You can be excused for not noticing that a scientist named Daniel Buehrer, a retired professor from the National Chung Cheng University in Taiwan, recently published a white paper proposing a new class of mathematics that many feel could one day lead to the birth of machine "consciousness," and perhaps even Artificial Super Intelligence (ASI) itself which is slated to arrive circa 2045. After all, keeping up with all the breakthroughs in the field of Artificial Intelligence (AI), from the development of new Artificial General Intelligence (AGI) architectures to the AI's, for example, from DeepMind, that are self-evolving and fighting each other, can be exhausting. Robot consciousness, or sentient machines, have long been a touchy subject if for no other reason than the fact that as of yet we still aren't able to describe what consciousness really is, let alone how it came to be, and this therefore makes it a touchy subject for anyone in AI circles. In order to have a discussion around the idea of a computer that can'feel' and'think,' and that has its own aspirations and motivations, you first have to find two people who actually agree on the semantics of sentience. And if you manage that, you'll then have to wade through a myriad of hypothetical objections to any theoretical living AI you can come up with.

For International Girls in ICT Day, and a few weeks ahead of the AI for Good Global Summit, ITU News caught up with Sarah Porter, CEO and Founder of InspiredMinds, World Summit AI, Intelligent Health, and Ada-AI, a non-profit dedicated to ensuring AI benefits all. Sarah is a humanitarian first-response trauma medic, ambassador for the Royal Marsden hospital in London and speaker for the United Nations on Lethal Autonomous Weapons. When I saw the story that a team of young girls from Afghanistan had against all odds made a robot but were then refused their visa [to attend an international robotics contest], it made me realise just how fortunate the Global North is with their right to free education in many disciplines including Science, Technology Engineering and Mathematics (STEM), unlimited access to wifi, and opportunities to learn how to code. The rapid progression of Artificial Intelligence (AI) by the wealthy corporations risks excluding the sectors of society that need it the most. Not only are women a minority in STEM education and in the tech teams building AI, the Global South is under-represented.

New Delhi: Indian scientists are using artificial intelligence (AI) to develop a smart diagnostics system to predict the onset of Alzheimer's disease early. Professor Pravat Mandal and his group at the National Brain Research Centre (NBRC), and Neuroimaging and Neurospectroscopy Laboratory (NINS) are together working to develop a model to map metabolic patterns in different brain regions in healthy and pathological conditions. Alzheimer's disease is a chronic degenerative brain disorder. Alzheimer's disease is an irreversible progressive brain degenerative disorder. It manifests as cognitive deterioration and associated behavioral disturbances, leading to impairment of activities of daily living.

SINGAPORE: This year's edition of the Singapore FinTech Festival will have a focus on Southeast Asia, given the country's chairmanship of the Association of Southeast Asian Nations (ASEAN), and artificial intelligence (AI), the Monetary Authority of Singapore (MAS) said. In a press release, the central bank said there will be two new elements in this year's festival: The ASEAN FinTech Showcase, which will highlight financial technology developments and opportunities in the region and a showcase of innovations from the 10 regional countries. There will also be the Artificial Intelligence in Finance Summit, which will explore emerging AI solutions in trading, investment management, customer service and risk management, said the MAS, which is partnering with The Association of Banks in Singapore (ABS) to organise the event. It will also include discussions on quantum computing as well as governance and ethics in the application of AI, it added. The co-organiser pointed out that popular segments from previous editions will be "refreshed and enhanced" as well.

The growing adoption of artificial intelligence (AI) is driving more organisations in Asia to turn to high-performance computing (HPC), according to a senior Lenovo executive. The race is on to get ready for GDPR next year. Computer Weekly looks at how to deal with data under the regulation, how compliance will affect businesses, and what organisations should do to prepare. You forgot to provide an Email Address. This email address doesn't appear to be valid.

In part 2 of China in AI, we look at how the biggest companies in China are positioning themselves to become global leaders in smart city solutions, autonomous driving, conversational AI, and predictive healthcare. China's internet may be sandboxed from the rest of the world, but China's big tech companies are bringing their AI capabilities to the global market. Tencent, Baidu, and Alibaba (collectively called BAT) are positioning themselves to become the AI platforms of the future. Join us for a live briefing as we dive into the Chinese government's AI strategy, what tech giants like Alibaba and Tencent are doing, startup activity, and much more. Tencent, which runs WeChat, has access to over 1B users on its platform, while Baidu is the country's largest search provider, and Alibaba is its biggest e-commerce platform. In addition, all 3 offer services well beyond their core products, and like the biggest tech giants in the US have far-reaching global ambitions. BAT is expanding into other countries in Asia, recruiting US talent and investing in US AI startups, and forming global partnerships to advance smart city solutions, autonomous driving, conversational AI, and predictive healthcare, among other initiatives.

With the new system in place, the Russian air-defense forces will be able to respond to all situational changes in real time, bypassing the stage of analysis at command posts. Currently, each anti-aircraft missile and radar installation has its own control which is absolutely vital given the high speed of modern aircraft and high density of air attacks. A combination of all existing air defense systems and the use of each one's fortes will create multiple lines of defense. For example the S-400 Triumf air-defense system, effective against high-altitude targets, could be used in sync with the Pantsir-S missile-gun system, which is ideal in close combat situations. This would ensure the effective destruction of aircraft, cruise and ballistic missiles, small drones and effective protection against fire by multiple rocket launchers.

Artificial intelligence has been pretty smart for the past two decades, and it's getting smarter fast. Not that long ago -- 1997 -- we were wowed by Kismet, an MIT robot that could turn sound and visual cues into facial expressions, vocal responses and basic movements. That same year, IBM built Deep Blue, which beat out world chess champion Garry Kasparov. Deep Blue could evaluate 200 million positions a second and think strategically. The match was called "the brain's last stand."

Considering the use of Fully Connected (FC) layer limits the performance of Convolutional Neural Networks (CNNs), this paper develops a method to improve the coupling between the convolution layer and the FC layer by reducing the noise in Feature Maps (FMs). Our approach is divided into three steps. Firstly, we separate all the FMs into n blocks equally. Then, the weighted summation of FMs at the same position in all blocks constitutes a new block of FMs. Finally, we replicate this new block into n copies and concatenate them as the input to the FC layer. This sharing of FMs could reduce the noise in them apparently and avert the impact by a particular FM on the specific part weight of hidden layers, hence preventing the network from overfitting to some extent. Using the Fermat Lemma, we prove that this method could make the global minima value range of the loss function wider, by which makes it easier for neural networks to converge and accelerates the convergence process. This method does not significantly increase the amounts of network parameters (only a few more coefficients added), and the experiments demonstrate that this method could increase the convergence speed and improve the classification performance of neural networks.

Real-time traffic flow prediction can not only provide travelers with reliable traffic information and thus save time, but also assist traffic management department to manage transportation system. It can greatly improve the efficiency of transportation. Traditional traffic flow prediction methods usually need a huge amount of data but still leaves a poor performance. With the development of deep learning, researchers begin to pay attention to artificial neural networks (ANNs) such as RNN and LSTM. However, these ANNs are very time-consuming. In our article, we improve the Deep Residual Network and build a dynamic model which previous researchers hardly use. Our result shows that our model can not only be trained efficiently but also have a higher accuracy. Additionally, our dynamic model is more suitable for practical applications.