TS04DanielaSilva CAPTCHA_TuringTest

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Apresentação CE-237

CAPTCHA, Innovations and Turing

Test

Prof. Dr. Adilson Marques da Cunha Prof. Dr. Luiz Alberto Vieira Dias

Prepared By: Daniela América da Silva

CE-230/CE-235/CE-237/CE-65 São José dos Campos

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Agenda

• What is CAPTCHA ?

• How CAPTCHA works

• CAPTCHA Types

• Breaking CAPTCHA

• CAPTCHA and Human Computation

• Human Computation Tools – Duolingo

• Invisible reCAPTCHA

• Amazon AI

• Other AI Examples

• Turing Test

• Turing Test – Contrary Views

• Turing Test – Arguments

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CAPTCHA

•CAPTCHA

– USUAL - A Program that can tell whether its user is a

HUMAN or A COMPUTER – ANOTHER DEFINITION

•A PROGRAM that can generate and grade tests that?

–Most humans can pass

–Current computer programs cannot pass •Program will?

–Gets a random strings of letters –Renders it into a distorted image

–And generate a test related to have the user typing the characters that appears in the image.

•CHALLENGE How to trust online pools if someone could vote thousands of times ?

•APPLICATIONS free email services, data collection, worms

and spam, preventing attacks

https://

www.youtube.com/watch?v=tx 082gDwGcM

Luis von Ahn of Carnegie Mellon University is one of the inventors of CAPTCHA.

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CAPTCHA – Technical details

•It is more than a distorced test that can be automatically generated and most human can pass but computer programs can not.

•PARODOX: CAPTCHA is a program that can generate and grade tests that it itself can not pass.

•It stands for: Completely Automated Public Turing Test to Tell Computers and Humans Apart.

•CAPTCHAs are Turing Tests. In the original Turing Test a human judge was allowed to ask a series of questions to 2 players, one of which was a

computer and the other a human. So CAPTCHAs are similar but they differ in that the judge is a computer. It is avoied the Reserve Turing Test because if is used when the two players pretend to be a computer.

•Original Turing test served as a measure for Artificial Intelligence – A computer would be said to be inteligente if it passed the Turing Test.

•The original Turing Test was conversational and the judge was only allowed to ask questions over a terminal. In the case of CAPTCHA the computer judge can ask any question that can be transmitted over a computer network.

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CAPTCHA – Types

•GYMPY and OCR Based. Consists of distorced image and directions e.g. type the three words appearing in the image

http://www.captcha.net/captcha_cacm.pdf •Bongo. Based on Pattern Recognition Problems.

Ask the user to solve a visual pattern recognition problem. The blocks in the left differs from the blocks in the right and the user must find the characteristics that set them apart. For the first figure please notice that the left side the lines are stronger than in the right side, In the second figure to which side does the block in the bottom belongs to.

•Pix. It has a large database of labeled images.

Pictures of concrete objects. (e.g. a horse , a table, a house, a flower). PIX itself is not a CAPTCHA since CAPTCHA shoudl use public images and then

asnwer the question what are these pictures is easy. It becomes a CAPTCHA if it randmly distort the

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CAPTCHA – Types

•Sound-based CAPTCHAS. It renders the word or numbers into a sound clip and it presents the distorced sound clip to the user and ask user to enter its contents. Based on the

difference in hability between humans and computers in recognizing spoken language. •Note. It is importante to have CAPTCHAs based on a variety of sensory abilities. Most of

CAPTCHAS presented rely on user being able to see an image. However since there are many visually impared people using the Web. CAPTCHAS based on sound are necessary for accessibility.

https://captcha.com/articles/audio-captcha.html

No Brasil, existem mais de 6,5 milhões

de pessoas com deficiência visual,

sendo 582 mil cegas e seis milhões com

baixa visão, segundo dados da fundação

com base no Censo 2010, feito pelo

Instituto Brasileiro de Geografia e

Estatística (IBGE).

http://

www.brasil.gov.br/cidadania-e-justica/2015/01/braile -aumenta-inclusao-de-cegos-na-sociedade

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•Marik and Moris algorithm (Recognizing objects in adversarial clutter – breaking a visual CAPTCHA)

–Greg Mori and Jitendra Malik published a paper detailing their approach to cracking the Gimpy version of CAPTCHA. One thing that helped them was that the Gimpy approach uses actual words rather than random strings of letters and numbers. With this in mind, Mori and Malik designed an algorithm that tried to identify words by examining the

beginning and end of the string of letters. They also used the Gimpy's 500-word dictionary.

–Mori and Malik ran a series of tests using their algorithm. They found that their algorithm could correctly identify the words in a Gimpy CAPTCHA 33 percent of the time [source:

Mori and Malik]. While that's far from perfect, it's also significant. Spammers can afford to have only one-third of their attempts succeed if they set bots to break CAPTCHAs

several hundred times every minute.

CAPTCHA – Breaking it

CAPTCHA implies a win-win situation – either the CAPTCHA is not broken and there is a way to differentiate humans from

computers, or the CAPTCHA is broken and a useful AI problem is solved.

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Human Computation 2002/2011 -

von Ahn, L (reCAPTCHA)

•He builds systems that combine humans and computers to solve large-scale problems that neither can solve

alone.

Power processing to solve problems that computers can not solve E.g. Labelling images with words. Example of search images in the web: query dog and will use filename to find the pictures.

•Majority of the web is not accessible for the visual impaired. Reason most images do not have a caption. •The ESP Game – could label all images in google in one

week. Object of the game: type the same word, only thing in common is a image. Purpose: Labbeling the entire web, 5000 people playing simultaneously can label all images in google in 2 months.

•OCR – it can not recognize words if pages turns yellow. •reCAPTCHA - Everytime you type a captcha it comes from

a book digitilized, then you get a word digitized properly. This was called reCAPTCHA.

https://

www.youtube.com/watch?v=t x082gDwGcM

https://www.youtube.com/watch ?v=cQl6jUjFjp4

In 10s your brain is doing

something that computer can not do.

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•Web partitioned on different languages •Existing softwares translates with a lot of

inaccuracies.

•How to motivate people to do it for free •Transform language translation in

something that many people would like to do. And not forced to do.

•Learning by doing

Duolingo 2011 - von Ahn, L

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Duolingo 2011 - von Ahn, L

https://www.youtube.com/watch?v=cQl6jUjFjp4 Why you learn you create value, and you do not pay with Money, but you pay with your time. Fair business model.

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•https://www.google.com/recaptcha/intro/invisible.html

•https://developers.google.com/recaptcha/docs/versions

•https://www.youtube.com/watch?time_continue=1&v=GeibaHfYW9o

Invisible reCaptcha

Service would rely on techniques like noticing how you move an onscreen pointer or analyzing your browsing habits to determine whether you are human or robot. Robots, for example, are more likely to click right in the middle of the box. As well as any past data

Google holds on you. This is called Invisible reCAPTCHA. If you seem suspicious (perhaps you are in fact a robot), you'll see one of the old reCAPTCHA challenges to solve as further verification[source: Titcomb].

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Amazon AI

Webinar AI -> "Artificial Intelligence Day“ https

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Amazon Services

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Amazon Recognition

://aws.amazon.com/pt/blogs/aws/aws-partner-webinar-series-september-october-2017/

Label Detection Face and Feelings

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Amazon Recognition - Customers

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Amazon Voice Services - Polly

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Amazon Voice Services - Lex

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Rise Conference – Co-Work with AI

Hong Kong 2018

• Focused in Startups -> https://riseconf.com/?

utm_source=facebook&utm_medium=social&utm_campaign=profilelink

https://www.facebook.com/RISEConfHQ/videos/835838516572661/

Challenges that machines can

not be creative, and proposes

co-work with AI

Project: AI to create creative directors, e.g. create the format about a thousand of TV comercials determining how a political series could be successfull.

Get client questions: claim, target, promotions, and then afterwards AI will come with suggestions of

series/movies that could be made.

Eg. Create the comercials with AI suggestions and other create by human.

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Rise Conference – Co-work with AI

Hong Kong 2018

•Two comercials: Lady one done by person and Dog one done by AI produced to Mondelez

•Work transition from operative to creative

https://www.facebook.com/RISEConfHQ/videos/835838516572661/

Co-work with AI

Meditation using Robots. It will speak like a meditation teacher and it will lead people meditation.

Comercial done by Human

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Other AI Examples - Random Computer

Science Paper Generator

Random Computer Science Paper Generator

https://pdos.csail.mit.edu/archive/scigen/

Adobe Acrobat Document

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Other AI Examples – Analyze Fake

Reviews

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• This question begins Alan Turing’s paper

‘Computing Machinery and Intelligence’ (1950).

Can Machines Think?

http://www.loebner.net/Prizef/TuringArticle.html,

http

://www.jackhoy.com/artificial-intelligence/2015/03/22/summary-of-computing-machinery-and-intelligence-a lan-turing.html

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•Turing found the form of the question unhelpful, that even the process of defining ‘machines’ and ‘think’ in common terms would be

dangerous, as it could mistakenly lead one to think the answer can be obtained from some kind of statistical survey.

•Instead, he replaced the question with ‘The Imitation Game’ - a game of two rooms. In one sits a man (A) and a woman (B). In the other sits an interrogator (C) who must determine which of the other two is the man and which is the woman through a series of

typewritten communication [1].

–A’s objective is to cause C to make the incorrect identification

–B’s objective is to help C to make the correct identification.

•Turing noted that the best strategy for the woman (B) is probably to give truthful answers, adding things like “I am the woman, don’t listen to him!” to her answers, but this alone will not be enough as the man (A) can make similar remarks.

•He then reframed the original question as ‘What happens when a machine takes the role of A?’ Will the interrogator still decide incorrectly as many times if the role is performed by a machine?

Can Machines Think?

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Can Machines Think? – Contrary

Views

Objections Argument: Turing: Theological Objection Thinking is a function of man’s

immortal soul. Hence no animal or

machine can think.

Turing could not accept any part of this argument and rejected the idea that

thinking machines would usurp God’s power of creating souls Head in the Sand

Objection The consequences of machines thinking would be too dreadful He claimed this argument would likely be quite strong in intellectual people,

since they value the power of

thinking more highly than others, and

are more inclined to base their belief in the superiority of Man on this power.

Mathematical

Objection There are a number of results of mathematical logic which can be used to show that there are

limitations to the powers of

discrete-state machines. The best

known of these results is known as Godel’s theorem (1931) and shows that in any sufficiently powerful logical system, statements can be

formulated which can neither be proved nor disproved within the system.

He acknowledged that there are limits to the powers of any machine but saw no proof that these limits do not also apply to human intellect and

expected that those holding the

mathematical argument would be willing to accept ‘The Imitation Game’ as a basis for discussion, unlike the previous two arguments.

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Can Machines Think? – Contrary

Views

Objections Argument: Turing: Argument from

Consciousness A machine cannot match a brain until it is conscious, moved to act

by it’s own thoughts and emotions.

According to this view, the only way to know that a man thinks is to be that particular man. He noted it may be the most logical view to hold but it makes communication of ideas difficult. A is liable to believe “A thinks but B does not” whilst B believes “B thinks but A does not.” instead of arguing

continually over this point it is usual to have the polite convention that everyone thinks. He accepted a certain mystery about the nature of consciousness but did not see it

having an impact on his question.

Arguments from Various Disabilities

Machines may do all the things you have mentioned but you will never

be able to make one to do X.” (Insert: fall in love, have a sense of humour etc.)

Naturally they conclude this is true of all machines. Turing believed that

diversity in behaviour would directly increase with storage capacity which was growing exponentially.

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Can Machines Think? – Contrary

Views

Objections Argument: Turing: Lady Lovelace’s

Objection Lady Lovelace’s memoir stated that Babbage’s Analytical Engine had no pretensions to originate any behaviour of it’s own.

Turing agreed with Hartree’s (1949) analysis that despite the fact machines constructed or projected at the time did not seem to have this property, it does

not imply the impossibility of

constructing electronic equipment that will ‘think for itself’.

Continuity in the Nervous System

The nervous system is certainly not a discrete-state machine. A

small error in the information about the size of a nervous impulse

impinging on a neuron, may make a large difference to the size of the outgoing impulse, [therefore] one

cannot expect to be able to mimic the behaviour of the nervous system with a discrete-state system.”

He agreed that a discrete-state

machine must be different from a continuous machine. But in adhering

to the conditions of ‘The Imitation

Game’, the interrogator will not be able to take any advantage of this

difference, that it would be possible

to sufficiently mimic continuous outputs where required.

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Can Machines Think? – Contrary

Views

Objections Argument: Turing: Informality of

Behaviour It is not possible to produce a set of rules purporting to describe what a

man should do in every conceivable set of

circumstances.

Turing reformed this argument as ‘if

men acted based on rules they would be no better than machines but there are no such rules, so men cannot be machines’.

Extrasensory Perception

Unlike machines, humans possess Extrasensory Perception (viz.,

telepathy, clairvoyance,

precognition and psychokinesis).

He acknowledged this was not much comfort and that the domain of

thinking is one where ESP may be

especially relevant. If telepathy is

admitted, the test may need to be modified to use a ‘telepathy-proof room’.

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•Consider the human mind to be like the skin of an onion, in each layer we find

mechanical operations that can be explained in mechanical terms but we say these layers do not correspond to the real mind - if that is true then where is it to be found? Do we ever peel back an onion layer and find the real mind?

•The only really satisfactory support for thinking machines will be provided by waiting for the end of the century and then playing ‘The Imitation Game’.

•The problem is mainly one of programming, rather than a engineering or data storage problem. Estimates of the storage capacity of the brain vary from 10^10 to 10^15 binary digits. Only a very small fraction is used for the higher types of thinking. Most of it likely

used for the retention of visual impressions. It would be surprising if more than 10^9 was required for satisfactory playing of ‘The Imitation Game’.

•Think about the process which has brought about the adult mind:The initial state of the mind (birth)

–The education to which it has been subjected

–Other experience, not to be described as education, to which it has been subjected –Why not separate the problem into two, first create a child’s brain, then educate it.

Through experimentation of the machine and teaching methods you could emulate an evolutiontionary process.

Can Machines Think? – Turing

Arguments

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Can Machines Think? – Turing

Arguments

•Regulating the order in which the rules of the logical system are applied is important as at each stage there would be a very large number of valid alternative steps. The choices mean the difference between a brilliant and a poor reasoner.

•An important feature of a learning machine is that its teacher will often be largely ignorant of what is going on inside, although he may still be able to some extent to predict his pupil’s behavior.

• This is in clear contrast with normal procedure when using a machine to do computations one’s object is then to have a clear mental picture of the state of the machine at each moment in the computation. Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation.

•It is probably wise to include a random element in a learning machine. A random element is rather useful when we are searching for a solution of some problem. A systematic

method has the disadvantage that there may be an enormous block without any solutions in the region which has to be investigated first. The learning process may be regarded as a search for a form of behaviour which will satisfy the teacher (or some other criterion). Since there is probably a very large number of satisfactory solutions the random

method seems to be better than the systematic. It should be noticed that it is used in the analogous process of evolution. But there the systematic method is not possible. How could one keep track of the different genetical combinations that had been tried, so as to avoid trying them again?

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How to Test AI ?

• PROBLEM: Neural network does not know how the machine learned • OPTIONS:

 Benchmarking with other algorithms  Neural networks - Black box test

 Open work ... There are not many tests in this area and/or suggestion to tests algorithms in artificial intelligence

 Data Flow - advantage, you look at the program, do not have to execute it and can detect if it is right or wrong.

• LITERATURE:

 Sandra Rapps  Elaine Weyuker

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