Artificial intelligence can perform feats that seem like sorcery. AI can drive cars and fly drones. It can compose original music, write poetry that isn't too awful, and design recipes that do sound awful (blueberry and spinach pizza, anyone?). AI can do some things better than humans: lip reading, diagnosing diseases such as pneumonia and some cancers, transcribing speech, and playing Jeopardy!, Go, Texas Hold'em, and a variety of video games. AI software can even learn to make its own AI software.

Artificial intelligence may be able to work out the maximum lifespans of extinct species and early humans. The technique relies on analysing specific regions of DNA that are linked to ageing. Benjamin Mayne at CSIRO, a research organisation in Australia, and his colleagues built an AI to predict the lifespan of different animals. To this the team first trained an AI on the known genomes of 252 species from five classes of animals, including mammals, reptiles and fish, and their maximum lifespans. The AI then narrowed down almost 30,000 DNA regions to just 42 that related to lifespan. This was then used to create a formula that can convert these 42 regions into a prediction of maximum lifespan.

In its 4 year existence the Israeli start-up Taranis has seen huge growth. Taranis started as a tool to provide farmers with the information to detect and prevent crop disease, weeds and insect damage based on weather forecasts gathered from aerial surveillance. The technology was then further developed by adding visual layers from satellites, planes and drones and leveraged with AI capabilities. Taranis also created a one-of-a-kind, patented hardware that can capture accurate images at a high resolution from a plane flying at 160 km/h, such as a specific insect on a leaf from 200 feet above ground. Taranis CEO Ofir Schlam says the future of the precision farming industry is looking bright, with thousands of start-ups emerging within the last 10 years. According to him, smart farming is projected to create a massive impact on the agricultural economy in the near future and will be dependent on precision technologies, such as the adoption of automated practices and indoor urbanised farming.

New centre aspires to be an international think-tank that promotes inter-disciplinary research into the interactions between technology and the law. The new Centre for Technology, Robotics, Artificial Intelligence & the Law (TRAIL), a research unit under the National University of Singapore Faculty of Law (NUS Law), was launched today by Mr Edwin Tong, Senior Minister of State for Law and Health, at the 8th Asian Privacy Scholars Network (APSN) Conference. Leveraging NUS Law's preeminent position amongst the top law schools in the world, TRAIL aspires to be an international think-tank that enables inter-disciplinary communities to research into legal, ethical, policy, philosophical and regulatory questions associated with the use and development of information technology (IT), artificial intelligence (AI), data analytics and robotics in the practice of law. The Centre plans to conduct research into the interactions between technology and the law in a more integrated and holistic manner. TRAIL also aims to provide a forum for legal and non-legal scholars interested in various aspects of technology law, to collaborate and advance inter-disciplinary research.

The months seem to be whipping by in a whirlwind and before long we will be into the new year. While it can be easy to get caught up in plans for marketing for the holidays, casting your eye further is necessary. Because 2020 is more than just a milestone, it is an opportunity to start implementing the hottest digital marketing trends that are going to be dominating the industry. Digital marketing has a wider scope and greater applications than ever before. Statista projected that by the close of 2019, active email accounts would be increasing to an astonishing 5.6 billion.

A new research project in Australia is using motion detectors and muscle sensors to track sheep shearers in an effort to minimize on the-job-injuries. Sheep shearers are six times more likely to be injured in the workplace than the average Australian worker. Data from sensors attached to sheep shearers will be used to model worker movement throughout the workday and test new ways of doing the job without risking injury. The study, a joint project between University of Melbourne and the trade group Australian Wool Innovation, uses sensors to measure electrical activity in muscles. These sensors are placed directly on the skin of the lower back and upper thighs, the ABC reported, while motion detectors are placed around the joints to track a worker's posture and shearing motions.

Graph Convolutional Networks (GCNs) have gained significant developments in representation learning on graphs. However, current GCNs suffer from two common challenges: 1) GCNs are only effective with shallow structures; stacking multiple GCN layers will lead to over-smoothing. 2) GCNs do not scale well with large, dense graphs due to the recursive neighborhood expansion. We generalize the propagation strategies of current GCNs as a \emph{"Sink$\to$Source"} mode, which seems to be an underlying cause of the two challenges. To address these issues intrinsically, in this paper, we study the information propagation mechanism in a \emph{"Source$\to$Sink"} mode. We introduce a new concept "information flow path" that explicitly defines where information originates and how it diffuses. Then a novel framework, namely Flow Graph Network (FlowGN), is proposed to learn node representations. FlowGN is computationally efficient and flexible in propagation strategies. Moreover, FlowGN decouples the layer structure from the information propagation process, removing the interior constraint of applying deep structures in traditional GCNs. Further experiments on public datasets demonstrate the superiority of FlowGN against state-of-the-art GCNs.

The modern world is changing more and more quickly with each passing year. If you don't pay attention to new changes or keep up the pace, it's easy to fall behind the times (and the market) while other companies beat you to the punch. To keep abreast of current changes – at least at a level of basic understanding. Adding to that, if you can't understand the buzzwords others are using in conversation, it's much harder to look smart while participating in that conversation. In this post, we're going to give you the 10 IT & technology buzzwords you won't be able to avoid in 2020 so that you can stay poised to take advantage of market opportunities and new conversations alike. The first in our definitive rundown of tech buzzwords 2020 is computer vision. Exciting and futuristic, the concept of computer vision is based on computing devices or programs gaining the ability to extract detailed information from visual images. In a computing context, 'vision' involves systems that can identify items, places, objects, or even humans from visuals mediums – images caught by a camera or sensor.

Singapore's Health Sciences Authority has approved the use of an artificial intelligence-powered (AI) software for the automated analysis and reporting of vascular ultrasound scans. Developed by See-Mode Technologies, the application taps deep learning, text recognition, and signal processing technologies with the aim of helping clinicians interpret such images -- a task that typically is performed manually, time-consuming, and error-prone. Such scans, used for patients with cardiovascular or heart diseases, are commonly analysed by a sonographer or radiologist who has to manually review between 50 and 150 images for each patient, according to See-Mode. No system is infallible and cybersecurity breaches are inevitable, but Singapore needs to do better in mitigating the risks and following through on its pledge to safeguard citizen data. "The end result is a hand-written, paper-based template filled with drawings, numbers, and measurements, which can take as long as 20 minutes per patient for severe cases," it said in a statement Tuesday.

Key Questions Answered in this Report: • What is the estimated global artificial intelligence in agriculture market size in terms of value during the period 2018-2024? Global Artificial Intelligence (AI) in Agriculture Market Forecast, 2019-2024 The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024. The reported growth in the market is expected to be driven by the increasing need to optimize farm operation planning, growing demand to derive insights from emerging complexities of data-driven farming, and rising development of autonomous equipment in agriculture. Artificial intelligence has emerged to be a strong driving force behind the growth of data-driven farming.Regions and countries where agriculture is the major source of livelihood and sustenance, the artificial intelligence technology has led to greater profitability in the farms of those economies. The reduction in expenditure and resultant positive RoI with AI's integration in farm equipment and operations has even reached above 30% in a few countries.