We've created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today. Codex is the model that powers GitHub Copilot, which we built and launched in partnership with GitHub a month ago. Proficient in more than a dozen programming languages, Codex can now interpret simple commands in natural language and execute them on the user's behalf--making it possible to build a natural language interface to existing applications. We are now inviting businesses and developers to build on top of OpenAI Codex through our API. OpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.

Deep learning, the artificial-intelligence technology that powers voice assistants, autonomous cars, and Go champions, relies on complicated "neural network" software arranged in layers. A deep-learning system can live on a single computer, but the biggest ones are spread over thousands of machines wired together into "clusters," which sometimes live at large data centers, like those operated by Google. In a big cluster, as many as forty-eight pizza-box-size servers slide into a rack as tall as a person; these racks stand in rows, filling buildings the size of warehouses. The neural networks in such systems can tackle daunting problems, but they also face clear challenges. A network spread across a cluster is like a brain that's been scattered around a room and wired together.

Tech giants are investing heavily in machine learning. In 2019, Microsoft invested in 11 artificial intelligence (AI) startups, with $1 billion for OpenAI alone. In that same year, Intel Capital made 19 investments, and Google Ventures made 16 investments. That huge influx of capital means that AI computing power is making rapid advancements in a range of sectors from healthcare to construction to marketing and search engine optimization. However, before we get into the implications of machine learning for SEO professionals, let's define what we mean by AI.

Despite the fact that the batch normalization technique is aimed at preventing the problem of exploding gradients*, it also helps with the overfitting problem. This technique was described in detail in this work done by Ioffe, and Szeged about 6 years ago. Exploding gradients problem: If not controlled, the deep learning network weights can become too large, which would lead to the extreme values of the gradients, i.e., making gradients to explode making the loss function to return infinite/NaN values. Normalization is a process of scaling the data into a range of -1 to 1 or 0 to 1. Which is more like a matter of preference. This makes the input layer normalized, while the hidden layers are not normalized.

Deep in the dense coastal forests and marshes of the American Southeast lie shell rings and shell mounds left by Indigenous people 3,000 to 5,000 years ago. Now an international team of researchers, using deep machine learning to assess remote sensing data, has located previously undiscovered shell rings. The researchers hope this will lead to a better understanding of how people lived in that area and a way to identify other, undiscovered shell rings. "The rings themselves are a treasure trove for archeologists," said Dylan S. Davis, doctoral candidate in anthropology at Penn State. "Excavations done at some shell rings have uncovered some of the best preservation of animal bones, teeth and other artifacts."

"A star does not compete with other stars around it; it just shines." Python is by far the most popular programming language in the field of Data Science. The rich list of libraries, simple syntax and high productiveness make Python an extremely popular language among beginners as well as seasoned practitioners. Therefore, it is not unusual to find countless articles praising the power of Python and it's famous data science libraries like Numpy, Pandas, Tensorflow, Matplotlib, etc. This blog will try to divert attention to look at some of the lesser-known Python libraries that are slowly gaining recognition among the Data Science community. Streamlit has been gaining tremendous popularity in recent times.

There are a ton of great explainers of what graph neural networks are. However, I find that a lot of them go pretty deep into the math pretty quickly. Yet, we still are faced with that age-old problem: where are all the pics?? As such, just as I had attempted with Bayesian deep learning, I'd like to try to demystify graph deep learning as well, using every tool I have at my disposal to minimize the number of equations and maximize intuition using pictures. Here's my attempt, I hope you find it useful! In my Network Analysis Made Simple tutorial, we see that the term "graph" are really nothing more than a synonym for networks.

We introduce a new domain expert mixture (DEMix) layer that enables conditioning a language model (LM) on the domain of the input text. A DEMix layer is a collection of expert feedforward networks, each specialized to a domain, that makes the LM modular: experts can be mixed, added or removed after initial training. Extensive experiments with autoregressive transformer LMs (up to 1.3B parameters) show that DEMix layers reduce test-time perplexity, increase training efficiency, and enable rapid adaptation with little overhead. We show that mixing experts during inference, using a parameter-free weighted ensemble, allows the model to better generalize to heterogeneous or unseen domains. We also show that experts can be added to iteratively incorporate new domains without forgetting older ones, and that experts can be removed to restrict access to unwanted domains, without additional training. Overall, these results demonstrate benefits of explicitly conditioning on textual domains during language modeling.

Reinforcement learning has become one of the most trending subjects in the recent decade. It has seen applications in various fields such as robot manipulations, autonomous driving, path planning, computer gaming, etc. We accomplished three tasks during the course of this project. Firstly, we studied the Q-learning algorithm for tabular environments and applied it successfully to an OpenAi Gym environment, Taxi. Secondly, we gained an understanding of and implemented the deep Q-network algorithm for Cart-Pole environment. Thirdly, we also studied the application of reinforcement learning in autonomous driving and its combination with safety check constraints (safety controllers). We trained a rough autonomous driving agent using highway-gym environment and explored the effects of various environment configurations like reward functions on the agent training performance.

To implement a good Content Based Image Retrieval (CBIR) system, it is essential to adopt efficient search methods. One way to achieve this results is by exploiting approximate search techniques. In fact, when we deal with very large collections of data, using an exact search method makes the system very slow. In this project, we adopt the Locality Sensitive Hashing (LSH) index to implement a CBIR system that allows us to perform fast similarity search on deep features. Specifically, we exploit transfer learning techniques to extract deep features from images; this phase is done using two famous Convolutional Neural Networks (CNNs) as features extractors: Resnet50 and Resnet50v2, both pre-trained on ImageNet. Then we try out several fully connected deep neural networks, built on top of both of the previously mentioned CNNs in order to fine-tuned them on our dataset. In both of previous cases, we index the features within our LSH index implementation and within a sequential scan, to better understand how much the introduction of the index affects the results. Finally, we carry out a performance analysis: we evaluate the relevance of the result set, computing the mAP (mean Average Precision) value obtained during the different experiments with respect to the number of done comparison and varying the hyper-parameter values of the LSH index.