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) …
The biggest issue facing machine learning is how to put the system into production. To conceptualize this framework, there is a significant paper from Google called ML Test Score -- A Rubric for Production Readiness and Technical Debt Reduction -- which is an exhaustive framework/checklist from practitioners at Google. It is a follow-up to previous work from Google, such as (1) Hidden Technical Debt in ML Systems, (2) ML: The High-Interest Credit Card of Technical Debt, and (3) Rules of ML: Best Practices for ML Engineering. As seen in Figure 1 from the paper above, ML system testing is more complex a challenge than testing manually coded systems, since ML system behavior depends strongly on data and models that cannot be sharply specified a priori. One way to see this is to consider ML training as analogous to the compilation, where the source is both code and training data.
To quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy. This retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 (n 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. A generative adversarial network (GAN) was trained to adapt the input image before segmentation. The LV segmentation performance, end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and LV ejection fraction (LVEF) were evaluated before and after manufacturer adaptation.
These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!
The whole backdrop of Artificial intelligence and deep learning is to imitate the human brain, and one of the most notable feature of our brain is it's inherent ability to transfer knowledge across tasks. Which in simple terms means using what you have learnt in kindergarten, adding 2 numbers, to solving matrix addition in high school mathematics. The field of machine learning also makes use of such a concept where a well trained model trained with lots and lots of data can add to the accuracy of our model. Here is my code for the transfer learning project I have implemented. I have made use of open cv to capture real time images of the face and use them as training and test datasets.
In 1963, Martin Luther King gave his "I have a dream" speech, words that reflected the thoughts and attitudes of civil rights activists at the time, and lit a torch that lives on in the hearts and minds of those who fight for civil liberties and equality in the western hemisphere. While the world has advanced since Dr. King ushered those words, it's hard to deny that discrimination still rears its ugly head in modern society. We know for a fact that racial discrimination in the workplace is illegal in most of America and Europe. And yet, just in the USA statistics show that things don't seem to have improved regarding hiring practices for black people and Hispanics in the last 25 years. In theory, AI-assisted hiring is built on an underlying model that makes unbiased decisions as long as the data itself isn't biased.
The federal government continues its halting effort to field an enterprise cloud strategy, with Lt. Gen. Jack Shanahan, who leads the Defense Department's Joint AI Center (JAIC), commenting recently that not having an enterprise cloud platform has made the government's efforts to pursue AI more challenging. "The lack of an enterprise solution has slowed us down," stated Shanahan during an AFCEA DC virtual event held on May 21, according to an account in FCW. However, "the gears are in motion" with the JAIC using an "alternate platform" for example to host a newer anti-COVID effort. This platform is called Project Salus, and is a data aggregation that is able to employ predictive modeling to help supply equipment needed by front-line workers. The Salus platform was used for the ill-fated Project Maven, a DOD effort that was to employ AI image recognition to improve drone strike accuracy.
Each Fourth of July for the past five years I've written about AI with the potential to positively impact democratic societies. I return to this question with the hope of shining a light on technology that can strengthen communities, protect privacy and freedoms, or otherwise support the public good. This series is grounded in the principle that artificial intelligence can is capable of not just value extraction, but individual and societal empowerment. While AI solutions often propagate bias, they can also be used to detect that bias. As Dr. Safiya Noble has pointed out, artificial intelligence is one of the critical human rights issues of our lifetimes.
As global technology has evolved over the years, we have moved from television to the internet, and today we are smoothly and gradually adapting Artificial Intelligence. The term AI was first coined by John McCarthy in 1956. It involves a lot of the main things ranging from process automation of robotics to the actual process of robotics. It has become highly popular among large enterprises today owing to the amount of data these companies are dealing in. Increase in the demand for understanding the data patterns has led to the growth in demand of AI.
A good dataset serves as the backbone of an Artificial Intelligence system. Data assists in various ways as it helps understand how the system is performing, understand meaning insights and others. At the premier annual Computer Vision and Pattern Recognition conference (CVPR 2020), several datasets have been open-sourced in order to help the community achieve higher accuracies and insights. Below here we have listed the top 10 Computer Vision datasets that are open-sourced at the CVPR 2020 conference. About: FaceScape is a large-scale detailed 3D face dataset that includes 18,760 textured 3D face models, which are captured from 938 subjects and each with 20 specific expressions.
New research in Scientific Reports conducted by Washington University shows how comprehending brain activity as a network rather than by electroencephalography readings, provides more accurate identification of epileptic seizures in real-time. The study, which mixes machine learning with systems theory, was steered by lead author Walter Bomela. "Our technique allows us to get raw data, process it and extract a feature that's more informative for the machine learning model to use," Bomela stated in a news release. "The major advantage of our approach is to fuse signals from 23 electrodes to one parameter that can be efficiently processed with much less computing resources." As explained by researchers, using an EEG, epileptic seizures can be observed through irregular brain activity in the form of spikes and waves during the measurement of electrical output.