singularity - fiction or future?
TRANSCRIPT
WHAT IS A SINGULARITY ?
civilization changes so much that its
rules and technologies are
incomprehensible to previous
generations
extremely rapid technological and
scientific changes
Imagine:
explaining the internet to somebody living in the year 1200
SINGULARITY MODEL
2 3
Nanobots & Gray Goo Cybernetics (Genome)
1
artificial intelligence
artificial intelligence
“Since the design of machines is an intellectual activity, an
ultraintelligent machine could design even better machines.
There will then be an “intelligence explosion”, and the
intelligence of man would be left far behind.
Thus the first ultraintelligent machine is the last invention of
mankind”
- I. J. Good, 1965
SINGULARITY MODEL
3
Cybernetics (Genome)
1
Artificial Intelligence
Nanobots & Gray Goo
The self-replicating molecular
machine.
• Ability to build machines that
manipulate matter at the
atomic level.
• Control our world in the most
granular way imaginable.
2
Nanobots & Gray Goo
SINGULARITY MODEL
2
Nanobots & Gray Goo
1
Artificial Intelligence
3
Cybernetics (Genome)
Cybernetics (Genome)
Synthetic biology, genetic engineering, and other life sciences
will eventually give us control of the human genome.
• We could engineer new forms of life and change the
course of human evolution in one generation with
enhanced capabilities. [Superpower]
• Allow us to tinker with the mechanisms that make us age,
thus dramatically increasing our lifespans. [Longevity]
SINGULARITY MODEL
2
Nanobots & Gray Goo
1
Artificial Intelligence
3
Cybernetics (Genome)
WILL THEY HAPPEN ?
Vs
ARTIFICIAL INTELLIGENCE
HUMAN INTELLIGENCE
• Learning & Adaptation
• Reasoning & Planning
• Perceiving & Reacting
• Feeling & Emotion
• Understanding of language
Theory and development of
computer systems able to
perform tasks that normally
require human intelligence
RAY KURZWEIL - GOOGLE
Ray Kurzweil“The Heir to Thomas Edison”
Google’s Director of Engineering
Author of “The Singularity Is Near”
“The Age of Spiritual Machines”
The Law of Accelerating Returns
“Fundamental measures of information technology development follow predictable and exponential trajectories."
"As exponential growth continues to accelerate into the first half of the twenty-first century and will likely explode into infinity soon after.
Kurzweil is optimistic. • Planning on creating a simulated version of his late father• Nanobots will enhance our immune systems by 2030. He’s
planning on living forever, more or less. • If he dies, he plans to be perfused with cryoprotectants,
vitrified in liquid nitrogen and hope that future medical technology will repair his tissues and revive him.
EVENT & INTERVIEW
“Are you ready for brain-chip implant?”
Captain HoffCaptain of Founder Space (SV)
Tech Evangelist & Angel Investor
• Top 10 Incubator in Inc. Magazine• #1 Accelerator in Silicon Valley for
overseas startups by Forbes.
“It is just a matter of when.”
Ted WillichCEO/Founder of NLP Logix
Expert in ML & Computer Vision
• Aim to deliver AI’s business value• Serial Techopreneur in Machine
Learning & AI field
EXISTING APPLICATIONS
INDUSTRIAL
Autonomous Vehicles
Fleet Telematics
AUTOMOTIVE
Machine Vision System
Predictive Analytics
MEDICAL
Predictive Diagnosis
Image Analysis
EXISTING APPLICATIONS
Stock Trading Model
Fraud Detections
FINANCE CONSUMER
Chatbot Technology
AI Private Assistant
BUSINESS
Gaming & Entertainment
Immersive Wearables
UTOPIAN TIMES AHEAD...?
Artificial Intelligence
Nanotechnology Genetic Engineering
3D Printing & robotics Cybernetics
Synthetic Biology
When AI Screws Up, It Screws Up Badly
On TV show Jeopardy players in 2011, IBM’s Watson was asked:“What country’s largest airport is named for a World War II hero; its second largest, for a World War II battle.” Watson: “What is Toronto?”
When AI Screws Up, It Screws Up Badly
“Watson absolutely surprises me. People ask: 'Why did it get that one wrong?' I don't know. People ask: 'Why did it get that one right?' I don't know.”
- David Ferrucci, the Lead Researcher of IBM Watson
AI RESEARCH & POLICY
NEXT 15 YEARS
• Large-scale machine learning
• Deep learning
• Reinforcement learning
• Robotics
• Computer vision
• Natural Language Processing
• Collaborative systems
• Crowdsourcing and human
computation
• Algorithmic game theory and
computational social choice
• Internet of Things (IoT)
• Neuromorphic Computing