- pyFrom the dataset WIDERFace and the film Marching Band (2009) with XML and LabelImg interface.Pascal XML to Tensorflow CSV indexWhen the data is converted to Pascal XML, an index is created.
- Transfer learning is a method in Machine Learning that is focused on applying knowledge gained from one problem to another problem.In the class train_input_reader, a link is made with the TFRecord files for training the model.
- $ tensorboard –logdir= model_outputTensorflow Tensorboard TotalLossConverting checkpoint to protobufTo use the model in Object Recognition with the Computer Vision library Tensorflow.
- config \–trained_checkpoint_prefix model_output/model.ckpt-12262 \–output_directory model/TL; DR;In the folder on the github repository is a frozen model of the Artificial Neural Network.
- The Chinese University of Hong Kong has WIDERFace and this dataset has been used to train model.Conclusion and use of the frozen modelIt has been possible to train a face recognition model.
In this blogpost I will focus on training my own classes. In the Tensorflow documentation is written how to setup on your local machine. We are going to train a real-time object recognition…
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- China has installed over 20 million cutting-edge security cameras in what is believed to be the world’s most advanced surveillance system.
- The camera system, facilitated with artificial intelligence technology, is part of the ‘Sky Net’ operation, which is China’s anti-corruption programme mainly aimed to track down fugitives.
- Over 20 million CCTV cameras equipped with AI technology have been installed in China, crowning to be the world’s most advanced surveillance system.
- Operation ‘Sky Net’ was launched in 2015 as the Chinese central government aimed to hunt for corrupt fugitive officials, crack down on underground banks and confiscate misappropriated assets, according to Bloomberg.
- In April, China has started to use facial recognition technology to catch jaywalkers in Shenzhen.
China has installed over 20 million cutting-edge security cameras. Such technology can identify a pedestrian or a motorist, which can help policemen in their search for criminals.
Continue reading “Big brother is watching you! China installs ‘the world’s most advanced video surveillance system’ with over 20 million AI-equipped street cameras”
- Because of the way quantum computers work—taking advantage of strange quantum mechanical effects like entanglement and superposition—algorithms running on them should in principle be able to solve problems much faster than the best known classical algorithms, a phenomenon known as quantum speedup.
- Quantum computers, unsurprisingly, deal with quantum data, but the majority of the problems humans want to solve relate to the classical world.
- The input problem could be mitigated to some extent by the development of quantum random access memory (qRAM)—the equivalent to RAM in a conventional computer used to provide the machine with quick access to its working memory.
- A qRAM can be configured to store classical data but allow the quantum computers to access all that information simultaneously as a superposition, which is required for a variety of quantum algorithms.
- At present, the authors say very little is known about how many gates—or operations—a quantum machine learning algorithm will require to solve a given problem when operated on real-world devices.
Quantum computers could give machine learning algorithms a dramatic speed up. But how far off are we really from quantum machine learning?
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- Artificial Intelligence (AI) is already in use on search engines and has been so for the past few years.
- RankBrain, which is part of Google, is an AI system that is used quite prominently in ranking websites for search engine results.
- Search Engine Optimization (SEO), which is still the primary means of passive marketing on the internet, has already been affected by AI in a powerful way.
- One area that has shown considerable improvement thanks to the use of AI is content marketing.
- However, the way search engine rankings will be affected over the foreseeable future will change regardless of how the advances in AI turn out.
Clear And Unbiased Facts About Impact Of Artificial Intelligence on Future of SEO (Without All the Hype). Happy reading.
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- Read the full ABC article and watch the video interview to learn more about Tanmay and his work in the field of AI.
- The Australian Broadcasting Corporation (ABC) recently profiled 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five, launched his first app at age nine, and has been working with IBM’s AI and cognitive APIs for a couple of years now.
- ABC notes: “the Canadian teen has become a global force in programming and commands more than 20,000 subscribers on his YouTube channel that teaches computer coding.”
- He is currently in Australia for the IBM Watson Summit, which brings together experts in artificial intelligence to discuss how the technology can help people and businesses in the future.
- You can also watch Tanmay’s video, “IBM Watson, Machine Learning: How to use the “Retrieve and Rank” service in IBM Bluemix”, one of 80 tutorials he has created and made available on the “Tanmay Teaches” YouTube channel.
A profile of 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five and works with IBM’s AI and cognitive APIs.
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- IBM and the Massachusetts Institute of Technology have inked a 10-year, $240 million deal to develop a research lab devoted entirely to artificial intelligence, with a heavy emphasis on healthcare.
- MIT professors and students will have access to IBM’s research lab which includes IBM Watson Health and IBM Security.
- Although the lab will broadly focus on AI’s impact across several industries, there’s a clear emphasis on how to integrate new algorithms into healthcare and cybersecurity.
- Beyond research, MIT and IBM plan to launch new companies to commercialize AI technologies and innovations developed in the lab.
- “The combined MIT and IBM talent dedicated to this new effort will bring formidable power to a field with staggering potential to advance knowledge and help solve important challenges.”
IBM Watson and the Massachusetts Institute of Technology have inked a 10-year, $240 million deal to develop a research lab devoted entirely to artificial intelligence, with a heavy emphasis on healthcare. Anantha Chandrakasan, the dean of MIT’s School of Engineering, said cancer care will be a particular focus.
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- This broad category of perturbation techniques involve perturbing the pixel intensity of input image with minimum noise and observing the change of prediction probability.
- The leftmost image is the original X-ray image, the middle one is the perturbed image as the black occluding patch moves across the image, the rightmost image is the plot of the probability of pleural effusion as different parts of the X-ray gets occluded.
- As is evident from above, the probability of pleural effusion drops as soon as the right CP angle and accumulated fluid region of the X-ray is occluded to the network, the probability of the pleural effusion drops suddenly.
- The algorithm for generating heatmap for input image X with pixel intensities x goes as follows – – Here’s a demo of how integrated gradients model works for the demo X-ray.
- The method perturbs the sensitivity heatmap and monitors the probability drop to refine the heatmap to minimum pixels that can preserve the prediction score.
At Qure, we’re building deep learning systems which help diagnose abnormalities from medical images. Most of the deep learning models are classification models which predict a probability of abnormality from a scan. However, just the probability score of the abnormality doesn’t amount much to a radiologist if it’s not accompanied by a visual interpretation of the model’s decision.
Continue reading “Visualizing Deep Learning Networks”
- In this article I will introduce the concept of reinforcement learning but with limited technical details so that readers with a variety of backgrounds can understand the essence of the technique, its capabilities and limitations.
- Q learning is a widely used reinforcement learning algorithm.
- In this algorithm, the agent learns the quality(Q value) of each action (action is also called policy) based on how much reward the environment gave it.
- As the agent interacts with the environment, the Q values get updated from random values to values that actually help maximize reward.
- This list will be helpful for those who are looking to get started with Reinforcement Learning: – – Figure 3.0 Deep Q Learning in action.
We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.
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- The annual report for the company’s 2017 fiscal year, which ended on June 30, now includes six references to AI, up from zero in the previous annual report.
- And the company has plunked AI into its corporate vision statement, too.
- “Our strategic vision is to compete and grow by building best-in-class platforms and productivity services for an intelligent cloud and an intelligent edge infused with AI,” the company wrote in the annual report, which was released on Wednesday.
- For the sake of comparison, here’s last year’s version: “Our strategic vision is to compete and grow as a productivity and platform company for the mobile-first and cloud-first world.”
- For Microsoft watchers, that old tagline should sound very familiar — pretty much ever since he became CEO of Microsoft in 2014, Satya Nadella has repeatedly spoken of the “mobile-first and cloud-first world” that the company was trying to target.
Artificial intelligence has replaced mobile in the company’s overall mission in its 2017 annual report.
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