Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.
Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’
Bernard Marr ist ein weltbekannter Futurist, Influencer und Vordenker in den Bereichen Wirtschaft und Technologie mit einer Leidenschaft für den Einsatz von Technologie zum Wohle der Menschheit. Er ist Bestsellerautor von 20 Büchern, schreibt eine regelmäßige Kolumne für Forbes und berät und coacht viele der weltweit bekanntesten Organisationen. Er hat über 2 Millionen Social-Media-Follower, 1 Million Newsletter-Abonnenten und wurde von LinkedIn als einer der Top-5-Business-Influencer der Welt und von Xing als Top Mind 2021 ausgezeichnet.
Bernards neueste Bücher sind ‘Künstliche Intelligenz im Unternehmen: Innovative Anwendungen in 50 Erfolgreichen Unternehmen’
What Really Is Natural Language Generation And Processing?
2 July 2021
In our everyday lives, we encounter natural language generation (NLG) in many ways that go beyond asking Alexa for the forecast or Siri for directions. As the technology for natural language generation improves, we will experience even more applications where machines produce easy-to-consume natural narratives that are on par or possibly even better than human-generated content.
What is natural language generation?
Natural language generation is a subset of artificial intelligence that takes data in and transforms it into language that sounds natural, as if a human was writing or speaking the content. While this capability isn’t new, it is much more sophisticated today and there is a significant uptick in adoption of NLG enterprise-wide to improve operational efficiency, human productivity and even customer engagement. A machine is able to process an extraordinary amount of data with a high level of accuracy and the goal of NLG systems is to determine how best to communicate the findings or analysis of the data. When organisations use machines to automate the more routine communication tasks, it frees up humans to focus on more complex communication initiatives.
We’re in the era of advanced NLG and machines can communicate just like humans. NLG systems identify what might be interesting or vital to communicate to a specific audience and then transforms that intelligent insight to create content packed with audience-relevant info and writes it in conversational language.
“Where NLG is really good is where you have information from a lot of sources that needs to be combined, integrated and presented to various audiences,” said Ehud Reiter, professor of computing science and chief scientist for Arria in an article for Computer Weekly.
What’s the difference between natural language generation and natural language processing (NLP)?
When trying to distil the differences of NLG and NLP into the most simplified version, you can think of NLG as the writer and NLP as the reader who consumes what NLG technology writes. NLG begins with the data and unlocks the meaning to turn the data into language and then communicates it. NLP then takes over to look at the language and then figures out what message is being communicated.
NLG industry leaders
Natural language generation and processing suppliers seek to find ways of applying their technology and tools to any industry that needs real-time data storytelling. Companies such as Arria, Narrative Science and Automated Insights have their own NLG platforms that automatically generate narratives on a huge scale that sound like they were created by humans. NLG technology has huge potential for many industries including journalism, finance, business service, healthcare and more. Applications for NLG and NLP in our everyday lives
Many of us already appreciate the conveniences of intelligent personal assistants such as Alexa, Siri and Cortana when they help us set timers, read the daily news headlines and play music per our requests throughout the day. Spurred on by its success in our everyday lives, conversational interfaces and natural language generation and processing are infiltrating many areas. Here are just a few examples:
Amazon recently announced its latest products and Alexa is central to all of them. And it’s clear that the latest instalment of Alexa is also more sophisticated and intuitive. While there is still some way to go before Alexa and accompanying products create the perfect scenario for a smart home, it’s headed in the right direction.
Not only has Amazon increased Alexa’s presence in its new product line-up, BMW plans to integrate Alexa into its vehicles and select Mini vehicles in the new year. BMW and Mini drivers will get Alexa’s comprehensive Internet abilities inside their cars.
Sonos speakers has had Alexa support in a limited private beta that they recently announced will roll out to all Sonos speakers for a public beta. The company’s newest model, the Sonos One has a microphone built in, but for older models Alexa integration is accomplished through a connected Alexa device.
Google is also pushing virtual assistant and is trying to catch up with Amazon. Google’s massive installation at this year’s Consumer Electronics Show in Las Vegas is one clear indication that the company is very serious about gaining market share.
Artificial intelligence has been put to work in the newsroom of media companies doing everything from eliminating fake news, putting together reports and stories from raw data like this one from Wired, crunching data and more. Google is actually funding a robot journalism project. The Associated Press uses NLG to generate and publish corporate earnings stories. Leaders of the project are quick to point out that “skilled journalists will still be vital in the process.”
You likely have interacted with NLG and you didn’t even know it.
Narrative Science’s Quill software supports clients such as Deloitte and the US Automobile Association with generating portfolio reviews that have easy-to-understand commentary to accompany charts and tables. They are also using NLG technology to personalise online interactions in a way that’s similar to conversations when they interact with customers on the phone.
We have only scratched the surface here for the ways that companies are using NLG to process and communicate vasts amounts of information. If one thing is for certain, NLG and NLP will continue to get better and lots more use cases will be realised in the near future.