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.
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.
CybelAngel is able to alert on a set of sensitive blueprints amidst the thousands of billions of documents available on the web. Ever wondered how we are able to do it? Let us introduce you to the latest Machine Learning model for data leak detection: Content Scoring. While nothing will replace cyber-analysts, technology can help lower the noise, speed up the detection of real threats, and contextualize the leak to facilitate the investigation. As CybelAngel deploys new detection capacities across file servers, we have drastically improved our Machine Learning algorithms used for filtering and contextualizing.
The automatic and accurate interlinking of geospatial data poses an important scientific challenge, with direct application in several business fields. The major requirement is achieving high accuracy in identifying similar entities within datasets. For example, in a cadastral database, it is crucial that the land parcels, that were gathered from several different databases, are uniquely and clearly identified. In another example, for a geo-marketing company, it is of high importance to be able to accurately cross-reference the location/addresses of customers and companies, so that they are properly targeted. LinkGeoML aims at researching, developing and extending machine learning methods, utilizing the vast amount of available, open geospatial data, in order to implement automated and highly accurate algorithms for interlinking geospatial entities. The proposed methods will implement novel training features, based on domain knowledge and on the analysis of open and proprietary geospatial datasets.
The coronavirus lockdown and the school closures that have resulted have had a huge impact on the education of 1.2 billion children across 186 countries. Teachers have been scrambling to keep in touch with their classes, while parents have been trying to keep bored children engaged in learning while cut off from school and fellow students. As a result, virtual classrooms, language apps, online tutoring, and online education software (and new hardware) have seen a surge popularity, with some reports suggesting the market could hit $350 billion by 2025. But can the digital revolution in education, long been promised but rarely achieved, take a step forward as a result of these changes? Speaking at the CogX 2020 conference, Rose Luckin, professor of Learner Centered Design at University College London's Knowledge Lab, argued that the only way for the industry to evolve was to build on what it has learnt recently.
Watching this playlist is an outstanding start to learn the fundamental concepts of deep learning and artificial neural networks. The lectures are deep dive into deep learning models for image classification. The lectures also explain training deep learning models. This specialization contains 5 courses to understand deep learning foundations and apply them (you can audit the courses for free). Deep learning is getting attention from the researchers.
Ever trained an image recognition model? What accuracy did you get? 90, 95, or maybe a near-perfect 99 percent? No matter what your answer is, we want to ask for a follow-up. If you get a great accuracy on training as well as test images, does it mean your model is ready to be deployed? Well, even though it once did, now it may not be ready.
Among the other questions being asked as a result of the current pandemic is, "What will the rise of artificial intelligence mean for K-12 education?" It would seem safe to assume that the rush to online learning and the adoption of new technologies will inevitably lead educators to embrace tools powered by artificial intelligence. But according to Robert F. Murphy, that more optimistic vision for AI will probably be tempered for now by budget shortfalls that "may seriously delay" school districts from making those types of investments anytime soon. Murphy is an independent education consultant with over two decades of research experience, including as a senior policy researcher for the international think tank RAND Corporation and as the director for evaluation research at SRI International, a scientific research center. In a paper authored last year for RAND, Murphy addressed the more fundamental issues of AI that need to be considered, regarding its further adoption.
The term artificial intelligence (AI) was coined 64 years ago at a scholarly conference. The AI field hasn't remained the theoretical province of computer scientists and mathematicians; it now is a pervasive part of everyday life. With a technology this powerful, it is critical to include the perspectives of all women, including those from underrepresented communities. AI applications -- based on algorithms -- are found in robotics, machine learning, natural language processing, machine vision, speech recognition and more. These applications are found in homes, vehicles and myriad other aspects of daily life.
Amid a growing backlash over AI's racial and gender biases, numerous tech giants are launching their own ethics initiatives -- of dubious intent. The schemes are billed as altruistic efforts to make tech serve humanity. But critics argue their main concern is evading regulation and scrutiny through "ethics washing." At least we can rely on universities to teach the next generation of computer scientists to make. Only 15% of instructors and professors said they're teaching AI ethics, and just 18% of students indicated they're learning about the subject.