Figure 5: An ablation of ELLA's relevance classifier, where it is replaced with an oracle, in comparison

In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable. Among the numerous approaches proposed to deal with these hard exploration problems, intrinsic motivation mechanisms are arguably among the most studied alternatives to date. Advances reported in this area over time have tackled the exploration issue by proposing new algorithmic ideas to generate alternative mechanisms to measure the novelty. However, most efforts in this direction have overlooked the influence of different design choices and parameter settings that have also been introduced to improve the effect of the generated intrinsic bonus, forgetting the application of those choices to other intrinsic motivation techniques that may also benefit of them. Furthermore, some of those intrinsic methods are applied with different base reinforcement algorithms (e.g. PPO, IMPALA) and neural network architectures, being hard to fairly compare the provided results and the actual progress provided by each solution. The goal of this work is to stress on this crucial matter in reinforcement learning over hard exploration environments, exposing the variability and susceptibility of avant-garde intrinsic motivation techniques to diverse design factors. Ultimately, our experiments herein reported underscore the importance of a careful selection of these design aspects coupled with the exploration requirements of the environment and the task in question under the same setup, so that fair comparisons can be guaranteed.

Standing around an empty parking lot in Arizona on a sunny, 100 F summer day was not a pleasant way to spend an afternoon. But excitement and anticipation overpowered our fears of heat exhaustion as we waited for Waymo's self-driving taxi. I was there with Kelly Funkhouser, CR's manager of vehicle technology. We looked back and forth wondering which direction the minivan would arrive from. "What if it gets stuck on the way here and never shows up?" Kelly was a little more optimistic.

Robot Framework is an open-source Test Automation framework. It was initially developed by Nokia Networks, however, it is now maintained by the Robot Framework Foundation. You will learn about the features, pros, and cons of the Framework along with instructions to install the needed software. Robot Framework is a Test Automation tool in which the test cases are written using keywords that makes it easy to learn and use. These keywords are written in a tabular form. With Robot Framework, the Test Scripts are replaced by a few keywords thereby replacing the need for large pieces of code.

Can you scale artificial intelligence (AI) in your organization? Of course, the answer is yes assuming you want your company to survive and gain competitive advantage. Boston Consulting Group (BCG) has a great read on this: "Artificial Intelligence: Ready to Ride the Wave" as part of its Executive Perspectives series, a tsunami of trends, charts, lists and insights helping C-suite executives better understand AI as a must-have technology investment. Scaling AI matters, even more so because most companies fail at it. According to BCG's report, only 11% of companies have found value in AI.

Question: can you scale #AI at your organization? Of course, the answer is yesssssssss, assuming you want your company to survive and gain competitive advantage. Scaling AI matters, even more so because most companies fail at it. According to BCG's report, only 11% of companies have found value in AI. The answer is a bit complicated.

Tuesday night was the night. I rode in one of Cruise's fleet of driverless robotaxis in San Francisco for the first time. Thanks to an Austrian friend who already had access to the Cruise app, we hopped into one of the driverless cars at around 11 p.m., went out for a few drinks, and then headed back in another Cruise robotaxi, again driverless. Here is the first video with some driverless Cruises we saw while waiting for ours. Also watch out for minute 7:56 to see the coyotes.

In many ways this year's CES looked a lot more like an autonomous-car show than a consumer electronics show. There were announcements aplenty from the likes of Ford, Baidu, Toyota, and others about self-driving vehicles, upcoming driving tests, and new partners. In a parking lot across from the Las Vegas Convention Center, several companies offered rides; you could even schedule a ride in a self-driving Lyft through the company's app and get dropped off at one of many casinos on the Strip. A couple of miles away in downtown Las Vegas, an eight-passenger autonomous shuttle bus ran in a loop around Fremont Street. It was part of an ongoing test between commuter transit company Keolis, autonomous-car maker Navya, and the city.

With a simple swipe of the band across sensors located throughout the park, the giant system knows where you are, what you're doing and what you need. The goal of the tech team who developed the MagicBands was to "root out all the friction within the Disney World experience." The goal would be to potentially offer customized guest experiences at those points. The more data Disney collects it can improve operational efficiency such as in the scheduling of 240,00 shifts for 80,000 employees each week, the better it is able to target marketing because the preferences and behaviors of past guests are used to create future packages and offers specific to them and Disney is even dabbling at making robotic versions of Mickey and Minnie and all of its characters that would move around among the guests and interact with them.