OmniXAI (short for Omni eXplainable AI) is a Python machine-learning library for explainable AI (XAI), offering omni-way explainable AI and interpretable machine learning capabilities to address many pain points in explaining decisions made by machine learning models in practice. OmniXAI includes a rich family of explanation methods integrated in a unified interface, which supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explaination methods including "model-specific" and "model-agnostic" methods (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, OmniXAI provides an easy-to-use unified interface to generate the explanations for their applications by only writing a few lines of codes, and also a GUI dashboard for visualization for obtaining more insights about decisions. The following table shows the supported explanation methods and features in our library. We will continue improving this library to make it more comprehensive in the future, e.g., supporting more explanation methods for vision, NLP and time-series tasks.
This post is co-written with Sowmya Manusani, Sr. Staff Machine Learning Engineer at Zendesk Zendesk is a SaaS company that builds support, sales, and customer engagement software for everyone, with simplicity as the foundation. It thrives on making over 170,000 companies worldwide serve their hundreds of millions of customers efficiently. The Machine Learning team at Zendcaesk is responsible for enhancing Customer Experience teams to achieve their best. By combining the power of data and people, Zendesk delivers intelligent products that make their customers more productive by automating manual work. Zendesk has been building ML products since 2015, including Answer Bot, Satisfaction Prediction, Content Cues, Suggested Macros, and many more.
Peltarion is a Swedish artificial intelligence (AI) software company and developer of a no-code, machine learning operations platform that empowers users to design, train and manage deep learning models in the Cloud at scale and at speed. King's acquisition of Peltarion will accelerate the current use of AI and machine learning technology in King's game platform, a key area of ongoing strategic direction for the company. With this investment, King aims to continue to build top-tier AI and machine learning capabilities and teams – enabling a new generation of innovative game design, development and live operations capabilities and becoming a hub for the world's top talent in game AI. Veritone, Inc., creator of aiWARE, a hyper-expansive enterprise AI platform, announced its sponsorship of the Snowflake Summit 2022, a flagship event running June 13 to 16 in Las Vegas that focuses on the "World of Data Collaboration." Zyxel Networks, a leader in delivering secure, AI- and cloud-powered business and home networking solutions, announced a family of WiFi 6E access points (APs) that enable businesses to enjoy the performance benefits provided through use of the newly-opened 6GHz WiFi spectrum.
Technology analyst Rob Enderle believes ownership of digital twins will become a defining question of the impending metaverse era. Artificial intelligence combined with the Internet of Things allows the digital construction of things that are constantly learning from and helping improve the real counterpart. French software company Dassault Systemes says thousands of firms are showing interest in its digital twin technology, which includes projects like Living Heart, a digital model of the human heart for mapping heart conditions, and trying out surgical procedures and devices. Meanwhile, U.S. software firm Nvidia's Earth-2 project is creating a digital twin of the Earth's surface, using deep learning models and neural networks. Technology analyst Rob Enderle anticipates that the first iterations of thinking human digital twins--virtual replicas of ourselves--will debut before the end of the decade.
PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupiter notebooks on an EC2 GPU instance.
Machine learning is rapidly evolving and the crucial focus of the software development industry. The infusion of artificial intelligence with machine learning has been a game-changer. More and more businesses are focusing on wide-scale research and implementation of this domain. Machine learning provides enormous advantages. It can quickly identify patterns and trends and the concept of automation comes to reality through ML.
According to Gartner, AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision-making, and take action. In essence, the concept of AI centres on enabling computer systems to think and act in a more'human' way, by learning from and responding to the vast amounts of information they're able to use. AI is already transforming our everyday lives. From the AI features on our smartphones such as built-in smart assistants, to the AI-curated content and recommendations on our social media feeds and streaming services. As the name suggests, machine learning is based on the idea that systems can learn from data to automate and improve how things are done – by using advanced algorithms (a set of rules or instructions) to analyse data, identify patterns and make decisions and recommendations based on what they find.
The Alphabet subsidiary DeepMind has done it again, and this time, they are testing the boundaries of AI in software development sectors. DeepMind's AlphaCode was tested against human performance on coding challenges and achieved rank among the top 54% of human coders on Codeforces. This is a remarkable achievement as it is one of its kind. There are other code generation machine learning models, such as OpenAI Codex, but none of them tried to compete with human programmers. A coding challenge is like solving puzzles. To solve these challenges, an individual must have an understanding of logic, math, and programming skills.
Long ago, I built a hand-written digit recognition web app using Flask and TensorFlow. It was my first ML project as a beginner which didn't end up dying in a notebook, so I think it's worth sharing. This is how it's gonna look: In this tutorial, we will build our digit recognition model using TensorFlow and the MNIST dataset, which contains 70,000 images of hand-written digits 0 to 9, convert it into a TFLite model, and then build the web app. We'll be using Google Colab throughout this guide, because it's the easiest way to get started. We'll use the Keras Datasets API to load our MNIST images, because it makes it extremely easy to load the data.
I bet that you have already seen in movies the IT guy hacking a system by writing commands inside a black window and thought "How cool is that!". Well, in reality, things are not that easy to hack but we do have some basic commands that can help interact with the computer, which is called command-line interface (CLI). The command-line interface is a program on your computer that allows you to create and delete files, run programs, and navigate through folders and files. On a Mac and Linux Systems, it's called Terminal, and on Windows, it's Command Prompt. CLI is not just a fancy method to interact with your computer.