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.
- Artificial Intelligence Still Isn’t All That Smart
Some routine jobs might be at risk someday, but work requiring judgment seems safe.
- What Data Scientists Really Do, According to 35 Data Scientists
“It’s true that data science is a varied field. The data scientists I’ve interviewed approach our conversations from many angles.”
- A small team of student AI coders beats Google’s machine-learning code
The success shows that advances in artificial intelligence aren’t the sole domain of elite programmers.
- Flagler Hospital uses AI to create clinical pathways that enhance care and slash costs
A pneumonia pathway saved $1,356 per patient, lowered length of stay by two days and significantly reduced readmission rates.
- What HBR Gets Wrong About Algorithms and Bias
“The Harvard Business Review recently published an article, Want Less-Biased Decisions? Use Algorithms. by Alex P. Miller. The article focuses on the fact that humans make very biased decisions (which is true), yet ignores many important related issues.”
- AI for cybersecurity is a hot new thing—and a dangerous gamble
Machine learning and artificial intelligence can help guard against cyberattacks, but hackers can foil security algorithms by targeting the data they train on and the warning flags they look for.
- Safety-first AI for autonomous data centre cooling and industrial control
“In 2016, we jointly developed an AI-powered recommendation system to improve the energy efficiency of Google’s already highly-optimised data centres. Our thinking was simple: even minor improvements would provide significant energy savings and reduce CO2 emissions to help combat climate change.”
- TimescaleDB vs. InfluxDB: purpose built differently for time-series data
An in-depth look into how two leading time-series databases stack up in terms of data model, query language, reliability, performance, ecosystem, operational management, and company/community support.
- NVIDIA Unveils Quadro RTX, World’s First Ray-Tracing GPU
Also interesting is this tidbit: “Turing Tensor Cores to accelerate deep neural network training and inference, which are critical to powering AI-enhanced rendering, products and services.”
- Machine learning links brain connectivity patterns with psychiatric symptoms
Using machine learning, researchers from the University of Pennsylvania found that four dimensions of psychopathology — mood, psychosis, fear and disruptive externalizing behavior — were linked to distinct patterns of connectivity in the brain.
- Shrinking Machine Learning Models for Offline Use
A new technique for compressing machine-learning models that reduces their memory footprints by 94% while leaving their performance almost unchanged.
- How Should We Evaluate Machine Learning for AI?: Percy Liang (video presentation)
“Machine learning has undoubtedly been hugely successful in driving progress in AI, but it implicitly brings with it the train-test evaluation paradigm. This standard evaluation only encourages behavior that is good on average; it does not ensure robustness as demonstrated by adversarial examples, and it breaks down for tasks such as dialogue that are interactive or do not have a correct answer. In this talk, I will describe alternative evaluation paradigms with a focus on natural language understanding tasks, and discuss ramifications for guiding progress in AI in meaningful directions.”
- AlphaGo Zero demystified
“DeepMind has shaken the world of Reinforcement Learning and Go with its creation AlphaGo, and later AlphaGo Zero. It is the first computer program to beat a human professional Go player without handicap on a 19 x 19 board. I will present my work on reproducing the paper as closely as I could.”
- Auto Keras
“Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors.”
- Furby source code (pdf)
A fun blast from the past (1998): AI before it was “deep”.