The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.

We extend the theory of PAC learning in a way which allows to model a rich variety of learning tasks where the data satisfy special properties that ease the learning process. For example, tasks where the distance of the data from the decision boundary is bounded away from zero. The basic and simple idea is to consider partial concepts: these are functions that can be undefined on certain parts of the space. When learning a partial concept, we assume that the source distribution is supported only on points where the partial concept is defined. This way, one can naturally express assumptions on the data such as lying on a lower dimensional surface or margin conditions. In contrast, it is not at all clear that such assumptions can be expressed by the traditional PAC theory. In fact we exhibit easy-to-learn partial concept classes which provably cannot be captured by the traditional PAC theory. This also resolves a question posed by Attias, Kontorovich, and Mansour 2019. We characterize PAC learnability of partial concept classes and reveal an algorithmic landscape which is fundamentally different than the classical one. For example, in the classical PAC model, learning boils down to Empirical Risk Minimization (ERM). In stark contrast, we show that the ERM principle fails in explaining learnability of partial concept classes. In fact, we demonstrate classes that are incredibly easy to learn, but such that any algorithm that learns them must use an hypothesis space with unbounded VC dimension. We also find that the sample compression conjecture fails in this setting. Thus, this theory features problems that cannot be represented nor solved in the traditional way. We view this as evidence that it might provide insights on the nature of learnability in realistic scenarios which the classical theory fails to explain.

In the process of intelligently segmenting foods in images using deep neural networks for diet management, data collection and labeling for network training are very important but labor-intensive tasks. In order to solve the difficulties of data collection and annotations, this paper proposes a food segmentation method applicable to real-world through synthetic data. To perform food segmentation on healthcare robot systems, such as meal assistance robot arm, we generate synthetic data using the open-source 3D graphics software Blender placing multiple objects on meal plate and train Mask R-CNN for instance segmentation. Also, we build a data collection system and verify our segmentation model on real-world food data. As a result, on our real-world dataset, the model trained only synthetic data is available to segment food instances that are not trained with 52.2% mask AP@all, and improve performance by +6.4%p after fine-tuning comparing to the model trained from scratch. In addition, we also confirm the possibility and performance improvement on the public dataset for fair analysis. Our code and pre-trained weights are avaliable online at: https://github.com/gist-ailab/Food-Instance-Segmentation

All the sessions from Transform 2021 are available on-demand now. Over the next two years, 75% of finance professionals believe their day jobs will significantly change, and 83% said they will have to learn new skills for AI and related technologies, according to a survey of finance processionals around the world from Unit4, a cloud leader in enterprise software. Above: More technical knowledge may be helpful, but the survey shows a surprising lack of emphasis on strategic leadership skills; only a quarter say interpersonal and influencing will be essential for future finance professionals. And only 21% think story telling will be important. In the next 12 months, more than four fifths of respondents are expecting to focus this upskilling on AI, machine learning, coding, analytics and data science capabilities, but a third of respondents accept that their organizations will need to grow their teams to fully implement the new technology, Unit4 said.

Africa Data School is 12 weeks of intensive training in Artificial intelligence, Data Science, Deep Learning, Machine learning, Computer vision, and Natural Language Processing. Africa Data School invites you to our virtual Open day Wednesday 28th 2021 from 4:00 pm to 5:00 pm EAT. Come hangout with Africa Data School as they give you first hand experience of what happens in the school.

There are many unsavory facts associated with AI-powered tools and applications. One of the more common ones is algorithmic discrimination based on race, gender, and ethnicity. Organizations around the world need to work in sync to get rid of bias in AI. By definition, AI is meant to replicate the working mechanism of the human brain for optimizing organizational activities. Unfortunately, while we have been able to get closer to actually recreating human intelligence artificially, AI also displays another distinctively human trait- prejudice against someone based on their race, ethnicity or gender.

The middle and working classes have seen a steady decline in their fortunes. Sending jobs to foreign countries, the hollowing out of the manufacturing sector, pivoting toward a service economy and the weakening of unions have been blamed for the challenges faced by a majority of Americans. According to a new academic research study, automation technology has been the primary driver in U.S. income inequality over the past 40 years. The report, published by the National Bureau of Economic Research, claims that 50% to 70% of changes in U.S. wages, since 1980, can be attributed to wage declines among blue-collar workers who were replaced or degraded by automation. Artificial intelligence, robotics and new sophisticated technologies have caused a wide chasm in wealth and income inequality.

First, let's take a look at why a project-based learning approach is so effective. First, building Python projects helps you learn more effectively because you can choose a project or topic that interests you. This helps you stay motivated, which is important in preventing you from giving up when things get tough. The second reason a project-based approach works is that there's no gap between learning the skill and putting it into practice. You won't waste time learning irrelevant things, because you'll be actively trying to learn the specific things you need to build your project.

RWTH receives funding for a network and an individual application in the federal-state initiative. RWTH Aachen has successfully emerged from the federal and state funding initiative "Artificial Intelligence in Higher Education". Both a joint project and an individual project are funded. With the funding initiative, which is endowed with around 133 million euros and reaches 81 universities across Germany, the federal and state governments are striving to develop the key technology of artificial intelligence (AI) more effectively across the university system. AIStudyBuddy The joint application "AIStudyBuddy: AI-based support for study planning" was submitted by RWTH as the applicant university together with the Ruhr University Bochum (RUB) and the Bergische Universität Wuppertal (BUW).

As homebound students and teachers looked for online resources during the pandemic, many turned to Scratch, a free coding system for kids developed by the MIT Media Lab. Scratch was already a popular option. It's been around since 2007 as a way to make animations and simple video games by combining Lego-like icons representing different coding functions. But in the 12 months beginning in March 2020--as schools across the country went remote for health reasons--usage spiked, and the number of projects shared on the service rose to 23 million, roughly double the amount from the previous year. Meanwhile the service has been going through some big changes behind the scenes.