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) …
Every once in a while, a machine learning framework or library changes the landscape of the field. In this article, we'll quickly understand the concept of object detection and then dive straight into DETR and what it brings to the table. In Computer Vision, object detection is a task where we want our model to distinguish the foreground objects from the background and predict the locations and the categories for the objects present in the image. Current deep learning approaches attempt to solve the task of object detection either as a classification problem or as a regression problem or both. For example, in the RCNN algorithm, several regions of interest are identified from the input image.
Doron Adler and Justin Pinkney, two software engineers, recently released a "Toonification translation" AI model that turns real faces into flawless cartoon representations. And while the toonification tool, "Toonify," was originally available to the public, it became too popular to sustain cheaply. But some people managed to Toonify a ton of celebrities before the tool was pulled, and all the animations are stellar. After much training of neural networks @Norod78 and I have put together a website where anyone can #toonify themselves using deep learning!https://t.co/OQ23p30isC In a series of blog posts, which come via Gizmodo, Pinkney outlines how he and Adler created Toonify.
Tiny robots that can transport individual neurons and connect them to form active neural circuits could help us study brain disorders such as Alzheimer's disease. The robots, which were developed by Hongsoo Choi at the Daegu Gyeongbuk Institute of Science and Technology in South Korea and his colleagues, are 300 micrometres long and 95 micrometre wide. They are made from a polymer coated with nickel and titanium and their movement can be controlled with external magnetic fields.
Summary: Since BERT NLP models were first introduced by Google in 2018 they have become the go-to choice. New evidence however shows that LSTM models may widely outperform BERT meaning you may need to evaluate both approaches for your NLP project. Over the last year or two, if you needed to bring in an NLP project quickly and with SOTA (state of the art) performance, increasingly you reached for a pretrained BERT module as the starting point. Recently however there is growing evidence that BERT may not always give the best performance. In their recently released arXiv paper, Victor Makarenkov and Lior Rokach of Ben-Gurion University share the results of their controlled experiment contrasting transfer-based BERT models with from scratch LSTM models.
"According to the filing, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks." Looks like Google won't be stopping its infamous patenting spree anytime soon. Earlier this month, Google filed a patent for capsule networks. Turing award recipient and Google researcher Geoff Hinton was named amongst the list of inventors in the filing. According to the patent filed, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks for traditional computer vision applications. Capsule networks are aimed at alleviating the extra dimensionality which surfaces with a convolutional neural network.
The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow.
Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person's behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the'bible' of mental health diagnosis. However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult. But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability.
Artificial intelligence (AI) can detect loneliness with 94 per cent accuracy from a person's speech, a new scientific paper reports. Researchers in the US used several AI tools, including IBM Watson, to analyse transcripts of older adults interviewed about feelings of loneliness. By analysing words, phrases, and gaps of silence during the interviews, the AI assessed loneliness symptoms nearly as accurately as loneliness questionnaires completed by the participants themselves, which can be biased. It revealed that lonely individuals tend to have longer responses to direct questions about loneliness, and express more sadness in their answers. 'Most studies use either a direct question of "how often do you feel lonely", which can lead to biased responses due to stigma associated with loneliness,' said senior author Ellen Lee at UC San Diego (UCSD) School of Medicine.
"We're entering a new world in which data may be more important than software." If you want to stay competitive in this rapidly evolving domain, you need to regularly update your skills with the latest changes. In the following section, we will share the top Data Science skills that not only a practicing Data Scientist would benefit from, but also anyone who's passionate about working his way around large volumes of data. If you code anything at all, we're sure you must've heard about GitHub. GitHub is among the most commonly used tools by the developers today after Stack Overflow.
It's way easier than you would think. Much of the content below is based on the Intro to Deep Learning with PyTorch course by Facebook AI. If you want to learn more, take the course, or just take a look here. Below is a graph that determines whether or not a student will be accepted into a university. Two pieces of data have been used: grades and tests each on a scale of 0–10.