Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback, as several applications in the real world are critical tasks, such as the medical field or the autonomous driving field. The explainability of models applied on time series has not gather much attention compared to the computer vision or the natural language processing fields. In this paper, we present an overview of existing explainable AI (XAI) methods applied on time series and illustrate the type of explanations they produce. We also provide a reflection on the impact of these explanation methods to provide confidence and trust in the AI systems.

Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.

In this contributed article, Taggart Bonham, Product Manager of Global AI at F5 Networks, discusses last June, OpenAI released GPT-3, their newest text-generating AI model. As seen in the deluge of Twitter demos, GPT-3 works so well that people have generated text-based DevOps pipelines, complex SQL queries, Figma designs, and even code. In the article, Taggart explains how enterprises need to prepare for the AI economy by standardizing their data collection processes across their organizations like GPT-3 so it can then be properly leveraged.

Over the past few decades, computer scientists have developed deep learning tools for a broad variety of applications, including for the analysis of pharmaceutical drugs. Most recently, deep learning models that predict the properties of pharmaceuticals have been trained to analyze and learn molecular representations. Researchers at Tsinghua University, the National University of Singapore, Fudan University's School of Pharmacy, and Zheijang University have recently developed MolMapNet, a new artificial intelligence (AI) tool that can predict the pharmaceutical properties of drugs by analyzing human-knowledge-based molecular representations. This tool, presented in a paper published in Nature Machine Intelligence, can also be used by people with little or no knowledge of computer science, biology or other sciences. "We were aware that pharmaceutical investigations require the learning of many molecular characters, particularly the rich collection of molecular properties (like volume) derived from human knowledge, but these molecular properties are tough to learn by AI (artificial intelligence)," Yu Zong Chen, one of the researchers who carried out the study, told TechXplore.

"AI models do not need to be interpretable to be useful." Interpretability in machine learning goes back to the 1990s when it was neither referred to as "interpretability" nor "explainability". Interpretable and explainable machine learning techniques emerged from the need to design intelligible machine learning systems and understand and explain predictions made by opaque models like deep neural networks. In general, the ML community is yet to agree on a definition for explainability or interpretability. Sometimes it is even called understandability.

Perhaps the most well-known resource for learning deep learning is Andrew Ng's series of 5 courses on Coursera. Those courses are still a great resource for anyone learning the fundamentals of the field but they are now a few years old (their launch was announced in August 2017). In this post, I will give you three main reasons why you should instead start from MIT's course that I am going to tell you about. Before I try to convince you to start your deep learning journey from there, here is a brief description of the course itself. This course is getting released now, as we speak (rather, … as you read).

Janelle Shane, a research scientist and author from Colorado, created a squad of courtship bots using one of the most sophisticated text-writing AI algorithms in the world: a language model known as the Generative Pre-trained Transformer 3 (GPT-3), which uses deep learning to produce human-like text. Researchers claim GPT-3's output is so advanced that it is often difficult to distinguish it from material written by a real, flesh-and-blood human--which, in this case, says some troubling things about the standards we've come to expect from real, flesh-and-blood humans.

You've probably seen the acronym "DLSS" appearing in more gaming and tech stories recently. You might know that it's an Nvidia graphics thing, and that it might be coming to the new Nintendo Switch console that's rumored to release later in 2021, according to a report from Bloomberg. But, really, what is it, and why does it matter? DLSS stands for deep learning super sampling, and it's a way for Nvidia's RTX graphics cards to work smarter, not necessarily harder, by running games at a lower resolution, then using dedicated AI cores to improve visual quality with less of the usual performance cost. The deep learning component works on the fly to make your game look as if you haven't lowered its resolution at all.

In this episode of the Data Exchange I speak Bharath ("Bart") Ramsundar, author and open source developer. While in graduate school, Bart created DeepChem, an open source project that aims to democratize deep learning for science. DeepChem historically was developed for researchers in the life sciences, so the working examples in its tutorials draw from areas like chemistry and bioinformatics. Researchers in other branches of science (e.g., physics and astronomy) have long embraced machine learning and big data management systems. In fact, I remember that during the early days of Hadoop and MPP databases, creators and vendors of big data systems approached research labs known to possess massive amounts of data.