I'm not sure either of these scooped anyone. I think last week I read about an example applied to moss classification, and even there I was unimpressed - it's the obvious mirror to doing random crops for data augmentation, which has been around for years, and I would bet dollars to donuts that'Cutout'/'Random Erasing' have been used before and might be implemented already in libraries because they're so obvious.

When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset, Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent?

Should I bother trying to turn the 3rd one into a paper? I considered it as my architecture/method was somewhat original but there is such an overflow of machine learning/deep learning papers right now that I'm wary of dumping more on the slush pile (and I'm not in academia so there is no pressure on me to publish).

In the quest to better understand climate change, there is plenty we still don't know. But the question isn't whether or not climate change is happening. "What we sometimes hear on the news is political manufactured uncertainty," said Auroop Ganguly, a professor of civil & environmental engineering at Northeastern. Instead, real climate change uncertainty stems from the challenge of simulating the future. What will happen to Boston's electric grid under long-term extreme weather conditions?

I've met some robotic humans, but I've yet to meet a robot that even remotely resembles a human. Artificial intelligence (AI), with its subfields of machine learning and deep learning, is supposed to fix all that. But, for a variety of reasons, AI is nowhere near delivering human-like understanding to machines. It turns out the very same traits that cause us to jump to conclusions make it impossible for machines to act human. Even so, deep learning, based on artificial neural networks, offers real promise.

DigitalMR is a London based tech company that specialises in Artificial Intelligence for Market Research and customer insights to be more specific . Our capabilities include high accuracy automated sentiment and semantic analysis (topics of conversation), emotions detection in text, and now image theme detection! The DigitalMR magic captioner was created to have the ability to describe an image in one sentence, using Artificial Intelligence. The fact that humans such as yourself can do this with remarkable ease does not mean that it is as easy for a machine. Non-trivial disciplines like computer vision (scene understanding) and deep learning are required.

Microsoft recently reached a new milestone in its ability to recognize conversational speech, achieving a 5.1% word error rate (WER). The achievement, detailed in a Sunday blog post, bests Microsoft's previous record of 5.9% and is closer to human parity. The new WER was achieved through the use of Switchboard. According to the blog, "Switchboard is a corpus of recorded telephone conversations that the speech research community has used for more than 20 years to benchmark speech recognition systems." Using Switchboard, speech recognition systems are tasked with transcribing conversations about topics such as politics or sports, for example.

Tesla CEO Elon Musk fired off a new and ominous warning on Friday about artificial intelligence, suggesting the emerging technology poses an even greater risk to the world than a nuclear conflagration with North Korea. Musk--a fierce and long time critic of A.I. who once likened it to "summoning the demon" in a horror movie--said in a Twitter post that people should be concerned about the rise of the machines than they are. Reacting to the news that autonomous tech had bested competitive players in an electronic sports competition, Musk posted what appeared to be a photo of a poster bearing the chilling words "In the end, the machines will win." Musk, who is spearheading commercial space travel with his venture SpaceX, is also the founder of OpenAI, a nonprofit that promotes the "safe" development of AI. His stance puts him at odds with much of the tech industry, but echoes remarks of prominent voices like Stephen Hawking--who has also issued dire warnings about machine learning.

There's a growing fear that artificial intelligence (A.I.) and machine learning will take over millions of human jobs, leading to social upheaval on an unimaginable scale. While some tech leaders believe that artificial-intelligence platforms will ultimately prove a blessing to the human race, there are others--most notably Tesla CEO Elon Musk--who think that unmanaged A.I. could doom us all. At this juncture in the evolution of A.I., it's too soon to tell how things will ultimately go. But there are a few signs that A.I. won't undermine human beings, at least in the near term. Exhibit A: the recent tournament that saw a bot created by OpenAI, a non-profit A.I. research company, beat human champions at "Dota 2," a real-time strategy game.

In my last submission, "The Emergence of the Age of Artificial Intelligence (AI):Part 1", I looked at examples of symbol manipulating programs in AI. Such programs operate by using explicitly stored symbols that are logically manipulated during the program execution. ANN's follow something known as Hebb's Rule which says that every time every time a correct decision is made, the neural pathways are reinforced / Image: pixabay Another class of AI software, called Artificial Neural Networks (ANNs), do not use explicit knowledge stored as rules of operation. Instead ANNs use implicit knowledge that is encoded in numeric parameters – called weights – and distributed over many connections. Unlike symbolic AI, ANNs have "black box" characteristics because they cannot explicate their reasoning in the same way that symbolic AI programs do.