- London: Oxford scientists have developed a new artificial intelligence system that can create fake videos of a person by using their still image and an audio clip.
- As the audio clip plays, the system then manipulates the mouth of the person in the still image so that it looks as if they are speaking.
- Although the results are not absolutely perfect, researchers believe that the software could soon make realistically fake videos only a single click away.
- Joon Son Chung from the University of Oxford, UK, said “The application we’re thinking of is redubbing a video into another language.”
- In the future, the audio from news clips could be automatically translated into another language and the images updated to fit.
Oxford University Scientists have developed a new artificial intelligence system; the system uses a person’s image and audio clip to create fake videos of the person.
Continue reading “Artificial Intelligence can now use a person’s image and audio to create fake videos”
- Communication is naively defined as content and the mode of transmission — symbols manifested as images, language transmitted through speech and writing, digital files sent through the internet.
- Most machine learning in today’s products is related to understanding — your phone can translate your voice into text and you can search photos for certain objects or people because of machine understanding.
- Generative modeling is a machine learning technique that creates new data that mimics the data that the machine was trained on.
- In an earlier example, image search was used as an example of computers aiding in communication by helping you find an image that approximates what your mind’s eye sees.
- Instead of returning an image that already exists, the generative system creates an entirely new image based on the text.
Communication is an essential pillar of society. Humanity’s progression over the past millennium was largely driven by the development and evolution of commu…
Continue reading “Machine Learning and Misinformation”
- TheTake, a site which launched as a way for consumers to buy that thing they saw in that movie, is set to begin selling an automated version of its service directly to businesses.
- The New York-based company is pitching studios and entertainment sites on a machine learning system that can identify products and locations as a way to generate revenue from product placements and experiential travel based on set locations.
- The new product is based on a year’s worth of work that TheTake’s development did to train a proprietary machine learning algorithm to identify images using a different technique than the industry standard, according to TheTake’s chief executive Ty the team behind TheTake would manually enter all the datasets and use an off-the-shelf computer visualization tool to identify images that fit the pre-defined parameters set by the company’s staff.
- While TheTake will continue to operate its service for consumers, which Cooper said has roughly half-a-million monthly users, it’s focus will shift to the business-to-business version of the service.
- Cooper, a former MGM Studios sales employee who managed partnerships with various cable companies from the studio’s New York office, launched the company with the help of a whiz kid from Columbia, Jared Browarnik, and consumer products developer from IAC and Amazon, Vincent Crossley.
TheTake, a site which launched as a way for consumers to buy that thing they saw in that movie, is set to begin selling an automated version of its service..
Continue reading “Working with major studios, TheTake launches AI image recognition engine for businesses”
- Use convert_images.py to create resized and gray images for training.
- The training attempts to obtain the resized color image when given the resized gray image.
- -n –normalize [y/n normalize training images]
- /images/train/ -n n to start training on the small amount of sample images.
- You can use this with some sample training images provided in images/train .
Colorful-Image-Colorization – A deep learning approach to colorizing images
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- Run python utils.py viewer samples/luigi_raceway to view the samples
- Training can take a while (~1 hour) depending on how much data you are training with.
- The program will save the model to disk when it is done.
- Position the emulator window so that the image is captured by the program (top left corner)
- Make sure you have a joystick connected and that mupen64plus is using the sdl input plugin
TensorKart – self-driving MarioKart with TensorFlow
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- With the tags in place, accessibility software can describe the image.
- Show Facebook Computer Vision Tags is a Chrome extension that reveals all those tags.
- Facebook uses software to look at the images you upload, then adds tags based on what it sees.
- If you’re curious about what tags your photos get, you can figure it out using Chrome’s dev tools , or just install the Computer Vision Tags extension, which adds a little overlay for each image with a list of the tags Facebook’s added to those photos.
- Chrome: Tons of photography apps, like Google Photos and Apple Photos , try and automatically make sense of objects in your photos and add automated tags, and it turns out Facebook does that too, even though you’d never know it.
Chrome: Tons of photography apps, like Google Photos and Apple Photos, try and automatically make sense of objects in your photos and add automated tags, and it turns out Facebook does that too, even though you’d never know it. Show Facebook Computer Vision Tags is a Chrome extension that reveals all those tags.
Continue reading “Show Facebook Computer Vision for Chrome Reveals Facebook’s Hidden Photo Tags”
- The training data consists of 25,000 images of cats and dogs.
- It reads in the external nippy file that contains the trained network description, takes a random image from the testing directory, and classifies it.
- We want all the dog images to be under a “dog” directory and the cat images under the “cat” directory so that the all the indexed images under them have the correct “label”.
- How many times it thought a cat was really a cat and how many times it got it wrong.
- We need all the images to be the same size as well as in a directory structure that is split up into the training and test images.
There is an awesome new Clojure-first machine learning library called Cortex that was open sourced recently. I’ve been exploring it lately and …
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- Amazon Rekognition Is An Image Recognition Service By Amazon
- According to Amazon’s CTO Werner Vogels, “Amazon Rekognition democratizes the application of deep learning technique for detecting objects, scenes, concepts, and faces in your images, comparing faces between two images, and performing search functionality across millions of facial feature vectors that your business can store with Amazon Rekognition.”
- Now it seems that Amazon wants in as well as they have announced Amazon Rekognition .
- As to who might be taking advantage of Amazon’s Rekognition service remains to be seen, as well as the various applications that it might be used for.
- 2016-11-28 Amazon To Start Cracking Down Harder On Counterfeit Products
One of the basic features of artificial intelligence (AI) is the ability to recognize images and process them. Companies like Microsoft and Google…
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- Figure 4: Generating training data in parallel using Microsoft R Server.
- We present the final tagged test image in Figure 8 where cars and boats are labeled with red and green bounding boxes respectively; you can also download the image .
- Each worker node returns a labelled list of moving window tile coordinates, which is then used to label the final test image in MRS running on HDInsight Spark edge node.
- We compress 2.3 million training images from 8.9GB of raw PNG images to 5.1GB with im2rec binary in 10 minutes for optimal training performance.
- MXNet DNN model training using NVIDIA Tesla K80 GPU using Microsoft R Server (MRS).
This post is by Max Kaznady, Data Scientist, Miguel Fierro, Data Scientist, Richin Jain, Solution Architect, T. J. Hazen, Principal Data Scientist Manager, and Tao Wu, Principal Data Scientist Manager, all at Microsoft.
Continue reading “Applying Deep Learning at Cloud Scale, with Microsoft R Server & Azure Data Lake”
- Specialized tools for seeing through blur and pixelation have been popping up throughout this year, like the Max Planck Institute’s work on identifying people in blurred Facebook photos.
- Just take a bunch of training data, throw some neural networks on it, throw standard image recognition algorithms on it, and even with this approach
- The algorithm doesn’t produce a deblurred image-it simply identifies what it sees in the obscured photo, based on information it already knows.
- Training data could be as simple as images on Facebook or a staff directory on a website.
- Shmatikov acknowledges that the Max Planck Institute’s work is more nuanced, taking into account contextual clues about identity.
It’s becoming much easier to crack internet privacy measures, especially blurred or pixelated images. Those methods make it tough for people to see sensitive information such as obscured license plate numbers or censored faces, but researchers from University of Texas at Austin and Cornell University say that the practice is wildly insecure in the age of machine learning. Using simple deep learning…
Continue reading “None of your pixelated or blurred information will stay safe on the internet — Quartz”