"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
With natural language processing, machine learning and advanced analytics, companies can make more informed decisions and generate human-like text from cues. Today, there are several powerful tools for creating AI-powered content online. GPT-3 from OpenAI is an autoregressive language model that is the most powerful natural language processing (NLP) model ever created. GPT-3 uses deep learning algorithms to create human-like text based on cues and can be used to create text, answer questions, perform tasks such as writing code, and much more. IBM Watson is a cognitive computing platform that uses natural language processing, machine learning and advanced analytics to help businesses make more informed decisions and create AI-based content such as news articles, blog posts and more.
Are you in the middle of creating a new machine-learning model and unsure of what activation function you should be using? But wait, what is an activation function? Activation functions allow machine learning models to understand and solve nonlinear problems. Using an activation function in neural networks specifically helps with the passing of the most important information from each neuron to the next. Today, the ReLU Activation Function is generally used in the architecture of Neural Networks, however, that does not necessarily mean it is always the best choice.
I recently started an AI-focused educational newsletter, that already has over 150,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. When it comes to the space of generative AI and foundational models, OpenAI seems to have hit escape velocity with the recent release of technologies such as ChatGPT. Given the computational requirements of these systems, it seems logical that the core competition of OpenAI will come from incumbent AI labs such as Google-DeepMind and Meta AI.
Deep Learning and Machine Learning are two subfields of Artificial Intelligence (AI) that use algorithms to learn patterns and make predictions based on data. Machine Learning algorithms, on the other hand, can have various structures, including decision trees, support vector machines, and more. Machine Learning algorithms, on the other hand, are typically designed for simpler problems with smaller data sets. Machine Learning algorithms, on the other hand, can be trained on smaller data sets and with less computational power. Machine Learning algorithms, on the other hand, are faster and easier to implement on simpler problems.
As new machine learning (ML) techniques continue to advance and provide the promise of better performance, platform teams everywhere are trying to adapt to support increasingly complex models. While many models served at Etsy still use "classic" model architectures (such as gradient-boosted trees), there has been a large shift and preference for deep learning techniques. The decision for the Search Ranking (SR) team to use deep learning in particular necessitated advances in ML Platform capabilities. In this post, we'll go over the workload-tuning and observability capabilities we created to combat the challenges serving deep learning ranking at scale within Etsy. Ranking use cases tend to be trickier to serve at low latency and low cost relative to other ML use cases.
Abstract: Sparse linear models are a gold standard tool for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains. Unfortunately, sparse linear models are far less flexible as functions of their input features than black-box models like deep neural networks. With this capability gap in mind, we study a not-uncommon situation where the input features dichotomize into two groups: explanatory features, which we wish to explain the model's predictions, and contextual features, which we wish to determine the model's explanations. This dichotomy leads us to propose the contextual lasso, a new statistical estimator that fits a sparse linear model whose sparsity pattern and coefficients can vary with the contextual features. The fitting process involves learning a nonparametric map, realized via a deep neural network, from contextual feature vector to sparse coefficient vector.
Abstract: Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data. Optimization-based meta-learning train a model on a variety tasks, such that it can solve new learning tasks using only a small number of training samples.However, these methods assumes that training and test dataare identically and independently distributed. To overcome such limitation, in this paper, we propose invariant meta learning for out-of-distribution tasks. Specifically, invariant meta learning find invariant optimal meta-initialization,and fast adapt to out-of-distribution tasks with regularization penalty. Abstract: Supervised learning typically optimizes the expected value risk functional of the loss, but in many cases, we want to optimize for other risk functionals.
Generative modeling is an unsupervised learning task involving automatically discovering and learning the patterns in input data so that the model can generate new outputs that plausibly could have been drawn from the original dataset. GANs are generative models that can create new data points resembling the training data. For instance, GANs can produce pictures resembling photographs of human faces, even though the faces depicted do not correspond to any actual individual. GANs consist of two models – a generator and a discriminator. The discriminator is a Convolutional Neural Network (CNN) consisting of various hidden layers and one output layer. The generator is an Inverse Convolutional Neural Net doing exactly the opposite of what a CNN does because.
Are you someone who's getting interested in computer vision or any state-of-the-art knowledge in deep learning? Did you know that Tensorflow is an open-source end-to-end platform that is being developed by the Google Brain team which was led by the Google senior fellow and AI researcher Jeff Dean built in November 2015. It can actually perform various tasks focused on training and inference of deep neural networks. This allows the developers to create better machine learning applications using the tools, libraries and community resources. In fact, it is one of the most known deep learning libraries globally which is Google's Tensorflow.