Robotic monitoring and artificial intelligence (AI) are widely used for physical surveillance of security and defence areas, factories, and residential premises. As robots can monitor a given area tirelessly and with precision, they make the task of surveillance reliable. Powered by the capabilities of precision, tirelessness, and reliability, robotic monitoring is being used by many forward looking organisations to deliver speckless customer experience across their digital properties. Let's examine this with a few cross-industry examples. Robotic monitoring can help companies deliver a dainty user experience.

Yext, Inc., a Digital Knowledge Management (DKM) firm, announced integrations with some of the largest global digital services used by Chinese travelers around the world, as part of Yext's Spring '19 Product Release. The integrations with Baidu Map (Overseas), Fliggy, CK Map, and PIRT put businesses outside China in control of their brand information in the services that hundreds of millions of Chinese travelers all across the globe use to find places to eat, shop, stay, and more. "The Chinese digital landscape is made up of an entirely different set of services from those in the West. When Chinese travelers who use services like Baidu and Fliggy at home travel overseas, they use these same services to find businesses in the cities they are visiting," said Howard Lerman, Founder and CEO of Yext. "If a business's information isn't in these services, it is invisible to these potential customers. We're integrating with some of the largest Chinese services so businesses using Yext can provide perfect answers to Chinese travelers."

In 1985, the TV film Max Headroom: 20 Minutes into the Future presented a science fictional cyberpunk world where an evil media company tried to create an artificial intelligence based on a reporter's brain to generate content to fill airtime. There were somewhat unintended results. Replace "reporter" with "redditors," "evil media company" with "well meaning artificial intelligence researchers," and "airtime" with "a very concerned blog post," and you've got what Ars reported about last week: Generative Pre-trained Transformer-2 (GPT-2), a Franken-creation from researchers at the non-profit research organization OpenAI. Unlike some earlier text-generation systems based on a statistical analysis of text (like those using Markov chains), GPT-2 is a text-generating bot based on a model with 1.5 billion parameters. With or without guidance, GPT-2 can create blocks of text that look like they were written by humans.

At a café called Ratio in Shanghai, a revolving, jointed robot arm with two fingerlike prongs knows just how you like your latte. That's because customers can order via a mini-app on social media platform WeChat, specifying the level of sweetness and choosing between coffee beans. Plus, because it syncs with your social media profile, the barista never spells your name wrong. Robots in restaurants sound like a futuristic novelty. But in China, kitchen-side automation has long been routine for some restaurants, fast food chains, and cafeterias. What's more, robotics are being used to standardize a centuries-old cuisine, potentially shaping the taste buds of future generations.

Russia's capital city of Moscow will soon be home to an ambitious autonomous tram project, joining Germany and China as the leading testbeds for the technology. Self-driving vehicles are not currently legal in Moscow or anywhere else. Autonomous trains and trams are stepping stones to full self-driving vehicles, thanks to their defined routes, but retain some of the hardest challenges cars face: knowing when obstacles, people, or vehicles are in their paths. That's because they use public space, and even though there may be fencing or other barriers in some locations, people, vehicles, and other obstacles are still real concerns. A view from the automated tram.Cognitive Technologies That's the challenge that Cognitive Technologies CEO Olga Uskova says her company is close to solving.

In this episode of The New Stack Makers podcast, we talk with Dr. Ayesha Khanna, co-founder and CEO of ADDO AI, an artificial intelligence solutions firm and incubator. Forbes named her one of southeast Asia's groundbreaking female entrepreneurs. Her bio is too extensive to encapsulate in a brief introduction, but she brings both depth and vision to all she does. She has been a strategic advisor on artificial intelligence, smart cities and fin-tech to leading corporations and governments around the globe. She is also the Founder of 21C GIRLS, a charity that delivers free coding and artificial intelligence classes to girls in Singapore and finds time to be an advisor to the startups Octa (a chatbot for young travelers) and Arro (a delivery robot for sports).

This paper presents a novel method for information interpretability in an MC-SEFRON classifier. To develop a method to extract knowledge stored in a trained classifier, first, the binary-class SEFRON classifier developed earlier is extended to handle multi-class problems. MC-SEFRON uses the population encoding scheme to encode the real-valued input data into spike patterns. MC-SEFRON is trained using the same supervised learning rule used in the SEFRON. After training, the proposed method extracts the knowledge for a given class stored in the classifier by mapping the weighted postsynaptic potential in the time domain to the feature domain as Feature Strength Functions (FSFs). A set of FSFs corresponding to each output class represents the extracted knowledge from the classifier. This knowledge encoding method is derived to maintain consistency between the classification in the time domain and the feature domain. The correctness of the FSF is quantitatively measured by using FSF directly for classification tasks. For a given input, each FSF is sampled at the input value to obtain the corresponding feature strength value (FSV). Then the aggregated FSVs obtained for each class are used to determine the output class labels during classification. FSVs are also used to interpret the predictions during the classification task. Using ten UCI datasets and the MNIST dataset, the knowledge extraction method, interpretation and the reliability of the FSF are demonstrated. Based on the studies, it can be seen that on an average, the difference in the classification accuracies using the FSF directly and those obtained by MC-SEFRON is only around 0.9% & 0.1\% for the UCI datasets and the MNIST dataset respectively. This clearly shows that the knowledge represented by the FSFs has acceptable reliability and the interpretability of classification using the classifier's knowledge has been justified.

We introduce the active exploration problem in Markov decision processes (MDPs). Each state of the MDP is characterized by a random value and the learner should gather samples to estimate the mean value of each state as accurately as possible. Similarly to active exploration in multi-armed bandit (MAB), states may have different levels of noise, so that the higher the noise, the more samples are needed. As the noise level is initially unknown, we need to trade off the exploration of the environment to estimate the noise and the exploitation of these estimates to compute a policy maximizing the accuracy of the mean predictions. We introduce a novel learning algorithm to solve this problem showing that active exploration in MDPs may be significantly more difficult than in MAB. We also derive a heuristic procedure to mitigate the negative effect of slowly mixing policies. Finally, we validate our findings on simple numerical simulations.

How do organisms recognize their environment by acquiring knowledge about the world, and what actions do they take based on this knowledge? This article examines hypotheses about organisms' adaptation to the environment from machine learning, information-theoretic, and thermodynamic perspectives. We start with constructing a hierarchical model of the world as an internal model in the brain, and review standard machine learning methods to infer causes by approximately learning the model under the maximum likelihood principle. This in turn provides an overview of the free energy principle for an organism, a hypothesis to explain perception and action from the principle of least surprise. Treating this statistical learning as communication between the world and brain, learning is interpreted as a process to maximize information about the world. We investigate how the classical theories of perception such as the infomax principle relates to learning the hierarchical model. We then present an approach to the recognition and learning based on thermodynamics, showing that adaptation by causal learning results in the second law of thermodynamics whereas inference dynamics that fuses observation with prior knowledge forms a thermodynamic process. These provide a unified view on the adaptation of organisms to the environment.