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Using Unsupervised Learning to Combat Cyber Threats

As the world enters a digital age, cyber threats are rising with massive data breaches, hacks into personal and financial data, and any other digital source that people can exploit. To combat these attacks, security experts are increasingly tapping into AI to stay a step ahead, using every tool in their toolbox, including unsupervised learning methods.

Machine learning in the cybersecurity space is still in its infancy stage, but there has been a lot of traction since 2020 to have more AI involved in combating cyber threats.

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Applying Convolutional Networks for Better Video Streaming Performance

In the wake of COVID, video streaming is no longer a fun diversion. Organizations are depending on it to keep their workforce moving... and parents are counting on it to keep their kids from going into all-out rebellion mode during lockdowns. We’re all familiar with the hiccups we experience using streaming platforms, so the application of Deep Learning to video encoding and streaming promises to be an interesting frontier. 

Convolutional Neural Networks (CNNs) are a form of Deep Learning – machine learning designed to mimic the human brain by creating multiple layers of ‘neuron’ connections based on weighted probabilities – that is commonly used in image recognition. Each neuron represents a combination of features from a dataset, which are activated for prediction through sigmoid, threshold and rectifier functions. 

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Graph-Based Recommendation System With Milvus

Background

A recommendation system (RS) can identify user preferences based on their historical data and suggest products or items to them accordingly. Companies will enjoy considerable economic benefits from a well-designed recommendation system.

There are three elements in a complete set of recommendation systems: user model, object model, and the core element—recommendation algorithm. Currently, established algorithms include collaborative filtering, implicit semantic modeling, graph-based modeling, combined recommendation, and more. In this article, we will provide some brief instructions on how to use Milvus to build a graph-based recommendation system.

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The Last Mile: How the Pandemic Revealed New Applications of Autonomous Vehicles

The Acceleration of Autonomous Vehicle Applications Due to COVID-19

Hint: It’s not focused on personal transportation.

Autonomous vehicles have long been a mainstay of both outlandish fiction and legitimate research, in some interpretations predating the invention of the car itself. By some accounts, full self-driving capabilities would be a major boon in terms of safety and promises to narrow wealth-based gaps in good access to transportation. Perhaps the impact could be as significant as the past adoption of safety bicycles, which expanded the distance one could travel by 3 to 4 times or more, without incurring the costs of owning a horse (or car) and the facilities to care for them.

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5G Experience in an Epidemic of Market Uncertainty

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“Want to see disfunction firsthand? Look no further than Telecoms...The disruption reshaping the telecommunications industry is so enormous and far-reaching that, if I ran a business school, I'd make it mandatory case study material.”

— Hans Geerdes, Head of Business Strategy at Nokia 

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Deep Learning for Manufacturing: Overview and Applications

Deep learning

Introduction to Deep Learning for Manufacturing

Before getting into the details of deep learning for manufacturing, it’s good to step back and view a brief history. Concepts, original thinking, and physical inventions have been shaping the world economy and manufacturing industry since the beginning of the modern era, i.e. early 18th century.

Ideas of economies-of-scale by the likes of Adam Smith and John Stuart Mill, the first industrial revolution and steam-powered machines, electrification of factories and the second industrial revolution, and the introduction of the assembly line method by Henry Ford are just some of the prime examples of how the search for high efficiency and enhanced productivity have always been at the heart of manufacturing.

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Examining the Transformer Architecture – Part 1: The OpenAI GPT-2 Controversy

“Recycling is NOT good for the world. It is bad for the environment, it is bad for our health, and it is bad for our economy. I’m not kidding. Recycling is not good for the environment. It is destructive to the earth and it is a major contributor to global warming. Recycling is not good for our health. It contributes to obesity and diseases like heart disease and cancer. Recycling is bad for our economy. It increases the cost of a product, and in turn, the price of everything that is made with that product. Recycling is not good for our nation. We pay a tremendous price for the privilege of having the world’s most advanced and efficient recycling system. Recycling is a huge, colossal waste of time, energy, money, and resources.”

 — GPT-2 model from OpenAI

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Top 10 Artificial Intelligence Quotes That Will Inspire You

Are you looking for motivation and inspiration in Artificial Intelligence, Deep Learning, and Machine Learning? With so many great minds, you’re bound to run across at least one great quote that puts Artificial Intelligence in perspective or inspires you to do great things.

The best AI quotes of all time are the ones that resonate with people in a way that the world will never forget. They’re the kind of wisdom that smacks you in the face and encourages you to be all you can be.

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AI Will Not Eat the World

So I work at the intersection of cybersecurity and machine learning. I use a variety of neural network architectures and machine learning techniques to try to create new ways to detect new malware. I've worked on other projects using machine learning and AI too.

And we have nothing to worry about.