PROVIDENCE, R.I. [Brown University] -- The past few years have witnessed a revolution in artificial intelligence. AI systems are beating humans on reading comprehension tests, clobbering board game champions and enabling cars to drive themselves. Even more mundane AI systems, like smartphone apps that recognize faces and personal assistants that understand verbal commands, were seemingly insurmountable challenges just a decades or so ago. These recent breakthroughs have been made possible in large part by a technology known as deep learning or deep neural networks -- algorithms that have become the unseen force behind modern AI. Every time a phone responds to "Hey Siri," or Google translates a sentence from Swedish to Swahili, deep neural networks are at play.

Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and compare different published works across rapidly evolving software frameworks, heterogeneous hardware platforms, compilers, libraries, algorithms, data sets, models, and environments. We present our community effort to develop an open co-design tournament platform with an online public scoreboard. It will gradually incorporate best research practices while providing a common way for multidisciplinary researchers to optimize and compare the quality vs. efficiency Pareto optimality of various workloads on diverse and complete hardware/software systems. We want to leverage the open-source Collective Knowledge framework and the ACM artifact evaluation methodology to validate and share the complete machine learning system implementations in a standardized, portable, and reproducible fashion. We plan to hold regular multi-objective optimization and co-design tournaments for emerging workloads such as deep learning, starting with ASPLOS'18 (ACM conference on Architectural Support for Programming Languages and Operating Systems - the premier forum for multidisciplinary systems research spanning computer architecture and hardware, programming languages and compilers, operating systems and networking) to build a public repository of the most efficient machine learning algorithms and systems which can be easily customized, reused and built upon.

We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm --- an iterative application of compressed sensing techniques for orthogonal polynomials --- requires only uniform sampling of the hyperparameters and is thus easily parallelizable. Experiments for training deep neural networks on Cifar-10 show that compared to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds significantly improved solutions, in some cases better than what is attainable by hand-tuning. In terms of overall running time (i.e., time required to sample various settings of hyperparameters plus additional computation time), we are at least an order of magnitude faster than Hyperband and Bayesian Optimization. We also outperform Random Search 8x. Additionally, our method comes with provable guarantees and yields the first improvements on the sample complexity of learning decision trees in over two decades. In particular, we obtain the first quasi-polynomial time algorithm for learning noisy decision trees with polynomial sample complexity.

Prior studies have proposed to detect or quantify macular fluid in OCT in an automated manner.5x5Chakravarthy,

Artificial intelligence (AI) is still in its infancy, but it is truly a transformational technology. Thus far, mainly the companies with the largest stores of data have been able to benefit from the science. When talking about AI, most are referring to machine learning and, more specifically, deep learning, a technique that requires the processing of massive amounts of data in order to train these systems. So far these innovations haven't been largely available to smaller companies, but that is changing. The widespread adoption of cloud computing is beginning to alter that dynamic, as companies are working to incorporate these capabilities into their cloud offerings.

Microsoft today is unveiling new artificial intelligence technology that's something of an artist – a "drawing bot." The bot is capable of creating images from text descriptions of an object, but it also adds details to those images that weren't included the text, indicating that the A.I. has a little imagination of its own, says Microsoft. "If you go to Bing and you search for a bird, you get a bird picture. But here, the pictures are created by the computer, pixel by pixel, from scratch," explained Xiaodong He, a principal researcher and research manager in the Deep Learning Technology Center at Microsoft's research lab in Redmond, Washington, in Microsoft's announcement. "These birds may not exist in the real world -- they are just an aspect of our computer's imagination of birds."

The promise of AI and other cognitive technologies is enticing companies to take on aggressive new initiatives. Authors Davenport and Ronanki share how to apply cognitive technologies from robotics to deep learning in bold new ways, based on their study of 152 cognitive projects and the results of Deloitte's 2017 state of cognitive survey. Taking an incremental approach--rather than a transformative approach--helps organizations avoid potential setbacks. In fact, "low-hanging fruit" projects that streamline business processes and augment, rather than replace, human capabilities are much more likely to be successful than the most highly ambitious projects. Here, you'll see how organizations are improving products and creating new ones, making better decisions, and freeing up workers to be more creative using cognitive technologies.

The "Industrial Machine Vision Market by Component (Hardware (Camera, Frame Grabber, Optics, Processor), and Software (Deep Learning, and Application Specific)), Product (PC-based, and Smart Camera-based), Application, End-User - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering. The overall industrial machine vision market was valued at USD 7.91 Billion in 2017 and is expected to reach USD 12.29 Billion by 2023, at a CAGR of 7.61% between 2017 and 2023. This is because of the increasing need for quality inspection and automation, growing demand for AI and IoT integrated machine vision system, increasing adoption of Industrial 4.0, development of new connected technologies, and government initiatives to support smart factories, among others. Governments of different countries worldwide are encouraging investments in manufacturing, which is necessitating the use of various automation products for structural development. Software component is expected to grow at the highest rate between 2017 and 2023.

Companies that have invested heavily in big data solutions want to know how to make smart, strategic investments that will distinguish them from the competition and enable the best possible return before making the decision to go all in. In the past, not all enterprise big data initiatives went as planned. These failures are not usually published, but the big data failure rate is unusually high. According to Gartner, only 15% of businesses make it past the pilot stage of these projects. Our fear, as leaders of technology companies, is that with so much attention surrounding AI, the pressure is on to apply the technology or risk falling behind the many decision makers who are adopting technologies without first establishing clear business goals and understanding the differences between AI and ML and how they should be applied. It's easy to get caught up in the allure of artificial intelligence as well as its hype, including breakthroughs like deep learning, but those looking to make an outsized impact should instead focus on its more practical counterpart: good old-fashioned machine learning -- or "cheap learning," as my colleague Ted Dunning and Ellen Friedman explain in their guide Practical Machine Learning: Innovations in Recommendation.

According to, Gary Nuttall MD of The Fintech Times, "The insurance sector is embracing both the InsurTech sector in general, and blockchain in particular, with alliances such as B3I (Blockchain Insurance Industry Initiative – with 15 insurance/reinsurance members) and R3 (which includes insurance companies from Asia and the USA). The establishment of consortia, along with industry standards bodies such as ACORD (a Global Insurance Standards organisation), will enable interoperability between blockchains. Customers experience in the insurance sector still lags way behind. Blockchain allows insurers to start developing models that reduce the burden on customers such as less data re-entry, one touch buying. This can be supported with greater control over digital identity using hash functions and will allow multiple platforms to be developed that can interact with each other. The FinTech community seems to be filled with challenger banks and alternative payment providers, all of whom are competing against existing banks and service providers. The InsurTech community seems to be more collaborative with startups working with existing insurers and we're likely to see this trend grow as blockchain startups aligns with organisations that already have a major foothold in insurance."