Nov
2019 Dec Opinions
All (81) | Events (5) | News (4) | Opinions (34) | Top Stories, Tweets (10) | Tutorials, Overviews (28)
- What is the most important question for Data Science (and Digital Transformation)
- Dec 31, 2019.
With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.
- Towards a Quantitative Measure of Intelligence: Breaking Down One of the Most Important AI Papers of 2019, Part II
- Dec 31, 2019.
AI scientist Francois Chollet proposes a better framework for measuring the intelligence of AI systems.
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How To “Ultralearn” Data Science: summary, for those in a hurry
- Dec 30, 2019.
For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science. - Towards a Quantitative Measure of Intelligence: Breaking Down One of the Most Important AI Papers of 2019, Part I
- Dec 30, 2019.
AI scientist Francois Chollet proposes a better framework for measuring the intelligence of AI systems.
- How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4
- Dec 27, 2019.
In this fourth and final part of the ultralearning data science series, it's time to take the final steps toward developing a deep understanding of the fundamentals and learning how to experiment -- the two aspects that are the ultimate keys to ultralearning.
- KDnuggets Cartoon in an English textbook?
- Dec 24, 2019.
KDnuggets is not only for learning about AI, Data Science, and Machine Learning. A KDnuggets cartoon has been included in an English language and culture textbook for French high-school students.
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What is a Data Scientist Worth?
- Dec 23, 2019.
What is the Salary of a Data Scientist in 2019? Let's have a look at some data to see how we can answer that question. - How To “Ultralearn” Data Science: optimization learning, Part 3
- Dec 20, 2019.
This third part in a series about how to "ultralearn" data science will guide you through how to optimize your learning through five valuable techniques.
- 5 Ways to Apply Ethics to AI
- Dec 19, 2019.
Here are six more lessons based on real life examples that I think we should all remember as people working in machine learning, whether you’re a researcher, engineer, or a decision-maker.
- The 4 fastest ways NOT to get hired as a data scientist
- Dec 18, 2019.
Ready to try to get hired as a data scientist for the first time? Avoiding these common mistakes won’t guarantee an offer, but not avoiding them is a sure fire way for your application to be tossed into the trash bin.
- Automatic Text Summarization in a Nutshell
- Dec 18, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used.
- The ravages of concept drift in stream learning applications and how to deal with it
- Dec 18, 2019.
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. These streams of data evolve generally over time and may be occasionally affected by a change (concept drift). How to handle this change by using detection and adaptation mechanisms is crucial in many real-world systems.
- How To “Ultralearn” Data Science: removing distractions and finding focus, Part 2
- Dec 17, 2019.
This second part in a series about how to "ultralearn" data science will guide you through several techniques to remove those distractions -- because your focus needs more focus.
- Industry AI, Analytics, Machine Learning, Data Science Predictions for 2020
- Dec 16, 2019.
Predictions for 2020 from a dozen innovative companies in AI, Analytics, Machine Learning, Data Science, and Data industry.
- Microsoft Introduces Icebreaker to Address the Famous Ice-Start Challenge in Machine Learning
- Dec 16, 2019.
The new technique allows the deployment of machine learning models that operate with minimum training data.
- Xavier Amatriain’s Machine Learning and Artificial Intelligence 2019 Year-end Roundup
- Dec 16, 2019.
It is an annual tradition for Xavier Amatriain to write a year-end retrospective of advances in AI/ML, and this year is no different. Gain an understanding of the important developments of the past year, as well as insights into what expect in 2020.
- KDnuggets Poll: How well do current AutoML solutions work?
- Dec 14, 2019.
Take part in our latest poll, asking readers their opinions on the effectiveness of current automated machine learning solutions.
- How To “Ultralearn” Data Science, Part 1
- Dec 13, 2019.
What is "ultralearning" and how can you follow the strategy to become an expert of data science? Start with this first part in a series that will guide you through this self-motivated methodology to help you efficiently master difficult skills.
- Dusting Under the Bed: Machine Learners’ Responsibility for the Future of Our Society
- Dec 13, 2019.
The Machine Learning community must shape the world so that AI is built and implemented with a focus on the entire outcome for our society, and not just optimized for accuracy and/or profit.
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AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020
- Dec 11, 2019.
We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment. - NeurIPS 2019 Outstanding Paper Awards
- Dec 11, 2019.
NeurIPS 2019 is underway in Vancouver, and the committee has just recently announced this year's Outstanding Paper Awards. Find out what the selections were, along with some additional info on NeurIPS papers, here.
- Scalable graph machine learning: a mountain we can climb?
- Dec 10, 2019.
Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability. We take a close look at scalability for graph machine learning methods covering what it is, what makes it difficult, and an example of a method that tackles it head-on.
- Moving Predictive Maintenance from Theory to Practice
- Dec 9, 2019.
Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance.
- The 4 Hottest Trends in Data Science for 2020
- Dec 9, 2019.
The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020
- Dec 9, 2019.
As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.
- DeepMind Unveils MuZero, a New Agent that Mastered Chess, Shogi, Atari and Go Without Knowing the Rules
- Dec 9, 2019.
The new model showed great improvements over the previous AlphaZero agent.
- Accuracy Fallacy: The Media’s Coverage of AI Is Bogus
- Dec 6, 2019.
Such as the gross exaggerations Stanford researchers broadcasted about their infamous "AI gaydar" project, there exists a prevalent "accuracy fallacy" in relation to AI from the media. Find out more about how the press constantly misleads the public into believing that machine learning can reliably predict psychosis, heart attacks, sexuality, and much more.
- Why software engineering processes and tools don’t work for machine learning
- Dec 5, 2019.
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.
- The Essential Toolbox for Data Cleaning
- Dec 5, 2019.
Increase your confidence to perform data cleaning with a broader perspective of what datasets typically look like, and follow this toolbox of code snipets to make your data cleaning process faster and more efficient.
- Artificial Friend or Virtual Foe
- Dec 5, 2019.
Is AI making more good than harm?
- Explainability: Cracking open the black box, Part 1
- Dec 4, 2019.
What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.
- A Non-Technical Reading List for Data Science
- Dec 2, 2019.
The world still cannot be reduced to numbers on a page because human beings are still the ones making all the decisions. So, the best data scientists understand the numbers and the people. Check out these great data science books that will make you a better data scientist without delving into the technical details.
- Top 7 Data Science Use Cases in Trust and Security
- Dec 2, 2019.
What are trust and safety? What is the role of trust and security in the modern world? Read this overview of 7 data science application use cases in the realm of trust and security.
- Google Open Sources MobileNetV3 with New Ideas to Improve Mobile Computer Vision Models
- Dec 2, 2019.
The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.
Free MIT Courses on Calculus: The Key to Understanding Deep Learning