Exploring Concepts in Artificial Intelligence · Exploring Concepts in Arti cial Intelligence Sonit...

Exploring Concepts in Artificial Intelligence

Sonit Singh

Macquarie University

July 13, 2018

Many of these slides in this workshop are either from or adapted from slides of workshop on

”Artificial Intelligence for Teachers” for the year 2016 provided by its original author Diego

Molla-Aliod http://web.science.mq.edu.au/ diego/pub/itteacher/

Sonit Singh Macquarie University


About me: Sonit Singh

Education

Pursing PhD in Applying Deep Learning Techniques in Languageand Vision

Master of Research in NLP and Machine Learning (2017),Macquarie University

Research Interests

Computer Vision and Natural Language Processing

Machine Learning, Deep Learning and Data Analytics

Knowledge Bases, Artificial Intelligence and CommonsenseReasoning

Signal, Image and Video Processing



Artificial Intelligence (AI)

If human beings can thinkthen why not machines?

What is Artificial Intelligence?



Defining AI

Wikipedia (18 September, 2017)

Artificial intelligence (AI, also machine intelligence, MI) is intelligence exhibited

by machines, rather than humans or other animals (natural intelligence, NI). In

computer science, the field of AI research defines itself as the study of ”intelli-

gent agents”: any device that perceives its environment and takes actions that

maximize its chance of success at some goal.

(https : //en.wikipedia.org/wiki/Artificial intelligence)

Nils. J. Nilsson (2010)

Artificial intelligence is that activity devoted to making machines intelli-gent, and intelligence is that quality that enables an entity to functionappropriately and with foresight in its environment.(https://ai100.stanford.edu/2016-report/section-i-what-artificial-intelligence/defining-ai)



What is involved in AI?

Ability to interact with the real-world

to perceive, understand and acte.g. speech recognition and understandinge.g. visual understanding

e.g. ability to take actions

Reasoning and Planning

modeling the external world

solving new problems, planning and decision making

ability to deal with uncertain problems

Learning and adaptation

continuous learning and adaptation

model is always being ”updated”



The Turing Test



The Turing Test as an Imitation Game



A Rebuttal: The Chinese Room Argument



The Chinese Room Argument

Searle says that whatever purely formal principles you put into acomputer, they will not be sufficient for real understanding just likewhatever rules he uses to translate Chinese will not be sufficient forhim to understand Chinese.

He concludes that you cannot get semantics (meaning) from syntax(rules for symbol manipulation).

Strong AI

Computers think and understand

Weak AI

Computers simulate thought and understanding



The Compromise: The Loebner Prize

Home Page of the Loebner Prize in AI

http://www.loebner.net/Prizef/loebner-prize.html Link



http://www.loebner.net/Prizef/loebner-prize.html

http://www.loebner.net/Prizef/loebner-prize.html

The First Machine who passed the Turing Test

(http://www.zdnet.com/article/computer-chatbot-eugene-goostman-passes-the-turing-test/)

On 7 June, 2014 (60th anniversary of Turing’s death), EugeneGoostman reportedly passed the Turing test.

33 % of the judges believed Eugene Goostman was a 13 year-oldUkranian boy.



ELIZA (Weizenbaum, 1966), the First Chatbot

http://www.manifestation.com/neurotoys/eliza.php3 Link



http://www.manifestation.com/neurotoys/eliza.php3

http://www.manifestation.com/neurotoys/eliza.php3

How Eliza works?

No deep language analysis or Artificial Intelligence

Look at specific keywords and sentence patterns”If the sentence has the keyword XXX in the sentence patternYYYY, then say ZZZ”Store specific topics mentioned by the user

This way it looks like Eliza ”remembers” the conversation

Use some ”catch-all” rules when everything fails

Many users believed the machine understood them

Even some psychologists thought of using Eliza as an assistant



Develop your own Chatbot using AIML

https://playground.pandorabots.com/en/quickstart/ Link



https://playground.pandorabots.com/en/quickstart/

https://playground.pandorabots.com/en/quickstart/

Key aspects of AIML

Preprocessing: Normalisation, sentence splitting

Categories: patterns, templates

Ultimate Default Category

Randomised responses

Wildcards

Variables: properties, predicates

Recursion and reduction: ”srai”

Sets and maps

Context: ”that”, topic

https://playground.pandorabots.com/en/tutorial/



https://playground.pandorabots.com/en/tutorial/

Task 1: Build a Sceptic Chatbot

Steps to follow

1 Create a chatbot and click on ”Files”

2 Click on ”pand initial” and replace the first category with

3 Save the file

4 Test the bot by clicking on ”Train”



Task 2: Different Greetings

Create a new Chatbot and create a new AIML file and type in this text



Task 3: Keeping track of the Context

Create a new Chatbot and insert this content in a new AIML file



Machine Learning (ML)

What is Machine Learning?

Rule-based Systems

People write specific rules

The Computer follows the rules to process the data.

Machine Learning Systems

The machine receives the data

The machine learns to generalise from the data

The machine responds to new data by applying what it has learntfrom the previous data.



Why Machine Learning?

People are very good at doing things but they can’t explain howthey did it.

Can you define what is Cat?Can you explain what decisions to make when you drive a car?

Sometimes it is better to instruct the machine how to learn

Machines can end learning something that the programmer did’ntknow.



Why Machine Learning. . .

No human experts

industrial/manufacturing control

mass spectrometer analysis, drug design, astronomical analysis

Black box human experts

face/handwriting/speech recognition

driving a car, flying a plane

Rapidly changing phenomenon

credit scoring, financial modeling

diagnosis, fraud detection

Need for customisation/personalisation

personalized news reader

movie/book recommendation



Why Machine Learning now?

Flood of available data (especially with the advent of the Internet)

Increasing computational power

Growing progress in available algorithms and theory developed byresearchers

Increasing support from industry



V’s in Machine Learning data

(http://bigdata.perfectial.com/img/vvvv.png)



http://bigdata.perfectial.com/img/vvvv.png

Types of Machine Learning

(https://cdn-images-1.medium.com/max/1600/0*Y4F8zOK84K5TlCEY.)



https://cdn-images-1.medium.com/max/1600/0*Y4F8zOK84K5TlCEY.

Typical Machine Learning Tasks

How will Artificial Intelligence affect your life-by Jeff Dean (https://www.youtube.com/watch?v=BfDQNrVphLQ)



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

Steps in Machine Learning

(https://upxacademy.com/introduction-machine-learning/)



https://upxacademy.com/introduction-machine-learning/

Build a Machine Learning System using Azure ML Studio

(https://studio.azureml.net/)



https://studio.azureml.net/

Azure ML Studio tutorial

Steps:

Sign In with Microsoft account

Go to Experiments and click on New

Drag and Drop the building blocks of the experiment

Run the experiment

Visualise the performance of the trained model

(https://studio.azureml.net/)



https://studio.azureml.net/

Experiment 1: Iris Flower Dataset: Binary Classification



Experiment 2: MNIST Dataset: Multiclass Classification



Text Classification: Sentiment Analysis

(https://gallery.cortanaintelligence.com/Experiment/Logistic-Regression-for-Text-Classification-Sentiment-Analysis-1#

Link



https://gallery.cortanaintelligence.com/Experiment/Logistic-Regression-for-Text-Classification-Sentiment-Analysis-1#

https://gallery.cortanaintelligence.com/Experiment/Logistic-Regression-for-Text-Classification-Sentiment-Analysis-1#

https://gallery.cortanaintelligence.com/Experiment/Logistic-Regression-for-Text-Classification-Sentiment-Analysis-1

Regression Analysis

(http://analyticstraining.com/2014/popular-applications-of-linear-regression-for-businesses/)



http://analyticstraining.com/2014/popular-applications-of-linear-regression-for-businesses/

Regression Analysis

Consider the following three scenarios

The CEO of the local Tourism Authority would like to know whethera family’s annual expenditure on recreation is related to their annualincome. This information could be used to tailor marketingcampaigns to certain consumer segments.

A food company is interested in determining a shelf-life for a newchilled food product and hence they would like to quantify therelationship between microbial activity and time.

A car buyer is interested in purchasing a second hand car and wouldlike to ascertain the relationship between a car’s age and advertisedpurchase price.

(http://www.massey.ac.nz/ mbjones/Book/Chap9.pdf)



Experiment x: Predicting price of number of candies



Screenshot of the Predictive Experimentson



Resulting Web Service



Microsoft Azure Cognitive Services

Microsoft Cognitive Services let you build apps with powerful algorithms to see, hear, speak,

understand and interpret our needs using natural methods of communication, with just a few lines

of code. Easily add intelligent features, such as emotion and sentiment detection, vision and

speech recognition, language understanding, knowledge and search, into your app, across devices

and platforms, such as iOS, Android and Windows, keep improving and are easy to set up.

(https://azure.microsoft.com/en-au/services/cognitive-services/)Link



https://azure.microsoft.com/en-au/services/cognitive-services/

https://azure.microsoft.com/en-au/services/cognitive-services/

Artificial Intelligence and Life

AI through the eyes of cinema

(https://www.theguardian.com/culture/gallery/2015/jan/08/the-top-20-artificial-intelligence-films-in-pictures)



AI through the eyes of cinema. . .

(https://www.theguardian.com/culture/gallery/2015/jan/08/the-top-20-artificial-intelligence-films-in-pictures)



Famous AI systems

Garry Kasporav vs IBM’s Deep Blue (1996)

(http://medicalfuturist.com/wp-content/uploads/2014/11/garry-kasparov-deep-blue-ibm.jpg)



http://medicalfuturist.com/wp-content/uploads/2014/11/garry-kasparov-deep-blue-ibm.jpg

Famous AI systems

DARPA Urban Challenge (2007)

(http://thefutureofthings.com/3019-darpas-urban-challenge-2007/)



http://thefutureofthings.com/3019-darpas-urban-challenge-2007/

Famous AI systems

IBM Watson wins jeopardy challenge (2011)

(http://medicalfuturist.com/ibm-watson-is-the-stethoscope-of-the-21st-century/)



http://medicalfuturist.com/ibm-watson-is-the-stethoscope-of-the-21st-century/

Famous AI systems

Google Deepmind’s AlphaGo vs Lee Sedol (2015)

(https://gogameguru.com/alphago-shows-true-strength-3rd-victory-lee-sedol/)



https://gogameguru.com/alphago-shows-true-strength-3rd-victory-lee-sedol/

Famous AI systems

Rise of Voice Assistants (2016)

(https://finance.yahoo.com/news/battle-of-the-voice-assistants-siri-cortana-211625975.html)



https://finance.yahoo.com/news/battle-of-the-voice-assistants-siri-cortana-211625975.html

Fears of Artificial Intelligence

The return of the machinery question: Will AI destroy the world?

(http://www.economist.com/sites/default/files/ai_mailout.pdf)



http://www.economist.com/sites/default/files/ai_mailout.pdf

Fears of Artificial Intelligence

The Technological Singularity

(http://rebaneruminations.typepad.com/rebanes_ruminations/2010/04/singularity-whats-that-wappendix.html)



http://rebaneruminations.typepad.com/rebanes_ruminations/2010/04/singularity-whats-that-wappendix.html

Artificial Intelligence and Life in 2030

(https://ai100.stanford.edu/2016-report)



https://ai100.stanford.edu/2016-report

How AI will change Life by 2030

Smart transportation

(http://www.sciencemag.org/news/2016/09/five-surprising-ways-ai-could-be-part-our-lives-2030-0)



http://www.sciencemag.org/news/2016/09/five-surprising-ways-ai-could-be-part-our-lives-2030-0


Home and Service robots

(https://singularityhub.com/2016/10/19/8-ways-ai-will-profoundly-change-city-life-by-2030/)



https://singularityhub.com/2016/10/19/8-ways-ai-will-profoundly-change-city-life-by-2030/


Healthcare


(https://medium.com/@d.healthcare/what-is-the-role-technology-might-play-in-healthcare)




https://medium.com/@d.healthcare/what-is-the-role-technology-might-play-in-healthcare


Education

(http://www.sciencemag.org/news/2016/09/five-surprising-ways-ai-could-be-part-our-lives-2030-0)




http://www.sciencemag.org/news/2016/09/five-surprising-ways-ai-could-be-part-our-lives-2030-0



Low resource communities

(http://analyticsindiamag.com/10-areas-artificial-intelligence-going-impact-lives-future/7-ai-will-prove-useful-for-assisting-low-resource-communities/)

(http://www.longevitylive.com/nutrition-body/agriculture-artificial-intelligence/)



http://analyticsindiamag.com/10-areas-artificial-intelligence-going-impact-lives-future/7-ai-will-prove-useful-for-assisting-low-resource-communities/

http://analyticsindiamag.com/10-areas-artificial-intelligence-going-impact-lives-future/7-ai-will-prove-useful-for-assisting-low-resource-communities/

http://www.longevitylive.com/nutrition-body/agriculture-artificial-intelligence/


Public Safety and Security


(https://venturebeat.com/2016/11/22/how-ai-will-transform-cybersecurity/)




https://venturebeat.com/2016/11/22/how-ai-will-transform-cybersecurity/


Employment and Workplace


(http://www.freegrab.net/jobswork.htm)




http://www.freegrab.net/jobswork.htm


personalised and interactive Entertainment

(http://dataconomy.com/2015/03/an-introduction-to-recommendation-engines/)




http://dataconomy.com/2015/03/an-introduction-to-recommendation-engines/


Preparing for the future ofArtificial Intelligence

US President Report and IBM’s response

(https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf)

(http://research.ibm.com/cognitive-computing/cognitive-horizons-network/downloads/IBM_Research_CSOC_OSTP_Response%

20v2.pdf)



https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

http://research.ibm.com/cognitive-computing/cognitive-horizons-network/downloads/IBM_Research_CSOC_OSTP_Response%20v2.pdf

http://research.ibm.com/cognitive-computing/cognitive-horizons-network/downloads/IBM_Research_CSOC_OSTP_Response%20v2.pdf

What to do When Machines do everything

Artificial intelligence has left the laboratory (and the movie lot) and is in your building. It’s in your

home. It’s in your office. From Alexa to Nest to Siri to Uber to Waze, we are surrounded by smart

machines running on incredibly powerful and self-learning software platforms

(http://www.whenmachinesdoeverything.com/?utm_source=perspectives&utm_medium=internal_banner&utm_campaign=

perspectives_internal Link



http://www.whenmachinesdoeverything.com/?utm_source=perspectives&utm_medium=internal_banner&utm_campaign=perspectives_internal



What great leaders say about future of AI?

”Artificial Intelligence is the future, not only for the Russia, but for allhumankind. It comes with colossal opportunities, but also threats thatare difficult to predict.Whoever becomes the leader in this sphere willbecome the ruler of the world” -Vladimir Putin

”AI will be the best or worst thing ever for the humanity” -Elon Musk

”The real question is, when will we draft an artificial intelligence bills ofrights? What will that consist of? And who will get to decide that?” -Gray Scot

”By far, the greatest danger of Artificial Intelligence is that peopleconclude too early that they understand it” - Eliezer Yudkowsky



So, Let’s start learningArtificial Intelligence for

Australia’s brighter future

How to start learning AI

(http://itenterprise.co.uk/start-learning-artificial-intelligence/)

Link

http://itenterprise.co.uk/start-learning-artificial-intelligence/

http://itenterprise.co.uk/start-learning-artificial-intelligence

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