Abstract-- We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model referred to as DeepMoTIon, is trained with pedestrian surveillance data to predict human velocity.The robot processes LiDAR scans via the trained network to navigate to the target location. We conduct extensive experiments to assess the different components of our network and prove the necessity of each to imitate humans. Our experiments show that DeepMoTIon outperforms state-ofthe art in terms of human imitation and reaches the target on 100% of the test cases without breaching humans' safe distance. I. INTRODUCTION Robots are gradually moving from factories and labs to streets, homes, offices, and healthcare facilities.

The post coincides topically with last years' first annual Conference on Robot Learning as well as the workshop on Challenges in Robot Learning at NIPS2017, the latter we had the pleasure of co-organising together with colleagues from Oxford, DeepMind, and MIT. The events, as well as this post, cover current challenges and potentials of learning across various tasks of relevance in robotics and automation. In this context, similar to the long-term discussion on how much innate structure is optimal for artificial general intelligence, there is the more short-term question of how to merge traditional programming and learning (not sure if I prefer the branding as differentiable programming or software 2.0) for more narrow applications in efficient, robust and safe automation. The question about structure as beneficial or limiting aspect becomes arguably easier to answer in the context of robotic near-term applications as we can simply acknowledge our ignorance (our missing knowledge about what will work best in the future) and focus on the present to benchmark and combine the most efficient and effective directions. Existing solutions to many tasks in mobile robotics, such as localisation, mapping, or planning, focus on prior knowledge about the structure of our tasks and environments. This may include geometry or kinematic and dynamic models, which therefore have been built into traditional programs. However, recent successes and the flexibility of fairly unconstrained, learned models shift the focus of new academic and industrial projects. Successes in image recognition (ImageNet) as well as triumphs in reinforcement learning (Atari, Go, Chess) inspire like-minded research. As the post has become a bit of a long read, I suggest to read it like a paper: intro, discussion & conclusions and then - only if you did not fall asleep after all - the rest. Similar to scientific papers, some paragraphs will require basic familiarity with the field. However, a coarse web search should be enough to illustrate most unexplained terminology. Additionally, to keep this engaging, I have added some of my favourite recent videos highlighting interesting research for each section. Finally, this is a high-level review with more details to be found in the respective references, which just represent a small subset of available work in each field, chosen based on personal interest as well as shameless self-promotion of our work.

The quest to give machines a mind of their own occupied the brightest AI specialists in 2017. Machine learning (and especially the newly hip branch, deep learning) practically delivered all of the most stunning achievements in artificial intelligence so far -- from systems that beat us at our own games to art-producing neural networks that rival human creativity. At the onset and in hindsight, experts have heralded 2017 as "The Year of AI". Following its stunning win over the best human Go player in 2016, AlphaGo was upgraded a year later into a generalized and more powerful incarnation, AlphaZero. Free of any human guidance except the basic game rules, AlphaZero learned how to play master-level chess by itself in just four hours.

Deep learning and Convolutional Neural Networks are the buzz words around now a days when we hear anything about AI. By the time I became enough familiar with these terms, I already had started my journey on the road to Computer Vision by reading the fascinating PyImageSearch blogs and joining the PyImageSearchGurus course designed by Dr Adrian Rosebrock. When my fascination about Computer Vision did not diminish a bit even after six months, I decided to pursue it to the best of my abilities. I watched a lot of You Tube videos, skimmed through different papers and books on this subject and finally settled by purchasing the Imagenet bundle of Deep Learning for Computer Vision with Python (DL4CV) by Dr. Adrian Rosebrock. DL4CV certainly exceeded my expectations mark. What I like about Adrian's writing is - his application oriented approach when teaching the theory.

Recent news from the OpenAI people is all about a bonus trio. They are releasing new Gym environments--a set of simulated robotics environments based on real robot platforms--including a Shadow hand and a Fetch research robot, said IEEE Spectrum. In addition to that toolkit, they are releasing an open source version of Hindsight Experience Replay (HER). As its name suggests, it helps robots learn from hindsight, for goals-based robotic tasks. Last but not least, they released a set of requests for robotics research.

Imagine you wanted to determine how much solar energy could be generated from adding solar cells to a particular house. This is what Google's Project Sunroof does with Deep Learning. Enter an address and Google uses a Deep Learning framework to estimate how much money you could save in energy costs with solar cells over 20 years (see Figure 1). But let's assume there "might" be an even better way to estimate solar energy savings. For example, you want to use Deep Learning to estimate how much solar energy we could generate with solar panels on the Golden Gate Bridge (that probably wouldn't be a very popular decision in San Francisco).

What a confused hairball of a book. Yet somehow, out of the terrible writing, forests of typo's, vast swaths of formatting errors, splatter-shot jumps of topic (from the minutiae of one company's website to settlement patterns of ancient Madagascar), SOMEHOW out of all the NOISE and IRRELEVANT and DISTRACTING signals, the errors, the garden paths, the red herrings,the level jumps, and general mayhem in this horrific mishmash of a "book", some kind of ... INSIGHT... may begin to organically ... EMERGE... and may contribute to a careful and well-seat-belted reader's useful and possibly even ultimately coherent .... INTUITION ... about Deep Learning.

NEW DELHI: Haptik, a leading Indian artificial intelligence based chatbot platform, has entered into a collaboration with Amazon Web Services (AWS) to offer cutting-edge chatbot solutions to customers in India. The tie-up aims to enable companies to leverage these conversational bots to automate some of their most critical processes across customer support, lead generation and sales funnel management. Chatbots allow for highly engaging, conversational experiences, through voice and text that can be customised and used on mobile devices, web browsers, and on popular chat platforms such as Facebook Messenger or Slack. The extended collaboration aims to enable seamless integration between Haptik's chatbots and AWS's advanced AI service portfolio, and to provide omnichannel support to customers. For its core technology platform, Haptik utilises several AWS tools and resources notably including Elastic Cloud Compute, RDS for data storage, CloudFront for scalability, Kinesis for data lake and Amazon Polly, a textto-speech service that uses advanced deep-learning technologies to synthesise speech that sounds like a human voice.

Martin Welker is the chief executive of Axonic. Even with the support of AI frameworks like TensorFlow or OpenAI, artificial intelligence still requires deep knowledge and understanding compared to a mainstream web developer. If you have built a working prototype, you are probably the smartest guy in the room. Congratulations, you are a member of a very exclusive club. With Kaggle, you can even earn decent money by solving real-world projects. All in all, it is an excellent position to be in, but is it enough to build a business?

Integrating geography and location information with AI brings a powerful new dimension to understanding the world around us. This has a wide range of applications in a variety of segments, including commercial, governmental, academic or not-for-profit. Geospatial AI provides robust tools for gathering, managing, analyzing and predicting from geographic and location-based data, and powerful visualization that can enable unique insights into the significance of such data. Available today, Microsoft and Esri will be offering the GeoAI Data Science Virtual Machine (DSVM) as part of our Data Science Virtual Machine/Deep Learning Virtual Machine family of products on Azure. This is a result of a collaboration between the two companies and will bring AI, cloud technology and infrastructure, geospatial analytics and visualization together to help create more powerful and intelligent applications.