We're in the midst of a wave of excitement around AI such as hasn't been seen for a few decades. But those previous periods of inflated expectations led to troughs of disappointment. Will this time be different? Applications of AI such as predictive analytics are already decreasing costs and improving reliability of industrial machinery. Furthermore, the funding and research going into AI now comes from a wide range of commercial firms.

To see what kind of talent we are currently looking for and submit your resume, please visit: https://a9.com/careers/ We are always looking for talented people with backgrounds in: · Computer Vision · Machine Learning · Natural Language Processing · Backend Infrastructure / Systems Software Development · Analytics Data Mining · Pattern Recognition · Artificial Intelligence · Optical Character Recognition · Server Infrastructure · Augmented Reality · DevOps / Operations Engineer · Software Developer in Test A9 solves some of the biggest challenges in search and advertising. We focus on helping people find the things they want. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.

Intel CEO Brian Krzanich says drones will play a vital role showing action at the Olympics. The Olympic Games don't just attract the world's best athletes, they're also a platform for emerging technologies like virtual reality, 5G connectivity, artificial intelligence and drones. Tech giant Intel said Wednesday it's now an official worldwide partner of the games through 2024. Intel CEO Brian Krzanich and International Olympics Committee President Thomas Bach signed off on the deal during an event in New York. The new deal will begin during the 2018 Winter Games in Pyeongchang, South Korea, in February, where 16 events will be shown through Intel's True VR.

Suppose, we are given a set of $n$ elements to be clustered into $k$ (unknown) clusters, and an oracle/expert labeler that can interactively answer pair-wise queries of the form, "do two elements $u$ and $v$ belong to the same cluster?". The goal is to recover the optimum clustering by asking the minimum number of queries. In this paper, we initiate a rigorous theoretical study of this basic problem of query complexity of interactive clustering, and provide strong information theoretic lower bounds, as well as nearly matching upper bounds. Most clustering problems come with a similarity matrix, which is used by an automated process to cluster similar points together. Our main contribution in this paper is to show the dramatic power of side information aka similarity matrix on reducing the query complexity of clustering. A similarity matrix represents noisy pair-wise relationships such as one computed by some function on attributes of the elements. A natural noisy model is where similarity values are drawn independently from some arbitrary probability distribution $f_+$ when the underlying pair of elements belong to the same cluster, and from some $f_-$ otherwise. We show that given such a similarity matrix, the query complexity reduces drastically from $\Theta(nk)$ (no similarity matrix) to $O(\frac{k^2\log{n}}{\cH^2(f_+\|f_-)})$ where $\cH^2$ denotes the squared Hellinger divergence. Moreover, this is also information-theoretic optimal within an $O(\log{n})$ factor. Our algorithms are all efficient, and parameter free, i.e., they work without any knowledge of $k, f_+$ and $f_-$, and only depend logarithmically with $n$. Along the way, our work also reveals intriguing connection to popular community detection models such as the {\em stochastic block model}, significantly generalizes them, and opens up many venues for interesting future research.

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity.

Let us discuss the applicability of these number systems. The real numbers are used in every aspect of physics. All quantities, measurements, and lengths of physical objects or processes are given as real numbers. Although complex numbers were formulated by mathematicians to help solve equations (i is the solution to the equation x2 -1), physicists started using complex numbers to discuss waves in the middle of the 19th century. In the 20th century, complex numbers became fundamental for the study of quantum mechanics. By now, the role of complex numbers is very important in many different branches of physics. The quaternions show up in physics but are not a major player. The octonions, the sedenions, and the larger number systems rarely arise in the physics literature.

As a kid, I saw the 1968 version of Planet of the Apes. As a future primatologist, I was mesmerized. Years later I discovered an anecdote about its filming: At lunchtime, the people playing chimps and those playing gorillas ate in separate groups. It's been said, "There are two kinds of people in the world: those who divide the world into two kinds of people and those who don't." And it can be vastly consequential when people are divided into Us and Them, ingroup and outgroup, "the people" (i.e., our kind) and the Others. The core of Us/Them-ing is emotional and automatic. Humans universally make Us/Them dichotomies along lines of race, ethnicity, gender, language group, religion, age, socioeconomic status, and so on. We do so with remarkable speed and neurobiological efficiency; have complex taxonomies and classifications of ways in which we denigrate Thems; do so with a versatility that ranges from the minutest of microaggression to bloodbaths of savagery; and regularly decide what is inferior about Them based on pure emotion, followed by primitive rationalizations that we mistake for rationality. But crucially, there is room for optimism. Much of that is grounded in something definedly human, which is that we all carry multiple Us/Them divisions in our heads. A Them in one case can be an Us in another, and it can only take an instant for that identity to flip.

SYS-CON Events announced today that HTBase will exhibit at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. HTBase (Gartner 2016 Cool Vendor) delivers a Composable IT infrastructure solution architected for agility and increased efficiency. It turns compute, storage, and fabric into fluid pools of resources that are easily composed and re-composed to meet each application's needs. With HTBase, companies can quickly provision resources and deploy unique, mission-critical, self-designed solutions to add-onto or create any type of infrastructure as per the business requirement. HTBase is the first company to enable a true multi-cloud strategy, enabling organizations to automate movement of data and workloads between private and public clouds.

Automation and robots are already a major part of most manufacturers, ranging from car factories to electronics production. But soon, smarter robots could significantly shake up the market for manufacturers. In a new report from Loup Ventures, the venture capital firm predicts that a smarter sub-series of robots could increasingly become a more popular option for manufacturers. Compared to the larger single-purpose robots that are commonly used in fields like automotive assembly, Loup argues that smarter robots, which the report calls co-bots, will be used by more factories over time. These types of robots are defined by their ability to use motion and force-detection sensors to analyze their surroundings and work alongside humans.

If you ask any experienced analytics or data science professional, what differentiates a good model from a bad model – chances are that you will hear a uniform answer. Whether you call it "characteristics generation" or "variable generation" (as it was known traditionally) or "feature engineering" – the importance of this step is unanimously agreed in the data science / analytics world. This step involves creating a large and diverse set of derived variables from the base data. The richer the set of variables that are generated, the better will be your models. Most of our time and coding efforts are usually spent in the area of feature engineering. Therefore, understanding feature engineering for specific data sources is a key success factors for us.