Machine Learning


AI Accelerators and open software

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Three years ago, we had maybe six or less AI accelerators, today there's over two dozen, and more are coming. One of the first commercially available AI training accelerators was the GPU, and the undisputed leader of that segment was Nvidia. Nvidia was already preeminent in machine learning (ML) and deep-learning (DL) applications and adding neural net acceleration was a logical and rather straight-forward step for the company. Nvidia also brought a treasure-trove of applications with their GPUs based on the company's proprietary development language CUDA. The company developed CUDA in 2006 and empowered hundreds of Universities to give courses on it.


AI Accelerators and open software

#artificialintelligence

Three years ago, we had maybe six or less AI accelerators, today there's over two dozen, and more are coming. One of the first commercially available AI training accelerators was the GPU, and the undisputed leader of that segment was Nvidia. Nvidia was already preeminent in machine learning (ML) and deep-learning (DL) applications and adding neural net acceleration was a logical and rather straight-forward step for the company. Nvidia also brought a treasure-trove of applications with their GPUs based on the company's proprietary development language CUDA. The company developed CUDA in 2006 and empowered hundreds of Universities to give courses on it.


AI Learns to Cheat at Hide and Seek #OpenAI #HideandSeek #MachineLearning #ArtificialIntelligence #ReinforcementLearning @OpenAI

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OpenAI recently posted on Twitter about teaching computer agents'hide and seek'. We've observed AIs discovering complex tool use while competing in a simple game of hide-and-seek. They develop a series of six distinct strategies and counter strategies, ultimately using tools in the environment to break our simulated physics. In the simulations, seekers are incentivized to maintain line of sight of hiders and hiders are incentivized to avoid line of sight from seekers. The agents environments contain various shelters including cubicles, movable partitions, blocks and ramps.


Fermat's Library Some Studies In Machine Learning Using the Game of Checkers annotated/explained version.

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This is his seminal paper originally published in 1959 where Samuel sets out to build a program that can learn to play the game of checkers. Checkers is an extremely complex game - as a matter of fact the game has roughly 500 billion billion possible positions - that using a brute force only approach to solve it is not satisfactory. Samuel's program was based on Claude Shannon's minimax strategy to find the best move from a given current position. In this paper he describes how a machine could look ahead "by evaluating the resulting board positions much as a human player might do".


Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers. - PubMed - NCBI

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Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology.


CyberSecurity: Machine Learning Artificial Intelligence Actionable Intelligence

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Overview The goal of artificial intelligence is to enable the development of computers to do things normally done by people -- in particular, things associated with people acting intelligently. In the case of cybersecurity, its most practical application has been automating human intensive tasks to keep pace with attackers! Progressive organizations have begun using artificial intelligence in cybersecurity applications to defend against attackers. However, on it's own, artificial intelligence is best designed to identify "what is wrong." What today's enterprise needs to know is not only "what is wrong" in the face of a breach, but to understand "why it's wrong" and "how to fix it!"


BeagleBoard.org Launches BeagleBone AI, Offering a Fast Track to Getting Started with Artificial Intelligence at the Edge

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Foundation today announces general availability of the newest, fastest, most powerful BeagleBoard.org Built on our proven open source Linux approach, BeagleBone AI fills the gap between small single board computers (SBCs) and more powerful industrial computers. Leveraging the Texas Instruments Sitara AM5729 processor, developers have access to powerful machine learning capabilities with the ease of the BeagleBone Black header and mechanical compatibility. BeagleBone AI makes it easy to explore how artificial intelligence (AI) and machine learning can be used in everyday life. Through BeagleBone AI, developers can take advantage of the TI C66x digital-signal-processor (DSP) cores and embedded-vision-engine (EVE) cores on the Sitara AM5729 processor.


Mac Malware that Spoofs Trading App Steals User Information, Uploads it to Website

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Unlike in the pre-internet era, when trading in the stock or commodities market involved a phone call to a broker -- a move which often meant additional fees for would-be traders -- the rise of trading apps placed the ability to trade in the hands of ordinary users. However, their popularity has led to their abuse by cybercriminals who create fake trading apps as lures for unsuspecting victims to steal their personal data. We recently found and analyzed an example of such an app, which had a malicious malware variant that disguised itself as a legitimate Mac-based trading app called Stockfolio. We found two variants of the malware family. The first one contains a pair of shell scripts and connects to a remote site to decrypt its encrypted codes while the second sample, despite using a simpler routine involving a single shell script, actually incorporates a persistence mechanism.


Vianai emerges with $50M seed and a mission to simplify machine learning tech – TechCrunch

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You don't see a startup get a $50 million seed round all that often, but such was the case with Vianai, an early-stage startup launched by Vishal Sikka, former Infosys managing director and SAP executive. The company launched recently with a big check and a vision to transform machine learning. Just this week, the startup had a coming out party at Oracle Open World, where Sikka delivered one of the keynotes and demoed the product for attendees. Over the last couple of years, since he left Infosys, Sikka has been thinking about the impact of AI and machine learning on society and the way it is being delivered today. He didn't much like what he saw.


How to Deploy Machine Learning Models on Mobile and Embedded Devices

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Thanks to libraries such as Pandas, scikit-learn, and Matplotlib, it is relatively easy to start exploring datasets and make some first predictions using simple Machine Learning (ML) algorithms in Python. Although, to make these trained models useful in the real world, it is necessary to make them available to make predictions on either the Web or Portable devices. In two of my previous articles, I explained how to create and deploy a simple Machine Learning model using Heroku/Flask and Tensorflow.js. Today, I will instead explain to you how to deploy Machine Learning models on Smartphones and Embedded Devices using TensorFlow Lite. TensorFlow Lite is a platform developed by Google to train Machine Learning models on mobile, IoT (Interned of Things) and embedded devices.