Human thinking is beyond imagination. Can a computer develop such ability to think and reason without human intervention? This is something programming experts at IBM Watson are trying to achieve. Their goal is to simulate human thought process in a computerized model. The result is cognitive computing – a combination of cognitive science and computer science.
As we observe the S&P 500 stock market index near all-time highs, it becomes increasingly challenging to find companies that still present great value. If you don't already own them, it might feel like you've missed out on some of the most popular high-growth technology stocks as they continue to trend higher. But there's one technology company the market might be overlooking right now. Artificial intelligence (AI) is an emerging industry, and C3.ai (NYSE:AI) has developed an entire market all for itself. Known as Enterprise AI, the company can build AI applications for just about any industry in the world, and customers are flocking to it.
Results released June 16, 2021 – Pew Research Center and Elon University's Imagining the Internet Center asked experts where they thought efforts aimed at ethical artificial intelligence design would stand in the year 2030. Some 602 technology innovators, developers, business and policy leaders, researchers and activists responded to this specific question. The Question – Regarding the application of AI Ethics by 2030: In recent years, there have been scores of convenings and even more papers generated proposing ethical frameworks for the application of artificial intelligence (AI). They cover a host of issues including transparency, justice and fairness, privacy, freedom and human autonomy, beneficence and non-maleficence, freedom, trust, sustainability and dignity. Our questions here seek your predictions about the possibilities for such efforts. By 2030, will most of the AI systems being used by organizations of all sorts employ ethical principles focused primarily on the public ...
Found hundreds of feet beneath the sea floor, these structures have been revealed in new detail by 3D seismic reflection scans that were analysed by researchers led from the British Antarctic Survey. Each structure is a so-called tunnel valley, a U-shaped gouge formed by meltwater draining underneath the vast ice sheets that formerly coated the area that today is the swathe of the North Sea between Scotland and Norway. Studying these ancient valleys shines a light on how ice sheets react to a warming climate, the team explained -- and could help us understand how glaciers in Antarctica and Greenland will respond to climate change. Huge channels -- each ten times wider than the Thames -- lie buried under the North Sea, remnants of Ice Age landscapes that were carved up by the glacial rivers that once crossed much of the UK and western Europe. Found hundreds of feet beneath the sea floor, these structures have been revealed in new detail by 3D seismic reflection scans (such as pictured top left, with an interpretation of the data showing four structures, labelled as'TV' 1–4, bottom right) that were analysed by researchers led from the British Antarctic Survey.
In the upcoming age of AI, two very different classes of companies appear well-positioned to leverage AI's capabilities: startup ventures and multi-billion-dollar giant corporations. Promising AI startups are being launched at an increasing pace in areas like health care, finance, retail, media and cross-industry tech, to name a few. And alongside tech giants like Google or Microsoft, traditional large corporations are employing AI to digitalize their business model and processes. Examples of AI-driven automation and augmentation range from automated customer loan approval and smart infotainment systems at car manufacturer Daimler to predictive maintenance at oil and gas behemoth Shell and AI-assisted medical image reading at industrial manufacturer Siemens. Corporate AI innovation is fairly concentrated with the top-10 patenting firms in the world accounting for more than 15% of AI patents in the period 2011 to 2016.
Researchers at the University of Oxford are seeking NHSX funding for an artificial intelligence (AI) COVID screening test. Results of a three-month evaluation study at John Radcliffe Hospital found the CURIAL-Rapide test could screen emergency department (ED) patients at the bedside within 10 minutes, without needing a laboratory. Results were available 45 minutes after patients arrived at the ED – 26% faster than with lateral flow tests (LFTs). When compared against PCR testing, the AI test was more likely to identify COVID patients than LFTs and corrected ruled out the infection 99.7% of the time. Collaborating with University Hospitals Birmingham NHS Foundation Trust, Portsmouth University Hospitals NHS Trust, and Bedfordshire Hospitals NHS Foundation trust, the study found CURIAL-Rapide performed consistently across 72,000 admissions to five UK hospitals. Another AI model named CURIAL-Lab, which uses routine blood tests performed in a laboratory alongside vital signs, was at least as effective as CURIAL-Rapide when tested at hospitals.
Artificial intelligence can enable busy NHS emergency departments to perform bedside checks for Covid-19 in just 10 minutes without the need for a laboratory, a study led by Oxford University shows. During a three-month evaluation at John Radcliffe Hospital, Oxford's main accident and emergency centre, the study found that AI test results were available 45 minutes after a patient arrived, 26% faster those for a lateral flow test. The AI screening test, known as CURIAL-Rapide, uses routine healthcare data (blood tests and vital signs) to screen patients for Covid-19. Compared to lateral flow tests, the AI test was more likely to identify Covid-19 in patients and correctly ruled out the infection 99.7% of the time, the research found. In addition, a collaboration with five NHS trusts between December 2020 and March 2021 – University Hospitals Birmingham, Portsmouth University and Bedfordshire Hospitals – the study found that the AI test performed consistently in 72,000 admissions. It provided reliable negative results for uninfected patients up to 98.8% of the time and was 21% more effective at identifying Covid-19 positive patients than lateral flow tests.
This lecture provided an overview on artificial intelligence and took a deep dive on machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Andrew Ng: Artificial Intelligence is the New Electricity. During his talk, Professor Ng discussed how artificial intelligence (AI) is transforming industry after industry. Putting Artificial Intelligence to Work. In this webinar, distinguished speaker Thomas Davenport, author of the recent book, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, as he brings us his practical advice and examples on how to achieve the business value that AI offers.
Fibre-optic Distributed Acoustic Sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterisation, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fibre-optic cables into seismic arrays with a spatial sampling density of the order of metres and a time sampling rate up to one thousand Hertz. The versatility of DAS enables dense instrumentation of traditionally inaccessible domains, such as urban, glaciated, and submarine environments. This in turn opens up novel applications such as traffic density monitoring and maritime vessel tracking. However, these new environments also introduce new challenges in handling various types of recorded noise, impeding the application of traditional data analysis workflows.
Hydraulic fracturing uses a water-based mixture to open up tight oil and gas formations. The process is mostly contained, but concerns remain about the potential for surface water contamination. Bonetti et al. found a small increase in certain ions associated with hydraulic fracturing across several locations in the United States (see the Perspective by Hill and Ma). These small increases appeared 90 to 180 days after new wells were put in and suggest some surface water contamination. The magnitude appears small but may require that more attention be paid to monitoring near-well surface waters. Science , aaz2185, this issue p. ; see also abk3433, p.  The impact of unconventional oil and gas development on water quality is a major environmental concern. We built a large geocoded database that combines surface water measurements with horizontally drilled wells stimulated by hydraulic fracturing (HF) for several shales to examine whether temporal and spatial well variation is associated with anomalous salt concentrations in United States watersheds. We analyzed four ions that could indicate water impact from unconventional development. We found very small concentration increases associated with new HF wells for barium, chloride, and strontium but not bromide. All ions showed larger, but still small-in-magnitude, increases 91 to 180 days after well spudding. Our estimates were most pronounced for wells with larger amounts of produced water, wells located over high-salinity formations, and wells closer and likely upstream from water monitors. : /lookup/doi/10.1126/science.aaz2185 : /lookup/doi/10.1126/science.abk3433