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) …
In some cases, AI-assisted cancer detection might be more than a convenience -- it could be the key to getting a diagnosis in the first place. Microsoft and SRL Diagnostics have developed an AI tool that helps detect cervical cancer, freeing doctors in India and other countries where the sheer volume of patients could prove overwhelming. The team trained an AI to spot signs of the cancer by feeding it "thousands" of annotated cervical smear images to help it spot abnormalities (including pre-cancerous examples) that warrant a closer look. Doctors would only have to look at those slides that justify real concern. A framework for using the AI is now ready for an "internal preview" at SRL.
The latest research out of Facebook sets machine learning models to tasks that, to us, seem rather ordinary -- but for a computer are still monstrously difficult. These projects aim to anonymize faces, improvise hand movements and -- perhaps hardest of all -- give credible fashion advice. The research here was presented recently at the International Conference on Computer Vision, among a few dozen other papers from the company, which has invested heavily in AI research, computer vision in particular. Modifying faces in motion is something we've all come to associate with "deepfakes" and other nefarious applications. But the Facebook team felt there was actually a potentially humanitarian application of the technology.
To help you fine-tune your Google Cloud environment, we offer a family of'recommenders' that suggest ways to optimize how you configure your infrastructure and security settings. But unlike many other recommendation engines, which use policy-based rules, some Google Cloud recommenders use machine learning (ML) to generate their suggestions. In this blog post, we'll take a look at one of our recommendation engines, the Cloud Identity and Access Management (IAM) Recommender, and take you on a behind-the-scenes look at the ML that powers its functionality. IAM Recommender helps security professionals enforce the principle of least privilege by identifying and removing unwanted access to GCP resources. It does this by using machine learning to help determine what users actually need by analyzing their permission usage over a 90 day period.
Which of these levels of personal detail do you feel comfortable sharing with your smartphone? And should every app on that device have the same level of knowledge about your personal details? Welcome to the concept of siloed sharing. If you want to keep relying on your favorite device to store and automatically sort through your data, it's time to start considering whether you want to trust device-, app-, and cloud-level AI services to share access to all of your information, or whether there should be highly differential access levels with silo-class safeguards in place. Your phone already contains far more information about you than you realize. Depending on who makes the phone's operating system and chips, that information might be spread across storage silos -- separate folders and/or "secure enclaves" -- that aren't easily accessible to the network, the operating system, or other apps.
AI itself in an umbrella term, coined at the Dartmouth Conference in 1956, that encompasses various applications of computational intelligence. It ranges from simple process automations, to more intelligent decision making. AI is an incredible opportunity to fundamentally change every corner of our world, for the better! These are dreamy ambitions that any leader wants to make a reality. But like landing a human on the Moon (or Mars now), no great achievement is ever easily grasped.
The stunning aerial dance of UFO's that wowed audiences in Steven Speilberg's Close Encounters of the Third Kind has become a reality thanks to the drone. The small aerial vehicles may be a potential threat in some circumstances but they can be beautiful in others. A spectacular aerial art form continues to evolve as operators use lighted drones and computer algorithms to create animations in the sky. Intel's 2018 light show in Sacramento used 1500 drones to put on this stunning display.
Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years.
Resampling is a way to reuse data to generate new, hypothetical samples (called resamples) that are representative of an underlying population. Two popular tools are the bootstrap and jackknife. Although they have many similarities (e.g. they both can estimate precision for an estimator θ), they do have a few notable differences. Bootstrapping is the most popular resampling method today. It uses sampling with replacement to estimate the sampling distribution for a desired estimator.
Artificial intelligence (AI) and machine learning (ML) technologies, although still relatively new concepts, are garnering a vast amount of interest in international development across sectors and geographies. The U.S. Agency for International Development (USAID)'s Center for Digital Development (CDD)'s Strategy & Research (S&R) team published a report in 2018, "Reflecting the Past, Shaping the Future: Making AI work for International Development", based on extensive research on this rapidly growing field. USAID would like to translate the report's recommendations into an actionable format so the lessons and good practices are accessible to USAID program staff and implementing partners that may have limited familiarity with, nor time, to devote to the topic. Today, the Digital Frontiers team has released a request for proposals (RFP) for qualified firms to work with Digital Frontiers and USAID's S&R team to create a modular, field-ready guidance product that translates findings from the report into concise, practical guidance for USAID staff and partners. Photo courtesy: Save the Children.
Scientists from the School of Energy and Power Engineering, Chongqing University, China, have discovered a highly efficient, time saving as well as a reliable machine learning (ML) method for the research and development of novel organic photovoltaic (OPV) materials. During the development of high performing OPV materials, if one can pre-establish the correlation between the structure of the designed material and its photovoltaic property, it becomes highly meaningful and time saving. The research is reported in the journal Science Advances. OPV cells are an easy and highly economical method for transforming the solar energy into electrical energy. Until now, the typical OPV materials-based research has focused on building a relationship between the newly developed OPV molecular material and its organic photovoltaic material properties.