Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.
- Fitting to Noise or Nothing At All: Machine Learning in Markets
Deep learning or “derp” learning? Zachary David takes a look at a recent application paper of deep learning in financial markets and shows all the ways how it fails.
- Elon Musk: Mark Zuckerberg’s understanding of AI is “limited”
Tech billionaires have differing views on where AI will take humankind.
- The AI Hierarchy of Needs
More often than not, companies are not ready for AI. Think of AI as the top of a pyramid of needs. Yes, self-actualization (AI) is great, but you first need food, water and shelter (data literacy, collection and infrastructure). Must read article!
- Cargo cult data science
Cargo cults describe situations where people emulate a set of behaviours, without understanding their underlying motivations. When this happens with data-science, organizations will emulate the technology behind data science, without creating a data-driven organizational culture.
- The human insights missing from big data (Ted talk)
Why do so many companies make bad decisions, even with access to unprecedented amounts of data? With stories from Nokia to Netflix to the oracles of ancient Greece, Tricia Wang demystifies big data and identifies its pitfalls, suggesting that we focus instead on “thick data” — precious, unquantifiable insights from actual people — to make the right business decisions and thrive in the unknown.
- The ‘creepy Facebook AI’ story that captivated the media
No, Facebook Did Not Panic and Shut Down an AI Program That Was Getting Dangerously Smart
“‘Robot intelligence is dangerous’: Expert’s warning after Facebook AI ‘develop their own language'” Is it time to panic and start preparing for apocalypse at the hands of machines?
- Cutting Edge Deep Learning for Coders—Launching Deep Learning Part 2
Fast.ai has launched part 2 in its deep learning course offering. Highly recommended!
- Thoughts on OpenAI, reinforcement learning, and killer robots
There is a lot of confusion about the term AI.
- Artificial Intelligence Is Stuck. Here’s How to Move It Forward
Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go.
- Robust Adversarial Examples
OpenAI has created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.
- Detecting Deviating Data Cells (pdf)
Interesting statistical cell-wise anomaly detection approach from our colleagues at the Department of Mathematics.
- Chinese chatbots shut down after anti-government posts
This story is as hilarious as what happened last time with Microsoft’s Tay bot. Here, Tencent shuts down its chat bot after it replies in a negative manner regarding China’s government.
- Deep Learning for NLP Best Practices
There has been a running joke in the NLP community that an LSTM with attention will yield state-of-the-art performance on any task. While this has been true over the course of the last two years, the NLP community is slowly moving away from this now standard baseline and towards more interesting models.
- Agents that imagine and plan
Imagining the consequences of your actions before you take them is a powerful tool of human cognition. If our algorithms are to develop equally sophisticated behaviours, they too must have the capability to ‘imagine’ and reason about the future, Deepming argues.
- Your ‘Anonymous’ Browsing Data Isn’t Actually Anonymous
Researchers said it was “trivial” to identify users and view their browsing habits in purchased ‘anonymous’ browsing data.
- An end to end implementation of a Machine Learning pipeline
A technical introduction of an end-to-end machine learning project.