A regular message in this column is that artificial intelligence (AI) won't spread widely until it's easier to use than the requirement to have programmers who can work at the model level. That challenge won't be solved instantly, and it's slowly changing. While technical knowledge is still too often required, there are ways in which development time can be shortened. One way that's been happening has been is the increased availability of pre-built models. A few years back, a tech CEO loved to talk about the "Cambrian Explosion" of deep learning models, as if a lot of models meant real progress in the business world.

Before I answer this question, two words of introduction. What you see in the picture below is a model that makes text look like an image -- AttnGAN (Attentional Generative Adversarial Networks). It changes descriptive texts into synthesized images. Thanks to its innovative generative network, AttnGAN it can synthesize small details in different subregions of an image, paying attention to the appropriate words in the natural language description. The text that answers the question of "what an AI is" is not a descriptive text, but let us see what our GAN will generate.

Description Lets learn basics to transform your career. I promise not to exhaust you with huge number of videos. Welcome to the most comprehensive Introduction to AI, Machine Learning and Data Science course! An excellent choice for beginners and professionals looking to expand their knowledge on Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised Learning. This is an introductory course for beginners to boost your knowledge. This course gives introduction to to AI, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised learning with real time examples where machine learning can be applied to solve or simplify real world business problems.

In December 2019, Utah startup Latitude launched a pioneering online game called AI Dungeon that demonstrated a new form of human-machine collaboration. The company used text-generation technology from artificial intelligence company OpenAI to create a choose-your-own adventure game inspired by Dungeons & Dragons. When a player typed out the action or dialog they wanted their character to perform, algorithms would craft the next phase of their personalized, unpredictable adventure. Last summer, OpenAI gave Latitude early access to a more powerful, commercial version of its technology. In marketing materials, OpenAI touted AI Dungeon as an example of the commercial and creative potential of writing algorithms.\

A regular message in this column is that artificial intelligence (AI) won't spread widely until it's easier to use than the requirement to have programmers who can work at the model level. That challenge won't be solved instantly, and it's slowly changing. While technical knowledge is still too often required, there are ways in which development time can be shortened. One way that's been happening has been is the increased availability of pre-built models. A few years back, a tech CEO loved to talk about the "Cambrian Explosion" of deep learning models, as if a lot of models meant real progress in the business world.

Mike Solomon will teach you how to do Machine Learning on Meeshkan. Meeshkan is an easy and inexpensive platform where people can explore ideas in AI, Machine Learning and Deep Learning. This course starts with a simple AI question: can a machine predict if a GitHub project will be successful by analyzing only the first few commits of that project? The first section of the course will run the Machine Learning project on Meeshkan. You'll see how quick and easy it is to do Machine Learning on Meeshkan.

Deep learning is a subset of machine learning, which in turn is a subset of artificial intelligence, but the origins of these names arose from an interesting history. In addition, there are fascinating technical characteristics that can differentiate deep learning from other types of machine learning... essential working knowledge for anyone with ML, DL, or AI in their skillset. If you are looking to improve your skillset or steer business/research strategy in 2021, you may come across articles decrying a skills shortage in deep learning. A few years ago, you would have read the same about a shortage of professionals with machine learning skills, and just a few years before that, the emphasis would have been on a shortage of data scientists skilled in "big data." Likewise, we've heard Andrew Ng telling us for years that "AI is the new electricity," and the advent of AI in business and society is constantly suggested to have an impact similar to that of the industrial revolution.

\textit{Stochastic gradient descent} (SGD) is of fundamental importance in deep learning. Despite its simplicity, elucidating its efficacy remains challenging. Conventionally, the success of SGD is attributed to the \textit{stochastic gradient noise} (SGN) incurred in the training process. Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of a \textit{stochastic differential equation} (SDE) driven by either Brownian or L\'evy stable motion. In this study, we argue that SGN is neither Gaussian nor stable. Instead, inspired by the long-time correlation emerging in SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by \textit{fractional Brownian motion} (FBM). Accordingly, the different convergence behavior of SGD dynamics is well grounded. Moreover, the first passage time of an SDE driven by FBM is approximately derived. This indicates a lower escaping rate for a larger Hurst parameter, and thus SGD stays longer in flat minima. This happens to coincide with the well-known phenomenon that SGD favors flat minima that generalize well. Four groups of experiments are conducted to validate our conjecture, and it is demonstrated that long-range memory effects persist across various model architectures, datasets, and training strategies. Our study opens up a new perspective and may contribute to a better understanding of SGD.

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.

The quantification of positively buoyant marine plastic debris is critical to understanding how concentrations of trash from across the world's ocean and identifying high concentration garbage hotspots in dire need of trash removal. Currently, the most common monitoring method to quantify floating plastic requires the use of a manta trawl. Techniques requiring manta trawls (or similar surface collection devices) utilize physical removal of marine plastic debris as the first step and then analyze collected samples as a second step. The need for physical removal before analysis incurs high costs and requires intensive labor preventing scalable deployment of a real-time marine plastic monitoring service across the entirety of Earth's ocean bodies. Without better monitoring and sampling methods, the total impact of plastic pollution on the environment as a whole, and details of impact within specific oceanic regions, will remain unknown. This study presents a highly scalable workflow that utilizes images captured within the epipelagic layer of the ocean as an input. It produces real-time quantification of marine plastic debris for accurate quantification and physical removal. The workflow includes creating and preprocessing a domain-specific dataset, building an object detection model utilizing a deep neural network, and evaluating the model's performance. YOLOv5-S was the best performing model, which operates at a Mean Average Precision (mAP) of 0.851 and an F1-Score of 0.89 while maintaining near-real-time speed.