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 the fall of 2017, Sam Bowman, a computational linguist at New York University, figured that computers still weren't very good at understanding the written word. Sure, they had become decent at simulating that understanding in certain narrow domains, like automatic translation or sentiment analysis (for example, determining if a sentence sounds "mean or nice," he said). But Bowman wanted measurable evidence of the genuine article: bona fide, human-style reading comprehension in English. So he came up with a test. In an April 2018 paper coauthored with collaborators from the University of Washington and DeepMind, the Google-owned artificial intelligence company, Bowman introduced a battery of nine reading-comprehension tasks for computers called GLUE (General Language Understanding Evaluation). The test was designed as "a fairly representative sample of what the research community thought were interesting challenges," said Bowman, but also "pretty straightforward for humans."
The first neural network you want to build using squaring of numbers. Every time you want to learn about NNs or data science or AI, you search through google, you go through Reddit, get some GitHub codes. There is MNIST dataset, GANs, convolution layers, everywhere. Everybody is talking about neural networks. You pick up your laptop, run the code, Voila! it works.
The goal of this research is to create physically simulated biped characters equipped with a rich repertoire of motor skills. The user can control the characters interactively by modulating their control objectives. We present a novel network-based algorithm that learns control policies from unorganized, minimally-labeled human motion data. The network architecture for interactive character animation incorporates an RNN-based motion generator into a DRL-based controller for physics simulation and control. The motion generator guides forward dynamics simulation by feeding a sequence of future motion frames to track.
Artificial intelligence (AI) is already re-configuring the world in conspicuous ways. Data drives our global digital ecosystem, and AI technologies reveal patterns in data. Smartphones, smart homes, and smart cities influence how we live and interact, and AI systems are increasingly involved in recruitment decisions, medical diagnoses, and judicial verdicts. Whether this scenario is utopian or dystopian depends on your perspective. The potential risks of AI are enumerated repeatedly.
Transfer learning is the reuse of a pre-trained model on a new problem. It's currently very popular in deep learning because it can train deep neural networks with comparatively little data. This is very useful since most real-world problems typically do not have millions of labeled data points to train such complex models. We'll take a look at what transfer learning is, how it works, why and when you it should be used. Additionally, we'll cover the different approaches of transfer learning and provide you with some resources on already pre-trained models.
Accurate image and video classification is important for a wide range of computer vision applications, from identifying harmful content, to making products more accessible to the visually impaired, to helping people more easily buy and sell things on products like Marketplace. Facebook AI is developing alternative ways to train our AI systems so that we can do more with less labeled training data overall, and also deliver accurate results even when large, high-quality labeled data sets are simply not available. Today, we are sharing details on a versatile new model training technique that delivers state-of-the-art accuracy for image and video classification systems. This approach, which we call semi-weak supervision, is a new way to combine the merits of two different training methods: semi-supervised learning and weakly supervised learning. It opens the door the door to creating more accurate, efficient production classification models by using a teacher-student model training paradigm and billion-scale weakly supervised data sets.
With AutoML Vision Edge, you can create custom image classification models for your mobile app by uploading your own training data. Firebase ML Kit has a lot of features that allows you to perform machine learning on the user's phone. AutoML allows you to create a custom solution exactly for your problem, the best part is you don't need to know machine learning for building your solution. You just have to upload images and AutoML takes care of everything for you. In this blog post we will build an app called SeeFood, the app sees food and tells you what food item it is.
Artificial intelligence, including machine learning, presents exciting opportunities to transform the health and life sciences spaces. It offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities. However, open issues around regulation and clinical relevance remain, causing both technology developers and potential investors to grapple with how to overcome today's barriers to adoption, compliance, and implementation. Over the past few years, the U.S. Food and Drug Administration (FDA) has been taking incremental steps to update its regulatory framework to keep up with the rapidly advancing digital health market. In 2017, the FDA released its Digital Health Innovation Action Plan to offer clarity about the agency's role in advancing safe and effective digital health technologies, and addressing key provisions of the 21st Century Cures Act.
Untrained dragons can cause a lot of damage. Likewise, as AI systems spread further and have more influence over our lives, it's getting far more important to make sure they're properly trained. Bias can creep into the reasoning of AI very easily, either via datasets that are not diverse enough or through irrelevant data attached to viable data points, leading to flawed results and in some cases prejudiced or dangerous conclusions. Despite regulations like GDPR to protect the privacy of our data, personal consumer data is increasingly being used by companies to improve services or to gain customer insight. Ironically, these regulations also make it more difficult for companies to gather enough data to train an AI system or to prove how their AI reaches its decisions (an impossible task for many deep learning systems).
Editor's note: Sayak is a speaker for ODSC West in San Francisco this November! Be sure to check out his talk, "Interpretable Machine Learning -- Fairness, Accountability and Transparency in ML systems," there! The problem is it is much harder to evaluate machine learning systems than to train them. "It requires responsibly requires doing more than just calculating loss metrics. Before putting a model into production, it's critical to audit training data and evaluate predictions for bias."