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) …
GPT-2 was a great success. OpenAI didn't want to publish the most enormous and mightiest version, with 1.5B parameters. At least, claiming that they were afraid of misusing it for less ethical purposes. Lately, they claimed that they didn't found shreds of evidence of such. All of this is legit, considering the volume of the false "news" generated using it. And the truth is that it can be very successful in developing false news/stories.
Compare the above two sentences "I drink coffee with milk" and "I drink coffee with friends". They only differ at their very last words, but their parses differ at earlier places, too. Now imagine you read sentences like these. This might be a daunting task when the sentences get longer and their structures more complex. In our work, we show that this task is also difficult for some leading machine learning models for parsing.
Signals in the brains of birds have been read by scientists, in a breakthrough that could help develop prostheses for humans who have lost the ability to speak. In the study silicon implants recorded the firing of brain cells as male adult zebra finches went through their full repertoire of songs. Feeding the brain signals through artificial intelligence allowed the team from the University of California San Diego to predict what the birds would sing next. The breakthrough opens the door to new devices that could be used to turn the thoughts of people unable to speak, into real, spoken words for the first time. Current state-of-the-art implants allow the user to generate text at a speed of about 20 words per minute, but this technique could allow for a fully natural'new voice'.
This paper introduces Deep4D a compact generative representation of shape and appearancefrom captured 4D volumetric video sequences of people. 4D volumetric video achieves highlyrealistic reproduction, replay and free-viewpoint rendering of actor performance from multipleview video acquisition systems. A deep generative network is trained on 4D video sequencesof an actor performing multiple motions to learn a generative model of the dynamic shapeand appearance. We demonstrate the proposed generative model can provide a compactencoded representation capable of high-quality synthesis of 4D volumetric video with two ordersof magnitude compression. A variational encoder-decoder network is employed to learn anencoded latent space that maps from 3D skeletal pose to 4D shape and appearance. Thisenables high-quality 4D volumetric video synthesis to be driven by skeletal motion, includingskeletal motion capture data. This encoded latent space supports the representation of multiplesequences with dynamic interpolation to transition between motions. Therefore we introduceDeep4D motion graphs, a direct application of the proposed generative representation. Deep4Dmotion graphs allow real-tiome interactive character animation whilst preserving the plausiblerealism of movement and appearance from the captured volumetric video. Deep4D motion graphsimplicitly combine multiple captured motions from a unified representation for character animationfrom volumetric video, allowing novel charact...
BERT is a state-of-the-art model by Google that came in 2019. In this blog, I will go step by step to finetune the BERT model for movie reviews classification(i.e positive or negative). Here, I will be using the Pytorch framework for the coding perspective. BERT is built on top of the transformer (explained in paper Attention is all you Need). Input text sentences would first be tokenized into words, then the special tokens ( [CLS], [SEP], ##token) will be added to the sequence of words.
We are sharing a new benchmark for continual learning (CL), a means for improving upon traditional machine learning (ML) methods by training AI models to mimic the way humans learn new tasks. In CL, an AI model applies knowledge from previous tasks to solve new problems, rather than restarting its training from scratch every time. We expect that CL models will require less supervision, sidestepping one of the most significant shortcomings of modern AI systems: their reliance on large human-labeled data sets. But developing effective CL models comes with its own challenges. When we fundamentally change how we train ML models, we must also change how we evaluate and compare them.
Deep learning models rely on numerical vectors to'understand' the input words. We can think of the numerical vectors as high dimensional features representing the input words. In this high dimensional space, words are located close together or far away from each other. Word representation is built by finding the proper numerical vector representations for all the words in a given corpus. The quality of word representation relies on the corpus. This can be easily understood in the way that two human beings can have a different understanding of the same word, depending on whether he likes to spend time reading the modern newspaper or Shakespeare's literature. Besides, the quality of word representation heavily relies on the methods to find numerical vector representations for all the words. There are several methods to generate word representation by learning from the words' context.
The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default.
Deep Learning: Recurrent Neural Networks in Python, GRU, LSTM, more modern deep learning, machine learning, and data science for sequences Created by Lazy Programmer Inc. English [Auto], Indonesian [Auto], 5 more Preview this Course - GET COUPON CODE Description Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.
Knee cartilage compartments with anatomic labels implemented in lateral (left side), central (middle), and medial (right side) MRI obtained with an intermediate weighted fat-saturated fast-spin-echo sequence (top row) and a spin-lattice relaxation time constant in rotating frame (T1r) magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots sequence (bottom row, T1r maps). Study was performed without administration of intravenous gadolinium-based contrast material. The lateral femur (LF)/medial femur (MF) and lateral tibia (LT)/medial tibia (MT) can be further divided into subcompartments on the basis of meniscus anatomy according to Eckstein et al.