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The world is filled with news and that is a challenge for any business. Some event like an earthquake will shake up the business model and let the company reinvent itself. Others are unimportant. Some will hurt competitors and others will help everyone in the same business segment. But how can anyone know which one it is? How can people spot moments when they’re happening?
This is the challenge that media, PR and media companies own artificial intelligence (AI), AI signal, aimed at solving. Last week it launched its new External Smart Graph, a data structure that continuously tracks the major and minor events that take place through the zeitgeist each day. The system is a continuously evolving natural language model to track how companies and topics are discussed.
“You also want to be able to say that, in terms of reputation, your company is doing great, but if it’s not really what the outsiders see, well, that’s probably something to go on, ” said Clancy Childs, product manager of Signal AI.
Re-imagine the PR business as ‘normal’
The company started out nine years ago as a media surveillance effort to collect data from news sources and social media. It mainly targeted keywords, and it discovered that there was a ready market for companies that needed to think strategically about their image.
The new announcement shows some of the results of 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 were calling “decision reinforcement”.
The external intelligence graph emerged from the company’s attempt to harness the capabilities of new algorithms emerging from machine learning (ML). Signal AI’s team wants to treat text data as not just a string of characters to be searched for, but 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 make people write big keyword-based queries trying to differentiate things.” Childs explained. “We’re really going to use natural language processing, entity resolution and all these fun toys, an effective way to make it easier for people. I don’t want to write a one-page query to explain to you what an Apple Computer is. I just want to be able to treat Apple as an AI-trained entity.”
Industry Trends
Signal AI is selling its services to both companies that want to keep themselves informed and investors who want help selecting potential investments. Some clients are professionals such as communications directors who aim to track mentions of their company and their competitors. Others simply want to understand which businesses are succeeding and failing in the world of public opinion, to ensure that their investments are sound.
These major linguistic patterns and facts are becoming more common. Google is supposed to use a big, internal model of language and the world to guide how to rank answers for the search engine. Essentially, Facebook and Twitter sell users’ knowledge through an ad marketplace, allowing advertisers to select audiences based on their interests.
Microsoft and Nvidia recently touted their big model, Megatron-Turing NLG 530B, a giant language model with 530 billion parameters arranged in 105 classes. This is the culmination of a research project, but both companies are putting similar results into their products on many levels.
Some are starting to open these large systems to customers. Microsoft has both helped companies build classification systems and also packaged pre-built models into a tool for tasks like sorting and classifying images. Google’s cloud provides a natural language API that can detect entities and analyze emotions in raw texts.
Under the hood
The new External Intelligence Graph combines similar algorithms with a large collection of news articles that Signal AI has accumulated over the years. Some come from licensed sources like LexisNexis, and others are gathered from the open web through scraping or other techniques.
Signal AI is selling its services through its web interface and API. They are allowing companies to train basic models of what they want to track, and then it will populate a dashboard with both live search results, as well as information on changing sentiment. how to change.
“Our External Mind Graph takes the world’s growing unstructured content and transforms it into actionable insights to power today’s business decisions, providing organizations with present the newest kind of critical intelligence.” Luca Grulla, CTO of Signal AI said: “We are able to deliver a whole new kind of data through our unique External Intelligence Graph and an exciting new chapter in non-data mining. structure awaits.”
While raw search results can be helpful, more helpful insights can come from seeing how the External Mind Graph evolves. That is, some companies gain or lose in mentions with positive sentiment. Or companies grow closer to certain themes over time.
Childs gives an example of the Tesla company. At one point, its name in the diagram could be closely connected with the tram. Recently, however, as news of its autonomous guiding algorithms comes out, it will grow closer to those entities.
“These kinds of connections and relationships between entities and topics make it easier for companies interested in managing their own reputations and determining their position relative to their goals,” he said. Childs said.
The job for company managers is getting more complicated as some investors and clients begin to demand better accounting for non-monetary goals such as environmental stewardship. Profit calculation is very simple. However, tracking the progress of building a trustworthy brand is more difficult.
“[Many businesses are] Childs explained. “This gives them quantifiable reputation indicators of things like ESG [environmental, social and governance] very useful for companies trying to manage this kind of stakeholder capitalism and ESG accountability. “
