Today, whilst exhibitors on the show floors work flat out to get their booths ready before the masses enter the show halls tomorrow morning, the worlds' media get to soak up a 12 hour shift of back to back press launches by the industry big guns. The Mandalay Bay conference centre is the setting; each organisation gets 45 minutes to make their pitch for media oxygen and airtime as to why what they have to say is news for those hungry for CES knowledge. Having fuelled up with coffee, bananas and muffins, I attended 10 of these launches to gain a flavour of what the talking points of this years' CES maybe. First out the traps at 8am are LG, and thanks to jet lag, we are all there early, with crowds gathering from 7.00am, waiting to get a ringside seat for the show. LG, led the commentary that many of the CE brands would cover today.

Data is the new oil, it has been said for years now. If data is the new oil, then China is already the largest producer with its factories packed with labourers working hard to annotate images and data for machine learning. Machine Learning needs loads of data to perform well and the need for high quality hand annotated data has skyrocketed in the last decade. "I used to think the machines are geniuses. Now I know we're the reason for their genius."

NEW DELHI: India may have to forgo as much as $1.97 trillion in gross domestic product (GDP) growth promised by investment in intelligent technologies over the next decade if the country fails to bridge the skill gap, a new report from Accenture said on Monday. Advanced technologies such as artificial intelligence (AI), augmented/virtual reality (AR/VR) and Blockchain can enable rapid reskilling and upskilling at scale, said the report titled "Fueling India's Skill Revolution". These technologies can help people learn new skills quickly, efficiently and cost effectively, Accenture said. "We must offer more experiential on-the-job training and help people adopt life-long learning as their jobs are transformed. Digital tools and applications -- like artificial itelligence, analytics and blockchain -- will be essential in delivering these new learning approaches," said Rekha M Menon, Chairman and Senior Managing Director at Accenture in India.

In November, Amazon also said it had created an inference chip. Amazon's chip is not a direct threat to Intel and Nvidia's business because it will not be selling the chips. Amazon will sell services to its cloud customers that run atop the chips starting next year. If Amazon relies on its own chips, it could deprive both Nvidia and Intel of a major customer. Also at the Consumer Electronics Show on Monday, Intel said that Dell Technologies Inc will feature Intel's next generation of processors in its XPS line of laptops.

Also at the conference, Amnon Shashua, the head of Intel's Mobileye self-driving car computer unit, said Mobileye has mapped out all of the roadways in Japan, using cameras that were already embedded in vehicles produced by Nissan Motor Co Ltd that come with Mobileye systems from the factory. Intel's tech rivals such as Alphabet Inc and Apple Inc are gathering mapping data through special vehicles with cameras mounted on top of them.

James: Dead set, that is Bruce.

In the beginning, archaeologists believe, the first breads were created using some of the most rudimentary technologies in human history: fire and stone. In the region that now encompasses Jordan, one of the world's most ancient examples -- a flatbread vaguely resembling pita and made from wild cereal grains and water -- was cooked in large fireplaces using flat basalt stones, according to Reuters. The taste is "gritty and salty," Amaia Arranz-Otaegui, a University of Copenhagen postdoctoral researcher in archaeobotany, told the news service. "But it is a bit sweet, as well." More than 10,000 years later, bread has clearly evolved but, perhaps, not as dramatically as the technology being used to bake it.

Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.

Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by the categorical nature of all attributes (e.g. {US, Chrome, Mobile}). With each attribute taking many values, the very large attribute combination set makes estimating audience size for any specific attribute combination challenging. We modify Eclat, a frequent itemset mining (FIM) algorithm, to accommodate categorical variables. For consequent frequent and infrequent itemsets, we then provide forecasts using time series analysis with conditional probabilities to aid approximation. An extensive simulation, based on typical characteristics of audience data, is built to stress test our modified-FIM approach. In two real datasets, comparison with baselines including neural network models, shows that our method lowers computation time of FIM for categorical data. On hold out samples we show that the proposed forecasting method outperforms these baselines.

The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the seafloor. Approximately 70.8% of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean-resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the Ocean Network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The Cognitive Ocean Network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the paper are as follows: (1) a CONet architecture is proposed and described in detail; (2) important and useful demonstration applications of the CONet are proposed; and (3) future trends in CONet research are presented.