Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems are heavily researched in literature. A less studied area is a pragmatic approach to the role of humans in a complex modern industrial environment where ML based systems are developed. Key stakeholders affect the system from inception and up to operation and maintenance. Product managers want to embed "smart" experiences for their users and drive the decisions on what should be built next; software engineers are challenged to build or utilise ML software tools that require skills that are well outside of their comfort zone; legal and risk departments may influence design choices and data access; operations teams are requested to maintain ML systems which are non-stationary in their nature and change behaviour over time; and finally ML practitioners should communicate with all these stakeholders to successfully build a reliable system. This paper discusses some of the challenges we faced in Atlassian as we started investing more in the ML space.

Identifying 3 moss species by deep learning, using the "chopped picture" method Graduate School of Agriculture, Kyoto University, Japan * corresponding author: ise@kais.kyoto-u.ac.jp Abstract In general, object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. As a result, the model correctly classified test images with accuracy more than 90%. Using this approach will help progress in computer vision studies. Introduction Especially in recent years, deep learning has become a very effective tool for object identification (Krizhevsky et al. 2012, Szegedy et al. 2015).

Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment. We exhaustively evaluate various design choices, identify performance bottlenecks, and more importantly propose multiple orthogonal ways to boost performance.

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.

Nervous about the thought of your connected car falling victim to hacks, especially when self-driving cars hit the streets in earnest? So is the British government -- it just issued tougher guidelines for the security of networked vehicles. It wants security to be part of the design process across every partner involved, even at the board of directors' level, and for companies to keep cars updated throughout their lifespans. UK officials also call for a "defence-in-depth" strategy that minimizes vulnerabilities (such as by walling off systems to limit the damage of a hack), and very limited use of personal data that gives you control over what the car transmits. The government has also reiterated its hopes to build a "new framework" for self-driving car insurance, making it clear who pays when an autonomous vehicle crashes.

Indium, rhodium, platinum, tellurium and gold: these are some of the rarest elements in the world. From smartphones (which contain a whopping 60 to 64 elements) to hybrid cars, wind turbines and medical equipment, much of the technology we depend upon contains a rich list of elemental ingredients. Meanwhile, demand for traditional metals such as copper and aluminium is rocketing, driven by the rapid growth of emerging economies in Asia and South America. If our voracious appetite for these minerals continues at the current rate then rare earth metals may be mined out in 15 to 20 years, and indium may only have another decade of supplies remaining. Even aluminium could run dry within the next century.

The number of jobs that machine learning could render redundant over the coming decades is a growing cause for concern. According to research by PwC, 38% of US jobs will be automated by 2030, while other parts of the world fare little better. In Germany, it is 35%, and in the UK, 30%. However, it may be inevitable that jobs will be lost, but as with all periods of great technological advance, new jobs will also be created. Many of these will in fact be focused on developing and supervising machine learning algorithms, helping businesses to integrate and implement the technology and bring in efficiencies hitherto unimaginable.

The government said it was concerned that smart vehicles, which allow drivers to do things such as access maps and travel information, could be targeted by hackers to access personal data, steal cars that use keyless entry systems, or take control of technology for malicious reasons.

Insilico Medicine develops the advanced artificial intelligence (AI) algorithms to study the ageing processes and discover new interventions in ageing, many of these molecules aim to induce the expression of certain genes involved in the endogenous repair processes to slow down and even reverse some of the aging-associated diseases. By applying a specific branch of artificial intelligence called Deep Learning (DL) on multi-modal data, the company aims to discover molecules that can stimulate the repair of the DNA. The objective of this collaboration is to increase health span for everyone on the planet. "Many of the diseases of aging are associated with the failure of the DNA repair mechanisms. The ageing processes accelerate as the DNA repair mechanisms lose function. The collaboration with Insilico Medicine will allow us to find the molecules that repair DNA and prevent accelerated ageing", said the head of the biology of ageing lab and Assistant Professor Morten Scheibye-Knudsen, Center for Healthy Ageing.

When I was a child, each year without exception our family would drive to Cornwall in a wheezing Ford Sierra for the summer holidays. We'd stay with my great-uncle, a retired army major (gruff bachelor, suspected womaniser, borderline alcoholic), who was perhaps the last person I'd ever meet who earnestly deployed the phrase: "Children should be seen and not heard." In order to preserve quiet in the house, we went out a lot. We'd eat the same sandy ham sandwiches and shoo the same crabs from under the same rocks. Familiarity might have bred contempt, were it not for the Game Boy my brother and I brought along for the ride.