B e t t e r F u t u r e s , T o g e t h e r Mytholo logy of AI Chry rysta tal Ball lls, Genie ies and Deit itie ies Victor Alexiev Black Swans and Black Elephants +65 9815 1543 victor@innovator.sg iRAHSs, 18 July 2017
What is is AI I Anyway?
The science of Intelligent Programs
Intelligent = improves its performance (accuracy or “cost”) over time
Why AI? I? Why Today?
Critical uncertainty: something that we know will be a game changer, but we are uncertain when, if, or how it will play out
Technology and Mega-Cycle les Info formation Steam Engin gine; Ra Railw lways; Ele lectrical Pe Petrochemicals; Te Technology; Cotton Steel engi gineering; Au Automobile les Con onnectivity Chemistry … A.I. Predictions P R D E Kondratiev Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 1 8 0 0 1 8 5 0 1 9 0 0 1 9 5 0 1 9 9 0 A new technology, powered by a widely available resource becomes P: Pr P: Pros osperit ity increasingly important across sectors. It turns into an ultimate driver of R: Recessio ion D: Depressio ion productivity, requires the build-up of new infrastructure and new forms of E: E: Exp Expansio ion organizing labour and capital... New skills and forms of education
Why it’s diffe iffere rent th this is time Data ta + Computi ting po power + + Communication inf infrastr tructure = Pre Predictions are re be becoming ch cheap and being embedded into everything Pre redic iction-empowered technology (smart) will gradually displace judgement and codified expertise as its costs continue decreasing, consuming knowledge-based tasks
International Community Open Source Technologies Data Everywhere Smart Hardware Intelligent Software Cheap Computing Power
B e t t e r F u t u r e s , T o g e t h e r Curr rrent Sta tate Victor Alexiev Black Swans and Black Elephants +65 9815 1543 victor@innovator.sg iRAHSs, 18 July 2017
50 years rs of f pro rogre ress “ • the generic visual object recognition capabilities of a two year old child • the manual dexterity of a six year old child • the social interaction and language capabilities of a ten year old child “ Rod Brooks-MIT, 2016
Recognizing Key Entities Fro rom an Im Image
Approximating human performance in: Understanding English Sentences Playing rule-based games Writing computer programs from spec Transcribing voice Translating text
Recognizing Key Entities Fro rom an Im Image
Playing Games … e.g. – Chess … or “Go”
Wri riti ting Computer r Pro rograms fr from Specification …
Lim imitations – Exp xplo loitable le Outc tcomes Darpa experiment in purposefully misleading an image recognition algorithm
Lim imitations – Manipula lating Learn rning Microsoft experiment with Generative AI twitter-bot went awfully wrong Learning quality depends on learning samples and quality of interactions Learning AI can be subject to manipulation and distortion http://www.darpa.mil/attachments/AIFull.pdf
What AI is is stru truggli ling wit ith.. ... Handle small changes of input (e.g. invert colours) Extract or derive causal structures (esp. about unknown entities) Transfer learning from one domain/context to another Reason ethically ...and many more
B e t t e r F u t u r e s , T o g e t h e r A Look In Into to th the Future Victor Alexiev Black Swans and Black Elephants +65 9815 1543 victor@innovator.sg iRAHSs, 18 July 2017
“The Future is is a Foreign Country. They do things differently there!”
What did id they get rig ight?
And more than anything...
Taking this is to extremes...
∞ / 0
Expla laining The Inexpli licable...
Crystal Balls Genies Scrying Evoking Deities Worshiping
Enchanted / Intelligent Devices (mirrors, orbs, cups... phones) All knowing, all seeing Peek into the future, albeit open to interpretation Answer questions / Advise
What? When? Where re? Who? Can I? I? How? Should ld I? I? Why? What if if I? I? Descri ribe Defi fine
Mirr irror r Mirr irror on the wall, who’s the fairest of them all?
Opport rtunitie ies Dec ecisio ion Qualit ity: Better decisions with perfect information Simulated Rea Sim Realit itie ies: Instant feedback of the consequences of our actions Co Communic ication: Ultimate communication device Tra ranscendence: Us as data (AI copies of real people)
Thre reats Open to to interpretatio ion: Perfect information ≠ perfect understanding Over-relia iance: How many of you follow GPS blindly? Arr Arrogance: Give us the confidence to do the wrong things at scale Pri rivacy? Truth?: Forget about it
Ove ver-re reli liance
Tra ranscendence? Fast Company, April 2017
Dri rive vers rs Usage : the more you use, the more data you provide Int Integration : More integrated data sets and applications = more utility Pro rocessing Pow ower : ETL and Prediction models at scale Talent : Access to talent to tie all this up
PA from another dimension (jinn, demon, spirits) Grant wishes/execute tasks at zero cost (obligatory and frictionless) Bound to an device/location (constrained autonomy) Sworn to serve their master (authentication)
FA FACTO CTORY RY WORKER RKER
POSTMAN TMAN
Shamelessly borrowed from my friend, Volker Hirsch : http://bit.ly/2nEFrhn
Shamelessly borrowed from my friend, Volker Hirsch : http://bit.ly/2nEFrhn
But not only “physical” tasks: Robo-advisory is on the rise • Lawyers • Accountants • Fund managers • HR assistants
Opport rtunitie ies Pro roductivit ity: We can get a lot more done Cos Cost of of Lea Learnin ing : once one “genie” learns a trick, all genies know it Ac Access: Lowering the cost of services opens the market Hu Human Sa Safety : We don’t need to put humans at risk
Thre reats Ag Agency: Who is responsible? Se Securit ity: What if a(all) genie(s) get(s) stolen/kidnapped/hacked? Ine Inequalit ity: Genies give an unfair advantage Pur urpose: If genies do most of the work, what are we gonna do?
Dri rive vers rs Localized Processing Power Batteries Progress in Voice and Image Recognition Progress in Intent Detection ...Teaching ethics to AI becomes really important here
Infinite transcendent beings who lord over humanity Excellence in some aspects, weakness in others Free-willed superpowers Can be “unleashed upon us” Us, mere humans, are merely pawns serving the superhuman agenda
Opport rtunitie ies Those “gods of the future” may just be augmented, benevolent humans With superhuman strength, we can take on our worst enemies/fears Our deities can protect us from the most terrible things in the universe ...
Thre reats Co Control : Deities act on their own will, and who knows what that would be Am Ambiguity : Supernatural behaviour can arise as an unintended consequence Co Complexit ity : An average peasant like myself will see intent in randomness Distraction : While we’re afraid of deities we are not addressing real problems
Dri rive vers rs Our imagination... ... Mixed with emergent behaviour of complex dynamic systems Insufficient education in systems thinking and complexity theory Proactive regulation Bad maths
B e t t e r F u t u r e s , T o g e t h e r Back to to Reali lity Victor Alexiev Black Swans and Black Elephants +65 9815 1543 victor@innovator.sg iRAHSs, 18 July 2017
Will AI overlords take over the world? What do they want from us? I’ve watched a lot of SciFi movies — is now a good time to panic?
AI/SW does not want or feel anything… yet Jobs are rationalized by businesses striving to attain profit , scale and competitiveness You risk being innovated out of your job!
60k x 5$/d > Technology
Key Worries
Safety and Securi rity Priv rivacy and nd Truth: We have too much data laying around Ce Centraliz ization: Centralized systems create single points of vulnerability Ru Rushing-in in: AI Arms race can lead to unintended consequences
Exp xpla lanatio ion and In Inte terp rpre retabili lity Opacity: Most advanced models are difficult to backtrack Ma Manip ipulatio ion: Statistical models are subjected to adversarial manipulations Ed Education: Complexity is exponential (model audit is difficult)
Fair irness and De-biasing Bia Bias: Embedding and scaling assumptions Arr Arrogance: Bad data = bad decisions Li Linear thin thinkin ing: Bad decisions + good outcomes = bad data
…A little bit of Game Theory as a Warning If there is a significant incentive in cheating – someone will cheat. Embedding Fairness and Ethics in AI, if not effectively enforced – will not suffice But how do you enforce something like that? Governance and Regula latory st stru ructures must be pro proactive in inste tead of f pla playing catc tch-up up
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