Intel is expected to report a sharp drop in quarterly earnings, hurt by a rapidly shrinking market for personal computers that its chips go into. The company after the closing bell Thursday is projected to post sales of about $15 billion during the quarter ended in September, a retreat of more than 21% from the year-earlier period, according to a FactSet survey of analysts. Net income likely fell by around 93% to $494 million, the analysts estimate. Intel and other chip makers cashed in on a boom in computer and electronics sales at the outset of the pandemic with the shift toward remote work and distance learning. The market has turned, though, with high inflation, rising interest rates and recession fears that have weighed on demand.

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, including facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and short experts-written telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarizers across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple-yet-effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls.

Microsoft likely recorded slower earnings and sales growth last quarter as a sharp decline in personal computer sales eroded demand for its Windows software, counteracting some of the demand for its cloud and other businesses serving companies. The Redmond, Wash., corporation's revenue growth is expected to slow to about 10% in the three months through September compared with a year earlier, while its net income is expected to edge up 1%, according to analysts surveyed by FactSet. They predicted the company would report sales of $49.66 billion and net income of $17.36 billion for the period. That would mean last quarter had the slowest revenue growth in more than five years and the lowest income growth in more than two years. The company is scheduled to announce results after the market closes on Tuesday. A weekly digest of tech reviews, headlines, columns and your questions answered by WSJ's Personal Tech gurus.

In this article, I would like to start with a recent announcement that Microsoft (NASDAQ:MSFT) made and then show how, even though it may be of little relevance, it offers once again the opportunity to understand how Microsoft runs its business and, most important, defends its wide moat. I really enjoy carrying out this kind of research, especially when I have to deal with a very large company such as Microsoft. In fact, I think that very often, understanding well how one particular choice works, enables me to get a grasp of the whole company better than if I were to analyze only its financials without diving into some of its operations. Let's get to the announcement: Microsoft is launching Microsoft Designer, a graphic design app in Microsoft 365 that helps users create social media posts, invitations, digital postcards, graphics, and more, all in a flash. The most important feature is that Microsoft Designer is powered by AI technology, including DALL E 2 by OpenAI, which enables to instantly generate a variety of designs with minimal effort.

In this piece we will take a look at the 11 best machine learning stocks to buy. If you want to skip our industry introduction and jump ahead to the top five stocks in this list, then head on over to 5 Best Machine Learning Stocks to Buy. Machine learning refers to a set of technologies that enable researchers and others to use large or small datasets to their advantage by making predictions. It often requires breaking the data set into pieces, and depending on the size of the data set, often requires large amounts of computing power too. The basics of machine learning involve two kinds, supervised and unsupervised.

In general, traders test their trading strategies by applying them on the historical market data (backtesting), and then apply to the future trades the strategy that achieved the maximum profit on such past data. In this paper, we propose a new trading strategy, called DNN-forwardtesting, that determines the strategy to apply by testing it on the possible future predicted by a deep neural network that has been designed to perform stock price forecasts and trained with the market historical data. In order to generate such an historical dataset, we first perform an exploratory data analysis on a set of ten securities and, in particular, analize their volatility through a novel k-means-based procedure. Then, we restrict the dataset to a small number of assets with the same volatility coefficient and use such data to train a deep feed-forward neural network that forecasts the prices for the next 30 days of open stocks market. Finally, our trading system calculates the most effective technical indicator by applying it to the DNNs predictions and uses such indicator to guide its trades. The results confirm that neural networks outperform classical statistical techniques when performing such forecasts, and their predictions allow to select a trading strategy that, when applied to the real future, increases Expectancy, Sharpe, Sortino, and Calmar ratios with respect to the strategy selected through traditional backtesting.

Commodities AI Ltd (ChAI), an AI-driven commodity intelligence company, has completed a seed round to expand its industry-leading services into new markets and provide real-time commodity price forecasts to new global audiences, including commodity companies and key supply chain providers. The funding announcement marks an important milestone in ChAI's product development and ability to provide important information to a range of stakeholders as commodity markets continue to experience significant volatility. Under current market conditions, supply chain providers need long-term forecasts to address global food security challenges. ChAI uses purpose-built AI technology to analyze thousands of data sets to determine what variables are driving market prices. At the same time, through the use of AI technology, the company is able to predict raw material costs, identify risks and help its customers to effectively purchase the necessary raw material products.

Gather AI, a startup using drones to inventory items in warehouses, today announced that it raised $10 million in a Series A round led by Tribeca Venture Partners with participation from Xplorer Capital, Dundee Venture Capital, Expa, Bling Capital, XRC Labs and 99 Tartans. The proceeds bring the company's total raised to $17 million, which CEO Sankalp Arora says is being put toward expanding Gather's deployment capacity and go-to-market plans as well as hiring new machine learning engineers. Arora co-founded Gather AI in 2019 with Daniel Maturana and Geetesh Dubey, graduate students at Carnegie Mellon's Robotics Institute. The trio had the idea to use drones to gather data -- specifically data in warehouses, such as the number of items on a shelf and the locations of particular pallets. Over the course of several years, they designed a prototype of an inventory monitoring system that used off-the-shelf autonomous drones, which became Gather's core product.

Intel Corp. is planning a major reduction in headcount, likely numbering in the thousands, to cut costs and cope with a sputtering personal-computer market, according to people with knowledge of the situation. The layoffs will be announced as early as this month, with the company planning to make the move around the same time as its third-quarter earnings report on Oct. 27, said the people, who asked not to be identified because the deliberations are private. The chipmaker had 113,700 employees as of July. Some divisions, including Intel's sales and marketing group, could see cuts affecting about 20% of staff, according to the people. Intel is facing a steep decline in demand for PC processors, its main business, and has struggled to win back market share lost to rivals like Advanced Micro Devices Inc.

WIRE)--Knightscope, Inc. (Nasdaq: KSCP) ("Knightscope" or the "Company"), a leading developer of autonomous security robots, today announced the signing of a definitive agreement to acquire CASE Emergency Systems ("CASE") and to close on the transaction during October. The acquisition is planned to boost the company's revenues while increasing its positive impact on the safety of communities nationwide. CASE is a leader in blue light emergency phones and an innovator in next generation wireless emergency communications technology, providing Knightscope with a strategic entry into a nationwide market. Audited full year results reflect CASE generated over $5.4 million of profitable revenue in 2021. The accretive transaction provides a significant increase in physical presence to Knightscope with over 7,000 devices currently deployed across the United States, 9 production and logistics facilities spread throughout California, Texas and New York, and a seasoned team located across 4 states.