AI in everything

  Four Approaches to Defining Artificial Intelligence Assoc Prof Sonika Tyagi Data Science & AI | RMIT School of Computational Technology| Australia When we ask  “What is AI?” , it turns out there isn’t just one answer. In fact, there are  four broad ways  to define and understand AI: based on whether we want machines to  think or act , and whether we want them to do so  like humans or like ideal rational agents . Let’s explore each of these four perspectives. 1. Acting Humanly: The Turing Test Approach One of the earliest and most well-known definitions of AI comes from  Alan Turing . In 1950, he proposed what we now call the  Turing Test , an operational test of a machine’s intelligence. Turing suggested that if a machine could carry out a conversation well enough to convince a human that it too was human, then we could say the machine was intelligent. In this view,  intelligent behavior  is about replicating  human-like perfo...

 What is AI? Assoc Prof Sonika Tyagi Data Science & AI | RMIT School of Computational Technology| Australia

 Authors: Navya Tyagi & Esha Srivastava, Tyagi Lab, RMIT University, Melbourne, Australia   Introduction   We are aware that machine learning models are built upon vast amounts of pre-existing context and are constantly learning. The advent of machine learning is meant to assist humans in making complex decisions. However, there are still many misconceptions and uncertainties about how much trust one should place in machine learning when it comes to decision-making—especially when these decisions could impact an individual’s life. But what if there was a way to at least understand or backtrack how machine learning makes decisions? Hence, in this blog, we are going to discuss interpretability and explainability. We often come across these terms and may use them interchangeably. However, it is important to understand the distinction between them. Interpretability refers to converting the implicit information within a neural network into human-interpretable insights. Th...

Author : Navya Tyagi  Genomic data consists of DNA, RNA, and protein sequences that can be represented as strings of unstructured text. These sequences can be very large in size. For instance, human DNA is made of 3 billion A,G,C, and T letters. There are hidden patterns that can be considered equivalent to "words" in a natural language. But all of these words are not known and more importantly the grammar that genomic lanaguege follow is not well understood. These biological words with critical functions are of interest to study disease and development processes. Sometime a mutation in these "words" may result in a disease condition. Determining these "words" with biological function is a computational challenge. Figure 1: a) Code of DNA can be written using letters A,C,G,T. A pairs wiht T and G pairs with C making a double stnraded (helical) structure out of two DNA strands. b) message from DNA is transcribed to RNA (also known as messenger RNA). The ...

Data is the basic unit of information and in case of health and clinical data this comes in many shapes and sizes. The clinical data varies from numerical measurements and quantities, to image, documentation, digital codes and narrative text with facts and observations. This data becomes available to a learning or accountability system by various means. For example,  it can be data acquisition from paper records, direct entry into a computer system, or reuse of data collected by others. The other aspect is the high throughput data generated from R&D. Thus, we can safely say that the data is Big, multi dimensional and multimodal. The individual data elements can be grouped based on a common criteria to form datasets e.g. vital measurement data from patients electronic health records. The data from biological and health domain is growing at the fastest rate being >90% of the data generated each year. It needs no convincing that big data will increase efficiency and accounta...

  Receiver operating curve (ROC)  An ROC curve is a commonly used technique to visualise, organise, and select or compare classifiers based on their performance. Where a classifier is usually binary with two possible outcomes. Historically, the use of ROCs comes from WW-II where it was used for assessing performance of signal detection and specifically to find out whether a signal from radar was a true positive or a false positive. The guy who operated the radar receiver would be known as ‘receiver operator’, and hence, ROC got its name :) Sometime in the 1970s ROC started making its appearance in the field of medicine, where it was used to evaluate and compare algorithms. Now, I will elaborate on understanding different components of an ROC, what they mean, and how they are used. Let's take an example  where an observed instance is mapped to one of two class labels, say COVID positive or negative. This test can be performed based on a variety of factors a.k.a features. A...