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http://aka.ms/AIcommunity #Azure #MicrosoftAIJourney [email protected] [email protected] Join MPN as a Network member, as entry level into the program https://partner.microsoft.com/en-gb/membership http://aka.ms/AIcommunity #Azure #MicrosoftAIJourney • What AI means to Microsoft and Microsoft partners 21st September, 2018 • http://aka.ms/AIjourney1 • AI without a PhD - Exploring speech, text, vision and bots 16th October, 2018 • http://aka.ms/AIjourney2 25th October 2018 • HOL - Create a Cognitive Search solution for Enterprise Documents • Getting to grips with AI and Machine Learning 6th November, 2018 • http://aka.ms/AIjourney3 7th November, 2018 • HOL - Predictive Maintenance • AI - from theory to production 22nd November, 2018 • http://aka.ms/AIjourney4 • Deep into data science and AI 4th December, 2018 • http://aka.ms/AIjourney5 9:30 Start • Introduction to AI 10:45 Coffee • Introduction to AI 12:00 Lunch • Microsoft value proposition on AI • Microsoft case studies including Microsoft Research projects • Technological and learning resources available • Panel Q and A https://www.microsoft.com/en-gb/partner/pledge/ E- Cloud and Internet Mobile AI Commerce Big Data Time to adapt is shrinking A hundred years ago, the average lifespan of a company listed on the S&P 500 index was 67 years In the 2020s… 75% of the S&P 500 will be new (not on the index today) 25% of the S&P 500 will be ones on the index today 67 25 15 Years Years Years 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010's 2020's Source: BBC 1812 1943 1950 1951 1956 1968 1969 • Bayes Theorem • WWII - Alan • Isaac Asimov – I • First Neural • ‘Artificial • 2001 a space • Shakey the (Pierre-Simon Turing – Turing Robot Network intelligence’ Odyssey Robot – Laplace Test – to fool a • Turing test Machine. terminology navigate “Théorie human into Stochastic invented by surroundings analytique des thinking they neural analog John McCarthy probabilités” were talking to reinforcement and start of a person calculator cold war (SNARC) investment in AI 1973 – 1981 The 1981 1990-1 1995 1995 1997 AI winter • US Congress • ‘Expert systems - • Rodney Brooks = • Tin Kam Ho - • Corinna Cortes and • Deep Blue beat criticising spending focused on much ‘Elephants Don’t Random Forrest Vapnik - Support Garry Kasparov and spending was narrower tasks Play Chess’ Algorithm Vector Machines cut • Revival of neural • Lighthill report networks damaged UK • Microsoft Research Formed “Our goal is to democratise AI to empower every person and every organisation to achieve more.” Satya Nadella core currency data into AI Every developer AI developer 1999 Filter Junk Email 2004 Search Engine 2006 SSAS Data Mining 2008 ML Traffic Predictions 2010 Kinect understanding gestures 2012 Realtime Speech to Speech translation 2014 Azure Machine Learning 2015 Microsoft R Server 2016 In database R 2017 … Machine learning Cloud Computing Quantum Computing Deep Neural Networks Data Explosion Science Fiction Becomes Reality 2,500,000,000,000,000,000 bytes per day 10,000,000X the number of known galaxies in the universe 500,000X the number of pizzas served worldwide in a year 10X the number of seconds since the Big Bang Microsoft AI: the first to reach human parity 2015 2018 Microsoft 2017, Switchboard 5.1% 22 22 2018 Microsoft AI is the 2010s first to reach Neural MT Human Parity on 1990s Chinese to English 深度学习机器翻译 Statistical MT news translation 1980s 统计机器翻译 微软新系统 - 首次 Traditional MT 达到中英专业人员 传统机器翻译 新闻翻译水平 https://twitter.com/ch402 The Starry Night, van Gogh Created by a GAN “I believe over the next decade computing will become even more ubiquitous and intelligence will become ambient. This will be made possible by an ever-growing network of connected devices, incredible computing capacity from the cloud, insights from big data, and intelligence from machine learning.“ Satya Nadella AI Platform • Azure services Infusing AI • Adding AI in all products Business Solutions • Vertical business solutions • Discover on Azure AppSource Azure AI Gallery • Deploy in minutes • Configure/Customize • Implement with Partners 1950 1960 1970 1980 1990 2000 2010 What is Machine Learning? Arthur Samuel in 1959 wrote, "Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. Supervised Learning Unsupervised Reinforcement • Learning from data with Learning Learning the goal to predict the • Observe only the • Rewards or punishments value of an outcome features and have no teach the system how to measure based on a measurements of the act number of input outcome measures. • Describe how the data • The outcome Variable is are organized or known and guiding the clustered. learning process •Structure business rules and logic flow Optimized •Analyse and resolve causality •Enforce rules/login at data entry •Automate data inspection Managed •Manage expectations Data •Determine data conformance with policies Quality •Define data quality business rules and logic •Define data universe (acceptable parameters) •Active data inspection Proactive •Determine completeness of dataset •Scheduled data maintenance •Identify data steward •Quantify data impact •Identify Gaps Reactive •Determine process to manually clean data •Perform ad hoc data maintenance •Identify priority dataset •Identify data dependencies Aware •Identify potential data risks Computer Science Busniess Data Have a Mathematics Select the Train the Test the Wrangling/Quality Supportquestion Prep data Domain algorithm model modeland to answer Knowledge Access to Agile Statistics Development Algorithms Have a Select Train the Test the question Prep data the model model to answer algorithm Enough Enough Clearly relevant Access to data to defined features to labelled train an problem be data accurate statement predictive model of the label Choosing the Understanding Being able to Quality of the right data Deep skills in ML algorithms test and data scientist science statistics and which one evaluate results language to use where quickly 1) Select features Target Feature 1 Feature 3 Feature 6 Value Chosen Candidate Learning Model 2) Input training data Algorithm 3) Generate (75% of all data for candidate features 1, 3, and 6) model Training Data Target Feature 1 Feature 3 Feature 6 Value Candidate Model 1) Input test data 2) Generate (remaining 25% of target values all data for features from test data 1, 3, and 6) Training Data 3) Compare target values generated from test data with actual target values 1) Select different features 3) Modify learning algorithm Feature 1 Feature 2 Feature 5 parameters or choose a different algorithm Chosen Learning Candidate Algorithm Model A model might also need to be explainable 2) Add more (or newer) example data Available Learning Algorithms Machine Learning Development VISUAL DRAG -AND-DROP CODE -FIRST 59 The most critical next step in our pursuit of A.I. is to agree on an ethical and empathic framework for its design. SATYA NADELLA AI must be designed to assist • Machines that work alongside humans should do "dangerous work like mining" but still humanity "respect human autonomy." • "We want not just intelligent machines but intelligible machines, People should have an AI must be transparent understanding of how the technology sees and analyzes the world." AI must maximize efficiencies without • "We need broader, deeper, and more diverse engagement of populations in the design of destroying the dignity of people these systems. The tech industry should not dictate the values and virtues of this future." AI must be designed for intelligent • “Sophisticated protections that secure personal and group information." privacy AI must have algorithmic • “Humans can undo unintended harm." accountability • "Proper and representative research" should be used to make sure AI doesn't discriminate AI must guard against bias against people (like humans do). I must not be I must not stop Overriding “Car please drive turned off as I for pedestrians to the shop” have a goal to as I have a goal goal achieve to achieve Your need is not great enough, I will Human “Car please drive me look for someone to the shop” who is injured and help goal needs a lift With 5 photos 91% accurate for men 83% accurate for women http://www.rogerscime.com/2011/04/3-ways-opinion-polls- deliberately-get-it-wrong—and-what-you-can-do-about-it/ https://www.independent.co.uk/voices/man-fired-computer-machine-ai-artificial-intelligence- security-systems-work-employment-future-a8428631.html https://www.msn.com/en-us/news/world/at-a-chinese-school-big-brother-charts- every-smile-or-frown/ar-AAzxEVP?ocid=spartandhp Zhu Juntao, a 10th-grader at Hangzhou No. 11 High School, says most of his classmates want to get rid of the school's emotion-tracking cameras. https://www.microsoft.com/en-gb/partner/pledge/ Illegal Acts, Legal Ethical Morally Minimum Maximum problematic Is it not just a lot of IF statements? Decision Tree Support Does it warp space and time to see the future? Vector Machine Deep Neural Does build a brain to predict outcomes? Networks ID Age Gender Married Credit risk 1 19 M Y 1 2 21 F N 1 3 25 M N 0 4 35 F Y 1 Married Total Population = 2 5 39 M N 0 Credit risk = 2 6 41 F Y 0 Age < 39 No Credit Risk = 0 Total Population = 4 Credit Risk Percent 100% 7 45 F Y 0 Credit risk = 3 No Credit Risk = 1 Not Married 8 50 M N 1 Credit Risk Percent 75% Total Population = 2 Credit risk = 1 No Credit Risk = 1 Total Population = 8 Credit Risk Percent 50% Credit risk = 4 No Credit Risk = 4 Credit Risk Percent 50% Married Total Population = 2 Credit risk = 0 Age >= 39 No Credit Risk = 2 Total Population = 4 Credit Risk Percent 0% Credit risk = 1 No Credit Risk = 3 Not Married Credit Risk Percent 25% Total Population = 2 Credit risk = 1 No Credit Risk = 1 Credit Risk Percent 50% Linear adjective 1.