Introhive is seeking an ambitious, well-rounded senior Data Scientist to join our growing Data Science department. This position would be based in our Fredericton office. Introhive offers a unique opportunity to work on very large data sets and solve challenging business problems using ML, Deep Learning and NLP. If this sounds intriguing, then we would like to talk to you about this key role in within the technical team.

Facebook has updated its popular open-source deep-learning library PyTorch. The latest version, PyTorch 1.3, includes PyTorch Mobile, quantization, and Google Cloud TPU support. The release was announced today at the PyTorch Developer Conference in San Francisco. PyTorch Mobile enables an end-to-end workflow from Python to deployment on iOS and Android. Facebook believes it is increasingly important to be able to run machine learning models on devices such as today's supercharged smartphones, as this delivers lower latency and can help preserve data privacy for example through federated learning approaches. Currently, PyTorch Mobile is in the experimental mode and remains under examination and development.

The paper presented at ICLR 2019 can be found here. I also have slides as well as a poster explaining the work in detail. Deep neural networks are an amazing piece of technology. With enough labelled data they can learn to produce very accurate classifiers for high dimensional inputs such as images and sound. In recent years the machine learning community has been able to successfully tackle problems such as classifying objects, detecting objects in images and segmenting images.

Please note on June 30, 2020, this program will be retiring and no longer available on edX. If you are interested in earning the Professional Certificate you must be complete the program by June 30, 2020, in order to earn the certificate. Artificial Intelligence (AI) will define the next generation of software solutions. Human-like capabilities such as understanding natural language, speech, vision, and making inferences from knowledge will extend software beyond the app. The AI Professional Certificate program takes aspiring AI engineers from a basic introduction of AI to mastery of the skills needed to build deep learning models for AI solutions that exhibit human-like behavior and intelligence.

People around the world marched for climate change on September 20, 2019, with protests taking place across 4,500 locations in 150 countries, all inspired by Swedish climate activist Greta Thunberg. It is obvious the call for a healthier planet is being demanded by more and more people internationally. But what is the answer? Millions of people across the globe marched on September 20, 2019 to demand urgent action on climate change. One of the questions being posed: Can Artificial Intelligence (AI) and tech companies help address climate change and save the planet?

Qualcomm executives this week attempted to draw contrasts and explain how their approach to cloud computing, artificial intelligence (AI), and mobile edge computing is different than other technology companies and operators. "Our focus is different than the rest of the industry," Jim Thompson, CTO and executive vice president at Qualcomm said during a media gathering at the company's headquarters. AI, an area of technology that Qualcomm has been working on for the better part of a decade, plays a big role in Qualcomm's vision for what it calls the "edge cloud," Thompson explained. "Our focus has been deep neural networks, deep learning for low power, for devices that you would have a battery in and have limited thermal capability," he said, adding that Qualcomm's interest is not the cloud essentially. "AI is very good at consuming large amounts of data. It comes from the edge of the network, it comes from what people do, it comes from sensors, and all of that is at the edge of the network," he said.

Build language model using large datasets which helps us capture general properties of the language. Using this type of modelling approach helps us learn the patterns within the language which we may not come across in the smaller datasets. In this approach the model doesn't have to learn from scratch and can generalise over smaller datasets thereby reaching higher accuracy with much less data and computation time

As we collectively experience the increasing pervasiveness of machine learning algorithms that drive so many services and functions in our society, it is clear to us that a new workforce of specialized programmers and computer scientists exist behind this reality. You may even be one of these wunderkinds who expanded your early coding education to include the development of learning algorithms for enhancing existing software applications or creating entirely new deep learning systems. Or, you may be an "older-kinder" who quickly circled back to catch the wave of AI excitement to coral it into your well-honed development tool kit. Either way, while the current generation of programmers is running with machine learning trends, the next round of professionals who will fill our shoes – those young ones who are learning PowerPoint and Common Core math in grade school today – are experiencing AI as something that is… just already something normal. AI-powered digital voice assistants are commonplace in homes, and kids are thrilled to ask what the weather is today repeatedly or to have the device tell a joke.There are people behind this magic, of course, and the fad of learning this trade could become just that if we don't consider how to ensure a pipeline of future machine learning developers to carry our torch.

How the new advances in semantics can help us be better at Machine Learning. Deep learning on graphs is taking more importance by the day. I've been talking about the data fabric in general, and giving some concepts of Machine Learning and Deep Learning in the data fabric. The Data Fabric is the platform that supports all the data in the company. How it's managed, described, combined and universally accessed.

Algorithms are increasingly making choices for young people, from recommending new TV shows to the friends they meet. But when machines are so intelligent that they can make all these decisions, who is actually responsible? Andrea Catherwood hosts a debate at the BBC Blue Room annual conference with Anne Longfield, Children's Commissioner for England, Dr Nejra van Zalk, lecturer in psychology at Imperial College London, Hanna Adan, documentary maker and Neil Lawrence, DeepMind Professor of Machine Learning at the University of Cambridge.