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Global Big Data Conference

#artificialintelligence

B2B software sales and marketing teams love hearing the term "artificial intelligence" (AI). AI has a smoke and mirrors effect. But, when we say "AI is doing this," our buyers often know so little about AI that they don't ask the hard questions. In industries like the DevTools space, it is crucial that buyers understand both what products do and what their limitations are to ensure that these products meet their needs. If the purpose of AI is to make good decisions for humans, to accept that "AI is doing this" is to accept that we don't really know how the product works or if it is making good decisions for us.


Top 10 Data Science Experts to Follow on Twitter

#artificialintelligence

The application of artificial intelligence (AI) and machine learning to the business and IT, from intelligent IT operations (AIOps) to service management to software testing, is keeping the data revolution moving at lightning speed. That's why data science remains a popular concentration for computer science students who have the talent for math and analytics. And it's why more organizations are clamoring for data scientists who can help make decisions faster and put their businesses ahead of competitors. In today's age data science expertise with desirable knowledge in relatable fields is rare to find and therefore we have enlisted top 10 data science experts who you can follow in Twitter. Hilary is the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel.


Bored from Quarantine? Make Your Data Science Skills Recession-Proof

#artificialintelligence

Data science is one of the most well-payed jobs in the contemporary market. It is even considered as the hottest job of the 21st century. Data science has been a game-changer across every industry. With high-level digitization of processes, the generation of data is at peak and thus data science technology and tools are deployed to drive more productivity across organizations. This tech-field as a whole has a bunch of perks to provide including technologies for Big Data, Data Mining, Machine Learning, Data Analysis, and Data Analytics.


50 Best Python Tutorial Online To Learn Python Fast 2019 JA Directives

#artificialintelligence

Are you looking for the Best Python Tutorial Online To Learn Python Fast? The best way to learn python is with the list of the Best Python Courses online, books, Training, and Certification Program, which will help you to become an expert in Python programming language and Python programmer. The largest curated list for everything you need to know about Python. Don't be afraid, you will be happy to know that if you have a little idea about programming experience than it's easy for beginners like you to use and learn Python, so let get started! Also, we have included some bonus python certification book to help you to become a Python certified programmer. Learning Python from different sources are now available and installing Python is easy. Many Linux and UNIX distributions include a recent Python. Also, many Windows computers now come with Python already installed. If you don't know how to install Python you can find a few notes on the BeginnersGuide /Download on the wiki page.


Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds

arXiv.org Machine Learning

We revisit the problem of online learning with sleeping experts/bandits: in each time step, only a subset of the actions are available for the algorithm to choose from (and learn about). The work of Kleinberg et al. [2010] showed that there exist no-regret algorithms which perform no worse than the best ranking of actions asymptotically. Unfortunately, achieving this regret bound appears computationally hard: Kanade and Steinke [2014] showed that achieving this no-regret performance is at least as hard as PAC-learning DNFs, a notoriously difficult problem. In the present work, we relax the original problem and study computationally efficient no-approximate-regret algorithms: such algorithms may exceed the optimal cost by a multiplicative constant in addition to the additive regret. We give an algorithm that provides a no-approximate-regret guarantee for the general sleeping expert/bandit problems. For several canonical special cases of the problem, we give algorithms with significantly better approximation ratios; these algorithms also illustrate different techniques for achieving no-approximate-regret guarantees.


No-Regret and Incentive-Compatible Online Learning

arXiv.org Machine Learning

We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm's predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respect to the best fixed expert in hindsight. Second, we want incentive compatibility, a guarantee that each expert's best strategy is to report his true beliefs about the realization of each event. To achieve this goal, we build on the literature on wagering mechanisms, a type of multi-agent scoring rule. We provide algorithms that achieve no regret and incentive compatibility for myopic experts for both the full and partial information settings. In experiments on datasets from FiveThirtyEight, our algorithms have regret comparable to classic no-regret algorithms, which are not incentive-compatible. Finally, we identify an incentive-compatible algorithm for forward-looking strategic agents that exhibits diminishing regret in practice.


A Drifting-Games Analysis for Online Learning and Applications to Boosting

Neural Information Processing Systems

We provide a general mechanism to design online learning algorithms based on a minimax analysis within a drifting-games framework. Different online learning settings (Hedge, multi-armed bandit problems and online convex optimization) are studied by converting into various kinds of drifting games. The original minimax analysis for drifting games is then used and generalized by applying a series of relaxations, starting from choosing a convex surrogate of the 0-1 loss function. With different choices of surrogates, we not only recover existing algorithms, but also propose new algorithms that are totally parameter-free and enjoy other useful properties. Moreover, our drifting-games framework naturally allows us to study high probability bounds without resorting to any concentration results, and also a generalized notion of regret that measures how good the algorithm is compared to all but the top small fraction of candidates.


Artificial Intelligence for Social Good: A Survey

arXiv.org Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.


50 Best Python Tutorial Online To Learn Python Fast 2019 JA Directives

#artificialintelligence

Are you looking for the Best Python Tutorial Online To Learn Python Fast? The best way to learn python is with the list of the Best Python Courses online, books, Training, and Certification Program, which will help you to become an expert in Python programming language and Python programmer. The largest curated list for everything you need to know about Python. Don't be afraid, you will be happy to know that if you have a little idea about programming experience than it's easy for beginners like you to use and learn Python, so let get started! Also, we have included some bonus python certification book to help you to become a Python certified programmer. Learning Python from different sources are now available and installing Python is easy. Many Linux and UNIX distributions include a recent Python. Also, many Windows computers now come with Python already installed. If you don't know how to install Python you can find a few notes on the BeginnersGuide /Download on the wiki page.


Here Are the Top Data Science Influencers in 2019

#artificialintelligence

The term "influencer marketing" may call to mind jet-setting travel vloggers on YouTube or cool gamer kids streaming on Twitch. However, there are also many influencers working in the business and marketing realm. Data science influencers educate and inform on the subject of the scientific approach to extracting insights from data for real-world applications. Here are eight of the data science thought leaders topping influencer discovery searches. Andrew Ng's credentials speak for themselves.