"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Facial recognition software could be used to detect hail storms - and their severity. That's according to scientists at the US National Center for Atmospheric Research, who've tested the software's effectiveness on meteorological data. Specifically, they found that a deep learning model called a convolutional neural network can spot the early signs as they happen - better than current methods. The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warnings. AI: The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warning Whether or not a storm produces hail hinges on myriad meteorological factors.
HOSTKEY deploys a well-established environment for machine learning applications such as neural networks with high-performance GPUs and dedicated servers with NVIDIA GTX 1080/1080Ti and RTX 2080Ti graphics cards. Just start your TensorFlow experience in a straightforward and user-friendly environment making it easy to build, train and deploy machine learning models at scale. TensorFlow runs up to 50% faster on our high-performance GPUs and scales easily. Now your machines learn in hours, not days. Deep Learning is a buzzword that will be familiar to most people.
Machine learning is a powerful tool in the AI toolbox, but its limitations must be understood to use effectively. Machine learning has become the latest darling of the IT marketing space, a secret sauce that is supposed to turbo-charge computers and bring us closer to the nirvana of artificial intelligence dominance ... or something like that. Like so much of what comes out of IT marketing, most of it is hype, and deceptive hype at that. While there is a lot of power in what machine learning can do, by assigning it magical capabilities the mavens of marketing may actually be setting the whole field up for yet another artificial intelligence winter, as expectations continue to fall short of reality. It's worth understanding what Machine Learning is, and isn't.
The first video shows the set-up and process of optimizing a simple parametric design for an neighborhood. The algorithm takes five fitness objectives into account: Solar comfort on the streets (weighted by pedestrian frequency); wind comfort; footfall through the neighborhood; access to the neighborhood, overall access to local transit stations; The latter two indicators are computed for the whole area, thus enabling to include a positive impact of the new quarter's spatial arrangement on the whole neighborhood as a goal dimension. By using our deep learning based predictions for solar and wind related measures, one iteration takes just about three seconds to be computed.
In vitro fertilization (IVF) technology has made enormous strides in recent decades, resulting in millions of successful pregnancies. Nevertheless, disparity in visual morphology assessment results between embryologists has raised serious questions about the efficacy of embryo selection (1). Attempting to solve this problem, Iman Hajirasouliha (Assistant Professor of Computational Biology) and his team at Cornell University the Englander Institute for Precision Medicine at Weill Cornell Medicine have developed a new tool – the aptly-named STORK – which is capable of driving robust assessment and selection of human blastocysts (2). "Often, to overcome uncertainties in embryo quality, many more embryo's than required are implanted into the patient," says Hajirasouliha. "Often, this leads to undesired multiple pregnancies and serious complications." Convinced there must be a more efficient way to screen for healthy, viable embryos, the team trained a deep neural network to select the best embryo's using time-stamped images of embryos from a large IVF center.
When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn't help reduce the confusion when every tech vendor rebrands their products as AI. So, what do these terms really mean? What are overlaps and differences? And most importantly, what can this do for your business?
There are many options to do the course work, e.g., AWS, PaperSpace, etc., but I found Google Colaboratory is the best and easiest option. Here is the instruction for the Fast.ai Unlike other option, Colab guarantees to work because Google starts with a clean, new virtual machine (VM) every time, and in the first few steps in the notebooks, it loads the required correct version of Pytorch and Fast.ai.
With all of the progress we've seen in deep learning tech in the past few years, it seems pretty inevitable that security cameras become smarter and more capable in regards to tracking, but there are more options than we think in how we choose to pull this off. Traces AI is a new computer vision startup, in Y Combinator's latest batch of bets, that's focused on helping cameras track people without relying on facial recognition data, something the founders believe is too invasive of the public's privacy. We can use your hair style, whether you have a backpack, your type of shoes and the combination of your clothing," co-founder Veronika Yurchuk tells TechCrunch. Tech like this obviously doesn't scale too well for a multi-day city-wide manhunt, and leaves room for some Jason Bourne-esque criminals to turn their jackets inside out and toss on a baseball cap to evade detection. As a potential customer, why forego a sophisticated technology just to stave off dystopia? Well, Traces AI isn't so convinced that facial recognition tech is always the best solution; they believe that facial tracking isn't something every customer wants or needs and there should be more variety in terms of solutions. "The biggest concern [detractors] have is, 'Okay, you want to ban the technology that is actually protecting people today, and will be protecting this country tomorrow?'
We need a lot of data thas a huge drawback. We are learning the representation directly and this is why it works so well. Even in RL, we need a lot of data and this can really be a drawback in every turn. And all supervised learning is backpropagation gradient descent derivative. Even 2005 some good progress were made.