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) …
Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study published in Radiology: Artificial Intelligence. The model has the potential to aid in early detection of cognitive impairment at an individual level. Amnestic mild cognitive impairment (aMCI) is a transition phase from normal aging to Alzheimer's disease (AD). People with aMCI have memory deficits that are more serious than normal for their age and education, but not severe enough to affect daily function. For the study, Ni Shu, Ph.D., from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, China, and colleagues used a machine learning approach to train a brain age prediction model based on the T1-weighted MR images of 974 healthy adults aged from 49.3 to 95.4 years.
Almost everyone in this world is utilising the power of Artificial Intelligence for efficient service in this fast-paced busy life. It is well-known that the combination of the digital transformation and the tech-driven era is contributing to the growth of AI and machine learning models. Reputed organisations and start-ups are motivated to adopt the power of Artificial Intelligence to boost productivity as well as ease the workflow. Ever wondered about what are the factors steering the widespread recognition of Artificial Intelligence? Let's dig into these five factors to understand the reasons behind this revolution.
If you're reading this article, you probably know about Deep Learning Transformer models like BERT. They're revolutionizing the way we do Natural Language Processing (NLP). In case you don't know, we wrote about the history and impact of BERT and the Transformer architecture in a previous post. These models perform very well. And why does BERT perform so well in comparison to other Transformer models? Some might say that there's nothing special about BERT.
During the past decade, machine learning has exploded in popularity and is now being applied to problems in many fields. Traditionally, a single machine learning model is devoted to one task, e.g. There are some advantages, however, to training models to make multiple kinds of predictions on a single sample, e.g. This is known as Multi-task learning (MTL). In this article, we discuss the motivation for MTL as well as some use cases, difficulties, and recent algorithmic advances.
Tesla's Senior Director of AI, Andrej Karpathy, unveiled the electric vehicle automaker's new supercomputer during a presentation at the 2021 Conference on Computer Vision and Pattern Recognition (CVPR). Last year, Elon Musk highlighted Tesla's plans to build a "beast" of a neural network training supercomputer called "Dojo". For several years, the company has been teasing its Dojo supercomputer, which Musk has hinted will be the world's fastest supercomputer, outperforming the current world leader, Japan's Fugaku supercomputer which runs at 415 petaflops. The new supercomputer seems to be a predecessor to the Dojo project, with Karpathy stating that it is the number five supercomputer in the world in terms of floating-point operations per second (FLOPS). This supercomputer is certainly not lacking in the processing department.
PyTorch online course has been designed for those students who can learn the concepts at a fast pace. We will provide in-depth knowledge with the help of different PyTorch examples. We will also provide PyTorch tutorial in which you will learn different concepts like how to install PyTorch. You will also learn the process of configuring PyTorch. First the instructors will tell you about what is PyTorch and then they will gradually move towards basic and then to advanced topics.
You can see a complete working example in our Colab Notebook, and you can play with the trained models on HuggingFace. Since being first developed and released in the Attention Is All You Need paper Transformers have completely redefined the field of Natural Language Processing (NLP) setting the state-of-the-art on numerous tasks such as question answering, language generation, and named-entity recognition. Here we won't go into too much detail about what a Transformer is, but rather how to apply and train them to help achieve some task at hand. The main things to keep in mind conceptually about Transformers are that they are really good at dealing with sequential data (text, speech, etc.), they act as an encoder-decoder framework where data is mapped to some representational space by the encoder before then being mapped to the output by way of the decoder, and they scale incredibly well to parallel processing hardware (GPUs). Transformers in the field of Natural Language Processing have been trained on massive amounts of text data which allow them to understand both the syntax and semantics of a language very well.
Computer scientists are questioning whether DeepMind, the Alphabet-owned U.K. firm that's widely regarded as one of the world's premier AI labs, will ever be able to make machines with the kind of "general" intelligence seen in humans and animals. In its quest for artificial general intelligence, which is sometimes called human-level AI, DeepMind is focusing a chunk of its efforts on an approach called "reinforcement learning." This involves programming an AI to take certain actions in order to maximize its chance of earning a reward in a certain situation. In other words, the algorithm "learns" to complete a task by seeking out these preprogrammed rewards. The technique has been successfully used to train AI models how to play (and excel at) games like Go and chess.
Almost everyone in this world is utilising the power of Artificial Intelligence for efficient service in this fast-paced busy life. It is well-known that the combination of the digital transformation and the tech-driven era is contributing to the growth of AI and machine learning models. Reputed organisations and start-ups are motivated to adopt the power of Artificial Intelligence to boost productivity as well as ease the workflow. Ever wondered about what are the factors steering the widespread recognition of Artificial Intelligence? Let's dig into the five key factors to understand the reasons behind this revolution.