Boosted ensemble of decision tree (DT) classifiers are extremely popular in international competitions, yet to our knowledge nothing is formally known on how to make them \textit{also} differential private (DP), up to the point that random forests currently reign supreme in the DP stage. Our paper starts with the proof that the privacy vs boosting picture for DT involves a notable and general technical tradeoff: the sensitivity tends to increase with the boosting rate of the loss, for any proper loss. DT induction algorithms being fundamentally iterative, our finding implies non-trivial choices to select or tune the loss to balance noise against utility to split nodes. To address this, we craft a new parametererized proper loss, called the M$\alpha$-loss, which, as we show, allows to finely tune the tradeoff in the complete spectrum of sensitivity vs boosting guarantees. We then introduce \textit{objective calibration} as a method to adaptively tune the tradeoff during DT induction to limit the privacy budget spent while formally being able to keep boosting-compliant convergence on limited-depth nodes with high probability. Extensive experiments on 19 UCI domains reveal that objective calibration is highly competitive, even in the DP-free setting. Our approach tends to very significantly beat random forests, in particular on high DP regimes ($\varepsilon \leq 0.1$) and even with boosted ensembles containing ten times less trees, which could be crucial to keep a key feature of DT models under differential privacy: interpretability.

During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historic data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.

Dubins tours represent a solution of the Dubins Traveling Salesman Problem (DTSP) that is a variant of the optimization routing problem to determine a curvature-constrained shortest path to visit a set of locations such that the path is feasible for Dubins vehicle, which moves only forward and has a limited turning radius. The DTSP combines the NP-hard combinatorial optimization to determine the optimal sequence of visits to the locations, as in the regular TSP, with the continuous optimization of the heading angles at the locations, where the optimal heading values depend on the sequence of visits and vice versa. We address the computationally challenging DTSP by fast evaluation of the sequence of visits by the proposed Windowing Surrogate Model (WiSM) which estimates the length of the optimal Dubins path connecting a sequence of locations in a Dubins tour. The estimation is sped up by a regression model trained using close to optimum solutions of small Dubins tours that are generalized for large-scale instances of the addressed DTSP utilizing the sliding window technique and a cache for already computed results. The reported results support that the proposed WiSM enables a fast convergence of a relatively simple evolutionary algorithm to high-quality solutions of the DTSP. We show that with an increasing number of locations, our algorithm scales significantly better than other state-of-the-art DTSP solvers.

You will focus on bringing state of the art machine learning technologies to many different platforms across the company by building state of the art prototypes, consulting on best practices and procedures, and mentoring all team members.

A very strong Java programmer in the other technologies might work out, too. Our platform automates the entire process of building predictive models starting from raw business data through data and feature engineering to machine learning all the way to production. We have offices in the USA, Japan, and Poland. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects. Unique to the dotData Platform is its AI-powered feature engineering, which eliminates the most time-consuming and labor- and skill-intensive aspects of the full data science process by discovering and evaluating millions of features derived from relational, transactional, temporal, geo-locational, or text data.

It might come as a surprising fact that there are presently 14 Chinese AI organizations valued at $1 billion. These unicorns worth consolidated comes to $40.5 billion, as per a report China Money Network released during the World Economic Forum's Summer Davos gathering in Beijing. Just to place these numbers in context. Google purchased DeepMind for over $500 million in 2014. Chinese voice recognition giant iFlytek Co. has a market capitalization of 63 billion yuan ($9.2 billion).

Intel said it is ending work on its Nervana neural network processors in favor of the artificial intelligence chips it gained with the chipmaker's recent $2 billion acquisition of Habana Labs. The Santa Clara, Calif.-based company said Friday it has ended development of its Nervana NNP-T training chips and will deliver on current customer commitments for its Nervana NNP-I inference chips, so that it can move forward with Habana Labs' Gaudi and Goya processors in their place. "Habana product line offers the strong, strategic advantage of a unified, highly-programmable architecture for both inference and training," Intel said in a statement provided to CRN. "By moving to a single hardware architecture and software stack for data center AI acceleration, our engineering teams can join forces and focus on delivering more innovation, faster to our customers." Analysts questioned whether Intel would move forward with Nervana after the chipmaker announced its acquisition of Habana Labs in mid-December. The deal was only announced a little over a month after Intel in November revealed more details of its Nervana chips, which were meant to compete with Nvidia's growing footprint of GPUs in the AI acceleration market.

Shelly Duvall is hiding from her crazed husband as he chops down the door with an axe. Jim Carrey sticks his head through the opening and cackles the iconic line: "Here's Johnny!" What you're seeing is not a Hollywood special effect. It wasn't done with After Effects, green screen, or with costuming and makeup. The video is a fake created by deep learning artificial intelligence – a deepfake.

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