We were delighted to be joined by over 100 Women in AI at the end of November for the first instalment of our Women in AI virtual evenings. The evening of virtual networking, discussion and keynote presentations, supported by TD Bank, covered topics including'Diversity's Critical Role in AI and Innovation', Action Recognition for Behaviour Understanding from Video in 2020 and more. Speakers included Jane Ho, Associate VP, Data & Analytics at TD Bank, Ashley Cohen, Principal Analytical Lead of Google, Tanmana Sadhu, Computer Vision Engineer at Huawei Canada, Inmar Givoni, Director of Engineering of Uber ATG, Sedef Akinli Kocak, Senior Lecturer at Ryerson University and Hakimeh Purmehdi, Senior Data Scientist at Ericsson. A summary of highlights is below, including a video recording of the panel discussion. Artificial intelligence and Machine Learning models are heavily reliant on the data that feed them. While AI can improve human decision making; however, since data can be biased based on human decisions made in the past, AI output may inherit or even amplify biases.

Many times AI has been put on a pedestal as the future of x y & z, however, many seem to agree that education is a sector in particular which will see stark changes in both admin, teaching styles, personalisation and more. I had the pleasure of speaking to three individuals working in the field, including, Vinod Bakthavachalam, Senior Data Scientist at Coursera, Kian Katanforoosh, Lecturer at Stanford University & Sergey Karayev, Co-Founder and CTO of Gradescope. We began by having Sergey of Gradescope walk us through his product, which has been recently acquired by turnitin. The concept, it seemed was formed from the simple and widespread issue of both lack of consistency, lack of insight through time constraint and delayed feedback on academic work. Sergey found that scanning the papers onto an online interface when paired with a rubric can allow for accurate marking in seconds across several papers.

Whilst we can all be consumed by the quick YouTube video or blog article for learning, sometimes it's nice to get engrossed into a good book. See more AI insights here. A Scientists Quest to reclaim our humanity by bringing emotional intelligence to technology. The recent release from Affectiva CEO and Founder, Rana el Kaliouby & Carol Colman addresses many questions including how we humanize our technology and how we connect with each other. To combat our fundamental loss of emotional intelligence online, Rana cofounded Affectiva, the pioneer in the new field of Emotion AI, allowing our technology to understand humans the way we understand one another.

Our expert-led blog series continues, with a new four-part edition, kicking off with finding out what our group of AI experts would see as their dream summit panel. Included are experts from MILA, Gartner, Google and more. Alexia chose the following for her dream summit panel: Firstly, Ian Goodfellow for his work on Generative Adversarial Networks (GANs) and adversarial examples (a big vulnerability in neural networks). Joining after was Anima Anandkumar for her work on Competitive Descent and Non-convex Optimization. Finally, Fei-Fei Li was added to the lineup for her work on ImageNet (the biggest categorized image dataset at the time and still a major benchmark for generative models), Robotics, and neuroscience applications.

Are GANs the next step in Deep Learning? Well, the subset of Machine Learning was once described by Yoshua Bengio as the most interesting idea in the last 10 years of ML, with the technique of using two neural networks against each other to generate new, synthetic instances of data that can pass for real data, opening many doors in the world of AI. That said, we wanted to explore some of the applications of GANs currently being used through the below 5 must-watch presentations from DeepMind, NASA, MIT, Insitro and Université de Montréal. In this presentation, Francesco introduces a new deep generative model for the genetic analysis of medical imaging, combining both convolutional neural networks and structured linear mixed models to extract latent imaging features in the context of genetic association studies. The linked presentation includes an application of the method to brain MRI images from the Alzheimer's Disease Neuroimaging Initiative dataset, where we reveal novel and known risk genes for neurological and psychiatric disorders. Genetic association studies and the process of evaluation during study is covered before looking at both the phenotypes and genetic variants of participants.

There are swathes of blogs covering the impact of AI on both the financial and insurance industries, however, many look at farfetched AI and ML concepts, not yet tested or applied in either. The below list of'uses' documents application methods or techniques which are currently being implemented, albeit quietly, slowly and behind the scenes. The below are six ways in which we think AI is best being utilised in both the finance and insurance industries. Considered one of the more sought after applications of AI in Finance, it is suggested that the use of AI for fraud detection could detect billions of dollars worth of fraudulent transactions. Whilst AI is already somewhat prevalent in the financial industry, it is expected that by the end of 2021, the amount spent on applying AI in finance with specific focus on fraud detection is set to triple.

Having covered some of our favourite AI books and AI podcasts in previous lists, this time we wanted to focus on audio books. Whilst a good book can't be beaten, many prefer to digest their information differently, seen by an increase in audio book sales in recent years. Algorithms to live by is an exploration into how computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind. In this book Brian explains the problems we face in every day life which could be solved through leveraging AI, machine processes and algorithms. This audiobook aims to teach its listeners a concept which they can, eventually after repetition, learn by heart, then allowing them to brainstorm the various opportunities for python and deep learning application.