Machine learning is an incredibly broad and diverse field, with a non-stop increase on research, along a multitude of applications. Thus writing a list enlisting the best machine learning researchers on the field proves challenging for a number of reasons. Please mind that this list encompasses researchers who are currently working on the field. Also, please mind that this list is by no means ranked. Everyone listed below has done extraordinary work to advance humanity's state of AI further.

At Wake Radiology in North Carolina, roughly 50 doctors scrutinize x-rays and other images for local medical providers. Within a few weeks, they should start to get help on some lung CT scans from machine-learning algorithms that highlight potentially cancerous tissue nodules. Although Wake is based in a region known as the Research Triangle, for its intensity of high-tech R&D, the lung-reading software hails from elsewhere--China. Infervision, a four-year-old Beijing startup, has amassed more than a million scans from Chinese hospitals that it's using to train and test algorithms. Gathering medical data is much easier for Chinese companies than for their US counterparts, because patient populations are larger and the burden of privacy regulations smaller. "In the US, particularly for big academic hospitals, you have to go through so many processes and it can take a really long time to access data," says Yufeng Deng, Infervision's chief scientist.

Japan has deployed robots to work in Tokyo subway stations to help tourists in preparation for the 2020 Olympic Games. The six-foot robot named Arisa, created by the Tokyo Metropolitan Government, will give subway travellers directions and show them the way to the restrooms. Tokyo is facing an influx of millions of spectators for the forthcoming international event which they will host for the second time in history, after 1964. An ageing population and low birth rate has resulted in a national labour shortage, prompting the need for multilingual Arisa to guide the swarm of foreign tourists. Japan has deployed robots to work in Tokyo subway stations to direct tourists in preparation for the 2020 Olympic Games. Arisa is a project by the Tokyo Metropolitan Government.

Robots that walk, talk, pour beer and play pingpong have taken over the CES gadget show in Las Vegas again. Just don't expect to find one in your home any time soon. Most home robot ventures have failed, in part because they're so difficult and expensive to design to a level of intelligence that consumers will find useful, says Bilal Zuberi, a robotics-oriented venture capitalist at Lux Capital. But that doesn't keep companies from trying. 'Roboticists, I guess, will never give up their dream to build Rosie,' says Zuberi, referring to the humanoid maid from'The Jetsons.' AvatarMind has developed service robots like iPal which is based on artificial intelligence, motion control, sensors and power management.

Baccini et al. (Reports, 13 October 2017, p. 230) report MODIS-derived pantropical forest carbon change, with spatial patterns of carbon loss that do not correspond to higher-resolution Landsat-derived tree cover loss. The assumption that map results are unbiased and free of commission and omission errors is not supported. The application of passive moderate-resolution optical data to monitor forest carbon change overstates our current capabilities. Baccini et al. (1) report net tropical forest aboveground carbon stock change from Moderate Resolution Imaging Spectroradiometer (MODIS) data and purport to capture all forest carbon dynamics resulting from both natural and anthropogenic processes. We believe their method and results overstate current monitoring capabilities and may confuse the global community of practitioners working to establish robust and defensible forest carbon monitoring systems.

Google is helping the Wikimedia Foundation achieve its goal of making Wikipedia articles available in a lot more languages. The Foundation has added Google Translate to its content translation tool, which human editors can use to add content to non-English Wikipedia websites. Those editors can take advantage of the new option -- "one of the most advanced machine translation systems available today," the foundation called it -- to generate an initial translation that they can then review and edit for readability in their language. The Foundation says volunteer Wikipedia editors have been asking for Google Translate integration for a long time now. According to VentureBeat, this move is an expansion of an earlier partnership, wherein Google promised to help Wikipedia make its English posts more accessible in Indonesia.

Answer Set Programming (ASP) is a purely declarative formalism developed in the field of logic programming and nonmonotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem; however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones; the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP system DLV, in particular into its grounding subsystem I-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal. Under consideration in Theory and Practice of Logic Programming (TPLP).

In the last decade or so we have seen tremendous progress in Artificial Intelligence (AI). AI is now in the real world, powering applications that have a large practical impact. Most of it is based on modeling, i.e. machine learning of statistical models that make it possible to predict what the right decision might be in future situations. The next step for AI is machine creativity, i.e. tasks where the correct, or even good, solutions are not known, but need to be discovered. Methods for machine creativity have existed for decades. I believe we are now in a similar situation as deep learning was a few years ago: with the million-fold increase in computational power, those methods can now be used to scale up to creativity in real-world tasks. In particular, Evolutionary Computation is in a unique position to take advantage of that power, and become the next deep learning.

This paper discusses cooperative stabilization control of rigid formations via an event-based approach. We first design a centralized event-based formation control system, in which a central event controller determines the next triggering time and broadcasts the event signal to all the agents for control input update. We then build on this approach to propose a distributed event control strategy, in which each agent can use its local event trigger and local information to update the control input at its own event time. For both cases, the triggering condition, event function and triggering behavior are discussed in detail, and the exponential convergence of the event-based formation system is guaranteed.

The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implicit feedback in order to understand user preferences across numerous product categories. This paper presents a deep learning based solution to this problem within the collaborative filtering with implicit feedback framework. Our approach combines neural attention mechanisms, which allow for context dependent weighting of past behavioral signals, with representation learning techniques to produce models which obtain extremely high coverage, can easily incorporate new information as it becomes available, and are computationally efficient. Offline experiments demonstrate significant performance improvements when compared to several alternative methods from the literature. Results from an online setting show that the approach compares favorably with current production techniques used to produce personalized product recommendations.