We have the reached the stage of AI Building AI. Our AI robots/machines are creating child AI robots/machines. How will humanity control children AI when humans didn't create them? Have we already lost control? AI building AI is the next phase humanity appears to be going through in its technological evolution.

Artificial intelligence could soon play a large role in aiding medical professionals dispatch emergency services to patients worldwide. Dispatchers in Copenhagen, Denmark have been working alongside an AI named Corti since 2016. Corti is able to understand both the words used in distress calls as well as the sounds taking place in the background and the AI uses this information to provide emergency personnel with the necessary questions for a more accurate diagnosis. The AI also helps out by reminding dispatchers to obtain crucial information such as the address of the incident and it can even ensure that the ambulance en route is travelling to the correct destination. However, Corti's ability to detect background clues is extremely valuable to medical personnel.

In Germany there also exists some manager game similar to fantasy football with soccer. I modeled the team selection as a constrained knapsack problem and optimized the team with an EA/SAT solver (maximize points for a given budget and under the game constraints). Unfortunately, I only have the points from last season and therefore I don't have any meaningful prediction model. Currently, my handpicked team outperforms the optimized team as the optimized team. Need to do some write up...

The pace of technological change can seem exciting and daunting in equal measure. From Amazon to Uber, tech giants saw their practices and business models scrutinised more deeply than ever before, while regulators sought to get their arms around a rapidly evolving "new economy", driven by disruptive technology. While we can only guess at what's around the corner, here are five things to watch out for this year. Machine learning comprises a key component of artificial intelligence whose use is set to grow. Many businesses, especially consumer-facing ones that analyse data (think Google Translate), will start to deploy it, with high-end smartphones being a good example of where we can expect continued early adoption. Machine learning is likely to make data analysis easier and improve efficiencies, thus empowering rather than replacing humans – at least for now.

Emergency dispatchers have a tough job assuring callers while trying to ask questions that could save the patient's life. But soon they could get backup from AI. Starting in 2016, dispatchers in Copenhagen began getting help from an artificial intelligence named Corti that understands the words and sounds during calls to recognize cardiac arrest, then prompts the emergency professional with the right questions to get a more accurate diagnosis. Corti helps out in other ways, too, like reminding to ask whoever's on the phone for the address of the incident and ensuring the ambulance en route is headed to the right place. But much of its value lies in refining its diagnosis by detecting background clues. In one incident, recounted by Fast Company, the dispatcher had concluded a man who'd fallen off the roof had broken his back.

In Belgium, Volvo is promoting its S90 model in an unusual way--it's getting the car to do its recruitment. Creative agency FamousGrey has refitted a Volvo S90 as the "HR90," equipping it with artificial intelligence that allows it to interview prospective technicians. The car will be "recruiting" at the Brussels Motor Show this week, and will then continue with a tour of job expos, schools and Volvo dealerships in search of new hires. Volvo asked candidates to submit their job application on a website in order to be considered for an interview. The car quizzes them via image recognition, mapping and analysis of preset parameters, analyzing the candidate's facial expressions and word use in order to assess their knowledge, motivation and social skills.

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.

The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification (including parameterizations). Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms.

How can we train a statistical mixture model on a massive data set? In this work we show how to construct coresets for mixtures of Gaussians. A coreset is a weighted subset of the data, which guarantees that models fitting the coreset also provide a good fit for the original data set. We show that, perhaps surprisingly, Gaussian mixtures admit coresets of size polynomial in dimension and the number of mixture components, while being independent of the data set size. Hence, one can harness computationally intensive algorithms to compute a good approximation on a significantly smaller data set. More importantly, such coresets can be efficiently constructed both in distributed and streaming settings and do not impose restrictions on the data generating process. Our results rely on a novel reduction of statistical estimation to problems in computational geometry and new combinatorial complexity results for mixtures of Gaussians. Empirical evaluation on several real-world datasets suggests that our coreset-based approach enables significant reduction in training-time with negligible approximation error.

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: 1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; 2) the ML model can be used to improve predictions of the same RSO at future epochs; and 3) the ML model based on a RSO can be applied to other RSOs that share some common features.