ThinkPad superfan: "Storytelling" machines key to unlocking artificial intelligence - Lenovo Think Stories

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In Winston's Genesis Group, which is part of MIT's Computer Science and Artificial Intelligence Laboratory, Winston and squadrons of his students have painstakingly built technology that can analyze roughly 100-line texts written for computers on subjects such as Shakespeare, international cyber conflict, and fairy tales. Genesis compares stories; detects concepts such as love or revenge, even when they are not named; concludes whether a short-term gain leads to a long-term loss; and explains acts based on personality traits. The system can even analyze a text through a filter of cultural bias, thus interpreting an event like the cyber attack on Estonia by Russia in 2007 from the point of view of people in one or the other country.


Why the right web analytics platform is (even more) business critical

ZDNet

The increase in the use of data analytics tools is failing to snuff out the role of the traditional management hunch when it comes to business decisions. Forrester just recently published The Forrester Wave: Web Analytics, Q4 2017. In it we rank the leading web analytics platform providers including Adobe, AT Internet, Cooladata, Google, IBM, Mixpanel, and Webtrekk. You may ask, "Why should I care?" After all, when it comes to understanding customers on digital touchpoints, most businesses have extended their intelligence capabilities beyond browser analytics to now include app, social, media, ads, and much more, including even IoT.


Characteristics of Good Visual Analytics and Data Discovery Tools

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Visual Analytics and Data Discovery allow analysis of big data sets to find insights and valuable information. See this article for more details and motivation: "Using Visual Analytics to Make Better Decisions: the Death Pill Exa...". Several tools are available on the market for Visual Analytics and Data Discovery. Take a look at available visual analytics tools on the market with the above list in mind and select the right one for your use cases.


3 keys to keep your data lake from becoming a data swamp

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Data lakes, and all big data initiatives, come from, one, pressure in the marketplace to have one, and secondly, real-world data generators spitting up gobs of data that you need to find a way to store." Even with a focused data set, gleaning insight from data at scale requires automation. "AI, machine learning, deep learning, whatever term you want to use, it's the magical solution for wading your way through your information. You could capture photographs of customers entering your stores and then use a convoluted neural network (CNN) -- a type of deep learning neural network that excels at computer vision problems -- to process the images.


Machine Learning And Analytics: What's Your First Step?

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So what is the first step for a tech department that wants to start using machine learning to improve its data analytics? The road to advanced analytics and machine learning starts with basic connectivity and data collection. This journey includes pinpointing the questions that need to be answered with data analysis, identifying the data needed to answer those questions, and putting processes in place to gather the correct type and amount of that data to properly support machine learning. Tech departments often approach machine learning as a science project, where the objective is to solve every piece of the puzzle at once.


Book: Python Machine Learning Blueprints

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Machine Learning is transforming the way we understand and interact with the world around us. Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling. Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling.


Top 10 Machine Learning Projects on Github

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While there are many sources of such tools on the internet, Github has become a de facto clearinghouse for all types of open source software, including tools used in the data science community. The following is an overview of the top 10 machine learning projects on Github. This is a curated list of machine learning libraries, frameworks, and software. It also includes data visualization tools, which opens it up as more of a generalized data science list in some sense... which is a good thing.



AI Ethics Missing as Machine Learning Advances « Techtonics

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But in this brave new world of artificial intelligence (AI) and machine learning, there are no ethical guidelines, no regulations, and no parameters to govern how this data is collected and used. "So on the one hand, the companies are saying'if you give us this data, we'll give you better services, more personalized services,'" said Ben Lorica, Chief Data Scientist for O'Reilly Media, which provides technology and business training. While AI and machine learning tools don't make it easier to collect data, they "dramatically change how the collected data is'used,'" said Cornell Tech Computer Science professor, Vitaly Shmatikov, in an email. And a lot of companies are already discussing transparency and fairness, and ethics training for data processing and machine learning algorithms.