You might be in high school reading this, or simply thinking of switching careers and delving into Artificial Intelligence - this is for you. The industry actually exists and is not just something unreachable which you see on television. First and foremost, you need in-depth knowledge of data science and statistics, as well as data processing and software engineering. Fortunately, these are subjects that you can find in South African higher education institutions. Getting a degree or certificate in any of those fields builds the foundation of your aspired job in AI.

Bob has started his own mobile company. He wants to give a tough fight to big companies like Apple, Samsung etc. He does not know how to estimate the price of mobiles his company creates. In this competitive mobile phone market, you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.

Angela Yochem, EVP Chief Transformation and Digital Officer at Novant Health, on the many detours she took in college, the meaning of community for female leaders, and why we need to tell young women that "doing the hard stuff is super fun and interesting" What initially sparked your interest in technology? It began when my father taught me to write code when I was around 11 years old. Unlike today, an 11-year-old coder was relatively anomalous back then. Even though I didn't know anything about systems building and how machines worked, I saw coding as a way to solve logic problems and I'd write little games to pass the time. After which, I had several detours along my technical journey.

Continual learning (CL) aims to develop techniques by which a single model adapts to an increasing number of tasks encountered sequentially, thereby potentially leveraging learnings across tasks in a resource-efficient manner. A major challenge for CL systems is catastrophic forgetting, where earlier tasks are forgotten while learning a new task. To address this, replay-based CL approaches maintain and repeatedly retrain on a small buffer of data selected across encountered tasks. We propose Gradient Coreset Replay (GCR), a novel strategy for replay buffer selection and update using a carefully designed optimization criterion. Specifically, we select and maintain a "coreset" that closely approximates the gradient of all the data seen so far with respect to current model parameters, and discuss key strategies needed for its effective application to the continual learning setting. We show significant gains (2%-4% absolute) over the state-of-the-art in the well-studied offline continual learning setting. Our findings also effectively transfer to online / streaming CL settings, showing upto 5% gains over existing approaches. Finally, we demonstrate the value of supervised contrastive loss for continual learning, which yields a cumulative gain of up to 5% accuracy when combined with our subset selection strategy.

This study is aimed at addressing the problem of fault tolerance of quadruped robots to actuator failure, which is critical for robots operating in remote or extreme environments. In particular, an adaptive curriculum reinforcement learning algorithm with dynamics randomization (ACDR) is established. The ACDR algorithm can adaptively train a quadruped robot in random actuator failure conditions and formulate a single robust policy for fault-tolerant robot control. It is noted that the hard2easy curriculum is more effective than the easy2hard curriculum for quadruped robot locomotion. The ACDR algorithm can be used to build a robot system that does not require additional modules for detecting actuator failures and switching policies. Experimental results show that the ACDR algorithm outperforms conventional algorithms in terms of the average reward and walking distance.

Class expression learning is a branch of explainable supervised machine learning of increasing importance. Most existing approaches for class expression learning in description logics are search algorithms or hard-rule-based. In particular, approaches based on refinement operators suffer from scalability issues as they rely on heuristic functions to explore a large search space for each learning problem. We propose a new family of approaches, which we dub synthesis approaches. Instances of this family compute class expressions directly from the examples provided. Consequently, they are not subject to the runtime limitations of search-based approaches nor the lack of flexibility of hard-rule-based approaches. We study three instances of this novel family of approaches that use lightweight neural network architectures to synthesize class expressions from sets of positive examples. The results of their evaluation on four benchmark datasets suggest that they can effectively synthesize high-quality class expressions with respect to the input examples in under a second on average. Moreover, a comparison with the state-of-the-art approaches CELOE and ELTL suggests that we achieve significantly better F-measures on large ontologies. For reproducibility purposes, we provide our implementation as well as pre-trained models in the public GitHub repository at https://github.com/ConceptLengthLearner/NCES

Creating the appropriate training and evaluation data is often the biggest challenge in developing AI in practice. This workshop will explore challenges and opportunities across the data-for-AI pipeline. We will discuss recent advances in curating, cleaning, annotating and evaluating datasets for AI. We will also investigate questions that arise from data regulations, privacy and ethics. The goal of the workshop is to help build an intellectual foundation for the emerging and critically important discipline of data-centric AI.

The graph represents a network of 1,541 Twitter users whose tweets in the requested range contained "kdnuggets", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 14 November 2021 at 03:28 UTC. The requested start date was Sunday, 14 November 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 5-hour, 23-minute period from Sunday, 31 October 2021 at 01:18 UTC to Saturday, 13 November 2021 at 06:42 UTC.

The pandemic actually gave us a whole new perspective on online learning. With the online meets replacing actual classes, both the students and the teachers now understand the working of the online classes very well. But now, with the pandemic on decline, we should not neglect the online mode of learning, instead nurture it with the use of AI.

Fifty years ago this winter, a young student teacher by the name of Don Rawitsch introduced his eighth grade American history class to a computer game on westward expansion that he had developed along with his colleagues Bill Heinemann and Paul Dillenberger. The game, called The Oregon Trail, would go on to sell over 65 million copies, many of them to educational institutions, making it one of the bestselling games of all time, right up there with Super Mario Bros. and Tetris. But when I talked to Rawitsch recently, he said that when he first came up with the idea, making money was the furthest thing from his mind. "Back in 1971, there was a lot of activity going on in the world of schools to upgrade curriculum and come up with innovative methods of teaching," Rawitsch said. Inspired by his teachers at Carleton College in Northfield, Minnesota, Rawitsch decided to pursue new types of pedagogy for his student teacher classes at Jordan Junior High School in Minneapolis.