Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise."

Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise."
Copyright : Futuretext Ltd. London0 Ajit Jaokar Enterprise AI Artificial Intelligence for the Enterprise https://www.meetup.com/Big-Data-Berlin/events/236608419/
Copyright : Futuretext Ltd. London1 Ajit Jaokar Oxford Uni – Data Science for IoT. Rated top influencer for DS and IoT by kdnuggets and DS central - World Economic Forum - Spoken at MWC(5 times), CEBIT, CTIA, Web 2.0, CNN, BBC, Oxford Uni, Uni St Gallen, European Parliament. @feynlabs – teaching kids Computer Science. Adivsory – Connected Liverpool
Copyright : Futuretext Ltd. London2 Ajit Jaokar
Copyright : Futuretext Ltd. London3 AI vs. Deep Learning vs. Machine Learning. The term Artificial Intelligence (AI) implies a machine that can Reason. A more complete list or AI characteristics Reasoning - Knowledge representation – Planning – Communication - Perception: http://cdn04.androidauthority.net/wp-content/uploads/2015/07/machine- learning-ai-artificial-intelligence-e1462471461626.jpg
Copyright : Futuretext Ltd. London4 Deep Learning algorithms are currently driving AI. Finally, in a broad sense, the term Machine Learning means the application of any algorithm that can be applied against a dataset to find a pattern in the data. This includes algorithms like supervised, unsupervised, segmentation, classification, or regression.
Copyright : Futuretext Ltd. London5 The holy grail of AI is artificial general intelligence (aka like Terminator!)
Copyright : Futuretext Ltd. London6 What we see today is mostly narrow AI (ex like the NEST thermostat). AI is evolving rapidly.
Copyright : Futuretext Ltd. London7 http://assets.inhabitat.com/wp- content/blogs.dir/1/files/2013/12/Volvo-self-driving-car- 706x369.jpg
Copyright : Futuretext Ltd. London8
Copyright : Futuretext Ltd. London10 Ajit Jaokar What problem does Deep Learning Address
Copyright : Futuretext Ltd. London11 Deep learning is really about automated feature engineering. Feature engineering involves finding connections between variables and packaging them into a new single variable Deep Learning suits problems where the target function is complex and datasets are large but with examples of positive and negative cases. Deep Learning also suits problems that involve Hierarchy and Abstraction. (image source: Yoshua Bengio)
Copyright : Futuretext Ltd. London13 Ajit Jaokar Twelve types of AI problems
Copyright : Futuretext Ltd. London14 With this background, we now discuss the twelve types of AI problems. 1) Domain expert: Problems which involve Reasoning based on a complex body of knowledge This includes tasks which are based on learning a body of knowledge like Legal, financial etc. and then formulating a process where the machine can simulate an expert in the field 2) Domain extension: Problems which involve extending a complex body of Knowledge Here, the machine learns a complex body of knowledge like information about existing medication etc. and then can suggest new insights to the domain itself – for example new drugs to cure diseases.
Copyright : Futuretext Ltd. London15 3) Complex Planner: Tasks which involve Planning Many logistics and scheduling tasks can be done by current (non AI) algorithms. But increasingly, as the optimization becomes complex AI could help. AI techniques help on this case because we have large and complex datasets where human beings cannot detect patterns but a machine can do so easily. 4) Better communicator: Tasks which involve improving existing communication AI and Deep Learning benefit many communication modes such as automatic translation, intelligent agents etc 5) New Perception: Tasks which involve Perception AI and Deep Learning enable newer forms of Perception which enables new services such as autonomous vehicles
Copyright : Futuretext Ltd. London16 6) Enterprise AI: AI meets Re-engineering the corporation! 7) Enterprise AI adding unstructured data and Cognitive capabilities to ERP and Data warehousing For reasons listed above, unstructured data offers a huge opportunity for Deep Learning and hence AI. 8) Problems which impact domains due to second order consequences of AI “The second-order consequences of machine learning will exceed its immediate impact. “ ex insurance 9) Problems in the near future that could benefit from improved algorithms A catch-all category for things which were not possible in the past, could be possible in the near future due to better algorithms or better hardware. Ex speech recognition and translation capabilities
Copyright : Futuretext Ltd. London17 10) Evolution of Expert systems Expert systems have been around for a long time. Much of the vision of Expert systems could be implemented in AI/Deep Learning algorithms in the near future. The IBM Watson strategy leads to an Expert system vision. Of course, the same ideas can be implemented independently of Watson today. 11) Super Long sequence pattern recognition I got this title from a slide from Uber’s head of Deep Learning. The application of AI techniques to sequential pattern recognition is still an early stage domain(and does not yet get the kind of attention as CNNs for example) – but in my view, this will be a rapidly expanding space. LSTMs fall in this category 12) Extending Sentiment Analysis using AI The interplay between AI and Sentiment analysis is also a new area.
Copyright : Futuretext Ltd. London18 Ajit Jaokar What is Deep Learning
Copyright : Futuretext Ltd. London19 How to train a Big Data algorithm? Start with the Rules and apply them to Data OR Start with the data and find the rules from the Data The Top-down approach involved writing enough rules for all possible circumstances. But this approach is obviously limited by the number of rules and by its finite rules base. Bottom up approach: where there are no rules : a) No models(schema), b) Linearity(sequence) and hierarchy is not known c) Non deterministic – output is not known d) Problem domain is not finite In contrast – transactional computing is straight forward Image source: https://www.simplilearn.com
Copyright : Futuretext Ltd. London20  10 million images from YouTube videos – recognise pictures of Cats - without telling what a cat is  Apply them to real problems” such as image recognition, search, and natural-language understanding,
Copyright : Futuretext Ltd. London22  10 million images from YouTube videos – recognise pictures of Cats - without telling what a cat is  Apply them to real problems” such as image recognition, search, and natural-language understanding,
Copyright : Futuretext Ltd. London23 1.Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. 2.Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. 3.Virtual Agents: Getting a lot of media attention Ex Amazon, Apple etc 4.Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. 5.AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Ex Nvidia. 6.Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.
Copyright : Futuretext Ltd. London24 7.Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. 8.Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. 9.Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion. 10.Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data.
Copyright : Futuretext Ltd. London25 Ajit Jaokar Deep Learning Libraries
Copyright : Futuretext Ltd. London26 Ajit Jaokar The Enterprise AI layer
Copyright : Futuretext Ltd. London27 A logical concept called the AI layer for the Enterprise. We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. One simple way is to think of it as an ‘Intelligent Data warehouse’ i.e. an extension to either the Data warehouse or the ERP system. For instance, an organization would transcribe call centre agents’ interactions with customers create a more intelligent workflow, bot etc using Deep learning algorithms.
Copyright : Futuretext Ltd. London28 Enterprise AI layer – What it mean to the Enterprise So, if we imagine such a conceptual AI layer for the enterprise, what does it mean in terms of new services that can be offered? Here are some examples • Bots : Bots are a great example of the use of AI to automate repetitive tasks like scheduling meetings. Bots are often the starting point of engagement for AI especially in Retail and Financial services • Inferring from textual/voice narrative: Security applications to detect suspicious behaviour, Algorithms that can draw connections between how patients describe their symptoms etc • Detecting patterns from vast amounts of data: Using log files to predict future failures, predicting cyberseurity attacks etc • Creating a knowledge base from large datasets: for example an AI program that can read all of Wikipedia or Github. • Creating content on scale: Using Robots to replace Writers or even to compose Pop songs • Predicting future workflows: Using existing patterns to predict future workflows • Mass personalization: in advertising • Video and image analytics: Collision Avoidance for Drones, Autonomous vehicles, Agricultural Crop Health Analysis etc
Copyright : Futuretext Ltd. London29 Ajit Jaokar How artificial Intelligence will redefine management
Copyright : Futuretext Ltd. London30 Practice 1: Leave Administration to AI Ex - juggle shift schedules because of staff members’ illnesses, vacations, or sudden departures. AI will automate many of these tasks. Including report writing. Recently, the data analytics company Tableau announced a partnership with Narrative Science, a Chicago-based provider of natural language generation tools. The result of the collaboration is Narratives for Tableau, a free Chrome extension that automatically creates written explanations for Tableau graphics.
Copyright : Futuretext Ltd. London31 Practice 2: Focus on Judgment Work Essence of human judgment — the application of experience and expertise to critical business decisions and practices. (knowledge of organizational history and culture, as well as empathy and ethical reflection. Also creative thinking and experimentation, data analysis and interpretation, and strategy development Practice 3: Treat Intelligent Machines as “Colleagues” Practice 4: Work Like a Designer Manager-designers bring together diverse ideas into integrated, workable, and appealing solutions. They embed design thinking into the practices of their teams and organizations.
Copyright : Futuretext Ltd. London32 Practice 5: Develop Social Skills and Networks
Copyright : Futuretext Ltd. London33 https://techcrunch.com/2016/05/13/robots-wont-just- take-jobs-theyll-create-them/amp/
Copyright : Futuretext Ltd. London34 http://cloudtimes.org/wp- content/uploads/2012/03/cloud-robotics.jpg

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Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise."