Deep supervised learning has achieved great success in the last decade. However, its deficiencies of dependence on manual labels and vulnerability to attacks have driven people to explore a better solution. As an alternative, self-supervised learning attracts many researchers for its soaring performance on representation learning in the last several years. Self-supervised representation learning leverages input data itself as supervision and benefits almost all types of downstream tasks. In this survey, we take a look into new self-supervised learning methods for representation in computer vision, natural language processing, and graph learning. We comprehensively review the existing empirical methods and summarize them into three main categories according to their objectives: generative, contrastive, and generative-contrastive (adversarial). We further investigate related theoretical analysis work to provide deeper thoughts on how self-supervised learning works. Finally, we briefly discuss open problems and future directions for self-supervised learning. An outline slide for the survey is provided.

This is a guest post by AWS Machine Learning Hero Cyrus Wong. Staying connected with family, friends, and colleagues is easy for most people who live with or close to others. For educators who need to communicate lessons and schedules with their students, or businesses who communicate with new and existing customers, staying connected can be hard, especially in times of crisis and isolation. Specifically, I wanted to make remote communication between educators and students easier. Communicating time-sensitive information and confirming that students received messages can be hard; scaling communication from tens to thousands of students can make the problem more complex, impacting educator and student productivity, time, and overall experience.

It stores all scraped samples locally and runs rules over this data to either aggregate and record new time series from existing data or generate alerts. Grafana or other API consumers can be used to visualize the collected data.

Heather Chmura '16 launched her career at the age of 14 as the youngest franchise owner of a Wetzel's Pretzels in Las Vegas, managing more than 50 employees. Busy with her business, she went to high school online. But she decided to attend college in person. After searching for a small school with overseas programs and a good religious studies department, she joined her sister, Becky Chmura '14, at Westmont. She majored in economics and business, participated in the Westmont in Northern Europe semester, took Emmaus Road trips to Japan, South Korea and Taiwan and studied Business at the Bottom of the Pyramid with professor Rick Ifland to help establish microfinance businesses in Haiti.

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The world of finance is changing. Since the financial crash in 2008, there has been a slow but steady move away from traditional finance models, as the value of embedding deeper approaches to social and environmental issues has become increasingly clear. Now, the world has taken a shocking blow from the COVID-19 pandemic. The tragic deaths, lost livelihoods, and curtailed freedoms are unprecedented, and no-one can tell how or when the global economy will recover from a downturn of this speed and scale. As we tackle one of the biggest global crises of our time, and look to rebuild in a way that ensures we emerge from this stronger and more resilient, sustainable finance – in other words, finance that takes account of positive and negative social and environmental factors, particularly the factors that tend to play out over the medium to long-term – will be more critical than ever.

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The "Artificial Intelligence in Education Market" globally is a standout amongst the most emergent and astoundingly approved sectors.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. One of the challenging elements of any deep learning solution is to understand the knowledge and decisions made by deep neural networks. While the interpretation of decisions made by a neural networks has always been difficult, the issue has become a nightmare with the raise of deep learning and the proliferation of large scale neural networks that operate with multi-dimensional datasets.

Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of Prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.