How-To Tutorials - Artificial Intelligence

What really excites me about data science and by extension machine learning is the sheer number of possibilities! You can think of so many applications off the top of your head: robo-advisors, computerized lawyers, digital medicine, even automating VC decisions when they invest in startups. You can even venture into automation of art and music, algorithms writing papers which are indistinguishable from human-written papers. It's like solving a puzzle, but a puzzle that's meaningful and that has real world implications. The things that we can do today weren’t possible 5 years ago, and this is largely thanks to growth in computational power, data availability, and the adoption of the cloud that made accessing these resources economical for everyone, all key enabling factors for the advancement of Machine learning and AI. Having witnessed the growth of data science as discipline, industries like finance, health-care, education, media & entertainment, insurance, retail as well as energy has left no stone unturned to harness this opportunity. Data science has the capability to offer even more; and we will see the wide range of applications in the future in places haven’t even been explored. In the years to come, we will increasingly see data powered/AI enabled products and services take on roles traditionally handled by humans as they required innately human qualities to successfully perform. In this article we have covered some use cases of Data Science being used differently and start-ups who have practically implemented it: The Nurturer: For elder care The world is aging rather rapidly. According to the World Health Organization, nearly two billion people across the world are expected to be over 60 years old by 2050, a figure that’s more than triple what it was in 2000. In order to adapt to their increasingly aging population, many countries have raised the retirement age, reducing pension benefits, and have started spending more on elderly care. Research institutions in countries like Japan, home to a large elderly population, are focusing their R&D efforts on robots that can perform tasks like lifting and moving chronically ill patients, many startups are working on automating hospital logistics and bringing in virtual assistance. They also offer AI-based virtual assistants to serve as middlemen between nurses and patients, reducing the need for frequent in-hospital visits. Dr Ben Maruthappu, a practising doctor, has brought a change to the world of geriatric care with an AI based app Cera. It is an on-demand platform to aid the elderly in need. The Cera app firmly puts itself in the category of Uber & Amazon, whereby it connects elderly people in need of care with a caregiver in a matter of few hours. The team behind this innovation also plans to use AI to track patients’ health conditions and reduce the number of emergency patients admitted in hospitals. A social companion technology - Elliq created by Intuition Robotics helps older adults stay active and engaged with a proactive social robot that overcomes the digital divide. AliveCor, a leading FDA-cleared mobile heart solution helps save lives, money, and has brought modern healthcare alive into the 21st century. The Teacher: Personalized education platform for lifelong learning With children increasingly using smartphones and tablets and coding becoming a part of national curricula around the world, technology has become an integral part of classrooms. We have already witnessed the rise and impact of education technology especially through a multitude of adaptive learning platforms that allow learners to strengthen their skills and knowledge - CBTs, LMSes, MOOCs and more. And now virtual reality (VR) and artificial intelligence (AI) are gaining traction to provide us with lifelong learning companion that can accompany and support individuals throughout their studies - in and beyond school . An AI based educational platform learns the amount of potential held by each particular student. Based on this data, tailored guidance is provided to fix mistakes and improvise on the weaker areas. A detailed report can be generated by the teachers to help them customise lesson plans to best suit the needs of the student. Take Gruff Davies’ Kwiziq for example. Gruff with his team leverage AI to provide a personalised learning experience for students based on their individual needs. Students registered on the platform get an advantage of an AI based language coach which asks them to solve various micro quizzes. Quiz solutions provided by students are then turned into detailed “brain maps”.  These brain maps are further used to provide tailored instructions and feedback for improvement. Other startup firms like Blippar specialize in Augmented reality for visual and experiential learning. Unelma Platforms, a software platform development company provides state-of-the-art software for higher-education, healthcare and business markets. The Provider: Farming to be more productive, sustainable and advanced Though farming is considered the backbone of many national economies especially in the developing world, there is often an outdated view of it involving a small, family-owned lands where crops are hand harvested. The reality of modern-day farms have had to overhaul operations to meet demand and remain competitively priced while adapting to the ever-changing ways technology is infiltrating all parts of life. Climate change is a serious environmental threat farmers must deal with every season: Strong storms and severe droughts have made farming even more challenging. Additionally lack of agricultural input, water scarcity, over-chemicalization in fertilizers, water & soil pollution or shortage of storage systems has made survival for farmers all the more difficult.   To overcome these challenges, smart farming techniques are the need of an hour for farmers in order to manage resources and sustain in the market. For instance, in a paper published by arXiv, the team explains how they used a technique known as transfer learning to teach the AI how to recognize crop diseases and pest damage.They utilized TensorFlow, to build and train a neural network of their own, which involved showing the AI 2,756 images of cassava leaves from plants in Tanzania. Their efforts were a success, as the AI was able to correctly identify brown leaf spot disease with 98 percent accuracy. WeFarm, SaaS based agritech firm, headquartered in London, aims to bridge the connectivity gap amongst the farmer community. It allows them to send queries related to farming via text message which is then shared online into several languages. The farmer then receives a crowdsourced response from other farmers around the world. In this way, a particular farmer in Kenya can get a solution from someone sitting in Uganda, without having to leave his farm, spend additional money or without accessing the internet. Benson Hill Bio-systems, by Matthew B. Crisp, former President of Agricultural Biotechnology Division, has differentiated itself by bringing the power of Cloud Biology™ to agriculture. It combines cloud computing, big data analytics, and plant biology to inspire innovation in agriculture. At the heart of Benson Hill is CropOS™, a cognitive engine that integrates crop data and analytics with the biological expertise and experience of the Benson Hill scientists. CropOS™ continuously advances and improves with every new dataset, resulting in the strengthening of the system’s predictive power. Firms like Plenty Inc and Bowery Farming Inc are nowhere behind in offering smart farming solutions. Plenty Inc is an agriculture technology company that develops plant sciences for crops to flourish in a pesticide and GMO-free environment. While Bowery Farming uses high-tech approaches such as robotics, LED lighting and data analytics to grow leafy greens indoors. The Saviour: For sustainability and waste management The global energy landscape continues to evolve, sometimes by the nanosecond, sometimes by the day. The sector finds itself pulled to economize and pushed to innovate due to a surge in demand for new power and utilities offerings. Innovations in power-sector technology, such as new storage battery options and smartphone-based thermostat apps, AI enabled sensors etc; are advancing at a pace that has surprised developers and adopters alike. Consumer’s demands for such products have increased. To meet this, industry leaders are integrating those innovations into their operations and infrastructure as rapidly as they can. On the other hand, companies pursuing energy efficiency have two long-standing goals — gaining a competitive advantage and boosting the bottom line — and a relatively new one: environmental sustainability. Realising the importance of such impending situations in the industry, we have startups like SmartTrace offering an innovative cloud-based platform to quickly manage waste at multiple levels. This includes bridging rough data from waste contractors, extrapolating to volume, EWC, finance and Co2 statistics. Data extracted acts as a guide to improve methodology, educate, strengthen oversight and direct improvements to the bottom line, as well as environmental outcomes. One Concern provides damage estimates using artificial intelligence on natural phenomena sciences. Autogrid organizes energy data and employs big data analytics to generate real-time predictions to create actionable data. The Dreamer: For lifestyle and creative product development and design Consumers in our modern world continually make multiple decisions with regard to product choice due to many competing products in the market.Often those choices boil down to whether it provides better value than others either in terms of product quality, price or by aligning with their personal beliefs and values.Lifestyle products and brands operate off ideologies, hoping to attract a relatively high number of people and ultimately becoming a recognized social phenomenon. While ecommerce has leveraged data science to master the price dimension, here are some examples of startups trying to deconstruct the other two dimensions: product development and branding. I wonder if you have ever imagined your beer to be brewed by AI? Well, now you can with IntelligentX. The Intelligent X team claim to have invented the world's first beer brewed by Artificial intelligence. They also plan to craft a premium beer using complex machine learning algorithms which can improve itself from the feedback given by its customers. Customers are given to try one of their four bottled conditioned beers, after the trial they are asked by their AI what they think of the beer, via an online feedback messenger. The data then collected is used by an algorithm to brew the next batch. Because their AI is constantly reacting to user feedback, they can brew beer that matches what customers want, more quickly than anyone else can. What this actually means that the company gets more data and customers get a customized fresh beer! In the lifestyle domain, we have Stitch Fix which has brought a personal touch to the online shopping journey. They are no regular other apparel e-commerce company. They have created a perfect formula for blending human expertise with the right amount of Data Science to serve their customers. According to Katrina Lake, Founder, and CEO, "You can look at every product on the planet, but trying to figure out which one is best for you is really the challenge” and that’s where Stitch Fix has come into the picture. The company is disrupting traditional retail by bridging the gap of personalized shopping, that the former could not achieve. To know how StitchFix uses Full Stack Data Science read our detailed article. The Writer: From content creation to curation to promotion In the publishing industry, we have seen a digital revolution coming in too. Echobox are one of the pioneers in building AI for the publishing industry. Antoine Amann, founder of Echobox, wrote in a blog post that they have "developed an AI platform that takes large quantity of variables into account and analyses them in real time to determine optimum post performance". Echobox pride itself to currently work with Facebook and Twitter for optimizing social media content, perform advanced analytics with A/B testing and also curate content for desired CTRs. With global client base like The Le Monde, The Telegraph, The Guardian etc. they have conveniently ripped social media editors. New York-based startup Agolo uses AI to create real-time summaries of information. It initially use to curate Twitter feeds in order to focus on conversations, tweets and hashtags that were most relevant to its user's preferences. Using natural language processing, Agolo scans content, identifies relationships among the information sources, and picks out the most relevant information, all leading to a comprehensive summary of the original piece of information. Other websites like Grammarly, offers AI-powered solutions to help people write, edit and formulate mistake-free content. Textio came up with augmented writing which means every time you wrote something and you would come to know ahead of time exactly who is going to respond. It basically means writing which is supported by outcomes in real time. Automated Insights, Creator of Wordsmith, the natural language generation platform enables you to produce human-sounding narratives from data. The Matchmaker: Connecting people, skills and other entities AI will make networking at B2B events more fun and highly productive for business professionals. Grip, a London based startup, formerly known as Network, rebranded itself in the month of April, 2016. Grip is using AI as a platform to make networking at events more constructive and fruitful. It acts as a B2B matchmaking engine that accumulates data from social accounts (LinkedIn, Twitter) and smartly matches the event registration data. Synonymous to Tinder for networking, Grip uses advanced algorithms to recommend the right people and presents them with an easy to use swiping interface feature. It also delivers a detailed report to the event organizer on the success of the event for every user or a social Segment. We are well aware of the data scientist being the sexiest job of the 21st century. JamieAi harnessing this fact connects technical talent with data-oriented jobs organizations of all types and sizes. The start-up firm has combined AI insights and human oversight to reduce hiring costs and eliminate bias.  Also, third party recruitment agencies are removed from the process to boost transparency and efficiency in the path to employment. Another example is Woo.io, a marketplace for matching tech professionals and companies. The Manager: Virtual assistants of a different kind Artificial Intelligence can also predict how much your household appliance will cost on your electricity bill. Verv, a producer of clever home energy assistance provides intelligent information on your household appliances. It helps its customers with a significant reduction on their electricity bills and carbon footprints. The technology uses machine learning algorithms to provide real-time information by learning how much power and money each device is using. Not only this, it can also suggest eco-friendly alternatives, alert homeowners of appliances in use for a longer duration and warn them of any dangerous activity when they aren’t present at home. Other examples include firms like Maana which manages machines and improves operational efficiencies in order to make fast data driven decisions. Gong.io, acts as a sales representative’s assistant to understand sales conversations resulting into actionable insights. ObEN, creates complete virtual identities for consumers and celebrities in the emerging digital world. The Motivator: For personal and business productivity and growth A super cross-functional company Perkbox, came up with an employee engagement platform. Saurav Chopra founder of Perkbox believes teams perform their best when they are happy and engaged! Hence, Perkbox helps companies boost employee motivation and create a more inspirational atmosphere to work. The platform offers gym services, dental discounts and rewards for top achievers in the team to firms in UK. Perkbox offers a wide range of perks, discounts and tools to help organizations retain and motivate their employees. Technologies like AWS and Kubernetes allow to closely knit themselves with their development team. In order to build, scale and support Perkbox application for the growing number of user base. So, these are some use cases where we found startups using data science and machine learning differently. Do you know of others? Please share them in the comments below.

Get ready to learn and experience the very latest in data science and AI with expert-led trainings, workshops, and talks at ​ODSC West 2019 in San Francisco and ODSC Europe 2019 in London. ODSC events are built for the community and feature the most comprehensive breadth and depth of training opportunities available in data science, machine learning, and deep learning. They also provide numerous opportunities to connect, network, and exchange ideas with data science peers and experts from across the country and the world. What to expect at ODSC West 2019 ODSC West 2019 is scheduled to take place in San Francisco, California on Tuesday, Oct 29, 2019, 9:00 AM – Friday, Nov 1, 2019, 6:00 PM PDT. This year, ODSC West will host several networking events, including ODSC Networking Reception, Dinner and Drinks with Data Scientists, Meet the Speakers, Meet the Experts, and Book Signings Hallway Track. Core areas of focus include Open Data Science, Machine Learning & Deep Learning, Research frontiers, Data Science Kick-Start, AI for engineers, Data Visualization, Data Science for Good, and management & DataOps. Here are just a few of the experts who will be presenting at ODSC: Anna Veronika Dorogush, CatBoost Team Lead, Yandex Sarah Aerni, Ph.D., Director of Data Science and Engineering, Salesforce Brianna Schuyler, Ph.D., Data Scientist, Fenix International Katie Bauer, Senior Data Scientist, Reddit, Inc Jennifer Redmon, Chief Data Evangelist, Cisco Systems, Inc Sanjana Ramprasad, Machine Learning Engineer, Mya Systems Cassie Kozyrkov, Ph.D., Chief Decision Scientist, Google Rachel Thomas, Ph.D., Co-Founder, fast.ai Check out the conference’s more industry-leading speakers here. ODSC also conducts the Accelerate AI Business Summit, which brings together leading experts in AI and business to discuss three core topics: AI Innovation, Expertise, and Management. Don’t miss out on the event You can also use code ODSC_PACKT right now to exclusively save 30% before Friday on your ticket to ODSC West 2019. What to expect at ODSC Europe 2019 ODSC Europe 2019 is scheduled to take place in London, the UK on Tuesday, Nov 19, 2019 – Friday, Nov 22, 2019. Europe Talks/Workshops schedule includes Thursday, Nov 21st and Friday, Nov 22nd. It is available to Silver, Gold, Platinum, and Diamond pass holders. Europe Trainings schedule includes Tuesday, November 19th and Wednesday, November 20th. It is available to Training,  Gold ( Wed Nov 20th only), Platinum, and Diamond pass holders. Some talks scheduled to take place include ML for Social Good: Success Stories and Challenges, Machine Learning Interpretability Toolkit, Tools for High-Performance Python, The Soul of a New AI, Machine Learning for Continuous Integration, Practical, Rigorous Explainability in AI, and more. ODSC has released a preliminary schedule with information on attending speakers and their training, workshop, and talk topics. The full schedule is going to be available soon. They’ve also recently added several excellent speakers, including Manuela Veloso, Ph.D. | Head of AI Research, JP Morgan Dr. Wojciech Samek | Head of Machine Learning, Fraunhofer Heinrich Hertz Institute Samik Chandanara | Head of Analytics and Data Science, JP Morgan Tom Cronin | Head of Data Science & Data Engineering, Lloyds Banking Group Gideon Mann, Ph.D. | Head of Data Science, Bloomberg, LP There are more chances to learn, connect, and share ideas at this year’s event than ever before. Don’t miss out. Use code ODSC_PACKT right now to save 30% on your ticket to ODSC Europe 2019.

If you want to learn how to build artificial intelligence systems, the first step is simple: forget all about artificial intelligence. Instead focus your attention on machine learning. That way, you can be sure you’re in the domain of the practical rather than the domain of hype. Okay, this position might sound a little too dramatic. But there are a number of jokes doing the rounds on Twitter along these lines. Mat Velloso, an adviser to Satya Nadella at Microsoft, wrote late last year that “if it’s written in Python, it’s machine learning. If it’s written in PowerPoint, it’s probably AI.” https://twitter.com/matvelloso/status/1065778379612282885 There are similar jokes that focus on the use of the different words depending on whether you’re talking to investors or colleagues - either way, it’s clear that if you’re starting to explore artificial intelligence and machine learning, understanding what’s important and what you can ignore will help you to get a better view on where you need to go as your learning journey unfolds. So, once you understand that artificial intelligence is merely the word describing the end goal we’re trying to achieve, and machine learning is a means of achieving that goal, you can begin to start trying to develop intelligent systems yourself. Clearly, a question will keep cropping up: where next? Well, this post should go some way to helping you. Do you want to learn artificial intelligence? Read Packt's extensive Learning Path Python: Beginner's Guide to Artificial Intelligence. For a more advanced guide, check out Python: Advanced Guide to Artificial Intelligence. The basics of machine learning If you want to build artificial intelligence, you need to start by learning the basics of machine learning. Follow these steps: Get to grips with the basics of Python and core programming principles - if you’re reading this, you probably know enough to begin, but if you don’t there are plenty of resources to get you started. (We suggest you start with Learning Python) Make sure you understand basic statistical principles - machine learning is really just statistics, automated by code. Venturing further into machine learning and artificial intelligence The next step builds on those foundations. This is where you begin thinking about the sorts of problems you want to solve and the types of questions you want to ask. This is actually a creative step where you set the focus for your project - whatever kind of pattern or relationship you want to understand, this is where you can do just that. One of the difficulties, however, is making sure you have access to the data you need to actually do what you want. Sometimes, you might need to do some serious web scraping or data mining to get hold of the data you want - that’s beyond the scope of this piece, but there are plenty of resources out there to help you do just that. But there are also plenty of ready made data sets available for you to use in your machine learning project in whichever way you wish. You can find 50 data sets for machine learning here, all for a range of different uses. (If you’re trying machine learning for the first time, we’d suggest using one of these data sets and playing around to save you collecting data). Getting to grips with data modelling Although machine learning modelling is the next step in the learning journey, arguably it should happen at the same time as you’re thinking about both the questions you’re asking and the different data sources you might require. This is because the model - or models - you decide to employ in your project will follow directly from the problems you’re trying to tackle and, indeed, the nature and size of the data sets you eventually use. It’s important to note that no model is perfect. There’s a rule in the data science and machine learning world called the ‘no free lunch’ rule - basically, there’s no model that offers a short cut. There will always be trade offs between different algorithms in how they perform in various factors. To manage this issue you need to understand what’s important to you - maybe you’re not worried about speed, for example? Or perhaps accuracy isn’t crucial, you just want to build something that runs quickly. Broadly, the models you use will fall into these categories: supervised or unsupervised. Supervised machine learning algorithms Supervised learning is where you have an input and an output and you use an algorithm to better understand the relationship between the two. Ultimately, you want to get to a point when your machine learning system understands the relationship in such a way that you could predict an output. Supervised learning can also be broken down into regression or classification. Regression is where the output is a number or value, while classification is a specific category, or descriptor. Some algorithms can be used for both regression and classification problems, such as random forest, while others can be used for one or the other. For example, support vector machines can be used for classification problems, while linear regression algorithms can, as the name indicates, be used for regression problems. Unsupervised machine learning algorithms Unsupervised machine learning contrasts from supervised machine learning in that there are no outputs on which the algorithm works. If supervised learning 'tells' the algorithm the answers from which it then needs to understand how those answers were generated, unsupervised learning aims to understand the underlying structure within a given set of data. There aren’t any answers to guide the machine learning algorithm. As above, there are a couple of different approaches to unsupervised machine learning: clustering and association. Clustering helps you understand different groups within a set of data, while association is simply a way of understanding relationship or rules: if this happens, then this will happen too. Okay, so what about artificial intelligence? By now you will have a solid foundation of knowledge in machine learning. However, this is only the tip of the iceberg - machine learning at its most basic provides a very limited form of artificial intelligence. Advances in artificial intelligence are possible through ever more powerful algorithms - artificial or deep neural networks - that have additional layers of complexity (quite literally additional neurons). These are the algorithms that are used to power sophisticated applications and tools. From image recognition to image identification, through to speech to text and machine translation, the applications of these algorithms are radically transforming our relationship with technology. But you probably already knew that. The important question is how you actually go about doing it. Well, luckily in many ways, if you know the core components of machine learning, more advanced elements of deep learning and artificial neural networks shouldn’t actually be as complex as you might at first think. There are, however, a couple of considerations that become more important as you move deeper into deep learning. Hardware considerations for deep learning One of the most important considerations for any deep learning projects you want to try is the hardware you’re using. For a basic machine learning problem, this shouldn’t be an issue. However, but as the computations on which your deep learning system is working become more extensive, the hardware you use to run will become a challenge you need to resolve. This is too big an issue to explore here, but you can look in detail at our comparison of different processors here. Getting started with deep learning frameworks One of the reasons the whole world is talking about artificial intelligence is because it’s easier to do. And this is thanks, in part, to the growth of new deep learning frameworks that make it relatively straightforward to build complex deep learning models. The likes of TensorFlow, Keras, and PyTorch are all helping engineers and data scientists build deep learning models of considerable sophistication. Although they each have their own advantages, and it’s well worth spending some time comparing them, there’s certainly a lot to be said for simply getting started with them yourself. What about cloud's impact on machine learning and artificial intelligence? An interesting development in the machine learning space is the impact of cloud based solutions. The likes of Azure, AWS and Google Cloud Platform are all offering a number of different services and tools from within their overarching cloud products that make performing machine and deep learning tasks much easier. While this is undoubtedly going to be an important development, and, indeed, one you may have encountered already, there is no substitute for simply getting your hands dirty with the data and seeing how the core principles behind machine learning and artificial intelligence actually work. Conclusion: Don’t be scared, take machine learning and artificial intelligence one step at a time Clearly, with so much hype around artificial intelligence its easy to get stuck before you begin. However, by focusing on the core principles and practical application of machine learning you will be well on your way to helping drive the future of artificial intelligence. Learn artificial intelligence from scratch with Python: Beginner's Guide to Artificial Intelligence. Dive deeper into deep learning and artificial intelligence with Python: Advanced Guide to Artificial Intelligence.  

The ’Strengthening African Machine Learning’ conference organized by Deep Learning Indaba, at Stellenbosch, South Africa, is ongoing right now. This 6-day conference will celebrate and strengthen machine learning in Africa through state-of-the-art teaching, networking, policy debate, and through support programmes. Yesterday, three conference organizers, Sebastian Ruder, Herman Kamper, and Stephan Gouws asked tech experts their view on the state of Natural Language Processing, more specifically these 4 questions: What do you think are the three biggest open problems in Natural Language Processing at the moment? What would you say is the most influential work in Natural Language Processing in the last decade, if you had to pick just one? What, if anything, has led the field in the wrong direction? What advice would you give a postgraduate student in Natural Language Processing starting their project now? The tech experts interviewed included the likes of Yoshua Bengio, Hal Daumé III, Barbara Plank, Miguel Ballesteros, Anders Søgaard, Lea Frermann, Michael Roth, Annie Louise, Chris Dyer, Felix Hill,  Kevin Knight and more. https://twitter.com/seb_ruder/status/1072431709243744256 Biggest open problems in Natural Language Processing at the moment Although each expert talked about a variety of Natural Language Processing open issues, the following common key themes recurred. No ‘real’ understanding of Natural language understanding Many experts argued that natural Language understanding is central and also important for natural language generation. They agreed that most of our current Natural Language Processing models do not have a “real” understanding. What is needed is to build models that incorporate common sense, and what (biases, structure) should be built explicitly into these models. Dialogue systems and chatbots were mentioned in several responses. Maletšabisa Molapo, a Research Scientist at IBM Research and one of the experts answered, “Perhaps this may be achieved by general NLP Models, as per the recent announcement from Salesforce Research, that there is a need for NLP architectures that can perform well across different NLP tasks (machine translation, summarization, question answering, text classification, etc.)” NLP for low-resource scenarios Another open problem is using NLP for low-resource scenarios. This includes generalization beyond the training data, learning from small amounts of data and other techniques such as Domain-transfer, transfer learning, multi-task learning. Also includes different supervised learning techniques, semi-supervised, weakly-supervised, “Wiki-ly” supervised, distantly-supervised, lightly-supervised, minimally-supervised and unsupervised learning. Per Karen Livescu, Associate Professor Toyota Technological Institute at Chicago, “Dealing with low-data settings (low-resource languages, dialects (including social media text "dialects"), domains, etc.).  This is not a completely "open" problem in that there are already a lot of promising ideas out there; but we still don't have a universal solution to this universal problem.” Reasoning about large or multiple contexts Experts believed that NLP has problems in dealing with large contexts. These large context documents can be either text or spoken documents, which currently lack common sense incorporation. According to, Isabelle Augenstein, tenure-track assistant professor at the University of Copenhagen, “Our current models are mostly based on recurrent neural networks, which cannot represent longer contexts well. One recent encouraging work in this direction I like is the NarrativeQA dataset for answering questions about books. The stream of work on graph-inspired RNNs is potentially promising, though has only seen modest improvements and has not been widely adopted due to them being much less straight-forward to train than a vanilla RNN.” Defining problems, building diverse datasets and evaluation procedures “Perhaps the biggest problem is to properly define the problems themselves. And by properly defining a problem, I mean building datasets and evaluation procedures that are appropriate to measure our progress towards concrete goals. Things would be easier if we could reduce everything to Kaggle style competitions!” - Mikel Artetxe. Experts believe that current NLP datasets need to be evaluated. A new generation of evaluation datasets and tasks are required that show whether NLP techniques generalize across the true variability of human language. Also what is required are more diverse datasets. “Datasets and models for deep learning innovation for African Languages are needed for many NLP tasks beyond just translation to and from English,” said Molapo. Advice to a postgraduate student in NLP starting their project Do not limit yourself to reading NLP papers. Read a lot of machine learning, deep learning, reinforcement learning papers. A PhD is a great time in one’s life to go for a big goal, and even small steps towards that will be valued. — Yoshua Bengio Learn how to tune your models, learn how to make strong baselines, and learn how to build baselines that test particular hypotheses. Don’t take any single paper too seriously, wait for its conclusions to show up more than once. — George Dahl I believe scientific pursuit is meant to be full of failures. If every idea works out, it’s either because you’re not ambitious enough, you’re subconsciously cheating yourself, or you’re a genius, the last of which I heard happens only once every century or so. so, don’t despair! — Kyunghyun Cho Understand psychology and the core problems of semantic cognition. Understand machine learning. Go to NeurIPS. Don’t worry about ACL. Submit something terrible (or even good, if possible) to a workshop as soon as you can. You can’t learn how to do these things without going through the process. — Felix Hill Make sure to go through the complete list of all expert responses for better insights. Google open sources BERT, an NLP pre-training technique Use TensorFlow and NLP to detect duplicate Quora questions [Tutorial] Intel AI Lab introduces NLP Architect Library  

Machine learning has emerged with a vast new ecosystem of techniques and infrastructure and we are just beginning to learn their full capabilities. But with the exciting innovations happening, there are also some really concerning problems arising. Forms of bias, stereotyping and unfair determination are being found in computer vision systems, object recognition models, and in natural language processing and word embeddings. The Conference on Fairness, Accountability, and Transparency (FAT) scheduled on Feb 23 and 24 this year in New York is an annual conference dedicating to bringing theory and practice of fair and interpretable Machine Learning, Information Retrieval, NLP, Computer Vision, Recommender systems, and other technical disciplines. This year's program includes 17 peer-reviewed papers and 6 tutorials from leading experts in the field. The conference will have three sessions. Session 3 of the two-day conference on Saturday, February 24, is in the field of fairness in computer vision and NLP. In this article, we give our readers a peek into the three papers that have been selected for presentation in Session 3. You can also check out Session 1 and Session 2, in case you’ve missed them. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification What is the paper about The paper talks about substantial disparities in the accuracy of classifying darker and lighter females and males in gender classification systems. The authors have evaluated bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, they have characterized the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience.  They have also evaluated 3 commercial gender classification systems using this dataset. Key takeaways The paper measures accuracy of 3 commercial gender classification algorithms by Microsoft, IBM, and Face++ on the new Pilot Parliaments Benchmark which is balanced by gender and skin type. On annotating the dataset with the Fitzpatrick skin classification system and testing gender classification performance on 4 subgroups, they found : All classifiers perform better on male faces than on female faces (8.1% − 20.6% difference in error rate) All classifiers perform better on lighter faces than darker faces (11.8% − 19.2% difference in error rate) All classifiers perform worst on darker female faces (20.8% − 34.7% error rate) Microsoft and IBM classifiers perform best on lighter male faces (error rates of 0.0% and 0.3% respectively) Face++ classifiers perform best on darker male faces (0.7% error rate) The maximum difference in error rate between the best and worst classified groups is 34.4% They encourage further work to see if the substantial error rate gaps on the basis of gender, skin type and intersectional subgroup revealed in this study of gender classification persist in other human-based computer vision tasks as well. Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies What is the paper about The paper studies gender stereotypes and cases of bias in the Hindi movie industry (Bollywood) and propose an algorithm to remove these stereotypes from text. The authors have analyzed movie plots and posters for all movies released since 1970. The gender bias is detected by semantic modeling of plots at sentence and intra-sentence level. Different features like occupation, introductions, associated actions and descriptions are captured to show the pervasiveness of gender bias and stereotype in movies. Next, they have developed an algorithm to generate debiased stories. The proposed debiasing algorithm extracts gender biased graphs from unstructured piece of text in stories from movies and de-bias these graphs to generate plausible unbiased stories. Key takeaways The analysis is performed at sentence at multi-sentence level and uses word embeddings by adding context vector and studying the bias in data. Data observation showed that while analyzing occupations for males and females, higher level roles are designated to males while lower level roles are designated to females. A similar trend has been observed for centrality where females were less central in the plot vs their male counterparts. Also, while predicting gender using context word vectors, with very small training data, a very high accuracy was observed in gender prediction for test data reflecting a substantial amount of bias present in the data. The authors have also presented an algorithm to remove such bias present in text. They show that by interchanging the gender of high centrality male character with a high centrality female character in the plot text, leaves no change in the story but de-biases it completely. Mixed Messages? The Limits of Automated Social Media Content Analysis What is the paper about This paper broadcasts that a knowledge gap exists between data scientists studying NLP and policymakers advocating for the wide adoption of automated social media analysis and moderation. It urges policymakers to understand the capabilities and limits of NLP before endorsing or adopting automated content analysis tools, particularly for making decisions that affect fundamental rights or access to government benefits. It draws on existing research to explain the capabilities and limitations of text classifiers for social media posts and other online content. This paper is aimed at helping researchers and technical experts address the gaps in policymakers knowledge about what is possible with automated text analysis. Key takeaways The authors have provided an overview of how NLP classifiers work and identified five key limitations of these tools that must be communicated to policymakers: NLP classifiers require domain-specific training and cannot be applied with the same reliability across different domains. NLP tools can amplify social bias reflected in language and are likely to have lower accuracy for minority groups. Accurate text classification requires clear, consistent definitions of the type of speech to be identified. Policy debates around content moderation and social media mining tend to lack such precise definitions. The accuracy achieved in NLP studies does not warrant widespread application of these tools to social media content analysis and moderation. Text filters remain easy to evade and fall far short of humans ability to parse meaning from text. The paper concludes with recommendations for NLP researchers to bridge the knowledge gap between technical experts and policymakers, including Clearly describe the domain limitations of NLP tools. Increase development of non-English training resources. Provide more detail and context for accuracy measures. Publish more information about definitions and instructions provided to annotators. Don’t miss our coverage on Session 4 and Session 5 on Fair Classification, Fat recommenders, etc.

At the 32nd annual  NeurIPS conference held earlier this month, Edward William Felten, a professor of computer science and public affairs at Princeton University spoke about how decision makers and tech experts can work together to make better policies. The talk was aimed at answering questions such as why should public policy matter to AI researchers, what role can researchers play in policy debates, and how can researchers help bridge divides between the research and policy communities. While AI and machine learning are being used in high impact areas and have seen heavy adoption in every field, in recent years, they have also gained a lot of attention from the policymakers. Technology has become a huge topic of discussion among policymakers mainly because of its cases of failure and how it is being used or misused. They have now started formulating laws and regulations and holding discussions about how society will govern the development of these technologies. Prof. Felten explained how having constructive engagement with policymakers will lead to better outcomes for technology, government, and society. Why tech should be regulated? Regulating tech is important, and for that researchers, data scientists, and other people in tech fields have to close the gap between their research labs, cubicles, and society. Prof. Felten emphasizes that it is up to the tech people to bridge this gap as we not only have the opportunity but also a duty to be more active and productive in participating in public life. There are many people coming to the conclusion that tech should be regulated before it is too late. In a piece published by the Wall Street Journal, three experts debated about whether the government should regulate AI. One of them, Ryan Calo explains, “One of the ironies of artificial intelligence is that proponents often make two contradictory claims. They say AI is going to change everything, but there should be no changes to the law or legal institutions in response.” Prof. Felten points out that law and policies are meant to change in order to adapt according to the current conditions. They are not just written once and for all for the cases of today and the future, rather law is a living system that adapts to what is going on in the society. And, if we believe that technology is going to change everything, we can expect that law will change. Prof. Felten also said that not only the tech researchers and policymakers but the society also should also have some say in how the technology is developed, “After all the people who are affected by the change that we are going to cause deserve some say in how that change happens, how it is used. If we believe in a society which is fundamentally democratic in which everyone has a stake and everyone has a voice then it is only fair that those lives we are going to change have some say in how that change come about and what kind of changes are going to happen and which are not.” How experts can work with decision makers to make good tech decisions The three key approaches that we can take to engage with policymakers to take a decision about technology: Engage in a two-way dialogue with policymakers As a researcher, we might think that we are tech experts/scientists and we do not need to get involved in politics. We need to just share the facts we know and our job is done. But if researchers really want to maximize their impact in policy debates, they need to combine the knowledge and preferences of policymakers with their knowledge and preferences. Which means, they need to take into account what policymakers might already have heard about a particular subject and the issues or approaches that resonate with them. Prof. Felten explains that this type of understanding and exchange of ideas can be done in two stages. Researchers need to ask several questions to policymakers, which is not a one-time thing, rather a multi-round protocol. They have to go back and forth with the person and need to build engagement over time and mutual trust. And, then they need to put themselves into the shoes of a decision maker and understand how to structure the decision space for them. Be present in the room when the decisions are being made To have their influence on the decisions that get made, researchers need to have “boots on the ground.” Though not everyone has to engage in this deep and long-term process of decision making, we need some people from the community to engage on behalf of the community. Researchers need to be present in the room when the decisions are being made. This means taking posts as advisers or civil servants. We already have a range of such posts at both local and national government levels, alongside a range of opportunities to engage less formally in policy development and consultations. Creating a career path and rewarding policy engagement To drive this engagement, we need to create a career path which rewards policy engagement. We should have a way through which researchers can move between policy and research careers. Prof. Felten pointed to a range of US-based initiatives that seek to bring those with technical expertise into policy-oriented roles, such as the US Digital Service. He adds that if we do not create these career paths and if this becomes something that people can do only after sacrificing their careers then very few people will do it. This needs to be an activity that we learn to respect when people in the community do it well. We need to build incentives whether it is in career incentives in academia, whether it is understanding that working in government or on policy issues is a valuable part of one kind of academic career and not thinking of it as deter or a stop. To watch the full talk, check out NeurIPS Facebook page. NeurIPS 2018: Rethinking transparency and accountability in machine learning NeurIPS 2018: Developments in machine learning through the lens of Counterfactual Inference [Tutorial] Accountability and algorithmic bias: Why diversity and inclusion matters [NeurIPS Invited Talk]

It’s the middle of December and it’s shivery cold in the North Pole at -20°C. A fat old man sits on a big brown chair, beside the fireplace, stroking his long white beard. His face has a frown on it, quite his unusual self. Mr. Claus quips, “Ruddy mailman should have been here by now! He’s never this late to bring in the li'l ones’ letters.” [caption id="attachment_3284" align="alignleft" width="300"] Nervous Santa Claus on Christmas Eve, he is sitting on the armchair and resting head on his hands[/caption] Santa gets up from his chair, his trouser buttons crying for help, thanks to his massive belly. He waddles over to the window and looks out. He’s sad that he might not be able to get the children their gifts in time, this year. Amidst the snow, he can see a glowing red light. “Oh Rudolph!” he chuckles. All across the living room are pictures of little children beaming with joy, holding their presents in their hands. A small smile starts building and then suddenly, Santa gets a new-found determination to get the presents over to the children, come what may! An idea strikes him as he waddles over to his computer room. Now Mr. Claus may be old on the outside, but on the inside, he’s nowhere close! He recently set up a new rig, all by himself. Six Nvidia GTX Titans, coupled with sixteen gigs of RAM, a 40-inch curved monitor that he uses to keep an eye on who’s being naughty or nice, and a 1000 watt home theater system, with surround sound, heavy on the bass. On the inside, he’s got a whole load of software on the likes of the Python language (not the Garden of Eden variety), OpenCV - his all-seeing eye that’s on the kids and well, Tensorflow et al. Now, you might wonder what an old man is doing with such heavy software and hardware. A few months ago, Santa caught wind that there’s a new and upcoming trend that involves working with tonnes of data, cleaning, processing and making sense of it. The idea of crunching data somehow tickled the old man and since then, the jolly good master tinkerer and his army of merry elves have been experimenting away with data. Santa’s pretty much self-taught at whatever he does, be it driving a sleigh or learning something new. A couple of interesting books he picked up from Packt were, Python Data Science Essentials - Second Edition, Hands-On Data Science and Python Machine Learning, and Python Machine Learning - Second Edition. After spending some time on the internet, he put together a list of things he needed to set up his rig and got them from Amazon. [caption id="attachment_3281" align="alignright" width="300"] Santa Claus is using a laptop on the top of a house[/caption] He quickly boots up the computer and starts up Tensorflow. He needs to come up with a list of probable things that each child would have wanted for Christmas this year. Now, there are over 2 billion children in the world and finding each one’s wish is going to be more than a task! But nothing is too difficult for Santa! He gets to work, his big head buried in his keyboard, his long locks falling over his shoulder. So, this was his plan: Considering that the kids might have shared their secret wish with someone, Santa plans to tackle the problem from different angles, to reach a higher probability of getting the right gifts: He plans to gather email and Social Media data from all the kids’ computers - all from the past month It’s a good thing kids have started owning phones at such an early age now - he plans to analyze all incoming and outgoing phone calls that have happened over the course of the past month He taps into every country's local police department’s records to stream all security footage all over the world [caption id="attachment_3288" align="alignleft" width="300"] A young boy wearing a red Christmas hat and red sweater is writing a letter to Santa Claus. The child is sitting at a wooden table in front of a Christmas tree.[/caption] If you’ve reached till here, you’re probably wondering whether this article is about Mr.Claus or Mr.Bond. Yes, the equipment and strategy would have fit an MI6 or a CIA agent’s role. You never know, Santa might just be a retired agent. Do they ever retire? Hmm! Anyway, it takes a while before he can get all the data he needs. He trusts Spark to sort this data in order, which is stored in a massive data center in his basement (he’s a bit cautious after all the news about data breaches). And he’s off to work! He sifts through the emails and messages, snorting from time to time at some of the hilarious ones. Tensorflow rips through the data, picking out keywords for Santa. It takes him a few hours to get done with the emails and social media data alone! By the time he has a list, it’s evening and time for supper. Santa calls it a day and prepares to continue the next day. The next day, Santa gets up early and boots up his equipment as he brushes and flosses. He plonks himself in the huge swivel chair in front of the monitor, munching on freshly baked gingerbread. He starts tapping into all the phone company databases across the world, fetching all the data into his data center. Now, Santa can’t afford to spend the whole time analyzing voices himself, so he lets Tensorflow analyze voices and segregate the keywords it picks up from the voice signals. Every kid’s name to a possible gift. Now there were a lot of unmentionable things that got linked to several kids names. Santa almost fell off his chair when he saw the list. “These kids grow up way too fast, these days!” It’s almost 7 PM in the evening when Santa realizes that there’s way too much data to process in a day. A few days later, Santa returns to his tech abode, to check up on the progress of the call data processing. There’s a huge list waiting in front of him. He thinks to himself, “This will need a lot of cleaning up!” He shakes his head thinking, I should have started with this! He now has to munge through that camera footage! Santa had never worked on so much data before so he started to get a bit worried that he might be unable to analyze it in time. He started pacing around the room trying to think up a workaround. Time was flying by and he still did not know how to speed up the video analyses. Just when he’s about to give up, the door opens and Beatrice walks in. Santa almost trips as he runs to hug his wife! Beatrice is startled for a bit but then breaks into a smile. “What is it dear? Did you miss me so much?” Santa replies, “You can’t imagine how much! I’ve been doing everything on my own and I really need your help!” Beatrice smiles and says, “Well, what are we waiting for? Let’s get down to it!” Santa explains the problem to Beatrice in detail and tells her how far he’s reached in the analysis. Beatrice thinks for a bit and asks Santa, “Did you try using Keras on top of TensorFlow?” Santa, blank for a minute, nods his head. Beatrice continues, “Well from my experience, Keras gives TensorFlow a boost of about 10%, which should help quicken the analysis. Santa just looks like he’s made the best decision marrying Beatrice and hugs her again! “Bea, you’re a genius!” he cries out. “Yeah, and don’t forget to use Matplotlib!” she yells back as Santa hurries back to his abode. He’s off to work again, this time saddling up Keras to work on top of TensorFlow. Hundreds and thousands of terabytes of video data flowing into the machines. He channels the output through OpenCV and ties it with TensorFlow to add a hint of Deep Learning. He quickly types out some Python scripts to integrate both the tools to create the optimal outcome. And then the wait begins. Santa keeps looking at his watch every half hour, hoping that the processing happens fast. The hardware has begun heating up quite a bit and he quickly races over to bring a cooler that’s across the room. While he waits for the videos to finish up, he starts working on sifting out the data from the text and audio. He remembers what Beatrice said and uses Matplotlib to visualize it. Soon he has a beautiful map of the world with all the children’s names and their possible gifts beside. Three days later, the video processing gets done Keras truly worked wonders for TensorFlow! Santa now has another set of data to help him narrow down the gift list. A few hours later he’s got his whole list visualized on Matplotlib. [caption id="attachment_3289" align="alignleft" width="300"] Santa Claus riding on sleigh with gift box against snow falling on fir tree forest[/caption] There’s one last thing left to do! He suits up in red and races out the door to Rudolph and the other reindeer, unties them from the fence and leads them over to the sleigh. Once they’re fastened, he loads up an empty bag onto the sleigh and it magically gets filled up. He quickly checks it to see if all is well and they’re off! It’s Christmas morning and all the kids are racing out of bed to rip their presents open! There are smiles all around and everyone’s got a gift, just as the saying goes! Even the ones who’ve been naughty have gotten gifts. Back in the North Pole, the old man is back in his abode, relaxing in an easy chair with his legs up on the table. The screen in front of him runs real-time video feed of kids all over the world opening up their presents. A big smile on his face, Santa turns to look out the window at the glowing red light amongst the snow, he takes a swig of brandy from a hip flask. Thanks to Data Science, this Christmas is the merriest yet!

Built by Facebook engineers and researchers, Pytorch is an open-source Python-based deep learning framework for developing new machine learning models, explore neural network architecture and deploy them at scale in production.  PyTorch is known for its advanced indexing and functions, imperative style, integration support, and API simplicity. This is one of the key reasons why developers prefer this framework for research and hackability. PyTorch is also the second-fastest-growing open source project on the GitHub community which includes anybody from developers starting to get acquainted with AI to some of the best known AI researchers and some of the best-known companies doing AI.  At its F8 annual developer conference, Facebook shared how production-ready PyTorch 1.0 is being adopted by the community and the industry. If you want to learn how you can use this framework to build projects in machine intelligence and deep learning, you may go through our book PyTorch Deep Learning Hands-On by authors Sherin Thomas and Sudhanshu Passi. This book demonstrates numerous examples and dynamic AI applications and demonstrates the simplicity and efficiency of PyTorch.  A number of companies are using PyTorch for research and for production. At F8 developer conference this year, Jerome Pesenti, Vice President of AI at Facebook introduced representatives from Microsoft, Airbnb, Genentech, and Toyota Research Institute who talked about how the framework is helping them build, train, and deploy production-ready AI. Below are some excerpts from their talks. Read also: How PyTorch is bridging the gap between research and production at Facebook: PyTorch team at F8 conference How Microsoft uses PyTorch for its language modeling service David Aronchick, Head of Open Source Machine Learning Strategy at Microsoft Azure  At Microsoft, PyTorch is being used in their language modeling service. Language modeling service uses state-of-the-art language models for both 1 P (first-party) and 3 P (third party). Microsoft explored a number of deep learning frameworks but was running into several issues. These included a slow transition from research to production, inconsistent and frequently changing APIs, and a trade-off between high-level ease-of-use and low-level flexibility.  To overcome these issues, in partnership with Facebook Microsoft built an internal language modeling toolkit on top of PyTorch. Using the native extensibility that PyTorch provided, Microsoft was able to build advanced/custom tasks and architecture. It also improved the onboarding of new users and was an active and inviting community. As a result of this work, Microsoft was able to scale the language modeling features to billions of words. It also led to intuitive, static, and consistent APIs which resulted in seamless migration from Language modeling toolkit v0.4 to 1.0. They also saw improvements in model sizes. Microsoft have partnered with ics.ai to deliver conversational AI bots across the public sector in the UK. ICS.ai, based in Basingstoke, have trained their Microsoft AI driven chat bots to scale to the demands of large county councils, healthcare trusts and universities. How Airbnb is using conversational AI tools in PyTorch to enhance customer experience Cindy Chen, Senior machine learning Data Scientist at Airbnb Airbnb has built a dialog assistant to integrate smart replies and enhance their customer experience. The core of their Dialog assistant for customer service at Airbnb is powered by PyTorch. They have built the smart replies recommendation model by treating it as a machine translation problem.  Airbnb is translating the customer's input message into agent responses by building a sequence to sequence model. They leverage PyTorch’s Open neural machine translation library to build the sequence to sequence model.  Using Pytorch has significantly sped up the Airbnb’s model development cycle as PyTorch provides them with state-of-the-art technologies such as various attention mechanisms and beam search.  How Genentech uses Pytorch in drug discovery and cancer therapy Daniel Bozinov, Head of AI - Early clinical development informatics, Genentech At Genentech, PyTorch is being used to develop personalized cancer medicine as well as for drug discovery and in cancer therapy.  For drug development, Genentech has built deep learning models for specific domains to make some predictions about the properties of molecules such as toxicity. They're also applying AI to come up with new cancer therapies. They identify unique molecules specific to cancer cells that are only produced by those cancer cells, potentially sensitizing the immune system to attack those cancer cells and basically treat them like an infection.   PyTorch has been their deep learning framework of choice because of features such as easier debugging, more flexible control structures, being natively pythonic, and it’s Dynamic graphs which yield in faster execution. Their model architecture is inspired by textual entailment in natural language processing. They use a partially recurrent neural network as well as a straightforward feed-forward network, combine the outputs of these two networks and predict the peptide binding. Toyota Research Institute adds new driver support features in cars Adrien Gaidon, Machine Learning Lead, Toyota Research Institute Toyota developed a cutting-edge cloud platform for distributed deep learning on high-resolution sensory inputs, especially video. This was designed to add new driver support features to the cars. PyTorch was instrumental in scaling up Toyota’s deep learning system because of features like simple API, integration with the global Python ecosystem, and overall a great user experience for fast exploration. It’s also fast for training on a very large scale. In addition to amping up TRI’s creativity and expertise, Pytorch has also amplified Toyota’s capabilities to iterate quickly from idea to real-world use cases. The team at TRI is excited for new Pytorch production features that will help them accelerate Toyota even further.  In this post, we have only summarized the talks. At F8, these researchers spoke in length about each of their company’s projects and how PyTorch has been instrumental in their growth. You can watch the full video on YouTube.  If you are inspired to build your PyTorch-based deep learning and machine learning models, we recommend you to go through our book PyTorch Deep Learning Hands-On. Facebook releases PyTorch 1.3 with named tensors, PyTorch Mobile, 8-bit model quantization, and more François Chollet, creator of Keras on TensorFlow 2.0 and Keras integration, tricky design decisions in Deep Learning, and more PyTorch announces the availability of PyTorch Hub for improving machine learning research reproducibility

Brendan Frey is the founder and CEO of Deep Genomics. He is the professor of engineering and medicine at the University of Toronto. His major work focuses on using machine learning to model genome biology and understand genetic disorders. This article attempts to bring our readers to Brendan’s Keynote speech at NIPS 2017. It highlights how the human genome can be reprogrammed using Machine Learning and gives a glimpse into some of the significant work going on in this field. After reading this article, head over to the NIPS Facebook page for the complete keynote. All images in this article come from Brendan’s presentation slides and do not belong to us. 65% of people in their lifetime are at a risk of acquiring a disease with a genetic basis. 8 million births per year are estimated to have a serious genetic defect. According to the US healthcare system, the lifetime average cost of such a baby is 5M$ per child. These are just some statistics. If we also add the emotional component to this data, it gives us an alarming picture of the state of the healthcare industry today. According to a recent study,  investing in pharma is no longer as lucrative as it used to be in the 90s. Funding for this sector is dwindling, which serves as a barrier to drug discovery, trial, and deployment. All of these, in turn, add to the rising cost of healthcare. Better to stuff your money in a mattress than put it in a pharmaceutical company! Genomics as a field is rich in data. Experts in genomics strive to determine complete DNA sequences and perform genetic mapping to help understand a disease. However, the main problem confronting Genome Biology and Genomics is the inability to decipher information from the human genome i.e. how to convert the genome into actionable information. What genes are made of and why sequencing matter Essentially each gene consists of a promoter region, which basically activates the gene. Following the promoter region, there are alternating Exons and Introns. Introns are almost 10,000 nucleotides long. Exons are relatively short, around 100 nucleotides long. In software terms, you can think of Exons as print statements. Exons are the part that ends up in proteins. Introns get cut out/removed. However, Introns contain crucial control logic. There are words embedded in introns that tells the cells how to cut and paste these exons together and make the gene. A DNA sequence is transcribed into RNA and then the RNA is processed in various ways to translate into proteins. However, the picture is much more complicated than this. Proteins go back and interact with the DNA. Proteins also interact with RNA. even, RNA interacts with protein. So all these entities are interrelated. All these technicalities and interrelationships make biology generally complex for researchers or even a group of researchers to fully understand and make sense of the data. Another way to look at this is, that in the recent years our ability to measure biology (fitbits, tabloids, genomes) and the ability to alter biology (DNA editing) has far surpassed our ability to understand biology. In short, in this field, we have become very good at collecting data but not as good with interpreting it. Machine Learning brought to genomes Deep Genomics, is a genetic medicine company that uses an AI-driven platform to support geneticists, molecular biologists and chemists in the development of genetic therapies. In 2010, Deep Genomics used machine learning to understand how words embedded in introns control print statements splicing which puts exons into proteins. They also used machine learning to reverse engineer to infer those code words using datasets. Another deep genomic research project talks about Protein-DNA binding data. There are datasets which allow you to measure interactions between protein and DNA and understand how that works.  In this research, they took a dataset from Ray et al 2013 which consisted of 240,000 designed sequences and then evaluated which are the proteins that the sequence likes to stick to. Thus generating a big data matrix of proteins and designed sequences. The machine learning task here was to learn to take a sequence and predict whether the protein will bind to that sequence. How was this done? They took batches of data containing the designed sequences and fed it into a convolutional neural network. The CNN swept across those sequences to generate an intermediary representation.  This representation was then fed into different layers of convolutional pooling and fully connected layers produced the output. The output was then compared to the measurement (the data matrix of proteins and designed sequences described earlier ) and backpropagation was used to update the parameters. One of the challenges was figuring out the right metric. For this, they compared the measured binding affinity (how much protein sticks to the sequence) to the output of the neural network and determined the right cos function for producing a neural network that is useful in practice. Usecase One of the use cases of this neural network is to identify the pathological mutation and fix them. The above illustration is a sequence of the cholesterol gene. The researchers artificially in silico looked at every possible mutation in the promoter. So for each nucleotide, say if the nucleotide had a value of A, they switched it to G, C, and T and for each of those possibilities, they ran the entire promoter through a neural network and looked at its output. The neural network then predicted the mutations that will disrupt the protein binding. The heights of the letter showed the measured binding affinity i.e the output of the neural network.  The white box displays how much the mutation changed the output.  Pink or bright red was used in case of positive mutation, blue in case of negative mutation and white for no change. This map was then compared with known results to see the accuracy and also make predictions never seen before in a clinical trial. As shown in the image, Blues, which are the potential or known harmful mutations have correctly fallen in the white spaces. But there are some unknown mutations as well. Machine learning output such as this can help researchers narrow their focus on learning about new diseases and also in diagnosing existing ones and treating them. Another group of researchers used a neural network to figure out the 3D structure or the chromatin interaction structure of the DNA. The data used was a matrix form and showed how strongly two parts of a DNA are likely to interact. The researchers trained a multilayer convolutional network to take as input the raw DNA sequence and also a signal called chromatin accessibility( tells how available the DNA is) and fed it into CNN. The output of that system predicted the probability of contact which is crucial for gene expression. Deep genomics: Using AI to build a new universe of digital medicines The founding belief at Deep Genomics is that the future of medicine will rely on artificial intelligence, because biology is too complex for humans to understand. The goal of deep genomics is to build AI platform for detecting and treating genetic disease. Genome tools Genome processing tools are tools which help in identification of mutation, e.g DeepVariant. At deep genomics, the tool used is called genomic kit which is 20 to 800 times faster than other existing tools. Disease mechanism Prediction This is about figuring whether the disease mechanism is pathological or the mutation which simply changes hair color. Therapeutic Development Helping patients by providing them with better medicines. These are the basics of any drug development procedure. We start with patient genetic data and clinical mutations. Then we find the disease mechanism and figure the mechanism of action( the steps to remediate the problem). However, the disease mechanism and mechanism of action of a potential drug may not be the inverse of one another. The next step is to design a drug. With Digital medicines, if we know the mechanism of action that we are trying to achieve, and we have ML systems, like the ones described earlier, we can simulate the effects of modifying DNA or RNA. Thus we can, in silico design the compound we want to test. Next, we test the experimental work in the wet lab to actually see if it alters the way in which ML systems predicted. The next thing is toxicity or off-target effects. This evaluates if the compound is going to change some other part of the genome or has some unintended consequences. Next, we have clinical trials. In case of clinical trials, the biggest problems facing pharmaceutical companies is patient’s gratification. Then comes the marketing and distribution of that drug which is highly costly. This includes marketing strategies to convince people to buy those drugs, insurance companies to pay for them, and legal companies to deal with litigations. Here’s how long it took Ionis and Biogen, to develop Spinraza, which is a drug for curing Spinal Muscular Atrophy (SMA). It is the most effective drug for curing SMA. It has saved hundreds of lives already. However, it costs 750,000$ per child per year. Why does it cost so much? If we look at the timeline of the development of Spinraza, the initial period of testing was quite long. The goal of deep Genomics is to use ML to accelerate the research period of drugs such as Spinraza from 8 years down to a couple of years. They also aim to use AI to accelerate clinical trials, toxicity studies, and other aspects of drug development. The whole idea is to reduce the amount of time needed to develop the drug. Deep genomics uses AI to automate and accelerate each of these steps and make it fast and accurate. However, apart from AI, they also test compounds at their wet lab in human cells to see if they work. They also use Cloud Laboratory. At cloud lab, they upload a python script. Once uploaded, it specifies the experimental protocols and then robots conduct these experiments. These labs rapidly scale up the ability to do experiments, test compounds, and solve other problems. Earning trust of stakeholders One of the major issues ML systems face in the genomics industry is earning the trust of the stakeholders. These stakeholders include the patients, the physicians treating the patients, the insurance companies paying for the treatments, different technology providers, and the hospitals. Machine learning practitioners are also often criticized for producing black boxes, that are not open to interpretation. The way to gain this trust is to exactly figure out what these stakeholders need.  For this, machine learning systems need to explain the intermediary steps of a prediction. For instance, instead of directly recommending double mastectomy, the system says you have a mutation, the mutation is going to cause splicing to go wrong, leading to malfunctioning protein, which is likely to lead to breast cancer. The likelihood is x%. The road ahead Researchers at Deep Genomics pare currently working primarily on Project Saturn. The idea is to use a Machine learning system to scan a vast space of 69 billion molecules all in silico and identify about a thousand active compounds. Active compounds allow us to manipulate cell biology. Think about it as 1000 control switches which we can turn and twist to adjust what is going inside a cell, a toolkit for therapeutic development. They plan to have 3 compounds in clinical trials within the next 3 years.

In this article by Paul Asjes, the author of the book, Building Slack Bots, we'll have a look at webhooks in Slack. (For more resources related to this topic, see here.) Slack is a great way of communicating at your work environment—it's easy to use, intuitive, and highly extensible. Did you know that you can make Slack do even more for you and your team by developing your own bots? This article will teach you how to implement incoming and outgoing webhooks for Slack, supercharging your Slack team into even greater levels of productivity. The programming language we'll use here is JavaScript; however, webhooks can be programmed with any language capable of HTTP requests. Webhooks First let's talk basics: a webhook is a way of altering or augmenting a web application through HTTP methods. Webhooks allow us to post messages to and from Slack using regular HTTP requests with a JSON payloads. What makes a webhook a bot is its ability to post messages to Slack as if it were a bot user. These webhooks can be divided into incoming and outgoing webhooks, each with their own purposes and uses. Incoming webhooks An example of an incoming webhook is a service that relays information from an external source to a Slack channel without being explicitly requested, such as GitHub Slack integration: The GitHub integration posts messages about repositories we are interested in In the preceding screenshot, we see how a message was sent to Slack after a new branch was made on a repository this team was watching. This data wasn't explicitly requested by a team member but automatically sent to the channel as a result of the incoming webhook. Other popular examples include Jenkins integration, where infrastructure changes can be monitored in Slack (for example, if a server watched by Jenkins goes down, a warning message can be posted immediately to a relevant Slack channel). Let's start with setting up an incoming webhook that sends a simple "Hello world" message: First, navigate to the Custom Integration Slack team page, as shown in the following screenshot (https://my.slack.com/apps/build/custom-integration): The various flavors of custom integrations Select Incoming WebHooks from the list and then select which channel you'd like your webhook app to post messages to: Webhook apps will post to a channel of your choosing Once you've clicked on the Add Incoming WebHooks integration button, you will be presented with this options page, which allows you to customize your integration a little further: Names, descriptions, and icons can be set from this menu Set a customized icon for your integration (for this example, the wave emoji was used) and copy down the webhook URL, which has the following format:https://hooks.slack.com/services/T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX This generated URL is unique to your team, meaning that any JSON payloads sent via this URL will only appear in your team's Slack channels. Now, let's throw together a quick test of our incoming webhook in Node. Start a new Node project (remember: you can use npm init to create your package.json file) and install the superagent AJAX library by running the following command in your terminal: npm install superagent –save Create a file named index.js and paste the following JavaScript code within it: const WEBHOOK_URL = [YOUR_WEBHOOK_URL]; const request = require('superagent'); request .post(WEBHOOK_URL) .send({ text: 'Hello! I am an incoming Webhook bot!' }) .end((err, res) => { console.log(res); }); Remember to replace [YOUR_WEBHOOK_URL] with your newly generated URL, and then run the program by executing the following command: nodemon index.js Two things should happen now: firstly, a long response should be logged in your terminal, and secondly, you should see a message like the following in the Slack client: The incoming webhook equivalent of "hello world" The res object we logged in our terminal is the response from the AJAX request. Taking the form of a large JavaScript object, it displays information about the HTTP POST request we made to our webhook URL. Looking at the message received in the Slack client, notice how the name and icon are the same ones we set in our integration setup on the team admin site. Remember that the default icon, name, and channel are used if none are provided, so let's see what happens when we change that. Replace your request AJAX call in index.js with the following: request .post(WEBHOOK_URL) .send({ username: "Incoming bot", channel: "#general", icon_emoji: ":+1:", text: 'Hello! I am different from the previous bot!' }) .end((err, res) => { console.log(res); }); Save the file, and nodemon will automatically restart the program. Switch over to the Slack client and you should see a message like the following pop up in your #general channel: New name, icon, and message In place of icon_emoji, you could also use icon_url to link to a specific image of your choosing. If you wish your message to be sent only to one user, you can supply a username as the value for the channel property: channel: "@paul" This will cause the message to be sent from within the Slackbot direct message. The message's icon and username will match either what you configured in the setup or set in the body of the POST request. Finally, let's look at sending links in our integration. Replace the text property with the following and save index.js: text: 'Hello! Here is a fun link: <http://www.github.com|Github is great!>' Slack will automatically parse any links it finds, whether it's in the http://www.example.com or www.example.com formats. By enclosing the URL in angled brackets and using the | character, we can specify what we would like the URL to be shown as: Formatted links are easier to read than long URLs For more information on message formatting, visit https://api.slack.com/docs/formatting. Note that as this is a custom webhook integration, we can change the name, icon, and channel of the integration. If we were to package the integration as a Slack app (an app installable by other teams), then it is not possible to override the default channel, username, and icon set. Incoming webhooks are triggered by external sources; an example would be when a new user signs up to your service or a product is sold. The goal of the incoming webhook is to provide information to your team that is easy to reach and comprehend. The opposite of this would be if you wanted users to get data out of Slack, which can be done via the medium of outgoing webhooks. Outgoing webhooks Outgoing webhooks differ from the incoming variety in that they send data out of Slack and to a service of your choosing, which in turn can respond with a message to the Slack channel. To set up an outgoing webhook, visit the custom integration page of your Slack team's admin page again—https://my.slack.com/apps/build/custom-integration—and this time, select the Outgoing WebHooks option. On the next screen, be sure to select a channel, name, and icon. Notice how there is a target URL field to be filled in; we will fill this out shortly. When an outgoing webhook is triggered in Slack, an HTTP POST request is made to the URL (or URLs, as you can specify multiple ones) you provide. So first, we need to build a server that can accept our webhook. In index.js, paste the following code: 'use strict'; const http = require('http'); // create a simple server with node's built in http module http.createServer((req, res) => { res.writeHead(200, {'Content-Type': 'text/plain'}); // get the data embedded in the POST request req.on('data', (chunk) => { // chunk is a buffer, so first convert it to // a string and split it to make it more legible as an array console.log('Body:', chunk.toString().split('&')); }); // create a response let response = JSON.stringify({ text: 'Outgoing webhook received!' }); // send the response to Slack as a message res.end(response); }).listen(8080, '0.0.0.0'); console.log('Server running at http://0.0.0.0:8080/'); Notice how we require the http module despite not installing it with NPM. This is because the http module is a core Node dependency and is automatically included with your installation of Node. In this block of code, we start a simple server on port 8080 and listen for incoming requests. In this example, we set our server to run at 0.0.0.0 rather than localhost. This is important as Slack is sending a request to our server, so it needs to be accessible from the Internet. Setting the IP of your server to 0.0.0.0 tells Node to use your computer's network-assigned IP address. Therefore, if you set the IP of your server to 0.0.0.0, Slack can reach your server by hitting your IP on port 8080 (for example, http://123.456.78.90:8080). If you are having trouble with Slack reaching your server, it is most likely because you are behind a router or firewall. To circumvent this issue, you can use a service such as ngrok (https://ngrok.com/). Alternatively, look at port forwarding settings for your router or firewall. Let's update our outgoing webhook settings accordingly: The outgoing webhook settings, with a destination URL Save your settings and run your Node app; test whether the outgoing webhook works by typing a message into the channel you specified in the webhook's settings. You should then see something like this in Slack: We built a spam bot Well, the good news is that our server is receiving requests and returning a message to send to Slack each time. The issue here is that we skipped over the Trigger Word(s) field in the webhook settings page. Without a trigger word, any message sent to the specified channel will trigger the outgoing webhook. This causes our webhook to be triggered by a message sent by the outgoing webhook in the first place, creating an infinite loop. To fix this, we could do one of two things: Refrain from returning a message to the channel when listening to all the channel's messages. Specify one or more trigger words to ensure we don't spam the channel. Returning a message is optional yet encouraged to ensure a better user experience. Even a confirmation message such as Message received! is better than no message as it confirms to the user that their message was received and is being processed. Let's therefore presume we prefer the second option, and add a trigger word: Trigger words keep our webhooks organized Let's try that again, this time sending a message with the trigger word at the beginning of the message. Restart your Node app and send a new message: Our outgoing webhook app now functions a lot like our bots from earlier Great, now switch over to your terminal and see what that message logged: Body: [ 'token=KJcfN8xakBegb5RReelRKJng', 'team_id=T000001', 'team_domain=buildingbots', 'service_id=34210109492', 'channel_id=C0J4E5SG6', 'channel_name=bot-test', 'timestamp=1460684994.000598', 'user_id=U0HKKH1TR', 'user_name=paul', 'text=webhook+hi+bot%21', 'trigger_word=webhook' ] This array contains the body of the HTTP POST request sent by Slack; in it, we have some useful data, such as the user's name, the message sent, and the team ID. We can use this data to customize the response or to perform some validation to make sure the user is authorized to use this webhook. In our response, we simply sent back a Message received string; however, like with incoming webhooks, we can set our own username and icon. The channel cannot be different from the channel specified in the webhook's settings, however. The same restrictions apply when the webhook is not a custom integration. This means that if the webhook was installed as a Slack app for another team, it can only post messages as the username and icon specified in the setup screen. An important thing to note is that webhooks, either incoming or outgoing, can only be set up in public channels. This is predominantly to discourage abuse and uphold privacy, as we've seen that it's simple to set up a webhook that can record all the activity on a channel. Summary In this article, you learned what webhooks are and how you can use them to get data in and out of Slack. You learned how to send messages as a bot user and how to interact with your users in the native Slack client. Resources for Article: Further resources on this subject: Keystone – OpenStack Identity Service[article] A Sample LEMP Stack[article] Implementing Stacks using JavaScript[article]

Yael Niv is an Associate Professor of Psychology at the Princeton Neuroscience Institute since 2007. Her preferred areas of research include human and animal reinforcement learning and decision making. At her Niv lab, she studies day-to-day processes that animals and humans use to learn by trial and error, without explicit instructions given. In order to predict future events and to act upon the current environment so as to maximize reward and minimize the damage. Our article aims to deliver key points from Yael Niv’s keynote presentation at NIPS 2017. She talks about the ability of Artificial Intelligence systems to perform simple human-like tasks effectively using State representations in the human brain. The talk also deconstructs the complex human decision-making process. Further, we explore how a human brain breaks down complex procedures into simple states and how these states determine our decision-making capabilities.This, in turn, gives valuable insights into the design and architecture of smart AI systems with decision-making capabilities. Staying Simple is Complex What do you think happens when a human being crosses a road, especially when it’s a busy street and you constantly need to keep an eye on multiple checkpoints in order to be safe and sound? The answer is quite ironical. The human brain breaks down the complex process into multiple simple blocks. The blocks can be termed as states - and these states then determine decisions such as when to cross the road or at what speed to cross the road. In other words, the states can be anything - from determining the incoming traffic density to maintaining the calculation of your walking speed. These states help the brain to ignore other spurious or latent tasks in order to complete the priority task at hand. Hence, the computational power of the brain is optimized. The human brain possesses the capability to focus on the most important task at hand and then breaks it down into multiple simple tasks. The process of making smarter AI systems with complex decision-making capabilities can take inspiration from this process. The Practical Human Experiment To observe how the human brain behaves when urged to draw complex decisions, a few experiments were performed. The primary objective of these experiments was to verify the hypothesis that the decision making information in the human brain is stored in a part of the frontal brain called as Orbitofrontal cortex. The two experiments performed are described in brief below: Experiment 1 The participants were given sets of circles at random and they were asked to guess the number of circles in the cluster within 2 minutes. After they guessed the first time, the experimenter disclosed the correct number of circles. Then the subjects were further given a cluster of circles in two different colors (red and yellow) to repeat the guessing activity for each cluster. However, the experimenter never disclosed the fact that they will be given different colored clusters next. Observation: The most important observation derived from the experiment was that after the subject knew the correct count, their guesses revolved around that number irrespective of whether that count mattered for the next set of circle clusters given. That is, the count had actually changed for the two color specimens given to them. The important factor here is that the participants were not told that color would be a parameter to determine the number of circles in each set and still it played a huge part in guessing the number of circles in each set. This way it acted as a latent factor, which was present in the subconscious of the participants and was not a direct parameter. And, this being a latent factor was not in the list of parameters which played an important in determining the number of circles. But still, it played an important part in changing the overall count which was significantly higher for the red color than for the yellow color cluster. Hence, the experiment proved the hypothesis that latent factors are an integral part of intelligent decision-making capabilities in human beings. Experiment 2 The second experiment was performed to ascertain the hypothesis that the Orbitofrontal cortex contains all the data to help the human brain make complex decisions. For this, human brains were monitored using MRI to track the brain activity during the decision making process. In this experiment, the subjects were given a straight line and a dot. They were then asked to predict the next line from the dot - both in terms of line direction and its length. After completing this process for a given number of times, the participants were asked to remember the length and direction of the first line. There was a minor change among the sets of lines and dots. One group had a gradual change in line length and direction and another group had a drastic change in the middle. Observation: The results showed that the group with a gradual change of line length and direction were more helpful in preserving the first data and the one with drastic change was less accurate. The MRI reports showed signs that the classification information was primarily stored in the Orbitofrontal cortex. Hence it is considered as one of the most important parts of the human decision-making process. Shallow Learning with Deep Representations The decision-making capabilities and the effect of latent factors involved in it form the basis of dormant memory in humans. An experiment on rats was performed to explain this phenomenon. In the experiment, 4 rats were given electric shock accompanied by a particular type of sound for a day or two. On the third day, they reacted to the sound even without being given electric shocks. Ivan Pavlov has coined this term as Classical Conditioning theory wherein a relatively permanent change in behavior can be seen as a result of experience or continuous practice. Such instances of conditioning can be deeply damaging, for example in case of PTSD (Post Traumatic Stress Disorder) patients and other trauma victims. In order to understand the process of State representations being stored in memory, the reversal mechanism, i.e how to reverse the process also needs to be understood. For that, three techniques were tested on these rats: The rats were not given any shock but were subjected to the sound The rats were given shocks accompanied by sound at regular intervals and sounds without shock The shocks were slowly reduced in numbers but the sound continued The best results in reversing the memory were observed in case of the third technique, which is known as gradual extinction. In this way, a simple reinforcement learning mechanism is shown to be very effective because it helps in creating simple states which are manageable efficiently and trainable easily. Along with this, if we could extract information from brain imaging data derived from the Orbitofrontal cortex, these simple representational states can shed a lot of light into making complex computational processes simpler and enable us to make smarter AI systems for a better future.

In our first resolution, we talked about learning the building blocks of data science i.e developing your technical skills. In this second resolution, we walk you through steps to stay relevant in your field and how to dodge jobs that have a high possibility of getting automated in the near future. 2nd Resolution: Stay relevant in your field even as job automation is on the rise (Time investment: half an hour every day, 2 hours on weekends) Once you have got your fundamentals right, it is important to stay relevant through continuous learning and reskilling. In addition to honing your technical skills, you must also deepen your domain expertise and keep adding to your portfolio of soft skills to stay ahead of not the just human competition but also to thrive in an automated job market. We list below some simple ways to do all these in a systematic manner. All it requires is a commitment of half an hour to one hour of your time daily for your professional development. 1. Commit to and execute a daily learning-practice-participation ritual Here are some ways to stay relevant. Follow data science blogs and podcasts relevant to your area of interest. Here are some of our favorites: Data Science 101, the journey of a data scientist The Data Skeptic for a healthy dose of scientific skepticism Data Stories for data visualization This Week in Machine Learning & AI for informative discussions with prominent people in the data science/machine learning community Linear Digressions, a podcast co-hosted by a data scientist and a software engineer attempting to make data science accessible You could also follow individual bloggers/vloggers in this space like Siraj Raval, Sebastian Raschka, Denny Britz, Rodney Brookes, Corinna Cortes, Erin LeDell Newsletters are a great way to stay up-to-date and to get a macro-level perspective. You don’t have to spend an awful lot of time doing the research yourself on many different subtopics. So, subscribe to useful newsletters on data science. You can subscribe to our newsletter here. It is a good idea to subscribe to multiple newsletters on your topic of interest to get a balanced and comprehensive view of the topic. Try to choose newsletters that have distinct perspectives, are regular and are published by people passionate about the topic. Twitter gives a whole new meaning to ‘breaking news’. Also, it is a great place to follow contemporary discussions on topics of interest where participation is open to all. When done right, it can be a gold mine for insights and learning. But often it is too overwhelming as it is viewed as a broadcasting marketing tool. Follow your role models in data science on Twitter. Or you could follow us on Twitter @PacktDataHub for curated content from key data science influencers and our own updates about the world of data science. You could also click here to keep a track of 737 twitter accounts most followed by the members of the NIPS2017 community. Quora, Reddit, Medium, and StackOverflow are great places to learn about topics in depth when you have a specific question in mind or a narrow focus area. They help you get multiple informed opinions on topics. In other words, when you choose a topic worth learning, these are great places to start. Follow them up by reading books on the topic and also by reading the seminal papers to gain a robust technical appreciation. Create a Github account and participate in Kaggle competitions. Nothing sticks as well as learning by doing. You can also browse into Data Helpers, a site voluntarily set up by Angela Bass where interested data science people can offer to help newcomers with their queries on entering the required field and anything else. 2. Identify your strengths and interests to realign your career trajectory OK, now that you have got your daily learning routine in place, it is time to think a little more strategically about your career trajectory, goals and eventually the kind of work you want to be doing. This means: Getting out of jobs that can be automated Developing skills that augment or complement AI driven tasks Finding your niche and developing deep domain expertise that AI will find hard to automate in the near future Here are some ideas to start thinking about some of the above ideas. The first step is to assess your current job role and understand how likely it is to get automated. If you are in a job that has well-defined routines and rules to follow, it is quite likely to go the AI job apocalypse route. Eg: data entry, customer support that follows scripts, invoice processing, template-based software testing or development etc. Even “creative” job such as content summarization, news aggregation, template-based photo-editing/video editing etc fall in this category. In the world of data professionals, jobs like data cleaning, database optimization, feature generation, even model building (gasp!) among others could head the same way given the right incentives. Choose today to transition out of jobs that may not exist in the next 10 years. Then instead of hitting the panic button, invest in redefining your skills in a way that would be helpful in the long run. If you are a data professional, skills such as data interpretation, data-driven storytelling,  data pipeline architecture and engineering, feature engineering, and others that require a high level of human judgment skills are least likely to be replicated by machines anytime soon. By mastering skills that complement AI driven tasks and jobs, you should be able to present yourself as a lucrative option to potential employers in a highly competitive job market space.    In addition to reskilling, try to find your niche and dive deep. By niche, we mean, if you are a data scientist, choose a specific technical aspect in your field, something that interests you. It could be anything from computer vision to NLP to even a class of algorithms like neural nets or a type of problem that machine learning solves such as recommender systems or classification systems. It could even be a specific phase of a data science project such as data visualization or data pipeline engineering. Master your niche while keeping up with what’s happening in other related areas. Next, understand where your strengths lie. In other words, what your expertise is, what industry or domain do you understand well or have amassed experience in. For instance, NLP, a subset of machine learning abilities, can be applied to customer reviews to mine useful insights, perform sentiment analysis, build recommendation systems in conjunction with predictive modeling among other things. In order to build an NLP model to mine some kind of insights from customer feedback, we must have some idea of what we are looking for. Your domain expertise can be of great value here. If you are in the publishing business, you would know what keywords matter most in reviews and more importantly why they matter and how to convert the findings into actionable insights - aspects that your model or even a machine learning engineer outside your industry may not understand or appreciate. Take the case of Brendan Frey and the team of researchers at Deep Genomics as a real-world example. They applied AI and machine learning (their niche expertise) to build a neural network to identify pathological mutations in genes (their domain expertise). Their knowledge of how genes get created and how they work, what a mutation looks like etc helped them feed the features and hyperparameters into their model. Similarly, you can pick up any of your niche skills and apply them in whichever field you find interesting and worthwhile. Based on your domain knowledge and area of expertise, it could range from sorting a person into a Hogwarts house because you are a Harry Potter fan to sorting them into potential patients with a high likelihood to develop diabetes because you have a background in biotechnology.   This brings us to the next resolution where we cover aspects related to how your work will come to define you and why it matters that you choose your projects well.   

This session of FAT 2018 is about Recommenders, etc. Recommender systems are algorithmic tools for identifying items of interest to users. They are usually deployed to help mitigate information overload. Internet-scale item spaces offer many more choices than humans can process, diminishing the quality of their decision-making abilities. Recommender systems alleviate this problem by allowing users to more quickly focus on items likely to match their particular tastes. They are deployed across the modern Internet, suggesting products in e-commerce sites, movies and music in streaming media platforms, new connections on social networks, and many more types of items. This session explains what Fairness, Accountability, and Transparency means in the context of recommendation. The session also includes a paper that talks about predictive policing, which is defined as ‘Given historical crime incident data for a collection of regions, decide how to allocate patrol officers to areas to detect crime.’ The Conference on Fairness, Accountability, and Transparency (FAT), which would be held on the 23rd and 24th of February, 2018 is a multi-disciplinary conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. The FAT 2018 conference will witness 17 research papers, 6 tutorials, and 2 keynote presentations from leading experts in the field. This article covers research papers pertaining to the 5th session that is dedicated to FAT Recommenders, etc. Paper 1: Runaway Feedback Loops in Predictive Policing Predictive policing systems are increasingly being used to determine how to allocate police across a city in order to best prevent crime. To update the model, discovered crime data (e.g., arrest counts) are used. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. This paper is in response to this system, where the authors have developed a mathematical model of predictive policing that proves why this feedback loop occurs.The paper also empirically shows how this model exhibits such problems, and demonstrates ways to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Key takeaways: The results stated in the paper establish a link between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. The paper also demonstrates ways in which reported incidents of crime (reported by residents) and discovered incidents of crime (directly observed by police officers dispatched as a result of the predictive policing algorithm) interact. In this paper, the authors have used the theory of urns (a common framework in reinforcement learning) to analyze existing methods for predictive policing. There are formal as well as empirical results which shows why these methods will not work. Subsequently, the authors have also provided remedies that can be used directly with these methods in a black-box fashion that improve their behavior, and provide theoretical justification for these remedies. Paper 2: All The Cool Kids, How Do They Fit In? Popularity and Demographic Biases in Recommender Evaluation and Effectiveness There have been many advances in the information retrieval evaluation, which demonstrate the importance of considering the distribution of effectiveness across diverse groups of varying sizes. This paper addresses this question, ‘do users of different ages or genders obtain similar utility from the system, particularly if their group is a relatively small subset of the user base?’ The authors have applied this consideration to recommender systems, using offline evaluation and a utility-based metric of recommendation effectiveness to explore whether different user demographic groups experience similar recommendation accuracy. The paper shows that there are demographic differences in measured recommender effectiveness across two data sets containing different types of feedback in different domains; these differences sometimes, but not always, correlate with the size of the user group in question. Demographic effects also have a complex— and likely detrimental—interaction with popularity bias, a known deficiency of recommender evaluation. Key takeaways: The paper presents an empirical analysis of the effectiveness of collaborative filtering recommendation strategies, stratified by the gender and age of the users in the data set. The authors applied widely-used recommendation techniques across two domains, musical artists and movies, using publicly-available data. The paper explains whether recommender systems produced equal utility for users of different demographic groups. The authors made use of publicly available datasets, they compared the utility, as measured with nDCG, for users grouped by age and gender. Regardless of the recommender strategy considered, they found significant differences for the nDCG among demographic groups. Paper 3: Recommendation Independence In this paper the authors have showcased new methods that can deal with variance of recommendation outcomes without increasing the computational complexity. These methods can more strictly remove the sensitive information, and experimental results demonstrate that the new algorithms can more effectively eliminate the factors that undermine fairness. Additionally, the paper also explores potential applications for independence enhanced recommendation, and discuss its relation to other concepts, such as recommendation diversity. Key takeaways from the paper: The authors have developed new independence-enhanced recommendation models that can deal with the second moment of distributions without sacrificing computational efficiency. The paper also explores applications in which recommendation independence would be useful, and reveal the relation of independence to the other concepts in recommendation research. It also presents the concept of recommendation independence, and discuss how the concept would be useful for solving real-world problems. Paper 4: Balanced Neighborhoods for Multi-sided Fairness in Recommendation In this paper, the authors examine two different cases of fairness-aware recommender systems: consumer-centered and provider-centered. The paper explores the concept of a balanced neighborhood as a mechanism to preserve personalization in recommendation while enhancing the fairness of recommendation outcomes. It shows that a modified version of the Sparse Linear Method (SLIM) can be used to improve the balance of user and item neighborhoods, with the result of achieving greater outcome fairness in real-world datasets with minimal loss in ranking performance. Key takeaways: In this paper, the authors examine applications in which fairness with respect to consumers and to item providers is important. They have shown that variants of the well-known sparse linear method (SLIM) can be used to negotiate the tradeoff between fairness and accuracy. This paper also introduces the concept of multisided fairness, relevant in multisided platforms that serve a matchmaking function. It demonstrates that the concept of balanced neighborhoods in conjunction with the well-known sparse linear method can be used to balance personalization with fairness considerations. If you’ve missed our summaries on the previous sessions, visit the article links to be on track. Session 1: Online Discrimination and Privacy Session 2: Interpretability and Explainability Session 3: Fairness in Computer Vision and NLP Session 4: Fair Classification

We are back with Part 2 of our analysis of intriguing AI trends in 2018 as promised in our last post.  We covered the first nine trends in part 1 of this two-part prediction series. To refresh your memory, these are the trends we are betting on. Artificial General Intelligence may gain major traction in research. We will turn to AI enabled solution to solve mission-critical problems. Machine Learning adoption in business will see rapid growth. Safety, ethics, and transparency will become an integral part of AI application design conversations. Mainstream adoption of AI on mobile devices Major research on data efficient learning methods AI personal assistants will continue to get smarter Race to conquer the AI optimized hardware market will heat up further We will see closer AI integration into our everyday lives. The cryptocurrency hype will normalize and pave way for AI-powered Blockchain applications. Advancements in AI and Quantum Computing will share a symbiotic relationship Deep learning will continue to play a significant role in AI development progress. AI will be on both sides of the cybersecurity challenge. Augmented reality content will be brought to smartphones. Reinforcement learning will be applied to a large number of real-world situations. Robotics development will be powered by Deep Reinforcement learning and Meta-learning A rise in immersive media experiences enabled by AI. A large number of organizations will use Digital Twin Without further ado, let’s dive straight into why we think these trends are important. 10. Neural AI: Deep learning will continue to play a significant role in AI progress. Talking about AI is incomplete without mentioning Deep learning. 2017 saw a wide variety of deep learning applications emerge in diverse areas from Self-driving cars, to Beating Video Games and Go champions, to Dreaming, to Painting pictures, and making scientific discoveries. The year started with Pytorch posing a real challenge to Tensorflow, especially in research. Tensorflow countered it by releasing dynamic computation graphs in Tensorflow Fold. As deep learning frameworks became more user-friendly and accessible, and the barriers for programmers and researchers to use deep learning lowered, it increased developer acceptance. This trend will continue to grow in 2018. There would also be improvements in designing and tuning deep learning networks and for this, techniques such as automated hyperparameter tuning will be used widely. We will start seeing real-world uses of automated machine learning development popping up. Deep learning algorithms will continue to evolve around unsupervised and generative learning to detect features and structure in data. We will see high-value use cases of neural networks beyond image, audio, or video analysis such as for advanced text classification, musical genre recognition, biomedical image analysis etc. 2017 also saw ONNX standardization of neural network representations as an important and necessary step forward to interoperability. This will pave way for deep learning models to become more transparent i.e., start making it possible to explain their predictions, especially when the outcomes of these models are used to influence or inform human decisions. 2017 saw a large portion of deep learning research dedicated to GANs. In 2018, We should see implementations of some of GANs ideas, in real-world use cases such as in cyber threat detection. 2018 may also see more deep learning methods gain Bayesian equivalents and probabilistic programming languages to start incorporating deep learning. 11. Autodidact AI: Reinforcement learning will be applied to a large number of real-world situations. Reinforcement learning systems learn by interacting with the environment through observations, actions, and rewards. The historic victory of AlphaGo, this year, was a milestone for reinforcement learning techniques. Although the technique has existed for decades, the idea to combine it with neural networks to solve complex problems (such as the game of Go) made it widely popular. In 2018, we will see reinforcement learning used in real-world situations. We will also see the development of several simulated environments to increase the progress of these algorithms. A notable fact about reinforcement learning algorithms is that they are trained via simulation, which eliminates the need for labeled data entirely. Given such advantages, we can see solutions which combine Reinforcement Learning and agent-based simulation in the coming year. We can expect to see more algorithms and bots enabling edge devices to learn on their own, especially in IoT environments. These bots will push the boundaries between AI techniques such as reinforcement learning, unsupervised learning and auto-generated training to learn on their own. 12. Gray Hat AI: AI will be on both sides of the cybersecurity challenge. 2017 saw some high-profile cases of ransomware attack, the most notable being WannaCry. Cybercrime is projected to cause $6 trillion in damages by 2021. Companies now need to respond better and faster to these security breaches. Since hiring and training and reskilling people is time-consuming and expensive, companies are turning to AI to automate tasks and detect threats. 2017 saw a variety of AI in cyber sec releases. From Watson AI helping companies stay ahead of hackers and cybersecurity attacks, to Darktrace—a company by Cambridge university mathematicians—which uses AI to spot patterns and prevent cyber crimes before they occur. In 2018 we may see AI being used for making better predictions about never seen before threats. We may also hear about AI being used to prevent a complex cybersecurity attack or the use of AI in incident management. On the research side, we can expect announcements related to securing IoT. McAfee has identified five cybersecurity trends for 2018 relating to Adversarial Machine Learning, Ransomware, Serverless Apps, Connected Home Privacy, and Privacy of Child-Generated Content. 13. AI in Robotics: Robotics development will be powered by Deep Reinforcement learning and Meta-learning Deep reinforcement learning was seen in a new light, especially in the field of robotics after Pieter Abbeel’s fantastic Keynote speech at NIPS 2017.  It talked about the implementation of Deep Reinforcement Learning (DRL) in Robotics, what challenges exist and how these challenges can be overcome. DRL has been widely used to play games (Alpha Go and Atari). In 2018, deep reinforcement learning will be used to instill more human-like qualities of discernment and complex decision-making in robots. Meta-learning was another domain which gained widespread attention in 2017. We Started with  model-agnostic meta-learning, which addresses the problem of discovering learning algorithms that generalize well from very few examples. Later in the year, more research on meta-learning for few shot learning was published, using deep temporal convolutional networks and, graph neural networks among others. We're also now seeing meta-learn approaches that learn to do active learning, cold-start item recommendation, reinforcement learning, and many more. More research and real-world implementations of these algorithms will happen in 2018. 2018 may also see developments to overcome the Meta-learning challenge of requiring more computing power so that it can be successfully applied to the field of robotics. Apart from these, there would be improvements in significant other challenges such as safe learning, and value alignment for AI in robotics. 14. AI Dapps: Within the developer community, the cryptocurrency hype will normalize and pave way for AI-powered Blockchain applications. Blockchain is expected to be the storehouse for 10% of the world GDP by 2025.  With such a high market growth, Amazon announced the AWS Blockchain Partners Portal to support customers’ integration of blockchain solutions with systems built on AWS. Following Amazon’s announcement, more tech companies are expected to launch such solutions in the coming year.  Blockchain in combination with AI will provide a way for maintaining immutability in a blockchain network creating a secure ecosystem for transactions and data exchange. AI BlockChain is a digital ledger that maximizes security while remaining immutable by employing AI agents that govern the chain. And 2018, will see more such security solutions coming up. A drawback of blockchain is that blockchain mining requires a high amount of energy.  Google’s DeepMind has already proven that AI can help in optimizing energy consumption in data centers. Similar results can be achieved for blockchain as well. For example, Ethereum has come up with proof of stake, a set of algorithms which selects validators based in part on the size of their respective monetary deposits instead of rewarding participants for spending computational resources, thus saving energy. Research is also expected in the area of using AI to reduce the network latency to enable faster transactions. 15. Quantum AI: Convergence of AI in Quantum Computing Quantum computing was called one of the three path-breaking technologies that will shape the world in the coming years by Microsoft CEO, Satya Nadella. 2017 began with Google unveiling a blueprint for quantum supremacy. IBM edged past them by developing a quantum computer capable of handling 50 qubits. Then came, Microsoft with their Quantum Development Kit and a new quantum programming language. The year ended with Rigetti Computing, a startup, announcing a new quantum algorithm for unsupervised machine learning. 2018 is expected to bring in more organizations, new and old, competing to develop a quantum computer with the capacity to handle even more qubits and process data-intensive large-scale algorithms at speeds never imagined before. As more companies successfully build quantum computers, they would also use them for making substantial progress on current efforts in AI development and for finding new areas of scientific discovery. As with Rigetti, new quantum algorithms would be developed to solve complex machine learning problems. We can also see tools, languages, and frameworks such as Microsoft's Q# programming language being developed to facilitate quantum app development. 16. AI doppelgangers: A large number of organizations will use Digital Twin Digital twin, as the name suggests, is a virtual replica of a product, process or service. 2017 saw some major work going in the field of Digital twin. The most important being GE, which now has over 551,000 digital twins built on their Predix platform. SAP expanded their popular IoT platform, SAP Leonardo with a new digital twin offering. Gartner has named Digital Twin as one of the top 10 Strategic Technology Trends for 2018. Following this news, we can expect to see more organizations coming up with their own digital twins. First to, monitor and control assets, to reduce asset downtime, lower the maintenance costs and improve efficiency. And later to organize and envision more complex entities, such as cities or even human beings. These Digital twins will be infused with AI capabilities to enable advanced simulation, operation, and analysis over the digital representations of physical objects. 2018 is expected to have digital twins make steady progress and benefit city architects, digital marketers, healthcare professionals and industrial planners. 17. Experiential AI: Rise in immersive media experiences based on Artificial Intelligence. 2017 saw the resurgence of Virtual Reality thanks to advances made in AI. Facebook unveiled a standalone headset, Oculus Go, to go on sale in early 2018. Samsung added a separate controller to its Gear VR, and Google's Daydream steadily improved from the remains of Google Cardboard. 2018 will see virtual reality the way 2017 saw GANs -  becoming an accepted convention with impressive use cases but not fully deployed at a commercial scale. It won’t be limited to just creating untethered virtual reality headgears, but will also combine the power of virtual reality, artificial intelligence, and conversational platforms to build a uniquely-immersive experience. These immersive technologies will come out of conventional applications(read the gaming industry) to be used in real estate industry, travel & hospitality industry, and other segments. Intel is reportedly working on is a VR set dedicated to sports events. It allows a viewer to experience the basketball game from any seats they choose. It uses AI and big data to analyze different games happening at the same time, so they can switch to watch them immediately. Not only that, Television will start becoming a popular source of immersive experiences. The next-gen televisions will be equipped with high definition cameras, as well as AI technology to analyze a viewer's emotions as they watch shows. 18. AR AI: Augmented reality content will be brought to smartphones Augmented Reality first garnered worldwide attention with the release of Pokemon Go. Following which a large number of organizations invested in the development of AR-enabled smartphones in 2017. Most notable was Apple’s ARKit framework, which allowed developers to create augmented reality experiences for iPhone and iPad.  Following which Google launched ARCore, to create augmented reality experiences at Android scale. Then came Snapchat, which released Lens Studio, a tool for creating customizable AR effects. The latest AR innovation came from Facebook, which launched AR Studio in open beta to bring AR into the everyday life of its users through the Facebook camera. For 2018, they are planning to develop 3D digital objects for people to place onto surfaces and interact within their physical space. 2018 will further allow us to get a taste of augmented reality content through beta products set in the context of our everyday lives. A recent report published by Digi-Capital suggests that mobile AR market will be worth an astonishing $108 billion by 2021. Following this report, more e-commerce websites will engage mobile users using some form of AR content seeking inspiration from the likes of the Ikea Place AR app. Apart from these, more focus would be on building apps and frameworks which consumes less battery life and have high mobile connectivity capability. With this, we complete our list of our 18 in 18’ AI trends to watch.  We would love to know which of our AI-driven prediction surprises you the most and the trends which you agree with. Please feel free to leave a comment below with your views. Happy New Year!

In this article, we will provide 3 tips for making an interactive conversational application using current chat and voice examples. This is an excerpt from the book Voicebot and Chatbot Design written by Rachel Batish. In this book, the author shares her insights into cutting-edge voice-bot and chatbot technologies Help your users ask the right questions Although this sounds obvious, it is actually crucial to the success of your chatbot or voice-bot. I learned this when I initially set up my Amazon Echo device at home. Using a complementary mobile app, I was directed to ask Alexa specific questions, to which she had good answers to, such as “Alexa, what is the time?” or “Alexa, what is the weather today?” I immediately received correct answers and therefore wasn’t discouraged by a default response saying, “Sorry, I don’t have an answer to that question.” By providing the user with successful experience, we are encouraging them to trust the system and to understand that, although it has its limitations, it is really good in some specific details. Obviously, this isn’t enough because as time passes, Alexa (and Google) continues to evolve and continues to expand its support and capabilities, both internally and by leveraging third parties. To solve this discovery problem, some solutions, like Amazon Alexa and Google Home, send a weekly newsletter with the highlights of their latest capabilities. In the email below, Amazon Alexa is providing a list of questions that I should ask Alexa in my next interaction with it, exposing me to new functionalities like donation. From the Amazon Alexa weekly emails “What’s new with Alexa?” On the Google Home/Assistant, Google has also chosen topics that it recommends its users to interact with. Here, as well, the end user is exposed to new offerings/capabilities/knowledge bases, that may give them the trust needed to ask similar questions on other topics. From the Google Home newsletter Other chat and voice providers can also take advantage of this email communication idea to encourage their users to further interact with their chatbots or voice-bots and expose them to new capabilities. The simplest way of encouraging usage is by adding a dynamic ‘welcoming’ message to the chat voice applications, that includes new features that are enabled. Capital One, for example, updates this information every now and then, exposing its users to new functionalities. On Alexa, it sounds like this: “Welcome to Capital One. You can ask me for things like account balance and recent transactions.” Another way to do this – especially if you are reaching out to a random group of people – is to initiate discovery during the interaction with the user (I call this contextual discovery). For example, a banking chatbot offers information on account balances. Imagine that the user asks, “What’s my account balance?” The system gives its response: “Your checking account balance is $5,000 USD.” The bank has recently activated the option to transfer money between accounts. To expose this information to its users, it leverages the bot to prompt a rational suggestion to the user and say, “Did you know you can now transfer money between accounts? Would you like me to transfer $1,000 to your savings account?” As you can see, the discovery process was done in context with the user’s actions. Not only does the user know that he/she can now transfer money between two accounts, but they can also experience it immediately, within the relevant context. To sum up tip #1, by finding the direct path to initial success, your users will be encouraged to further explore and discover your automated solutions and will not fall back to other channels. The challenge is, of course, to continuously expose users to new functionalities, made available on your chatbots and voice-bots, preferably in a contextual manner. Give your bot a ‘personality’, but don’t pretend it’s a human Your bot, just like any digital solution you provide today, should have a personality that makes sense for your brand. It can be visual, but it can also be enabled over voice. Whether it is a character you use for your brand or something created for your bot, personality is more than just the bot’s icon. It’s the language that it ‘speaks’, the type of interaction that it has and the environment it creates. In any case, don’t try to pretend that your bot is a human talking with your clients. People tend to ask the bot questions like “are you a bot?” and sometimes even try to make it fail by asking questions that are not related to the conversation (like asking how much 30*4,000 is or what the bot thinks of *a specific event*). Let your users know that it’s a bot that they are talking to and that it’s here to help. This way, the user has no incentive to intentionally trip up the bot. ICS.ai have created many custom bots for some of the leading UK public sector organisations like county councils, local governments and healthcare trusts. Their conversational AI chat bots are custom designed by name, appearance and language according to customer needs. Chatbot examples Below are a few examples of chatbots with matching personalities. Expand your vocabulary with a word a day (Wordsworth) The Wordsworth bot has a personality of an owl (something clever), which fits very well with the purpose of the bot: to enrich the user’s vocabulary. However, we can see that this bot has more than just an owl as its ‘presenter’, pay attention to the language and word games and even the joke at the end. Jokes are a great way to deliver personality. From these two screenshots only, we can easily capture a specific image of this bot, what it represents and what it’s here to do. DIY-Crafts-Handmade FB Messenger bot The DIY-Crafts-Handmade bot has a different personality, which signals something light and fun. The language used is much more conversational (and less didactic) and there’s a lot of usage of icons and emojis. It’s clear that this bot was created for girls/women and offers the end user a close ‘friend’ to help them maximize the time they spend at home with the kids or just start some DIY projects. Voicebot examples One of the limitations around today’s voice-enabled devices is the voice itself. Whereas Google and Siri do offer a couple of voices to choose from, Alexa is limited to only one voice and it’s very difficult to create that personality that we are looking for. While this problem probably will be solved in the future, as technology improves, I find insurance company GEICO’s creativity around that very inspiring. In its effort to keep Gecko’s unique voice and personality, GEICO has incorporated multiple MP3 files with a recording of Gecko’s personalized voice. https://www.youtube.com/watch?v=11qo9a1lgBE GEICO has been investing for years in Gecko’s personalization. Gecko is very familiar from TV and radio advertisements, so when a customer activates the Alexa app or Google Action, they know they are in the right place. To make this successful, GEICO incorporated Gecko’s voice into various (non-dynamic) messages and greetings. It also handled the transition back to the device’s generic voice very nicely; after Gecko has greeted the user and provided information on what they can do, it hands it back to Alexa with every question from the user by saying, “My friend here can help you with that.” This is a great example of a cross-channel brand personality that comes to life also on automated solutions such as chatbots and voice-bots. Build an omnichannel solution – find your tool Think less on the design side and more on the strategic side, remember that new devices are not replacing old devices; they are only adding to the big basket of channels that you must support. Users today are looking for different services anywhere and anytime. Providing a similar level of service on all the different channels is not an easy task, but it will play a big part in the success of your application. There are different reasons for this. For instance, you might see a spike in requests coming from home devices such as Amazon Echo and Google Home during the early morning and late at night. However, during the day you will receive more activities from FB Messenger or your intelligent assistant. Different age groups also consume products from different channels and, of course, geography impacts as well. Providing cross-channel/omnichannel support doesn’t mean providing different experiences or capabilities. However, it does mean that you need to make that extra effort to identify the added value of each solution, in order to provide a premium, or at least the most advanced, experience on each channel. Building an omnichannel solution for voice and chat Obviously, there are differences between a chatbot and a voice-bot interaction; we talk differently to how we write and we can express ourselves with emojis while transferring our feelings with voice is still impossible. There are even differences between various voice-enabled devices, like Amazon Alexa and Google Assistant/Home and, of course, Apple’s HomePod. There are technical differences but also behavioral ones. The HomePod offers a set of limited use cases that businesses can connect with, whereas Amazon Alexa and Google Home let us create our own use cases freely. In fact, there are differences between various Amazon Echo devices, like the Alexa Show that offers a complimentary screen and the Echo Dot that lacks in screen and sound in comparison. There are some developer tools today that offer multi-channel integration to some devices and channels. They are highly recommended from a short and long-term perspective. Those platforms let bot designers and bot builders focus on the business logic and structure of their bots, while all the integration efforts are taken care of automatically. Some of those platforms focus on chat and some of them on voice. A few tools offer a bridge between all the automated channels or devices. Among those platforms, you can find Conversation.one (disclaimer: I’m one of the founders), Dexter and Jovo. With all that in mind, it is clear that developing a good conversational application is not an easy task. Developers must prove profound knowledge of machine learning, voice recognition, and natural language processing. In addition to that, it requires highly sophisticated and rare skills, that are extremely dynamic and flexible. In such a high-risk environment, where today’s top trends can skyrocket in days or simply be crushed in just a few months, any initial investment can be dicey. To know more trips and tricks to make a successful chatbot or voice-bot, read the book Voicebot and Chatbot Design by Rachel Batish. Creating a chatbot to assist in network operations [Tutorial] Building your first chatbot using Chatfuel with no code [Tutorial] Conversational AI in 2018: An arms race of new products, acquisitions, and more