Collaborating Authors

The future of sports? There's a robot sinking basketballs at the Tokyo Olympics.


The Tokyo Olympics are in full swing, as anyone who has the ability to set the TV channel to NBC is no doubt aware. And while the biennial showdown between world-class athletes and the nations they represent is entirely a celebration of human excellence, in 2021 at least one robot is being allowed to have a little fun, as a treat. Fresh from the Tokyo Olympics Twitter account we have this clip of a tiny-headed robot -- WHY IS ITS HEAD SO SMALL, Mashable's EiC asked in Slack -- shooting, and sinking, free throws Look at this mad nonsense. Nothing but net! (The robot moved further back right after this and immediately sunk a three-point shot with ease as well.) Why is this robot wearing a jersey with the number 95 on it?

Five Predictions for the use of AI in Fintech


In the year ahead, organisations across financial services will be turning to the technologies that can deliver the most value in a short amount of time. Inevitably, AI and machine learning, and a focus on harnessing data, will be key to bolstering business strategies and enabling new areas of growth. There are, however, immediate challenges that must be overcome following the disruption brought about by the coronavirus crisis, as well as key problems data scientists must seek to address to ensure AI continues to deliver on its promise. Retraining models for the post-pandemic world: The pandemic has had a catastrophic effect on many businesses and individuals. Now, more than ever, access to finance is vital for so many.

Deep Learning and Glaucoma


Glaucoma is a leading cause of irreversible blindness worldwide. A recent global meta-analysis of 50 population-based studies reported the pooled glaucoma prevalence (age range, 40-80 years) to be 3.5%, corresponding to an estimated 64.3 million individuals worldwide. Li, Zhixi et al. used deep learning system to detect referable GON (glaucomatous optic neuropathy) with high sensitivity and specificity. The study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON.

What is machine learning?


Machine learning is used to build products we interact with every day. An algorithm is the set of rules that a machine is programmed to follow in order to complete a task. Machine learning is the practice of training a computer to develop its own algorithm, so that it can complete increasingly difficult tasks. The computer creates an algorithm from samples of data, often called'training data', which it uses to generate predictions of appropriate answers and actions. The computer can, therefore, make decisions on its own, without being explicitly programmed in the unique context of that decision.

Python codes for types of Classification Algorithms


These classification algorithms are used for the calculation of metrics accuracy of the data by using python. The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. Classification can be performed on structured or unstructured data. Classification is a technique where we categorize data into a given number of classes. The main goal of a classification problem is to identify the category/class to which new data will fall.

Regular Expressions in Python -A Beginners Guide - Analytics Vidhya


Regular Expressions, also known as "regex" or "regexp", are used to match strings of text such as particular characters, words, or patterns of characters. It means that we can match and extract any string pattern from the text with the help of regular expressions. I have used two terms, match and extract and both the terms have a slightly different meaning. There may be cases when we want to match a specific pattern but extract a subset of it. For example, we want to extract the names of PhD scholars from a list of names of people in an organization. In this case, we will match the "Dr XYZ" keyword and extract only the name, i.e. "XYZ" not the prefix "Dr." from the list.

What Should Happen To Our Data When We Die?


The new Anthony Bourdain documentary, "Roadrunner," is one of many projects dedicated to the larger-than-life chef, writer and television personality. But the film has drawn outsize attention, in part because of its subtle reliance on artificial intelligence technology. Using several hours of Bourdain's voice recordings, a software company created 45 seconds of new audio for the documentary. The AI voice sounds just like Bourdain speaking from the great beyond; at one point in the movie, it reads an email he sent before his death by suicide in 2018. "If you watch the film, other than that line you mentioned, you probably don't know what the other lines are that were spoken by the AI, and you're not going to know," Morgan Neville, the director, said in an interview with The New Yorker.

Research Associate in Machine Learning-Based Spatial Audio


We have a research associate (postdoc) position to work on spatial audio processing and spatial hearing using methods from machine learning. The aim of the project is to design a method for interactively fitting individualised filters for spatial audio (HRTFs) to users in real-time based on their interactions with a VR/AR environment. We will use meta-learning algorithms to minimise the time required to individualise the filters, using simulated and real interactions with large databases of synthetic and measured filters. The project has potential to become a very widely used tool in academia and industry, as existing methods for recording individualised filters are often expensive, slow, and not widely available for consumers. The role is initially available for up to 18 months, ideally starting on or soon after 1st January 2022 (although there is flexibility).

No More "What" Without the "Why"


Throughout the last months, I had the chance to enable various organizations and leaders leveraging their large databases with machine learning. I was particularly engaging with member organisations which struggle with rising dropout rates (churns) -- an issue that became even more serious throughout the pandemic when individual income has been on a declining and the fear of job loss on a rising path. With machine learning, we used very large membership databases with individual-level information (e.g. Machine Learning tells us the "What", Causal Inference the "Why" Despite the overall good performance of the machine learning models, our clients were always interested in one obvious question: Why does an individual member leave? Unfortunately, machine learning models are not suited to identify the causes of things but rather they are built to predict things.

How to teach computers to recognize dogs and cakes in images


Artificial intelligence is a very interesting topic that evokes a lot of emotions. This is due to the fact that the development of new technologies involves many opportunities and threats. Some artificial intelligence technologies have been around for a long time, but advances in computational power and numerical optimization routines, the availability of huge amounts of data, have led to great breakthroughs in this field. Artificial intelligence is widely used to provide personalized recommendations when shopping or simply searching for information on the web. More advanced inventions include autonomous self-driving cars -- which, in a simplified way, make decisions about the next movements of vehicles based on data collected from various types of sensors installed in them.