Overview

It's another graph neural networks survey paper today! Clearly, this covers much of the same territory as we looked at earlier in the week, but when we're lucky enough to get two surveys published in short succession it can add a lot to compare the two different perspectives and sense of what's important. In particular here, Zhou et al., have a different formulation for describing the core GNN problem, and a nice approach to splitting out the various components. Rather than make this a standalone write-up, I'm going to lean heavily on the Graph neural network survey we looked at on Wednesday and try to enrich my understanding starting from there. For this survey, the GNN problem is framed based on the formulation in the original GNN paper, 'The graph neural network model,' Scarselli 2009.

The first time I mentioned chatbots (or bot-based technology) on this blog was back in 2016 in a post titled "Messaging is the Past, Present, and Future." There are a lot of non-HE chatbots in operation at the moment. For example, the Transport for London TravelBot within Facebook Messenger is daily go-to for anyone who uses the Tube. And the chatbots from Duolingo are a great way to practice learning a new language. However, as with a lot of buzzword-driven technology (and this isn't a bad thing), chatbots are increasingly becoming part of the mobile-app landscape for higher education.

Microsoft and Google have been among the leaders in development of artificial intelligence solutions. Both companies say AI is a major part of their future endeavors and will boost revenue through streamlining services. However, both Microsoft and Google admit AI could also cause harm to their business if something goes wrong. Both companies made the declaration in 10-K forms discovered by Wired. Created for investors, the documents give an overview of business and finance performance.

While many financial institutions are talking about robotics and artificial intelligence, some have already implemented these technologies; some have made significant strides including impressive cost savings of up to 60 per cent in some areas. Over the last two decades, the invasion of technology has changed the entire landscape and the way we perceive things around us. Banking has taken a paradigm shift in this technological revolution. While many new technologies are emerging, it is also bringing in a host of changes to the industry and generating newer forms of employment. Being a critical aspect of the economy, technological progression in the banking sector has become an important topic.

Every business must become a digital business. In the era of multiple innovation, where businesses have shifted from exploring emerging technologies to implementations of disruptive technologies like mobile, social, cloud, artificial intelligence (AI), and Internet of Things (IoT), the digital playing field will eventually even out. According to the Accenture Technology Vision 2019 research report -- based on a survey of over 6,000 business and IT executives, companies will need to find their competitive edge in this new'post-digital' era. Also: What is AI? Everything you need to know The Accenture Technology Vision 2019 report was written by Paul Daugherty, Accenture's chief technology and innovation officer (CTIO) and leader of the company's Technology Innovation and Ecosystem group, Marc Carrel-Billiard, Accenture's global senior managing director of Accenture Labs, the company's dedicated R&D organization, and Michael Biltz, Managing Director of Accenture's annual Strategic Technology Visioning efforts. Daugherty is also the co-author of'Human Machine: Reimagining Work in the Age AI'.

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If you've been at machine learning long enough, you know that there is a "no free lunch" principle -- there's no one-size-fits-all algorithm that will help you solve every problem and tackle every dataset. I work for Springboard -- we've put a lot of research into machine learning training and resources. At Springboard, we offer the first online course with a machine learning job guarantee. What helps a lot when confronted with a new problem is to have a primer for what algorithm might be the best fit for certain situations. Here, we talk about different problems and data types and discuss what might be the most effective algorithm to try for each one, along with a resource that can help you implement that particular model.

RL algorithm learns how to act best through many attempts and failures. Trial-and-error learning is connected with the so-called long-term reward. This reward is the ultimate goal the agent learns while interacting with an environment through numerous trials and errors. The algorithm gets short-term rewards that together lead to the cumulative, long-term one. So, the key goal of reinforcement learning used today is to define the best sequence of decisions that allow the agent to solve a problem while maximizing a long-term reward. And that set of coherent actions is learned through the interaction with environment and observation of rewards in every state. Reinforcement learning is distinguished from other training styles, including supervised and unsupervised learning, by its goal and, consequently, the learning approach. Three ML training styles compared.

As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve problems in computer vision, natural language processing, games, autonomous driving, robotics, and beyond. This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094: Deep learning is representation learning: the automated formation of useful representations from data. How we represent the world can make the complex appear simple both to us humans and to the machine learning models we build. My favorite example of the former is the publication in 1543 by Copernicus of the heliocentric model that put the Sun at the center of the "Universe" as opposed to the prior geocentric model that put the Earth at the center.

Since humans learned how to communicate, knowledge-sharing has been the core of our success, both as a species and as individuals. Around the campfire, after a successful hunt, our hunter-gatherer ancestors shared their stories of bravery and stories of misfortune too. By passing experience to one another, we've become more adept at surviving though times of hunger, famine and drought. Over time, multiple technologies and approaches have been invented, which increase the reach or the speed of knowledge transfer. Sometimes that improvement is so significant that it creates a paradigm shift, and we deem that to be a Knowledge Revolution.