In today's fast-evolving business landscape, having insight into the technology stacks that organizations use is crucial for forging partnerships, uncovering market openings, and informing strategic choices. However, conventional technology mapping, which typically hinges on keyword searches, struggles with the sheer scale and variety of data available, often failing to capture nascent technologies. To overcome these hurdles, we present STARS (Semantic Technology and Retrieval System), a novel framework that harnesses Large Language Models (LLMs) and Sentence-BERT to pinpoint relevant technologies within unstructured content, build comprehensive company profiles, and rank each firm's technologies according to their operational importance. By integrating entity extraction with Chain-of-Thought prompting and employing semantic ranking, STARS provides a precise method for mapping corporate technology portfolios. Experimental results show that STARS markedly boosts retrieval accuracy, offering a versatile and high-performance solution for cross-industry technology mapping.

Computer vision is a field of artificial intelligence that teaches computers to interpret and understand the visual world. Its applications are critical for the development of AI-powered technologies such as self-driving cars, autonomous drones, industrial robots, and augmented reality headsets, among numerous other technologies. Companies that are into the development of critical components of computer vision systems are in all rights computer vision companies. This includes computer vision chip makers and companies offering full computer vision solutions. The continuous increase in computer vision technologies also provides an excellent opportunity for investors to make fortunes by investing in recession-proof computer vision stocks.

DUBLIN, April 26, 2022--(BUSINESS WIRE)--The "Global Artificial Intelligence of Things (AIoT) in Telecommunications Growth Opportunities" report has been added to ResearchAndMarkets.com's offering. This report examines the strategic position of telecommunication service providers (TSPs) in using artificial intelligence (AI) and the Internet of Things (IoT) to offer enterprises Artificial Intelligence of Things (AIoT) solutions. TSPs play a vital role in deploying enterprise AIoT solutions amid the increasing deployment of 5G networks, edge infrastructure capabilities, and location-based data at their disposal. Given their network and connectivity capabilities and AI and services focus, TSPs are in a unique position to monetize AIoT opportunities. They increasingly offer solutions by industry vertical as part of their AIoT focus.

Because there is so much data to be screened, processed, and analyzed investors at times forgo tapping into data products. Even if one is sitting on mountains of great data, human analysis of this information is frequently too time intensive to be of use. In the age of AI why not just let machines have a crack at the problem? They take fewer coffee breaks anyways -- that's exactly what researchers from Carnegie Mellon University did and in this blog we are presenting their findings. The amount of data out there is growing at an exponential rate, with 90% of the data currently available created only in the last two years.How does one keep up with this growing complexity?

The internet and search engines like Google have made the world our oyster when it comes to sourcing information, but in the world of business, there remains a persistent need for more targeted market intelligence, a way to get reliable data quickly to get on with your work. Today, one of the startups hoping to build a lucrative operation of its own around that premise is announcing a round of funding to get there. Crunchbase -- a directory and database of company-related information that originally got its start as a part of TechCrunch before being spun off into a separate business several years ago -- has raised $30 million, a Series C that it plans to use to continue expanding its base of paid subscribers and expanding its product to include more predictive, personalised information for its users by way of more machine learning and other AI-based technology. CEO Jager McConnell, who has long viewed Crunchbase as the "LinkedIn for company profiles," said that of the 55 million people who visit the site each year, the company currently has "tens of thousands" of subscribers -- subscriptions are priced at $29/user/month varying by size of company contract -- which works out to less than 1% of its active users. That's "growing quickly," he added, speaking to site's potential.

Aug 21, 2018 (Heraldkeeper via COMTEX) -- New York, August 22, 2018: Artificial intelligence (AI) Robots is arguably the foremost exciting field in artificial intelligence. It's definitely the foremost controversial: everyone agrees that a mechanism will add a production line, however there is not any consensus on whether a robot will ever be intelligent. Factors like the growing adoption of customer-centric marketing methods, increased use of social media for advertising, and increase in demand for virtual assistants are conducive to the expansion of the AI in promoting market. The Artificial Intelligence (AI) Robots Market is expected to exceed more than US$ 12 Billion by 2024 at a CAGR of 29% in the given forecast period. The Artificial Intelligence (AI) Robots Market is segmented on the lines of its application, offering, robot type and regional.

The Artificial Intelligence Market is segmented on the Basis of Technology Type, End-User Type and Regional Analysis. By Technology Type this market is segmented on the basis of Machine learning, Natural language processing, Image processing and Speech recognition. By End-User Type this market is segmented on the basis of Media & advertising, BFSI, IT & telecom, Retail, Healthcare, Automotive & transportation and Others. By Regional Analysis this market is segmented on the basis of North America, Europe, Asia Pacific, Latin America, Middle East and Africa.

New industry research that focuses on Artificial Intelligence Chips Market and delivers in-depth market analysis and future prospects of Artificial Intelligence Chips market.

Merger and Acquisition (M&A) prediction has been an interesting and challenging research topic in the past a few decades. However, past work has only adopted numerical features in building models, and yet the valuable textual information from the great variety of social media sites has not been touched at all. To fully explore this information, we used the profiles and news articles for companies and people on TechCrunch, the leading and largest public database for the tech world, which anybody can edit. Specifically, we explored topic features via topic modeling techniques, as well as a set of other novel features of our design within a machine learning framework. We conducted experiments of the largest scale in the literature, and achieved a high true positive rate (TP) between 60% to 79.8% with a false positive rate (FP) mostly between 0% and 8.3% over company categories with a small number of missing attributes in the CrunchBase profiles.

This paper presents a new approach to disambiguate company names in the Twitter social network. We have focused on making lighter the processing of comparing company profiles with tweets in order to obtain a competitive real-time system. With this aim, we only use the home page of each company as information source to create a unique profile. On the other hand, we compute the similarity of a tweet in connection to a profile by comparing the content of the tweet with the profile. Both steps do not use any other external information source and all the process is developed in an unsupervised way. We have tested our application with the test WePS-3 CLEF ORM corpus obtaining encouraging results.