Here are three possible interpretations of the phrase "Time flies like an arrow":
1. The passage of time seems to go by very quickly, in the same way that an arrow flies through the air.
2. Certain types of insects that lay their eggs on decaying matter, known as flies, move through the air in a similar way to arrows.
3. The idiom is using "flies" to refer to time passing quickly in an abstract sense, similar to an arrow moving swiftly through space.
The key challenges with natural language understanding are ambiguity and context. Even a short phrase like this one could have multiple meanings without additional context clues. Determining the intended interpretation requires commonsense reasoning abilities that computers still lack
This is used for brief talk about AI and its recent application in Machine Learning and Deep Learning field.
I'd like to ask your understanding about any missing references.
I appreciate you would comment about it.
I will immediately update the slides.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on faces.
The Turing Test - A sociotechnological analysis and prediction - Machine Inte...piero scaruffi
The 'singularity" may be near not because we are making smarter machines but because we are making dumber humans. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
This is used for brief talk about AI and its recent application in Machine Learning and Deep Learning field.
I'd like to ask your understanding about any missing references.
I appreciate you would comment about it.
I will immediately update the slides.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on faces.
The Turing Test - A sociotechnological analysis and prediction - Machine Inte...piero scaruffi
The 'singularity" may be near not because we are making smarter machines but because we are making dumber humans. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
The Relationship Of AI With Human Thinking.pptxssusere5168c
Do you believe you make all your decisions on your own? Not, even as I am writing this blog. Artificial Intelligence (AI) may shape our judgments. We make several decisions daily, such as where to go, what to eat, where to shop, what to read, and so on. This helps humans in all aspects of their daily lives. You might listen to someone or browse for reviews to help you decide. To make a choice. But what if the information is biased?
The Relationship Of AI With Human Thinking.pdfVijayRout1
Can Artificial Intelligence Replace Our Friends And Teachers?
Artificial Intelligence (AI) is being used to help people make decisions. It collects and analyses huge amounts of data and provides us with conclusions. AI not only allows us to direct, purchase, and so on. But it also allows us to make more critical decisions about our current societal benefits, such as medical treatment, verdicts, health insurance, and so on.
Tahoe Silicon Mountain, a network of technology professionals who live and work in the Tahoe-Truckee area, is pleased to welcome Gunnar Newquist to present: “Improving Artificial Intelligence by Studying the Brain”
Current artificial intelligence (AI) only works with big data and lots of processing power, and it only delivers a solution for the particular problem for which it was designed. The next revolution in AI, called artificial general intelligence, is for a machine to learn, plan, and acquire new skills in complex environments.
Gunnar Newquist, CEO of Brain2Bot, received his Ph.D. in Molecular Neuroscience at the University of Nevada, Reno and went on to found Brain2Bot, which is using principles of intelligence derived from neuroscience to build artificial general intelligence.
Newquist will discuss how neuroscience can help us deliver AI that is both safe and effective and how some of the biggest advances in AI have been inspired by advances in neuroscience.
You can learn more about Newquist and his business here: www.brain2bot.com
The meeting will be on Monday, October 10th, 6-8 pm at Pizza on the Hill, in Tahoe Donner at 11509 Northwoods Blvd., Truckee. A $5 fee includes pizza and salad. Before and after the presentation, there will be time for networking with other technology professionals who live and work in the Tahoe-Truckee region.
The event will also be livestreamed and available online as it happens on YouTube: bit.ly/YouTubeTSM
This month’s event is sponsored by New Leaders, Clear Capital and Holland & Hart LLP.
You can find us on LinkedIn and Facebook and at TahoeSiliconMountain.com or sign up for email meeting announcements here: bit.ly/14XGofL.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
2. Why taking AI could change your life…..
• As we begin the new millennium
– science and technology are changing rapidly
– “old” sciences such as physics are relatively well-
understood
– computers are ubiquitous
• Grand Challenges in Science and Technology
– understanding the brain
• reasoning, cognition, creativity
– creating intelligent machines
• is this possible?
• what are the technical and philosophical challenges?
– arguably AI poses the most interesting challenges and
questions in computer science today
3. What is Intelligence?
• Intelligence:
– “the capacity to learn and solve problems” (Websters
dictionary)
– in particular,
• the ability to solve novel problems
• the ability to act rationally
• the ability to act like humans
• Artificial Intelligence
– build and understand intelligent entities or agents
– 2 main approaches: “engineering” versus “cognitive
modeling”
4. Computers versus humans
• A computer can do some things better than a
human can
– Adding a thousand four-digit numbers
– Drawing complex, 3D images
– Store and retrieve massive amounts of data
• However, there are things humans can do
much better.
6. Computer or human?
• Which of the following occupations could (or
should) be performed by computers?
– Postman
– Bookstore clerk
– Librarian
– Doctor
– Lawyer
– Judge
– Professor
7. What’s involved in Intelligence?
• Ability to interact with the real world
– to perceive, understand, and act
– e.g., speech recognition and understanding and synthesis
– e.g., image understanding
– e.g., ability to take actions, have an effect
• Reasoning and Planning
– modeling the external world, given input
– solving new problems, planning, and making decisions
– ability to deal with unexpected problems, uncertainties
• Learning and Adaptation
– we are continuously learning and adapting
– our internal models are always being “updated”
• e.g., a baby learning to categorize and recognize animals
8. Our Working Definition of AI
Artificial intelligence is the study of how to make
computers do things that people are better at or would
be better at if:
• they could extend what they do to a World Wide
Web-sized amount of data and
• not make mistakes.
9. First things first…
• Of course, first we have to understand why
we use the term “intelligence” in regard to
humans.
– What defines “intelligence”?
– Why is it that we assume humans are intelligent?
– Are monkeys intelligent? Dogs? Ants? Pine
trees?
10. Early History
• In 1950 English mathematician Alan Turing wrote a
landmark paper titled “Computing Machinery and
Intelligence” that asked the question: “Can
machines think?”
• Further work came out of a 1956 workshop at
Dartmouth sponsored by John McCarthy. In the
proposal for that workshop, he coined the phrase a
“study of artificial intelligence”
11. Can Machines Think?
• So Turing asked: “Can machines think?” He
felt that such machines would eventually be
constructed.
• But he also realized a bigger problem. How
would we know if we’ve succeeded?
12. 13
The Turing Test
Turing test
A test to empirically determine whether a
computer has achieved intelligence
Figure 13.2
In a Turing test, the
interrogator must
determine which
respondent is the computer
and which is the human
13. 14
The Turing Test
•Passing the Turing Test does not truly show that the
machine was thinking. It simply shows that it
generated behavior consistent with thinking.
•weak equivalence: the two systems (human and
computer) are equivalent in results (output), but they
do not necessarily arrive at those results in the same
way
•Strong equivalence: the two systems use the same
internal processes to produce results
14. 15
Knowledge Representation
•We want to compare the way that computers
and humans work to see if we can better
understand why each have their
(computational) strengths.
– Processing Models
– Knowledge Representation
– Reasoning
15. 16
Neural Network
A biological neuron
Neuron
A single cell that conducts a chemically-based electronic signal, it has a lot of tentacles.
At any point in time a neuron is in either an excited state or an inhibited state
Excited state
Neuron conducts a strong signal
Inhibited state
Neuron conducts a weak signal
16. Can we build hardware as complex as the brain?
• How complicated is our brain?
– a neuron, or nerve cell, is the basic information processing unit
– estimated to be on the order of 10 12
neurons in a human brain
– many more synapses (10 14
) connecting these neurons
– cycle time: 10 -3
seconds (1 millisecond)
• How complex can we make computers?
– 108
or more transistors per CPU
– supercomputer: hundreds of CPUs, 1012
bits of RAM
– cycle times: order of 10 -9
seconds
• Conclusion
– YES: in the near future we can have computers with as many basic
processing elements as our brain, but with
• far fewer interconnections (wires or synapses) than the brain
• much faster updates than the brain
– but building hardware is very different from making a computer
behave like a brain!
17. What About Things that People Do Easily?
• Common sense reasoning
• Vision
• Moving around
• Language
18. What About Things People Do Easily?
• If you have a problem, think of a past situation where you
solved a similar problem.
• If you take an action, anticipate what might happen next.
• If you fail at something, imagine how you might have done
things differently.
• If you observe an event, try to infer what prior event might
have caused it.
• If you see an object, wonder if anyone owns it.
• If someone does something, ask yourself what the person's
purpose was in doing that.
19. They Require Knowledge
•Why do we need it?
•How can we represent it and use it?
•How can we acquire it?
Find me stuff about dogs who save people’s lives.
20. Why?
•Why do we need it?
•How can we represent it and use it?
•How can we acquire it?
Find me stuff about dogs who save people’s lives.
Two beagles spot a fire.
Their barking alerts
neighbors, who call 911.
21. Even Children Know a Lot
A story described in Charniak (1972):
Jane was invited to Jack’s birthday party. She wondered if he
would like a kite. She went into her room and shook her piggy
bank. It made no sound.
22. We Divide Things into Concepts
• What’s a party?
• What’s a kite?
• What’s a piggy bank?
37. How Can We Teach Things to Computers?
A quote from John McCarthy:
In order for a program to be capable of learning something, it
must first be capable of being told it.
Do we believe this?
38. Some Things are Easy
If dogs are mammals and mammals are animals, are dogs
mammals?
39. Some Things Are Harder
If most Canadians have brown eyes, and most brown eyed people
have good eyesight, then do most Canadians have good eyesight?
40. Some Things Are Harder
If most Canadians have brown eyes, and most brown eyed people
have good eyesight, then do most Canadians have good eyesight?
Maybe not for at least two reasons:
It might be true that, while most brown eyed people have good
eyesight, that’s not true of Canadians.
Suppose that 70% of Canadians have brown eyes and 70% of
brown eyed people have good eyesight. Then assuming that
brown-eyed Canadians have the same probability as other brown-
eyed people of having good eyesight, only 49% of Canadians are
brown eyed people with good eyesight.
41. Can Computers beat Humans at Chess?
• Chess Playing is a classic AI problem
– well-defined problem
– very complex: difficult for humans to play well
• Conclusion:
– YES: today’s computers can beat even the best human
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
1966 1971 1976 1981 1986 1991 1997
Ratings
Human World Champion
Deep Blue
Deep Thought
PointsRatings
42. Can Computers Talk?
• This is known as “speech synthesis”
– translate text to phonetic form
• e.g., “fictitious” -> fik-tish-es
– use pronunciation rules to map phonemes to actual sound
• e.g., “tish” -> sequence of basic audio sounds
• Difficulties
– sounds made by this “lookup” approach sound unnatural
– sounds are not independent
• e.g., “act” and “action”
• modern systems (e.g., at AT&T) can handle this pretty well
– a harder problem is emphasis, emotion, etc
• humans understand what they are saying
• machines don’t: so they sound unnatural
• Conclusion:
– NO, for complete sentences
– YES, for individual words
43. Can Computers Recognize Speech?
• Speech Recognition:
– mapping sounds from a microphone into a list of words
– classic problem in AI, very difficult
• “Lets talk about how to wreck a nice beach”
• (I really said “________________________”)
• Recognizing single words from a small vocabulary
• systems can do this with high accuracy (order of 99%)
• e.g., directory inquiries
– limited vocabulary (area codes, city names)
– computer tries to recognize you first, if unsuccessful hands you over to a
human operator
– saves millions of dollars a year for the phone companies
44. Recognizing human speech (ctd.)
• Recognizing normal speech is much more difficult
– speech is continuous: where are the boundaries between words?
• e.g., “John’s car has a flat tire”
– large vocabularies
• can be many thousands of possible words
• we can use context to help figure out what someone said
– e.g., hypothesize and test
– try telling a waiter in a restaurant:
“I would like some dream and sugar in my coffee”
– background noise, other speakers, accents, colds, etc
– on normal speech, modern systems are only about 60-70% accurate
• Conclusion:
– NO, normal speech is too complex to accurately recognize
– YES, for restricted problems (small vocabulary, single speaker)
45. Can Computers Understand speech?
• Understanding is different to recognition:
– “Time flies like an arrow”
• assume the computer can recognize all the words
• how many different interpretations are there?
46. Can Computers Understand speech?
• Understanding is different to recognition:
– “Time flies like an arrow”
• assume the computer can recognize all the words
• how many different interpretations are there?
– 1. time passes quickly like an arrow?
– 2. command: time the flies the way an arrow times the flies
– 3. command: only time those flies which are like an arrow
– 4. “time-flies” are fond of arrows
47. Can Computers Understand speech?
• Understanding is different to recognition:
– “Time flies like an arrow”
• assume the computer can recognize all the words
• how many different interpretations are there?
– 1. time passes quickly like an arrow?
– 2. command: time the flies the way an arrow times the flies
– 3. command: only time those flies which are like an arrow
– 4. “time-flies” are fond of arrows
• only 1. makes any sense,
– but how could a computer figure this out?
– clearly humans use a lot of implicit commonsense knowledge in
communication
• Conclusion: NO, much of what we say is beyond the
capabilities of a computer to understand at present
48. Can Computers Learn and Adapt ?
• Learning and Adaptation
– consider a computer learning to drive on the freeway
– we could teach it lots of rules about what to do
– or we could let it drive and steer it back on course when it heads for the
embankment
• systems like this are under development (e.g., Daimler Benz)
• e.g., RALPH at CMU
– in mid 90’s it drove 98% of the way from Pittsburgh to San Diego without any
human assistance
– machine learning allows computers to learn to do things without explicit
programming
– many successful applications:
• requires some “set-up”: does not mean your PC can learn to forecast the stock
market or become a brain surgeon
• Conclusion: YES, computers can learn and adapt, when presented with
information in the appropriate way
49. • Recognition v. Understanding (like Speech)
– Recognition and Understanding of Objects in a scene
• look around this room
• you can effortlessly recognize objects
• human brain can map 2d visual image to 3d “map”
• Why is visual recognition a hard problem?
Conclusion:
– mostly NO: computers can only “see” certain types of objects under limited
circumstances
– YES for certain constrained problems (e.g., face recognition)
Can Computers “see”?
50. Can computers plan and make optimal decisions?
• Intelligence
– involves solving problems and making decisions and plans
– e.g., you want to take a holiday in Brazil
• you need to decide on dates, flights
• you need to get to the airport, etc
• involves a sequence of decisions, plans, and actions
• What makes planning hard?
– the world is not predictable:
• your flight is canceled or there’s a backup on the 405
– there are a potentially huge number of details
• do you consider all flights? all dates?
– no: commonsense constrains your solutions
– AI systems are only successful in constrained planning problems
• Conclusion: NO, real-world planning and decision-making is still beyond the capabilities
of modern computers
– exception: very well-defined, constrained problems
51. Summary of State of AI Systems in Practice
• Speech synthesis, recognition and understanding
– very useful for limited vocabulary applications
– unconstrained speech understanding is still too hard
• Computer vision
– works for constrained problems (hand-written zip-codes)
– understanding real-world, natural scenes is still too hard
• Learning
– adaptive systems are used in many applications: have their limits
• Planning and Reasoning
– only works for constrained problems: e.g., chess
– real-world is too complex for general systems
• Overall:
– many components of intelligent systems are “doable”
– there are many interesting research problems remaining
52. Intelligent Systems in Your Everyday Life
• Post Office
– automatic address recognition and sorting of mail
• Banks
– automatic check readers, signature verification systems
– automated loan application classification
• Customer Service
– automatic voice recognition
• The Web
– Identifying your age, gender, location, from your Web surfing
– Automated fraud detection
• Digital Cameras
– Automated face detection and focusing
• Computer Games
– Intelligent characters/agents
53. AI Applications: Machine Translation
• Language problems in international business
– e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no common
language
– or: you are shipping your software manuals to 127 countries
– solution; hire translators to translate
– would be much cheaper if a machine could do this
• How hard is automated translation
– very difficult! e.g., English to Russian
– “The spirit is willing but the flesh is weak” (English)
– “the vodka is good but the meat is rotten” (Russian)
– not only must the words be translated, but their meaning also!
– is this problem “AI-complete”?
• Nonetheless....
– commercial systems can do a lot of the work very well (e.g.,restricted vocabularies in software
documentation)
– algorithms which combine dictionaries, grammar models, etc.
– Recent progress using “black-box” machine learning techniques
Editor's Notes
Try friend in Wordnet
From Push Singh Open Mind paper
Two beagles spot fire. Their barking alerts neighbors who call the police.