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The state news agency of China has launched a digital version of its regular news presenter. Powered by artificial intelligence (AI) this version can keep breaking news stories 24 hours a day. Working in collaboration with search engine company Sogou, Chinese state news agency Xinhua showcased a digital version of one of its human reporters at China’s annual World Internet Conference in Wuzhen. The AI Anchor ingests and analyzes vast quantities of news stories all day, every day and then “learns from live broadcasting videos and can read texts as naturally as a professional news anchor,” according to a statement released by Xinhua.
Xinhua is not the first company to deploy AI in its news reporting. In 2014 the Associated Press wire service started using sophisticated computer algorithms to write 3,000 automated stories a quarter, with plans to grow this number over time. Whilst the initial focus was company earnings reporting, where speed and accuracy are critical, the clear plan was to use automated story generation to generate more nuanced and complex news stories over time.
Xinhua’s showcase takes things several steps further. The accomplishment of generating a life-like virtual news presenter, based on a real human being, that can report relevant new stories is impressive. However, what is impactful is the use of AI or machine learning to scour thousands of news stories, identify the most relevant ones for an audience and then deliver this in near real time. This demonstrates how far machine learning and AI have progressed in enabling the processing of huge volumes of data into actionable outcomes quickly; in this case a live news report featuring new and breaking stories.
Xinhua’s showcase demonstrates how AI and machine learning are impacting the future of news reporting and journalism. It is also a timely reminder of how any organization can quickly interrogate huge data sets to achieve specific goals. Using the same AI technology that can be deployed to deliver virtual news reports, banks are analyzing huge data sets of financial transactions in real time to identify and report potentially fraudulent activity. Academics can access and analyze millions of papers from across the globe to help inform more effective research. Machine learning is also being deployed to help inventors identify others working in similar areas and collaborate. Insurance companies are looking to offer more personalized quotes to people based on big data sets of geographical and demographic information.
This insight is being powered by several facilitating technologies: on demand computing enables a company of any size to generate insight from even the largest data sets without the need to invest directly in expensive servers; computer storage has become so commoditized that companies can now afford to manage and process petabytes of information.Often companies will have granular data sets about their own customers or operations. The missing part of the jigsaw can be external data sources that help contextualize this information and drive actionable business insight. Data sets need to be normalized, with a volume and variety of data, that is enriched and structured effectively to drive insight. Near real-time data can help businesses quickly identify opportunities or risk.Nexis® Data as a Service (DaaS) provides companies with access to comprehensive and relevant news and corporate biographical information from a wide range of reputable sources. From analyzing social commentary for emerging patterns to understanding more about risk potential of customers you are doing business with—this data can be quickly and readily deployed.
Recent developments in access to computing and storage, combined with machine learning, is transforming the insight that companies can glean from data. Critical to this is not only accurate and timely internal information but access to on demand external data to help drive further insight. Just as Xinhua plans to transform its operations through relevant analysis and insight from big data, so businesses in finance, risk, supply chain, marketing and sales can use AI-powered big data analytics to find the nuggets of insight in huge data sets.