Earlier this week, Google announced that it was piloting a machine learning intensive for college students. Today, its broader Machine Learning Crash Course is adding a new training module on fairness when building AI. As adoption of machine learning continues, ethics and fairness are very important considerations. While AI can have the "potential to be fairer and more inclusive at a broader scale than decision-making processes based on ad hoc rules or human judgments," there might be underlying biases present in the data used to train these models. Other issues involve insuring that AI is fair in all situations, while more broadly there is "no standard definition of fairness."
Your next professor could be a robot. Bina48 became the first robot to co-teach a university class when she helped lead a course at West Point, the U.S. Military academy, according to Axios. The humanoid AI taught two sessions of a philosophy course, with topics ranging from ethics, just war theory and use of artificial intelligence in society, which is pretty meta. Bina48 (pictured) became the first robot to co-teach a university class when she helped lead a course at West Point, the U.S. Military academy. William Barry, who has been using Bina48 to teach for several years, decided to put the robot in front of students in the classroom to see if she could'support a liberal education model.'
Evaluating a machine learning model 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. This module looks at different types of human biases that can manifest in training data. It then provides strategies to identify them and evaluate their effects.
I honestly can't understand the multiple 5 star reviews presented on this site about the course. I'm giving it a 1 star which is a bit harsh I know but I'm doing it to offset the number of 5 star reviews here. Honestly I think the course deserves something between 2 and 3 stars depending on your approach to it. Yes Prof. Hinton is a leading expert in the field but the course materials and the way they are presented are pretty bad! I honestly can't understand the multiple 5 star reviews presented on this site about the course.
If you're at all interested in Artificial Intelligence (AI), it's unlikely to be news to you that there is an AI skills shortage. Businesses are increasingly looking to invest in AI and are on the hunt for suitably skilled workers since traditional software teams without the experience of AI often encounter a number of challenges, as I described in a recent article over on DZone. Anyone thinking about joining the AI workforce will want to learn the subject, initially by doing some reading and research, but without committing to paying too much. As the need to recruit skilled AI staff has grown, so a number of businesses and individuals have set out to provide training courses, books, and e-learning, and the price and quality of these vary, as you would expect. As with all education, if you commit a chunk of your time, you don't want to find it wasted on out-of-date or incorrect information or to find that you are missing out on key skills after spending time and money on a course that promises to equip you appropriately.
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka "deep learning") approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
Artur Garcez gave a lecture on Relational Neuro-Symbolic AI at the EurAI Advanced Course on AI, 2018, which took place in beautiful Ferrara, Italy. All the lectures, with overarching theme Statistical Relational AI, are available from the University of Ferrara's YouTube channel: https://youtu.be/KeFhKi-tOTs?list Artur Garcez gave two talks: Part 1 gives an overview of two decades of research on neuro-symbolic AI. Part 2 describes in some detail two neuro-symbolic systems for relational learning: Connectionist ILP and the Logic Tensor Networks framework.
The course contains more than 4 hours of content and 2 articles. Its step by step approach is great for beginners and Martin has done a wonderful job to keep this course hands-on and simple. You will start by setting up your own development environment by installing the R and RStudio interface, add-on packages, and learn how to use the R exercise database and the R help tools. After that, you will learn various ways to import data, first coding steps including basic R functions, loops, and other graphical tools, which is the strength of R The whole course should take approx.
Helsinki University is offering a free online Artificial Intelligence course in English to anyone, anywhere. This free Artificial Intelligence (AI) online course is made for non-technical people so no special knowledge or skills are needed to take the course. AI is embedded in so many part of our lives - this course is meant to dispel any mystery around the technology that AI uses, the impact it has on our lives and how AI will develop in the coming years. Do you wonder what AI really means? Are you thinking about the kind of impact AI might have on your job or life?