Transformers have significantly changed the way in which Natural Language Processing tasks can be performed. This architecture, which trumps the classic recurrent one – and even LSTM-based architectures in some cases, has been around since 2017 and is the process of being democratized today. And in fact, many tasks can use these developments: for example, text summarization, named entity recognition, sentiment analysis – they can all be successfully used with this type of model. In this tutorial, we will be looking at the task of machine translation. We'll first take a look at how Transformers can be used for this purpose, and that they effectively perform a sequence-to-sequence learning task.

From exploratory data analysis to automated machine learning, look to these techniques to get your data science project moving — and to build better models.

Artificial Intelligence is rapidly creeping into the workflow of many businesses across various industries and functions. Due to advancements in Natural Language Processing (NLP), Natural Language Understanding (NLU), and Machine Learning (ML), humans are now able to develop technologies that are capable of imitating human-like interactions which include recognizing speech, as well as text. In this article, we are going to build a Chatbot using NLP and Neural Networks in Python. Before we can begin to think of any coding, we need to set up an intents JSON file that defines certain intentions that could occur during the interactions with our chatbot. To perform this we would have to first create a set of tags that users queries may fall into.

Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs' precision might have a similar effect as the learning rate during DNN training, and advocate dynamic precision along the training trajectory for further boosting the time/energy efficiency of DNN training. Specifically, we propose Cyclic Precision Training (CPT) to cyclically vary the precision between two boundary values to balance the coarse-grained exploration of low precision and fine-grained optimization of high precision. Through experiments and visualization we show that CPT helps to (1) converge to a wider minima with a lower generalization error and (2) reduce training variance, which opens up a new design knob for simultaneously improving the optimization and efficiency of DNN training. Please refer to our paper for more results.

In the 1970s, Pong was a very popular video arcade game. It is a 2D video game emulating table tennis, i.e. you got a bat (a rectangle) you can move vertically and try to hit a "ball" (a moving square). If the ball hits the bounding box of the game, it bounces back like a billiard ball. If you miss the ball, the opponent scores. A single-player adaptation Breakout came out later, where the ball had the ability to destroy some blocks on the top of the screen and the bat moved to the bottom of the screen.

A scientist has taught various artificial intelligence learning algorithms how to flirt, resulting in peculiar responses. Janelle Shane, a research scientist from Colorado, trained four versions of the GPT-3 artificial intelligence – DaVinci, Curie, Babbage, and Ada - to generate pick-up lines. The way that neural networks (which are networks similar to the connections in the human brain) such as this'learn' things is by matching patterns in speech and language. This is how they create content that seems similar to that a normal human but without any understanding of the texts' meaning. GPT-3 has previously been used to design websites, write stories and has capably disguised itself as a human on the internet.

GPT-3 AI Examples - The Good, The Bad and The Ugly AF // Wanna 10x your content creation with GPT 3? GPT 3 is powerful AI for content creation that uses large datasets of text crawled from the internet to create human-sounding, AI generated content. Created by the startup OpenAI, this AI content generator is taking the tech world by storm. But is it really any good, you might be wondering? Today I'm going to answer that question in a GPT 3 demo by showing you GPT 3 AI examples of how I used GPT-3 in my own business to create content snippets. OpenAI GPT 3 was released for public use in June 2020 and now has been used by TONS of data entrepreneurs creating SAS products that run off of GPT-3, such as Copy.ai and Writesonic.

Created by Lazy Programmer Inc. Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other. Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data. Once we've learned that structure, we can do some pretty cool things.

We are motivated by the goal of generalist robots that can complete a wide range of tasks across many environments. Critical to this is the robot's ability to acquire some metric of task success or reward, which is necessary for reinforcement learning, planning, or knowing when to ask for help. For a general-purpose robot operating in the real world, this reward function must also be able to generalize broadly across environments, tasks, and objects, while depending only on on-board sensor observations (e.g. RGB images). While deep learning on large and diverse datasets has shown promise as a path towards such generalization in computer vision and natural language, collecting high quality datasets of robotic interaction at scale remains an open challenge. In contrast, "in-the-wild" videos of humans (e.g. YouTube) contain an extensive collection of people doing interesting tasks across a diverse range of settings. In this work, we propose a simple approach, Domain-agnostic Video Discriminator (DVD), that learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task, and can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos. We find that by leveraging diverse human datasets, this reward function (a) can generalize zero shot to unseen environments, (b) generalize zero shot to unseen tasks, and (c) can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.

We present a deep reinforcement learning-based artificial intelligence agent that could provide optimized development plans given a basic description of the reservoir and rock/fluid properties with minimal computational cost. This artificial intelligence agent, comprising of a convolutional neural network, provides a mapping from a given state of the reservoir model, constraints, and economic condition to the optimal decision (drill/do not drill and well location) to be taken in the next stage of the defined sequential field development planning process. The state of the reservoir model is defined using parameters that appear in the governing equations of the two-phase flow. A feedback loop training process referred to as deep reinforcement learning is used to train an artificial intelligence agent with such a capability. The training entails millions of flow simulations with varying reservoir model descriptions (structural, rock and fluid properties), operational constraints, and economic conditions. The parameters that define the reservoir model, operational constraints, and economic conditions are randomly sampled from a defined range of applicability. Several algorithmic treatments are introduced to enhance the training of the artificial intelligence agent. After appropriate training, the artificial intelligence agent provides an optimized field development plan instantly for new scenarios within the defined range of applicability. This approach has advantages over traditional optimization algorithms (e.g., particle swarm optimization, genetic algorithm) that are generally used to find a solution for a specific field development scenario and typically not generalizable to different scenarios.