forecast


Automotive Radar 2020-2040: Devices, Materials, Processing, AI, Markets, and Players: IDTechEx

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This report investigates the market for radar technology, specifically focusing on automotive applications. It develops a comprehensive technology roadmap, examining the technology at the levels of materials, semiconductor technologies, packaging techniques, antenna array, and signal processing. It demonstrates how radar technology can evolve towards becoming a 4D imaging radar capable of providing a dense 4D point cloud that can enable object detection, classification, and tracking. The report examines the latest product innovations. It identifies and reviews promising start-ups worldwide.


Artificial Intelligence Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025

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Artificial intelligence (AI) within the consumer, enterprise, government, and defense sectors is migrating from a conceptual "nice to have" to an essential technology driving improvements in quality, efficiency, and speed. According to a new report from Tractica, the top industry sectors where AI is likely to bring major transformation remain those in which there is a clear business case for incorporating AI, rather than pie-in-the-sky use cases that may not generate return on investment for many years. "The global AI market is entering a new phase in 2020 where the narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level," says principal analyst Keith Kirkpatrick. According to the market intelligence company, AI is likely to thrive in consumer (Internet services), automotive, financial services, telecommunications, and retail industries. Not surprisingly, the consumer sector has demonstrated its ability to capture AI, thanks to the combination of three key factors – large data sets, high-performance hardware and state of the art algorithms.


Roundup Of Machine Learning Forecasts And Market Estimates, 2020

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IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the ... [ ] $37.5B that will be spent in 2019. Machine learning's growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Open jobs requiring TensorFlow experience is a useful way to quantify how prevalent machine learning is becoming in business today. There are 4,134 open positions in the U.S. on LinkedIn that require TensorFlow expertise and 12,172 open positions worldwide as of today. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses.


Roundup Of Machine Learning Forecasts And Market Estimates, 2020

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IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the ... [ ] $37.5B that will be spent in 2019. Machine learning's growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Open jobs requiring TensorFlow experience is a useful way to quantify how prevalent machine learning is becoming in business today. There are 4,134 open positions in the U.S. on LinkedIn that require TensorFlow expertise and 12,172 open positions worldwide as of today. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses.


Indian Ocean Dipole can be better predicted thru machine learning, say researchers

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Researchers in Japan and The Netherlands have, for the first time, used machine learning techniques, in particular artificial neural networks (ANNs), to predict the Indian Ocean Dipole (IOD), a positive phase of which has affected weather and climate in India and Australia in a spectacular fashion so far in 2019-20. The IOD has both positive and negative phases, and signals large socio-economic impacts on many countries and hence predicting the IOD well in advance will benefit the affected societies, note authors JV Ratnam and Swadhin K Behera (Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama) and HA Dijkstra (Institute for Marine and Atmospheric Research Utrecht, Utrecht University in The Netherlands) in a paper published by Nature. The IOD is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole in Sumatra (Indonesia) and the other near East Africa. Therefore, the IOD is represented by an index derived from the gradient between the western equatorial Indian Ocean and the south-eastern equatorial Indian Ocean. It starts sometime in May-June, peaks in September-October and ends in November (2019's rather strong positive phase of the IOD lasted into early January of 2020).


Machine-Learning Real Estate Valuation: Not Only a Data Affair

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Valuations are relatively straightforward yet still involved exercises when similar properties in terms of hedonic variables[i] (also called comparables) transacted in the market close to the valuation date. In the absence of reliable comparable transactions, the possible value of a piece of real estate (be it residential or commercial) needs to be assessed using a valuation method. From back-of-the envelope cap rate models, transparent discounted cash-flow spreadsheets to sophisticated econometric models, any reliable valuation stands to benefit from accurate forecasts of expected levels of cash-flows and discount rates. The buying or selling decision is further influenced by the perceived current state of the real estate cycle but also the projected direction of the cycle. Predicting rents requires a good understanding of demand and supply forces at work in the space market, construction and how its financed, the evolution of the natural vacancy rate and possible migration flows of both firms and workers, among the more prominent determinants.


How Google's New Weather AI Will Make Sure You Never Get Caught in the Rain

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Among the many things we've become addicted to on our smartphones is checking the weather. If you're anything like me, you open a weather app at least twice a day: in the morning to know what to expect for the day ahead, maybe before your commute home so you can prepare for possible rain or snow, and sometimes before bed to get an idea of what to wear or what activities to plan for the next day. Depending where you live, how much time you spend outside, and how prone your area is to rapid weather changes, maybe you check the forecast even more frequently than that. The fact that our phones now contain hour-by-hour breakdowns of temperature and likelihood of precipitation means we can be well-informed and well-prepared. But these forecasts are coming at a greater cost than we know, and they're not always right.


Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry

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The Asia Pacific artificial intelligence in fashion market accounted for US$ 55. 1 Mn in 2018 and is expected to grow at a CAGR of 39. 0% over the forecast period 2019-2027, to account for US$ 1015. GNW Real-time consumer behavior insights and increased operational efficiency are driving the adoption of artificial intelligence in fashion industry. Moreover, the availability of a large amount of data originating from different data sources is one of the key factors driving the growth of AI technology across the fashion industry. Artificial Intelligence has already disrupted several industries, including the retail and fashion industry. The fashion industry so far has been one of the primary adopters of the technology.


Google AI model trumps traditional methods of weather prediction – small tech news

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A few weeks ago, Google's artificial intelligence (AI) used a machine learning model to improve screening for breast cancer,media reported. Now, the company has used convolutional neural networks (CNN) in instant forecasts of precipitation. Google AI researchers mentioned its use of CNN in short-term precipitation forecasts in an article called Machine Learning for Precipitation Now Fromcasting Radar Images. The results look promising, and according to Google itself, the results are better than the traditional method: this precipitation forecast focuses on 0-6 hours of forecasts, which produce a resolution of 1 km and a total delay of only 5-10 minutes (including data collection delays). Even in the early stages of development, it outperforms traditional models.


Google says new AI model allows for near 'instantaneous' weather forecasts

Daily Mail - Science & tech

Google is throwing the power of its AI and machine-learning algorithms behind developing faster and more accurate weather forecasts. In a blog post, Google describes a new model developed by the company called'nowcasting' which it says has shown initial success in being able to accurately predict weather patterns with'nearly instantaneous' results. According to a new paper, the method is able to produce forecasts for up to six hours in advance in only five to 10 minutes - figures that it says outperform traditional models even in early stages. While some traditional forecasts generate massive amounts of data, they can also take hours to complete. 'A significant advantage of machine learning is that inference is computationally cheap given an already-trained model, allowing forecasts that are nearly instantaneous and in the native high resolution of the input data,' Google writes.