America’s intelligence collectors are already using AI in ways big and small, to scan the news for dangerous developments, send alerts to ships about rapidly changing conditions, and speed up the NSA’s regulatory compliance efforts. But before the IC can use AI to its full potential, it must be hardened against attack. The humans who use it — analysts, policy-makers and leaders — must better understand how advanced AI systems reach their conclusions.
Dean Souleles is working to put AI into practice at different points across the U.S. intelligence community, in line with the ODNI’s year-old strategy. The chief technology advisor to the principal deputy to the Director of National Intelligence wasn’t allowed to discuss everything that he’s doing, but he could talk about a few examples. Read More
Monthly Archives: January 2020
Operationalizing AI
When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don’t call it deployment. Instead the term that’s used is “operationalizing”. This might be confusing for traditional IT operations managers and applications developers. Why don’t we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment? Read More
AI on the Edge — An AI Nerd Series |#01-Pilot
In the beginning, there was one big computer. Soon we started connecting to it using terminals ( the UNIX era). Next, we had personal computers, and this was the first time regular people owned hardware that did the work. Today we’re firmly in the Cloud Computing era, which means today’s world is centralized with a central cloud doing all the required processing. The truly amazing things about the cloud are a large percentage of all companies in the world now rely on the infrastructure, hosting, machine learning, and compute power of a very select few cloud providers: Amazon, Microsoft, Google, and IBM. But things got little stirred up when Peter Levine of Andressen Horowitz presented his interesting working theory at a16z; His presentation was titled “The End of Cloud Computing”(!!!) Read More
Amazon explores a way to preserve privacy in natural language processing
Can privacy and security be preserved in the course of large-scale textual data analysis? As it turns out, yes. A team of Amazon researchers in a recently published study proposed a way to anonymize customer-supplied data. They claim that their approach, which works by rephrasing samples and basing the analysis on the new phrasing, results in at least 20-fold greater guarantees on expected privacy. Read More
Funds Using AI and Machine Learning Had Worse than Random Performance in 2019
AI and machine learning funds rose 6.4% in 2019, according to popular gauge. As shown in the article, these funds performed worse than random traders. We list a few possible reasons for these disappointing results.
According to Eurekahedge, AI and machine learning funds rose 6.4% in 2019 while the S&P 500 index rose 28.9% and 31.5% on a total return basis. Read More
Quantum experiments explore power of light for communications, computing
A team from the Department of Energy’s Oak Ridge National Laboratory has conducted a series of experiments to gain a better understanding of quantum mechanics and pursue advances in quantum networking and quantum computing, which could lead to practical applications in cybersecurity and other areas. Read More
Whoever leads in artificial intelligence in 2030 will rule the world until 2100
A couple of years ago, Vladimir Putin warned Russians that the country that led in technologies using artificial intelligence will dominate the globe. He was right to be worried. Russia is now a minor player, and the race seems now to be mainly between the United States and China. But don’t count out the European Union just yet; the EU is still a fifth of the world economy, and it has underappreciated strengths. Technological leadership will require big digital investments, rapid business process innovation, and efficient tax and transfer systems. China appears to have the edge in the first, the U.S. in the second, and Western Europe in the third. One out of three won’t do, and even two out three will not be enough; whoever does all three best will dominate the rest. Read More
Putting An End to End-to-End:Gradient-Isolated Learning of Representations
We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal, we split a deep neural network into a stack of gradient-isolated modules. Each module is trained to maximally preserve the information of its inputs using the InfoNCE bound from Oord et al. [2018]. Despite this greedy training, we demonstrate that each module improves upon the output of its predecessor, and that the representations created by the top module yield highly competitive results on downstream classification tasks in the audio and visual domain. The proposal enables optimizing modules asynchronously, allowing large-scale distributed training of very deep neural networks on unlabelled datasets. Read More
Researchers use AI to deblur human faces in photos
We’ve all been there: You’re snapping pics with your phone — perhaps of a high-speed bike ride or of a hockey match — and don’t think to check whether the autofocus is in lockstep with the action. It isn’t, as you later discover, and you’re stuck with a gallery of unusably blurry photos.
In search of a solution, scientists at the Inception Institute of Artificial Intelligence in the United Arab Emirates, the Beijing Institute of Technology, and Stony Brook University developed an AI system that removes blur from images in post-production. Read More
The Secretive Company That Might End Privacy as We Know It
The New York Times has a long story about a little-known start-up, Clearview AI, that helps law enforcement match photos of unknown people to their online images — and “might lead to a dystopian future or something,” a backer says. Read More
#image-recognition, #privacy
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