A modern corporation needs to be concerned about things like climate change, supply chain risk, and competition intelligence. Signal AI’s External Intelligence Graph maps these relationships and shows how an organization is “associated” with these crucial issues. This brand-new type of data, which makes sense of the massive amounts of unstructured content that are now accessible, can reveal how relationships are changing. It also brings up “unknown unknowns,” which surface new, unconnected links to a company or person.
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External Intelligence Graph continuously monitors important events
This is the problem that Signal AI, a PR and communications startup powered by artificial intelligence (AI), hopes to solve. It launched its new External Intelligence Graph last week, a data structure that continuously monitors the significant and incidental events that permeate the zeitgeist every day. The technology monitors how businesses and subjects are discussed using an ever-evolving natural language model.
“You also want to be able to say that, reputationally, your company is doing a lot of good work, but if it’s not actually what external people see, well, that’s probably something that needs to be worked on,” said the chief product officer of Signal AI, Clancy Childs.
Nine years ago, the business started as a media monitoring project that would collect information from news sources and social media. It was mostly focusing on keywords and discovered that there was a ready market for businesses that needed to consider their brand strategy. For example, you can check out how brands are using AI for enhanced creativity.
The company’s most recent $50 million fundraising round, which took place in December, is partially reflected in the current news. At the time, Highland Europe made investments alongside Redline Capital, MMC, Hearst, and GMG Ventures to create better systems for “decision augmentation.”
The company’s efforts to take use of the capabilities of the newest, emerging machine learning algorithms led to the creation of the External Intelligence Graph (ML). The team at Signal AI intended to reframe the way that text data was perceived as a collection of things with relationships that could be tracked and assessed rather than just a stream of characters to be searched.
“We’re not going to follow an approach where we make people write massive keyword-based queries to try to disambiguate things. 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 page-long query to explain to you What Apple Computer is. I just want to be able to look for Apple as a trained entity by the AI,” explained Childs.
The target audience of Signal AI
Companies who want to stay up with news independently as well as investors who want assistance selecting possible investments are both customers of Signal AI. Some clients are professionals, such as chief communications officers who seek to monitor mentions of their own business and rivals. Others simply want to know which companies are doing well and poorly in the eyes of the general public so they may make wise investment decisions.
Events and huge language models like these are occurring more frequently. Google apparently bases how it ranks results for the search engine on a sizable internal model of language and the outside world. Through the ad market, Facebook and Twitter essentially sell user data, enabling advertisers to choose an audience based on their preferences. Check out the ABC’s of data transformation for in-depth data insights.
Megatron-Turing NLG 530B, a massive language model with 530 billion parameters grouped in 105 layers, was recently hailed by Microsoft and Nvidia. This was the result of a research initiative, but both businesses are incorporating related findings into their goods on numerous levels.
Some are beginning to make these vast systems accessible to users. Microsoft offers prebuilt models in a tool for tasks like picture sorting and classification in addition to assisting businesses in developing classifier systems. The natural language API that can recognize entities and assess sentiment in unprocessed texts is available through Google’s Cloud.
How this system operates?
Similar algorithms are combined with a vast library of news stories that Signal AI has accumulated over time to create the new External Intelligence Graph. Others are collected from the public internet using scraping or other methods, while some come from authorized sources like LexisNexis.
For some more affluent users, an API is also available for purchase via Signal AI. Companies are able to train the fundamental models they wish to track, and after that, the models will populate a dashboard with both direct search results and data on sentiment changes.
“Our External Intelligence Graphtakes the world’s burgeoning unstructured content and turns it into actionable insight to augment today’s business decisions, providing organizations with a new kind of in-the-moment critical intelligence. We are able to provide an entirely new kind of data through our unique External Intelligence Graph, and an exciting new chapter in harnessing unstructured data awaits,” said Luca Grulla, the CTO of Signal AI.
Even though the unfiltered search results can be helpful, keeping an eye on how the External Intelligence Graph develops may yield more insightful data. That is, do certain businesses benefit or suffer from positive mentions? Or do businesses get more interested in certain subjects over time?
Childs used the business Tesla as an illustration. Its name in the graph may have previously had a close association with electric automobiles. It will, however, get closer to such entities as more information about its autonomous guidance algorithms becomes available.
“These kinds of connections and relationships between these entities and topics make it easier for companies who are interested in managing their own reputation and to identify where they stand relative to their goals,” explained Childs.
Since some investors and customers have begun to request better accounting regarding non-financial goals like environmental stewardship, the job for firm management has only gotten more challenging. Profit calculation is easy. It is more difficult to monitor development toward establishing a reliable brand, though.
“Businesses no longer just sort of interested in the single bottom line of ‘Are we making enough profit? This gives them quantifiable reputation metrics on things like ESG (environmental, social and governance) which are very helpful for companies that are trying to manage their sort of stakeholder capitalism and ESG responsibilities,” Childs added.