Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment. In this program, you'll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment, and you will build projects in NumPy and PyTorch. You will learn from experts in the field, and gain exclusive insights from working professionals.

If you're new to Python programming, consider starting with our AI Programming with Python Nanodegree program. If you're new to computer science algorithms, we recommend our Data Structures & Algorithms Nanodegree program. Learn to write programs using the foundational AI algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero. This program requires experience with linear algebra, statistics, and Python (including object-oriented programming). Use constraint propagation and search to build an agent that reasons like a human would to efficiently solve any Sudoku puzzle.

Deep neural networks have gained fame for their capability to process visual information. And in the past few years, they have become a key component of many computer vision applications. Among the key problems neural networks can solve is detecting and localizing objects in images. Object detection is used in many different domains, including autonomous driving, video surveillance, and healthcare. In this post, I will briefly review the deep learning architectures that help computers detect objects.

Melanie Mitchell, a professor of complexity at the Santa Fe Institute and a professor of computer science at Portland State University, acknowledges the powerful accomplishments of "black box" deep learning neural networks. But she also thinks that artificial intelligence research would benefit most from getting back to its roots and exchanging more ideas with research into cognition in living brains. This week, she speaks with host Steven Strogatz about the challenges of building a general intelligence, why we should think about the road rage of self-driving cars, and why AIs might need good parents. Listen on Apple Podcasts, Spotify, Android, TuneIn, Stitcher, Google Podcasts, or your favorite podcasting app, or you can stream it from Quanta. Melanie Mitchell: You know, you give it a new face, say, and it gives you an answer: "Oh, this is Melanie." And you say, "Why did you think that?" "Well, because of these billions of numbers that I just computed." Steve Strogatz [narration]: From Quanta Magazine, this is The Joy of x. Mitchell: And I'm like, "Well, I can't under-- Can you say more?" And they were like, "No, we can't say more." Steve Strogatz: Isn't that unnerving, that it's this great virtuoso at these narrow tasks, but it has no ability to explain itself? Strogatz: Melanie Mitchell is a computer scientist who is particularly interested in artificial intelligence. Her take on the subject, though, is quite a bit different from a lot of her colleagues' nowadays. She actually thinks that the subject may be adrift and asking the wrong questions. And in particular, she thinks that it would be better if artificial intelligence could get back to its roots in making stronger ties with fields like cognitive science and psychology, because these artificially intelligent computers, while they're smart, they are smart in a way that is so different from human intelligence. Melanie's been intrigued by these questions for really quite a long time, but her journey got started in earnest when she stumbled across a really big and really important book that was published in 1979.

Humans have been using antibiotics for about 100 years. For 30 years the competent authorities have been warning of the future problem of antibiotic resistance, since higher doses or different antibiotics are needed to end them. Pathogens, whether bacteria, fungi or protists, that have been traditionally stopped with antibiotics have naturally developed resistance to the drugs used against them. This is due to the process of constant evolution that occurs in nature, but health authorities point out that the misuse and abuse of antibiotics has helped this adaptation to take place much faster than expected. Health authorities suggest that by the end of the 21st century the current antibiotics will no longer be useful and that from 2050 we may already notice the lack of response from many of them. It is estimated that by then about 10 million people will die each year from resistant infections.

In this special guest feature, Sahar Mor, founder of AirPaper, discusses DALL-E – a new powerful API from OpenAI that creates images from text captions. With this, Sahar is planning to build a few products such as a chart generator based on text and a text-based tool to generate illustrations for landing pages. Sahar has 12 years of Engineering Product Management experience, both focused on products with AI in their core. Previously he worked as an Engineering Manager in early-stage startups and at the elite Israeli intelligence unit – 8200. Several months ago OpenAI published their latest research model DALL-E – an advanced neural network that generates images from text prompts and a natural progression of its powerful language model GPT-3.

It's easy to get pulled into using popular platforms like TensorFlow and PyTorch, but there are a number of other great open-source resources that can help you in your AI research. The truth is there is so much interesting work and so many brilliant new tools being developed on a daily basis in open-source artificial intelligence. It can be difficult to keep up with the ever-accelerating developments in AI and deep learning. So, we've taken the time to curate some interesting tools that you may be able to use. In this article, we'll take a look at 7 interesting libraries for doing a wide variety of cutting-edge research in artificial intelligence and related areas. The diversity of the libraries on this list is significant, and if at least one of the libraries isn't an exact fit for your next project (or perhaps an inspiration for one), they are all licensed under permissive open source licenses so you can contribute, fork, and modify these libraries to your heart's content.

Standardized body region labelling of individual images provides data that can improve human and computer use of medical images. A CNN-based classifier was developed to identify body regions in CT and MRI. 17 CT (18 MRI) body regions covering the entire human body were defined for the classification task. Three retrospective databases were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. The test databases originated from a different healthcare network. Accuracy, recall and precision of the classifier was evaluated for patient age, patient gender, institution, scanner manufacturer, contrast, slice thickness, MRI sequence, and CT kernel. The data included a retrospective cohort of 2,934 anonymized CT cases (training: 1,804 studies, validation: 602 studies, test: 528 studies) and 3,185 anonymized MRI cases (training: 1,911 studies, validation: 636 studies, test: 638 studies). 27 institutions from primary care hospitals, community hospitals and imaging centers contributed to the test datasets. The data included cases of all genders in equal proportions and subjects aged from a few months old to +90 years old. An image-level prediction accuracy of 91.9% (90.2 - 92.1) for CT, and 94.2% (92.0 - 95.6) for MRI was achieved. The classification results were robust across all body regions and confounding factors. Due to limited data, performance results for subjects under 10 years-old could not be reliably evaluated. We show that deep learning models can classify CT and MRI images by body region including lower and upper extremities with high accuracy.

Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable. We are interested in identifying novel concepts in a dataset through representation learning based on the examples in both labeled and unlabeled classes, and extending the horizon of recognition to both known and novel classes. To address this challenging task, we propose a combinatorial learning approach, which naturally clusters the examples in unseen classes using the compositional knowledge given by multiple supervised meta-classifiers on heterogeneous label spaces. We also introduce a metric learning strategy to estimate pairwise pseudo-labels for improving representations of unlabeled examples, which preserves semantic relations across known and novel classes effectively. The proposed algorithm discovers novel concepts via a joint optimization of enhancing the discrimitiveness of unseen classes as well as learning the representations of known classes generalizable to novel ones. Our extensive experiments demonstrate remarkable performance gains by the proposed approach in multiple image retrieval and novel class discovery benchmarks.

Quantifying entropy production (EP) is essential to understand stochastic systems at mesoscopic scales, such as living organisms or biological assemblies. However, without tracking the relevant variables, it is challenging to figure out where and to what extent EP occurs from recorded time-series image data from experiments. Here, applying a convolutional neural network (CNN), a powerful tool for image processing, we develop an estimation method for EP through an unsupervised learning algorithm that calculates only from movies. Together with an attention map of the CNN's last layer, our method can not only quantify stochastic EP but also produce the spatiotemporal pattern of the EP (dissipation map). We show that our method accurately measures the EP and creates a dissipation map in two nonequilibrium systems, the bead-spring model and a network of elastic filaments. We further confirm high performance even with noisy, low spatial resolution data, and partially observed situations. Our method will provide a practical way to obtain dissipation maps and ultimately contribute to uncovering the nonequilibrium nature of complex systems.