This list was started a while back and rather small, but it grew up to 200 articles in the past few weeks. It will reach 400 when completed. Essentially, this is the best of all our weekly digests. Also, it features all the articles (double-starred in red) that will be part of my upcoming book Data Science 2.0. So if you missed many of our recent tweets, here's a chance to see all this content at once, on one web page.

Comparing 4 Machine Learning APIs: Amazon Machine Learning, BigML, Google Prediction API and PredicSis on a real data from Kaggle, we find the most accurate, the fastest, the best tradeoff, and a surprise last place. By Louis Dorard UPDATE - NEW BIGML RESULTS: As pointed out by Francisco Martin, if you just change the objective field (SeriousDlqin2yrs) to be numeric instead of categorical, BigML's accuracy for a single model goes to 0.853 (whereas it was initially reported as 0.790 - the accuracy in the table above and the Kaggle rank below have been updated to reflect that). Amazon ML (Machine Learning) made a lot of noise when it came out last month. Shortly afterwards, someone posted a link to Google Prediction API on HackerNews and it quickly became one of the most popular posts. Google's product is quite similar to Amazon's but it's actually much older since it was introduced in 2011.

The next time you enter a query into Google's search engine or consult the company's map service for directions to a movie theater, remember that a big brain is working behind the scenes to provide relevant search results and make sure you don't get lost while driving. As Fortune's Roger Parloff wrote, the Google Brain research team has created over 1,000 so-called deep learning projects that have supercharged many of Google's products over the past few years like YouTube, translation, and photos. With deep learning, researchers can feed huge amounts of data into software systems called neural nets that learn to recognize patterns within the vast information faster than humans. In an interview with Fortune, one of Google Brain's co-founders and leaders, Jeff Dean, talks about cutting-edge A.I. research, the challenges involved, and using A.I. in its products. The following has been edited for length and clarity. A lot of human learning comes from unsupervised learning where you're just sort of observing the world around you and understanding how things behave.

I recently found a great new service on Product Hunt. My new PA is an artificial intelligence (AI) application – Amy – and she's insanely clever. Let me show you how to save hours on scheduling using this service, and then I'll ask you a question about how AI should be applied to planning. I would love to hear your feedback. Below is the test email exchange between my new AI personal assistant (Amy) and myself, as well as a couple of my internal colleagues (Nick and Brett), to set up a meeting that is good for the three of us.

Our brains are good at letting us navigate the physical world and interact with each others because their specialized mechanisms and structure are the result of selective competition. This structure makes that the brain, at birth, is not an empty box ready to be filled with knowledge pouring from our senses. Instead, the brain is more like the rough outline of a fully functional mind. The silhouette it there, the details just need to be carved out. This analogy is quite good, as a lot of the early learning and brain development consists in pruning unused neural connections.

A few short months ago, Intel acquired Nervana Systems for 400 million dollars with the intention of using the technology they developed in order to be competitive in the deep learning market currently dominated by GPU-based solutions from NVIDIA. Artificial Intelligence is a big market for Intel and the company sees it as a pivotal ground that they must put a stake in or risk falling behind like they did on the mobile front. With Nervana's technology, Intel is expecting to produce "a breakthrough 100-fold increase in performance in the next three years to train complex neural networks", says Intel CEO Bryan Krzanich in a recent editorial. Nervana's technology will be a PCIe add-in card expected to hit be out sometime around the first half of 2017, codenamed Lake Crest and incorporates HBM technology that is directly targeting current GPU solutions. Intel believes that GPGPU architecture is not uniquely advantageous for AI and that their approach can support much larger models and is much more highly scalable.

Over the past decade, B2B marketing has undergone a massive transformation. According to Forrester Research, the entire B2B sector represents more than $1 trillion in digital commerce every year, more than double the size of the B2C economy. Is artificial intelligence the rocket fuel to take us through the next decade of marketing technology? AI is not about inventing a new task or a new way of being intelligent, but simply mimicking human intelligence. Given that it's a machine doing the work, AI can be done at infinite scale since it can read and process billions of data points with perfect memory.

Nintendo Reports Second Quarter Losses But 3DS Sales Are Up Thanks To'Pokmon GO' Increasingly, the world is becoming an intelligent, digitally enabled mesh of people, things and services. Technology will be embedded in everything in the digital business of the future, and ordinary people will experience a digitally-enabled world where the lines between what is real and what is digital blur. Rich digital services will be delivered to everything, and intelligence will be embedded in everything behind the scenes. We call this mesh of people, devices, content and services the intelligent digital mesh, and this forms the basis for our Top 10 Strategic Technology Trends for 2017. Artificial Intelligence (AI) and machine learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing or application.

Summary: If you want to capitalize on all the amazing advancements in data science take a look at these two hot growth areas for IoT. It's likely that these will be where a lot of venture capital is invested over the next year or two. A lot of well deserved attention is being directed at speech, image, and text processing. The tools in this area are the CNNs and RNNs we've reviewed in recent articles. We'll continue to exploit and refine these capabilities probably for several more years but if you want to get out in front you really need to be looking for the next wave.

Numerical Optimization is one of the central techniques in Machine Learning. For many problems it is hard to figure out the best solution directly, but it is relatively easy to set up a loss function that measures how good a solution is - and then minimize the parameters of that function to find the solution. I ended up writing a bunch of numerical optimization routines back when I was first trying to learn javascript. Since I had all this code lying around anyway, I thought that it might be fun to provide some interactive visualizations of how these algorithms work. The cool thing about this post is that the code is all running in the browser, meaning you can interactively set hyper-parameters for each algorithm, change the initial location, and change what function is being called to get a better sense of how these algorithms work.