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OK Computer [FOWA, with notes]

OK Computer [FOWA, with notes]

Peter Gasston

October 07, 2015

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  1. @stopsatgreen ASCI Red. Intel’s last supercomputer. £43 million (today). Could

    process 1.8 teraflops. 1,800,000,000,000. Retired in 2000.
  2. @stopsatgreen In 2012, Google used 16,000 connected processors to teach

    a computer how to detect a cat in a photo. >> Jim Wilson, NYT.
  3. @stopsatgreen In 2015, it recognises this as “man using his

    laptop while his cat looks at the screen”. 5% error rate. https://twitter.com/timClicks/status/619734363362557953
  4. @stopsatgreen IBM Watson. Built to beat human contestant at Jeopardy.

    10 years of development. Hard to get costs, but at least £12 million.
  5. @stopsatgreen Artificial Intelligence is becoming very good, very quickly. Lots

    of different strands to AI, but the revolution has been in deep learning.
  6. How deep learning works Input Input Output Classification Artificial neural

    network. Data is passed through layers; each layer does a job, then passes the result up to the next layer.
  7. How deep learning works Layers can perform pattern analysis or

    classification. Can give quick rough answer, slower perfect answer. Even this is simplified; can pass 25 layers in smartest systems.
  8. @stopsatgreen Computer scientists in Germany have used this to understand

    the content of an image and the painting styles of old masters.
 >> http://arxiv.org/abs/1508.06576 https://github.com/jcjohnson/neural-style
  9. @stopsatgreen B. Turner, C. Van Gogh, D. Munch Not just

    adding a filter; it understands the areas of differentiation (sky, buildings, water).
  10. Is your job at risk? Abstractions People Pictures Words Numbers

    Routine Variety You’re alright Get nervous Bye Bye >> http://blogs.gartner.com/martin-kihn/how-to-know-if-a-robot-will-take-your-marketing-job/
  11. @stopsatgreen One in five of us
 could be out of

    work. Not saying we’ll all be laid off, but increased efficiency will shrink the job pool.
  12. @stopsatgreen –Travis Gertz “The work we produce is repeatable and

    predictable. It panders to a common denominator. We build buckets and templates to hold every kind of content, then move on to the next component of the system. Digital design is a human assembly line.” Travis Gertz, ‘Design Machines’ >> https://louderthanten.com/articles/story/design-machines
  13. Is your job at risk? Abstractions People Pictures Words Numbers

    Routine Variety You’re alright Get nervous Bye Bye A human assembly line…
  14. @stopsatgreen Developers might be feeling a bit smug, but they

    really shouldn’t be. Hone lets designers change apps without writing code. https://hone.tools/
  15. @stopsatgreen – Wired “[muScalpel] successfully transplanted a video coding format

    from one media player to another. It took the automated system 26 hours to complete the transplant, while VLC's manual addition of the code happened over a period of 20 days.” Automated system to take code from one base and move it to another. Currently C only, but could work in any language. http://www.wired.co.uk/news/archive/2015-07/30/code-organ-transplant-software-myscalpel
  16. @stopsatgreen – MIT news “Helium is a system that revamps

    and fine- tunes code without ever needing the original source, in a matter of hours or even minutes.” Performance increases between 75% and 500% in tests. http://news.mit.edu/2015/computer-program-fixes-old-code-faster-than-expert-engineers-0609 The developers are probably feeling less smug now.
  17. @stopsatgreen AI can’t do our jobs, but it can do

    bits of our jobs. We become more efficient, can do more, fewer staff needed. 20% of jobs lost, probably starting with the junior ones.
  18. @stopsatgreen – Travis Gertz “While we’ve been streamlining our processes

    and perfecting our machine-like assembly techniques, others have been watching closely and assembling their own machines. We’ve designed ourselves right into an environment ripe for automation.” All of the workflow tools we’ve built make our jobs automatable. These quotes are from a fairly mandatory article, ‘Design Machines’ by Travis Gertz. But the best thing about the article is that he also suggests a solution to the problem:
  19. @stopsatgreen Deepdream is Google’s tool for visualising neural networks. >>

  20. @stopsatgreen Turing vs Lovelace. Creativity should be the benchmark of

    humanlike intelligence. Lovelace Test: artificial agent must produce an original program (music, poem, etc) that it was not engineered to produce. Must be reproducible, and unexplainable. http://www.slate.com/articles/health_and_science/new_scientist/2014/12/lovelace_test_of_artificial_intelligence_creativity_better_than_the_turing.html
  21. @stopsatgreen – Cameron Moll “Mastery of creation and composition is

    much more important than mastery of tools.” http://aneventapart.com/news/post/mastery-of-creation-is-more-important-than-mastery-of-tools
  22. @stopsatgreen – Andrew Ng “We need to enable a lot

    of people to do non- routine, non-repetitive tasks.
 Teaching innovation and creativity could be one way to get there.” Andrew Ng is a major player in Google and Baidu’s AI. He has major concerns about the effects of automation and AI on employment. http://www.huffingtonpost.com/2015/05/13/andrew-ng_n_7267682.html
  23. @stopsatgreen You can do what The Grid does. It’s all

    existing tech. Smart, rather than intelligent.
  24. @stopsatgreen – Cennydd Bowles “AI is becoming a cornerstone of

    user experience. This is going to be interesting (read: difficult) for designers.” http://www.cennydd.com/blog/ai-and-future-user-experience
  25. @stopsatgreen – Cennydd Bowles “We’ll have to create frameworks /

    scaffolds / templates for AIs to deliver output through. These scaffolds may be sonic, tactile, and linguistic as well as visual.”
  26. @stopsatgreen Artificial Intelligence is becoming very good, very quickly. Artificial

    Intelligence is becoming very available, very quickly. Previously the domain of a few internet giants, now many products and services.
  27. @stopsatgreen IBM Developer Cloud gives you access to all of

    Watson’s capabilities, using Node/Java and a range of RESTFUL APIs. They just brought a whole range into general availability. They want to be the ubiquitous platform of AI, as Windows was to the home PC and Android to mobile. http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/
  28. Watson tech stack BLUEMIX WATSON NODE.JS WWW Bluemix is a

    cloud platform like Amazon Web Services, Google Cloud Platform, or Microsoft Azure.
  29. @stopsatgreen Visual Recognition goes beyond simple face detection, to understand

    the content of an image. It’s like the Google tool I showed at the start.
  30. @stopsatgreen At a certain point your users’ expectations will be

    raised. Your dumb photo system will seem outdated.
  31. @stopsatgreen Personality Insights find persona characteristics from the content provided

    by users. Better targeted promotions. This is the type of information that other sites gather on you. Now you can gather it on your users!
  32. @stopsatgreen Tradeoff Analytics are used to make comparisons, helping users

    make better choices between products or objectives.
  33. @stopsatgreen Watson not the only game in town. MS have

    Project Oxford, the power behind Cortana. Although you have to know .Net or Android for now. https://www.projectoxford.ai/
  34. @stopsatgreen Has visual recognition. This seems to not be quite

    as evolved as Watson’s, but also adds ‘racy’ content detection, clever colour extraction, character recognition and intelligent cropping.
  35. @stopsatgreen Project Oxford also has very good Face APIs, able

    to detect, recognise, verify, deduce age, find similar faces, and so on. I like it because it guessed my age as a few years younger than I really am.
  36. @stopsatgreen https://www.metamind.io/ is a new startup with image and text

    tools. Also http://www.skymind.io/ for Enterprise.
  37. @stopsatgreen Natural language understanding to get answers from the content

    of your site. Understand what a question is about. Temperature or weather.
  38. @stopsatgreen Pocket and Shutterstock recommendations. Amazon, Netflix, Google News, etc,

    all use these types of service. 2/3 of Netflix views are from recommendations.
  39. @stopsatgreen Alchemy API, an IBM acquisition, analyses text for sentiment,

    relations, entities… it learns and recommends based on that learning. http://www.alchemyapi.com/
  40. @stopsatgreen Machine: hi, this is <name> from helpdesk connect, what

    can i help you with today? Human: hi, i forgot my password. can you tell me how i can recover it? Machine: i’ll need to verify who it is at that machine. can we do a hangout? Human: yes Machine: <a hangout ensues> Human: cool, i am good now Google trained the system using old support calls. http://www.wired.com/2015/06/google-made-chatbot-debates-meaning-life/
  41. @stopsatgreen –Kyle Dent “A conversation is a sequence of turns

    where each utterance follows from what’s already been said and is relevant to the overall interaction. Dialog systems must maintain a context over several turns.” http://radar.oreilly.com/2015/09/talking-to-the-iot.html
  42. @stopsatgreen Amazon Echo is the first home manifestation of the

    virtual assistant. Siri in Apple TV, Apple Car, Apple Watch etc. Google Voice Search, also in Android, Chrome, Wear, Auto, TV, etc. … in your Windows 10 laptop.
  43. @stopsatgreen In browsers using the Web Speech API, supported in

    Chrome, Opera, Safari, Firefox (behind flag)
  44. @stopsatgreen https://www.ivona.com/ just one of many artificial voice services available.

    Also http://developer.att.com/apis/speech and http://neospeech.com/
  45. @stopsatgreen Making computers listen is easy… now. Bell Labs, 1952.

    Could recognise numbers spoken by one person. 1980s Hidden Markov method. Recently deep learning.
  46. @stopsatgreen Since 2012 Google’s voice recognition error rate has dropped

    from 26% to 8%. That’s from one word in 4 to one word in 12. This was a few months ago - a recent update is even better. Baidu, ‘China’s Google’, says theirs is 6% - one word in 17.
  47. @stopsatgreen Making computers understand is hard. But possible. Only recognises

    words (string matching), don’t understand intent.
  48. @stopsatgreen Why natural language is good. Train machines, multiple steps,

    takes minutes for what can be a quite simple query.
  49. @stopsatgreen Note that I don’t have to enter dates manually.

    Not all options are available, but I can train it to use them. I used Chicago because an unfortunate drawback is that they don’t always recognise British English.
  50. @stopsatgreen This is what powers M. AI & HI. This

    is the wave of the future. https://www.facebook.com/Davemarcus/posts/10156070660595195
  51. @stopsatgreen (A) Future of Web Apps:
 From GUI to CUI.

    >> http://www.wired.com/2013/03/conversational-user-interface/
  52. @stopsatgreen There’s a reason that WhatsApp is valued at $19billion.

    Image: Shutterstock via http://www.wired.co.uk/news/archive/2014-02/19/whatsapp-exclusive
  53. @stopsatgreen Conversational UI is an idea whose time has come.

    Natural language understanding is now at the point where this is possible.
  54. @stopsatgreen Old problems still apply to
 new interaction models. Re:

    Jenn Riggins’ talk, The Good, The Bad & The Ugly.
  55. @stopsatgreen Security is hard. Privacy even harder. Deep learning is

    impossible without some kind of human training. Samsung TV.
  56. @stopsatgreen The ready availability of deep learning happened so quickly

    that we barely realised. This is an opportunity.
  57. @stopsatgreen A.I. is improving rapidly. A.I. will take some jobs.

    A.I. can’t create like people. A.I. can improve your work.