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- Explainable AI or Halting Faulty Models ahead of Disaster
- Mar 27, 2019.
A brief overview of a new method for explainable AI (XAI), called anchors, introduce its open-source implementation and show how to use it to explain models predicting the survival of Titanic passengers.
- The Four Levels of Analytics Maturity
- Mar 26, 2019.
We outline our four-step model to categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them.
- Data Science for Decision Makers: A Discussion with Dr Stelios Kampakis
- Mar 26, 2019.
This article contains an interview veteran data scientist, Dr Stylianos (Stelios) Kampakis, in which he discusses his career, and how he helps decision makers across a range of businesses understand how data science can benefit them.
- The AI Black Box Explanation Problem
- Mar 25, 2019.
Introducing Black Box AI, a system for automated decision making often based on machine learning over big data, which maps a user’s features into a class predicting the behavioural traits of the individuals.
- The Problem With Self-Serve Analytics
- Mar 22, 2019.
A hands-off approach would seem reckless for questions about things like security. And yet that approach is not just the norm for analytical questions in most organizations; it's often the ideal.
- How to Capture Data to Make Business Impact
- Mar 21, 2019.
We take a look at the formula for calculating the efficiency of a data capturing method, before going onto explain the concept of Smart Data.
- Top 8 Data Science Use Cases in Manufacturing
- Mar 21, 2019.
Data science is said to change the manufacturing industry dramatically. Let's take under consideration several data science use cases in manufacturing that have already become common and brought benefits to the manufacturers.
- My Best Tips for Agile Data Science Research
- Mar 21, 2019.
This post demonstrates how to bring maximum value in minimal time using agile methods in data science research.
- Overcoming distrust on the path to productive analytics
- Mar 18, 2019.
We outline the importance of overcoming distrust in data and analytics, with tips on how to align all stakeholders, being a data optimist, streamlining the process, and more.
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How To Work In Data Science, AI, Big Data
- Mar 18, 2019.
There are many facets to working in Data Science. Your role will depend greatly on the industry you pick and the area of Data Science you want to pursue. A Data Science career is very dynamic and requires a team effort to succeed. - My favorite mind-blowing Machine Learning/AI breakthroughs
- Mar 14, 2019.
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
- Cartoon: AI and March Madness
- Mar 14, 2019.
AI has mastered chess, Go, and other games, but can AI master March Madness? KDnuggets Cartoon imagines one scenario when this happens.
- AI: Arms Race 2.0
- Mar 12, 2019.
An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.
- The 7 Myths of Data Anonymisation
- Mar 12, 2019.
Anonymisation has always been rather seen as a necessary evil instead of a helpful tool. That’s why plenty of myths have arisen around that technology over the years.
- The Pareto Principle for Data Scientists
- Mar 11, 2019.
In this article, I’ll share a few ways in which we, as data scientists, can use the power of the Pareto Principle to guide our day-to-day activities.
- Beating the Bookies with Machine Learning
- Mar 8, 2019.
We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.
- 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning
- Mar 8, 2019.
For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.
- Designing Ethical Algorithms
- Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis
- Mar 7, 2019.
CRM/Consumer analytics, health care, banking, finance, and science were the top sectors in 2018. The greatest increases were in mobile apps, investment, security, entertainment, and social policy, while fraud detection, retail, advertising, direct marketing, and social media saw the greatest declines.
- Breaking neural networks with adversarial attacks
- Mar 7, 2019.
We develop an intuition behind "adversarial attacks" on deep neural networks, and understand why these attacks are so successful.
- Beyond news contents: the role of social context for fake news detection
- Mar 7, 2019.
Today we’re looking at a more general fake news problem: detecting fake news that is being spread on a social network. This is a summary of a recent paper which demonstrates why we should also look at the social context: the publishers and the users spreading the information!
- 3 Reasons Why AutoML Won’t Replace Data Scientists Yet
- Mar 6, 2019.
We dispel the myth that AutoML is replacing Data Scientists jobs by highlighting three factors in Data Science development that AutoML can’t solve.
- Neural Networks seem to follow a puzzlingly simple strategy to classify images
- Mar 5, 2019.
We explain why state-of-the-art Deep Neural Networks can still recognize scrambled images perfectly well and how this helps to uncover a puzzlingly simple strategy that DNNs seem to use to classify natural images.
- The Difference Between Data Scientists and Data Engineers
- Mar 4, 2019.
ODSC East 2019 has multiple tracks for both Data Scientists and Data Engineers, including workshops, talks, and training sessions. Save 45% with code KDN45.
- On Building Effective Data Science Teams
- Mar 4, 2019.
We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.
- OpenAI’s GPT-2: the model, the hype, and the controversy
- Mar 4, 2019.
OpenAI recently released a very large language model called GPT-2. Controversially, they decided not to release the data or the parameters of their biggest model, citing concerns about potential abuse. Read this researcher's take on the issue.
- What no one will tell you about data science job applications
- Mar 1, 2019.
For every person who has a question relating to a data science job application, and asks it, there are ten people who have the same question, but don’t ask it. If you’re one of those ten, then this post is for you.
- Most impactful AI trends of 2018: The rise of ML Engineering
- Mar 1, 2019.
As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018’s foundation and truly blossom in 2019.
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