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How randomness can protect neural networks against adversarial attacks

As we continue to learn about the unique security threats of deep learning algorithms entail, one of the areas of focus are adversarial attacks, perturbation in input data that cause artificial intelligence algorithms to behave in unexpected (and perhaps dangerous) ways. Researchers are working on ways to build robust AI models that are more resilient against adversarial examples. Protecting deep learning algorithms against adversarial perturbation will be key to deploying AI in more sensitive settings.

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Algorithms Are Replacing Leadership Strategies

Machine learning (ML) and artificial intelligence (AI) applications are booming in the corporation world.  While algorithms aren’t always replacing humans, they are usually changing the way we work. No leader is safe from the rapid change we are seeing in the age of algorithms.  Businesses are transforming at rapid pace and there is no time to dilly dally. Learn how to make use of algorithms to build on your human skills or risk being replaced entirely.

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What is reinforcement learning?

Reinforcement learning, the special AI technique is considered by many the holy grail of artificial intelligence, because it can create autonomous systems that truly self-learn tasks without human intervention though things are a bit more complicated in reality. While machine learning and its more advanced subset deep learning, can solve many problems that were previously thought to be out of bounds for computers, they are dependent on vast amounts of quality, annotated training data. This makes their application limited in domains where labeled data is scarce. This is where reinforcement learning comes into play.

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Machine Learning Is A Moneyball Moment For Companies

Despite a great deal of lip service and a small amount of capital invested, most corporations are still not data-driven, nor do they use machine learning (ML) and artificial intelligence (AI) to guide their strategic investments in business models. Companies are finally embracing analytics, but still have shown little appetite to be data driven, let alone use ML and AI to help them understand the key drivers of value in today’s highly competitive environment: capital allocation and business model design.

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How to Prepare for an Automated Future: 7 Steps to Machine Learning

Organisations that want to prepare for an automated future should have a thorough understanding of AI. However, AI is an umbrella term that covers multiple disciplines, each affecting the business in a slightly different way. Artificial intelligence can be divided into three different domains consisting of the seamless integration of robotics, cognitive systems, and machine learning. The objective of machine learning is to derive meaning from data. Therefore, data is the key to unlock machine learning. There are seven steps to machine learning, and each step revolves around data.

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What is computer vision?

Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. Until recently, computer vision only worked in limited capacity. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.

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Machine Learning In Warehouse Management

Machine learning, a branch of artificial intelligence, has already been used across many industries to improve efficiency and productivity. The technology has been in development for some time, with the uptake being slowed down by some industry’s reluctance to adopt it. However, it’s now being used by many businesses, including logistics companies and retailers seeking help for warehouse management.

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Industrial Machine Learning: Where are We Now?

The rapid growth of Machine Learning and Artificial Intelligence applications is not only in the software domain. As the use of sensors in industrial applications becomes more mainstream, the large amount of data gathered can be used to train algorithms. This, in turn, results in rapid increases in efficiency.

In this article, chemical engineer and researcher Hermes Ribeiro Sant Anna gives an overview of how Machine Learning and Artificial Intelligence are used in industrial production facilities. 

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