Every day, millions of articles and papers are published. While there is a lot of knowledge hidden in those articles, it is virtually impossible to read all of them. Even if you only focus on a specific domain, it is still hard to find all relevant articles and read them to get valuable insights. However, there are tools that could help you avoid manual labor and extract those insights automatically. I am, of course, talking about various NLP tools and services. In this blog post, I will present a solution of how you can combine the power of NLP with knowledge graphs to extract valuable insights from relevant articles automatically.

This is a problem if we want AIs to be trustworthy. That's why Diffbot takes a different approach. It is building an AI that reads every page on the entire public web, in multiple languages, and extracts as many facts from those pages as it can. Like GPT-3, Diffbot's system learns by vacuuming up vast amounts of human-written text found online. But instead of using that data to train a language model, Diffbot turns what it reads into a series of three-part factoids that relate one thing to another: subject, verb, object.

Wouldn't it be fabulous to know for sure that an article you read online is authentic and contains trusted sources? If everyone used AI to fact check, fake news and data could be eliminated permanently from online news sites. Menlo Park, CA-based AI startup, Diffbot has announced an official partnership with the European Journalism Centre to combat fake news. The company is the only other US company aside from Microsoft and Google to crawl and index the entire web to create its Knowledge Graph. Journalists can access the DKG through the Data Journalism platform created by the European Journalism Centre to provide resources, materials, online courses and community forums for data journalists all over the world.

You can say this for Diffbot: It's not shying away from a big challenge. The San Francisco startup, which just closed $10 million in Series A investment, wants to scrape all the data on the web (all of it) to put it into a structured format, which thus makes it useful for all sorts of business purposes. And perhaps more to the point, the five-year-old company became profitable last year, according to founder and chief executive Mike Tung. Of course that isn't really verifiable from the outside, but it's worth noting especially as far larger companies are struggling to find a good business model for AI or cognitive computing or whatever the next name for this self-teaching technology will be. The company claims big customers including Amazon (amzn), DuckDuckGo, and Salesforce (crm) which uses the service in its Radian6 "social listening" business.

Diffbot is trying to reorganize all the data on the Web so it can be put to better use. The service "converts the existing Web into a structured database-like representation that can essentially be used for all sorts of intelligent applications," said Mike Tung, Diffbot CEO. On Thursday, Diffbot said it had received $500,000 in funding from Bloomberg Beta, the investment arm of the Bloomberg media company. Andy Bechtolsheim, a founder of Sun MIcrosystems and the first major investor in Google, is also a backer. Diffbot says it already has paying customers for the service, which is being used by Microsoft's Bing, Adobe, Salesforce.com, and eBay.

Diffbot is a geeky and incredibly interesting technology that uses bots, algorithms, computer vision and artificial intelligence to process the content on the Web the way a human being can. "The entire Internet can be broken down into 30 different page types" explains Co-founder Mike Tung, also known as "Diffbot Mike," and "Diffbot can identify them all." Diffbot knows the difference between a social network profile, a blog post, a site's front page, a product page, an event page and dozens more. Today, Diffbot is releasing its first set of APIs, now open to all developers for free. The launch has the potential to dramatically impact the types of applications developers can build, and for consumers, it means a whole host of intelligent applications are about to emerge.

Rather recently, Microsoft released an app using AI to detect a dog's breed. In my non-SitePoint time, I also work for Diffbot – the startup you may have heard of over the past few weeks – who also dabble in AI. To test how they compare, in this tutorial we'll recreate Microsoft's application using Diffbot's technology to see if it does a better job at recognizing the adorable beasts we throw at it! We'll build a very primitive single-file "app" for uploading images and outputting the information about the breed under the form. If you'd like to follow along, please register for a free 14-day token at Diffbot.com, if you don't have an account there yet.

Diffbot, an artificial intelligence company that helps clients extract and combine data from multiple Web sources, announced today it raised 10 million from investors including Tencent and Felicis Ventures to expand its "knowledge-as-a-service" offerings to businesses and consumer apps. The Palo Alto, CA-based startup, founded in 2009, still has a tiny staff of 14. But Diffbot's ambition is huge: to catalog trillions of facts across the Web--many of them drawn from page elements such as comment forums, which can't be mined by traditional search engines. The startup says it has made a significant start on that goal, having indexed 1.2 billion entities such as people, products, and places since the middle of last year. Its Global Index also encompasses 10 to 20 times that number of facts, says Diffbot founder and CEO Mike Tung.

When Satya Nadella made machine learning the centerpiece of the Microsoft Build conference, I think it became official: 2016 is the year of machine learning. All the major clouds now (or will soon) have machine learning APIs. In fact, InfoWorld's Martin Heller has already reviewed the machine learning services offered by AWS, Azure, and IBM Cloud. Even more telling, a couple of years ago only a handful of machine learning startups were out of stealth. Now there are -- what -- a thousand?

When Satya Nadella made machine learning the centerpiece of the Microsoft Build conference, I think it became official: 2016 is the year of machine learning. All the major clouds now have (or will soon have) machine learning APIs. In fact, InfoWorld's Martin Heller has already reviewed the machine learning services offered by AWS, Azure, and IBM Cloud. Even more telling, just a couple of years ago only a handful of machine learning startups were out of stealth; now there are, what, a thousand? Oddly, Nadella's Build talk revolved around intelligent bots, which operate a little like today's highly annoying interactive voice response systems.