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How Efficient is EfficientNet?

#artificialintelligence

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.


HR by Spreadsheet vs. HR by Algorithm

#artificialintelligence

"…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.


Air Force Betting on New Robotic Wingman

#artificialintelligence

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.


RSNA Launches Pulmonary Embolism AI Challenge

#artificialintelligence

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.


Researchers reveal the perfect 'flirty' face for women: study

FOX News

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.


Machine Learning Summits '20 - Home

#artificialintelligence

If you are in the ML/AI field, and are interested in enhancing your skills, while networking and learning from Google's ML/AI experts, this is the event for you! Attendees should have prior knowledge and experience with Machine Learning and/or AI technologies. We want to create a learning journey for developers around Google's ML content - from data to decisions.


The 4 Top Artificial Intelligence Trends For 2021

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Before the global pandemic struck in 2020 and the world was turned on its head, artificial intelligence (AI), and specifically the branch of AI known as machine learning (ML), were already causing widespread disruption in almost every industry. The 4 Top Artificial Intelligence Trends For 2021 Adobe Stock The Covid-19 pandemic has impacted many aspects of how we do business, but it hasn't diminished the impact AI is having on our lives. In fact, it's become apparent that self-teaching algorithms and smart machines will play a big part in the ongoing fight against this outbreak as well as others we may face in the future. AI undoubtedly remains a key trend when it comes to picking the technologies that will change how we live, work, and play in the near future. So, here's an overview of what we can expect during what will be a year of rebuilding our lives as well as rethinking business strategies and priorities.


Is Tesla Entering Machine Learning As A Service Market?

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Tesla may be introducing machine-learning training as a web service with its upcoming'Dojo' supercomputer, CEO Elon Musk said on Twitter. Project Dojo was initially revealed by Musk last year and is a supercomputer which Tesla has been working on. The supercomputer has been designed to ingest massive amounts of video data and perform massive levels of unsupervised training on the visual data. The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do massive unsupervised training of vast amounts of video data. Dojo uses our own chips & a computer architecture optimized for neural net training, not a GPU cluster. Could be wrong, but I think it will be best in world.


The use of graph neural networks to discover particles

#artificialintelligence

Machine learning algorithms can beat the world's hardest video games in minutes and solve complex equations faster than the collective efforts of generations of physicists. But the conventional algorithms still struggle to pick out stop signs on a busy street. Object identification continues to hamper the field of machine learning--especially when the pictures are multidimensional and complicated, like the ones particle detectors take of collisions in high-energy physics experiments. However, a new class of neural networks is helping these models boost their pattern recognition abilities, and the technology may soon be implemented in particle physics experiments to optimize data analysis. This summer, Fermilab physicists made an advance in their effort to embed graph neural networks into the experimental systems.


Scientists combat anti-Semitism with artificial intelligence – IAM Network

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BERLIN (AP) -- An international team of scientists have joined forces to combat the spread of anti-Semitism online with the help of artificial intelligence. The Alfred Landecker Foundation, which supports the team, said Monday that the project named Decoding Anti-Semitism includes discourse analysts, computational linguists and historians. They will develop a "highly complex, AI-driven approach to identifying online anti-Semitism." The team includes researchers from Berlin's Technical University, King's College in London and other scientific institutions in Europe and Israel. Computers will run through vast amounts of data and images that humans wouldn't be able to assess because of their sheer quantity.