Intuition Robotics has been working on its ElliQ "proactive social robot for older adults" for only a few years--the company, founded in 2016, has managed to secure funding from Toyota AI Ventures, Samsung, and iRobot, among others. For nearly a year, Intuition has been testing ElliQ in the homes of beta testers aged 62-97 in the San Francisco Bay Area, and things have apparently gone well enough that they've decided that the robot is ready to go on sale. If you're wondering what ElliQ actually does, the website is a bit more informative, but not much: ElliQ is specially designed with and for older adults to give them everything they need to stay sharp, connected and engaged. Interacting with ElliQ and the world is easy and fun, and through AI she becomes even more helpful by learning what you like and need. ElliQ enables family members to easily check-in with you and help with the day-to-day, creating more quality time together wherever you live. ElliQ suggests personalized activities at the right time, keeping you sharp, active and engaged.

One noninvasive way to study frog communities is by analyzing long-term samples of acoustic material containing calls. This immense task has been optimized by the development of Machine Learning tools to extract ecological information. We explored a likelihood-ratio audio detector based on Gaussian mixture model classification of 10 frog species, and applied it to estimate presence-absence in audio recordings from an actual amphibian monitoring performed at Yasun ı National Park in the Ecuadorian Amazonia. A modified filter-bank was used to extract 20 cepstral features that model the spectral content of frog calls. Experiments were carried out to investigate the hyperparameters and the minimum frog-call time needed to train an accurate GMM classifier. With 64 Gaussians and 12 seconds of training time, the classifier achieved an average weighted error rate of 0.9% on the 10-fold cross-validation for nine species classification, as compared to 3% with MFCC and 1.8% with PLP features. For testing, 10 GMMs were trained using all the available training-validation dataset to study 23.5 hours in 141, 10-minute long samples of unidentified real-world audio recorded at two frog communities in 2001 with analog equipment. To evaluate automatic presence-absence estimation, we characterized the audio samples with 10 binary variables each corresponding to a frog species, and manually labeled a subset of 18 samples using headphones. The one-vs-all Receiver Operating Characteristics curves were used to tune the likelihood-ratio detector per class in order to set operating points that minimize false positives while still allowing moderately noisy calls to be detected. A recall of 87.5% and precision of 100% with average accuracy of 96.66% suggests good generalization ability of the algorithm, and provides evidence of the validity of this approach Finally, we applied the algorithm to the available corpus, and show its potentiality to gain insights into the temporal reproductive behavior of frogs. Introduction In long term ecological studies, it is important to quantify changes that occur on biodiversity and the ecosystem as a whole. Large scale temporal and spatial studies to understand the natural and anthropogenic induced population dynamics are demanded by the scientific community. In addition, recent anuran population declines around the world have motivated studies to gain an understanding of the phenomenon [1].

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction.

When the Apollo 11 spacecraft was orbiting the Moon prior to the first lunar landing, Nasa officials told the astronauts on board to look out for the'lovely girl with a big rabbit'. They were jokingly referring to a story from Chinese mythology in which the goddess Chang'e escapes Earth to live on the Moon with her pet, Jade Rabbit. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon'. The robotic probe was named Chang'e 4, a product of China's £3.9 billion a year space exploration project. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon' If ever there was a metaphor for the Communist super-power's obsessive secrecy and soaring global ambition, then this audacious secret mission provides it.

The South China Sea is host to a number of countries vying for control in the area. Attempting to develop new tactics and technologies to swing the balance in its favor, China may have found its key advantage – artificial intelligence (AI). Described as an "enabling" technology, in the same way as the combustion engine or electricity, applications range from deep-sea exploration and international investment, to cybersecurity and combat operations. Chinese scientists are currently developing plans for the first-ever AI-run colony on Earth. Designed for unmanned submarine science and defense operations, the project started at the Chinese Academy of Sciences following a visit from President Xi Jinping in April to the deep-sea research institute in Sanya, Hainan province.

Mia Dand is the CEO of Lighthouse3.com, Mia is an experienced marketing leader who helps F5000 companies innovate at scale with digital and emerging technologies. She has built and led new emerging technology programs for global brands including Google, Symantec, HP, eBay and others. Mia is a strong champion for diversity & inclusion in tech.

Functional data analysis is a statistical framework where data are assumed to follow some functional form. This method of analysis is commonly applied to time series data, where time, measured continuously or in discrete intervals, serves as the location for a function's value. Gaussian processes are a generalization of the multivariate normal distribution to function space and, in this paper, they are used to shed light on coastal rainfall patterns in British Columbia (BC). Specifically, this work addressed the question over how one should carry out an exploratory cluster analysis for the BC, or any similar, coastal rainfall data. An approach is developed for clustering multiple processes observed on a comparable interval, based on how similar their underlying covariance kernel is. This approach provides significant insights into the BC data, and these insights can be described in terms of El Nino and La Nina; however, the result is not simply one cluster representing El Nino years and another for La Nina years. From one perspective, the results show that clustering annual rainfall can potentially be used to identify extreme weather patterns.

Anthony Levandowski, the controversial engineer at the heart of a lawsuit between Uber and Waymo, claims to have built an automated car that drove from San Francisco to New York without any human intervention. The 3,099-mile journey started on 26 October on the Golden Gate Bridge, and finished nearly four days later on the George Washington Bridge in Manhattan. The car, a modified Toyota Prius, used only video cameras, computers and basic digital maps to make the cross-country trip. Levandowski told the Guardian that, although he was sitting in the driver's seat the entire time, he did not touch the steering wheels or pedals, aside from planned stops to rest and refuel. "If there was nobody in the car, it would have worked," he said.

The world's first ever underwater Artificial Intelligence colony will be created on the South China sea bed, Chinese President Xi Jinping claims. The base has been described as a'deep sea Atlantis' and will be used for unmanned submarine science and defence operations. Chinese officials and scientists familiar with the plans say that the deep sea station will analyse samples from the sea bed and send reports to the surface. Xi urged the scientists and engineers to'dare to do something that has never been done before' on a recent visit to the deep sea research institute in Hainan Province. China's unmanned submarine vehicle Qianlong III, pictured, could help to drive a subsea exploration programme and herald the arrival of an AI colony on the South China Sea bed, Chinese scientists and officials say'There is no road in the deep sea, we do not need to chase after other countries, we are the road,' President Xi said.