Columbia University's Fu Foundation School of Engineering and Applied Science is working with 2U to launch the Columbia Artificial Intelligence Program, an online executive education offering aimed at advancing the next generation of technology leaders. The program will "explore the practical aspects of AI and machine learning through the study of cutting-edge research and hands-on application," through live, seminar-style online classes as well as interactive course content, according to a news announcement. Curriculum will be created and taught by Columbia Engineering faculty and delivered through the 2U platform. Participants will learn to build, lead and manage AI teams and projects, drive AI strategy and adoption, and contribute to policy and regulations of AI technologies, the announcement said. The program can be completed in 9 months (full-time) or 18 months (part-time).

Many interactive data systems combine visual representations of data with embedded algorithmic support for automation and data exploration. To effectively support transparent and explainable data systems, it is important for researchers and designers to know how users understand the system. We discuss the evaluation of users' mental models of system logic. Mental models are challenging to capture and analyze. While common evaluation methods aim to approximate the user's final mental model after a period of system usage, user understanding continuously evolves as users interact with a system over time. In this paper, we review many common mental model measurement techniques, discuss tradeoffs, and recommend methods for deeper, more meaningful evaluation of mental models when using interactive data analysis and visualization systems. We present guidelines for evaluating mental models over time that reveal the evolution of specific model updates and how they may map to the particular use of interface features and data queries. By asking users to describe what they know and how they know it, researchers can collect structured, time-ordered insight into a user's conceptualization process while also helping guide users to their own discoveries.

This paper describes problems in AI research and how the SP System may help to solve them. Most of the problems are described by leading researchers in AI in interviews with science writer Martin Ford, and reported by him in his book Architects of Intelligence. These problems, each with potential solutions via SP, are: how to overcome the divide between symbolic and non-symbolic kinds of knowledge and processing; eliminating large and unexpected errors in recognition; the challenge of unsupervised learning; the problem of generalisation, with under- and over-generalisation; learning from a single exposure or experience; the problem of transfer learning; how to create learning that is fast, economical in demands for data and computer resources; the problems of transparency in results and processing; problems in the processing of natural language; problems in the development of probabilistic reasoning; the problem of catastrophic forgetting; how to achieve generality across several aspects of AI. The SP System provides a relatively promising foundation for the development of artificial general intelligence.

As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about---summary quality. In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL;DR dataset of Reddit posts and find that our models significantly outperform both human reference summaries and much larger models fine-tuned with supervised learning alone. Our models also transfer to CNN/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We conduct extensive analyses to understand our human feedback dataset and fine-tuned models. We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want.

A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.

K-12 classrooms consistently integrate collaboration as part of their learning experiences. However, owing to large classroom sizes, teachers do not have the time to properly assess each student and give them feedback. In this paper we propose using simple deep-learning-based machine learning models to automatically determine the overall collaboration quality of a group based on annotations of individual roles and individual level behavior of all the students in the group. We come across the following challenges when building these models: 1) Limited training data, 2) Severe class label imbalance. We address these challenges by using a controlled variant of Mixup data augmentation, a method for generating additional data samples by linearly combining different pairs of data samples and their corresponding class labels. Additionally, the label space for our problem exhibits an ordered structure. We take advantage of this fact and also explore using an ordinal-cross-entropy loss function and study its effects with and without Mixup.

Then, this course is for you. The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithms work, its limitations and its underlying assumptions. It always pays to know the machinery under the hood, rather than being a guy who is just behind the wheel with no knowledge about the car. Linear Algebra is one of the area where everyone agrees to be a starting point in learning curve of Machine Learning, Data Science and Artificial intelligence.

Deloitte announced the launch of the Deloitte AI Institute, a center that focuses on artificial intelligence (AI) research, eminence, and applied innovation across industries. The Institute will bring together the brightest minds in the field of AI to apply cutting-edge research to help address a wide spectrum of relevant AI use cases. "The Deloitte AI Institute is being established to advance the conversation and development of AI for enterprises," said Nitin Mittal, AI co-leader and principal, Deloitte Consulting LLP. "Our goal is to blend Deloitte's deep experience in applied AI with a robust network of some of the most intelligent AI minds in the world to challenge the status quo. Through the power of this center, we aim to deliver impactful and game-changing research; and innovation to help our clients lead in the'Age of With,' a world where humans work side-by-side with machines."

Online Courses Udemy How to Start a Career in Data Science 2020, The Ultimate Guide to Starting a Data Science Career: Create a Project Portfolio, Build Your Resume, Get an Interview Created by 365 Careers, Ken Jee Students also bought Python 3 Programming: Beginner to Pro Masterclass Data Science 2020: Data Science & Machine Learning in Python Tidy Data: Updated Data Processing With tidyr and dplyr in R Complete Data Science Training with Python for Data Analysis Ensemble Machine Learning in Python: Adaboost, XGBoost The Product Management for Data Science & AI Course 2020 Preview this course GET COUPON CODE Description Data science jobs are hyper-competitive. For each position, there are multiple other highly qualified candidates eyeing the same role. It is like you are all competing for a $100,000 prize. If you frame it this way, wouldn't you want to go the extra mile? By taking this course, you will be doing just that.

These introductory courses play a key role in educating the future of machine learning professionals. DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm. When used standalone, the DirectML API is a low-level DirectX 12 library and is suitable for high-performance, low-latency applications such as frameworks, games, and other real-time applications. The seamless interoperability of DirectML with Direct3D 12 as well as its low overhead and conformance across hardware makes DirectML ideal for accelerating machine learning when both high performance is desired, and the reliability and predictability of results across hardware is critical.