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Interaction with AI: individual assignment

Module 1 Concepts, definition and history of interaction with AI

History of AI

The term artificial intelligence, AI, was first used in 1956 by John McCarthy.

AI originates in mathematics and engineering and was at first hand

focusing on devising better algorithms. In the 1960 and 1970 AI researchers

believed that machines would be so intelligent that they could compete

with humans on equal terms before the twentieth century. Through the

last decades AI have had a changing interest and actuality. Today we can

say that AI was way oversold and is not in anywhere near the predictions

that were made. (Grudin, 2009)

What is AI?

“The study of how to produce machines that have some of the qualities

that the human mind has, such as the ability to understand language,

recognize pictures, solve problems, and learn.” (Cambridge dictionary,

2019).

By comparing the human mind to how a computer works seems kind of

far off doesn’t it? How the human brain works is in no way related to how

the different processes in a computer works. Mike Loukides and Ben Lorica

is questioning the definition of intelligence:

“Defining artificial intelligence isn’t just difficult; it’s impossible, not the

least because we don’t really understand human intelligence.

Paradoxically, advances in AI will help more to define what human

intelligence isn’t than what artificial intelligence is.” (OReilly, 2016).

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By this I want to go back to the earlier definition by John McCarthy:

“It is the science and engineering of making intelligent machines,

especially intelligent computer programs. It is related to the similar task of

using computers to understand human intelligence, but AI does not have

to confine itself to methods that are biologically observable.” (McCarthy,

1998).

A computer is only intelligent in that way that can make a dissection

between what is right and wrong. I will now try to make a definition of my

own:

AI is a way of tricking us into believing that machines can take their own

decisions and, in some way, have the same (or even more) intelligence as

humans.

AI at Google

Google has a vision to bring the benefits of AI to everyone. They present AI

as something that can help and be used by everyone, every day for

anything. Their research can be accessed by everyone and they are a big

believer of sharing their knowledge. The technology should be used to help

people by being social beneficial, fair, accountable and works for everyone.

(Google, 2019).

AI in Black Mirror

Black Mirror portraits a twisted and scary future where human innovation

has gone totally wrong. Every episode is based on a new type of high-tech

innovation which pushes the limits of the society and makes us question

our use of technology. The episodes explore the negative effects of AI and

makes you want to take a reality check. Human interaction with AI is

portrayed as a danger to the society and the series is trying to show us that

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we have a dark future ahead if we don't change the course we are in right

now.

Robots and AI systems

How robot came about

Robotics is a field in change and what the term robot means have changes

over decades. The Robot Institute of America defined in 1979 a robot as “a

reprogrammable, multifunctional manipulator designed to move

materials, pars tools, or specialized devices through various programmed

motion for the performance of a variety of tasks”. (Thrun, 2004). Robotics

have been categorized into three different categories; industrial robotics,

professional service robotics, and personal service robotics.

What is robots?

NASA defines a robot as “a machine that is built to do a certain job again

and again, or to do work that might be dangerous for humans.“ (NASA,

2019).

LEO Robotics have a more detailed definition: "A robot is an actuated

mechanism programmable in two or more axes with a degree of

autonomy, moving within its environment, to perform intended tasks.

Autonomy in this context means the ability to perform intended tasks

based on current state and sensing, without human intervention." (LEO

Robotics, 2019).

It is hard to define what a robot actually is, but i like how Joseph

Engelberger puts it:

"I can't define a robot, but I know one when I see one."

I want to take a different approach to define a robot:

Robots is a physical machine that can do your job better than you.

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The relation between AI and robots

However you define a robot it’s not the same as an AI. A robot can be

controlled by an AI system and therefore be an artificial intelligent robot.

But an AI system don’t need to have anything to do with robots, and robots

don’t need AI to function.

AV1

AV1 is a communication robot made by No Isolation and is specifically

designed for children with a long-term illness. The robot will take the place

of the child that can’t attend school, and be their eyes, ears and voice in the

classroom. The owner of the robot can control it through an app on their

phone or tablet. If the child wants to ask a question, they can press a

button on the app and the robot will have a flash on the top of the head.

You can also change the eye expression of the robot.

Universal Design and AI systems

What is universal design?

Universal Design is “the design and composition of an environment so that

it can be accessed, understood and used to the greatest extent possible

by all people regardless of their age, size, ability or disability.” (The centre

for excellence in universal design, 2019).

Universal Design is something that you should find in every part of the

society, ether it’s to get into a building, take the tram or be able to read the

information on a web page. No exception and maybe even more important

when it comes to AI and robots. It’s about that everyone should feel

included and be able to use a system. If a system is using face recognition

it should be able to recognize people with dark skin on equal terms as

people with light skin.

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“...simply put, universal design is good design”. (The centre for excellence in

universal design, 2019).

The potential of AI

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Module 2

Characteristics of AI infused systems

Amershi er al. (2019) describes AI-infused systems as ' systems that have

features harnessing AI capabilities that are directly exposed to the end

user'. Key characteristics of AI-infused systems can be learning, improving,

black box and to be fueled by large data sets. I will furthermore describe

and discuss these characteristics. Amershi et al. (2019) have developed 18

generally applicable design guidelines for human AI-interaction that I will

use to exemplify these characteristics.

Learning

AI-infused systems can be designed to be dynamic; this means that it is

designed for change and will learn and develop while it is being used.

Often this can mean that the AI-infused system is not fully developed

when you start using it but will give you a better user-experience and a

more personalized experience after it learn from your use. In this case G1,

make clear what the system can do, is important to bring into the design

of the AI-infused system. When interacting with a chatbot it will inform you

of what kind of information it can give you. This will make sure that the

user knows what to expect and will not be disappointed. Furthermore, it is

important to make clear how well the system can do what it can do (G2)

so that the user can use the system in the best way. Using the chatbot-

example, it can be beneficial to inform the user how to write so the chatbot

understands in the best way. This will work as a kind of “user manual” so

that the user knows how to interact with the chatbot and “speak” the same

language. The more the system is used the better it will get since it learns

from every time it is used.

Improving

An important aspect of the user experience for AI-infused systems is that

the user can correct if the AI-infused system doesn’t give the best results or

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show the wrong information. If the system supports efficient correction(G9)

will make it easy for the user to edit, refine or recover. When the user is

shown the advertisement or news that is not suited or wanted you should

easily be able to correct by for example hitting a “show less/don’t show“

button. In other contexts, it would be beneficial for the user to be provided

with multiple options if the AI-infused system is uncertain about the user’s

goals (G10). When you are designing an AI-infused system you should take

uncertainty into consideration because you cannot predict how the user

will use your system.

Black box

Often can AI-systems seem fairly magical and the user may ask themselves

why the system behaved as it did. To give the user this explanation the AI

system should make clear why the system did what it did (G11) by bringing

explainability into the design. This can be done by showing the data the AI

system used or have the option to press “why am I seeing this

advertisement?”.

Fuelled by large data sets

Many AI-infused systems are designed to collect your data and use this to

give you a better user experience. By collecting and learning from your

actions and use over time the AI system can give you a more personalizes

experience. Tesla for example collects data from all of their users to develop

autonomous software.

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Search for photos on iPhone

One AI-infused system I recently where made aware of is Apple’s

embedded image search function. Here you can easily search for words,

persons or places and the image

recognition will show you all the photos

that fits your search. This way you can

easily find a specific photo using one or

multiple keywords.

To make this possible I believe that the

image recognition is fuelled by large

data sets from everyone that own an

iPhone or iPad, and practice on all of the

photos the users take. My experience is

that the image recognition is fairly

accurate, and it does not take me long

to find the exact photo I am looking for.

The search function will get better each time you use it and learn by using

the image recognition on your photos, and therefor fits the characteristics

of a learning AI-infused system. The system makes it clear what it can do

(G1) by showing the user keywords that

can be searched for and by making

predefined groups of keywords.

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The system supports efficient correction(G9) and makes it easy for the user

to correct the search function if it makes mistakes. The image recognition

is an improving AI-infused system and

makes the user experience better by

optimizing the recognition of people. The

function “bekreft ekstra bilder” gives the

user an option to go thru photos the image

recognition is uncertain about and in this

way you can teach the AI system how a

person look in different angles etc.

Human AI-interaction design

Summary of Anashi et al. (2019)

In this paper we are introduced to 19 generally applicable design guidelines

for human-AI interaction. Through multiple rounds of evaluation with

people within the field and different popular AI-infused products they have

validated these guidelines. Their goal is to address the challenges for

designing user interface in the context of AI and frame the opportunities

within the field.

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Summary of Kockielnik et al. (2019)

This paper investigates the expectation of the end-users of AI-infused

systems and how this affect their perception an acceptance of such

systems. Some of the main factors that affect the users are external

information, knowledge and understanding, and firsthand experience. In

their work they used a Scheduling Assistant with an AI system for

automated meeting detection and comparing how the focus on high recall

and high precision would affect the user’s satisfaction of the system. Their

finding indicate that high precision should be the main focus rather than

high recall as the users feel more satisfied with a false negative, and that it

is easier to correct than a false negative.

Chatbots

Some of the key challenges in the design of chatbots and conversational

user interfaces discussed by Luger & Sellen(2016) and Følstad & Brandtzaeg

(2017) are to meet the user’s expectations or mental model of what the AI

system can do. The lack of system feedback made the users uncertain

about how to interact with the system and their knowledge about the

limitations and possibilities to the system. Luger and Sellen (2016) discuss

key factors that limited the user’s interaction; understanding of what the

system can do, what it was doing and how it was doing it. By using G1

“make clear what the system can do” you could limit these challenges,

and the user would be more confident on how to interact with the system.

Users of chatbots and conversational agents states issues with both trust of

the system, and its ability to learn. If G2 “make clear how well the system

can do what it can do” was implemented properly it would be better

received by the users as they would be familiar with its limitations and

open for it to get better by doing mistakes.

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Module 3

Collaboration and levels of automation

Philips at al. (2016) argues that human-animal teams can serve as an

analog for developing effective human-robot teams. They serve as an

inspiration to look at the robot as part of an interactive team rather than

just a tool. Humans and animals have worked side by side for decades and

are capable of completing a wide variety of work by leveraging the unique

capabilities of each team member. The article explores the future of robots

where they can serve as team members alongside soldiers, working to

achieve common goals and complete team tasks.

By basing human-robot interaction on human-animal interaction we can

benefit of people’s mental model. As people already have knowledge about

how to interact with animals, this would be beneficial in their first meeting

with the robot. Philips at al. (2016) argues that this relationship between

physical forms and mental models has been shown to influence initial

perceptions of robot trustworthiness.

Replace physical capabilities: Big Dog

By looking at how human-animal teams are structured to give the human

physical benefits we can start to identify team structure elements in

human-robot interaction. The robot Big Dog is an example of this where

the robot serves as a pack transporter and give a more efficient locomotion

for transportation. The design is based on a large dog and benefit of not

having wheels in uncertain terrain. Endsley (2011) present a variety of

automation levels from manual control to full automation, and I would

argue that Big Dog have the level of automation described as Automated

decision making. The robot generates recommended options along with

humans, the system selects best and carries it out. In the human-robot

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interaction the human serves as the direction giver, and the dog will

choose the best way to get from A to B and follow along with the human.

Augment and extend natural human functions: The FRIEND

Service animals such as dogs can help people with impaired vision, and in

many cases help to enhance an individual’s independence and execution

of daily activities that they would not be able to perform safely on their

own. This is a field robots are starting to emerge, and will be at great

means for people to live more independently. The FRIEND robot is an

example of a care providing robot that has the purpose to help people that

suffer from paralysis of other muscular disorders. The user will not be as

depended of other humans and can manage to do simple everyday tasks

that seem rater hard for them. The robot will also reduce stress for the user

by letting them retain a greater degree of self-determination. The FRIEND

robot have an automation level described by Endsley (2011) as action

support, where the robot aims at doing each action as instructed.

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References Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., ... &

Teevan, J. (2019, April). Guidelines for human-AI interaction. In Proceedings

of the 2019 CHI Conference on Human Factors in Computing Systems (p.

3). ACM.

Bratteteig, Tone. Verne, Guri. (2018), Does AI make PD absolute? Exploring

Challenges from Artificial Intelligence to Participatory Design. Belgium:

Hasselt and Genk. Cambridge dictionary. (2019). Artificial intelligence.

Retrieved from:

https://dictionary.cambridge.org/dictionary/english/artificial-intelligence

Endsley, M. R. (2011). Designing for Situation Awareness: Understanding

situation awareness in system design. CRC Press.

Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of

HCI. interactions, 24(4), 38-42.

Grudin, Jonathan. (2009). AI and HCI: Two Fields Divided by a Common

Focus. AI magazine. (48-57).

Kocielnik, R., Amershi, S., & Bennett, P. N. (2019, April). Will You Accept an

Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI

Systems. In Proceedings of the 2019 CHI Conference on Human Factors in

Computing Systems (p. 411). ACM.

LEO Robotics. (2019). Defining robots and robotics. Retrieved from:

http://www.leorobotics.nl/definition-robots-and-robotics

Luger, E., & Sellen, A. (2016, May). Like having a really bad PA: the gulf

between user expectation and experience of conversational agents.

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In Proceedings of the 2016 CHI Conference on Human Factors in

Computing Systems (pp. 5286-5297). ACM.

McCarthy, J. (1998). What is artificial intelligence? NASA. (2015). Robot.

Retrived from: https://www.nasa.gov/audience/forstudents/k-

4/dictionary/Robot.html

OReilly. (2016). What is Artificial Intelligence? Retrieved from:

https://www.oreilly.com/radar/what-is-artificial-intelligence/

Phillips, E. K., Schaefer, K., Billings, D. R., Jentsch, F., & Hancock, P. A. (2016).

Human-Animal Teams as an Analog for Future Human-Robot Teams:

Influencing Design and Fostering Trust. Journal of HumanRobot

Interaction, 5(1), 100.

The centre for excellence in universal design. (2019). What is universal

design. Reterived from: http://universaldesign.ie/What-is-Universal-Design/

Thrun, Sebastian. (2004). Toward a FRamework for Human-Robot

Interaction,. Human-Computer Interaction. (9-24)

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Appendix: feedback

It has been nice getting feedback after each iteration and have helped my

motivation and understanding of my own assignments. After reading the

feedback I went back to my deliveries and learned from my mistakes

before starting the next assignment.