Recently,self-supervised learning methods like MoCo [22], SimCLR [8], BYOL [20] and SwAV [7] have reduced the gap with supervised methods.These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our final SElf-supERvised (SEER) model,a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy,surpassing the best self-supervised pretrained model by 1%and confirming that self-supervised learning works in areal world setting. Interestingly, we also observe that self-supervised models are good few-shot learners achieving77.9% top-1 with access to only 10% of ImageNet. Read More
Tag Archives: Big7
AI Moving to the Edge
As edge computing demands increase, major cloud providers are announcing solutions to fill that need: Google with Coral, Amazon with Panorama, and now Microsoft with Percept. As Microsoft’s John Roach said, there “millions of scenarios becoming possible thanks to a combination of artificial intelligence and computing on the edge. Standalone edge devices can take advantage of AI tools for things like translating text or recognizing images without having to constantly access cloud computing capabilities.” Read More
#iot, #big7Google’s Model Search automatically optimizes and identifies AI models
Google today announced the release of Model Search, an open source platform designed to help researchers develop machine learning models efficiently and automatically. Instead of focusing on a specific domain, Google says that Model Search is domain-agnostic, making it capable of finding a model architecture that fits a dataset and problem while minimizing coding time and compute resources. Read More
Hackers are finding ways to hide inside Apple’s walled garden
The iPhone’s locked-down approach to security is spreading, but advanced hackers have found that higher barriers are great for avoiding capture.
You’ve heard of Apple’s famous walled garden, the tightly controlled tech ecosystem that gives the company unique control of features and security. All apps go through a strict Apple approval process, they are confined so sensitive information isn’t gathered on the phone, and developers are locked out of places they’d be able to get into in other systems. The barriers are so high now that it’s probably more accurate to think of it as a castle wall.
Virtually every expert agrees that the locked-down nature of iOS has solved some fundamental security problems, and that with these restrictions in place, the iPhone succeeds spectacularly in keeping almost all the usual bad guys out. But when the most advanced hackers do succeed in breaking in, something strange happens: Apple’s extraordinary defenses end up protecting the attackers themselves. Read More
Is Google’s AI research about to implode?
What does Timnit Gebru’s firing and the recent papers coming out of Google tell us about the state of research at the world’s biggest AI research department.
The high point for Google’s research in to Artifical Intelligence may well turn out to be the 19th of October 2017. This was the date that David Silver and his co-workers at DeepMind published a report, in the journal Nature, showing how their deep-learning algorithm AlphaGo Zero was a better Go player than not only the best human in the world, but all other Go-playing computers.
What was most remarkable about AlphaGo Zero was that it worked without human assistance. … But there was a problem. Maybe it wasn’t Silver and his colleagues’ problem, but it was a problem all the same. The DeepMind research program had shown what deep neural networks could do, but it had also revealed what they couldn’t do. Read More
Adversarial Threats to DeepFake Detection: A Practical Perspective
Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the credibility of social media platforms and other media sharing web sites. State-of-the art DeepFake detection techniques rely on neural network based classification models which are known to be vulnerable to adversarial examples. In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point. We perform adversarial attacks on DeepFake detectors in a black box setting where the adversary does not have complete knowledge of the classification models. We study the extent to which adversarial perturbations transfer across different models and propose techniques to improve the transferability of adversarial examples. We also create more accessible attacks using Universal Adversarial Perturbations which pose a very feasible attack scenario since they can be easily shared amongst attackers. We perform our evaluations on the winning entries of the DeepFake Detection Challenge (DFDC) and demonstrate that they can be easily bypassed in a practical attack scenario by designing transferable and accessible adversarial attacks. Read More
Employee Surveillance Is Rising to New Dystopian Heights
Amazon’s new driver surveillance cameras may put employee privacy in the backseat.
The way to becoming a trillion-dollar company isn’t paved with philanthropy — on the contrary, it lies in pushing legal limits to new dystopian heights.
Recently, Amazon revealed the use of constant monitoring cameras in company vehicles — to “improve driver behavior,” according to an informational video from the firm.
However, in light of how Amazon develops surveillance techniques to monitor warehouse workers — both on and off the clock — Amazon delivery drivers’ lives could become a lot harder. Read More
Artificial Intelligence in Research: Where do China and USA stand?
What’s the current situation of Artificial Intelligence in the Research and funding sector?
Today, we are on the cusp of witnessing a widespread implementation of artificial intelligence (AI) across several sectors. As artificial intelligence technologies are pushing the frontiers of usability and innovation, countries are racing to diffuse its applications on public, private and social front. While we inch close to achieving total disruption, artificial intelligence becomes a key driver of productivity and GDP growth for every nation. With the USA and China having occupied the leading ranks in AI research, nations like the UK, Singapore, Japan, Brazil, India and others are striving to etch themselves on global map.
… Nations like India, Japan are powerhouses of digital data. However, according to a new report from the Center for Data Innovation, USA still holds a substantial lead globally. With tech behemoths like Amazon, Google, Microsoft, Facebook and IBM investing heavily in artificial intelligence, the USA still managed to hold the axial position in AI research.
An article in Tech Wire Asia reveals that thanks to beaming investment in startups and research and development funding, the USA achieved an overall score of 44.6 points in a new study by the Information Technology and Innovation Foundation (ITIF). Read More
Tech giants open up about their algorithms
Google, Facebook, TikTok and others are starting to talk more about how their algorithms work in a bid to win trust.
Yes, but: It’s hard to know what isn’t being revealed.
- Google on Monday published a blog post that shows users how to access more information about their search results, the day ahead of its Q4 earnings report.
- Facebook similarly released a post last week about how its News Feed algorithm works the day before its Q4 earnings.
- TikTok last year, amid the threat of a ban from the Trump administration, walked Axios and other reporters through an extensive presentation of how its prized algorithm works.
The AI Squad
According to Mark Cuban, “The companies that have harnessed AI the best are the companies dominating. To paraphrase a great movie line, ‘They keep getting smarter while everyone else stays the same ‘ It’s the foundation of how I invest in stocks these days. ‘How good is the company at AI’ ” This “AI Squad” includes the US based members of the Big 7: Alphabet, Amazon, Facebook, and Microsoft, plus Apple. Read More
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