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A Guide to Analyzing Experimental Data

#artificialintelligence

Have you ever run an experimental study, or performed some A/B testing? If so, you should be familiar with the pre-analysis panic: how can you make the data reveal whether your experiment has worked? Every day -- in economics, public policy, marketing, and business analytics -- we face the challenges that come from running experiments and analyzing what comes out of them. As researchers -- who struggle with a clean and efficient experimental workflow ourselves -- we have decided to share with you a practical guide, complete with all the steps you need to follow when you want to analyze experimental data. We cannot promise that the journey will be short, but we assure you it will be fun!


9 Examples of Machine Learning in Action

#artificialintelligence

The term "machine learning" makes it sound like computers will solve problems for us without much human guidance. But, some fascinating careers are paving the way for artificial intelligence to help us all out in our daily lives and at work. Machine Learning Engineers and Data Scientists that specialize in machine learning get to work in pretty diverse industries. That's one of the best things about a career in programming or data science -- you can take those skills just about anywhere. It also means that you can work in a field that excites you or one in which you feel like you're making a positive contribution.


How Mobile Integration Will Change the Casino Industry Moving Forward

#artificialintelligence

It's an understatement to say that casinos have been slow to adapt to mobile technology. Consumers use smartphones to find a ride, order groceries and coordinate their business and social lives; however, stepping inside a casino, is almost like travelling back in time to 2005. Want to play a game of blackjack or try your luck on your favorite slot machine? You'll have to use cash. Loyalty rewards are only earned by using a physical player card, and the most common marketing offers are distributed via direct mail.


Top 8 Use Cases & Benefits of RPA in Manufacturing

#artificialintelligence

The global automation market size is expected to generate $214B by the end of 2021, of which $29B (14%) will come from manufacturing and factory automation. This is because numerous processes in manufacturing are repetitive, rule-based, and can be automated using RPA bots. For instance, bill of materials (BOM), data migration and analytics, invoices, and inventory reporting are highly repetitive and time consuming tasks if done manually. A typical rule-based process can be 70%-80% automated. RPA bots handle rule-based repetitive tasks and minimize the need for human interference.


Dialysis adequacy predictions using a machine learning method - Scientific Reports

#artificialintelligence

Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman’s rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.


Short Introduction to Generative Adversarial Networks (GANs)

#artificialintelligence

General Adversarial Network (GAN) are a generative modelling approach using deep learning neural networks such as CNN. There are two types of modelling techniques, i) Discriminative modelling and ii) generative modelling. Discriminative models are typical one that are used for classification in machine learning. They take input as features X (image, for image classification) and predict the output Y(probability of the image) for the given features. On the other hand, generative models outputs features X (image) given a random value.


AI and Retail: It is a Match!

#artificialintelligence

Artificial Intelligence (AI) and retail are a good fit. The COVID-19 pandemic has accelerated digital transformation worldwide and is whipping up different business verticals to adopt various AI technologies. As per the UNCTAD survey, more than half of consumers of the emerging and developed economies are shopping online. The part of AI in the retail market in 2020 was valued at USD 1,80 billion and is expected to reach USD 10,90 billion at a CAGR of 35% by 2026. It seems like it is high time for going big or going home for retailers.


Want hands-free lighting? These are the best smart bulbs to use with Google Home.

Mashable

If you're looking to bolster your smart home with smart bulbs, utilizing Google Home compatibility is a tempting proposition for those of us with Android phones or other Google compatible devices, i.e. pretty much everyone. Equipping your home with a smart lighting system that offers voice controls and the ability to manipulate your lights far more efficiently than through conventional means is great, but where do you begin? It feels like there are countless different smart light bulbs out there, all offering different features from Amazon Alexa support to Apple HomeKit compatibility or simply the ability to change the color temperature at a moment's notice. To help narrow things down, we've taken a look at the very best smart bulbs for Google Home and the Google Home app, offering you voice command support so you can "speak" to your lighting system. While it might seem like something expensive and substantial such as the Philips Hue bulb system is the solution for everyone, it's possible to implement smart lighting on a tight budget providing you know where to look.


AIhub monthly digest: July 2021 – ICML, protein folding for all, and AI Song Contest winner announced

AIHub

Welcome to our July 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. In this edition we cover ICML 2021, celebrate award winners, check out new AI reports and strategies, and find out who won the AI Song Contest. This month saw the running of the thirty eighth International Conference on Machine Learning (ICML). There were a huge variety of events, including talks, workshops, tutorial, and socials. We were in (virtual) attendance and managed to catch all of the invited talks.


A Gentle Introduction to Multivariate Calculus

#artificialintelligence

It is often desirable to study functions that depend on many variables. Multivariate calculus provides us with the tools to do so by extending the concepts that we find in calculus, such as the computation of the rate of change, to multiple variables. It plays an essential role in the process of training a neural network, where the gradient is used extensively to update the model parameters. In this tutorial, you will discover a gentle introduction to multivariate calculus. A Gentle Introduction to Multivariate Calculus Photo by Luca Bravo, some rights reserved.