AI News Thursday, April 26 Vast data sets, Ai research project, Artificial intelligence & more…

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What’s new?

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Reproducibility Issues Hinder Machine Learning Progress

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  • One of the biggest pieces of the puzzle is being able to record and factor in small changes, such as GPU driver updates in mid-job, or changes to the data set during a training run by an outside source.
  • This in-development model is run on a training data set on the researchers machine, which gets a GPU driver update in the middle of running.
  • Small changes like this can have a powerful effect on the end product, especially in scenarios where a machine learning system is set to work largely unsupervised, training itself on vast data sets.
  • Even if the researcher logged every tiny codebase change they made from start to finish, which is mostly impractical, others would still have issues reproducing their results by following the same procedure with the same algorithm, machine and data set.
  • Given the nature of AI research, needing vast data sets and lots of training across tons of machines for grander data processing tasks, industrywide collaboration across national borders is exactly what the AI field needs to make its next growth breakthrough.

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[/vc_column_text] [vc_column_text el_class=”topfeed-embedly”] Reproducibility Issues Hinder Machine Learning Progress | [/vc_column_text] [/vc_column] [/vc_row] [vc_row el_id=”www_bloomberg_com_news_articles_2018_04_25_u_k_announces_1_4_billion_drive_into_artificial_intelligence_cmpid_socialflow_twitter_business_utm”] [vc_column width=”1/2″] [vc_separator] [vc_column_text]

U.K. Announces $1.4 Billion Drive Into Artificial Intelligence

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  • Antony Phillipson, the U.K. Trade Commissioner for North America and Consul General in New York, said in an interview ahead of the announcement that the U.K. could not compete with China in terms of total funding or scale of some government-run AI projects.
  • As part of the U.K. governments program, Canadian VC firm Chrysalix will set up a U.K. office and invest 110 million pounds in AI and robotics.
  • Japans Global Brain, which has invested in similar businesses, will open a European headquarters in the U.K. and invest 35 million pounds in technology startups over the next five years.
  • The government said it will fund 1,000 new PhD places for those working on AI and related subjects, and train 8,000 new computer science teachers for U.K. secondary schools.
  • It is also investing 9 million pounds in a new Center for Data Ethics and Innovation at the Alan Turing Institute, which will research principles and codes of conduct for AI safety and ethical use of machine learning.

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[/vc_column_text] [vc_column_text el_class=”topfeed-embedly”] U.K. Announces $1.4 Billion Drive Into Artificial Intelligence [/vc_column_text] [/vc_column] [/vc_row] [vc_row el_id=”www_cultofmac_com_543285_name_your_price_for_10_machine_learning_courses_deals__”] [vc_column width=”1/2″] [vc_separator] [vc_column_text]

This lesson bundle offers a great chance to master machine learning.

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  • Machine learning is shaping up to reshape the world, and theres plenty of opportunity for developers.
  • The 2018 Machine Learning Bundle takes a deep dive into the languages and techniques of this quickly expanding field.
  • The courses cover basic, advanced, and large scale applications of Python in machine learning.
  • So if youre interested in expanding your coding skills to include machine learning, this bundle is for you.
  • Buy now: Name your price for the 2018 Machine Learning Bundle, thats a deal worth $844.

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[/vc_column_text] [vc_column_text el_class=”topfeed-embedly”] This lesson bundle offers a great chance to master machine learning. [/vc_column_text] [/vc_column] [/vc_row] [vc_row el_id=”www_datasciencecentral_com_profiles_blogs_how_industrial_iot_is_influenced_by_cognitive_anomaly_detection_”] [vc_column width=”1/2″] [vc_separator] [vc_column_text]

How Industrial IoT is Influenced by Cognitive Anomaly Detection

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  • A Cognitive approach to Anomaly Detection, powered by Machine Learning and excellent data and analytics, is providing IIoT businesses with solutions, and helping them to overcome the limitations of traditional statistical approaches.
  • In Industrial IoT, one main objectives is the automatic monitoring and detection of these abnormal events, or changes and shifts in the collected data, including all the techniques aimed at identifying data patterns that deviate from the expected behavior.
  • With the help of Data Scientist Taj Darra from DataRPM, we can understand the importance of a bottom up approach to anomaly detection, which you can see here: – – When Machine Learning is enhanced with a cognitive IoT framework, it enables IIoT businesses to detect anomalies from the initial…
  • Lets break down the phases of anomaly detection: – – Cognition is giving businesses the means to gain control over enormous quantities of sensor data generating from every machine.
  • There are opportunities for businesses to take advantage of Cognitive Anomaly Detection now.

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[/vc_column_text] [vc_column_text el_class=”topfeed-embedly”] How Industrial IoT is Influenced by Cognitive Anomaly Detection [/vc_column_text] [/vc_column] [/vc_row] [vc_row el_id=”www_ibm_com_blogs_research_2018_04_ai_adversarial_robustness_toolbox__”] [vc_column width=”1/2″] [vc_separator] [vc_column_text]

Securing AI Against Adversarial Threats with Open Source Toolbox

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  • IBM Research Ireland is releasing the Adversarial Robustness Toolbox, an open-source software library, to support both researchers and developers in defending DNNs against adversarial attacks and thereby making AI systems more secure.
  • The Adversarial Robustness Toolbox is designed to support researchers and developers in creating novel defense techniques, as well as in deploying practical defenses of real-world AI systems.
  • This first release of the Adversarial Robustness Toolbox supports DNNs implemented in the TensorFlow and Keras deep learning frameworks.
  • Currently, the library is primarily intended to improve the adversarial robustness of visual recognition systems, however, we are working on future releases that will comprise adaptations to other data modes such as speech, text or time series.
  • We hope the Adversarial Robustness Toolbox project will stimulate research and development around adversarial robustness of DNNs, and advance the deployment of secure AI in real world applications.

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[/vc_column_text] [vc_column_text el_class=”topfeed-embedly”] Securing AI Against Adversarial Threats with Open Source Toolbox [/vc_column_text] [/vc_column] [/vc_row]