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- Posted by Karen Hurst in categories: robotics/AI , space
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- After a year of breakthroughs, experts believe they are on the brink of revolutionising our daily lives through artificial intelligence – and Asia can play a leading role in this brave new world.
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The Machines are Coming: China’s role in the future of artificial intelligence
Continue reading “The Machines are Coming: China’s role in the future of artificial intelligence”
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Algorithms have become part of our daily lives and can be found in any aspect of business. What are the algorithms every data scientist should know?
Continue reading “12 Algorithms Every Data Scientist Should Know”
- In summary, the thesis of this post is that visual perception is NOT a solved problem and requires more work, likely a fundamental shift.
- An adversarial example constructed from the category “Egyptian cat” classified with 98% confidence.
- BTW: since I uploaded these cat images to ClarifAI, they may become part of their training set future results from them might be different than those presented .
- The “best cat” is quite confidently classified as a dalmatian (0.74) and only with 0.04 confidence as the “Egyptian cat”.
- It does classify my best cat as a cat (success!
In this post I will explore the capabilities of contemporary deep learning models on the vitally important task of detecting a cat. Not an ordinary cat though, but a sketch of an abstract cat. This task matters because success tells us something about whether a visual system has learned generalization and abstraction — at least on par with a 2-year old. This post is inspired by my ex co-worker Peter O’Connor who tried similar experiments on LeNet several years ago. In addition, this post is a continuation of this blog’s highly popular “Just how close are we to solving vision?” which to-date has amassed nearly 15,000 hits. Let’s begin by introducing my menagerie:
Continue reading “Can a deep net see a cat? – Piekniewski’s blog”
- Snap’s expected IPO coming sometime next year will likely keep a fire lit under Snap to further innovate.
- While GBB campaigns are still charged on a CPM basis, Snap says the campaigns end up being more effective.
- Snap has also been making a big push to improve measurement.
- Snapchat is adding ways to optimize campaign performance with the help of machine learning.
- According to CBInsights , Snap Inc. acquired four patents in 2016, and while it’s nowhere near the 16 it received in 2015 or the 18 it received in 2014, it’ll likely need to keep innovating next year.
Snapchat is adding ways to optimize campaign performance with the help of machine learning. Earlier this month, Snap Inc. began rolling out what it calls Goal-Based Bidding (GBB). The option, available to marketers buying ads through Snapchat’s API, uses machine learning to know which users are most likely to swipe a certain type of ad.
Continue reading “Snapchat Is Beginning to Use Machine Learning to Improve Ad Targeting”
- Researchers use an ingenious design to make a soft robot that moves on its own.
- But the octobot is purposefully minimalist, meant just to show that such a soft robot can be made at all.
- A researcher measures a silicone mixture that will form the body of the octobot.
- The octobot is made out of materials that most microfluidics labs have on hand.
- The platinum ink will help turn liquid hydrogen peroxide into gas to move the tentacles; another ink will pave the way for vessels throughout the bot that the gas will travel through.
Researchers use an ingenious design to make a soft robot that moves on its own.
Continue reading “This incredible soft robot needs no batteries or computer chips”