Organizations have found a lot of new value for themselves and their customers over the past year, thanks to Internet of Things (IoT), machine learning, blockchain and other SAP Leonardo-related technologies. These successes usually result from a focus on solving a specific problem -- and they often lead to the creation of new business models. "SAP Leonardo is on the frontlines of next practices in 25 distinctly different industries," SAP CEO Bill McDermott said during his opening keynote at SAPPHIRE NOW this month. SAP Leonardo is a system of intelligent technologies that include IoT, machine learning and blockchain. "Last year, we said a combination of maturing technologies -- IoT, machine learning and blockchain -- would help reimagine business processes," SAP CEO Bill McDermott said during his opening keynote at SAPPHIRE NOW this month.

"What commonly happens in the invention process is that people are driven by the constant irritations and annoyances of life," Hobson said. "And traffic lights appear to have a malicious intent whenever you approach them." Waiting at a red light, he noticed a tremendous amount of traffic in one direction but nobody in the other. Yet the traffic lights still obediently kept to their programmed pace of red, yellow, green. Worse: the light tried to accommodate non-existent pedestrians, further delaying things.

China has performed outstandingly well in scientific research and technological innovation among the G20 countries, and the country's scientific and technological strength in the field of AI is seeing rapid growth, second only to the US, a report shows. According to the report, in terms of the overall scientific and technological strength of AI, the US ranks first among the G20 countries followed by China, whose technological strength in AI has increased significantly, especially in the past five years. China has performed outstandingly well in the first three fields among the four branches of AI--machine learning, natural language processing, computer vision, and speech processing. But while the output of the country's scientific research has surpassed that of the US, improvements are needed in terms of the quality of the output. The report focuses on the scale of research output, academic impact, and international cooperation among the G20 countries, as an indication of China's position in the competitive landscape, as well as the challenges it faces.

Have you put a bet on the FIFA World Cup? If yes, the chances are you've made a pretty educated guess, right? You know which team has the strongest players or most favourable odds. Or maybe you've put some cash on your country's team, (which normally I'd avoid England, but given their recent performance, I could be wrong to!) Either way, you might be best casting your bets in line with San Francisco based Unanimous AI. They use a technology called Swarm AI - algorithms modelled on swarms in nature that amplifies human intelligence. By using human intelligence and artificial intelligence together, they can predict outcomes better than humans or AI acting alone.

Artificially intelligent software can drive robots to perform the most menial tasks, such as reaching out and gripping objects. However, there's one thing they can't, er, grasp easily. And that's dealing with things that move unexpectedly, which right now rules them out of a lot of real-world labor. However, it does make them good for picking up stuff that typically stays still, such as clothes on the floor or boxes in a warehouse. It's a surprisingly difficult skill to master, according to a paper to be presented at the Robotics: Science and Systems conference starting on Tuesday at Carnegie Mellon University in the US.

BANKS have been rolling out chatbots, which allow users to bank without interacting with a human. Banking giant Citi is joining the mix, planning to launch its own chatbot in Hong Kong, except it's on (Facebook) Messenger. This makes sense for Citi. As reported by the South China Morning Post, Facebook has over five million users in Hong Kong. By rolling out the chatbot on a commonly used platform, Citi is going where its users are.

SYDNEY – Australia will invest 7 billion Australian dollars ($5.2 billion) to develop and buy high-tech U.S. drones for joint military operations and to monitor waters including the South China Sea, it said Tuesday. Canberra has been embarking on its largest peacetime naval investment through a massive shipbuilding strategy that includes new submarines, offshore patrol vessels and frigates to shore up its defense capabilities. As part of this, the government will spend AU$1.4 billion to buy the first of six MQ-4C Triton maritime surveillance drones, with the aircraft to enter service from mid-2023, complementing seven P-8A Poseidon planes currently in use. "Together these aircraft will significantly enhance our anti-submarine warfare and maritime strike capability, as well as our search and rescue capability," Prime Minister Malcolm Turnbull said in a statement. "This investment will protect our borders and make our region more secure."

Australia will buy six U.S. Triton remotely piloted aircraft to beef up its maritime patrols, with the initial investment of A$1.4 billion ($1 billion) for the first drone, Prime Minister Malcolm Turnbull said on Tuesday. The government said the Triton drones, made by Northrop Grumman Corp, would be used alongside P-8A Poseidon aircraft for long range operations and intelligence gathering, and would improve anti-submarine warfare and marine strike capability. "This investment will protect our borders and make our region more secure," Turnbull and Australia's defence ministers said in a joint statement. The total cost for the six drones, including facilities upgrades and support, will be A$6.9 billion, a person familiar with the transaction said. Defence Industry Minister Christopher Pyne's office declined to comment on the total cost of the aircraft, which can fly for up to 24 hours and have sensors that can view the surrounding area over 2,000 nautical miles (3,700 kms).

We specialize the decoupled extended Kalman filter (DEKF) for online parameter learning in factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through simulations. Learning model parameters through the DEKF makes factorization models more broadly useful by allowing for more flexible observations through the entire exponential family, modeling parameter drift, and producing parameter uncertainty estimates that can enable explore/exploit and other applications. We use a more general dynamics of the parameters than the standard DEKF, allowing parameter drift while encouraging reasonable values. We also present an alternate derivation of the regular extended Kalman filter and DEKF that connects these methods to natural gradient methods, and suggests a similarly decoupled version of the iterated extended Kalman filter.

Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind's WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work.