data mining


Healthcare Machine Learning Startup Cogitativo Closes $5M Series A Financing

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The strategic investment by HCSC Ventures, Inc., a wholly-owned subsidiary of Health Care Service Corporation which specializes in investments in innovative health care companies, will support the product-line expansion for anomaly detection and real-time operational decision support solutions for healthcare payers. Cogitativo brings a new scientific paradigm to the rapidly growing market for healthcare performance improvements by enabling payers and providers to challenge system complexity through Cogitativo's machine learning platform. "Cogitativo brings a unique blend of computational scientists with nationally recognized health care operators and advanced data science capabilities to help address complex health care challenges." Within the next several months, Cogitativo expects to release expanded machine learning solutions for improving payment accuracy, care anomaly detection and real-time monitoring of payers' care delivery networks.


Artificial Intelligence: Get your users to label your data

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What I want to tell you about is an often neglected aspect of machine learning: data labeling. We discussed that to avoid paying for data labeling, you want some magic data annotation solution. In a similar example, the messages that I pin to inbox in Google Inbox indicate to Google what unlabeled messages I might want to pin as important items in the future. For example, users have a high incentive to press the back button in a user flow when the AI made a mistake, but a low incentive to do so otherwise.


Tech industry must secure against 'unintended consequences': Elop

ZDNet

The path to disruption is paved by unintended consequences, Telstra group executive of Technology, Innovation and Strategy Stephen Elop has said, with the tech industry needing to secure machine-learning and artificial intelligence (AI) applications against unconscious biases and breaches of security and trust. According to Elop -- who served as CEO of Nokia before being added to the Telstra team last year after the telco created the new role of innovation head to lead its CTO, chief scientist, software group, and corporate strategy -- while AI machines learn from the data input into their systems, this data comes tainted by humans with unconscious biases. "At the heart of artificial intelligence is big data, and the insights that can be gleaned from advanced data analytics ... how we use data, and the data we select to train our machines can have a profound outcome on our analytics," Elop said. Most importantly, Elop said, is when developers fail to secure systems against the unintended consequence of breach of trust.


Gaining Control Over AI, Machine Learning

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As artificial intelligence and machine learning technologies make their way into advanced data management platforms, the emphasis for developers and data scientists is broadening to include not just deployment but "control" of the data accessed by these automation tools. To gain control of algorithm-driven business models, the company argues: "Organizations require greater control over the data being used by machine learning and AI models." Immuta's data management platform is designed to provide greater control of the data fed into algorithms, speeding deployment as well as increasing visibility into how automation tools are functioning. Among the tasks burdening data scientists are compliance with complex data security regulations and information governance policies such as rules for accessing personal data.


Comprehensive Repository of Data Science and ML Resources

@machinelearnbot

Here are 29 resources, mostly in the form of tutorials, covering most important topics in data science: This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, sign up on DSC.


Google Cloud Dataprep now available in public beta

ZDNet

Google announced Thursday that it's making Google Cloud Dataprep, a serverless data preparation tool, available in public beta. Dataprep helps to quickly ready data for immediate analysis or for training machine learning models. With machine learning, it also suggests different ways of cleaning the data, which should help make data preparation faster and less prone to error. Last year, Amazon rolled out its own serverless data preparation tool called AWS Glue.


Brighterion CEO On Artificial Intelligence PYMNTS.com

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Akli Adjaoute, founder, president and CEO of artificial intelligence and machine learning technologies provider Brighterion, told Karen Webster in a recent episode of the "In Their Own Words" podcast series that the main difference between then and now is scale. Legacy AI and data scientists are still focused on statistics, neural networks, data mining and deep learning, just as they were almost 50 years ago. Real artificial intelligence and machine learning can solve these issues." With a true AI, said Adjaoute, the same predictive analytics platform can be applied across fields -- including financial services, fraud, payments, healthcare and homeland security, to name a few -- and for many different uses -- such as forecasting market volatility and manipulation, predicting market movers and trades, risk assessment, fraud prevention or detection and anti-money laundering compliance, among others.


The 6 types of artificial intelligence 7wData

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Wow – lots and lots and lot of uses for Machine Learning, once the machine has figured out how to do the recognition – the "training". Initial training is hard work, both for the humans who prepare the training data and the computer that's trying to create the models from the training data. If we give a machine learning system a goal and lot of power, it might come to the unfortunate decision that humans get in the way of achieving its goals, and that if it could get rid of those humans, it would reach its goal faster. There is the inference part - the part that takes in the data and figures out what's going on.


The 6 types of artificial intelligence 7wData

#artificialintelligence

Wow – lots and lots and lot of uses for Machine Learning, once the machine has figured out how to do the recognition – the "training". Initial training is hard work, both for the humans who prepare the training data and the computer that's trying to create the models from the training data. If we give a machine learning system a goal and lot of power, it might come to the unfortunate decision that humans get in the way of achieving its goals, and that if it could get rid of those humans, it would reach its goal faster. There is the inference part - the part that takes in the data and figures out what's going on.


Pentagon Turns to DAVE for More Efficient Buying

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The Defense Department is known as the biggest spender among agencies in the federal government, but it'd like to be known as one of the more efficient agencies at some point. The undersecretary then uses that data to oversee programs, make decisions on them and report to Congress various spending data, Krzysko said. DAVE's API-driven nature "gives us the ability to move that data around" and ultimately provide better spending information to program managers and senior executives across the Pentagon. "That translates to a data management framework and infrastructure together that can support what you're trying to do."