Signal AI opens External Intelligence Graph for enterprise use


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The world is inundated with news, and that’s a challenge for any business. Some events are like earthquakes that will shake up the business model and for the company to reinvent itself. Others are inconsequential. Some will hurt competitors and some will help everyone in the same industry. But how can anyone know which is which? How can someone spot the moments as they unfold?

This is the challenge that the public relations and communications company, powered by artificial intelligence (AI), AI signal, aim to attack. Last week, it rolled out its new External Intelligence Graph, a data structure that continuously tracks major and minor events that cross the air every day. The system is an ever-evolving natural language model that tracks how businesses and topics are discussed.

“You also want to be able to say that, reputation-wise, your company does a lot of good work, but if that’s not really what external people see, well, that’s probably something he have to work,” said Clancy Childs, product manager at Signal AI.

Reinvent PR activities as “usual”

The company began nine years ago as a media monitoring effort that would collect data from news sources and social media. He was largely targeting keywords and found that there was a ready market for companies that needed to think strategically about their image.

The new announcement shows some results from the company’s recent $50 million fundraising round last December. At the time, Highland Europe along with Redline Capital, MMC, Hearst and GMG Ventures invested in building better mechanisms for what they called “decision augmentation”.

The External Intelligence Graph was born out of the company’s efforts to harness the capabilities of new emerging machine learning (ML) algorithms. The Signal AI team wanted to think of text data as more than a stream of characters to search, but rather as a collection of entities with relationships between them that can be tracked and measured.

“We’re not going to take an approach where we force people to write massive keyword-based queries to try to disambiguate.” explained Childs. “We’re actually going to use natural language processing, entity resolution, and all these cool toys, effectively to make it easier for people. I don’t want to write a one-page query to tell you what Apple Computer is. I just want to be able to search for Apple as an AI formed entity.

Signal AI sells its service both to businesses that want to keep tabs on the news themselves, and to investors that want to help pick out potential investments. Some clients are professionals like communications managers who aim to track mentions of their own company and their competitors. Others just want to understand which companies are succeeding and failing in the world of public opinion, to make sure their investments are sound.

These big language patterns and events are becoming more and more common. Google would use its internal big language model and the world to guide how it ranks responses for the search engine. Facebook and Twitter essentially sell user knowledge through the ad marketplace, allowing advertisers to select an audience based on their interests.

Microsoft and Nvidia recently touted their great model, Megatron-Turing NLG 530B, a huge language model that has 530 billion parameters arranged in 105 layers. This was the culmination of a research project, but both companies incorporate similar results into their products on many levels.

Some are starting to open these large systems up to customers. Microsoft both helps companies create classification systems and also bundles pre-made templates into a tool for jobs like sorting and classifying images. Google’s Cloud offers the Natural Language API capable of entity detection and sentiment analysis in plain text.

under the hood

The new External Intelligence Graph combines similar algorithms with an extensive collection of news articles that Signal AI has amassed over the years. Some come from licensed sources like LexisNexis, and some are collected from the open web through scraping or other techniques.

Signal AI sells its service through a web interface and, for some advanced customers, an API. They let companies form basic models of what they want to track, and then they’ll populate a dashboard with both direct search results, as well as information on changing sentiment.

“Our External Intelligence Graph takes the world’s booming unstructured content and transforms it into actionable insights to augment today’s business decisions, providing organizations with a new kind of real-time critical intelligence.” said Luca Grulla, CTO of Signal AI, “We are able to deliver a whole new kind of data through our unique external intelligence graph, and an exciting new chapter in unstructured data mining awaits. ”

While raw search results can be helpful, the most useful information can come from the evolution of the external intelligence graph. In other words, some companies win or lose mentions with positive sentiment. Or are companies getting closer to certain topics over time.

Childs gave an example from the Tesla company. At one point, his name in the chart may be closely related to electric vehicles. Lately, however, as news about its autonomous guidance algorithms emerges, it will get closer to these entities.

“These kinds of connections and relationships between these entities and subjects make it easier for businesses to manage their own reputation and identify where they stand in relation to their goals,” Childs said.

The job of business leaders has only become more complicated as some investors and customers have begun to demand better accountability for non-monetary goals such as environmental stewardship. Calculating profits is simple. However, it is harder to track progress towards building a trustworthy brand.

“[Many businesses are] is no longer simply interested in the single bottom line of ‘Are we making enough profit?’ “Explained Childs. “It gives them quantifiable reputation metrics on things like ESG [environmental, social and governance] which are very useful for companies trying to manage their type of stakeholder capitalism and their ESG responsibilities. »

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