Artificial intelligence research began with early attempts to build mechanical people and the development of logic in the 1800s and 1900s, leading to the first general purpose computers in the 1940s and 1950s which helped spark interest in building intelligent machines, with the field of AI being established at the 1955 Dartmouth conference to study how to make computers exhibit intelligent behavior. Subsequent decades saw continued research in symbolic and subsymbolic approaches to AI with successes in games, expert systems, and machine learning but challenges in domains requiring commonsense knowledge like natural language understanding.
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal
Artificial intelligence and the Singularity - History, Trends and Reality Checkpiero scaruffi
A lecture given at the second LAST festival (www.lastfestival.org) by Piero Scaruffi on Artificial intelligence and the Singularity - History, Trends and Reality Check. This is a very old presentation. See the updated one at www.scaruffi.com/singular
Alan Turing and the Programmable Universe (lite version)piero scaruffi
Alan Turing, the cultural context of his world, and what would Turing say of today's high-tech world. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
Lectures on Silicon Valley at Beijing and other cities in China - September 2014 - excerpted from my book http://www.amazon.com/History-Silicon-Valley-Almost-3rd/dp/1500262226/ref=sr_1_3_bnp_1_pap?ie=UTF8&qid=1405191978&sr=8-3&keywords=scaruffi+silicon+valley
Demystifying Machine Intelligence: Why the Singularity is not Coming any Time Soon And Other Meditations on the Post-Human Condition and the Future of Intelligence. A more updated version can be found at www.scaruffi.com/singular
Intelligence is not Artificial - Stanford, June 2016piero scaruffi
A critical analysis of the state of A.I. and predictions about its realistic future. Based on the book of the same title, see http://www.scaruffi.com/singular/ where i keep updating these slides
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal
Artificial intelligence and the Singularity - History, Trends and Reality Checkpiero scaruffi
A lecture given at the second LAST festival (www.lastfestival.org) by Piero Scaruffi on Artificial intelligence and the Singularity - History, Trends and Reality Check. This is a very old presentation. See the updated one at www.scaruffi.com/singular
Alan Turing and the Programmable Universe (lite version)piero scaruffi
Alan Turing, the cultural context of his world, and what would Turing say of today's high-tech world. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
Lectures on Silicon Valley at Beijing and other cities in China - September 2014 - excerpted from my book http://www.amazon.com/History-Silicon-Valley-Almost-3rd/dp/1500262226/ref=sr_1_3_bnp_1_pap?ie=UTF8&qid=1405191978&sr=8-3&keywords=scaruffi+silicon+valley
Demystifying Machine Intelligence: Why the Singularity is not Coming any Time Soon And Other Meditations on the Post-Human Condition and the Future of Intelligence. A more updated version can be found at www.scaruffi.com/singular
Intelligence is not Artificial - Stanford, June 2016piero scaruffi
A critical analysis of the state of A.I. and predictions about its realistic future. Based on the book of the same title, see http://www.scaruffi.com/singular/ where i keep updating these slides
Art/Science Interaction - Case study: Silicon Valleypiero scaruffi
Presentation for the Alpbach Technology Forum of August 2014 on Art/Science and Silicon Valley. I keep updating my presentations on Silicon Valley at www.scaruffi.com/svhistory
This quiz was held at Cambridge School, Indirapuram on 9th November, 2015 as a part of the annual IT fest, Infoyage 2015, organized by In-X Union, the IT club of Cambridge.
Artificial Intelligence: Should You Be Worried?Harry Blanchard
An introduction to the what artificial intelligence is and a cultural history of the fear of creation of intelligence. A realistic assessment is made of the so-called singularity and what we really should be worried about: artificial "semi-intelligence." Talk given to the Northern Monmouth County Branch of the AAUW.
Access 2016 - Junior Quiz Prelims Questions+AnswersBits N Bytes
The Questions and Answers of the Preliminary Round of the Junior Quiz at Access 2016, held on 16th and 17th of December, 2016
Prepared by - Apratim Chandra Singh, Ayan Marwaha, Paavas Bhasin
Access 2016 - Sub Junior Quiz Prelims Questions+AnswersBits N Bytes
The Questions and Answers of the Preliminary Round of the Sub-Junior Quiz at Access 2016, held on 16th and 17th of December, 2016
Prepared by - Apratim Chandra Singh, Ayan Marwaha, Paavas Bhasin
The singularity is coming by Takuya Matsuda - CODE BLUE 2015CODE BLUE
An Artificial Intelligence (AI) extremely surpassed the human intelligence is called "Superintelligence". In a short while, the Superintelligence will be developed for the first time in our history. The Superintelligence raises an exponential development of the scientific technology and affects the human society and civilization. The time is called "Singularity (Technological Singularity)". An American futurist Ray Kurzweil, who bruits the concept of Singularity, predicts that the time will come in the year 2045. And he also predicts that the capacity of the AI will gets up to that of a human in the year 2029. I would like to call the period prior to 2045 as "Pre-Singularity". An AI used for a specific purpose is called "Narrow AI", and an AI used for general purposes is called "Artificial General Intelligence (AGI)“. Today there is only Narrow AI, but it will greatly affect the human society such as Technological Unemployment in the coming future. If AGI comes into being, the influence increases dramatically. Researchers all over the world endeavors to develop the AGI. In recent years, researches of the Superintelligence are implemented in Japan. I will discuss what is the Superintelligence and political, economic, technological and military significances. I will especially introduce the roadmap for the development of the Super intelligence in Japan. I will also discuss about the possibility that Singularity will be occurred from Japan in 2020s much earlier than the year 2045.
1.0 Introduction
1.1 Objectives
1.2 Some Simple Definition of A.I.
1.3 Definition by Eliane Rich
1.4 Definition by Buchanin and Shortliffe
1.5 Another Definition by Elaine Rich
1.6 Definition by Barr and Feigenbaum
1.7 Definition by Shalkoff
1.8 Summary
1.9 Further Readings/References
Art/Science Interaction - Case study: Silicon Valleypiero scaruffi
Presentation for the Alpbach Technology Forum of August 2014 on Art/Science and Silicon Valley. I keep updating my presentations on Silicon Valley at www.scaruffi.com/svhistory
This quiz was held at Cambridge School, Indirapuram on 9th November, 2015 as a part of the annual IT fest, Infoyage 2015, organized by In-X Union, the IT club of Cambridge.
Artificial Intelligence: Should You Be Worried?Harry Blanchard
An introduction to the what artificial intelligence is and a cultural history of the fear of creation of intelligence. A realistic assessment is made of the so-called singularity and what we really should be worried about: artificial "semi-intelligence." Talk given to the Northern Monmouth County Branch of the AAUW.
Access 2016 - Junior Quiz Prelims Questions+AnswersBits N Bytes
The Questions and Answers of the Preliminary Round of the Junior Quiz at Access 2016, held on 16th and 17th of December, 2016
Prepared by - Apratim Chandra Singh, Ayan Marwaha, Paavas Bhasin
Access 2016 - Sub Junior Quiz Prelims Questions+AnswersBits N Bytes
The Questions and Answers of the Preliminary Round of the Sub-Junior Quiz at Access 2016, held on 16th and 17th of December, 2016
Prepared by - Apratim Chandra Singh, Ayan Marwaha, Paavas Bhasin
The singularity is coming by Takuya Matsuda - CODE BLUE 2015CODE BLUE
An Artificial Intelligence (AI) extremely surpassed the human intelligence is called "Superintelligence". In a short while, the Superintelligence will be developed for the first time in our history. The Superintelligence raises an exponential development of the scientific technology and affects the human society and civilization. The time is called "Singularity (Technological Singularity)". An American futurist Ray Kurzweil, who bruits the concept of Singularity, predicts that the time will come in the year 2045. And he also predicts that the capacity of the AI will gets up to that of a human in the year 2029. I would like to call the period prior to 2045 as "Pre-Singularity". An AI used for a specific purpose is called "Narrow AI", and an AI used for general purposes is called "Artificial General Intelligence (AGI)“. Today there is only Narrow AI, but it will greatly affect the human society such as Technological Unemployment in the coming future. If AGI comes into being, the influence increases dramatically. Researchers all over the world endeavors to develop the AGI. In recent years, researches of the Superintelligence are implemented in Japan. I will discuss what is the Superintelligence and political, economic, technological and military significances. I will especially introduce the roadmap for the development of the Super intelligence in Japan. I will also discuss about the possibility that Singularity will be occurred from Japan in 2020s much earlier than the year 2045.
1.0 Introduction
1.1 Objectives
1.2 Some Simple Definition of A.I.
1.3 Definition by Eliane Rich
1.4 Definition by Buchanin and Shortliffe
1.5 Another Definition by Elaine Rich
1.6 Definition by Barr and Feigenbaum
1.7 Definition by Shalkoff
1.8 Summary
1.9 Further Readings/References
Lecture Description:
Lecture video by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://www.jarrar.info/courses/AI/
Other courseshttp://www.jarrar.info/courses/
Introduction to Artificial Intelligence
An Introduction to Artificial IntelligenceSeth Juarez
Ever want to know how computers think? In this session attendees will learn the foundations of artificial intelligence through a collaborative discussion centered around the creation of an intelligent game. Attendees will also learn how to use advanced search techniques to solve complex problems using specialized heuristics. In short, attendees will understand how to make intelligent programs by learning how to pose an AI problem in order to maximize desired outcomes.
Search techniques in ai, Uninformed : namely Breadth First Search and Depth First Search, Informed Search strategies : A*, Best first Search and Constraint Satisfaction Problem: criptarithmatic
CCN notes for &th EC students, VTU, UNIT 1 Network MOdels explanation like OSI model ,TCP/IP model and tlepohone networks and cable network for data transmission
CS6659 Artificial Intelligence
Slides in the features of Artificial Intelligence, Definition of Artificial Intelligence
Can be used by undergraduate students
Artificial Intelligence Robotics (AI) PPT by Aamir Saleem AnsariTech
Artificial intelligence (AI) is the intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at an arbitrary goal.Colloquially, the term "artificial intelligence" is likely to be applied when a machine uses cutting-edge techniques to competently perform or mimic "cognitive" functions that we intuitively associate with human minds, such as "learning" and "problem solving".The colloquial connotation, especially among the public, associates artificial intelligence with machines that are "cutting-edge" (or even "mysterious"). This subjective borderline around what constitutes "artificial intelligence" tends to shrink over time; for example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" as it is nowadays a mundane routine technology.Modern examples of AI include computers that can beat professional players at Chess and Go, and self-driving cars that navigate crowded city streets.
AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
Chapter 10 of a university course in media history by Prof. Bill Kovarik, based on the book Revolutions in Communication: Media History from Gutenberg to the Digital Age (Bloomsbury, 2nd ed., 2015).
Slides for the talk given at Desachate 2017, Montevideo.
It's an exploration of narratives that have the potential to bend the arc of human history this century and mess up our personal lives: AI, robots, AI+HI, longevity & space settlement. Talking notes here: https://medium.com/@erikailves/the-shapes-of-21st-century-stories-5ffbd24016cd
The evolution of an agent - Betty. A reminiscence companion for older people who have experienced isolation and loneliness as well as ageing memory loss.
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal
From the first attempt to make a machine working with punch cards, through Alan Turing, the ENIAC Six women, Apple, Microsoft, Facebook, War Games, Youtube, Stuxnet, Red October to 2016 Presidential Election. It's interesting and it's ugly.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
5. What is Artificial Intelligence?
A.I. is the study of how to make computers do things at
which, at the moment, people are better.
6. Or, Stepping Back Even Farther, Can We
Build Artificial People?
•Historical attempts
•The modern quest for robots and intelligent agents
•Us vs. Them
9. Historical Attempts - Euphonia
Joseph Faber's Amazing Talking Machine (1830-40's). The Euphonia and other early
talking devices are described in detail in a paper by David Lindsay called "Talking Head",
Invention & Technology, Summer 1997, 57-63.
About this device, Lindsay writes:
It is "... a speech synthesizer
variously known as the Euphonia and
the Amazing Talking Machine. By
pumping air with the bellows ... and
manipulating a series of plates,
chambers, and other apparatus
(including an artificial tongue ... ),
the operator could make it speak any
European language. A German
immigrant named Joseph Faber spent
seventeen years perfecting the
Euphonia, only to find when he was
From http://www.haskins.yale.edu/haskins/HEADS/
finished that few people cared." SIMULACRA/euphonia.html
10. Historical Attempts - RUR
In 1921, the Czech author Karel Capek produced the play R.U.R.
(Rossum's Universal Robots).
"CHEAP LABOR. ROSSUM'S ROBOTS."
"ROBOTS FOR THE TROPICS. 150 DOLLARS EACH."
"EVERYONE SHOULD BUY HIS OWN ROBOT."
"DO YOU WANT TO CHEAPEN YOUR OUTPUT?
ORDER ROSSUM'S ROBOTS"
Some references state that term "robot" was derived from the Czech word
robota, meaning "work", while others propose that robota actually means "forced
workers" or "slaves." This latter view would certainly fit the point that Capek was
trying to make, because his robots eventually rebelled against their creators, ran
amok, and tried to wipe out the human race. However, as is usually the case
with words, the truth of the matter is a little more convoluted. In the days when
Czechoslovakia was a feudal society, "robota" referred to the two or three days
of the week that peasants were obliged to leave their own fields to work without
remuneration on the lands of noblemen. For a long time after the feudal system
had passed away, robota continued to be used to describe work that one wasn't
exactly doing voluntarily or for fun, while today's younger Czechs and Slovaks
tend to use robota to refer to work that’s boring or uninteresting.
http://www.maxmon.com/1921ad.htm
11. The Roots of Modern Technology
5thc B.C. Aristotelian logic invented
1642 Pascal built an adding machine
1694 Leibnitz reckoning machine
12. The Roots, continued
1834 Charles Babbage’s
Analytical Engine
Ada writes of the engine, “The
Analytical Engine has no
pretensions whatever to originate
anything. It can do whatever we
know how to order it to perform.”
The picture is of a model built in the late 1800s by Babbage’s son
from Babbage’s drawings.
13. The Roots: Logic
1848 George Boole The Calculus of Logic
chocolate and ¬ nuts and mint
chocolate
nuts
mint
14. Mathematics in the Early 20th Century –
(Looking Ahead: Will Logic be the Key to
Thinking?)
1900 Hilbert’s program and the effort to formalize
mathematics
1931 Kurt Gödel’s paper, On Formally Undecidable
Propositions
1936 Alan Turing’s paper, On Computable Numbers with an
application to the Entscheidungs problem
15. The Advent of the Computer
1945 ENIAC The first electronic digital computer
17. The Dartmouth Conference and the Name
Artificial Intelligence
J. McCarthy, M. L. Minsky, N. Rochester, and C.E.
Shannon. August 31, 1955. "We propose that a 2
month, 10 man study of artificial intelligence be
carried out during the summer of 1956 at
Dartmouth College in Hanover, New Hampshire.
The study is to proceed on the basis of the
conjecture that every aspect of learning or any
other feature of intelligence can in principle be
so precisely described that a machine can be
made to simulate it."
18. Time Line – The Big Picture
academic $ academic and routine
50 60 70 80 90 00 10
1956 Dartmouth conference.
1981 Japanese Fifth Generation project launched as the
Expert Systems age blossoms in the US.
1988 AI revenues peak at $1 billion. AI Winter begins.
19. How Will We Recognize AI?
1950 Alan Turing’s paper, Computing Machinery and
Intelligence, described what is now called “The Turing
Test”. http://www.abelard.org/turpap/turpap.htm
1990 Loebner Prize established. Grand Prize of
$100,000 and a Gold Medal for the first computer whose
responses are indistinguishable from a human.
20. The Origins of AI Hype
1950 Turing predicted that in about fifty years "an average
interrogator will not have more than a 70 percent chance of
making the right identification after five minutes of
questioning".
1957 Newell and Simon predicted that "Within ten years a
computer will be the world's chess champion, unless the rules
bar it from competition."
21. Was Turing Right?
A conversation with Jabberwacky, 2003
Jabberwacky: Have you been to the Moon?
User: No. Few have. Have you?
Jabberwacky: Many a time my dear chap!
User: What was the most interesting thing you saw?
Jabberwacky: Fred.
User: Why did you find him interesting?
Jabberwacky: It just came into my head.
User: What sorts of things do you generally find interesting?
Jabberwacky: Nothing but smelly socks.
22. Another One
A Conversation with Alice, 2004
>What's the best place to eat Chinese food?
ALICE: My favorite places are California and Maine. Holland
is cool too.
>What do you like about Maine?
ALICE: The special effects are the best.
http://www.alicebot.org/
23. Alice, Again
>Who invented paper towels?
ALICE: The Chinese, long before Europeans.
>Who invented pet rocks?
ALICE: The Wright Brothers.
>Who invented Barbie dolls?
ALICE: Thomas Edison.
24. Were Newell and Simon Right?
•1950 Claude Shannon published a paper describing how
a computer could play chess.
•1957 Newell and Simon predicted that a computer will
beat a human at chess within 10 years.
•1967 MacHack was good enough to achieve a class-C
rating in tournament chess.
25. Chess Today
In 1997, Deep Blue beat Gary
Kasparov.
26. Why Did They Get it Wrong?
They failed to understand at least three key things:
•The need for knowledge (lots of it)
•Scalability and the problem of complexity and exponential
growth
•The need to perceive the world
27. Scalability
Solving hard problems
requires search in a large
space.
To play master-level
chess requires searching
about 8 ply deep. So
about 358 or 2⋅1012 nodes
must be examined.
29. But Chess is Easy
•The rules are simple enough to fit on one page
•The branching factor is only 35.
30. A Harder One
John saw a boy and a girl with a red wagon with one blue and
one white wheel dragging on the ground under a tree with huge
branches.
31. How Bad is the Ambiguity?
•Kim (1)
•Kim and Sue (1)
•Kim and Sue or Lee (2)
•Kim and Sue or Lee and Ann (5)
•Kim and Sue or Lee and Ann or Jon (14)
•Kim and Sue or Lee and Ann or Jon and Joe (42)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak (132)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel (469)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy (1430)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy and Jan
(4862)
The number of parses for an expression with n terms is the n’th Catalan number:
2n 2 n
Cat (n) = −
n n − 1
33. How Much Compute Power Does it Take?
From Hans Moravec, Robot Mere Machine to Transcendent Mind 1998.
34. How Much Compute Power is There?
From Hans Moravec, Robot Mere Machine to Transcendent Mind 1998.
35. Evolution of the Main Ideas
•Wings or not?
•Games, mathematics, and other knowledge-
poor tasks
•The silver bullet?
•Knowledge-based systems
•Hand-coded knowledge vs. machine learning
•Low-level (sensory and motor) processing
and the resurgence of subsymbolic systems
•Robotics
•Natural language processing
•Programming languages
•Cognitive modeling
36. Symbolic vs. Subsymbolic AI
Subsymbolic AI: Model
intelligence at a level similar to
the neuron. Let such things as
knowledge and planning emerge.
Symbolic AI: Model such
things as knowledge and (blueberry (isa fruit)
planning in data structures that (shape round)
make sense to the (color purple)
programmers that build them. (size .4 inch))
37. The Origins of Subsymbolic AI
1943 McCulloch and Pitts A Logical Calculus of the Ideas
Immanent in Nervous Activity
“Because of the “all-or-none” character of nervous
activity, neural events and the relations among them can
be treated by means of propositional logic”
39. Low-level (Sensory and Motor) Processing
and the Resurgence of Subsymbolic Systems
•Computer vision
•Motor control
•Subsymbolic systems perform cognitive tasks
•Detect credit card fraud
•The backpropagation algorithm eliminated a formal
weakness of earlier systems
•Neural networks learn.
41. Games
•Chess
•Checkers:
•1952-1962 Art Samuel built the first checkers
program
•Chinook became the world checkers champion in
1994
•Othello:
•Logistello beat the world champion in 1997
42. Games
•Chess
•Checkers: Chinook became the world checkers champion in
1994
•Othello: Logistello beat the world champion in 1997
•Go:
•Role Playing Games: now we need knowledge
43. Mathematics
1956 Logic Theorist (the first running AI program?)
1961 SAINT solved calculus problems at the college
freshman level
1967 Macsyma
Gradually theorem proving has become well enough
understood that it is usually no longer considered AI
1996 J Moore and others verified the correctness of the
AMD5k86 Floating-Point Division algorithm
44. The Silver Bullet?
Is there an “intelligence algorithm”?
1957 GPS (General Problem Solver)
Start Goal
45. But What About Knowledge?
•Why do we need it?
Find me stuff about dogs who save people’s lives.
Around midnight, two beagles spotted a
fire in the house next door. Their
barking alerted their owners, who called
911.
•How can we represent it and use it?
•How can we acquire it?
46. Representing Knowledge - Logic
1 McCarthy’s paper, “Programs with Common Sense”
at(I, car) ⇒ can (go(home, airport, driving))
at(I, desk) ⇒ can(go(desk, car, walking))
1965 Resolution theorem proving invented
48. Representing Knowledge – Capturing
Experience
Representing Experience with Scripts, Frames, and Cases
1977 Scripts
Joe went to a restaurant. Joe ordered a hamburger. When the
hamburger came, it was burnt to a crisp. Joe stormed out
without paying.
The restaurant script:
Did Joe eat anything?
49. Representing Knowledge - Rules
Expert knowledge in many domains can be captured in
rules.
From XCON (1982):
If: the most current active context is distributing
massbus devices, and
there is a single-port disk drive that has not been
assigned to a massbus, and
there are no unassigned dual-port disk drives, and
the number of devices that each massbus should support is known,
and
there is a massbus that has been assigned at least one disk drive that
should support additional disk drives, and
the type of cable needed to connect the disk drive to the previous
device on the massbus is known
Then: assign the disk drive to the massbus.
50. Representing Knowledge – Probabilistically
1975 Mycin attaches probability-like numbers to rules
If: (1) the stain of the ogranism is gram-positive, and
(2) the morphology of the organism is coccus, and
(3) the growth conformation of the organism is clumps
Then: there is suggestive evidence (0.7) that the identity of
the organism is stphylococcus.
1970s Probabilistic models of speech recognition
1980s Statistical Machine Translation systems
1990s large scale neural nets
51. The Rise of Expert Systems
1967 Dendral – a rule-based system that infered
molecular structure from mass spectral and NMR data
1975 Mycin – a rule-based system to recommend
antibiotic therapy
1975 Meta-Dendral learned new rules of mass
spectrometry, the first discoveries by a computer to appear in
a refereed scientific journal
1979 EMycin – the first expert system shell
1980’s The Age of Expert Systems
52. Expert Systems – The Heyday
1979 Inference
1980 IntelliCorp Selling expert system shells
1981 Teknowledge
1983 Carnegie Group
1980 XCON (R1) – first real commercial expert system at
DEC, configures VAX systems
1981 Japanese Fifth Generation project launched as the
Expert Systems age blossoms in the US.
1984 Gold Hill Common Lisp
1986 neural net start up companies appear
1988 AI revenues peak at $1 billion. AI Winter begins.
53. Expert Systems – Today: Medicine
One example domain, medicine, has expert systems whose
tasks include:
•arrhythmia recognition from electrocardiograms
•coronary heart disease risk group detection
•monitoring the prescription of restricted use antibiotics
•early melanoma diagnosis
•gene expression data analysis of human lymphoma
•breast cancer diagnosis
54. Expert Systems – Today: Build Your Own
eXpertise2Go: http://www.expertise2go.com/
AcquiredIntelligence: http://www.aiinc.ca/demos/
(whales, graduate school)
55. Taking the SAT Exam
QUESTION: Sodium azide is used in air bags to rapidly produce
gas to inflate the bag. The products of the decomposition reaction
are:(a) Na and water.(b) Ammonia and sodium metal.(c) N2 and
O2(d) Sodium and nitrogen gas.(e) Sodium oxide and nitrogen
gas.
(d) Sodium and nitrogen gas.
56. SYSTEM-GENERATED ANSWER:
# What are the products of the given decomposition reaction?
•By definition, oxidation-reduction reactions occur when electrons are
transferred from the atom that is oxidized to the atom that is reduced. We need
to look for changes in the oxidation states of the elements in the reaction.
•In the reactants, the oxidation state(s) of the element Na is/are (1). In the
product, the oxidation state(s) is/are (0).
Therefore, the reaction causes a change in oxidation state.
•Therefore, this is an oxidation reduction reaction.
•By definition, a Binary Ionic-Compound Decomposition Reaction occurs when
a binary ionic compound is heated.
•Therefore, this reaction is a Binary-Ionic Compound Decomposition reaction.
•In general, a Binary Ionic-Compound Decomposition Reaction converts a
binary ionic-compound into basic elements.
•In this reaction, NaN3 reacts to produce Na and N2.
# The products of the decomposition reaction are:
(d) Sodium and nitrogen gas.
The work of Bruce Porter et al here at UT
57. What About Things that People Do Easily?
•Common sense
•Moving Around
•Language
58. What About Things that People Do Easily?
•Common sense
•CYC
•UT (http://www.cs.utexas.edu/users/mfkb/RKF/tree/ )
•WordNet (http://www.cogsci.princeton.edu/~wn/)
•Moving around
•Language
59. Hand-Coded Knowledge vs. Machine Learning
•How much work would it be to enter knowledge by hand?
•Do we even know what to enter?
1952-62 Samuel’s checkers player learned its evaluation
function
3 Winston’s system learned structural descriptions
from examples and near misses
1984 Probably Approximately Correct learning offers a
theoretical foundation
mid 80’s The rise of neural networks
60. Robotics - Tortoise
1950 W. Grey Walter’s light seeking tortoises. In this
picture, there are two, each with a light source and a light
sensor. Thus they appear to “dance” around each other.
61. Robotics – Hopkins Beast
1964 Two versions of the Hopkins beast, which used sonar to
guide it in the halls. Its goal was to find power outlets.
62. Robotics - Shakey
1970 Shakey (SRI)
was driven by a remote-
controlled computer,
which formulated plans
for moving and acting.
It took about half an
hour to move Shakey
one meter.
63. Robotics – Stanford Cart
1971-9 Stanford cart.
Remote controlled by
person or computer.
1971 follow the white
line
1975 drive in a straight
line by tracking skyline
1979 get through
obstacle courses. Cross
30 meters in five hours,
getting lost one time
out of four
64. Planning vs. Reacting
In the early days: substantial focus on planning (e.g., GPS)
1979 – in “Fast, Cheap and Out of Control”, Rodney
Brooks argued for a very different approach. (No, I’m not
talking about the 1997 movie.)
The Ant, has 17 sensors.
They are designed to work
in colonies.
http://www.ai.mit.edu/people/brooks/papers/fast-cheap.pdf
http://www.ai.mit.edu/projects/ants/
65. Robotics - Dante
1994 Dante II (CMU) explored
the Mt. Spurr (Aleutian Range,
Alaska) volcano. High-
temperature, fumarole gas
samples are prized by volcanic
science, yet their sampling poses
significant challenge. In 1993,
eight volcanologists were killed
in two separate events while
sampling and monitoring
volcanoes.
Using its tether cable anchored at the crater rim, Dante II is able
to descend down sheer crater walls in a rappelling-like manner to
gather and analyze high temperature gasses from the crater floor.
66. Robotics - Sojourner
Oct. 30, 1999 Sojourner on Mars. Powered by a 1.9 square foot
solar array, Sojourner can negotiate obstacles tilted at a 45
degree angle. It travels at less than half an inch per second.
http://antwrp.gsfc.nasa.gov/apod/ap991030.html
67. Robotics – Mars Rover
Tutorial on Rover:
http://marsrovers.jpl.nasa.gov/gallery/video/animation.html
68. Sandstorm
March 13, 2004 - A DARPA Grand Challenge: an unmanned
offroad race, 142 miles from Barstow to Las Vegas.
69. Moving Around and Picking Things Up
Phil, the drug robot, introduced in 2003
71. What Can You Do with an Aibo?
1997 – First official Rob-Cup soccer match
Picture from 2003
competition
72. Robotics - Cog
1998 – now Cog
Humanoid
intelligence requires
humanoid
interactions with the
world.
http://www.eecs.mit.edu/100th/images/Brooks-Cog-Kismet.html
73. At the Other End of the Spectrum - Roomba
2001 A robot
vacuum cleaner
74. Natural Language Processing
1964 STUDENT solves algebra word problems
The distance from New York to Los Angeles is 3000 miles. If the
average speed of a jet plane is 600 miles per hour, find the time it
takes to travel from New York to Los Angeles by jet.
1965 ELIZA models a Rogerian therapist
young woman: Men are all alike.
eliza: In what way?
young woman: They're always bugging us about something
specific or other.
eliza: Can you think of a specific example?
young woman: Well, my boyfriend made me come here.
eliza: Your boyfriend made you come here?
76. NLP, continued
1973 Schank – a richer limited domain: children’s stories
Suzie was invited to Mary’s birthday party. She knew
she wanted a new doll so she got it for her.
1977 Schank – scripts add a knowledge layer – restaurant
stories
1970’s and 80’s sophisticated grammars and parsers
But suppose we want generality? One approach is “shallow”
systems that punt the complexities of meaning.
77. NLP Today
•Grammar and spelling checkers
•Spelling: http://www.spellcheck.net/
•Chatbots
•See the list at:
http://www.aaai.org/AITopics/html/natlang.html#chat/
•Speech systems
•Synthesis: The IBM system:
•http://www.research.ibm.com/tts/coredemo.html
78. Machine Translation: An Early NL
Application
1949 Warren Weaver’s memo suggesting MT
1966 Alpac report kills government funding
Early 70s SYSTRAN develops direct Russian/English system
Early 80s knowledge based MT systems
Late 80s statistical MT systems
79. MT Today
Austin Police are trying to find the person responsible for robbing a
bank in Downtown Austin.
El policía de Austin está intentando encontrar a la persona
responsable de robar un banco en Austin céntrica.
The police of Austin is trying to find the responsible person to rob a
bank in centric Austin.
80. MT Today
A Florida teen charged with hiring an undercover policeman to
shoot and kill his mother instructed the purported hitman not to
damage the family television during the attack, police said on
Thursday.
Un adolescente de la Florida cargado con emplear a un policía de
la cubierta interior para tirar y para matar a su madre mandó a
hitman pretendida para no dañar la televisión de la familia durante
el ataque, limpia dicho el jueves.
An adolescent of Florida loaded with using a police of the inner
cover to throw and to kill his mother commanded to hitman tried not
to damage the television of the family during the attack, clean said
Thursday.
81. MT Today
I have a dream, that my four little children will one day live in a
nation where they will not be judged by the color of their skin but
by the content of their character. I have a dream today – Martin
Luther King
I am a sleepy, that my four small children a day of alive in a
nation in where they will not be judged by the color of its skin but
by the content of its character. I am a sleepy today. (Spanish)
http://www.shtick.org/Translation/translation47.htm
82. Why Is It So Hard?
Sue caught the bass with her new rod.
83. Why Is It So Hard?
Sue caught (the bass) (with her new rod).
84. Why Is It So Hard?
Sue caught the bass with the dark stripes.
85. Why Is It So Hard?
Sue caught (the bass with the dark stripes).
86. Why Is It So Hard?
Sue played the bass with her new bow.
87. Why Is It So Hard?
Sue played the bass with her new bow.
Sue played the bass with her new beau.
94. MT Today
Is MT an “AI complete” problem?
•John saw a bicycle in the store window. He wanted it.
•John saw a bicycle in the store window. He pressed his
nose up against it.
•John saw the Statue of Liberty flying over New York.
•John saw a plane flying over New York.
95. Text Retrieval and Extraction
•Try Ask Jeeves: http://www.askjeeves.com
•To do better requires:
•Linguistic knowledge
•World knowledge
•Newsblaster: http://www1.cs.columbia.edu/nlp/newsblaster/
96. Programming Languages
1958 Lisp – a functional programming language with a
simple syntax.
(successor SitA ActionP)
1972 PROLOG - a logic programming language whose
primary control structure is depth-first search
ancestor(A,B) :- parent(A,B)
ancestor(A,B) :- parent(A,P), ancestor(P,B)
1988 CLOS (Common Lisp Object Standard) published.
Draws on ideas from Smalltalk and semantic nets
97. Cognitive Modeling
Symbolic Modeling
1957 GPS
1983 SOAR
Neuron-Level Modeling
McCulloch Pitts neurons: all or none response
More sophisticated neurons and connections
More powerful learning algorithm
98. Making Money – Software
•Expert systems to solve problems in particular domains
•Expert system shells to make it cheaper to build new systems
in new domains
•Language applications
•Text retrieval
•Machine Translation
•Text to speech and speech recognition
•Data mining
99. Making Money - Hardware
1980 Symbolics founded
1986 Thinking Machines introduces the Connection Machine
1993 Symbolics declared bankruptcy
Symbolics 3620 System c 1986:
Up to 4 Mwords (16 Mbytes)
optional physical memory, one
190 Mbyte fixed disk, integral
Ethernet interface, five backplane
expansion slots, options include
an additional 190 Mbyte disk or
1/4'' tape drive, floating point
accelerator, memory, RS232C
ports and printers.
100. Making Money - Robots
1962 Unimation, first industrial
robot company, founded. Sold a
die casting robot to GM.
1990 iRobot founded, a spinoff
of MIT
2000 The UN estimated that
there are 742,500 industrial robots
in use worldwide. More than half
of these were being used in Japan.
2001 iRobot markets Roomba
for $200.
106. Today: Computer as Artist
Two paintings done by Harold Cohen’s Aaron program:
107. Why AI?
"AI can have two purposes. One is to use the power of
computers to augment human thinking, just as we use
motors to augment human or horse power. Robotics
and expert systems are major branches of that. The
other is to use a computer's artificial intelligence to
understand how humans think. In a humanoid way. If
you test your programs not merely by what they can
accomplish, but how they accomplish it, they you're
really doing cognitive science; you're using AI to
understand the human mind."
- Herb Simon
Editor's Notes
But there’s still a fear: how would we eat?
Picture from http://www.fourmilab.ch/babbage/hpb1910.html
Herbert A. Simon and Allen Newell, "Heuristic Problem Solving: The Next Advance in Operations Research," Operations Research, January-February 1958, 1-10.
http://www.jabberwacky.com 2003 Loebner bot winner, beaten by two people
http://www.alicebot.org/
Note here that the language processing seems fine. It’s a lack of specific knowledge that is killing Alice.
Is this AI? Is it just search? Is chess a representative “intelligent problem”? What about role playing games? Need a lot more knowledge.
Parses: read bottom up. It gets worse at the end. 8064 total. With huge branches: tree, ground, dragging, wheel, wagon, girl, boy and girl, saw (8) under a tree can attach to ground, dragging, wheel, wagon, girl, boy and girl, saw. (7) On the ground (6) Dragging: wheel, wagon, girl, boy and girl (4) And: (one blue and one white) wheel, one blue and (one white wheel) (2) With a red wagon: girl, boy and girl, saw (3) How hard to check each? Current approaches: use statistics to guess right a lot of the time. But note that this one is ambiguous even for people.
From Church and Patil, 1982, via http://www.cogs.susx.ac.uk/lab/nlp/gazdar/teach/nlp/nlpnode11.html Binomial coefficients: (a b) = a!/(b!(a-b)!) Cat(n) is the number of ways to parenthesize an expresssion of length n with two conditions: 1) Equal number of open and closes. That’s the first term. 2) They must be properly nested. That’s the second term, so it subtracts out the improper ones.
Maybe the problem is representation. Can we punt search if we have trained a neural net to simply do the right thing? Maybe but now we have to search in a different space, the space of all neural nets.
Neural net picture from http://hem.hj.se/~de96klda/NeuralNetworks.htm#2.1.2%20The%20Artificial%20Neuron
Sandstorm is a modified Humvee. It cost $3M. The prize for the grand challenge is $1M. Sandstorm made it 9 miles. 15 teams entered. Sandstorm got the farthest of all: At mile 7.4, on switchbacks in a mountainous section, vehicle went off course, got caught on a berm and rubber on the front wheels caught fire, which was quickly extinguished. Vehicle was command-disabled.
Doctors will write the equivalent of 10 prescriptions for every person in the country, and as the population ages and drugs are more aggressively marketed, the numbers are growing. A shortage of pharmacists needed to keep up with the demand hit its peak two years ago, but it still exists in some areas. There are about 5,000 open pharmacy positions nationwide. And because of the paperwork and overwork associated with the enormous volume of prescriptions, mistakes still are being made. Most estimates put the number of mistakes at about 4 percent. Still, that represents more than 100-million mistakes a year - sometimes with tragic consequences.
Why build machines to play games? That’s what we want to do. But what about knowledge-intensive yet boring tasks? BabelFish
Using BabelFish babelfish.altavista.com Going both ways is particularly difficult since the first pass may create some nonstandard prose, which may make sense to people but be really hard to translate.
What does bass mean? What role is the with phrase playing? To what does it attach? (see next slide)
With is the instrument and attaches to caught
With is an attribute and attaches see next slide to bass
With is an attribute and attaches to bass
Now the meaning of bass changes.
But, if we’re speaking, how do we differentiate these two?
Assuming we don’t mean Olive Oil
Assuming we don’t mean Olive Oil
Jacket used for riding
Please go buy some baby oil.
What’s the weather like in Austin? Who invented the computer? Will computers ever be smarter than people?
http://www.robotics.utexas.edu/rrg/learn_more/history/#firstrobot http://news.bbc.co.uk/1/hi/in_depth/sci_tech/2001/artificial_intelligence/1531432.stm (UN estimate)
A Commemorative Certificate for ascending the great wall in China. (input) I have ascended the great wall (output)