Careers at A9

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To see what kind of talent we are currently looking for and submit your resume, please visit: https://a9.com/careers/ We are always looking for talented people with backgrounds in: · Computer Vision · Machine Learning · Natural Language Processing · Backend Infrastructure / Systems Software Development · Analytics Data Mining · Pattern Recognition · Artificial Intelligence · Optical Character Recognition · Server Infrastructure · Augmented Reality · DevOps / Operations Engineer · Software Developer in Test A9 solves some of the biggest challenges in search and advertising. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. A9 advertising drives the publisher products for Amazon's ad programs. To see all of our current openings, please visit: https://a9.com/careers/ To see all of our current openings, please visit: https://a9.com/careers/


By merging AI with human intelligence, you can provide the superhuman interactions consumers have come to expect.

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Certainly, none of this is a secret: Whether we're talking AI or apps, technology has fundamentally altered the way we interact with companies. For those of us who work to create digital experiences, the challenge then is to strike a meaningful balance between artificial and human intelligence. AI is fantastic at sparking a conversation, but at points of complexity, make it easy for customers to pivot to human interactions. Leverage human intelligence and instantaneous connectivity wherever ambiguity exists.


Qualcomm selected by DARPA's HIVE Project to accelerate the future of deep learning

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References to "Qualcomm"; may mean Qualcomm Incorporated, or subsidiaries or business units within the Qualcomm corporate structure, as applicable. Qualcomm Incorporated includes Qualcomm's licensing business, QTL, and the vast majority of its patent portfolio. Qualcomm Technologies, Inc., a wholly-owned subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of Qualcomm's engineering, research and development functions, and substantially all of its products and services businesses. Qualcomm products referenced on this page are products of Qualcomm Technologies, Inc. and/or its subsidiaries.


What is Narrow, General and Super Artificial Intelligence

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You might have also heard about narrow, general and super artificial intelligence, or about machine learning, deep learning, reinforced learning, supervised and unsupervised learning, neural networks, Bayesian networks and a whole lot of other confusing terms. But then it gives a more understandable definition of machines that mimics cognitive functions such as problem solving and learning. General AI, also known as human-level AI or strong AI, is the type of Artificial Intelligence that can understand and reason its environment as a human would. According to University of Oxford scholar and AI expert Nick Bostrom, when AI becomes much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills, we've achieved Artificial Super Intelligence.


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With the help of the Kaggle data science community, the Department of Homeland Security (DHS) is hosting an online competition to build machine learning-powered tools that can augment agents, ideally making the entire system simultaneously more accurate and efficient. Kaggle, acquired by Google earlier this year, regularly hosts online competitions where data scientists compete for money by developing novel approaches to complex machine learning problems. The TSA is making its data set of images available to competitors so they can train on images of people carrying weapons. Thankfully, Google, Facebook and others are heavily investing in lighter versions of machine learning frameworks, optimized to run locally, at the edge (without internet).


Facebook Uses Artificial Intelligence to Fight Terrorism

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"We want to find terrorist content immediately, before people in our community have seen it," read the message posted Thursday. AI, Facebook says, is also useful for identifying and removing "terrorist clusters." So when we identify pages, groups, posts or profiles as supporting terrorism, we also use algorithms to "fan out" to try to identify related material that may also support terrorism." Facebook said AI has helped identify and remove fake accounts made by "repeat offenders."


Deep Learning Research Review Week 1: Generative Adversarial Nets

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This week, I'll be doing a new series called Deep Learning Research Review. The way the authors combat this is by using multiple CNN models to sequentially generate images in increasing scales. The approach the authors take is training a GAN that is conditioned on text features created by a recurrent text encoder (won't go too much into this, but here's the paper for those interested). In order to create these versatile models, the authors train with three types of data: {real image, right text}, {fake image, right text}, and {real image, wrong text}.


YOLO: Core ML versus MPSNNGraph

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The Core ML conversion tools do not support Darknet, so we'll first convert the Darknet files to Keras format. However, as I'm writing this the Core ML conversion tools only support Keras version 1.2.2. Now that we have YOLO in a format that the Core ML conversion tools support, we can write a Python script to turn it into the .mlmodel Note: You do not need to perform these steps if you just want to run the demo app. This means we need to put our input images into a CVPixelBuffer object somehow, and also resize this pixel buffer to 416 416 pixels -- or Core ML won't accept it.


A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R

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From each of the unlabeled points (Markov states) a random walk with Markov transition matrix (computed from the row-stochastic kernelized distance matrix) will be started that will end in one labeled state, which will be an absorbing state in the Markov Chain. As can be seen, with increasing iterations, the probability that the state ends in that particular red absorbing state with state index 323 increases, the length of a bar in the second barplot represents the probability after an iteration and the color represents two absorbing and the other unlabeled states, where the w vector shown contains 1000 states, since the number of datapoints 1000. Each time a new unlabeled (black) point is selected, a random walk is started with the underlying Markov transition matrix and the power-iteration is continued until it terminates to one of the absorbing states with high probability. Since only two absorbing states are there, finally the point will be labeled with the label (red or blue) of the absorbing state where the random walk is likely to terminate with higher probability.


The world's first protein database for Machine Learning and AI

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I am incredibly proud and excited to present the very first public product of Peptone, the Database of Structural Propensities of Proteins. Database of Structural Propensities of Proteins (dSPP) is the world's first interactive repository of structural and dynamic features of proteins with seamless integration for leading Machine Learning frameworks, Keras and Tensorflow. As opposed to binary (logits) secondary structure assignments available in other protein datasets for experimentalists and the machine learning community, dSPP data report on protein structure and local dynamics at the residue level with atomic resolution, as gauged from continuous structural propensity assignment in a range -1.0 to 1.0. Seamless dSPP integration with Keras and Tensorflow machine learning frameworks is achieved via dspp-keras Python package, available for download and setup in under 60 seconds time.