We've been experimenting with using eldr AI for NLP on the input side, output side, and both sides of an Artificial Neural Network (ANN) using various settings including Softmax and Cross Entropy. Here I talk about these fascinating results.
AI Recommendation Engines are powerful and common uses of AI. They are able to learn from all customer data and work out accurately what products particular customers may be interested in based on the behaviour and demographic of other customers. How a real AI Recommendation Engine/ Recommendation System (Recsys) works is different to how you might think - if you're used to traditional software techniques. It uses some sophisticated maths that are actually interesting, using three dimensional mappings as opposed to tonnes of database calls and bog standard code. Here I discuss how it all works.
Customer Churn is becoming one of the common uses of AI. Customer Churn is essentially Customer Retention Analysis - that is the ability to know how likely a customer is to remain with you during initial contact/browsing, following a purchase, and/or long term or permanently. Knowing or accurately predicting whether a customer will buy products or services off you at every stage (from their first visit to your store or website, or as they use your services) is absolutely key to modifying your behaviour, appeal and incentives to that particular customer in order to maximise sales and promote growth.
With AI use and discussion growing rapidly, ethics has been increasingly brought up as a genuine concern in some cases such as Human Resources (HR) - or any scenario where AI is taking over the decision process when selecting or judging human beings. One major talking point is bias and/or discrimination that might be present in AI systems e.g. when selecting a candidate for a new role does AI automatically disregard candidates with a certain education or background? Here I will discuss where this bias comes from and why it isn't AI that we should be concerned with.
Anomaly detection using AI is a powerful and efficient way of spotting anything unusual in any process, data or system - it's easy to do using an Artificial Neural Network such as eldr.ai, it's fast and it's accurate.
When a patient first presents at the NHS - generally either through a GP or Accident and Emergency, their correct journey through treatment and services is absolutely essential for a successful therapeutic outcome, as well as for the NHS to provide their services within time and budget. Generally the patient journey involves Diagnosis, Treatment, Recovery, Referrals, Second Opinion and Private Options, in various orders.
AI is being used in almost all industries. One industry or use that many people might not be aware of is in professional sport. Using AI in professional sport such as football including the Premier League and motor racing such Formula 1 can massively improve efficiency at all levels, ultimately promoting more wins.
Smart Cities use a variety of sensors such as cameras and movement/pressure monitors dotted around towns, cities and infrastructure in order to gather vast amounts of data about the entities operating in that space, e.g. People and Traffic. The goal is to improve efficiency.
We've put in NLP functionality meaning that eldr AI can now learn from full text, sentences, words etc, at the same time learning from values, categories and continuous data in the same Artificial Neural Network.
ELDR AI can learn anything. We built it that way - to tackle any learning task in any industry. We realised this is just in our heads, and people who use ELDR will need to know how it can be used by them. For this, we have created Flavours and Recipes.
We've been busy adding extra features to ELDR AI prior to release of v1.0 at the end of January. We have been able to successfully integrate dynamic Embeddings, Batch Normalisation and Dropouts in relevant layers throughout the AI Engine making it even more adaptable, robust and powerful.
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