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) …
We ask whether recent progress on the ImageNet classification benchmark continues to represent meaningful generalization, or whether the community has started to overfit to the idiosyncrasies of its labeling procedure. We therefore develop a significantly more robust procedure for collecting human annotations of the ImageNet validation set. Using these new labels, we reassess the accuracy of recently proposed ImageNet classifiers, and find their gains to be substantially smaller than those reported on the original labels. Furthermore, we find the original ImageNet labels to no longer be the best predictors of this independently-collected set, indicating that their usefulness in evaluating vision models may be nearing an end. Nevertheless, we find our annotation procedure to have largely remedied the errors in the original labels, reinforcing ImageNet as a powerful benchmark for future research in visual recognition.
"It's the representation in gaming I've waited for my whole life." Marvel's Avengers are assembling once again, not on the big screen, but for a blockbuster video game. It features many of the superheroes you might expect, including Iron Man, Hulk and Captain America. But they are joined by a new addition: Kamala Khan. The Muslim-American teenager of Pakistani heritage, who has shape-shifting abilities, is the latest character to adopt the Ms Marvel moniker.
To follow along, you can either download our Jupyter notebook here, or continue reading and typing in the following code as you proceed through the walkthrough. Unsupervised machine learning methods can allow us to understand and explore data in situations where we are not given explicit labels. One type of unsupervised machine learning methods falls under the family of clustering. Getting a general idea of groups or clusters of similar data points can inform us of any underlying structural patterns in our data, such as geography, functional similarities, or communities when we otherwise would not know this information beforehand. We will be applying our dimensional reduction techniques to Microbiome data acquired from UCSD's Qiita platform.
Imagine there's a marquee game coming out next year from one of the coolest AAA video game studios in the world, and its first round of marketing has just gone live. It's nighttime in the game and the streamer, playing as a middle-aged man, approaches a group of people standing outside of a club. They're all broad-shouldered with cut biceps, and they're wearing an assortment of wigs, crop tops, mini skirts, lace stockings and bikini bottoms. Chest hair pokes out from some of their shirts, and under layers of dramatic makeup, a few jawlines are dusted with stubble. The tight clothing highlights obvious crotch-level bulges.
While BERT is a significant improvement in how computers'understand' human language, it is still far away from understanding language and context in the same way that humans do. We should, however, expect that BERT will have a significant impact on many understanding focused NLP initiatives. The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another. The datasets are designed to test a model's language understanding and are useful for evaluating models like BERT. As the GLUE results show, BERT makes it possible to outperform humans even in comprehension tasks previously thought to be impossible for computers to outperform humans.
In computer vision, one key property we expect of an intelligent artificial model, agent, or algorithm is that it should be able to correctly recognize the type, or class, of objects it encounters. This is critical in numerous important real-world scenarios--from biomedicine, where an intelligent system might be tasked with distinguishing between cancerous cells and healthy ones, to self-driving cars, where being able to discriminate between pedestrians, other vehicles, and road signs is crucial to successfully and safely navigating roads. Deep learning is one of the most significant tools for state-of-the-art systems in computer vision, and its use has resulted in models that have reached or can even exceed human-level performance in important and challenging real-world image classification tasks. Despite their successes, these models still have difficulty generalizing, or adapting to tasks in testing or deployment scenarios that don't closely resemble the tasks they were trained on. For example, a visual system trained under typical weather conditions in Northern California may fail to properly recognize pedestrians in Quebec because of differences in weather, clothes, demographics, and other features.
Neural networks are very resource intensive algorithms. They not only incur significant computational costs, they also consume a lot of memory in addition. Even though the commercially available computational resources increase day by day, optimizing the training and inference of deep neural networks is extremely important. If we run our models in the cloud, we want to minimize the infrastructure costs and the carbon footprint. When we are running our models on the edge, network optimization becomes even more significant. If we have to run our models on smartphones or embedded devices, hardware limitations are immediately apparent.
The integrity and trustworthiness of data or any other master entity is enforced via data quality rules. Customers no longer want to rely on hand crafted rules that can number in the thousands, which in turn also need a lot of maintenance. Riding on the machine learning (ML) wave, customers can break free from their rule-based business logic and rely on data driven decisions within product information management systems (PIM). These processes are necessary for decreasing effort and saving time and costs. The IBM InfoSphere Master Data Management (MDM) suite offers these ML capabilities in IBM MDM Product Master to help organize product and service information across the enterprise. As a PIM solution, IBM Product Master (formerly IBM InfoSphere Master Data Management Collaborative Edition) aggregates information from any upstream system, enforces business processes to ensure data accuracy and consistency, and synchronizes trusted information with downstream systems.
CVPR 2020 is yet another big AI conference that takes place 100% virtually this year. Here we've picked up the research papers that started trending within the AI research community months before their actual presentation at CVPR 2020. These papers cover the efficiency of object detectors, novel techniques for converting RGB-D images into 3D photography, and autoencoders that go beyond the capabilities of generative adversarial networks (GANs) with respect to image generation and manipulation. Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries. If you'd like to skip around, here are the papers we featured: Model efficiency has become increasingly important in computer vision.