IBM is currently helping to build Petascale super computers
IBM Z systems in just a few years will include petascale capabilities.
By 2035, petascale digital worker capabilities will be routinely available at smartphone scale to everyone on the planet. Today, democratizing the ability to build AI systems is key focus of DAIO (Data AI Open) and Cognitive Opentech Group (COG)
Time dimension (x-axis) is plus or minus 10 years….
Daniel Pakkala (VTT) URL: https://aiimpacts.org/preliminary-prices-for-human-level-hardware/
Dan Gruhl: https://www.washingtonpost.com/archive/business/1983/11/06/in-pursuit-of-the-10-gigaflop-machine/012c995a-2b16-470b-96df-d823c245306e/?utm_term=.d4bde5652826
In 1983 10 GF was ~10 million.
That's 24.55 million in today's dollars.
or 2.4 billion for 1 TF in 1983
Today 1 TF is about $3k http://www.popsci.com/intel-teraflop-chip
1950 Nathaniel Rochester (IBM) 701 first commercial computer that did super-human levels of numeric calculations routinely. He worked at MIT on arithmetic unit of WhirlWind I programmable computer.
Dota 2 is most recent August 11, 2017 as a super-human game player in Valve Dota 2 competition – Elon Musk’s OpenAI result.
Miles Bundage tracks gaming progress: http://www.milesbrundage.com/blog-posts/my-ai-forecasts-past-present-and-future-main-post
Learning by doing related
The team has successfully applied the One Button Machine in various data science competitions where it outperformed most human teams and ranked among the top 16螄% of participants
One Button Machine works by traversing the graph defined by the entities (tables) and relations (primary/foreign keys) of a relational database. The aggregation functions can be specified by the user, or chosen generically for certain data types. To deal with the combinatorial explosion of related entities, the One Button Machine deploys heuristics and sub-sampling strategies. Scalability to big databases is achieved by dynamic caching of intermediate results and a parallelisable implementation in Apache Spark, a distributed computing framework for analysing massive amounts of data.
Who is winning: https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/
Leaderboards and reproducibility: Hugo Larochelle (Google Brain) (@hugo_larochelle) 8/21/17, 7:36 AM My slides for my talk at ICML 2017 Reproducibility Workshop, on incentives for open source and on open research: https://drive.google.com/file/d/0B8lLzpxgRHNQZ0paZWQ0cTcxMlNYYnc0TnpHekMxMjVBckVR/view Slide 20: Conclusions: "Open source is the key to better reproducibility"
Nathaniel Rochester: In 1948, Rochester moved to IBM where he designed the IBM 701, the first general purpose, mass-produced computer. He wrote the first symbolic assembler, which allowed programs to be written in short, readable commands rather than pure numbers or punch codes.
The nature of reality changes when there is more than one intelligent species, and we are not the smartest.
The nature of reality also changes when the cost of exploring alternate experience pathways are made less risky – the notions of time and identity changes as a result.
Mitigate risks and harvest benefits of existence, by learning to evermore efficiently and rapidly rebuild from scratch to higher states of value and capability of entities.
The evolving ecology of service system entities their value co-creation and capability co-elevation mechanisms, as well as their capabilities, constraints, rights, and responsibilities at each stage in time. Human progress as well as the development of individuals, and the arc of institutions can be viewed in this way. Entities exist as individuals and populations. Generations of entities, generations of species (populations), generations of individuals (cohorts).
URL: http://www.mercurynews.com/2016/08/04/cupertino-teens-score-20000-for-24-hours-of-work/ Karan Mehta and Anish Krishnan
Cost of dw 20170909 v18
Preparing for the Future
of Artificial Intelligence (AI)
September 9, 2017
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Cost of Digital Workers:
Narrow (Petascale) and Broad (Exascale)
• Moore’s Law can be
thought of as lowering
costs by 1000 every 20
years, and a million
every 40 years
• AI Pattern Recognition
• Narrow AI (Fast) I-Shaped
• AI Reasoning
• Broad AI (Slow) T-Shaped
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+/- 10 years
IBM-MIT $240M over 10 year AI mission
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Sept 7, 2017
• What is the timeline for solving AI and IA?
• Who are the leaders driving AI progress?
• What will the biggest benefits from AI be?
• What are the biggest risks associated with AI, and are they real?
• What technologies may have a bigger impact than AI?
• What are the implications for stakeholders: individuals, businesses
and other organizations, industries, cities, states, and nations?
• How should we prepare to get the benefits and avoid the risks?
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AI to IA Timeline: Hard unsolved AI problems
• 2012-2019 AI Pattern Recognition and
Learning from Massive Labeled Data
• Speech, image, translation, driverless, games
• Chatbots as digital assistants
• 2020 Video Understanding
• 2021 Episodic Memory
• 2022 Learning from Watching
• 2023 Commonsense Reasoning **
• 2024 Learning from Doing
• 2025 Fluent Conversation
• 2026 Learning from Reading
• 2027-2035 Cognitive Collaborator and
Mediator; Intelligence Augmentation (IA)
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• Who is winning?* (depends on search terms)
• Nations by Publications
• Companies by Publications and Patents
• SQuAD – Question Answering
• EFF Measuring AI Progress
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History of AI
• The Dartmouth
organized by Marvin
McCarthy and two
Claude Shannon and
Nathan Rochester of
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Brief History of
• 1943 Threshold Logic
• 1958 Perceptron
• 1986 Hinton Backprop
• 1989 Lecunn for digits
• 2009 Ng GPUs
• 2012 Hinton ImageNet
• 2014 Facebook DeepFace
• 2016 DeepMind AlphaGo
• 2017 IBM Scaling 95%
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Hillery Hunter (IBM)
Distributed Deep Learning
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• Extremely Low Power
• Fast Inference
• High Accuracy
• 6000 frames/sec/watt
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• Access to expertise
• “Insanely great” labor productivity for trusted service providers
• Digital workers for healthcare, education, finance, etc.
• Better choices
• ”Insanely great” collaborations with others on what matters most
• AI for IA = Augmented Intelligence and higher value co-creation interactions
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• Job Loss
• Shorter term bigger risk
• Shorter term bigger risk
= bad actors
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Other Technologies: Bigger impact?
• Augmented Reality (AR)/
Virtual Reality (VR)
• Game worlds
• Trust and security
• Advanced Materials/
• Manufacturing as cheap,
local recycling service
(utility fog, artificial leaf, etc.)
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• Businesses and
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• Open AI code + data + models
+ stacks + community
• Ethical conduct
• Learn 3 R’s of IBM’s Cognitive
Opentech Group (COG)
• Read arXiv
• Redo with Github
• Report with Jupyter notebooks
on DSX and/or
• Improve your skills of rapidly
rebuilding from scratch
• Build your open code eminence
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Open APIs to win…
• IBM Watson on Bluemix
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AI for NLP