Software development has gone through massive paradigm shifts over the past decade. Once limited to developers with years of study or access to expensive servers, web development has now become a trade where bootcamps crank out developers in a matter of weeks. We are rapidly approaching our next paradigm shift, which will be AI-based code generation. When we reach that inflection point, web development will have officially died, and the labor force is woefully unprepared. Here are some of the paradigm shifts that have brought us to this point. WordPress launched on May 27th, 2003.

Google Brain's Neural Network AI has reportedly created its own universal language, which allows the system to translate between other languages without knowing them. By simply teaching the AI "how to translate from Portuguese to English and English to Spanish," the system was able to then translate from Spanish to Portuguese on its own without any outside guidance. "In the last 10 years, Google Translate has grown from supporting just a few languages to 103, translating over 140 billion words every day," declared Google in their research blog. "To make this possible, we needed to build and maintain many different systems in order to translate between any two languages, incurring significant computational cost. With neural networks reforming many fields, we were convinced we could raise the translation quality further, but doing so would mean rethinking the technology behind Google Translate." "In'Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation,' we address this challenge by extending our previous GNMT system, allowing for a single system to translate between multiple languages," they continued.

DeepMind, the British AI firm owned by Google's parent company Alphabet, has signed a new five-year deal to use data collected by the UK's National Health Service. The agreement with the NHS Royal Free Hospital Trust in London replaces a previous deal that attracted controversy over its lack of official oversight. Under the terms of the new deal, DeepMind will handle personally identifiable medical records for some 1.6 million patients, including medical history dating back five years. The agreement also includes stricter data regulation, including "technical audits" of DeepMind's systems. Using data from the Royal Free, DeepMind has built an app named Streams that alerts doctors when patients are in danger of developing acute kidney injury (AKI) -- a common but often overlooked condition.

On January 2016, the White House announced its aim to deliver a decade's worth of advances in cancer prevention, diagnosis, and treatment, in five years with this initiative. This initiative includes the building of an AI framework named CANDLE (Cancer Distributed Learning Environment) being developed by multiple organizations. It will help us change the way we understand cancer. READ MORE 6. "GPU Deep Learning has given us a new tool to tackle grand challenges that have, up to now, been too complex for even the most powerful supercomputers. Together with the Department of Energy and the National Cancer Institute we are creating an AI supercomputing platform for cancer research."

Abstract: Although Linear Regression is arguably one of the most popular analytical techniques, I believe it isn't understood well. Several fundamental assumptions are violated during application. The objective of this note is to provide an overview of the assumptions and possible fixes. Linear regression is arguably one of the most widely used techniques in the data science world. But, a comprehensive understanding of this technique is not universal and it is at a level that is less than desired.

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.