Steve Brown - The Innovation Ultimatum

EarthNow, an ambitious startup funded by Bill Gates, Airbus, Softbank, and others, is a splendid example of machine vision's future potential. EarthNow will operate a constellation of satellites, each containing four powerful, high-definition cameras. The company's goal is essentially to create a real-time version of Google Earth, though with an important twist: Artificial intelligence, built into the satellites, runs applications to interpret camera images and add intelligent insight. These applications will reveal important details about activity on our planet.

A lightning strike in a remote location can start a devastating forest fire that rages out of control. Detection, rapid response, and early containment may save millions of acres of land from incineration, prevent damage to structures, and save lives. With EarthNow, global fire detection is just another application that runs on the satellites. The network becomes an eye in the sky that watches for fire starts and automatically alerts local authorities, 24/7/365.

EarthNow has proposed a range of other exciting applications for their satellite constellation. The system could provide real-time traffic information to city managers, real-time crop health information to farmers, and alert law enforcement or government agencies to illegal fishing, mining, and logging activity. Marine biologists will be able to track whale migration and volcanologists will monitor volcanic activity. Global asset tracking applications include tracking ships at sea, trucks on the road, planes in the air, and shipping containers in transit. Other applications include improved weather forecasting, law enforcement, and news coverage. EarthNow is a powerful platform that raises serious privacy concerns. This is why EarthNow chose to build machine learning capabilities into the satellites themselves. Users have very limited access to data from applications—real-time traffic data, the GPS locations of whales—but not the image data itself. The machine vision capabilities of the EarthNow platform present an exciting new set of opportunities for research scientists, public safety professionals, local governments, and a wide range of businesses.

Human language is complex, powerful, and full of rich nuance. It's a tough task to build a computer than can “hear” spoken natural human language, break it down into its component parts, infer meaning and intent, and then act on that information to do something useful. Artificial intelligence is behind many incredible applications of natural language processing. It can find spelling and grammatical errors, translate from one language to another, review legal contracts, and summarize the important points of a report.

Natural language processing (NLP) is an umbrella term that describes a machine's ability to understand, process, and communicate using natural human language. NLP consists of two pieces: natural language understanding (NLU) and natural language generation (NLG). You can think of one as being language input and the other as language output. In more technical terms, NLU converts unstructured human language data to structured data that a computer can understand while NLG converts structured data to unstructured data in the form of human language. Artificial intelligence is essential to both NLU and NLG. Natural language processing sits at the heart of voice interfaces, language translation services, email sentiment analysis, and many other applications that involve human language.

Computers that understand human language perform many useful business tasks. Sentiment detection assesses text to determine if it conveys positive or negative emotion, for example, to highlight positive online product reviews or surface negative customer emails that require a swift response. NLU is also used to detect profanity, hate speech, threats, abuse, and other conversation that may be deemed inappropriate.

Natural language understanding is used to analyze documents and provide decision support. Scriptbook, a startup from Antwerp, Belgium, reviews movie screenplays to predict their likely box office failure or success. The software helps studios to make greenlight decisions on scripts. Scriptbook analyzed the scripts of 62 movies released in 2015 and 2016. Thirty of these movies were box office successes; 32 were failures and lost money. Scriptbook's AI correctly predicted all 30 of the box office hits and correctly called 22 of the movies that were duds. With 52 correct calls made on 62 movies, the AI scored far better than the Hollywood moguls had. Scriptbook also uses NLU to predict a film's likely MPAA rating, the likeability of characters, and the countries where a movie will find most success, all based on its script.

LawGeex, an Israeli company, uses natural language processing to automate the review of legal contracts and nondisclosure agreements. LawGeex challenged 20 U.S.-trained lawyers to identify legal issues in five real-life nondisclosure agreements (NDAs), faster than their AI. The test was overseen by an independent lawyer and performed with input from legal experts and law professors. The lawyers took an average of 92 minutes to review all five NDAs and achieved an average accuracy rate of 85%. The LawGeex AI scored an accuracy of 94%, equal to the best lawyer's score, and completed the entire task in just 26 seconds. Lawyers that I've told about this AI are generally delighted. Reviewing NDAs is not a favorite part of their work and they are excited to offload routine tasks and focus more time on higher value, higher revenue work.

Natural language understanding speeds data entry. AIs identify email addresses, physical addresses, dates of birth, and phone numbers from nonstandard forms, automatically. This technology, sometimes combined with handwriting recognition (thanks again, AI!), makes short work of data entry.

Voice recognition combines speech-to-text capabilities with NLU. This technology automatically creates subtitles for videos and presentations. Microsoft now includes this capability with some versions of its PowerPoint application.

Computers with the ability to write or speak in natural human language are a huge breakthrough. AIs generate language either from source data or from source text. For example, an auto-generated weather report is created from weather forecast data, while a translation from one language to another is performed based on source text.

Natural language generation (NLG) has many valuable business applications. Language translation is an obvious one. Another is the automatic creation of summaries and abstracts. These might be summaries of financial reports, legal documents, operations reports, performance reviews, news articles, or medical records. NLG is combined with image recognition to automatically caption or describe images. This is a valuable function for people with visual impairment and also improves the quality of image searches.

In March 2014, the Los Angeles Times published a short story on an earthquake in Beverly Hills, California. The article described the location, time, and strength of the earthquake (4.4 on the Richter scale, if you're interested) and posted it to the L.A. Times website within three minutes of the earthquake. The article was written by a simple piece of software that sourced seismic data from the U.S. Geological Survey. This simple automation populated data into a prewritten template but demonstrates the power of automation. In a world of 24-hour news cycles and shrinking ad revenue, NLG frees human reporters to focus on higher-value stories and investigative journalism. More advanced automation now writes weather and traffic reports, summarizes business results, and covers sports events. Wordsmith, an automated reporting platform used by the Associated Press (AP), uses NLG to generate stories about minor league baseball games, college basketball, and quarterly corporate earnings reports. The AP claims that Wordsmith produces more than 4,400 corporate recaps each quarter, more than 15 times the number it could previously handle using a human writing staff. Wordsmith offers a glimpse of the more sophisticated NLG capabilities coming in the near future.