X is perhaps the only enterprise on the planet where regular investigation into the absurd is not just permitted but encouraged, and even required. X has quietly looked into space elevators and cold fusion. It has tried, and abandoned, projects to design hoverboards with magnetic levitation and to make affordable fuel from seawater. It has tried—and succeeded, in varying measures—to build self-driving cars, make drones that deliver aerodynamic packages, and design contact lenses that measure glucose levels in a diabetic person’s tears.
The purpose of X is not to solve Google’s problems; thousands of people are already doing that. Nor is its mission philanthropic. Instead X exists, ultimately, to create world-changing companies that could eventually become the next Google.
At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. We are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework for TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you, and that you can use to analyze English text.
We’ve been building the best AI team and tools for years, and recent breakthroughs will allow us to do even more. This past March, DeepMind’s AlphaGo took on Lee Sedol, a legendary Go master, becoming the first program to beat a professional at the most complex game mankind ever devised. The implications for this victory are, literally, game changing—and the ultimate winner is humanity. This is another important step toward creating artificial intelligence that can help us in everything from accomplishing our daily tasks and travels, to eventually tackling even bigger challenges like climate change and cancer diagnosis.
All of this represents the conclusion of a test that began all the way back in 2010, and has been tweaked over the years since then. Search Engine Land readers — particularly some outside the U.S. — notified us late last year that they were seeing the top-only ads more frequently.
Data-driven design. I love stuff like this.
It’s easy for sites with rich content to run into performance issues on mobile devices. If you’ve ever browsed a content site that has a heavy footprint on desktop, chances are, the site wasn’t the fastest you’ve ever visited when you viewed it on your phone or tablet.
Google’s Accelerated Mobile Pages (AMP) project aims to solve these issues and make the user’s browsing experience “instant”, especially on resource-constrained mobile devices. The AMP project relies on existing standards and current technologies, so how exactly does it accomplish better performance? Largely by restricting what developers are able to incorporate into their sites.
This is a really good initiative.
A great piece on the unification of Google’s design process. I disagree with the title, though. I define good design (in the context of software) as UIs and accompanying UXs that are intuitive and easy to grasp for as many users as possible. Save for maybe Google+, I’ve never noticed a significant amount of uproar regarding older Google UIs/UXs. They were pretty good; it’s not like Google didn’t “get” design before Material Design.