AI For the Rest of Us: Part 2

This is part 2 of a post that is seeking to explain AI in a way anyone can understand it. Part 1 had some background and introduction. This post has an example hopefully anyone can follow! For ease of understanding I will use a simple example. The process for more complicated examples is the same?-?just (many!) more numbers to implement! My simple example is?-?how many ice creams are sold in a given day based on what the temperature is. This example is so simple that you would never use machine learning to figure this out?-?but it still can be used as an example for how a machine actually learns.


AI For the Rest Of Us: Part 1

There is huge amounts of hype swirling around today about AI. There is also a huge amount of discussion about it’s impact on society today and in the future. Yet – I’ve seen very little material explaining well how an AI actually works. How is an AI built? What does it actually do inside the black box? Albert Einstein said once that ‘If you can’t explain it simply, you don’t understand it well enough.’ I rarely see explanations about AI that are simple. So I am going to try. I’ve called this blog ‘AI for the Rest of Us’ – for those who are not currently participating in the AI revolution and want to understand what is going on. I suspect there are many people who fit into this category.


How to generate QR codes with R and publish with R Markdown

Have you ever wondered about creating a QR code and adding on your R markdown report? According to Wikipedia, the QR code (abbreviated from Quick Response Code) is a type of matrix barcode designed in Japan for the automotive industry. Nowadays QR codes are scanned by a smartphone and contain numeric or alphabetic data and often link to a website or application.


A Brief History of ASR: Automatic Speech Recognition

This moment has been a long time coming. The technology behind speech recognition has been in development for over half a century, going through several periods of intense promise – and disappointment. So what changed to make ASR viable in commercial applications? And what exactly could these systems accomplish, long before any of us had heard of Siri? The story of speech recognition is as much about the application of different approaches as the development of raw technology, though the two are inextricably linked. Over a period of decades, researchers would conceive of myriad ways to dissect language: by sounds, by structure – and with statistics.


Microsoft Introduction to AI – Part 1

Are you a bit like me and have wanted to learn about Artificial Intelligence although felt a little intimidated by the maths involved? Maybe you thought the concepts were too difficult to understand and you would be out of your depth. I recently completed the Microsoft Introduction to AI course and wrote course notes to help me retain the knowledge that I have learnt. I have tried to write these notes in a basic way to make it easy to consume. I’ve recently become an aunt and have bought a few children’s books related to technology and space, I really love how the authors and illustrators have managed to simplify complicated topics. So, I’ve been inspired to treat these topics in a similar way by simplifying them to make it a lot more accessible.


Conversational AI: Design & Build a Contextual AI Assistant

Though Conversational AI has been around since the 1960s, it’s experiencing a renewed focus in recent years. While we’re still in the early days of the design and development of intelligent conversational AI, Google quite rightly announced that we were moving from a mobile-first to an AI- first world, where we expect technology to be naturally conversational, thoughtfully contextual, and evolutionarily competent. In other words, we expect technology to learn and evolve. Most chatbots today can handle simple questions and respond with prebuilt responses based on rule-based conversation processing. For instance, if user says X, respond with Y; if user says Z, call a REST API, and so forth. However, at this juncture, we expect more from conversation. We want contextual assistants that transcend answering simple questions or sending push notifications. In this series, I’ll walk you through the design, development and deployment of a contextual AI assistant that designs curated travel experiences.


I trained an AI to imitate my own art style. This is what happened.

Alright, let’s talk about artificial intelligence. AI is everywhere today, and if you think we just DO NOT NEED another AI article, you’re probably right. But before you close this tab, please hear me out. This one is different. First of all, I’m not a developer or software engineer. I did not create another AI. I’m a designer / digital artist with a little bit of coding skills. However, I’m very passionate about all things technology. When I was researching AI for a university project, I came across some interesting image-generating scripts. There was especially one that caught my attention: a method called Neural Style Transfer (NST).


The significance of Interaction Plots in Statistics

Interaction plots are used to understand the behavior of one variable depends on the value of another variable. Interaction effects are analyzed in regression analysis, DOE (Design of Experiments) and ANOVA (Analysis of variance). This blog will help you to understand the interaction plots and its effects, how to interpret them in statistical designs, and the problems you will face if you don’t include them in your statistical models. In any statistical study, whether it’s a product development, manufacturing process, simulation, health, testing and so on. Many variables can affect the expected outcome (Response). Changing/adjusting these variables can affect the outcome directly.


Educating for AI – one of the most critical problems in AI

One of the hardest problem in AI is not technical. It is social. Specifically, it is the problem of ‘educating people for living and working in a world dominated by AI’


Using the Digital Transformation Journey Workbook to Deliver “Smart” Spaces

Key points of this blog include:
• Digital Transformation sweeps aside traditional industry borders to create new sources of customer and operational value
• Unfortunately, Digital Transformation and ‘smart’ initiatives are struggling, and organizations will need a more pragmatic approach to successful execution per Forrester research
• Organizations must define their ‘smart’ initiatives from perspective of stakeholder journey maps that identify, validate, value and prioritize the use cases that support ‘smart’
• ‘Smart’ spaces are comprised of interlocking subsystems that decompose into a series of use cases that are identified, validated and valued from the journey maps of your key stakeholders and constituents
• The Digital Transformation Journey Workbook identifies ‘smart’ spaces and digital transformation requirements from perspectives of key stakeholders – the sources of economic value.


A Healthcare Problem in Need of a Data Analytics Solution

A recent visit to a local hospital provided some insight on a particular problem in health care. After speaking to a few healthcare professionals, it was revealed that patients who have surgery are at a significantly higher risk at developing a Pulmonary Embolism (PE) as a result of a Deep Vein Thrombosis (DVT) or blood clot. Clots are also referred to as a thrombus. However, once a clot begins to travel it is called an embolus. You may have heard of these terms in the context of the myriad of commercials about blood-thinning medications. DVTs typically originate in the legs or arms and are associated with clots that are formed in the venous system. These clots are associated with either a genetic disorder, surgery or pre-disposition as a result of the nature of a disease. In particular, cancer patients are at higher risk for developing PEs.
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