We are near the end of the hype cycle for artificial intelligence (AI). The human champion of the game of Go decided to retire, saying AI cannot be beaten after AlphaGo defeated him. Domain-specific chatbots are engaging with customers and providing them with the answers they need. AI is about to revolutionize our broken health-care system. Is your company ready for AI? Anyone with deep data claims to be using AI. Credible pilots and use cases have succeeded in many different sectors.

Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders, such as Alzheimer's disease (AD). It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons; however, fMRI has not been widely used in clinical diagnosis. Their limited use is due to the fact fMRI data are highly susceptible to noise, and the fMRI data structure is very complicated compared to a traditional x-ray or MRI scan. Scientists from Texas Tech University now report they developed a type of deep-learning algorithm known as a convolutional neural network (CNN) that can differentiate among the fMRI signals of healthy people, people with mild cognitive impairment, and people with AD. Their findings, "Spatiotemporal feature extraction and classification of Alzheimer's disease using deep learning 3D-CNN for fMRI data," is published in the Journal of Medical Imaging and led by Harshit Parmar, doctoral student at Texas Tech University.

Reinforcement Learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. It can be used to teach a robot new tricks, for example. Reinforcement learning is a behavioral learning model where the algorithm provides data analysis feedback, directing the user to the best result. It differs from other forms of supervised learning because the sample data set does not train the machine.

Deep learning pioneer Yoshua Bengio has provocative ideas about the future of AI. For the first part of this article series, see here. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today.

Let's start with the one minute version: I was part of the EF12 London cohort in 2019, where I met my co-founder. A privacy-preserving medical-data marketplace and AI platform built around federated deep learning. The purpose of the platform would have been to allow data scientists to train deep learning models on highly sensitive healthcare data without that data ever leaving the hospitals. At the same time, thanks to a novel data monetization strategy and marketplace component, hospitals would have been empowered to make money from the data they are generating. We received pre-seed funding, valued at $1 million. Then the race for demo day began with frantic product building and non-stop business development.

For the first part of this article series, see here. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

Happy Halloween to everybody, hope people had a good bank holiday weekend and didn't miss us this week. Kicking off the talks we're joined by Ali Karaali who is going to share his research on deep fakes and how to spot them. We're joined by a couple of folks from Huawei who are join tell about their efforts in putting machine at scale on mobile devices. AGENDA: [18:40 - 19:00] Getting Online [19:00 - 19:10] Welcome [19:10 - 19:30] Ali Karaali, Postdoctoral Research@ Sigmedia Group & ADAPT Centre, TCD How to spot fake videos of real people [19:30 - 19:50] Giovanni Laquidara, Developer Advocate @ Huawei Mindspore is an all-scenario deep learning framework optimized for parallel distributed training, easy adaptable for IOT and open source [19:50- 20:10] William Zhang, Product Manager @ Huawei Machine Learning Kit Open machine learning service inspiring your life This event is strictly for Machine Learning professionals, researchers and students only • If you finally can't make it, please RSVP to "NO" as soon as possible so that other people can take your place.

Recurrent neural network (RNN) is one of the earliest neural networks that was able to provide a break through in the field of NLP. The beauty of this network is its capacity to store memory of previous sequences due to which they are widely used for time series tasks as well. High level frameworks like Tensorflow and PyTorch abstract the mathematics behind these neural networks making it difficult for any AI enthusiast to code a deep learning architecture with right knowledge of parameters and layers. In order to resolve these type of inefficiencies the mathematical knowledge behind these networks is necessary. Coding these algorithms from scratch gives an extra edge by helping AI enthusiast understand the different notations in research papers and implement them in practicality. If you are new to the concept of RNN please refer to MIT 6.S191 course, which is one of the best lectures giving a good intuitive understanding on how RNN work.

Generally speaking, distributed machine learning (DML) is an interdisciplinary domain that involves almost every corner of computer science -- theoretical areas (such as statistics, learning theory, and optimization), algorithms, core machine learning (deep learning, graphical models, kernel methods, etc), and even distributed and storage systems. There are countless problems to be explored and studied in each of these sub-domains. On the other hand, DML is also the most widely adopted and deployed ML technology in industrial production because of its faculty with Big Data. It's easiest to understand DML if you break it into four classes of research problems. Please note, however, that these classes are absolutely not mutually exclusive.

Deci, the deep learning company dedicated to transforming the AI lifecycle, today announced it has raised $9.1 million in a seed round led by Israel-based VC firm Emerge and global VC fund Square Peg. The company is building an AI-based platform that can automatically craft robust, scalable, and efficient deep neural network solutions ready for production at scale. Deci aims to help AI practitioners build the next generation of deep learning models. Advancements in AI, mainly powered by deep learning, have triggered groundbreaking innovations in medicine, manufacturing, transportation, communication, and retail. But, prolonged development cycles, high computing costs, and unsatisfying inference performance are making it nearly impossible for enterprises to productize AI.