There are two ways to categorize Machine Learning algorithms you may come across in the field.
Generally, both approaches are useful. However, we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.
Back in 2016 (oh how time flies), the tech industry was abuzz with the word "chatbots." Many were dubbing chatbots as the "new apps", believing that they would completely transform the way we perceive mobile technology. Although we’ll refrain from making such decisive statements, it’s true that this technology has left quite the mark, particularly in the area of user experience. Instead of replacing apps, chatbots have demonstrated an ability to enhance UX. In order to better understand this use case, we’ve handpicked five particular apps that have harnessed the power of chatbots to increase engagement and bolster retention.
Yet before diving into some cool examples, let’s take a brief glance at the way they work, to understand their power and to see how far we’ve come in terms of upgrading the digital interface.
This article illustrates how Geocoding uncovers the untapped value within generally overlooked insurance categories, such as Life and Annuity, and how it can help address modern-day business challenges remarked by Orszag. While Geocoding in Big Data is gaining prominence within Property and Casualty (P&C), we believe the real opportunity lies in the actuarial adoption of AI framework capable of processing consumable inputs that weren’t visible in the erstwhile "Ease of Geocoding" era.
Establishing this premise for Life and Annuity, we then pivot towards crafting a general purpose Geo-inclusive architecture that can help actuaries of all disciplines apply Machine Learning to solve new generation of business problems, such as, dwindling subscribers or risk-attributed challenges, such as, Adverse Selection.
Finance is probably one of the first fields to adopt innovations. These days, IoT, AI, and blockchain are the technologies reshaping multiple industries, especially FinTech. AI solutions attract immense investments and for the right reasons. Analysts have counted that Artificial Intelligence is going to save the industry more than a trillion dollars (!) through the year 2030.
How exactly are financial institutions planning to leverage AI? Is it already a part of the processes? Skelia dug into this issue.
Thanks to António Alegria, Head of AI at OutSystems for taking me through how OutSystems is using AI to improve the quality and speed of software development. António also heads up OutSystems’ AI Center of Excellence — Project Turing.
António began his presentation explaining that tools were key to humanity’s progress and that software became the ultimate tool to drive great achievements.
Before anything else, you will need to Install Python on your machine. So, here are the steps to install Python.
You can download from the official website:
Last time, we looked at how to use TensorFlow from within SAP HANA, express edition. This allows you to surface your TensorFlow ModelServer models inside your instances and use them as a regular stored procedure.
This allows, for example, to process images or documents stored as blobs with an image classification model or something as simple as a classification on the Iris dataset.
If you’re at all interested in Artificial Intelligence (AI) — and it seems likely that you are, since you’re reading this in the AI Zone here on DZone — it’s unlikely to be news to you that there is an AI skills shortage. Businesses are increasingly looking to invest in AI and are on the hunt for suitably skilled workers since traditional software teams without the experience of AI often encounter a number of challenges, as I described in a recent article in this zone.
Anyone thinking about joining the AI workforce will want to learn the subject, initially by doing some reading and research, but without committing to paying too much. But where to start? As the need to recruit skilled AI staff has grown, so a number of businesses and individuals have set out to provide training courses, books, and e-learning, and the price and quality of these vary, as you would expect. As with all education, if you commit a chunk of your time, you don’t want to find it wasted on out-of-date or incorrect information or to find that you are missing out on key skills after spending time and money on a course that promises to equip you appropriately.
In this tutorial, our aim is to write a schema and load it into our knowledge graph; phone_calls. One that describes the reality of our dataset.
First off, let’s look at the dataset we are going to be working with. Simply put, we’re going to have:
Last time, we looked at how to leverage the SAP HANA R integration, which opens the door to about 11,000 packages. So, if you feel like the built-in libraries (APL and PAL) don’t offer what you need or if you feel like doing something your way too, now you can!
I hope you all managed to try this out, and probably some of you already started comparing the PAL implementation with R algorithms. Feel free to share your feedback!
