CORRELATION


Classification with Scikit-Learn

#artificialintelligence

With the dataset splitted into training and test sets, we can start building a classification model. Actually, classifiers like Random Forest and Gradient Boosting classification performs best for most datasets and challenges on Kaggle (That does not mean you should rule out all other classifiers). Again, we will split the dataset into a 70% training set and a 30% test set and start training and validating a batch of the eight most used classifiers. For datasets, where this is not the case we can play around with the features in the dataset, add extra features from additional datasets or change the parameters of the classifiers in order to improve the accuracy.


Hybrid content-based and collaborative filtering recommendations with {ordinal} logistic regression (1): Feature engineering

@machinelearnbot

Content based systems (CF) rely on a typical description of items over feature vectors, and then recommend novel items to users by computing some similarity metric between them and the items that the user has already rated. The content-based component of the system encompasses two matrices: the user-user and the item-item proximity matrices, both obtained from applying the relevant distance metric over a set of features that characterize users and items, respectively. The CF component of the system, relies on the typical user-user and item-item similarity matrices computed from the known, past user-item ratings, providing for a memory component of the recommender. The Jaccard distance, on the other hand, neglects the covariances from the rating scales altogether, preserving only the information about the extent of the shared ratings.


AI is from Venus, Machine Learning is from Mars

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Unlike AI which seeks to understand the world through conceptual models, machine learning has no such interest. The underlying hypothesis of machine learning as applied to log files is that correlation can serve as a proxy for causation. The class of questions for which an answer can be verified in polynomial time is called NP, which stands for "nondeterministic polynomial time." AI emulates human intelligence and is P. Machine learning simulates it and is NP.


Why Math Is the Best Way to Make Sense of the World

WIRED

Goldin, a professor of mathematical sciences at George Mason, has made it her life's work to improve quantitative literacy. In 2004, she became the research director of George Mason's Statistical Assessment Service, which aimed "to correct scientific misunderstanding in the media resulting from bad science, politics or a simple lack of information or knowledge." Quanta Magazine spoke with Goldin about finding beauty in abstract thought, how STATS is arming journalists with statistical savvy, and why mathematical literacy is empowering. Our mission at STATS has changed to focus on offering journalists two things.


Data management a chore? These three tips can improve your data relationship - Watson

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The "sexy stuff" is analysis and modeling to enrich the data relationship. Watson Discovery Service improves the developer relationship with really big data using cognitive capabilities with simple tooling and APIs to quickly upload, enrich, and index large collections of data. Discovery Services suite of APIs provides a pipeline to ingest, store, and enrich your data and get to the good stuff. Embedded Watson algorithms enrich documents with natural language understanding, sentiment and emotion analysis, and concept tagging.


US spy agencies hope artificial intelligence can predict future events

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Dawn Meyerriecks, the CIA's deputy director for technology development, said this week the CIA currently has 137 different AI projects, many of them with developers in Silicon Valley. These range from trying to predict significant future events, by finding correlations in data shifts and other evidence, to having computers tag objects or individuals in video that can draw the attention of intelligence analysts. Officials of other key spy agencies at the Intelligence and National Security Summit in Washington this week, including military intelligence, also said they were seeking AI-based solutions for turning terabytes of digital data coming in daily into trustworthy intelligence that can be used for policy and battlefield action. "If we were to attempt to manually exploit the commercial satellite imagery we expect to have over the next 20 years, we would need 8 million imagery analysts," Robert Cardillo, director of the National Geospatial-Intelligence Agency, said in a speech in June.


Hiring Algorithms Are Not Neutral

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These software systems can in some cases be so efficient at screening resumes and evaluating personality tests that 72% of resumes are weeded out before a human ever sees them. They reflect human biases and prejudices that lead to machine learning mistakes and misinterpretations. If the algorithm learns what a "good" hire looks like based on that kind of biased data, it will make biased hiring decisions. The result is that automatic resume screening software often evaluates job applicants based on subjective criteria, such as one's name.


Behind the hype: Machine learning in investment management

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Although big data is usually directly associated with machine learning, there is still a debate whether new data sources, such as web crawling through news or social media, credit card data, geolocation data, and so on, is helpful in the investment process. The Barclays report states that 54% of surveyed investment managers use alternative data, such as web crawling social media data, satellite data, or credit card data. Despite the widespread use of alternative data, 80% of surveyed investment managers in the Barclays report said that their biggest challenge was in assessing the usefulness of the data. The Barclays report confirms this potential by noting that the most popular use case for machine learning among respondents is to clean traditional data sources, such as tick data, with 88% of those managers who use machine learning in the investment process using it as a data processing tool.


Data swamped US spy agencies put hopes on artificial intelligence

#artificialintelligence

Provided by AFP The US National Security Agency, which operates this ultra-secure data collection center in Utah, is one of the key US spying operations turning to artifical intelligence to help make sense of massive amounts of digital data they collect every day. Dawn Meyerriecks, the Central Intelligence Agency's deputy director for technology development, said this week the CIA currently has 137 different AI projects, many of them with developers in Silicon Valley. These range from trying to predict significant future events, by finding correlations in data shifts and other evidence, to having computers tag objects or individuals in video that can draw the attention of intelligence analysts. Officials of other key spy agencies at the Intelligence and National Security Summit in Washington this week, including military intelligence, also said they were seeking AI-based solutions for turning terabytes of digital data coming in daily into trustworthy intelligence that can be used for policy and battlefield action.


Data swamped US spy agencies put hopes on artificial intelligence

@machinelearnbot

Dawn Meyerriecks, the Central Intelligence Agency's deputy director for technology development, said this week the CIA currently has 137 different AI projects, many of them with developers in Silicon Valley. These range from trying to predict significant future events, by finding correlations in data shifts and other evidence, to having computers tag objects or individuals in video that can draw the attention of intelligence analysts. Officials of other key spy agencies at the Intelligence and National Security Summit in Washington this week, including military intelligence, also said they were seeking AI-based solutions for turning terabytes of digital data coming in daily into trustworthy intelligence that can be used for policy and battlefield action. "If we were to attempt to manually exploit the commercial satellite imagery we expect to have over the next 20 years, we would need eight million imagery analysts," Robert Cardillo, director of the National Geospatial-Intelligence Agency, said in a speech in June.