Today, the Forbes Technology Council published ‘9 Ways Your Business Can Plan For Artificial Intelligence’. They discuss the need for companies to do their due diligence in deploying AI and the fact that AI is a must, and not an option, for startups. Also, we couldn’t agree more that “not everyone needs a plan, but everyone needs to research.”
Intel has just announced their newly minted single cross-Intel organization: the Artificial Intelligence Products Group, which will be led by Naveen Rao. The new organisation will align resources from across the company to include engineering, labs, software and more as they build on their current AI portfolio.
In addition, they will be creating an applied AI research lab dedicated to pushing the forefronts of computing. They will be exploring novel architectural and algorithmic approaches to inform future generations of AI. This includes a range of solutions from the data centre to edge devices, and from training to inference – all designed to enable Intel and its customers to innovate faster.
Prominent VC Octopus Ventures has raised a £120m tech investment fund to continue supporting AI startups in the UK. Octopus Ventures has so far invested in 50 companies across many different verticals.
Speaking to The Financial Times, Alex Macpherson, CEO at Octopus Ventures, said: “We have expertise
in the machine-learning field, but the challenge today is pretty much every business that comes through to us is machine learning or artificial intelligence.” Octopus is currently looking to participate in the Seed rounds of two AI startups in the UK over the coming months.
The Government of Canada announced on Wednesday that it is funding a Pan-Canadian Artificial Intelligence Strategy for research and talent that will cement Canada’s position as a world leader in AI.
The $125 million strategy will attract and retain top academic talent in Canada, increase the number of post-graduate trainees and researchers studying artificial intelligence, and promote collaboration between Canada’s main centres of expertise
in Montreal, Toronto-Waterloo and Edmonton. The programme will be administered through CIFAR, the Canadian Institute for Advanced Research.
Delay discounting, a behavioural measure of impulsivity, is often used to quantify the human tendency to choose a smaller, sooner reward (e.g., $1 today) over a larger, later reward ($2 tomorrow). Delay discounting and its relation to human decision making is a hot topic in economics and behaviour science since pitting the demands of long-term goals against short term desires is among the most difficult tasks in human decision making [Hirsh et al., 2008].
Previously, small-scale studies based on
questionnaires were used to analyse an individual’s delay discounting rate (DDR) and his/her real-world behaviour (e.g., substance abuse) [Kirby et al., 1999]. In this research, they employ large-scale social media analytics to study DDR and its relation to people’s social media behaviour (e.g., Facebook Likes). They also build computational models to automatically infer DDR from Social Media Likes.
When you first start learning about data science, one of the first things you learn about are classification algorithms. The concept behind these algorithms is pretty simple: take some information about a data point and place the data point in the correct group or class.
Bryan Berend, Lead Data Scientist at Nielsen, teaches about this notion with a more fun example than spam filters and takes a stab at using classification algorithms on Harry Potter, trying to build a classifier to sort characters into the different houses. Although the classifier is not particularly successful, you will learn a lot about APIs and more in the process.
A startup called Floyd has developed a cloud service for deep learning. Yesterday Floyd’s founders talked about their product onstage at Silicon Valley accelerator Y Combinator’s demo day.
Floyd is seeking to build out a marketplace rich with data sets and algorithms. But at its core, it’s a
managed service for training neural networks and then running machine-learned models on an ongoing basis. In that sense, it competes with existing machine learning services from public clouds like Microsoft Azure, Google Cloud Platform, and of course the market-leading Amazon Web Services (AWS), on which Floyd is itself hosted. Check out this article and ProductHunt to see what makes Heroku unique.
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Their approach builds upon recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network.
Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Their contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom CNN layer through which they can backpropagate.