A paper coauthored by researchers at IBM describes an AI system -- Navsynth -- that generates videos seen during training as well as unseen videos. While this in and of itself isn't novel -- it's an acute area of interest for Alphabet's DeepMind and others -- the researchers say the approach produces superior quality videos compared with existing methods. If the claim holds water, their system could be used to synthesize videos on which other AI systems train, supplementing real-world data sets that are incomplete or marred by corrupted samples. As the researchers explain, the bulk of work in the video synthesis domain leverages GANs, or two-part neural networks consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples. They're highly capable but suffer from a phenomenon called mode collapse, where the generator generates a limited diversity of samples (or even the same sample) regardless of the input.
Though golf has a tendency to move slower than most industries, the technology innovations we've seen this week beg to differ. Artificial intelligence and robotics have been terms perhaps thrown around in the past, implemented by only the biggest companies, but now we're actually seeing the results of intense research and development. And that's especially true in the golf instruction realm, where lessons can have so much added value with the right set of data and smart products. There were too many items to say this is a definitive list. But this is at least what caught our eye at the 2020 PGA Merchandise Show in the ever-expanding tech/instruction space.
When someone thinks of artificial intelligence technology, often the mind goes to some of the world's biggest tech companies like Google, Amazon or even Tesla. But one company that creates products for a game that's existed since the mid-18th century is also getting involved in that realm. Senior Director of Brand and Product Management for Callaway Golf Dave Neville sat down with Kyle Porter of CBS Sports HQ at the PGA Merchandise Show to discuss the new MAVRIK driver. The club's name comes from a common compliment given to founder Ely Callaway Jr. for his knowledge of product, marketing and sales. Though the name is old-fashioned, the development of the driver is anything but, according to Neville.
When nine Callaway Mavrik drivers showed up on the USGA conforming list in mid-December, there was the expected here we go again refrain mixed in with a couple of sideways glances and some descriptions which were anything but parliamentary. That said, as noted in MyGolfSpy's 2019 Editor's Choice awards, the AI (Artificial Intelligence) component of Callaway's signature Flash Face technology is a new club technology that's likely to impact club design throughout the industry – and with the Mavrik irons, it's clear Callaway is dedicated to extending its use throughout its hardgoods lineup. So not to bury the lede, Callaway's Mavrik irons (3 models) incorporate for the first time, AI face design…in every iron. Yes, each individual iron will have a different face thanks to AI capabilities though as sets progress toward shorter irons (8-iron, 9-iron, PW) the designs are more similar than different due to the role loft plays in performance. Specifically, clubs with more loft result in less blunt impact conditions and therefore the face technology (materials, design, etc) has a reduced impact.
Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can lead to poor estimates, as some relevant available information is ignored. In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. We leverage a weighting scheme, derived from the minimization of the error on the model-based policy gradient estimator, in order to define a suitable objective function that is optimized for learning the approximate transition model. Then, we integrate this procedure into a batch policy improvement algorithm, named Gradient-Aware Model-based Policy Search (GAMPS), which iteratively learns a transition model and uses it, together with the collected trajectories, to compute the new policy parameters. Finally, we empirically validate GAMPS on benchmark domains analyzing and discussing its properties.
Item response theory (IRT) models are widely used in psychometrics and educational measurement, being deployed in many high stakes tests such as the GRE aptitude test. IRT has largely focused on estimation of a single latent trait (e.g. ability) that remains static through the collection of item responses. However, in contemporary settings where item responses are being continuously collected, such as Massive Open Online Courses (MOOCs), interest will naturally be on the dynamics of ability, thus complicating usage of traditional IRT models. We propose DynAEsti, an augmentation of the traditional IRT Expectation Maximization algorithm that allows ability to be a continuously varying curve over time. In the process, we develop CurvFiFE, a novel non-parametric continuous-time technique that handles the curve-fitting/regression problem extended to address more general probabilistic emissions (as opposed to simply noisy data points). Furthermore, to accomplish this, we develop a novel technique called grafting, which can successfully approximate distributions represented by graphical models when other popular techniques like Loopy Belief Propogation (LBP) and Variational Inference (VI) fail. The performance of DynAEsti is evaluated through simulation, where we achieve results comparable to the optimal of what is observed in the static ability scenario. Finally, DynAEsti is applied to a longitudinal performance dataset (80-years of competitive golf at the 18-hole Masters Tournament) to demonstrate its ability to recover key properties of human performance and the heterogeneous characteristics of the different holes. Python code for CurvFiFE and DynAEsti is publicly available at github.com/chausies/DynAEstiAndCurvFiFE. This is the full version of our ICDM 2019 paper.
Some golfers are blessed with an enormous drive, but many are cursed with a shoddy short game. Now, Nissan has tried to cure these woes by creating a fully-functioning self-driving golf ball which putts itself perfectly in the hole every time. A video released by the Japanese manufacturer shows it being hit in a wayward direction by a toddler, before self-adjusting and bagging a birdie. A publicity video released by the Japanese manufacturer shows the'self-driving' ball being hit in a wayward direction by a toddler, before self-adjusting and bagging a birdie When the ball is hit, a monitoring system calculates the correct route based on the ball's movement and adjusts its trajectory. Combining sensing technology with an internal electric motor, the ProPILOT golf ball stays on route until reaching the cup.
The ParOne Steamathalon took place at the Els Club in Dubai where kids from schools around the city competed in a golf match. But this match was competed between robots. It was a great initiative and a good excuse to show the kids my robot dancing . GUY IN DUBAI Guy in Dubai is an insight into how to experience the wild side of Dubai, UAE. Attempting every extreme adventure, challenge and living the amazing social life the Dubai has to offer.
While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.
Golf fans who are planning to watch the Masters this weekend will have yet more ways to check out the action. For the first time at a golf tournament, practically every one of the more than 20,000 shots from the first major of the year will be available to view on the Masters website and app within five minutes of a player striking the ball. While these videos won't be live, you'll essentially be able to watch full rounds from the likes of Tiger Woods, Rory McIlroy and Jordan Speith without such trivial matters as watching them walk between shots. There is a caveat in that cameras might not capture shots in some instances, such as those from unusual lies, or if a group's tee shots end up in wildly different spots. The Masters attracts sports aficionados who might not typically watch golf as well as devotees, so it's a high-profile way to debut this technology after a few years of development.