The continental United States is jam-packed with reminders of our ravenous oil appetite. Since the 1850s, there have been an estimated 3.5 million oil and gas wells drilled across the country. Many of those were abandoned after the companies running them ran out of business or otherwise ceased operating. These forgotten fossil fuel artifacts, referred to officially as "undocumented orphan wells" (UOWs) are often left behind without meaningful efforts taken to safely seal them. Unplugged orphan wells can leak out dangerous methane, oil, and other chemicals for years which can pollute the air and potentially contaminate nearby water sources.

The prediction of streamflows and other environmental variables in unmonitored basins is a grand challenge in hydrology. Recent machine learning (ML) models can harness vast datasets for accurate predictions at large spatial scales. However, there are open questions regarding model design and data needed for inputs and training to improve performance. This study explores these questions while demonstrating the ability of deep learning models to make accurate stream temperature predictions in unmonitored basins across the conterminous United States. First, we compare top-down models that utilize data from a large number of basins with bottom-up methods that transfer ML models built on local sites, reflecting traditional regionalization techniques. We also evaluate an intermediary grouped modeling approach that categorizes sites based on regional co-location or similarity of catchment characteristics. Second, we evaluate trade-offs between model complexity, prediction accuracy, and applicability for more target locations by systematically removing inputs. We then examine model performance when additional training data becomes available due to reductions in input requirements. Our results suggest that top-down models significantly outperform bottom-up and grouped models. Moreover, it is possible to get acceptable accuracy by reducing both dynamic and static inputs enabling predictions for more sites with lower model complexity and computational needs. From detailed error analysis, we determined that the models are more accurate for sites primarily controlled by air temperatures compared to locations impacted by groundwater and dams. By addressing these questions, this research offers a comprehensive perspective on optimizing ML model design for accurate predictions in unmonitored regions.

The most promising startups using artificial intelligence are U.S.-based companies working in the fields of health care, retail and transportation, according to a study that looked at budding AI companies around the world. Of the top 100 startups in AI, 65% were based in the U.S., though some of those had dual headquarters in China or elsewhere, according to the analysis by CB Insights, a tech research group that analyzed data on close to 5,000 startups around the world. "These would be companies to watch that are doing really interesting research in AI," said Deepashri Varadharajan, the lead analyst on the report. "Some of them might get acquired. Some might have successful product launches."

When SAP veteran Bill McDermott took over the CEO spot at digital workflow company ServiceNow in October, his mandate focused on growth. "Should we choose to do'tuck-ins' to compliment what our customers need, to get us somewhere faster, we'll do that very carefully," he told CNBC. ServiceNow kicked off 2020 with one such "tuck-in": the acquisition of Israeli company Loom Systems, an AIOps company that uses artificial intelligence to give enterprise users insights into digital operations and fix IT issues. The acquisition symbolizes a bigger trend in enterprise technology: Acquiring AI startups enables technology vendors to capitalize, enhance or expand their capabilities while bringing scarce talent aboard. Last year, consolidation in the AI market hit record numbers.

In an AI industry marked by consolidation, a handful of companies are scooping up the lion's share of leading startups. According to a report from analyst firm CB Insights, AI acquisitions rose six-fold from 2013 to 2018. Last year, acquisitions in that market hit a record 166 deals -- a 38% year-over-year spike. The firm's projections say 2019 is on track to sprint past last year, with over 140 acquisitions in the first eight months. Since 2010, there have been 635 AI acquisitions.

AI-related startups received record funding in 2019. This year marked the biggest year in funding for artificial intelligence ventures yet. According to a new report by CB Insights, the second quarter of 2019 saw a record of $7.4 billion invested in AI startups, with the majority going to transportation and health care-related companies. The data, which tracks capital received between 2013 and 2019, also shows that seed funding is still going strong. SEE ALSO: Why the AI Apocalypse Isn't the End of the World The number of unicorn-status AI startups has spiked in recent years, jumping from just two in 2016 to 11 in 2017.

Of all the places where artificial intelligence is gaining a foothold, nowhere is the impact likely to be as great--at least in the near term--as in healthcare. A new report from Accenture Consulting, entitled Artificial Intelligence: Healthcare's New Nervous System, projects the market for health-related AI to grow at a compound annual growth rate of 40% through 2021--to $6.6 billion, from around $600 million in 2014. In that regard, the Accenture report, authored by senior managing director Matthew Collier and colleagues, echoes earlier assessments of the market. A comprehensive research briefing last September by CB Insights tech analyst Deepashri Varadharajan, for example--which tracked AI startups across industries from 2012 through the fall of 2016--showed healthcare dominating every other sector, from security and finance to sales & marketing. Varadharajan calculated there were 188 deals across various healthcare segments from Jan. 2012 to Sept. 2016, worth an aggregate $1.5 billion in global equity funding.

Shepherds – yes, this is a reference to the popular medical drama "Grey's Anatomy" – who work in hospitals around the world may soon get a new assistant. No, not just another intern but an extra pair of virtual eyes to help them better diagnose stroke victims. Tel Aviv-based MedyMatch Technology Ltd., which hopes to have its first commercially available product as soon as the first half of 2017, is developing an artificial intelligence (AI) platform for critical areas of patient care. The platform is meant to help study data more quickly and accurately than the human eye, and help physicians with their clinical decisions in a wide set of healthcare issues. MedyMatch's first area of focus will be for stroke patients.