If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.
Mixing quantum computing and Artificial Intelligence (AI) may sound like a new buzzword. However, since quantum computing advances are hinting at profound changes in the very notions of computation, it is natural to reexamine various branches of computer science in the light of these disruptions. As usual, before entering the quantum realm, it is important to get an overview of the classical world. Artificial Intelligence is difficult to define. Probably because intelligence, by itself, is difficult to define.
Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.
TensorFlow remains the dominant AI modeling framework. Most AI (artificial intelligence) developers continue to use it as their primary open source tool or alongside PyTorch, in which they develop most of their ML (machine learning), deep learning, and NLP (natural language processing) models. In the most recent O'Reilly survey on AI adoption in the enterprise, more than half of the responding data scientists cited TensorFlow as their primary tool. This finding is making me rethink my speculation, published just last month, that TensorFlow's dominance among working data scientists may be waning. Neverthless, PyTorch remains a strong second choice, having expanded its usage in the O'Reilly study to more than 36 percent of respondents, up from 29 percent in the previous year's survey.
Google Brain had recently launched the TensorFlow Developer Certificate program which would enable machine learning (ML) enthusiasts to demonstrate their skills in using TensorFlow to solve deep learning and ML problems. According to the blog post, the goal of this certificate is to provide them with the opportunity to showcase their expertise in ML in an increasingly AI-driven job market. TensorFlow is one of the popular open-source libraries in ML which provides a suitable abode with essential tools for ML researchers and developers to perform SOTA ML applications. The developers at Google Brain claim that this is intended as a foundational certificate for students, developers, and data scientists who want to demonstrate practical ML skills through building and training of models using TensorFlow. Currently, this is a level one certificate exam which tests a developer's foundational knowledge of integrating ML into tools and applications.
Hi everyone, I've recently built Mimicry, a PyTorch library for GANs which I hope can make GAN research findings more reproducible. The general idea is to have an easily accessible set of implementations (that reproduce the original scores as closely as possible), baseline scores for comparisons, and metrics for GANs which researchers can quickly use to produce results and compare. For reproducibility, I re-implemented the original models and verified their correctness by checking their scores against the reported ones under the same training and evaluation conditions. On the metrics part, to ensure backward compatibility of existing scores, I adopted the original TensorFlow implementations of Inception Score, FID, and KID so new scores produced can be compared with other works directly. I've also included a tutorial to implement a more sophisticated GAN like Self-supervised GAN (SSGAN) from the ground up, again with a focus on reproducing the results.
I am creating the web deployment for a book I am writing for Manning Publications on deep learning with structured data. The audience for this book is interested in how to deploy a simple deep learning model. They need a deployment example that is straightforward and doesn't force them to wade through a bunch of web programming details. For this reason, I wanted a web deployment solution that kept as much of the coding as possible in Python. With this in mind, I looked at two Python-based options for web deployment: Flask and Django.
Cocky children as young as four have the same levels of overconfidence as city bankers and business leaders, according to a new study. UK researchers demonstrated that high levels of confidence in one's own abilities – a trait common among high achievers – is apparent from an extremely early age. This suggests that cocky city types developed their'cognitive bias' from infancy rather than later life, they say. Researchers conducted a card game with young girls and boys with the objective of collecting as many stickers as possible, and compared their different strategies. More than 70 per cent of four-year-olds and half of five and six-year-olds were overconfident in their expectations - comparable to big shot bankers and traders.
In a classical time series forecasting task, the first standard decision when modeling involves the adoption of statistical methods or other pure machine learning models, including three based algorithms or deep learning techniques. The choice is strongly related to the problem we are carrying out but in general: statistical techniques are adequate when we face an autoregressive problem when the future is related only to the past; while machine learning models are suitable for more complex situations when it's also possible to combine variegated data sources. In this post, I try to combine the ability of the statistical method to learn from experience with the generalization of deep learning techniques. Our task is a multivariate time series forecasting problem, so we use the multivariate extension of ARIMA, known as VAR, and a simple LSTM structure. We don't produce an ensemble model; we use the ability of VAR to filter and study history and provide benefit to our neural network in predicting the future.
Machine Learning and Deep Learning are ongoing buzzwords in the industry. Branding ahead of functionalities led to Deep Learning being overused in many artificial intelligence applications. This post will provide a quick grasp at constraint satisfaction, a powerful yet underused approach which can tackle a large number of problems in AI and other areas of computer science, from logistics and scheduling to temporal reasoning and graph problems. Let's consider a factual and highly topical problem. Hospitals must organize quickly to treat ill people.