Opinions, Interviews, Reports
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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Two Major Difficulties in AI and One Applied Solution - Feb 22, 2019.
Some of AI’s biggest problems can be solved by focusing on modelling our own human abilities instead of admiring NN and ML “intelligence”. We present an example that takes us in that direction in the form of chess. -
Word Embeddings in NLP and its Applications - Feb 20, 2019.
Word embeddings such as Word2Vec is a key AI method that bridges the human understanding of language to that of a machine and is essential to solving many NLP problems. Here we discuss applications of Word2Vec to Survey responses, comment analysis, recommendation engines, and more. -
6 Books About Open Data Every Data Scientist Should Read - Feb 20, 2019.
Check out this collection of six books which tackle the hard skills required to make sense of the changing field known as open data and muse on the ethical implications of a digitally connected world. -
Automatic Machine Learning is broken - Feb 19, 2019.
We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics. -
Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019 - Feb 18, 2019.
We recap some of the major highlights in data science and AI throughout 2018, before looking at the some of the potential newest trends and technological advances for the year ahead. -
The Persuasion Paradox – How Computers Optimize their Influence on You - Feb 16, 2019.
How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence, considering it's so important? In this season finale episode, Eric Siegel introduces machine learning methods designed to persuade. -
Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues. -
Learn How to Listen: One of the hardest parts of being a data scientist - Feb 15, 2019.
Listen, Be Humble, Be Present and Transform ideas. A Data Scientist will spend a large amount of their time in meetings where you can understand the business, the goals of the area, their KPIs, and their requirements. -
Top 10 Data Science Use Cases in Telecom - Feb 14, 2019.
In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication. -
The Analytics Engineer – new role in the data team - Feb 13, 2019.
In a constantly changing landscape and with many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing, forcing the introduction of a new role: The Analytics Engineer. -
How AI can help solve some of humanity’s greatest challenges – and why we might fail - Feb 12, 2019.
AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short. -
Natural Language Processing for Social Media - Feb 12, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Natural Language Processing and how it is used in social media analytics. -
Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks. -
Data Science For Our Mental Development - Feb 11, 2019.
In this blog, I aim to generalize how AI can help us with mental development in the future as well as discuss some of the present-day solutions. -
The Best and Worst Data Visualizations of 2018 - Feb 8, 2019.
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved. -
Is Domain Knowledge a Hurdle to Start a Career in Data? - Feb 8, 2019.
How would I decide which domain to choose, while starting my career in data? Is it an obstacle? -
Neural Networks – an Intuition - Feb 7, 2019.
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this. -
Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry. - Feb 7, 2019.
The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology. -
Top 10 Technology Trends of 2019 - Feb 7, 2019.
This article outlines 10 top trending technologies for 2019, a list which covers diverse topics such as security, IoT, reinforcement learning, energy sustainability, smart cities, and much more. -
How I used NLP (Spacy) to screen Data Science Resumes - Feb 6, 2019.
A real life example of when using NLP can help filter down a list of candidates for a job opening, with full source code and methodology. -
The Essential Data Science Venn Diagram - Feb 4, 2019.
A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition. -
Five Ways Your Safety Depends on Machine Learning - Feb 2, 2019.
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime. -
Data Scientists: Why are they so expensive to hire? - Feb 1, 2019.
We provide some reasoning behind the high cost factor of hiring a data scientist, including the increasing amount of data ready to be analyzed, the structural shortage of people with the appropriate skills, and more.
January:
- What Is Dimension Reduction In Data Science?
- The Algorithms Aren’t Biased, We Are
- Cracking the Data Scientist Interview
- Monetizing the Math – are you ready?
- AI is a Big Fat Lie
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Your AI skills are worth less than you think
We are in the middle of an AI boom. That doesn’t mean that making your AI startup succeed is easy. I think there are some important pitfalls ahead of anyone trying to build their business around AI. - The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them
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What were the most significant machine learning/AI advances in 2018?
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights. - How AI and Data Science is Changing the Utilities Industry
- Data Science and Ethics – Why Companies Need a new CEO (Chief Ethics Officer)
- Cartoon: Is this how you do the blockchain thing?
- Why Ice Cream Is Linked to Shark Attacks – Correlation/Causation Smackdown
- How to Monitor Machine Learning Models in Real-Time
- Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples
- 10 Exciting Ideas of 2018 in NLP
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How to go from Zero to Employment in Data Science
We propose the quickest and surest way to go from zero experience to landing a job, either in data science generally, or specifically in a new programming language or a new technology. - The 6 Most Useful Machine Learning Projects of 2018
- Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated
- Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data
- The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup
- Explainable Artificial Intelligence
- The Role of the Data Engineer is Changing
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data
- Core Principles of Sustainable Data Science, Machine Learning and AI Product Development: Research as a core driver
- A Non-Compromising Approach to Privacy-Preserving Personalized Services
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The Five Best Data Visualization Libraries
There are plenty of library options out there to make great visualizations. We outline five of the best, complete with code examples and explanations, that will enable you to create and build interactive visualizations.
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The cold start problem: how to build your machine learning portfolio
This post outlines what makes a good machine leaning portfolio, with useful examples to help you begin to understand the type of project that gets noticed by big companies.
- Ensemble Learning: 5 Main Approaches