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Microsoft AI Journey #Azure #Microsoftaijourney Oct 16St 2019 http://aka.ms/AIcommunity Microsoft AI Journey http://aka.ms/AIJourney #Azure #MicrosoftAIJourney Oct 16st 2019 [email protected] Wifi - msevent439sh Housekeeping Join MPN as a Network member, as entry level into the program https://partner.microsoft.com/en-gb/membership • An introduction to AI and what it means to Microsoft and its 16th October 2019 Partners 30th October 2019 • AI without a PhD - exploring speech, vision, bots and more 14th November 2019 • Getting to grips with AI and machine learning essentials 20th November 2019 • Do It Yourself AI solutions - developing intelligence in your app 26th November 2019 • Partner Hackathon (3 days) 4th December 2019 • Taking your AI ideas from theory to production 11th December 2019 • The Future Recoded https://aka.ms/AIJourney http://aka.ms/AIcommunity #Azure #MicrosoftAIJourney 26th November 2019 (3 days) • Partner Hackathon http://aka.ms/UKPartnerHackathon 10:00 Start • Introduction + Health and Safety • Nigel Willson Keynote • Robin Lester Introduction to the technical track and what is AI? • Telematicus Practical examples and war stories 13:00 Lunch • The Data Analysis Bureau AI Readiness 14:30 Coffee • PWC Big picture and responsible AI • Solveig Moro Le Mao Technological and learning resources available • Panel Q and A • How do we get started in AI? • Why do we get started in AI? • What does an AI project look like? • What should I be focusing on to make this work? • What kind of successes have others had and can I also do this? • What is the difference between the hype and the reality? https://aka.ms/AIJourneySurvey1 http://aka.ms/AIcommunity Microsoft AI Journey http://aka.ms/AIJourney #Azure #MicrosoftAIJourney Oct 16st 2019 [email protected] 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 “UK businesses and public sector organisations that forgo or delay implementing AI solutions risk missing the boat on driving down costs, increasing their competitive advantage and empowering their workers,” Cindy Rose, Chief Executive of Microsoft UK https://news.microsoft.com/en-gb/2019/10/01/uk-companies-risk-falling-behind-foreign-rivals- unless-they-use-more-ai-microsoft-report-reveals/ “In the past jobs were about muscles, now they’re about brains, but in future they’ll be about the heart.” - Minouche Shafik (director of the London School of Economics) Term “Machine Learning” invented (1959) 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. 1950 1960 1970 1980 1990 2000 2010 Artificial Narrow Artificial General Artificial Intelligence (ANI) Intelligence (AGI) Superintelligence ASI AGI ANI • A narrow • Making machines (ASI) understanding of a behave, think and be • “an intellect that is specific problem like humans much smarter than • Frame Reference • not about applying the best human brains Problem intelligence to specific in practically every • Symbol grounding problems field, including problem scientific creativity, • Feature engineering general wisdom and problem social skills.” Nick Bostrom Machine learning Cloud Computing Quantum Computing Deep Neural Networks Data Explosion Modern Data Science Deep Cloud Quantum Machine Neural Data Computing Computing Networks learning Explosion Microsoft Research Turning ideas into reality for 27 years MSR Cambridge MSR Redmond MSR Montreal MSR New York MSR New England MSR Beijing MSR Shanghai MSR India MSR Labs Researchers Fields WW Patents Papers Driving innovation Fueled by breakthrough research MSR Cambridge MSR Redmond MSR Montreal MSR New York MSR New England MSR Beijing MSR Shanghai MSR India Switchboar d Meeting speech Broadcast Switchbo speech ard cellular IBM Switchboard First FPGA deployed Speech recognition Machine translation Conversational Q&A Object detection in a datacenter human parity human parity human parity human parity 39.5 Teraflops with 94.9% on 69.9% with MT 89.4% on Stanford 96% on RESNET Intel Stratix 10 Switchboard test Research system CoQA test vision test Speech recognition human parity What does she say? Speech-recognition word-error rates A.Switchboard Switchboard Meeting cellular speech B. Broadcast speech IBM, Switchboard Human-level C. 1993 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Speech synthesis human parity Which is artificial? A. B. #1 https://www.microsoft.com/en- us/research/welcome-to-canada-demo/ https://www.nextrembrandt.com/ https://drawingbot.azurewebsites.net/ “Our goal is to democratise AI to empower every person and every organisation to achieve more.” Satya Nadella The core currency of any business going forward will be the ability to convert their data into AI that drives competitive advantage Every developer can be an AI developer, and every company can become an AI company New capabilities in Azure Machine Learning service Automated Visual interface Machine learning notebooks machine learning UI The most comprehensive pre-trained AI Text Analytics Personalizer Translator Text Bing Spell Check Decision Computer Ink Recognizer Vision Language Language Content Moderator Face Understanding Immersive Anomaly Detector QnA Maker Custom Reader Video Vision Vision Indexer Form Recognizer Conversation Custom Speech transcription capability Bing Custom Bing Bing Entity Search Search Video Search Bing News Bing Speech-to-Text Search Local Business Speech Web search Search Bing Web Text-to-Speech Search Bing Image Search Bing Autosuggest Neural Text-to-Speech Bing Visual Search New container support for additional services Anomaly Detector Speech-to-Text Azure Cognitive Services Text-to-Speech Wherever your data resides Vision Speech Language Decision Ready to serve at any stage of your AI journey Azure Search + Cognitive Search Capability + Cognitive Search Capability with Custom Skills 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 Enough Enough Clearly relevant Access to data to defined features to labelled train an problem be data accurate statement predictive model of the label Toolings Building an AI practice Envisioning Bot Framework • Understanding the tooling and how it might fit into products and services Cognitive Services Selling • Talk to customers to gain buy in or understand the marketplace Pilot • Create a pilot to fulfil and AI goal Skilling up / Going deeper Azure ML SDK • After low hanging fruit are picked go deeper to create more AI IP “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 Everyone should have access to the benefits of AI, including the tools it takes to create and transform your work. We want to remove barriers and help every developer on the planet to create the next generation of AI-powered products. We believe that you should choose the technology and platforms that you prefer and we've designed our comprehensive stack to reflect this. AI Platform • Azure services Infusing AI • Adding AI in all products • Deploy in minutes • Configure/Customize • Implement with Partners 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 https://www.microsoft.com/en-gb/partner/pledge/ With 5 photos 91% accurate for men 83% accurate for women AI could be used to data mine private information Ena Matsuoka Boston Dynamics http://www.rogerscime.com/2011/04/3-ways-opinion-polls- deliberately-get-it-wrong—and-what-you-can-do-about-it/ At a Chinese school, Big Brother charts every smile or frown 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. Don’t Know, 15 Disagree, 17 Neither, 21 Agreee, 48 https://www.microsoft.com/en-gb/partner/pledge/ Illegal Acts, Legal Ethical Morally Minimum Maximum problematic Our approach The Partnership on AI to Benefit People and Society The Partnership on AI to Benefit People and Society was established to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society. 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). aka.ms/uber-selfies aka.ms/seahawks aka.ms/seeing-ai .
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