We love bringing the community together and as such, we have some great events coming up within the next few months. We’ve added an events section in the newsletter so you can keep track and ensure you don’t miss out on tickets. Please do send us suggestions of other events, speakers and panelists, or put yourself forward ?.
We are also excited to announce that Siavash Mahdavi will be joining our “Investing in AI” panel. He’s got the full spectrum of experience, having started Within– a 3D printing company with AI at the heart (which was sold to Autodesk for $90m) and most recently started a new venture A.I.Music *AND* he’s an angel investor. Check out our latest blogpost to learn more about him.
Monday March 27: Investing in AI (if you want to know how many real investment opportunities you can expect to see this year, how to craft a sensible investment strategy, or how your startup can access investment, then come to our event to network and meet the people you need to know)
Tuesday May 2: Why Women in AI (empathy, nurturing, listening, multi-tasking, intuition, teaching and mothering are skills and qualities we need to be involved in training AI machines. The question is how to ensure they are at the table)
Adobe, one of the world’s largest and most powerful software companies, is trying something new: It’s applying machine learning and image recognition to graphic and web design. In an unnamed project, the company has created tools that automate designers’ tasks, like cropping photos and designing web pages. Should designers be worried?
The new project, which uses Adobe’s AI and machine learning program Sensei and integrates into the Adobe Experience Manager CMS, will debut at the company’s Sneaks competition later in March. While Adobe hasn’t committed to integrating it into any of its products, it’s one of the most ambitious attempts to marry machine learning and graphic design to date. There have been efforts to use AI in the design world before—for instance, Wix’s Advance Design Intelligence and automated projects like Mark Maker, but Adobe’s is notable because of the company’s sheer reach in the design world. Although it’s just a prototype, it’s one to watch closely.
The Cochrane Transform Project is now applying AI and machine learning to analyze thousands of reports to automatically select ones to include in systematic reviews. Systematic reviews bring together the best available research evidence from individual clinical trials and study data from around the world to inform the development of guidelines, individual practice decisions and national-scale health policymaking.
This new approach is successfully saving weeks of monotonous work, freeing up the expert health care reviewers to spend their time and energy on high-level analysis. This will speed up bringing the latest health interventions to the UK National Health Service, and beyond.
Professor James Thomas at University College London is using Cortana Intelligence to quickly develop and deploy AI solutions in the cloud. He explains, “What makes this particularly efficient is the fact that I can build a classifier using the studio and then just deploy it as a web service with the click of a button, without deploying a server.”
A new stock image search engine, Everypixel, is beta testing its unique algorithm to measure the aesthetics of stock images through neural networks.
Everypixel’s team trained a neural network to see the beauty in photos the same way humans do. While the company specifically built the algorithm to identify and weed out the most aesthetically-pleasing stock images from the ugly ones, it also works with basically any type of image.
To develop its algorithm, the team at Everypixel asked designers, editors and experienced stock photographers to help generate a training dataset.They tested 956,794 positive and negative patterns, and their “‘Heartless algorithm’ learned to see the beauty of shots in the same way as you do.”
Computer programs that learn to perform tasks also typically forget them very quickly. Demis Hassabis et. al. show that the learning rule can be modified so that a program can remember old tasks when learning a new one. This is an important step towards more intelligent programs that are able to learn progressively and adaptively.
The ability to learn tasks in succession without forgetting is a core component of biological and artificial intelligence. In this work they show that an algorithm that supports continual learning—which takes inspiration from neurobiological models of synaptic consolidation—can be combined with deep neural networks to achieve successful performance in a range of challenging domains. In doing so, they demonstrate that current neurobiological theories concerning synaptic consolidation do indeed scale to large-scale learning systems. This provides prima facie evidence that these principles may be fundamental aspects of learning and memory in the brain.
Japan, perhaps more than any nation on Earth, has a deep history with autonomous drones. Its companies have been using them for decades to assist with agriculture, infrastructure inspection, and construction. Yesterday one of Japan’s biggest tech companies, Rakuten, announced it was forming a joint venture with the American startup AirMap. The goal is to develop a robust traffic management system for unmanned aerial vehicles, allowing large numbers of drones to operate autonomously in the same airspace.
Rakuten, which is best known as an e-commerce company, has been experimenting with drone delivery since June of last year. Like Amazon, it wants to enable customers to order something online and have it delivered to their doorstep, or windowsill, in under an hour. The company sees low-altitude airspace as a wide-open market, where the only competition comes from birds and radio waves.
“AI is changing the way we interact with technologies across multiple industries. In a fast-growing market such as India, AI helps making technology-based companies more efficient,” says Sachin Jaiswal, cofounder of Niki.ai. This AI startup has recently launched the chatbot SDK to help brands deliver the “conversational” experience consumers are demanding on mobile and web apps.
HDFC Bank, Oxigen Wallet, Intex Smartphones and booking service Ticketgoose are some of the early adopters of its chatbot service. Two years ago, Niki.ai started out by developing chatbot programmed to respond to the chat requests of users for services such as cab ordering, food delivery and phone credit top-ups, and has now served over 50 million interactions.
“One might find that an adversary is able to control, in a big-data environment, enough of that data that they can feed you in misdirection,” said Dr Deborah Frincke, head of the Research Directorate (RD) of the US National Security Agency/Central Security Service (NSA/CSS).
As one example, an organisation may decide to use machine learning to develop a so-called “sense of self” of its own networks, and build a self-healing capability on top of that. But what if an attacker gets inside the network or perhaps was even inside the network before the machine learning process started?
“Their behaviour now becomes part of the norm. So in a sense, then, what I’m doing is that I’m protecting the insider. That’s a problem,” Frincke said.
It can be difficult to install a Python machine learning environment on Mac OS X. Python itself must be installed first, and then there are many packages to install, and it can be confusing for beginners.
In this tutorial, you will discover how to setup a Python 3 machine learning and deep learning development environment using macports. After completing this tutorial, you will have a working Python 3 environment to begin learning, practicing, and developing machine learning and deep learning software.