Solving the transcription start site identification problem with ADAPT-CAGE

Cap Analysis of Gene Expression (CAGE) has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicing byproducts, alternative isoforms and capped molecules overlapping introns and exons. We present ADAPT-CAGE, a Machine Learning framework which is trained to distinguish between CAGE signal derived from TSSs and transcriptional noise. ADAPT-CAGE provides highly accurate experimentally derived TSSs on a genome-wide scale.

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Far-field Speech Recognition Market Size by Global Industry Analysis 2020

Far-field speech recognition is an essential technology for speech interactions, and aims to enable smart devices to recognize distant human speech (usually 1m-10m). This technology is applied to many scenarios such as smart home appliances (smart loudspeaker, smart TV), meeting transcription, and onboard navigation. Microphone array is often used to collect speech signals for far-field speech recognition. However, in a real environment, there is a lot of background noise, multipath reflection, reverberation, and even human voice interference, leading to decreased quality of pickup signal. Generally, the accuracy of far-field speech recognition is significantly less than near-field speech recognition.

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What are Important AI & Machine Learning Trends for 2020?

Companies ranging from high tech startups to global multinationals see artificial intelligence as a key competitive advantage in an increasingly competitive and technical market.

But, the AI industry moves so quickly that it’s often hard to follow the latest research breakthroughs and achievements, and even harder to apply scientific results to achieve business outcomes. To help you develop a robust AI strategy for your business in 2020, I’ve summarized the latest trends across different research areas, including natural language processing, conversational AI, computer vision, and reinforcement learning. I’ve also included external education you can follow to further your expertise.

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How machine learning is helping Credit Karma reintroduce itself to users

Personal finance company Credit Karma has always relied on the use of consumer credit data to power its services and fuel its business model.

But it's only recently that the company is turning to machine learning to make sense of hundreds of billions of data points and deliver personalized insights and recommendations to individual members at scale.When it launched in 2008 at the height of the financial crisis, Credit Karma's primary service leveraged credit report data to help consumers understand, track, and improve their credit scores. The company managed to gain traction as a provider of simulated credit information despite a wave of consumer skepticism stemming from credit monitoring scams, amassing over one million US users in less than two years.  

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Why Apple And Microsoft Are Moving AI To The Edge

Artificial intelligence (AI) has traditionally been deployed in the cloud, because AI algorithms crunch massive amounts of data and consume massive computing resources. But AI doesn’t only live in the cloud. In many situations, AI-based data crunching and decisions need to be made locally, on devices that are close to the edge of the network.

AI at the edge allows mission-critical and time-sensitive decisions to be made faster, more reliably and with greater security. The rush to push AI to the edge is being fueled by the rapid growth of smart devices at the edge of the network—smartphones, smart watches and sensors placed on machines and infrastructure. Earlier this month, Apple spent $200 million to acquire, a Seattle-based AI startup focused on low-power machine learning software and hardware. Microsoft offers a comprehensive toolkit called Azure IoT Edge that allows AI workloads to be moved to the edge of the network.

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Facebook speeds up AI training by culling the weak

Training an artificial intelligence agent to do something like navigate a complex 3D world is computationally expensive and time-consuming. In order to better create these potentially useful systems, Facebook  engineers derived huge efficiency benefits from, essentially, leaving the slowest of the pack behind.

It’s part of the company’s new focus on “embodied AI,” meaning machine learning systems that interact intelligently with their surroundings. That could mean lots of things — responding to a voice command using conversational context, for instance, but also more subtle things like a robot knowing it has entered the wrong room of a house. Exactly why Facebook is so interested in that I’ll leave to your own speculation, but the fact is they’ve recruited and funded serious researchers to look into this and related domains of AI work.

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