While many companies view artificial intelligence as an integral part of their future success, very few have fully embraced the technology. According to a global Accenture survey, business executives believe that within the next two years, artificial intelligence will work next to humans in their organisations as a co-worker, collaborator and trusted advisor. Accenture predicts by 2022, firms that adopt AI can boost revenues by up to 38 per cent. But rolling out AI at scale across a company is far easier said than done. And despite the lofty ambitions, very few businesses locally have taken AI beyond the experimental stage. There are still significant hurdles to be overcome when adopting AI in a significant way.

According to the DJI's report, around 65 people have been rescued by drones in the last year. The report, "More Lives Saved: A Year Of Drone Rescues Around The World," features the innovative drone technology, and the rapid adoption by first responders to increase the pace of drone use in critical public safety missions. Reports have also stated that drones have dropped buoys to struggling swimmers in Australia and Brazil, and found helpless people in fields, rivers, and mountains. Approximately, 22 out of 65 "were at great risk of death, such as stranded in a body of water or exposed to hazardous weather." "Drones allow rescuers a way to find missing people, deliver supplies like food and life vests, and cut search and response times from hours to minutes," says Brendan Schulman, DJI's Vice-President of policy and legal affairs.

The training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training set, and test it on a holdout test set. This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning. In this paper, we propose a novel training approach inspired by how radiologists are trained. In particular, we explore the use of meta-training that models a classifier based on a series of tasks. Tasks are selected using teacher-student curriculum learning, where each task consists of simple classification problems containing small training sets. We hypothesize that our proposed meta-training approach can be used to pre-train medical image analysis models. This hypothesis is tested on the automatic breast screening classification from DCE-MRI trained with weakly labeled datasets. The classification performance achieved by our approach is shown to be the best in the field for that application, compared to state of art baseline approaches: DenseNet, multiple - instance learning and multi-task learning.

Some say artificial intelligence needs to learn like a child. Babies are born with instincts that help us learn common sense, so far elusive for AI algorithms. It's a Saturday morning in February, and Chloe, a curious 3-year-old in a striped shirt and leggings, is exploring the possibilities of a new toy. Her father, Gary Marcus, a developmental cognitive scientist at New York University (NYU) in New York City, has brought home some strips of tape designed to adhere Lego bricks to surfaces. Chloe, well-versed in Lego, is intrigued. But she has always built upward. Could she use the tape to build sideways or upside down?

In promoting his 10-year tax plan, Malcolm Turnbull suggested people want governments to undertake "long term planning". However, a new research paper out this week from the IMF highlights how economies could be set for a major shake-up in the future and how sticking with the belief that better wages for workers comes from reducing company tax in order to spur capital investment is a rather wishful proposition. Economics research papers generally are not known for their optimism, but the IMF paper titled "Should We Fear the Robot Revolution? This research goes very much to the heart of primary political debate in this country about jobs, equality and the role of government. When Malcolm Turnbull took over the prime ministership he loved to talk about how it was the most exciting time to be alive – innovation was on the rise, agility was all the go! But for many workers it is a rather worrying time. In the past decade since the global financial crisis real wages have stalled, underemployment has risen, wage growth has plummeted, businesses have sought to move away from enterprise bargaining, and the growth areas have been mostly in lower-paid services sectors such as social care. And underneath all that is the feeling – perhaps overstated – that we are soon in danger of being replaced by robots. The IMF paper on the topic notes that there are essentially two camps on this issue – the first is optimistic and believes that, as in the past, greater automation will see some jobs lost, but the demand for many jobs – especially those "that place a premium on creativity, flexibility, and abstract reasoning" – will grow and overall the economy is better off. The other side of the coin are those who note these are not your grandparents' robots we're talking about. These are robots that make use of AI in order to do work previously believed to be non-automatable precisely because it was seen as creative, flexible, or needing abstract reasoning. The paper considered a range of scenarios – from the more traditional one where robots replace only low skilled work to where robots are able to replicate a range of work and then a final one where robots "can do anything". And the results are not good for workers. Essentially the shift sees national income move from labour to capital – as the returns from investing in robots to do work previously done by people increase. They note that "the most common arguments for technology optimism do not stand up to scrutiny". Even in scenarios that fit with the more optimistic view of automation, the paper concludes that "automation is very good for growth and very bad for equality". The authors suggest that "in scenarios where the traditional technology disappears and robots take over the automatable sector, the economy either ascends to a virtuous circle of ongoing endogenous growth or descends into a death spiral of perpetual contraction.

Europe is ahead in the digital game, says Tata's Consultancy Services COO. But businesses and governments will need to utilise Big Data and Artificial Intelligence to improve services and the quality of life of customers. N. Ganapathy Subramaniam is the Chief Operating Officer of Tata Consultancy Services (TCS), an Indian multinational IT service, consulting and business solutions company headquartered in Mumbai. He spoke to EURACTIV's Alexandra Brzozowski on the sidelines of the European Business Summit in Brussels. How could digital economy improve business and public-sector performance?

AIXIjs is a JavaScript demo for running General Reinforcement Learning (RL) agents in the browser. In particular, it provides a general and extensible framework for running experiments on Bayesian RL agents in general (partially observable, non-Markov, non-ergodic) environments. UPDATE (May 2017): I'll be presenting a conference paper containing a literature survey along with some experiments based on AIXIjs at IJCAI 2017, in Melbourne, Australia. The paper (to appear) is: J. S. Aslanides, Jan Leike, and Marcus Hutter. See the main site for more background, documentation, references, and demos.

This week Dezeen released Elevation, an 18-minute documentary that explores the impact drones will have on our lives. Here, we take a look at 10 innovative ways drones will change the world. Customers of supermarket giant Walmart may soon be able to summon assistance from unmanned aerial vehicles using mobile electronic devices. The vehicles will help locate products in store and advise on prices by crosscheck information stored on the store's central databases. PriestmanGoode's fleet of urban delivery drones, called Dragonfly, are featured in Dezeen's documentary.

Artificial intelligence and machine learning saw a significant spike of attention in the past few years – whether it's through partnerships, acquisitions, or in-house developments. The largest financial institutions in the US have been involved in one way or another in bringing artificial intelligence into operations and customer-facing functions. A recent study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) by MEDICI Team found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor. Some of the most prominent banks in this space across regions are Bank of America, OCBC, ABN Amro, YES BANK, etc. While front-office applications have certainly seen a higher intensity, scope, and adoption, the AI strategy in the US banking industry, in reality, is far more diverse.

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modeling the interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN's Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC's Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modeling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/. Keywords: Ontology, Sensor, Observation, Actuator, Linked Data, Web of Things, Internet of Things, Schema.org 1. Introduction and Motivation In their broadest definition sensors detect and react to changes in the environment that directly or indirectly reveal the value of a property. The process of determining this, not necessarily numeric, value is called an observation.