Investment professionals rely on extrapolating company revenue into the future (i.e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision. This task is manual and empirical, leaving the forecast quality heavily dependent on the investment professionals' experiences and insights. Furthermore, financial data on scaleups is typically proprietary, costly and scarce, ruling out the wide adoption of data-driven approaches. To this end, we propose a simulation-informed revenue extrapolation (SiRE) algorithm that generates fine-grained long-term revenue predictions on small datasets and short time-series. SiRE models the revenue dynamics as a linear dynamical system (LDS), which is solved using the EM algorithm. The main innovation lies in how the noisy revenue measurements are obtained during training and inferencing. SiRE works for scaleups that operate in various sectors and provides confidence estimates. The quantitative experiments on two practical tasks show that SiRE significantly surpasses the baseline methods by a large margin. We also observe high performance when SiRE extrapolates long-term predictions from short time-series. The performance-efficiency balance and result explainability of SiRE are also validated empirically. Evaluated from the perspective of investment professionals, SiRE can precisely locate the scaleups that have a great potential return in 2 to 5 years. Furthermore, our qualitative inspection illustrates some advantageous attributes of the SiRE revenue forecasts.

IBM announced plans to acquire Dialexa, a leading U.S. digital product engineering services firm, to help companies drive innovation and achieve their digital growth agendas. The acquisition is expected to deepen IBM's product engineering expertise and provide end-to-end digital transformation services for clients. Dialexa will be the sixth acquisition IBM has made in 2022, further bolstering the company's hybrid cloud and AI skills and capabilities. Since Arvind Krishna became CEO in April 2020, IBM has acquired more than 25 companies. Thirteen of those acquisitions, including Dialexa, have been in IBM Consulting.

A startup backed by a billionaire Brazilian family is partnering with e-commerce platform Farmers Business Network to offer robots that spray fertilizer and pesticides to US farmers. Solinftec's robots, which run on solar panels, are autonomous and were designed to apply fertilizer and weed killer only where needed. The company said the technology, already in use in Brazil, can reduce product use by as much as 70%. As a result of the deal, both companies will work with farmers in the US to commercialize the robot and to develop new methods for agrochemicals to be used with robotics technology.

Seattle-based venture capital firm Madrona Venture Group announced today it has raised a bumper $690 million for two oversubscribed funds that will be used to back promising technology startups in the Pacific Northwest region, Silicon Valley and beyond. The $430 million Madrona Fund 9 and the $260 million Acceleration Fund 3 saw 100% of existing institutional investors participate, Madrona said. Combined, they represent an almost 40% increase over the funds Madrona raised in 2020. The funds will be used to invest in technology firms ranging from pre-seed all the way through to Series C startups. Madrona is one of the more prominent of tech-focused venture capital firms, focusing on seed, early-stage and acceleration-stage startups that leverage technology and promise to disrupt huge existing markets.

Venture capital (VC) funding slid more deeply in AI & ML than in software overall in Q2, "falling 27.8% quarter-over-quarter (QoQ) compared to 21.6% for IT more generally." Both deal value and deal count "fell to their lowest levels since Q4 2020, also lagging the rest of IT," the report from Pitchbook revealed. Of the 70 product categories the firm tracks, "only 21 are on pace to grow in VC funding in 2022, driven by leading vertical applications including sales & marketing, information security, and drug discovery." The update from Pitchbook also mentioned that horizontal platform investment "is lagging, as outsized revenue projections face market realities, with AI-as-a service (AIaaS) investment on pace to decrease 87.7% in 2022." Numerous AI platform companies "have struggled to reach revenue forecasts in the current financial environment, creating a greater emphasis on vertical applications that can rapidly deliver value."

Chinese internet giant Baidu Inc. on Tuesday announced second-quarter revenues of 29.6 billion yuan ($4.3 billion), down five percent from last year after the company faced a challenging economic climate and tight controls on China's once-thriving tech sector. Other Chinese tech giants, including Tencent and e-commerce behemoth JD.com, had also reported disappointing results in recent weeks. China's major tech companies have been grappling with economic uncertainty, Covid-19 restrictions that have kept consumers jittery, and heightened scrutiny from regulators in recent months. Baidu, which operates China's most widely used search engine, saw revenues decrease but posted a net profit of 3.6 billion yuan ($522 million), buoyed by a 31 percent year-on-year growth in its cloud computing business. "Despite a challenging macro environment caused by Covid-19, Baidu Core generated RMB23.2 billion in revenues in the second quarter," CEO Robin Li was quoted as saying in an official press release.

Starting Monday, drive-thru customers at two Panera Bread locations in upstate New York will have their orders taken by a computer in a test of artificial intelligence technology's accuracy and ability to decrease service times. The sandwich chain is the latest restaurant company to invest in potential improvements to the drive-thru experience. A surge in drive-thru ordering during the Covid pandemic led to long lines of cars wrapped around restaurants, pushing chains to focus on speed of service and order accuracy. For example, McDonald's has also been working to automate its drive-thru lane, announcing a partnership last year with IBM to work toward that goal. Yum Brands' Taco Bell and Restaurant Brands International's Burger King have been building double drive-thru lanes at some locations to allow customers to pick up their digital orders more quickly.

Amazon has reportedly made a bid to buy Electronic Arts (EA), the US video game company behind the hugely successful FIFA series. According to Stockholm-based gaming content agency GLHF, Amazon will confirm its bid to take over the gaming firm on Friday. Rumours have circulated since the spring that EA had been looking to be acquired by a large company, including Disney, Apple or Amazon. The rumoured takeover was first reported by USA Today, which said'Amazon has finally made an offer', citing GLHF as its source. Such a purchase could broaden Amazon's push into sports, following its purchase of live streaming rights of NFL and the Premier League on its Prime platform.

We mined Apple's acquisitions, investments, and partnerships to discern the company's emerging strategic priorities. Apple is looking for the next big thing. As the world's most valuable company, with a $2T market cap and over $100B in annual profit, Apple has long been known for its cell phones, laptops, tablets, and watches. Today, the company is investing aggressively in high-tech areas like AR/VR, AI, and semiconductors to lay the groundwork for products and features that affect health & wellness, mobility, digital connections, and more. Download our full report to find out the top trends poised to reshape industries in 2022.

Models for bankruptcy prediction are useful in several real-world scenarios, and multiple research contributions have been devoted to the task, based on structured (numerical) as well as unstructured (textual) data. However, the lack of a common benchmark dataset and evaluation strategy impedes the objective comparison between models. This paper introduces such a benchmark for the unstructured data scenario, based on novel and established datasets, in order to stimulate further research into the task. We describe and evaluate several classical and neural baseline models, and discuss benefits and flaws of different strategies. In particular, we find that a lightweight bag-of-words model based on static in-domain word representations obtains surprisingly good results, especially when taking textual data from several years into account. These results are critically assessed, and discussed in light of particular aspects of the data and the task. All code to replicate the data and experimental results will be released.