If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial Intelligence (AI) is developing fast. It will change our lives by improving healthcare (e.g. At the same time, AI entails a number of potential risks, such as opaque decision-making, gender-based or other kinds of discrimination, intrusion in our private lives or being used for criminal purposes. In order to foster an active dialogue between multiple stakeholders and set out policy options to tackle these challenges, the European Commission has just released a White Paper entitled "On Artificial Intelligence -A European approach to excellence and trust" which is available for download. Interested to learn more on the implications of AI for intellectual property and the themes relevant for legal practice in this field?
Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. Conclusions: Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.
Artificial intelligence (AI) often raises concerns about privacy and deception apropos of facial recognition and forged entities. However, the massive outbreak of the novel coronavirus is now driving most of the technology companies and experts to look for AI's abet. Since the first report of coronavirus (COVID-19) in Wuhan, China – the virus has now spread to at least 100 other countries. Nevertheless, the undisclosed information holds the key to the certainty that the artificial intelligence warning system, run by Toronto startup "BlueDot" flagged a news report from China about enigmatic pneumonia distressing the residents of Wuhan, back in December 2019. As China leans on its strong technology sector, specifically artificial intelligence and data science to track and combat this pandemic, tech leaders like Alibaba, and Huawei chose to accelerate their company's healthcare initiatives.
The Radiological Society of North America (RSNA) has received numerous inquiries seeking access to COVID-19 related imaging data, both from radiology sites interested in sharing such data for use in research and education and from researchers. RSNA is committed to accelerating open source collaborative research on the uses of medical imaging in addressing the COVID-19 pandemic, including the use of new tools like artificial intelligence (AI). This form will enable institutions with COVID-19 data to express interest in participating in a planned open data repository for international COVID-19 imaging research and education efforts. Please complete this form if your institution has COVID-19 data that you may be willing and able to share for research purposes. Completing this brief survey does not represent a final commitment to collaborate with us or to share your data.
This course is designed to equip you with the theoretical and practical knowledge of Machine Learning as applied for geospatial analysis, namely Geographic Information Systems (GIS) and Remote Sensing. By the end of the course, you will feel confident and completely understand the Machine Learning applications in GIS technology and how to use Machine Learning algorithms for various geospatial tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation). This course will also prepare you for using GIS with open source and free software tools. In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. On top of that, you will practice GIS by completing an entire GIS project by exploring the power of Machine Learning, cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world.
It's a common frustration--software updates intended to make our applications run faster inadvertently end up doing just the opposite. These bugs, called performance regressions in the field of computer science, are time-consuming to fix because locating software errors normally requires substantial human intervention. To overcome this obstacle, researchers at Texas A&M University, in collaboration with computer scientists at Intel Labs, developed a completely automated way of identifying the source of the errors. Their algorithm, based on a specialized form of machine learning called deep learning, is not only turnkey, but also quick. It finds performance bugs in a matter of a few hours instead of days.
Semiconductor Engineering sat down to discuss the issues and challenges with machine learning in semiconductor manufacturing with Kurt Ronse, director of the advanced lithography program at Imec; Yudong Hao, senior director of marketing at Onto Innovation; Romain Roux, data scientist at Mycronic; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. SE: Machine learning is a hot topic. This technology uses a neural network to crunch data and identify patterns, then matches certain patterns and learns which of those attributes are important. We also have more advanced forms called deep learning.
ThetaRay, a provider of Big Data and artificial intelligence (AI)-enhanced analytics tools, has joined Microsoft's (NASDAQ:MSFT) partner program, One Commercial Partner, which provides various cloud-powered solutions. ThetaRay's anti-money laundering (AML) solution for correspondent banking can be accessed through Microsoft's Azure Marketplace. A large US bank has reportedly signed an agreement to use the solution. "We are proud to join the One Commercial Partner program and offer Microsoft Azure customers access to our industry-leading AML for Correspondent Banking solution." "Global banks are increasingly de-risking or abandoning their correspondent banking relationships due to a lack of transparency and fears of money laundering and regulatory fines. Our solution provides banks with the … ability to reverse the trend and grow their business by allowing full visibility into all links of the cross-border payment chain, from originator to beneficiary."
According to Deltec Bank, Bahamas - 'The presence of AI produces several specific benefits that banks can use to generate new revenue streams through individualization.' We all understand that artificial intelligence and data analytics are excellent teammates. Over the next several years, the banking sector will use the power of this combination to create and deliver essential products and strategies that can help consumers and businesses grow their wealth through new and unique methods. The success of this process depends on where the industry focuses its energy, and AI enables institutions to concentrate its initiatives on crucial tasks instead of bureaucratic responsibilities. The banking sector already uses a standardized analytics report to understand the reasons why specific behaviors and actions happen.
Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems.