The economic numbers from China are pointing to the realities of the trade war – a slowing China's economy. Results of Published Model Trades for THU 10/17 Find below the detailed outcome tracking of our models' trading plans for the day, as well as the results for the last month. Note: Our daily "S&P 500 Outlook, Forecast, and Trading plan" will be posted around 9:00am EDT, every trading day. Overnight futures markets are cheering the last minute deal struck between the UK and the EU on Brexit, despite serious doubts whether the UK parliament would approve the deal. Results of Published Model Trades for WED 10/16 Find below the detailed outcome tracking of our models' trading plans for the day, as well as the results for the last month.

If Facebook and Elon Musk's ambitions to directly connect human brains to machines are any indication, it seems we will become increasingly dependent on smart devices. Less bombastic than this proposal, but already widespread, are more mundane forms of augmentation – neural prostheses allow brains to control replacement body parts, artificial organs can be designed to specific bodies, and embedded devices like insulin pumps can intelligently support their hosts. With this is mind, we asked six experts the following question: How will technologically augmenting humans affect sustainability? The implications of human augmentation are so complex that it is difficult to succinctly assess what their implications will be for sustainability. There are, of course, potential benefits.

Over this past summer, I was fortunate enough to be given the opportunity to deliver a speech to the State University of New York (SUNY) College of Optometry residency class of 2019. During this 20 minutes (which they likely perceived as just over an hour), I recommended that residents take a few moments and conduct a search of the world's literature using the key words "deep learning" with the disease of their choice. Conducting such a search myself gave me a better understanding of the likely direction of health care in my clinical lifetime. A study recently published in JAMA Ophthalmology describes a deep learning system which appears to show high sensitivity and specificity for the detection of glaucoma.1 Previously by Dr. Casella: Consider IOP fluctuations when diagnosing glaucoma Deep learning So, just what exactly is deep learning? In the arena of artificial intelligence, this subset of machine learning is based on so-called "neural networks" that process data into concepts.

Every year, around 50,000 individuals graduate to become certified doctors. In order to maintain the minimum doctor-patient ratio, as suggested by WHO, India will need 2.3 Mn doctors by 2030. If there was ever a requirement to push healthcare in India into the future, it is now! Today is the time when we can see significant disruption in the Indian healthcare industry. Much of this is credited to the level of involvement of artificial intelligence, big data, cloud, machine learning and deep learning, and wearables or fitness trackers which are connecting the organizations with the individuals.

Report explores how organizations using AI are making it work, what technologies they're using, and what best practices can maximize an organization's success with AI and machine learning Seattle, WA, Oct. 18, 2019 (GLOBE NEWSWIRE) -- TDWI Research has released its newest Best Practices Report, Driving Digital Transformation using AI and Machine Learning. This original, survey-based report looks at the many dimensions of artificial intelligence (AI) and machine learning (ML) so data professionals and their business counterparts can understand the benefits of the technology, how it's used (and by whom), and how enterprises are achieving success with it. The author of the report, Fern Halper, is vice president and senior director of TDWI Research for advanced analytics. She explains that organizations are embracing AI and ML to gain better insights, make better decisions, and improve competitive advantage. "In fact," she writes, "AI is at the heart of the digital revolution around analytics occurring today. AI promises to help organizations improve their operations and processes and to drive new revenue opportunities."

There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently. Artificial intelligence is gradually becoming a constant presence in many technological applications.

Coined the fourth industrial revolution, the advancement of artificial intelligence and machine learning brings interesting discussion to the table. Because AI is so comprehensive and covers several industries, we find ourselves asking obscure questions such as "Do we need to legalize predictive AI policing?" With these questions arising, the key one that remains unanswered surrounds ethics. How do we ensure that AI technologies are ethically designed? To answer this question, there are essentially four aspects that dictate the result: the dilemma, the impact, adoption, and institutionalization.

Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different period of drifts in the source and target domains. Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes. ATL offers an online domain adaptation strategy via the generative and discriminative phases coupled with the KL divergence based optimization strategy to produce a domain invariant network while putting forward an elastic network structure. It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain. The rigorous numerical evaluation has been conducted along with a comparison against recently published works. ATL demonstrates improved performance while showing significantly faster training speed than its counterparts.

We study how to adapt to smoothly-varying (`easy') environments in well-known online learning problems where acquiring information is expensive. For the problem of label efficient prediction, which is a budgeted version of prediction with expert advice, we present an online algorithm whose regret depends optimally on the number of labels allowed and $Q^*$ (the quadratic variation of the losses of the best action in hindsight), along with a parameter-free counterpart whose regret depends optimally on $Q$ (the quadratic variation of the losses of all the actions). These quantities can be significantly smaller than $T$ (the total time horizon), yielding an improvement over existing, variation-independent results for the problem. We then extend our analysis to handle label efficient prediction with bandit feedback, i.e., label efficient bandits. Our work builds upon the framework of optimistic online mirror descent, and leverages second order corrections along with a carefully designed hybrid regularizer that encodes the constrained information structure of the problem. We then consider revealing action-partial monitoring games -- a version of label efficient prediction with additive information costs, which in general are known to lie in the \textit{hard} class of games having minimax regret of order $T^{\frac{2}{3}}$. We provide a strategy with an $\mathcal{O}((Q^*T)^{\frac{1}{3}})$ bound for revealing action games, along with an one with a $\mathcal{O}((QT)^{\frac{1}{3}})$ bound for the full class of hard partial monitoring games, both being strict improvements over current bounds.

Two important problems in preference elicitation are rank aggregation and label ranking. Rank aggregation consists of finding a ranking that best summarizes a collection of preferences of some agents. The latter, label ranking, aims at learning a mapping between data instances and rankings defined over a finite set of categories or labels. This problem can effectively model many real application scenarios such as recommender systems. However, even when the preferences of populations usually change over time, related literature has so far addressed both problems over non-evolving preferences. This work deals with the problems of rank aggregation and label ranking over non-stationary data streams. In this context, there is a set of $n$ items and $m$ agents which provide their votes by casting a ranking of the $n$ items. The rankings are noisy realizations of an unknown probability distribution that changes over time. Our goal is to learn, in an online manner, the current ground truth distribution of rankings. We begin by proposing an aggregation function called Forgetful Borda (FBorda) that, using a forgetting mechanism, gives more importance to recently observed preferences. We prove that FBorda is a consistent estimator of the Kemeny ranking and lower bound the number of samples needed to learn the distribution while guaranteeing a certain level of confidence. Then, we develop a $k$-nearest neighbor classifier based on the proposed FBorda aggregation algorithm for the label ranking problem and demonstrate its accuracy in several scenarios of label ranking problem over evolving preferences.