Where NLP is heading?
Natural language processing (NLP) has become an invaluable tool to researchers, organizations, and even hobbyists. It lets us summarize documents, analyze sentiment, categorize content, translate languages and one day potentially even converse at a human level. NLP is a fast-moving discipline undergoing rapid change as practitioners and researchers dive into the prospects it presents. Here are some of the trends and opportunities I see on the horizon.
A First Principles Theory of Generalization
Some new research from University of California, Berkeley shades some new light into how to quantify neural networks knowledge. Understanding how neural networks learn remains one of the biggest mysteries in modern ML. The BAIR team relied on two recent breakthroughs in deep learning: the theory of infinite-width networks. This idea suggests that, by studying theoretically infinite neural networks, we can gain insights into the generalization of the finite equivalent.
Break-It-Fix-It: Unsupervised Learning for Fixing Source Code Errors
Both beginner and professional programmers spend 50% of time fixing code errors during programming. Automating code repair can dramatically enhance the programming productivity. Recent works use machine learning models to fix code errors by training the models on human-labeled (broken code, fixed code) pairs. However, collecting this data for even a single programming language is costly, much less the dozens of languages commonly used in practice.
New technique protects contrastive ML against adversarial attacks
Machine learning and security researchers are worried about the effect of adversarial attacks on ML models trained through contrastive learning. A new paper by researchers at the MIT-IBM Watson AI Lab sheds light on the sensitivities of contrastive machine learning to. The paper introduces a new technique that helps protect contrastive. learning models against. adversarial. attacks while also preserving their accuracy.
Transformers and the future of conversational AI
There are plenty of ways to use existing models to give your devices some extra smarts. In this article, we use a simple implementation of the Question Answering (QA) model to turn your computer into a trivia genius. The current standard for testing QA models in English is the SQuAD22.0 data set. For accurate comparisons, you will want to look out for models evaluated using this benchmark.
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
When we apply robot learning methods to real-world systems, we must usually collect new datasets for every task, every robot, and every environment. To obtain truly generalizable robotic behaviors, we may need large and diverse datasets. To this end, we collected a dataset of 7200 demonstrations for 71 different kitchen-themed tasks. We refer to this dataset as the BRIDGE dataset (Broad Robot Interaction Dataset for boosting GEneralization)
Inside recommendations: how a recommender system recommends
Each time you visit Amazon or Netflix, you see recommended items or movies that you might like the product of recommender systems incorporated by these companies. Recommender systems rely on a combination of data stemming from explicit and implicit information on users and items. The idea underlying them is that if a user was interested in an item in the past, they would be interested in similar items later. Such systems make recommendations based on the user’s item and profile features.
Parts of Speech Tagging, Explained
Modern approaches to Natural Language Processing are offering a streamlining of the process of document analysis by way of simplification. Simply put, there’s a tendency to drop the hard stuff (i.e., understanding the content) for more direct techniques like looking at words, how often they appear in documents, what other words show up next to them. This kind of statistical information is collected and carefully optimized during what is known in Machine Learning as the Training stage.
7 Top Open Source Datasets to Train Natural Language Processing (NLP) & Text Models
The main problem with getting started with NLP is the dearth of proper guidance and the excessive breadth of the domain. There are countless open source datasets being released daily focusing on words, text, speech, sentences, slang, and just about anything else you can think of. The following list is what we recommend as some of the best open-source datasets to start learning NLP.
19 Data Science Project Ideas for Beginners
This article features 19 data science projects for beginners, categorized into 7 full project tutorials and 7 skills-based projects. The projects are a great way for beginners to get to grips with some of the very basic data science skills and languages that you’ll need to pursue data science as a hobby or a career. You’ll learn how to use APIs, how to run predictions, touch on deep learning, and look at regression.
Creating Sparse, Multitask Neural Networks
Machine Learning (ML) has become increasingly central to the acquisition and interpretation of data. Modern ML methods can be really, really great at one specific, narrow task. Robustly learning multiple similar or disparate tasks within the same model is currently a limitation due to catastrophic forgetting. When shooting for cutting-edge performance, some architectures can balloon to the 100’s of billions of parameters. These models can be dense and inefficient, only able to run on high-end hardware.
Solving Math Word Problems
We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset. This is important because today’s AI is still quite weak at commonsense multistep reasoning, which is easy for grade school kids.
A developer’s guide to machine learning security
Machine learning has become an important component of many applications we use today. Many ML libraries and online services don’t even require a thorough knowledge of machine learning. However, even easy-to-use machine learning systems come with their own challenges. The first step to countering them is to understand the different types of adversarial attacks and the weak spots of the machine learning pipeline.
Building Scalable, Explainable, and Adaptive NLP Models with Retrieval
Natural language processing (NLP) has witnessed impressive developments in answering questions, summarizing or translating reports, and analyzing sentiment or offensiveness. Much of this progress is owed to training ever-larger language models, such as T5 or GPT-3, that use deep monolithic architectures to internalize how language is used within text from massive Web crawls. In particular, existing large language models are generally inefficient and expensive to update.
Meta-Learning Student Feedback to 16,000 Solutions
A new AI system based on meta-learning trains a neural network to ingest student code and output feedback. Given a new assignment, this AI system can quickly adapt with little instructor work. On a dataset of student solutions to Stanford’s CS106A exams, we found the AI system to match human instructors in feedback quality. This is, to the best of our knowledge, the first successful deployment of machine feedback.
Designs from Data: Offline Black-Box Optimization via Conservative Training
Offline model-based optimization (offline MBO) is a method that examines a large dataset of previously tested designs. The goal in offline MBO is to maximize a black-box objective function f(x) with respect to its input x Instead, the algorithm is provided access to a static dataset of designs x_i and corresponding objective values y_i. The algorithm consumes this dataset and produces an optimized candidate design.
What are graph neural networks (GNN)?
This article is part of a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Graph neural networks (GNN) are a type of machine learning algorithm that can extract important information from graphs and make useful predictions. Graphs are excellent tools to visualize relations between people, objects, and concepts. They are also good sources of data to train machine learning models for complicated tasks.
Running ML Python Code in Parallel With RAY
Forecasting is an important part of running every business. You need to have an idea about what and how much to produce in order to have stock available for your customers. If you order too much, you’ll have excess inventory which carries cost. Forecasters need to be able to predict how much they need to produce and what they will need to do.
A Time Series Anomaly Detection Model for All Types of Time Series
Insight Data Science Fellow worked with Lazy Lantern, a computer software company that uses machine learning to provide autonomous analytics. The company is developing a machine-driven analytics tool that can be used in the future to provide an accurate and reliable data analysis. Lazy lantern is a company that provides autonomous analytics using machine learning and machine-recognition software to provide insights into data science.