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Deep Learning

Deep Learning for House Number Detection


This is a Stanford collected Dataset and is available for the public to experiment and to learn. SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST(e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. The images are, in no way, preprocessed or ready to be used yet.

Is Fine Art the Next Frontier of AI?


In 1950, Alan Turing developed the Turing Test as a test of a machine's ability to display human-like intelligent behavior. "Are there imaginable digital computers which would do well in the imitation game?" In most applications of AI, a model is created to imitate the judgment of humans and implement it at scale, be it autonomous vehicles, text summarization, image recognition, or product recommendation. By the nature of imitation, a computer is only able to replicate what humans have done, based on previous data. This doesn't leave room for genuine creativity, which relies on innovation, not imitation.

AI Generated Synthetic Media, aka deepfakes


Imagine a few days before an election, a video of a candidate is released, showing them using hate speech, racial slurs, and epithets that undercut their image as pro minorities. Imagine a teenager watching embarrassingly an explicit video of themselves going viral on social media. Imagine a CEO on the road to raise money when an audio clip stating her fears and anxieties about the product is sent to the investors, ruining her chances of success. All the above scenarios are fake, made up, and not actual, but can be made real by AI-generated synthetic media, also called deepfakes[1]. The same technology that can enable a mother, losing her voice to Lou Gehrig's disease to talk to her family using a synthetic voice can also be used to generate a political candidate's fake speech to damage their reputation.

Deep learning and metamaterials make the invisible visible


By combining purpose-built materials and neural networks, researchers at EPFL have shown that sound can be used in high-resolution imagery. Imaging allows us to depict an object through far-field analysis of the light- and sound-waves that it transmits or radiates. However, the level of detail is limited by the size of the wavelength in question--until now. Researchers at EPFL's Laboratory of Wave Engineering have successfully proven that a long, and therefore imprecise, wave (in this case a sound wave) can elicit details that are 30 times smaller than its length. Their research, which has just been published in Physical Review X, is creating exciting new possibilities, particularly in the fields of medical imaging and bioengineering.

Computer vision: Why it's hard to compare AI and human perception


This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Those are terms you hear a lot from companies developing artificial intelligence systems, whether it's facial recognition, object detection, or question answering. And to their credit, the recent years have seen many great products powered by AI algorithms, mostly thanks to advances in machine learning and deep learning. But many of these comparisons only take into account the end-result of testing the deep learning algorithms on limited data sets. This approach can create false expectations about AI systems and yield dangerous results when they are entrusted with critical tasks.

How to choose a cloud machine learning platform


In order to create effective machine learning and deep learning models, you need copious amounts of data, a way to clean the data and perform feature engineering on it, and a way to train models on your data in a reasonable amount of time. Then you need a way to deploy your models, monitor them for drift over time, and retrain them as needed. You can do all of that on-premises if you have invested in compute resources and accelerators such as GPUs, but you may find that if your resources are adequate, they are also idle much of the time. On the other hand, it can sometimes be more cost-effective to run the entire pipeline in the cloud, using large amounts of compute resources and accelerators as needed, and then releasing them. The major cloud providers -- and a number of minor clouds too -- have put significant effort into building out their machine learning platforms to support the complete machine learning lifecycle, from planning a project to maintaining a model in production.

7 Popular AI Projects On Gesture Gaming One Must Know


AI has made several breakthroughs when it comes to implementation into games. The functionalities of AI in video games include various domains such as real-time facial emotion recognition, automated difficulty adaptation, sentiment analysis, non-verbal bodily motion, lip-synchronised speech and more. This technique has been used in games to enhance graphical realism, to generate levels, sceneries and storylines, to establish player profiles, balance complexity or to add intelligent behaviours to non-playing characters. In this article, we list down the seven popular AI projects that work on gesture gaming. About: This project will help you understand how to use TensorFlow object detection API with the computer's webcam to play a snake game by using hand gestures.

Deep Instinct Contracts with T-Systems Poland, Furthering Strategic Expansion into EMEA


LONDON--(BUSINESS WIRE)--Deep Instinct, the first and only cybersecurity company to apply end-to-end deep learning to predict, identify, and prevent cyberattacks, is continuing its strategic expansion into EMEA, contracting with T-Systems (Poland), one of the region's largest IT services providers, to utilize and distribute Deep Instinct's protection to its customers. Deep Instinct also signed strategic partnership agreements with Cyber Monks and Spinnakar to distribute Deep Instinct's deep learning-based solution across the region. Leading Deep Instincts' EMEA expansion is Brooks Wallace, VP Sales EMEA, a veteran cybersecurity sales leader with over 20 years of experience in building sales teams. Wallace will oversee the newly opened sales and support office in the UK and forge additional strategic partnerships with MSSPs across the region. "Our expansion into EMEA comes at a critical time for the region, and contracting with T-Systems Poland attests to the unique value of our deep learning-based cyber-attack prevention solution," said Guy Caspi, CEO and Co-founder of Deep Instinct.

A Gentle Introduction to Probabilistic Programming Languages


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. Probabilistic thinking is an incredibly valuable tool for decision making. From economists to poker players, people that can think in terms of probabilities tend to make better decisions when faced with uncertain situations.