Enterprises and investors are increasing their use of natural language APIs to assist processing in tasks like data mining for sales intelligence, tracking how marketing campaigns change over time, and better defending against phishing and ransomware attacks. Still, AI products that use natural language engines to analyze text have a long way to go to capture more than a fraction of the nuance humans use to communicate with each other. The company this week announced new advanced features for its cloud-based natural language API designed to help AI developers "[extract] emotions in large-scale texts and [identify] stylometric data driving a complete fingerprint of content," Expert.ai said in a statement. Based in Modena, Italy and with U.S. headquarters in Rockville, Maryland, Expert.ai The company's customers include media outlets like the Associated Press, which uses NL software for content classification and enrichment; business intelligence consultants like L'Argus de la Presse, which conducts brand reputation analysis with NL processing; and financial services firms like Zurich Insurance, which uses Expert.ai's

In a recent blog post, Google announced the public preview of two new fully-managed AI tools: Healthcare Natural Language API and AutoML Entity Extraction for Healthcare. Both tools can assist healthcare professionals in reviewing and analyzing medical documents in a repeatable, scalable way. By delivering these new tools, the public cloud vendor hopes to reduce workforce burnout and increase healthcare productivity, both in the back-office and in clinical practice. The new Healthcare Natural Language API uses Artificial Intelligence (AI) to help medical staff like doctors to extract the most pertinent information they need to know about their patients they treat from the stacks of medical records related to each individual. The API has been trained on thousands of medical documents to extract the information medical staff needs to know - using machine learning to classify clinically important attributes based on the surrounding context in medical records.

The global Artificial Intelligence company Expert System announced the release of the expert.ai NL API, the cloud-based Natural Language API that enables data scientists, computational linguists, knowledge engineers and developers to easily embed advanced Natural Language Understanding and Natural Language Processing capabilities (NLU / NLP) into their applications. This release is the first step in executing on the company's strategy to become the global platform of reference for AI-based Natural Language problem solving. The growing need for accessible and accurate AI-based NLU / NLP applications in the enterprise places increased demand on the developer ecosystem to bring speed, scale and precision to linguistic analysis. According to Gartner, "during recent years, advances in the application of machine learning (including neural networks) and knowledge graphs to natural language processing have enabled machine-based attribution that diminishes the need for human oversight. Application of the technology is broadening as well as deepening -- across industries and functional domains, and into use cases -- pushing this innovation from many years in the Tough of Disillusionment toward the Slope of Enlightenment."

This past May I worked with the Internet Archive's Television News Archive to apply Google's suite of cloud AI APIs to analyze a week of television news coverage to examine how AI "sees" television and what insights we might gain into the world of non-consumptive deep learning-powered video understanding. Using Google's video, image, speech and natural language APIs as lenses, more than 600GB of machine annotations trace how deep learning algorithms today understand video. What lessons can we learn about the state of AI today and how it can be applied in creative ways to catalog and explore the vast world of video? Working with the Internet Archive's Television News Archive, a week of television news was selected covering CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news. This week was selected due to it having two major stories, one national (the Mueller report release on April 18th) and one international (the Notre Dame fire on April 15th).

Television news coverage is typically thought of as a visual medium, yet most of the narrative we consume from television comes in the form of spoken narration. Watching a news show with the audio muted and closed captioning off reinforces that the visual elements of television act more as enrichment than primary information conveyor. This means that quantifying this spoken narrative is imperative to understanding what television news is paying attention to and how it is framing and covering those events. Using Google's Cloud Speech-to-Text API to transcribe a week of television news coverage and annotating it with Google's Natural Language API, what might we learn about how television news covers the world? In the United States, most television stations provide closed captioning for their news programming, meaning they already come with a textual human-produced transcript.

Most television stations still rely on human transcription to generate the closed captioning for their live broadcasts. Yet even with the benefit of human fluency, this captioning can vary wildly in quality, even within the same broadcast, from a nearly flawless rendition to near-gibberish. At the same time, automatic speech recognition has historically struggled to achieve sufficient accuracy to entirely replace human transcription. Using a week of television news from the Internet Archive's Television News Archive, how does the station-provided primarily human-created closed captioning compare with machine-generated transcripts generated by Google's Cloud Speech-to-Text API? Automated high-quality captioning of live video represents one of the holy grails of machine speech recognition. While machine captioning systems have improved dramatically over the years, there has still been a substantial gap holding them back from fully matching human accuracy.

Google has started out with a mission to empower everything and everyone with artificial intelligence (AI). It has open-sourced Tensorflow and supporting libraries to enable developers and enterprises to build and train models, and infer (predict) using those. Building useful enterprise services with this may take time. Google has also exposed many of the AI services (via Cloud Machine Learning APIs) that can be quite useful in your applications. Extracting features and text from images, translating text from one language to another, and doing sentiment analysis on text can help improve user experience dramatically.

Bots are the new black, everyone wants to build one. If you want to do it too, unless you have a Natural Language Processing expert on your team, public APIs are your safest bet. For building an app that has to understand a single command (ex. Siri), current APIs may solve your problem. If you want to build a conversational agent, things get more complicated.

Google announced today that its Cloud Video Intelligence API is generally available, along with a new Content Classification feature for its Cloud Natural Language API. The updates are aimed at giving customers new capabilities for making their applications smarter using prebuilt machine learning systems. As their names imply, the two services are designed to provide developers with tools that they can use to make applications understand the content of videos and text. That, in turn, is supposed to help with the creation of more intelligent apps that would have previously been the domain of the tech titans. In addition to becoming generally available, the Cloud Video Intelligence API can now be used to transcribe the contents of video fed into it, along with its existing support for detecting objects within footage, spotting shot changes in a single video, and spotting explicit content in footage.

Update 10/25/17 3:53 PM: A Google spokesperson responded to Motherboard's request for comment and issued the following statement: "We dedicate a lot of efforts to making sure the NLP API avoids bias, but we don't always get it right. This is an example of one of those times, and we are sorry. We take this seriously and are working on improving our models. We will correct this specific case, and, more broadly, building more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone." John Giannandrea, Google's head of artificial intelligence, told a conference audience earlier this year that his main concern with AI isn't deadly super-intelligent robots, but ones that discriminate.