The pre-Singularity An uncertain road ahead David

Slides:



Advertisements
Similar presentations
Approaches, Tools, and Applications Islam A. El-Shaarawy Shoubra Faculty of Eng.
Advertisements

Artificial Intelligence By: David Hunt Lee Evans Jonathan Moreton Rachel Moss.
The Logic of Intelligence Pei Wang Department of Computer and Information Sciences Temple University.
Big Data Chapter 1 Verónica Morales Márquez,
Aaron Summers. What is Artificial Intelligence (AI)? Great question right?
Wrap-Up Wednesday/Friday 10th Week. Goals of this course Give students a broader, more realistic view of the discipline of computer science as they decide.
Computer Science Prof. Bill Pugh Dept. of Computer Science.
Prologue: An Inexorable Emergence By Ray Kurzweil from book.
Formal verification Marco A. Peña Universitat Politècnica de Catalunya.
D Goforth - COSC 4117, fall Notes  Program evaluation – Sept Student submissions  Mon. Sept 11, 4-5PM  FA 181 Comments to committee are.
CMSC 426: Image Processing (Computer Vision) David Jacobs.
Whole Brain Emulation, as a platform for creating safe AGI Anna Salamon and Carl Shulman.
Vedrana Vidulin Jožef Stefan Institute, Ljubljana, Slovenia
Last Words COSC Big Data (frameworks and environments to analyze big datasets) has become a hot topic; it is a mixture of data analysis, data mining,
The Thinking Machine Based on Tape. Computer Has Some Intelligence Now Playing chess Solving calculus problems Other examples:
How We’re Going to Solve the AI Problem Pedro Domingos Dept. Computer Science & Eng. University of Washington.
If the human brain were so simple that we could understand it, we would be so simple that we couldn't. —Emerson M. Pugh.
Eric Hand March19.3m, Brains: a few key facts & concepts - How facts & ideas about the brain may predict the Singularity - Project Blue Brain: Supporting.
ITR: Collaborative research: software for interpretation of cosmogenic isotope inventories - a combination of geology, modeling, software engineering and.
Information Explosion. Reality: New Machine-Generated Data Non-relational and relational data outside of the EDW † Source: Analytics Platforms – Beyond.
MICHAEL FINE Artificial Intelligence and The Singularity 1.
Last Words DM 1. Mining Data Steams / Incremental Data Mining / Mining sensor data (e.g. modify a decision tree assuming that new examples arrive continuously,
Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong A.I.
INSTANT PEOPLE INSIGHTS Injazat partners with Qlearsite to bring ‘People Analytics’ to Leaders across the UAE. The art of leadership is getting a boost.
Course Overview  What is AI?  What are the Major Challenges?  What are the Main Techniques?  Where are we failing, and why?  Step back and look at.
Artificial Intelligence: Research and Collaborative Possibilities a presentation by: Dr. Ernest L. McDuffie, Assistant Professor Department of Computer.
An insight into the posthuman era Rohan Railkar Sameer Vijaykar Ashwin Jiwane Avijit Satoskar.
Quantum Computers By Ryan Orvosh.
Super-Intelligent Machines Jared Schmidt "I mean, being a robot's great; but we don't have emotions and sometimes that makes me very sad."
AI: AlphaGo European champion : Fan Hui A feat previously thought to be at least a decade away!!!
Review Platforms. Final Exam April 28th Course Evaluations Available starting Thursday, April 7, 2016 at esff.temple.eduesff.temple.edu The last full.
Exponential Technologies – Singularity & ExOs
Technical Problems in Long-Term AI SafetyAndrew Critch Technical (and Non-Technical) Problems in Long-Term AI Safety Andrew Critch.
Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall) Chapter 12: Artificial Intelligence and Expert Systems.
Artificial Intelligence and Machine Learning in Big Data and IoT: The Market for Data Capture, Analytics, and Decision Making 2016 – 2021 Phone No.: +1.
How to Create a Mind The Secret of Human Thought Revealed by Ray Kurzweil Slides by Prof. Tappert.
It’s Time for Cognitive Computing
Bachelors Degree in Consciousness Studies: Proposed 4-Year Curriculum
Conclusions on CS3014 David Gregg Department of Computer Science
AP CSP: What is Big Data?.
Talking, feedback, inhibition, emotions and learning...
Cost Models for HPC and Supercomputing
Technology, Disruption & The Digital Enterprise
Siemens Enables Digitalization: Data Analytics & Artificial Intelligence Dr. Mike Roshchin, CT RDA BAM.
Done Done Course Overview What is AI? What are the Major Challenges?
Week 6 Innovation Process
DSS: Decision Support Systems and AI: Artificial Intelligence
HSCB Focus 2010 Overview August 5-7, 2009 Chantilly, Virginia
Videos NYT Video: DeepMind's alphaGo: Match 4 Summary: see 11 min.
Artificial intelligence and cloning
Artificial Intelligence
Artificial Intelligence
Solving Legal And Human Resource Problems With Artificial Intelligence
Options for Stage 3 16th March 2018.
Summary of Chapter 10 The Computer Scientist: Artificial Intelligence
Introduction Artificial Intelligent.
How Emerging Technology is Transforming Actuarial Science
Introduction to AI Tuomas Sandholm Professor
Introduction to Digital Electronics
Information Systems in Organizations 6.0 Artificial Intelligence
Enabling ML Based Research
If your internal knowledge isn’t easy to search, I’ll organize and unify it for you. When a member of your team creates a new asset,
Integrating Deep Learning with Cyber Forensics
Artificial Intelligence
UNIT 5 EMBEDDED SYSTEM DEVELOPMENT
UNIT 5 EMBEDDED SYSTEM DEVELOPMENT
Hansheng Lei Univ. of Texas Rio Grande Valley
The Intelligent Enterprise and SAP Business One
ARTIFICIAL INTELLIGENCE APPLICATION IN HEALTH CARE by
AI In By
Presentation transcript:

The pre-Singularity An uncertain road ahead David Wood @dw2 Chair, London Futurists Principal, Delta Wisdom londonfuturists.com deltawisdom.com

Q: Will all human translators be replaced before the Singularity? A: It’s too early to tell. There are many things we still understand insufficiently… The mind is still mysterious, and software progress is unpredictable.

The set of credible future scenarios Futurists… 1. Identify scenarios 2. Assess scenarios 3. Explore actions Opportunities The set of credible future scenarios Threats Trend analysis Extrapolation  Disruptions Brakes  Accelerators  Business as usual Interactions

Patiently built a platform for collaboration Spotted trends Anticipated convergence Positive feedback cycle Patiently built a platform for collaboration Vision: June 1998

Smartphone Capability Phase 2 smartphones (superphones) Mini-computers Software critical Software important Supercomputers Phase 1 smartphones Software relatively unimportant Feature phones (phase 0) The future arrives in waves “Software is eating the world” “Technology is eating the world” Time 1990 2000 2010

“Technology is eating the world” #1 #2 #3 #4 #5 2001 2006 “Technology is eating the world” 2011 2016 http://www.visualcapitalist.com/chart-largest-companies-market-cap-15-years/

Pre-Singularity AI Smartphones Huge amounts of software Intense co-opetition of many developers & many companies Many delays in the course of development (spurts too) Many surprises in the course of development Full social implications not clear in advance Overall potential very positive Huge amounts of software Intense co-opetition of many developers & many organisations Many delays in the course of development (AI winters…) Many surprises in the course of development(?) Full social implications not clear in advance Overall potential very positive(?)

The future arrives in waves Smartphone Capability AI Capability Phase 2 smartphones (superphones) Superhuman AI (ASI)? Works out own goals(?) Phase 1 smartphones AI with Deep Learning? Feature phones (phase 0) Works out own methods, follows given goals The future arrives in waves Narrow AI Follows given goals & methods Time 1990 2000 2010

“The Master Algorithm” Pedro Domingos, 2015 Multi-convergence of “tribes” Tribe Origin Core algorithm Symbolists Logic & philosophy inverse deduction Connectionists Neuroscience back-propagation Evolutionaries Evolutionary biology genetic programming Bayesians Statistics probability inference Analogizers Psychology kernel machines “How the quest for the ultimate learning machine will remake our world”

Likely date of advent of HL-AGI Population 10% 50% 90% Conference: Philosophy & Theory of AI Conference: Artificial General Intelligence Greek Association for Artificial Intelligence Top 100 cited academic authors in AI Combined (from above) Nick Bostrom: Superintelligence

Likely date of advent of HL-AGI Population 10% 50% 90% Conference: Philosophy & Theory of AI 2048 Conference: Artificial General Intelligence 2040 Greek Association for Artificial Intelligence 2050 Top 100 cited academic authors in AI Combined (from above) Nick Bostrom: Superintelligence

Likely date of advent of HL-AGI Population 10% 50% 90% Conference: Philosophy & Theory of AI 2048 2080 Conference: Artificial General Intelligence 2040 2065 Greek Association for Artificial Intelligence 2050 2093 Top 100 cited academic authors in AI 2070 Combined (from above) 2075 Nick Bostrom: Superintelligence

Likely date of advent of HL-AGI Population 10% 50% 90% Conference: Philosophy & Theory of AI 2023 2048 2080 Conference: Artificial General Intelligence 2022 2040 2065 Greek Association for Artificial Intelligence 2020 2050 2093 Top 100 cited academic authors in AI 2024 2070 Combined (from above) 2075 Nick Bostrom: Superintelligence

“Computers will have developed ‘common sense’ within a decade and we could be counting them among our friends not long afterwards” Geoffrey Hinton University of Toronto and Google http://www.macleans.ca/society/science/the-meaning-of-alphago-the-ai-program-that-beat-a-go-champ/ https://www.theguardian.com/science/2015/may/21/google-a-step-closer-to-developing-machines-with-human-like-intelligence

Exponential growth? ? ASI>>HL Technology Technology AGI=HL Time 2050 Technology Time 2050 AGI=HL Ray Kurzweil Eliezer Yudkowsky

Going nuclear: hard to calculate First hydrogen bomb test, 1st March 1954, Bikini Atoll Explosive yield was expected to be from 4 to 6 Megatons Was 15 Megatons, two and a half times the expected maximum Physics error by the designers at Los Alamos National Lab Wrongly considered the lithium-7 isotope to be inert in bomb The crew in a nearby Japanese fishing boat became ill in the wake of direct contact with the fallout. One of the crew died http://en.wikipedia.org/wiki/Castle_Bravo

The pre-Singularity: 5 unpredictable forces Hardware with higher performance: Continuation of Moore’s Law? “18 different candidates” in Intel labs to add extra life to that trend Hard-to-predict breakthroughs with Quantum Computing? Software algorithm improvements? Can speed things up faster than hardware gains – e.g. chess computers Compare: Andrew Wiles, unexpected proof of Fermat’s Last Theorem (1993) Learnings from studying the human brain? Improved scanning techniques -> “neuromorphic computing” etc Philosophical insight into consciousness/creativity?! More people studying these fields than ever before Stanford University online course on AI: 160,000 students (23,000 finished it) More components / databases / tools /methods ready for re-combination Unexpected triggers for improvement (malware wars, games AI, financial AI…) Transformation in society’s motivation? Financial motivation (Smarter people?!) “Sputnik moment!?” http://intelligence.org/2013/05/15/when-will-ai-be-created/

AI, Deep Learning => Pattern detection (Artificial Intuition) The acceleration of acceleration “We are 20 years away…” March 2016 AlphaGo 4, Lee Sedol 1 AI, Deep Learning => Pattern detection (Artificial Intuition)

Who here wanted to merge again? Jaan Tallinn: http://prezi.com/xku9q-v-fg_j/intelligence-stairway/

The pre-Singularity An uncertain road ahead David Wood @dw2 Chair, London Futurists Principal, Delta Wisdom londonfuturists.com deltawisdom.com