AMP Robotics Corp. ("AMP"), a pioneer in artificial intelligence (AI), robotics, and infrastructure for the waste and recycling industry, has raised $91 million in corporate equity in a Series C financing, led by Congruent Ventures and Wellington Management as well as new and existing investors including Blue Earth Capital, Sidewalk Infrastructure Partners (SIP), Tao Capital Partners, XN, Sequoia Capital, GV, Range Ventures, and Valor Equity Partners. This new round of funding follows a $55 million Series B financing led by XN in January 2021. "Our focus from the outset has been our application of AI-powered automation to economically and sustainably improve our global recycling system" "Advancements in robotics and automation are accelerating the transformation of traditional infrastructure, and AMP is seeking to reshape the waste and recycling industries," said Michael DeLucia, sector lead for Climate Investing, Wellington Management. "By bringing digital intelligence to the recycling industry, AMP can sort waste streams and extract additional value beyond what is otherwise possible." AMP will use the latest funding to scale its business operations while continuing its international expansion.

In this article, we will be taking a look at the 11 best metaverse stocks to buy. To skip our detailed analysis of these stocks and the rise of the metaverse, you can go directly to see the 5 Best Metaverse Stocks to Buy. With the rise of the Internet, many other new developments have come to the forefront. This includes the rise of the metaverse, which is a hypothetical iteration of the Internet as a single, universal, and immersive virtual world. Such a world includes the use of virtual reality and augmented reality technology, to facilitate its growth and development.

Consumer research featured in the Residential Security Tracker reveals that in Q2 2022, nearly 40% of US internet households reported owning some security solution, such as a home security system, networked cameras, or a video doorbell. Seven percent of US internet households, approximately seven million households, own a network camera or video doorbell but no security system. "Together, these smart home products add to the market for security solutions, as millions of households find a camera solution is'safe enough' for their needs," said Jennifer Kent, Vice President, Research, Parks Associates. "The current market for residential security solutions is a mix of devices, systems, installation methods, and attached services that offer consumers more choice than ever before." In the firm's July 2022 survey, Parks Associates expanded its definition of home security services to better capture fee-based self-monitoring and video storage services.

SureDone has launched SureFit, a Year, Make and Model search engine built for automotive, motorsports and powersports parts and accessory sellers using BigCommerce, Shopify or SureDone's integrated storefront and shopping cart. SureFit was designed with the input of brands, enterprise companies and high growth sellers to support fitment searches on their e-commerce websites. Visitors to parts and accessory websites want to find the specific parts that fit their vehicle. Leveraging the SureFit Year, Make and Model Search on a website results in visitors finding the parts they need and being confident they will fit. In addition, visitors will see additional available parts for their vehicle resulting in increased time on site and increasing multiple part purchases with a lower cart abandonment rate.

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.