bridge


Life will soon be like 'Her' -- and we'll fall in love with AI

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It has penetrated almost every industry and is helping them become innovative, develop authentic tools, and build strategies towards a sustainable future. Researchers are eagerly exploring new use cases of artificial intelligence that have the power to radically transform societies around us. But as we develop intelligence artificially, will be there a room for this AI to rewire us as humans? Artificial intelligence has come a long way since its inception. We are at the brink of a massive change where world leaders are constantly discussing whether AI will take over the world and start controlling us.


3 Inconvenient Truths about AI and ML - RTInsights

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To bridge the gap between the data we're collecting and the way organizations interface with it, we need to address some uncomfortable realities. As we step into the next decade, there's a growing sense – almost an inevitable momentum – that we're headed towards a golden age of AI. Over the past year, we've witnessed incredible advances in applying artificial intelligence techniques to image recognition, language processing, planning, and information retrieval. There are more amusing applications, too, including one team teaching AI how to craft puns. See also: Will the Consumerization of AI Set Unrealistic Expectations?


Artificial intelligence, digital marketing, and design thinking to be top skills to drive future growth: Survey

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A survey amongst 307 corporates, ranging from SMEs to large corporates, highlights that tech upskilling in skills like artificial intelligence, machine learning, digital marketing and design thinking is crucial for boosting their organizational performance. Conducted by Great Learning, the survey aimed to find out the top skills that organizations will need to drive future growth and how they plan to bridge the impending skill deficit amongst their ranks. Inspite of the increased awareness around upskilling, the survey found that 47% of the companies have still not assigned budgets for upskilling their workforce. Hari Krishnan Nair, co-founder, Great Learning said: "The technology skill gap among employees is one of the biggest challenges that organizations in India are beset with. While it is encouraging to see that a majority of companies are aware of the need for upskilling, the time to act is now. Skilled employees will continue to be the biggest asset for any organization going ahead and while options like lateral hiring and outsourcing may help in the short term, from a cost and effectiveness point of view, upskilling is the best way to stay competitive in the long run."


The almost Comprehensive Guide to AI in Infrastructure Asset Management

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Artificial Intelligent systems are generally defined as computer systems which are to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation. In the context of Infrastructure Asset Management, there are many applications for AI which can replace or augment substantial levels of human effort in order to improve the performance of the Asset or extend its life. As many public organisations are making significant efforts to understand'best practice' asset management, often referring to ISO 550001 for guidance on the The goal of Machine Learning is to learn from data, ingesting a large volume of data a ML algorithm is predominantly focused on a certain task to maximize the performance of machine in performing that task. Artificial Intelligence, on the other hand, is primarily focused on decision making. So while ML allows a system to learn new things from data, and AI attempts to mimic human behavior in a circumstances.


The Bridge Between AI And Bitcoin

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Cryptocurrency and blockchain firms are already exploring how AI can help improve their products and services. Indeed, the development of the blockchain does have big implications for the future of AI and Machine Learning too, especially when we consider data storage and fast access. The blockchain allows huge amounts of data to be stored securely and of course, shared across other entities on that network. These entities, could very well be computers or programmes that have been built to learn from the blockchain, and thus, we see a huge amount of information spread that these programmes can learn from, making Artificial Intelligence, better.


Anybody Aboard?

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Artificial intelligence will soon be making a career in the maritime industry: Because specialist personnel and cargo space are scarce and transport costs are high, more and more ship owners are relying on ships with state-of-the-art assistance systems and autonomous driving functions. Autonomous ships will get by completely without captain and crew. When autonomous vessels plough through the waves in the future, the history of ghost ships will have to be rewritten. Legends like the Flying Dutchman and the Marie Celeste have one thing in common. Both vessels had a crew on board before fate befell them in the vastness of the oceans.


Normalizing Constant Estimation with Gaussianized Bridge Sampling

arXiv.org Machine Learning

Department of Physics, Department of Astronomy University of California, Berkeley, CA 94720, USA and Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA 94720, USA Abstract Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate. Here we develop a new approach, starting from posterior samples obtained with a standard Markov Chain Monte Carlo (MCMC). We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant. We compare our method which we call Gaussianized Bridge Sampling (GBS) to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both significantly faster and substantially more accurate than these methods, and comes with a reliable error estimation. Keywords: Normalizing Constant, Bridge Sampling, Normalizing Flows 1. Introduction Normalizing constant, also called partition function, Bayesian evidence, or marginal likelihood, is the central object of Bayesian methodology.


Taking Your AI Projects from Pilot to Production

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The rise of AI has made it possible for automated visual inspection systems to identify anomalies in manufactured products with high accuracy. If implemented successfully, these systems can greatly improve quality control and optimize costs. Although many manufacturers are trying to implement such systems into their workflow, very few have managed to reach full-scale production. The disconnect occurs because proof of concept solutions are put together in a controlled setting, largely by trial and error. However, when pushed into the real world with real-world constraints like variable environmental conditions, real-time requirements, and integrations with existing workflows, proof of concept often breaks down.


Good tech talent is still hard to find (how to bridge the gap)

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In today's era of digital business, companies are intoxicated by the potential of technology-driven transformation. Retailers are wielding big data analytics to drive new customer experiences and increase sales while players in banking, insurance, and pretty much every other industry are making a beeline to machine learning (ML) and artificial intelligence (AI) to automate and reimagine key business processes. The possibilities are endless, and the future of digital business seems bright. Yet a persistent trouble spot remains the serious shortage of IT talent, particularly among candidates with proven expertise in coveted skills in areas like cloud, security and AI. The 2019 State of the CIO confirmed that finding and nurturing the right skills to support digital transformation and the ongoing IT agenda is a significant hurdle for many IT organizations.


The {\alpha}{\mu} Search Algorithm for the Game of Bridge

arXiv.org Artificial Intelligence

{\alpha}{\mu} is an anytime heuristic search algorithm for incomplete information games that assumes perfect information for the opponents. {\alpha}{\mu} addresses the strategy fusion and non-locality problems encountered by Perfect Information Monte Carlo sampling. In this paper {\alpha}{\mu} is applied to the game of Bridge.