Researchers at Recorded Future have uncovered what appears to be a new, growing social media-based influence operation involving more than 215 social media accounts. While relatively small in comparison to influence and disinformation operations run by the Russia-affiliated Internet Research Agency (IRA), the campaign is notable because of its systematic method of recycling images and reports from past terrorist attacks and other events and presenting them as breaking news—an approach that prompted researchers to call the campaign “Fishwrap.”
The campaign was identified by researchers applying Recorded Future’s “Snowball” algorithm, a machine-learning-based analytics system that groups social media accounts as related. Read More
Daily Archives: June 12, 2019
AI Software Reveals the Inner Workings of Short-Term Memory
Research by neuroscientists at the University of Chicago shows how short-term, working memory uses networks of neurons differently depending on the complexity of the task at hand.
The researchers used modern artificial intelligence (AI) techniques to train computational neural networks to solve a range of complex behavioral tasks that required storing information in short term memory. The AI networks were based on the biological structure of the brain and revealed two distinct processes involved in short-term memory. One, a “silent” process where the brain stores short-term memories without ongoing neural activity, and a second, more active process where circuits of neurons fire continuously. Read More
Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding
In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales. We propose a locale-agnostic universal domain classification model based on selective multi-task learning that learns a joint representation of an utterance over locales with different sets of domains and allows locales to share knowledge selectively depending on the domains. The experimental results demonstrate the effectiveness of our approach on domain classification task in the scenario of multiple locales with imbalanced data and disparate domain sets. The proposed approach outperforms other baselines models especially when classifying locale-specific domains and also low-resourced domains. Read More
The 3 critical AI research questions
AI is dramatically enhancing industries, products, and core capabilities. But to make AI truly ubiquitous, it needs to run on end devices within a tight power and thermal budget. To learn more about the research that is advancing AI adoption, don’t miss this VB Live event featuring Qualcomm’s Senior Director of Engineering, Jilei Hou, and analystJack Gold.
“We’re not anywhere near a steady state with AI,” says Jack Gold, tech analyst and founder and president of J. Gold Associates. “AI is starting to take off, but we’re nowhere near the top of the hockey stick.” Read More
5 Key Learnings To Set-up A High Impact AI Strategy
How to get started and develop an AI Strategy in your organization that actually works.
In our 1 hour webinar with Master Inventor and Fortune Global 500 advisor Neil Sahota who has helped more than 1000 organizations to build AI solutionsand AI Expert Rudradeb Mitra, we talked how to get started with AI, what characterizes a high-performing team and how to build ethical and trustworthy A.I. Solutions. How to get started and develop an AI Strategy in your organization that actually works. Read More
Detecting Kissing Scenes in a Database of Hollywood Films
Detecting scene types in a movie can be very useful for application such as video editing, ratings assignment, and personalization. We propose a system for detecting kissing scenes in a movie. This system consists of two components. The first component is a binary classifier that predicts a binary label (i.e. kissing or not) given a features exctracted from both the still frames and audio waves of a one-second segment. The second component aggregates the binary labels for contiguous non-overlapping segments into a set of kissing scenes. We experimented with a variety of 2D and 3D convolutional architectures such as ResNet, DesnseNet, and VGGish and developed a highly accurate kissing detector that achieves a validation F1 score of 0.95 on a diverse database of Hollywood films ranging many genres and spanning multiple decades. The code for this project is available at http://github.com/amirziai/kissing-detector. Read More
Better Future through AI: Avoiding Pitfalls and Guiding AI Towards Its Full Potential
Articial Intelligence (AI) technology is rapidly changing many areas of society. While there is tremendous potential in this transition, there are several pitfalls as well. Using the history of computing and the world-wide web as a guide, in this article we identify those pitfalls and actions that lead AI development to its full potential. If done right, AI will be instrumental in achieving the goals we set for economy, society, and the world in general. Read More
If DARPA Has Its Way, AI Will Rule the Wireless Spectrum
In the early 2000s, Bluetooth almost met an untimely end. The first Bluetooth devices struggled to avoid interfering with Wi-Fi routers, a higher-powered, more-established cohort on the radio spectrum, with which Bluetooth devices shared frequencies. Bluetooth engineers eventually modified their standard—and saved their wireless tech from early extinction—by developing frequency-hopping techniques for Bluetooth devices, which shifted operation to unoccupied bands upon detecting Wi-Fi signals.
Frequency hopping is just one way to avoid interference, a problem that has plagued radio since its beginning. Long ago, regulators learned to manage spectrum so that in the emerging wireless ecosystem, different radio users were allocated different frequencies for their exclusive use. While this practice avoids the challenges of detecting transmissions and shifting frequencies on the fly, it makes very inefficient use of spectrum, as portions lay fallow. Read More
Lecture Notes by Andrew Ng : Full Set
The following notes represent a complete, stand alone interpretation of Stanford’s machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programming. Read More
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