- Now, the company behind Pixelmator is introducing some of those extra features itself, unveiling a new version of its software today named Pixelmator Pro.
- Pixelmator Pro won’t do everything that Adobe’s full suite can, but it looks to be a big step up from the company’s original software.
- The other exciting change is a set of new machine learning-enhanced tools, integrated into Pixelmator Pro using Apple’s new Core ML API.
- These include a new Quick Selection tool that Pixelmator says snaps to boundaries more intelligently than ever before; a feature that automatically labels different layers based on their content; and a Repair tool that will quickly and seamlessly remove and replace parts of any photo.
- These flashy features aside, Pixelmator Pro also introduces some practical functions missing from the original software, including support for processing RAW images (a must for photographers looking to do professional-grade editing).
Pixelmator Pro will go on sale later this year, but the new set of features looks promising
Continue reading “Pixelmator unveils new Pro software with machine learning features”
- We think technology can help.Today, Google and Jigsaw are launching Perspective, an early-stage technology that uses machine learning to help identify toxic comments.
- Through an API, publishers — including members of the Digital News Initiative — and platforms can access this technology and use it for their sites.HOW IT WORKSPerspective reviews comments and scores them based on how similar they are to comments people said were “toxic” or likely to make someone leave a conversation.
- Each time Perspective finds new examples of potentially toxic comments, or is provided with corrections from users, it can get better at scoring future comments.Publishers can choose what they want to do with the information they get from Perspective.
- Publishers could even just allow readers to sort comments by toxicity themselves, making it easier to find great discussions hidden under toxic ones.We’ve been testing a version of this technology with The New York Times, where an entire team sifts through and moderates each comment before it’s posted — reviewing up to 11,000 comments every day.
- We’ve worked together to train models that allows Times moderators to sort through comments more quickly, and we’ll work with them to enable comments on more articles every day.WHERE WE GO FROM HEREPerspective joins the TensorFlow library and the Cloud Machine Learning Platform as one of many new machine learning resources Google has made available to developers.
Imagine trying to have a conversation with your friends about the news you read this morning, but every time you said something, someone shouted in your face, called you a nasty name or accused you…
Continue reading “WHEN COMPUTERS LEARN TO SWEAR: – Jigsaw – Medium”
- Unfortunately, this happens all too frequently online as people try to discuss ideas on their favorite news sites but instead get bombarded with toxic comments.
- According to the same report, online harassment has affected the lives of roughly 140 million people in the U.S., and many more elsewhere.
- News organizations want to encourage engagement and discussion around their content, but find that sorting through millions of comments to find those that are trolling or abusive takes a lot of money, labor, and time.
- As a result, many sites have shut down comments altogether.
- Through an API, publishers—including members of the Digital News Initiative—and platforms can access this technology and use it for their sites.
Google and Jigsaw announce the launch of Perspective, an early-stage technology that uses machine learning to identify toxic comments.
Continue reading “When computers learn to swear: Using machine learning for better online conversations”
- Machine Learning is one of the most exciting fields in the world.
- The week’s top Machine Learning stories, including machine learning for heart diagnoses, autonomous vehicles, and writing other AI agents!
- That’s why we created This Week in Machine Learning!
- Never miss a story from Udacity Inc , when you sign up for Medium.
- Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.
This week’s top Machine Learning stories, including machine learning for heart diagnoses, autonomous vehicles, and writing other AI agents! Machine Learning is one of the most exciting fields in the…
Continue reading “This Week in Machine Learning, 20 January 2017 – Udacity Inc – Medium”
- News New neural-network algorithm learns directly from human instructions instead of examples
- For example, you could train a neural network to identify sky in a photograph by showing it hundreds of pictures with the sky labeled.
- Abstract of Hair Segmentation Using Heuristically-Trained Neural Networks
- Humans conventionally “teach” neural networks by providing a set of labeled data and asking the neural network to make decisions based on the samples it’s seen.
- Applying the method to the binary classification of hair versus nonhair patches, we obtain a 2.2% performance increase using the heuristically trained NN over the current state-of-the-art hair segmentation method.
Conventional neural-network image-recognition algorithm trained to recognize human hair (left), compared to the more precise heuristically trained algorithm
Continue reading “New neural-network algorithm learns directly from human instructions instead of examples”
- We purposely use “pattern language” to reflect that the field of Deep Learning is a nascent, but rapidly evolving, field that is not as mature as other topics in computer science.
- Each pattern describes a problem and offers alternative solutions.
- You can find more details on this book at: A Pattern Language for Deep Learning .
- Pattern Languages are languages derived from entities called patterns that when combined form solutions to complex problems.
- Or you can check for updates at Design Patterns for Deep Learning
Deep Learning can be described as a new machine learning toolkit that has a high likelihood to lead to more advanced forms of artificial intelligence. The evidence for this is in the sheer number of breakthroughs that had occurred since the beginning of this decade. There is a new found optimism in the air and we are now again in a new AI spring. Unfortunately, the current state of deep learning appears to many ways to be akin to alchemy. Everybody seems to have their own black-magic methods of designing architectures. The field thus needs to move forward and strive towards chemistry, or perhaps even a periodic table for deep learning. Although deep learning is still in its early infancy of development, this book strives towards some kind of unification of the ideas in deep learning. It leverages a method of description called pattern languages.
Continue reading “Design Patterns for Deep Learning Architectures”
- Google is building a new artificial intelligence lab in Montreal dedicated to deep learning, a technology that’s rapidly reinventing not only Google but the rest of the internet’s biggest players.
- Google Opens Montreal AI Lab to Snag Scarce Global Talent
- Last year, Facebook opened an AI lab in Paris, another deep learning hotbed , after building its first lab around New York Univeristy professor Yann LeCun in Manhattan.
- The team will operate as an extension of Google Brain, the central operation that works to spread AI across the entire company.
- Google, Facebook, and Microsoft Are Remaking Themselves Around AI
Google is trying to get its hooks into the world’s top deep learning talent before its competitors do.
Continue reading “Google Opens Montreal AI Lab in Global Race for Scarce Talent”
- What is even more surprising is that the algorithm also outperformed its own training by nine per cent.
- The algorithm does not learn from an existing set of examples, but rather takes its data directly from human instructions.
- The left and centre columns show an aggressive and conservative image-recognition algorithm trained to recognized human hair, compared to the more precise heuristically trained algorithm at right.
- Humans normally teach computer networks that learn dynamically (neural networks) by providing a set of labeled data.
- You could for example show a neural network hundreds of pictures with the sky labeled, to train it to identify sky in a photograph.
U of T Engineering researchers Wenzhi Guo (ECE MASc 1T5) and Parham Aarabi (ECE) have designed a new machine learning algorithm that may soon enable your smartphone to give you an honest answer based on logic.
Continue reading “AI Algorithm Surpasses What it Was Taught by Humans”
- But while computers have become faster, the way chess engines work has not changed.
- Lai says Giraffe takes about 10 times longer than a conventional chess engine to search the same number of positions.
- Deep Learning Machine Teaches Itself Chess in 72 Hours, Plays at International Master Level
- Lai says that matches the best chess engines in the world.
- Lai has created an artificial intelligence machine called Giraffe that has taught itself to play chess by evaluating positions much more like humans and in an entirely different way to conventional chess engines.
In a world first, a machine plays chess by evaluating the board rather than using brute force to work out every possible move.
Continue reading “Deep Learning Machine Teaches Itself Chess in 72 Hours, Plays at International Master Level”
- Every job executed in the Neptune machine learning platform is registered and available for browsing.
- Every job executed in the machine learning platform can be monitored in the Neptune Web UI.
- To run the job we need to use the Neptune CLI command: neptune run main .
- A job is an experiment registered in Neptune.
- Thanks to a complete history of job executions, data scientists can compare their jobs with jobs executed by their teammates.
Today we’ll look at how the Neptune machine learning platform won a Kaggle machine learning computation at analyzed photos of whales using Python and Neptune.
Continue reading “Neptune”