Articles

2 Things You Need to Know About Reinforcement Learning: Computational and Sample Efficiency

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The High Cost of Deep Learning

Have you ever put on a sweater because the air conditioning was too cold? Forgotten to turn off the lights in another room before heading to bed? Do you commute to work more than 30 minutes every day just for the sake of “filling seats” at the office, even though everything you do at work could be done via laptop from home? 

In the counter-intuitive trade-offs between sample and computational efficiency in Reinforcement Learning, choosing evolution strategies can be smarter than it looks.

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Google’s New ML-Powered Voice Recording App

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Google is heavily investing its resources in AI and machine learning research intending to shell out products and services for the future. So whether it has to do with computational photography or email suggestion features, Google has always been active on this front. Recently, Google also launched the famed “Google Recorder”. You might wonder that there are several voice recorder apps in the market so why this? But we all know it, if it is from Google it has to be a contender for the top slot! 

Before we explore further, let us see whether Google reads the race or not! And, yes we see right there that Google has done a great job when it comes to AI-based research and launches. 

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Accelerating ML Inference on Raspberry Pi With PyArmNN

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Overview 

A neural network, trained to recognize images that include a fire or flames, can make fire-detection systems more reliable and cost-effective. This tutorial shows how to use the newly released Python APIs for Arm NN inference engine to classify images as “Fire” versus “Non-Fire.”

What Is Arm NN and PyArmNN? 

Arm NN is an inference engine for CPUs, GPUs, and NPUs. It executes ML models on-device in order to make predictions based on input data. Arm NN enables efficient translation of existing neural network frameworks, such as TensorFlow Lite, TensorFlow, ONNX, and Caffe, allowing them to run efficiently and without modification across Arm Cortex-A CPUs, Arm Mali GPUs, and Arm Ethos NPUs.

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Building a Recommendation System Using Deep Learning Models

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In this article, I am going to explain how we integrate some deep learning models, in order to make an outfit recommendation system. We want to build an outfit recommendation system. We used four deep learning models to get some important characteristics of the clothing used by the user.

The recommendation systems can be classified into 4 groups:

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10 Steps to Execute an AI Transformation Project

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AI Transformation in Medical Diagnoses

Within every aspect of healthcare, time is considered the most valuable component. Even minutes of delay can result in the loss of life. Early diagnosis lies at the heart of healing patients, and timely execution of treatment is of primary importance. At an average, doctors spend 15 minutes with each patient, which when considered intently, is grossly insufficient in providing a comprehensive diagnosis of the illness. In an ideal situation, a diagnosis should be made after careful consideration of all relevant patient information, including similar cases and demographics.

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As the healthcare industry gradually moves toward an AI-driven world, things that were previously considered a hindrance or unlikely are now fairly simple tasks. Over the years, more than 90% of hospitals in the county have moved from paper-based systems to electronic processes. When it comes to medical diagnoses, patients’ records are of primary importance. Risks towards critical illnesses can be caught through predictive analysis, thereby saving lives and costs. Early diagnosis is no longer a distant hope, but an actuality that can be easily accomplished through advanced systems.

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Talk to Your Database

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Talk to Your Database
DISCLAIMER: This post in based on personal experiences and the situations explained here may not apply in other context.

Figures displayed on the examples are just samples for demo purposes, not actual data.

In every company in the world, employees need access to information. Most companies purchase and install expensive software solutions or even spend years developing complex reporting systems on-site.

However, they all fall short satisfying user needs. They are either too complex, and non-technical people can’t understand how to use those tools, or they are too user-friendly and they lack the flexibility these users need.

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Creating a Conversational Order Process in Teneo

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Creating a conversational order process

Think about how much faster you could sell or help customers by having a chatbot or virtual assistant handling orders. An order process has certain information that needs to be filled out in order to complete the process. When you are building a bot, this is called slot filling. This guide is a walkthrough on how you create a slot filling flow.

You might also like:  Slot-Filling Chatbots Will Never Meet Human Expectations

Slot filling is about collecting certain bits of information from the user before a final response can be given. A typical use-case is to make an order of some kind where certain parameters need to be settled before the order can be placed, for example booking a flight or ordering a pair of shoes.


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Is It Worth It? ROI of Recommender Systems

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Netflix recommender system

Recommender systems promise to reduce churn and increase sales. But how do you measure their actual success? What is it that you should measure? And what challenges should you look out for when you’re building your recommendation engine? In this article, I’ll discuss some challenges of recommendation engines, the ROI, and standard metrics to help evaluate their performance.

You might also like:  Building a Recommendation System Using Deep Learning Models

Challenges of Recommender Systems

Most articles about recommendation engines focus on all the bright sides of recommendations: personalized customer experience, lower churn, increase in sales, and more revenue. While all of that is true, as we can see looking at the examples of numerous companies including Amazon, adopting a new technology requires a strategic approach — so you should be realistic and well-prepared and not only optimistic about the future outcomes. There are some challenges that you have to be aware of.

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AI Movie Restoration: Scarlett O’Hara HD

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With convolutional neural networks and state-of-the-art image recognition techniques it is possible to make old movie classics shine again. Neural networks polish the image, reduce the noise, and apply colors to the aged images.

The first movies were created in the late nineteenth century with celluloid photographic film used in conjunction with motion picture cameras.

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3 Ways Healthcare Apps Make Use of Machine Learning

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The healthcare industry has generated plenty of data. The new method of data collection, such as sensor-generated data, has helped this industry to find a spot in the top.

What if this data can be used to provide better healthcare services at lower costs and increase patient satisfaction? Yes, you heard it right. It’s actually possible by applying machine learning (ML) techniques in the healthcare industry.

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