It's been a relatively tame week for stocks this week, and Wednesday was no different. The Dow Jones rose 3 points, the S&P 500 continued trading just below its record high and gained nearly 0.2%, while the Nasdaq NDAQ gained 0.4%. Reopening plays like Carnival Corp. and American Airlines AAL led the way, while meme stocks once again had another manic day. Clover Health rose another 23% following yesterday's 85% rally, while Wendy's rose again after gaining 25% yesterday. For investors looking to find the best opportunities, the deep learning algorithms at Q.ai have crunched the data to give you a set of Top Buys.

OpenAI presented GPT-3 in May 2020 in a paper titled Language Models are Few-Shot Learners. In July 2020, the company released a beta API for developers to play and the model became an AI-rockstar overnight. GPT-3 is the third version of a family of Generative Pre-Trained language models. Its main features are multitasking and meta-learning abilities. Being trained in an unsupervised way on 570GB of Internet text data, it's able to learn tasks it hasn't been trained on by seeing a few examples (few-shot). It can also learn from zero- and one-shot settings, but the performance is usually worse.

Module 2 Deep Learning with Python Theoretical introduction to Neural Networks for Deep Learning, Python libraries for Deep Learning (Tensorflow), creation of a Neural Network and application to real datasets, Regression applied to different types of structured and unstructured data, such as numerical, categorical, or textual.

In order to develop artificial general intelligence (AGI), the sort of all-encompassing AI that we see in science fiction, we might need to merely sit back and let a simple algorithm develop on its own. Reinforcement learning, a kind of gamified AI architecture in which an algorithm "learns" to complete a task by seeking out preprogrammed rewards, could theoretically grow and learn so much that it breaks the theoretical barrier to AGI without any new technological developments, according to research published by the Google-owned DeepMind last month in the journal Artificial Intelligence and spotted by VentureBeat. While reinforcement learning is often overhyped within the AI field, it's interesting to consider that engineers could have already built all the tech needed for AGI and now simply need to let it loose and watch it grow. The kind of artificial intelligence that we encounter every day of our lives, whether it's machine learning or reinforcement learning, is narrow AI: an algorithm designed to accomplish a very specific task like predicting your Google search, spotting objects in a video feed, or mastering a video game. By contrast, AGI -- sometimes called human-level AI intelligence -- would be more along the lines of C-3PO from "Star Wars," in the sense that it could understand context, subtext, and social cues.

Travis DeShazo is, to paraphrase Cake's 2001 song "Comfort Eagle," building a religion. He is building it bigger. He is increasing the parameters. The results are fairly convincing, too, at least as far as synthetic scripture (his words) goes. "Not a god of the void or of chaos, but a god of wisdom," reads one message, posted on the @gods_txt Twitter feed for GPT-2 Religion A.I. "This is the knowledge of divinity that I, the Supreme Being, impart to you. When a man learns this, he attains what the rest of mankind has not, and becomes a true god. Another message, this time important enough to be pinned to the top of the timeline, proclaims: "My sayings are a remedy for all your biological ills.

In the summer of 2009, the Israeli neuroscientist Henry Markram strode onto the TED stage in Oxford, England, and made an immodest proposal: Within a decade, he said, he and his colleagues would build a complete simulation of the human brain inside a supercomputer. They'd already spent years mapping the cells in the neocortex, the supposed seat of thought and perception. "It's a bit like going and cataloging a piece of the rain forest," Markram explained. "How many trees does it have? What shapes are the trees?"

Create a Text Generation Web App with 100% Python (NLP) - Harness GPT-Neo -- a natural language processing (NLP) text generation model. Demonstrate it with a 100% Python web app Created by Vennify Inc., Eric FillionPreview this Course - GET COUPON CODE GPT-3 is a state-of-the-art text generation natural language processing (NLP) model created by OpenAI. You can use it to generate text that resembles text generated by a human. This course will cover how to create a web app that uses an open-source version of GPT-3 called GPT-Neo with 100% Python. That's right, no HTML, Javascript, CSS or any other programming language is required.

Fine-grained location prediction on smart phones can be used to improve app/system performance. Application scenarios include video quality adaptation as a function of the 5G network quality at predicted user locations, and augmented reality apps that speed up content rendering based on predicted user locations. Such use cases require prediction error in the same range as the GPS error, and no existing works on location prediction can achieve this level of accuracy. We present a system for fine-grained location prediction (FGLP) of mobile users, based on GPS traces collected on the phones. FGLP has two components: a federated learning framework and a prediction model. The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system. FGLP represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. The model merges Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. FGLP uses federated learning to protect user privacy and reduce bandwidth consumption. Our experimental results, using a dataset with over 600,000 users, demonstrate that FGLP outperforms baseline models in terms of prediction accuracy. We also demonstrate that FGLP works well in conjunction with transfer learning, which enables model reusability. Finally, benchmark results on several types of Android phones demonstrate FGLP's feasibility in real life.

The Reinforcement Learning (RL) building blocks, i.e. Q-functions and policy networks, usually take elements from the cartesian product of two domains as input. In particular, the input of the Q-function is both the state and the action, and in multi-task problems (Meta-RL) the policy can take a state and a context. Standard architectures tend to ignore these variables' underlying interpretations and simply concatenate their features into a single vector. In this work, we argue that this choice may lead to poor gradient estimation in actor-critic algorithms and high variance learning steps in Meta-RL algorithms. To consider the interaction between the input variables, we suggest using a Hypernetwork architecture where a primary network determines the weights of a conditional dynamic network. We show that this approach improves the gradient approximation and reduces the learning step variance, which both accelerates learning and improves the final performance. We demonstrate a consistent improvement across different locomotion tasks and different algorithms both in RL (TD3 and SAC) and in Meta-RL (MAML and PEARL).

Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) for few-shot conditional image generation. D2C uses a learned diffusion-based prior over the latent representations to improve generation and contrastive self-supervised learning to improve representation quality. D2C can adapt to novel generation tasks conditioned on labels or manipulation constraints, by learning from as few as 100 labeled examples. On conditional generation from new labels, D2C achieves superior performance over state-of-the-art VAEs and diffusion models. On conditional image manipulation, D2C generations are two orders of magnitude faster to produce over StyleGAN2 ones and are preferred by 50% - 60% of the human evaluators in a double-blind study.