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      "body": "Congratulations @kapsalisv! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@kapsalisv/birthday1.png</td><td>Happy Birthday! - You are on the Steem blockchain for 1 year!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@kapsalisv) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=kapsalisv)_</sub>\n\n\n**Do not miss the last post from @steemitboard:**\n<table><tr><td><a href=\"https://steemit.com/steemfest/@steemitboard/the-new-steemfest-badge-is-ready\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmRUkELn2Fd13pWFkmWU2wBMMx39EBX5V3cHBEZ2d7f3Ve/image.png\"></a></td><td><a href=\"https://steemit.com/steemfest/@steemitboard/the-new-steemfest-badge-is-ready\">The new SteemFest⁴  badge is ready</a></td></tr></table>\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
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      "body": "When watching a film, you may be the sort of person who immerses themselves in the story and special effects with a view to a couple of hours of escapism. Or, perhaps like me, you are the type who wants to work out what is real and what is computer generated imagery (CGI) and how realistic it really is. Either way, filmmakers continue to push the boundaries to improve the quality and variety of the special effects they deliver with the purpose of enhancing the audience experience and keep us coming back to the box-office. Films are now leaving the studio and the location shoot and moving in a steady stream towards the data center. The latest wave of technology seeing adoption includes areas such as Machine Learning and Deep Learning, which are all subcategories of artificial intelligence (AI).\n\nMachine Learning has already made good progress in the world of film...and it isn’t all about recasting Nicolas Cage in every role (though there is an App for that)! Streaming services already use Machine Learning to recommend new content based on your preferences, going beyond the capabilities of traditional recommendation engines. Behind the scenes, algorithms are also used to support many of the processes involved in pre and post production. For example, the use of cognitive data science has been widespread in the process of pre-screening potential scripts to accelerate the process of finding good stories suitable to go into production - one great example being the service offered by ScriptBook. Video content reviewing techniques are also used on completed sequences to determine which clips might be most suited for use in the film trailer with the aim of maximising audience figures.\n\nAI has of course also been used to enhance the quality of digitally animated characters such as the swarms of Zombies in World War Z. It is also beginning to tackle many more routine tasks like smoothing out special effects; as well as a few more complex ones such as adding realism to a CGI character by enhancing bodily movement. At the very high end, this may also include difficult and subtle tasks such as improving facial expressions - check out Serbian company 3Lateral who deliver amazing work on facial morphing.\n\nThe benefits of AI in these endeavours is that they free up the time of the artist to concentrate on the more creative aspects of their work. Data Scientists are tasked with automating the repetitive activities, freeing the artist to unleash their muse and with the time saved produce better films.\n\nStill, AI does have its limitations for the sector, though over time many of these may be addressed by a newer form of AI called Deep Learning that tries to approximate processes in the human brain. So while Machine Learning can be used to help support the creation of engaging characters and worlds, overall AI hasn’t yet progressed to the point where scriptwriters or actors can be replaced, and some will argue that is unlikely to ever happen given the advantages a human brain has over an artificial one.\n\nAI is not yet ready to create engaging and exciting stories that will achieve emotional engagement from an audience and similarly, it lacks the subtleties and understanding of the human condition that is the key to many great acting performances, plots and scripts. While we may see increasing use of CGI and much more photo-realistic images, for the time being, these CGI characters will still be driven from behind the scenes by talented writers and actors playing an alter-ego.\nUnderpinning these current and emerging capabilities are ever more powerful technology, both hardware, and software, the latter of which will be further enhanced over time by Deep Learning and Neural Networks trained with copious quantities of data.\n\nAs a provider for industrial scale Deep Learning and Machine Learning platforms and environments, Verne Global is ideally positioned to support film and media organisations leveraging the latest in AI technology. Not only is Verne Global’s infrastructure uniquely optimised for the most advanced forms of Deep Learning, but we also support the high-performance graphics processing (GPUs) as well as more traditional processors (CPU) infrastructure needed for the most demanding rendering requirements. Our specialist platform - hpcDIRECT also includes the ability to pre-install and optimise the latest rendering applications such as MaxonCinema4D, Autodesk Maya, Autodesk 3ds Max with associated plug-in’s. To deliver the specialist application services, Verne Global is delighted to be working closely with its partner RenderNation, who has over 15 years of experience in advanced rendering services to the film, television, game and architectural sector.\n\nAll of this capability is underpinned by Verne Global’s Icelandic data center, which offers low-cost green energy from geothermal and hydro-electric sources, along with free air cooling (vital for keeping all of this power-hungry IT equipment cool). So not only is Iceland the ideal location for filming with its stunning natural views such as waterfalls, glaciers, moonlike volcanic landscape and 21 hours of daylight during the summer, but it is also the ideal location for your compute-intensive post-production services, especially those levering AI. This is why Verne Global’s services have been used in films such as Contraband, 2 Guns, and Everest to name but a few. The Iceland Film Commission is also very welcoming to filmmakers and even offers a 25% rebate on production costs incurred in the country.\n\nIf you’re working within the graphics, rendering, or pre-and-post production industries and looking for a highly scalable, low-cost solution to use for your latest compute intensive film project, let us know. Verne Global, together with the specialist support from RenderNation are here to help.\n\nReproduced from my original blog at: https://verneglobal.com/blog/ai-is-taking-centre-stage-in-todays-film-making\n\nVasilis Kapsalis is the Director of Machine Learning and HPC at Verne Global",
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      "body": "Congratulations @kapsalisv! You have completed the following achievement on the Steem blockchain and have been rewarded with new badge(s) :\n\n<table><tr><td>https://steemitimages.com/60x70/http://steemitboard.com/@kapsalisv/voted.png?201811211447</td><td>You received more than 10 upvotes. Your next target is to reach 50 upvotes.</td></tr>\n</table>\n\n<sub>_[Click here to view your Board of Honor](https://steemitboard.com/@kapsalisv)_</sub>\n<sub>_If you no longer want to receive notifications, reply to this comment with the word_ `STOP`</sub>\n\n\n\n**Do not miss the last post from @steemitboard:**\n<table><tr><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-the-results-the-winners-and-the-prizes\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png\"></a></td><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-the-results-the-winners-and-the-prizes\">Meet the Steemians Contest - The results, the winners and the prizes</a></td></tr><tr><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-special-attendees-revealed\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png\"></a></td><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-special-attendees-revealed\">Meet the Steemians Contest - Special attendees revealed</a></td></tr><tr><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-intermediate-results\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmeLukvNFRsa7RURqsFpiLGEZZD49MiU52JtWmjS5S2wtW/image.png\"></a></td><td><a href=\"https://steemit.com/steemfest/@steemitboard/meet-the-steemians-contest-intermediate-results\">Meet the Steemians Contest - Intermediate results</a></td></tr></table>\n\n> Support [SteemitBoard's project](https://steemit.com/@steemitboard)! **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**!",
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      "body": "![AI brain.PNG](https://cdn.steemitimages.com/DQmUA8JiCtBrrS3GPZz1xr3qbX366y8vzEfAtaPjtczXU6M/AI%20brain.PNG)\n\nSC18 in Dallas proved once again to be a fascinating melting pot of HPC insights and observations, and it's intriguing to see the continuing convergence of AI into the supercomputing ecosystem. Along these lines I started to think about the movement towards 'Explainable AI'. Being able to explain and understand how models work when making predictions about the real world is a fundamental tenet of science. Whether solving equations in a dynamic system for precise answers or using statistical analysis to examine a distribution of events, the results sought from these methods are intended to increase our clarity and knowledge of how the world works.\n\n**Too much detail?**\n\nHuman researchers historically have been biased towards using models and tools that yield to our intuition. Nonlinear systems are seen as more chaotic and harder to understand. In recent decades iterative methods using computers to perform repetitive steps have helped address some of these challenges, although how they actually obtain their results can be more difficult for humans to understand. This has in part led the boom in data visualisation techniques, to overcome some of these challenges.\n\nAs AI gets more widely deployed, the importance of having explainable models will increase. With AI being used in tasks where incidents may arise resulting in legal action, it will be essential that not only are models and their associated training data archived and subject to version control, but also that the actions of the model are explainable.\n\nDeep Learning adds significant complexity to the form of the models used. Deep Learning models may be constructed from many interconnected nonlinear layers supporting feature extraction and transformation. This high level of coupling between very large numbers of non-linear functions drives the need for extremely complex, highly parallel computations. This complexity is leveraged in Deep Learning to provide models that can address fine details and identify features within a problem that cannot be addressed by traditional means, but it is achieved at the cost of sacrificing simplicity of insight.\n\n**Explainable AI (XAI)**\n\nExplainable AI is a movement focused on the interpretability of AI models. This is not just about simplifying models, which can often remove the benefits achieved from complexity. Instead, XAI can and does focus on delivering techniques to support human interpretability. A range of approaches can be used, for example, simple methods such as:\n\n- 2D or 3D projections (this involves taking a larger multi-dimensional space and presenting in a lower dimensional order (2D or 3D)\n- Correlation graphs (2D graphs where the nodes represent variables and the thickness of the lines between them represent the strength of the correlation).\n\nBut, with XAI, there is often a decision point at the start of the modelling process as to how interpretable the data scientist wants the model to be. Machine Learning techniques such as Decision Trees, Monotonic Gradient Boosted Machines and Rules-based systems do lead to good results, but in cases where accuracy is more important than interpretability it often falls to visualisation techniques to support human insight. There exist a range of tools that can support these objectives such as:\n\n- Decision tree surrogates: this is essentially a simple to understand model, used to explain to explain a more complex one by using a simplified decision flow\n- Partial dependence plots: These provide a view of how on average the machine learning model functions. This provides a coarse, high-level overview that does lack detail\n- Individual conditional expectation (ICE): these provide a focus on local relationships and are often a good complement to partial dependence plots – in effect ICE can provide a drill down from partial dependence plots.\n\nThese techniques can help aid clarity. They may not be representing the full complexity of the data, but instead, serve to provide a better feel for the data in human terms. These capabilities are going to be key as we advance Deep Learning and AI, and in particular, there will also be intense demand for expert witness skills to help articulate understanding to non-data scientist and non-technical audiences. Part of this process will rely on good visualisation of large data sets leveraging powerful GPU technology to support these representations. So in a sense whilst our abilities to use GPUs for AI has in part created challenges of complexity they will undoubtedly also be part of the solution to enhancing understanding. It is therefore likely that one outcome of the explainable AI movement will be AIs that can help humans with the tricky task of model interpretation.\n\nVasilis Kapsalis is the Director of Deep Learning and HPC at Verne Global\nOriginal Source: https://verneglobal.com/blog/explainable-ai\n\nFor more information on how Verne Global can support you AI or HPC implementation visit: https://verneglobal.com/",
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      "body": "Organisations developing machine learning models will, in general, have a business objective in mind when they set about creating them. Normally, the end goal for the data science team and the model is relatively clear, and so it should then be a case of ensuring the sufficient quality and quantity of data to train the model; leaving it to the data scientist to determine the most appropriate tools and frameworks to use.\n\nAs best practice, a portion of the data should also be held back to support verification and testing, perhaps using a K-fold cross-validation technique. The aim of verification and testing before deployment should be obvious to most, but for non-data scientists, it is worth mentioning that models can suffer from a problem called “overfitting”, which is when a model or function is too closely fit to a limited set of data points. In effect, the model has memorised the training data, and is great at recalling that, but fails when it comes to making useful future predictions.\n\nAssuming your data science team have carried out the right steps to verify and test the veracity of the model, this then brings you to the next challenge of machine learning. And this is that what you are trying to predict might be within a changing environment or subject to a relationship that changes over time. These moving targets are commonly called “shifts.”\n\nModels can typically suffer from three forms of shift, which means that the model will require some retraining. These are:\n\n- Covariate Shift: this refers to changes in the distribution of the input variables used in the model. In the real world, an example might include a medical researcher trying to predict the health needs of a town and finding that the population make-up of the town has changed (i.e. shifted) due to new house building bringing in new incomers with different age profiles, ethnicities, dietary preferences and incomes. For this reason, this type of shift can also be called a population drift. A similar example can arise when looking to filter spam email; over time the format and nature of these emails may change in a similar way to the town’s population.\n\n- Prior Probability Shift: This is when the there is a change in the outcome distribution without a change in the input. It may be observed as a change in the outcomes between the training datasets and the test data set. An example of this is where a health model is trained with data for one town and is then applied to another, but it turns out that despite having similar inputs distributions (e.g. age, ethnicity, wealth, education levels etc.) there is a change in the outcome distribution. This may be due to hidden features not captured in the data.\n\n- Concept Drift: This type of shift is when there is a change in the probability of the target variable over time. These changes can often impact the distribution in unforeseen ways. The clearest examples of this can be changes in consumer behaviour due to changes in fashion, while it can also be due to cyclical matters such as the run-up Black Friday or another event such as the World Cup.\n\nFor the data scientist, it is important to be able to understand the above shifts, as well as how any actions around the data that might impact results: for instance, selection bias, or errors in the input data such as movie reviews on a website contaminated with computer game reviews.  \n\nSolving the above challenges is very much the role of the data scientist, but what the commissioning company/organisation needs to acknowledge is that machine learning, and deep learning models are not static pieces of code to be run for years without change. The changing nature of the data and the distributions within it, mean that investment in data science and the supporting infrastructure and software is an ongoing activity and commitment. But those that succeed the rewards are significant!",
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