Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC). These documents are typically very long and tend to contain valuable soft information about a company's performance. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs). Whereas there has been a great progress in pre-trained language models (LMs) that learn from tremendously large corpora of textual data, they still struggle in terms of effective representations for long documents. Our work fills this critical need, namely how to develop better models to extract useful information from long textual documents and learn effective features that can leverage the soft financial and risk information for text regression (prediction) tasks. In this paper, we propose and implement a deep learning framework that splits long documents into chunks and utilizes pre-trained LMs to process and aggregate the chunks into vector representations, followed by self-attention to extract valuable document-level features. We evaluate our model on a collection of 10-K public disclosure reports from US banks, and another dataset of reports submitted by US companies. Overall, our framework outperforms strong baseline methods for textual modeling as well as a baseline regression model using only numerical data. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs in representing long documents can improve the quality of representation of textual data, and therefore, help in improving predictive analyses.

AI can power phenomenal revenue growth – until it doesn't. That lesson is being learned the hard way at a growing number of companies where issues with AI systems are not caught and remedied before materially impacting revenue. The latest example is Unity Software, a platform for creating and operating interactive and real-time 3D (RT3D) content. On its most recent earnings call, Unity revealed that it missed top line expectations and lowered its revenue guidance for the rest of the year due in part to a "self-inflicted wound" in AI. Specifically, the company's CEO and Executive Chairman John Riccitiello cited several issues related to machine learning (ML) models that caused an estimated impact to the business of approximately $110 million in 2022: When AI fails on the public stage like this, the temptation to pile onto whatever company is on the chopping block is sometimes irresistible (see: Zillow).

In a nutshell, artificial intelligence (AI) is a computer system that exhibits self-learning behavior or cognition. Cognitive computing systems are computers that use techniques such as machine learning to automatically determine how best to execute tasks without being explicitly programmed using rules. For example, let's say you have data showing that people who bought your product were going to buy something new with about a 30% chance of higher profit margin. You also have an algorithm that determines what percent of your sales staff should be responsible for finding new customers in this specific area. With all these variables, why not just test them and see which one generates revenue growth the most?

In Meta Platform's (FB -3.82%) most recent earnings report, there were some signs of a pullback with net income down year over year and more competition has played a part. In this video clip from "The Virtual Opportunities Show" on Motley Fool Live, recorded on May 24, Fool.com contributor Jose Najarro discusses how the company's investment in artificial intelligence is encouraging for the business going forward. Jose Najarro: First if we take a quick look, Meta Platforms for their financial results total revenue was $27.9 billion dollars. That was up 7% year over year. Again, this is a company that was probably growing at strong double digits and now we're seeing a bit of a pullback.

PRNewswire/ – Minerva Intelligence Inc. (TSXV: MVAI) (OTCQB: MVAIF) ("Minerva" or the "Company"), an artificial intelligence software company …

It's no secret that the market is on shaky ground in 2022. We are experiencing wild swings, especially in the tech sector. According to Bankrate, 82% of investors are investing less in 2022 than they did in 2021. We know that buying high and selling low is not the way to generate the best long-term returns, but it's easier said than done. Luckily, there are a couple of tried-and-true strategies that our future selves will thank us for. First, we don't need to time the bottom; that's nearly impossible.

May 26 (Reuters) - China's search engine giant Baidu Inc surpassed quarterly revenue estimates on Thursday as a resurgence of COVID-19 in China and accompanying restrictions boosted demand for its cloud and artificial intelligence (AI) products. The news drove Baidu's U.S.-listed shares more than 5% up in pre-market trading even as the company cautioned that the second quarter would be more challenging. Revenue for the three months to March 31 rose 1% to 28.41 billion yuan ($4.22 billion), the slowest growth in six quarters, but topped an analysts' average estimate of 27.82 billion, IBES data from Refinitiv showed. It posted a net loss of 885 million yuan, or 2.87 yuan per American Depository Share (ADS), amid an economic downturn and pandemic resurgence in China. A year earlier it had posted a profit of 25.65 billion yuan, or 73.76 yuan per ADS.

While data science and machine learning represents a substantial growth opportunity for Snowflake, it might not necessarily need to over-invest in …

C3.ai (NYSE:AI) is a leading software company, which provides Artificial Intelligence services to enterprises. The company is poised to ride the wave of growth forecasted for AI. The global Artificial Intelligence (AI) market is forecasted to grow at a meteoric 20.1% CAGR from $387 billion in 2022 to over $1.3 trillion by 2029. C3.ai serves an envious list of large reputable customers from The US Air Force and the Department of Defence, to large energy companies such as Shell & Engie. They have been growing revenues at a 40% CAGR over the past couple of years, while the stock price has declined massively.

In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.