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Artificial Intelligence for the Kingdom

Chris Lim
October 01, 2016

Artificial Intelligence for the Kingdom

Artificial intelligence is hot. From Amazon's Echo, to Google's DeepMind, to Facebook's News Feed algorithm and Messenger Bots, AI is touching and transforming every part of our lives. How can these advances be leveraged to get the Gospel to everyone on earth? What role does AI play in God's Kingdom?

Talk video: https://www.youtube.com/watch?v=IyHNc0-xtcc&list=PLRuVp8-8rrEQw4lK-s0eWkKDNyLUBTXYA

Real time speech captioning and translation: https://www.spf.io

Chris Lim

October 01, 2016

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  1. Overview • Part 1: Today’s Opportunity ◦ Why AI? ◦

    What is it? ◦ What’s possible today? ◦ How can we use it for the Kingdom? • Part 2: Q&A + Future Speculation ◦ Humans and Machines, AI Ethics ◦ Is/will AI become demonic? (Elon Musk’s words, not mine.) ◦ Impact on Society and Job Loss ◦ An AI Pastor?
  2. Recent AI History Conception: 1952-1956 Golden years: 1956-1974 AI Winter:

    1974-1980 Boom: 1980-1987 Bust: 1987-1993 AI: 1993-2001 Present: 2001-2016 Adapted from: https://en.wikipedia.org/wiki/History_of_artificial_intelligence
  3. Affiliate Link: http://amzn.to/2de0pPW Affiliate Link: http://amzn.to/2dxNZlU Two Books to Read

    to Understand AI http://qz.com/698334/bill-gates-says-these-are-the-two-books-we-should-all-read-to-understand-ai/
  4. “Certainly it’s the most exciting thing going on...It’s the holy

    grail, it’s the big dream that anybody who’s ever been in computer science has been thinking about.” —Bill Gates On Artificial Intelligence: http://qz.com/698334/bill-gates-says-these-are-the-two-books-we-should-all-read-to-understand-ai/
  5. “So the basic human senses like seeing, hearing, language...I think

    it's possible to get to the point in the next five to 10 years where we have computer systems that are better than people...That doesn't mean that the computers will be thinking or be generally better” —Mark Zuckerberg, CEO, Facebook http://www.theverge.com/2016/4/28/11526436/mark-zuckerberg-facebook-earnings-artificial-intelligence-future
  6. “We have this vision of a shift from mobile-first to

    an AI-first world” --Sundar Pichai, CEO Google http://www.forbes.com/sites/miguelhelft/2016/06/15/google-is-about-to-change-everything-again/#548ccde73f1e “We are in the process of transforming into a machine-learning company” —Jeff Dean, Senior Fellow, Google Brain http://www.nytimes.com/2016/09/29/technology/google-assistant.html
  7. “AI will become like electrical current – invisible and augmenting

    almost every part of our lives.” —Marc Benioff, CEO, Salesforce https://www.project-syndicate.org/commentary/artificial-intelligence-revolution-by-marc-benioff-2016-09
  8. We want to democratize AI just like we brought information

    to your fingertips. —Satya Nadella, CEO, Microsoft http://www.cmswire.com/digital-experience/microsoft-ceo-satya-nadella-our-ai-will-fuel-a-better-society-msignite/
  9. “AI will make [smartphones] even more essential to you. It

    will become [an] even better assistant than it is today. So where you probably aren’t leaving home without it today — you’re really going to be connected to it in the future.” —Tim Cook, CEO, Apple http://www.recode.net/2016/8/14/12474794/tm-cook-apple-interview-highlights-washington-post
  10. AI + Machine Learning in Household Gadgets: “It’s hard to

    overstate how big of an impact this will have on society over the next 20 years. It is big.” —Jeff Bezos, CEO, Amazon http://www.usatoday.com/story/tech/2016/05/31/amazon-founder-s-impact-gigantic/85200740/
  11. Example: If-Then Rules He replied, “When evening comes, you say,

    ‘It will be fair weather, for the sky is red,’ and in the morning, ‘Today it will be stormy, for the sky is red and overcast.’ You know how to interpret the appearance of the sky, but you cannot interpret the signs of the times. (Matthew 16:2-3 NIV)
  12. Example: If-Then Rules He replied, “When evening comes, you say,

    ‘It will be fair weather, for the sky is red,’ and in the morning, ‘Today it will be stormy, for the sky is red and overcast.’ You know how to interpret the appearance of the sky, but you cannot interpret the signs of the times. (Matthew 16:2-3 NIV) P: “the sky is red” Q: “fair weather” P→Q: If “the sky is red” then “it will be fair weather”
  13. Example: If-Then Rules He replied, “When evening comes, you say,

    ‘It will be fair weather, for the sky is red,’ and in the morning, ‘Today it will be stormy, for the sky is red and overcast.’ You know how to interpret the appearance of the sky, but you cannot interpret the signs of the times. (Matthew 16:2-3 NIV) P: “the sky is red” and “the sky is overcast” Q: “stormy weather” P→Q: If “the sky is red” and “overcast” then “it will be stormy”
  14. Example: If-Then Rules function predictWeather (sky) { if (sky.color ===

    'red') { if (sky.overcast === true) { return 'stormy'; } else { return 'fair'; } } }
  15. Example: If-Then Rules function predictWeather (sky, time) { if (time

    > 6 && time < 12) { // morning if (sky.color === 'red') { if (sky.overcast === true) { return 'stormy'; } else { if (time > 18 && time < 24) { // evening return 'fair'; } } } } }
  16. Example: Bayesian Belief Net He replied, “When evening comes, you

    say, ‘It will be fair weather, for the sky is red,’ and in the morning, ‘Today it will be stormy, for the sky is red and overcast.’ You know how to interpret the appearance of the sky, but you cannot interpret the signs of the times. (Matthew 16:2-3 NIV) fair stormy red sky overcast
  17. Recommended MIT Lectures Neural Networks: https://www.youtube.com/watch?v=uXt8qF2Zzfo Deep Neural Networks: https://www.youtube.com/watch?v=VrMHA3yX_QI

    Stanford & Coursera courses Deep learning for Natural Language Processing: http://cs224d.stanford.edu/syllabus.html Neural Networks for Machine Learning: https://www.coursera.org/learn/neural-networks
  18. Example: Neural Networks Photos: Public Domain NIH photo of a

    Neuron (left), Tensorflow Playground (right)
  19. Example: Neural Networks Photos: Public Domain NIH photo of a

    Neuron (left), Tensorflow Playground (right)
  20. What’s possible today? Image Captioning Automatic Speech Recognition Machine Translation

    What Every Data Scientist Needs to Know About Deep Learning https://www.youtube.com/watch?v=O0VN0pGgBZM http://www.slideshare.net/ExtractConf/andrew-ng-chief-scientist-at-baidu
  21. Show & Tell: A Neural Image Captioner GitHub: https://github.com/tensorflow/mo dels/tree/master/im2txt

    Examples from NeuralTalk: http://cs.stanford.edu/people/kar pathy/deepimagesent/generation demo/
  22. Deep Visual-Semantic Alignments for Generating Image Descriptions Andrej Karpathy, Li

    Fei-Fei http://cs.stanford.edu/people/karpathy/deepimagesent/
  23. A simple Jonathan Edwards “AI” language model? • Trained on

    a corpus of 2276 lines, ~1 million characters from his sermons • Trained with 3 layer LSTM, with 512 hidden states • Predicts the next character based on the previous sequence of characters. • Sample command: th sample.lua -checkpoint ../../edwards/checkpoint_17350_2.t7 -length 2000 -gpu -1 -start_text 'i always'
  24. Auto-generated sermons? why should I apprehend them, when slain down

    with her to take children, to call a brimstone, that they return from him. Lool degree of sorrows to sink undetermined, and so many contained in our other truth, who shall know that the interest by themselve, answer you, when once heart, or so much change of those souls; and 3 Cor. 12:60. "Would though them, that we are looks His love only began to answer the wicked. I have been makes us Satan in this hungry, we should never had the night is gleated. As Christ's sore earnestny of God and who go the conversion was borne hardness, that they do not the throne of grace. They clear strongly and quickened are very dull, I think that the moth of the loud, as I answer, And Thus it is very abate to oppose, as iverected the curse, that he is always flesh to God. Try your own prompts?
  25. Neural NLP Neural Machine Translation Tutorial: https://www.tensorflow.org/versions/r0.11/tutorials/seq2seq/index.html TensorFlow Speech Recognition:

    https://github.com/pannous/tensorflow-speech-recognition Deep Speech & Deep Speech 2: https://arxiv.org/abs/1412.5567 & https://arxiv.org/abs/1512.02595 ”Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages.”
  26. Phrase based vs Neural Machine Translation “The advantage of this

    approach is that it requires fewer engineering design choices than previous Phrase-Based translation systems.” -- Quoc Le & Mike Schuster Source: Domain Adaptation through phrase generalization Source: A neural network for Machine Translation, at Production Scale
  27. Demo SPF.IO: Speech to text translation in multiple languages simultaneously.

    • Designed for public speaking. • Can use different MT engines. • Lets you script things for QA if needed.
  28. Jesus quoting Isaiah “The Spirit of the Lord is on

    me, because he has anointed me to proclaim good news to the poor. He has sent me to proclaim freedom for the prisoners and recovery of sight for the blind, to set the oppressed free, to proclaim the year of the Lord’s favor.” Luke 4:18 NIV
  29. Jesus speaking of the end And this gospel of the

    kingdom will be preached in the whole world as a testimony to all nations, and then the end will come. Matthew 24:24 NIV
  30. Discussion Questions • What would Jesus say about AI? Would

    he agree or disagree with world technology leaders? • How would quality automatic translation change global missions and local evangelism? How would it change your community? • What would be an example of a godly use of AI and an ungodly one? How can you influence these outcomes? • What will happen to people whose lives are impacted by the automation of their work? • What place could AI have in the future Kingdom of God?
  31. On Demonic AI - Revelation 13 11 Then I saw

    a second beast, coming out of the earth. It had two horns like a lamb, but it spoke like a dragon. 12 It exercised all the authority of the first beast on its behalf, and made the earth and its inhabitants worship the first beast, whose fatal wound had been healed. 13 And it performed great signs, even causing fire to come down from heaven to the earth in full view of the people. 14 Because of the signs it was given power to perform on behalf of the first beast, it deceived the inhabitants of the earth. It ordered them to set up an image in honor of the beast who was wounded by the sword and yet lived. 15 The second beast was given power to give breath to the image of the first beast, so that the image could speak and cause all who refused to worship the image to be killed. 16 It also forced all people, great and small, rich and poor, free and slave, to receive a mark on their right hands or on their foreheads, 17 so that they could not buy or sell unless they had the mark, which is the name of the beast or the number of its name. 18 This calls for wisdom. Let the person who has insight calculate the number of the beast, for it is the number of a man.[e] That number is 666.
  32. On the nature of the mind 28 Then the Lord

    opened the donkey’s mouth, and it said to Balaam, “What have I done to you to make you beat me these three times?” Numbers 22:28 NIV Let his mind be changed from that of a man and let him be given the mind of an animal, till seven times[d] pass by for him. Daniel 4:16 NIV
  33. Two Virtuous Cycles Data => Learning => Automation => Usefulness

    => Data Content => Engagement => Personalization => Traffic => Content
  34. Money Alexa Fund OpenAI AI2 AI for the Common Good

    • Microsoft • Amazon • Facebook • Oracle
  35. Other Resources TensorFlow: https://www.tensorflow.org/versions/r0.11/tutorials/index.html AI Weekly: http://aiweekly.co/ Alexa Skills Kit:

    https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/getting-started -guide Messenger Platform: https://messengerplatform.fb.com/
  36. Asimov’s 3 Laws of Robotics A robot may not injure

    a human being or, through inaction, allow a human being to come to harm. A robot must obey orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
  37. Deep Learning Leaders There are a number of notable players

    in the deep learning space. On the academic side, the Geoffrey Hinton's lab at University of Toronto, Yann LeCun's group at New York University and Stanford's AI lab are some of the major leaders in deep learning research. On the private side, Google has led the way in applying deep learning to search and computer vision, and Baidu's Chief Scientist, Andrew Ng, is a major contributor to the scientific literature around deep learning on top of being the cofounder of Coursera. Adapted from http://blog.deepgram.com/how-to-get-a-job-in-deep-learning/
  38. 20 Hours Bay Area Deep Learning School Day 1 at

    CEMEX auditorium, Stanford 10 Hour Deep Learning School: https://www.youtube.com/watch?v=eyovmAtoUx0 https://www.youtube.com/watch?v=9dXiAecyJrY
  39. Nadella First, we want to build intelligence that augments human

    abilities and experiences. Ultimately, it’s not going to be about human vs. machine. We humans have creativity, empathy, emotion, physicality, and insight that can then be mixed with powerful A.I. computation—the ability to reason over large amounts of data and do pattern recognition more quickly—to help move society forward. Second, we also have to build trust directly into our technology. We must infuse technology with protections for privacy, transparency, and security. A.I. devices must be designed to detect new threats and devise appropriate protections as they evolve. And third, all of the technology we build must be inclusive and respectful to everyone. http://www.slate.com/articles/technology/future_tense/2016/06/microsoft_ceo_satya_nadella_humans_and_a_i_can_work_togeth er_to_solve_society.html?mod=djemCIO_h Computer pioneer Alan Kay quips, “The best way to predict the future is to invent it.” In the A.I. context, he’s basically saying, Stop predicting what the future will be like and create it in a principled way.
  40. Nadella Writing for the New York Times, cognitive scientist and

    philosopher Colin Allenconcludes, “Just as we can envisage machines with increasing degrees of autonomy from human oversight, we can envisage machines whose controls involve increasing degrees of sensitivity to things that matter ethically. Not perfect machines, to be sure, but better.” The most critical next step in our pursuit of A.I. is to agree on an ethical and empathic framework for its design. http://www.slate.com/articles/technology/future_tense/2016/06/microsoft_ceo_saty a_nadella_humans_and_a_i_can_work_together_to_solve_society.html?mod=dje mCIO_h
  41. We have this vision of a shift from mobile-first to

    an AI-first world over many years, - Sundar Pichai, CEO of Google http://www.forbes.com/sites/miguelhelft/2016/06/15/google-is-about-to-change-everything-again/#548ccde73f 1e “We are in the process of transforming into a machine-learning company,” Jeff Dean, who is in charge of Google Brain, the company’s artificial intelligence project, told me this year. http://www.nytimes.com/2016/09/29/technology/google-assistant.html
  42. Just as we would have talked about design principles for

    good user experience, what are the design principles for good AI? That, to me, is one of those fascinating problems—what does it mean to have algorithmic accountability when you’re training a deep neural net? -- Satya Nadella, CEO Microsoft Salesforce is selling Einstein as a system that can work predictive magic without having to look at your data, in what Mr. Benioff calls a “democratizing” move that will create millions of A.I. users who are not engineers. http://www.nytimes.com/2016/09/19/technology/artificial-intelligence-software-is-booming-but-why-now.html? _r=0 https://www.project-syndicate.org/commentary/artificial-intelligence-revolution-by-marc-benioff-2016-09
  43. Marc Benioff As in past periods of economic transformation, AI

    will unleash new levels of productivity, augment our personal and professional lives, and pose existential questions about the age-old relationship between man and machine. It will disrupt industries and dislocate workers as it automates more tasks. But just as the Internet did 20 years ago, AI will also improve existing jobs and spawn new ones. We should expect this and adapt accordingly by providing training for the jobs of tomorrow, as well as safety nets for those who fall behind. https://www.project-syndicate.org/commentary/artificial-intelligence-revolution-by-marc-benioff-2016-09
  44. “the ability to reason over large amounts of data and

    convert it into intelligence” http://winsupersite.com/ignite/microsoft-ignite-satya-nadella-outlines-democratization-ai
  45. Democratizing AI? In the midst of this abundance of information,

    we’re still constrained by our human capacity to absorb it. The question is, how can we use all we have in terms of computational power to solve this fundamental constraint? To make better sense of the world? That’s the essence of what AI is. It’s not about having AI that beats humans in games, it’s about helping everyone achieve more — humans and machines working together to make the world a better place. Read more at http://news.microsoft.com/features/democratizing-ai/#1CxzIZc4cdPeIUE0.99
  46. Two books to read to understand AI http://qz.com/698334/bill-gates-says-these-are-the-two-books-we-should-all-read-t o-understand-ai/ Super

    Intelligence and the Master Algorithm http://qz.com/335768/bill-gates-joins-elon-musk-and-stephen-hawking-in-saying-ar tificial-intelligence-is-scary/
  47. That dramatic progress has sparked a burst of activity. Equity

    funding of AI-focused startups reached an all-time high last quarter of more than $1 billion, according to theCB Insights research firm. There were 121 funding rounds for such startups in the second quarter of 2016, compared with 21 in the equivalent quarter of 2011, that group says. More than $7.5 billion in total investments have been made during that stretch—with more than $6 billion of that coming since 2014. (In late September, five corporate AI leaders—Amazon, Facebook, Google, IBM, and Microsoft—formed the nonprofitPartnership on AI to advance public understanding of the subject and conduct research on ethics and best practices.)