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SOFTWARE 2.0
David Parker
Software Engineer-
Microsoft
SPRING 2019
• CSD 439 –Big Data Application Development
• Basically learn how solve cool problems at scale…
ABOUT
THE
SPEAKER
SKYNET
Source:[19]
AMAZON GO
Source:[21]
Source:[13]
Source:[12]
Source:[11]
Source:[10]
CUURENT MAJOR
ACHIEVEMENTS WITH AI AND
MACHINE LEARNING
WHATISSOFTWARE 2.0?
• Andrej Karpathy, Director of AI, Tesla Identified a fundamental paradigm shift in
how we build software
• He callsthisshift, “Software 2.0”
• Emerging trend in industry that is rapidly advancing our ability to solve problems
• “AIisthe new electricity” (Andrew Ng)
HOW WE BUILD SOFTWARE TODAY
• Decompose large problems into smaller, easier problems to solve
Source:[1]
• This process of an explicit description of problem solving steps is what we will
call Software 1.0
• Software 1.0 is the technology stack
we have become accustom to
SOFTWARE 1.0
Source:[2]
SOFTWARE 1.0
• Great for problems that are well defined, i.e.
• TCP/IP Stack: layered stack of protocols to facilitate reliable data transfer
over the internet
• Computer Graphics: geometric shapes to 2D pixels on a screen
• Some tasks are too hard for Software 1.0
• Consider the problem of image recognition
Sobel
Source:[4]
DECOMPOSE?
COMPUTER SEESPIXELS
Pixels:
[255, 0, 0]
[152, 52, 86]
…
[200, 100, 1]
[ 0, 50, 9]
DIFFERENT PERSPECTIVES
Source:[7]
DIFFERENT PERSPECTIVES
Source:[7]
CONV NETS
• The problem of image recognition is easily solved using Convolutional Neural
Networks (CNNs aka Conv Nets)
Source:[5]
COMPUTER VISION CHART
Source:[3]
WHATISA NEURAL NETWORK?
• A data structure that connects the outputs of nodes to inputs of new nodes
in a layered fashion
• Inspired by biological neural networks, like the ones in our brains
y =activate(w1*
x1 +w2*x2 +w3*x3)
Source:[5]
WHATISA NEURAL NETWORK?
• Chain the outputs of the perceptrons to inputs of others
Source:[6]
PROGRAMS FROM DATA
Source:[8]
PROGRAMS FROM DATA
PROGRAMS FROM DATA
PROGRAMS FROM DATA
PROGRAMS FROM DATA
PROGRAMS FROM DATA
P
CONCLUSION?
Source:[20]
Q*BERT
Q*BERT
QUICK RECAP
• Software 1.0 was about explicitly engineering systems
• Software 2.0 is about finding programs through optimization i.e. directed
search using training data as the guide
NOTEXACTLY…
• Still many limitations that need to be overcome… more on that in a bit
Source:[9]
PROGRAMS FROM DATA
• Learning programs from data means we need… more data
• “Accumulating a nice, varied, large, clean dataset for all the different tasks
you want to do, and worrying about all the edge cases and massaging it is
where most of the action is” (Andrej Karpathy, 2018).
SOFTWARE 2.0 TOOLCHAIN
• Does not yet exist..
• Companies are investing in it
• But what does it look like?
SOFTWARE 2.0 TOOLCHAIN
Source:[15]
Source:[14]
?
Zoph, Barret; Le, Quoc V. (2016-11-04). "Neural
Architecture Search with Reinforcement
Learning". arXiv:1611.01578
SOFTWARE 2.0 TOOLCHAIN
Source:[18]
OPEN ISSUESAND CONCERNS
• It’s not all rainbows and butterflies…
?
Source:[16]
Bus Noise
OPEN ISSUESAND CONCERNS
• It’s not all rainbows and butterflies…
Source:[16]
Bus Noise
OPEN ISSUESAND CONCERNS
• It’s not all rainbows and butterflies…
Ostrich
Bus Noise
Source:[16]
OPEN ISSUESAND
CONCERNS
• What is the minimum pixelsthat
need to be changed in order to
perform this adversarial attack?
OPEN ISSUESAND
CONCERNS
• What is the minimum pixelsthat
need to be changed in order to
perform this adversarial attack?
• One.
OPEN ISSUESAND
CONCERNS
• What is the minimum pixelsthat
need to be changed in order to
perform this adversarial attack?
• One.
Source:[17]
ECONOMIC
OPPORTUNITIES
• Blue River T
echnology
• Founded by a couple of Stanford
students
• Uses object detection to identify
weeds and selectively sprays
herbicide
ECONOMIC
OPPORTUNITIES
• Reduces the amount of herbicide
used by 90%=less cost to farmers
and combats herbicide resistance
• Sold to John Deere for $300mil
CLOSING REMARKS
• Software 2.0 doesn’t “replace” Software 1.0. Itsimply gives us a new tool for
solving problems.
CLOSING REMARKS
• Software 2.0 doesn’t “replace” Software 1.0. Itsimply gives us a new tool for
solving problems.
• “Practice makes perfect for a pretty good local minimum”. –David Parker,
just now.
THANK YOU.
Q & A
MEDIA SOURCES
• [1] https://codehs.gitbooks.io/apjava/content/static/karel/movie_diagram.png
• [2] https://pixabay.com/en/source-code-code-programming-c-583537/
• [3] https://www.youtube.com/watch?v=zywIvINSlaI&t=305s
• [4] https://www.pexels.com/photo/adorable-animal-cat-cute-358464/
• [5] https://towardsdatascience.com/build-your-own-convolution-neural-network-in-5-mins-4217c2cf964f
• [6] https://chatbotslife.com/how-neural-networks-work-ff4c7ad371f7
• [7] https://www.petmd.com/cat/behavior/evr_ct_what-does-it-mean-when-a-cat-wags-tail
• [8] https://alykhantejani.github.io/images/gradient_descent_line_graph.gif
• [9] https://xkcd.com/1838/
• [10] https://www.shellypalmer.com/2016/03/alphago-vs-not-fair-fight/
• [11] https://www.inverse.com/article/35449-elon-musk-dota-2-openai-the-international-dendi-1v1
• [12] https://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of/karras2018iclr-paper.pdf
• [13] https://www.engadget.com/2016/01/25/tesla-autopilot-promo-shows-what-it-can-do-for-your-commute/
• [14] https://danielmiessler.com/blog/the-difference-between-decompilers-disassemblers-debuggers-and-hex-editors/
• [15] https://www.rsipvision.com/wp-content/uploads/2015/04/Slide5.png
• [16] https://arxiv
.org/pdf/1312.6199.pdf
• [17] https://arxiv.org/pdf/1710.08864.pdf
• [18] https://poloclub.github.io/ganlab/
• [19] https://www.marketwatch.com/story/this-is-what-happens-when-skynet-from-terminator-takes-over-the-stock-market-2018-02-14
• [20] https://medium.com/@karpathy/software-2-0-a64152b37c35
• [21] https://www.amazon.com/b?ie=UTF8&node=16008589011

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software-2.0.pptx

  • 2. SPRING 2019 • CSD 439 –Big Data Application Development • Basically learn how solve cool problems at scale…
  • 7. WHATISSOFTWARE 2.0? • Andrej Karpathy, Director of AI, Tesla Identified a fundamental paradigm shift in how we build software • He callsthisshift, “Software 2.0” • Emerging trend in industry that is rapidly advancing our ability to solve problems • “AIisthe new electricity” (Andrew Ng)
  • 8. HOW WE BUILD SOFTWARE TODAY • Decompose large problems into smaller, easier problems to solve Source:[1]
  • 9. • This process of an explicit description of problem solving steps is what we will call Software 1.0 • Software 1.0 is the technology stack we have become accustom to SOFTWARE 1.0 Source:[2]
  • 10. SOFTWARE 1.0 • Great for problems that are well defined, i.e. • TCP/IP Stack: layered stack of protocols to facilitate reliable data transfer over the internet • Computer Graphics: geometric shapes to 2D pixels on a screen
  • 11. • Some tasks are too hard for Software 1.0 • Consider the problem of image recognition Sobel Source:[4]
  • 13. COMPUTER SEESPIXELS Pixels: [255, 0, 0] [152, 52, 86] … [200, 100, 1] [ 0, 50, 9]
  • 16. CONV NETS • The problem of image recognition is easily solved using Convolutional Neural Networks (CNNs aka Conv Nets) Source:[5]
  • 18. WHATISA NEURAL NETWORK? • A data structure that connects the outputs of nodes to inputs of new nodes in a layered fashion • Inspired by biological neural networks, like the ones in our brains y =activate(w1* x1 +w2*x2 +w3*x3) Source:[5]
  • 19. WHATISA NEURAL NETWORK? • Chain the outputs of the perceptrons to inputs of others Source:[6]
  • 26. P
  • 30. QUICK RECAP • Software 1.0 was about explicitly engineering systems • Software 2.0 is about finding programs through optimization i.e. directed search using training data as the guide
  • 31. NOTEXACTLY… • Still many limitations that need to be overcome… more on that in a bit Source:[9]
  • 32. PROGRAMS FROM DATA • Learning programs from data means we need… more data • “Accumulating a nice, varied, large, clean dataset for all the different tasks you want to do, and worrying about all the edge cases and massaging it is where most of the action is” (Andrej Karpathy, 2018).
  • 33. SOFTWARE 2.0 TOOLCHAIN • Does not yet exist.. • Companies are investing in it • But what does it look like?
  • 34. SOFTWARE 2.0 TOOLCHAIN Source:[15] Source:[14] ? Zoph, Barret; Le, Quoc V. (2016-11-04). "Neural Architecture Search with Reinforcement Learning". arXiv:1611.01578
  • 36. OPEN ISSUESAND CONCERNS • It’s not all rainbows and butterflies… ? Source:[16] Bus Noise
  • 37. OPEN ISSUESAND CONCERNS • It’s not all rainbows and butterflies… Source:[16] Bus Noise
  • 38. OPEN ISSUESAND CONCERNS • It’s not all rainbows and butterflies… Ostrich Bus Noise Source:[16]
  • 39. OPEN ISSUESAND CONCERNS • What is the minimum pixelsthat need to be changed in order to perform this adversarial attack?
  • 40. OPEN ISSUESAND CONCERNS • What is the minimum pixelsthat need to be changed in order to perform this adversarial attack? • One.
  • 41. OPEN ISSUESAND CONCERNS • What is the minimum pixelsthat need to be changed in order to perform this adversarial attack? • One. Source:[17]
  • 42. ECONOMIC OPPORTUNITIES • Blue River T echnology • Founded by a couple of Stanford students • Uses object detection to identify weeds and selectively sprays herbicide
  • 43. ECONOMIC OPPORTUNITIES • Reduces the amount of herbicide used by 90%=less cost to farmers and combats herbicide resistance • Sold to John Deere for $300mil
  • 44. CLOSING REMARKS • Software 2.0 doesn’t “replace” Software 1.0. Itsimply gives us a new tool for solving problems.
  • 45. CLOSING REMARKS • Software 2.0 doesn’t “replace” Software 1.0. Itsimply gives us a new tool for solving problems. • “Practice makes perfect for a pretty good local minimum”. –David Parker, just now.
  • 47. Q & A
  • 48. MEDIA SOURCES • [1] https://codehs.gitbooks.io/apjava/content/static/karel/movie_diagram.png • [2] https://pixabay.com/en/source-code-code-programming-c-583537/ • [3] https://www.youtube.com/watch?v=zywIvINSlaI&t=305s • [4] https://www.pexels.com/photo/adorable-animal-cat-cute-358464/ • [5] https://towardsdatascience.com/build-your-own-convolution-neural-network-in-5-mins-4217c2cf964f • [6] https://chatbotslife.com/how-neural-networks-work-ff4c7ad371f7 • [7] https://www.petmd.com/cat/behavior/evr_ct_what-does-it-mean-when-a-cat-wags-tail • [8] https://alykhantejani.github.io/images/gradient_descent_line_graph.gif • [9] https://xkcd.com/1838/ • [10] https://www.shellypalmer.com/2016/03/alphago-vs-not-fair-fight/ • [11] https://www.inverse.com/article/35449-elon-musk-dota-2-openai-the-international-dendi-1v1 • [12] https://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of/karras2018iclr-paper.pdf • [13] https://www.engadget.com/2016/01/25/tesla-autopilot-promo-shows-what-it-can-do-for-your-commute/ • [14] https://danielmiessler.com/blog/the-difference-between-decompilers-disassemblers-debuggers-and-hex-editors/ • [15] https://www.rsipvision.com/wp-content/uploads/2015/04/Slide5.png • [16] https://arxiv .org/pdf/1312.6199.pdf • [17] https://arxiv.org/pdf/1710.08864.pdf • [18] https://poloclub.github.io/ganlab/ • [19] https://www.marketwatch.com/story/this-is-what-happens-when-skynet-from-terminator-takes-over-the-stock-market-2018-02-14 • [20] https://medium.com/@karpathy/software-2-0-a64152b37c35 • [21] https://www.amazon.com/b?ie=UTF8&node=16008589011