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. Humans build knowledge in images. Every time we are presented with an idea or an experience, our brain immediately formulates visual representations of it.

Machine Learning is an important technology for handling data in today's world. It is used to derive models of reality from data. For example, you can use it to segment customer data in an online store or to optimize a performance marketing campaign. This usually requires the use of a programming language with a large number of program libraries for the selected language. Very often "Python" or "R" are used here today and libraries like "Scikit Learn" and "TensorFlow".

In the first part of our tutorial on neural networks, we explained the basic concepts about neural networks, from the math behind them to implementing neural networks in Python without any hidden layers. We showed how to make satisfactory predictions even in case scenarios where we did not use any hidden layers. However, there are several limitations to single-layer neural networks. In this tutorial, we will dive in-depth on the limitations and advantages of using neural networks in machine learning. We will show how to implement neural nets with hidden layers and how these lead to a higher accuracy rate on our predictions, along with implementation samples in Python on Google Colab.

All students joining the Institute of Technical Education (ITE) from this year must take a module in the basics of artificial intelligence (AI) in their first year of study.The institute, which sees about 14,000 enrolling each year, is making a big push to teach them skills like basic programming and analytics, to meet the demands of emerging jobs. Please subscribe or log in to continue reading the full article.

Machine vision is commonly defined as the use of computer vision in the context of an industrial application, and the first use of machine vision for industrial purposes is often attributed to Electric Sorting Machine Company in the 1930s. They used a type of vacuum tube called a photomultiplier or PMT to sort food. Using this technology, machines could sort red apples from green and later recyclable glass bottles from ones with cracks. Much of the history of machine vision in the industrial sector has involved sorting one thing from another, the good from the bad. As camera technologies have improved, machine vision has been deployed for ever more precise quality control use cases, especially ones that involve parts that would be too small or hazardous for human inspectors.

If you've taken a look at the state of the art benchmarks/leaderboards for ImageNet sometime in the recent past, you've probably seen a whole lot of this thing called "EfficientNet." Now, considering that we're talking about a dataset of 14 million images, which is probably a bit more than you took on your last family vacation, take the prefix "Efficient" with a fat pinch of salt. But what makes the EfficientNet family special is that they easily outperform other architectures that have a similar computational cost. In this article, we'll discuss the core principles that govern the EfficientNet family. Primarily, we'll explore an idea called compound scaling which is a technique that efficiently scales neural networks to accommodate more computational resources that you might have/gain. In this report, I'll present the results I got from attempting to try the various EfficientNet scales on a dataset much smaller than ImageNet which is much more representative of the real world.

"…In many organizations, the human resource department is responsible for many strategic tasks from managing the hiring to [the] termination of employee[s], for example monitoring of employees' at all the levels, handling payroll, managing employee[s'] benefits and so on. To make this work easier[,] organizations across the world are investing in HR automation [to] [carry] out the best human capital decision[s]…" I know what you're thinking: "…my company's board of directors is too visually impaired to consider what kind of impact these new-flanged capabilities will have on the company to actually consider them-- let alone implement them…" but you would be wrong to think this way; because the change is not only already happening, but it is accelerating. While it is true that some companies have not fully considered implementing a complete, top-to-bottom HR automation strategy -- largely because such a thing is still too abstract a problem and a not-so-clear-opportunity right now -- news like Amazon's drive to automate hiring and onboarding for its hourly warehouse workers will not stay secret for long. Do not kid yourselves, while corporate boards are not known for being bastions of innovation and forward-thinking, they know it's possible -- even if they are unable to see its affect on the corporation's current business -- at least, not yet, anyway.

The next year will be pivotal for the Air Force's effort to acquire a new class of autonomous drones, as industry teams compete for a chance to build a fleet of robotic wingmen that will soon undergo operational experimentation. The "Skyborg" program is one of the service's top science-and-technology priorities under the "Vanguard" initiative to deliver game-changing capabilities to its warfighters. The aim is to acquire relatively inexpensive, attritable unmanned aircraft that can leverage artificial intelligence and accompany manned fighter jets into battle. "I expect that we will do sorties where a set number are expected to fly with the manned systems, and we'll have crazy new [concepts of operation] for how they'll be used," Assistant Secretary of the Air Force for Acquisition, Technology and Logistics Will Roper said during an online event hosted by the Mitchell Institute for Aerospace Studies. The platforms might even be called upon to conduct kamikaze missions.

The Radiological Society of North America (RSNA) has launched its fourth annual artificial intelligence (AI) challenge, a competition among researchers to create applications that perform a clearly defined clinical task according to specified performance measures. The challenge for competitors this year is to create machine-learning algorithms to detect and characterize instances of pulmonary embolism. RSNA collaborated with the Society of Thoracic Radiology (STR) to create a massive dataset for the challenge. The RSNA-STR Pulmonary Embolism CT (RSPECT) dataset is comprised of more than 12,000 CT scans collected from five international research centers. The dataset was labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Sometimes, all it takes is a look. For some single people, it can be hard to tell when someone is actually flirting with you. Fortunately for them, new research suggests that there may be a specific facial expression that women use when they're flirting.