Personal Note: I'm really honored to share this conversation. I really hope you enjoy listening to it as much as I enjoyed talking to Dr. Marc Lanctot. In this interview, they talk all about Research at DeepMind, Deep Learning Research, AlphaGo. They also talk all about Swift For Tensorflow and OpenSpiel. Dr. Marc Lanctot is a research scientist at Google DeepMind.

Webinar Overview & Objectives Artificial intelligence is the most transformative technology ever created by humankind and touches almost all aspects of our lives. But, as with all powerful tools, misapplication can cause real harm. When used correctly, AI offers tremendous benefits to candidates and organizations by creating a more fundamentally fair and personal hiring process that leads to greater job fit and satisfaction. To realize these benefits, AI must be deployed according to scientific principles and standards that ensure ethical application and practice. Attend this webinar to learn: - Hazards of using AI without rigorous oversight - Benefits AI offers talent acquisition leaders and their candidates - Standards for using AI in hiring to maximize benefit and eliminate risk AI is a revolutionary technology that has vast potential to benefit humankind, but the unintended consequences of its improper use can be lasting and destructive.

Artificial Intelligence (AI) has a rapidly growing presence in today's world. Applications are present in industries ranging from manufacturing to education. It has become clear that AI has the potential to revolutionize how the everyday world works. Join us to learn more about how AI can support your business and personal life. Researchers in the Faculty of Business and IT will provide real-life examples and present how AI changes our everyday life, with topics ranging from digital security, gaming, healthcare, online social connections and more!

This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of "machine learning" and "operations", a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle.

Machine Learning is considered a part of Artificial Intelligence in the field of Computer Science. It often uses statistical techniques to give computers the ability to "learn" ( progressively enhance the performance of a particular task) with data, without being specifically programmed. Machine Learning is often related to computational statistics, which also concentrates on prediction -making through the use of computers. Machine Learning has wide applications as it is used in various industries like Banking, Retail, Publishing, Financial Sector etc. Top companies like Facebook and Google to push pertinent advertisements which are based on users past search behaviour. Machine Learning is basically used for managing multi-dimensional and multi-variety data in dynamic environments.

As an HR tech analyst, author and brand strategist, Meghan is sought after for her ideas about the future of work, is a regularly featured speaker at global business conferences, and serves on boards for leading HR and technology brands.

Model selection is the problem of choosing one from among a set of candidate models. It is common to choose a model that performs the best on a hold-out test dataset or to estimate model performance using a resampling technique, such as k-fold cross-validation. An alternative approach to model selection involves using probabilistic statistical measures that attempt to quantify both the model performance on the training dataset and the complexity of the model. Examples include the Akaike and Bayesian Information Criterion and the Minimum Description Length. The benefit of these information criterion statistics is that they do not require a hold-out test set, although a limitation is that they do not take the uncertainty of the models into account and may end-up selecting models that are too simple.

Although Neo4j is often used by application developers, there's a growing trend of data scientists using graphs to help with their work. In this session, we'll look at how to combine Neo4j and the Cypher query language with the Python data science stack including libraries such as Pandas and matplotlib. We'll look at how to do exploratory data analysis as well as find insights into networked datasets using the newly released graph algorithms package through the use of hands-on tutorials. You will need your own laptop. Please download and install Neo4j and Python prior to the session.

In addition to the traditional task of getting machines to answer questions, a major research question in question answering is to create interesting, challenging questions that can help systems learn how to answer questions and also reveal which systems are the best at answering questions. We argue that creating a question answering dataset---and the ubiquitous leaderboard that goes with it---closely resembles running a trivia tournament: you write questions, have agents (either humans or machines) answer the questions, and declare a winner. However, the research community has ignored the decades of hard-learned lessons from decades of the trivia community creating vibrant, fair, and effective question answering competitions. After detailing problems with existing QA datasets, we outline the key lessons---removing ambiguity, discriminating skill, and adjudicating disputes---that can transfer to QA research and how they might be implemented for the QA community.

We present a machine-checked, formal proof of PAC learnability of the concept class of decision stumps. A formal proof has every step checked and justified using fundamental axioms of mathematics. We construct and check our proof using the Lean theorem prover. Though such a proof appears simple, a few analytic and measure-theoretic subtleties arise when carrying it out fully formally. We explain how we can cleanly separate out the parts that deal with these subtleties by using Lean features and a category theoretic construction called the Giry monad.