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An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
An	Introduc+on	to	AI	
and	Automa+on	
Ray	Poynter	
NewMR
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Agenda	
•  Automa+on	
•  Automa+on	and	market	research	
•  Automa+on	and	jobs	
•  Ar+ficial	intelligence	
•  Ar+ficial	intelligence	and	market	research	
•  The	near	future
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Automa+on	
•  Glass	blowing	automated	New	York	
1905	for	boGle	produc+on.	
•  BoGles	per	person	up	600%	
•  Price	per	boGle	fell	to	8%	of	previous	
price	
•  Many	skilled	glass	blowers	lost	their	jobs		
•  Reduced	costs	and	increased	consistency	led	to	new	uses	for	glass	boGles,	
including	foods,	drinks	and	medicines	
•  Massive	increase	in	the	use	of	glass,	lots	of	new	jobs,	economic	growth,	health	
improvements	(because	food	was	safer)
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
4	Key	Benefits	of	Automa+on	
1.  Cheaper	
2.  Faster	
3.  Scalable	
4.  BeGer
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Cheaper	
Costs	Fall	 Prices	Fall	
Market	
Grows	
Or	not!
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Be?er	
✗	 ✔
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Not	every	project	delivers	all	four!	
1.  Cheaper	
2.  Faster	
3.  Scalable	
4.  BeGer
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Automa+on	&	Market	Research	
•  Herman	Hollerith,	US	Census	1890	
•  1970s	and	80s	
–  Op+cal	scanning	of	surveys,	CATI,	CAPI,	SPSS,	spreadsheets,	
desktop	publishing,	presenta+on	&	char+ng	so]ware	
•  1990s	&	2000s	
–  Online	surveys,	CAQDAS,	DIY	surveys,	Online	access	panels,	
dashboards	and	reportals	
•  2010s	
–  ‘Shrink-wrapped’	research,	chatbots,	text	&	sen+ment	analysis,	
image	and	video	analysis,	report	automa+on,	advanced	analysis	
e.g.	semio+cs	and	conjoint	analysis
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Automa+on	and	DIY
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Automa+on	and	Jobs	
Automa+on	has	already	cost	hundreds	of	thousands	of	jobs	in	MR	
–  Interviewers,	data	punchers,	call	centre	staff	etc	
Automa+on	will	change	exis+ng	projects	and	jobs	
–  Faster,	cheaper,	more	standardised,	and	needing	fewer	people	hours	
–  Many	jobs	will	go,	and	some	new	jobs	will	be	created	
•  E.g.	‘clerical’	jobs	will	decline,	storytelling	and	client	success	managers	will	
grow	
If	the	market	grows,	there	will	be	a	growth	in	jobs	
–  Even	in	clerical	jobs,	but	the	+me	spent	on	each	project	will	be	much	
shorter	than	before,	so	more	projects	will	be	handled	and	the	role	will	
be	more	Q&A,	interpreta+on,	sales	etc.
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Ar+ficial	Intelligence
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
AI	
Ar+ficial	Intelligence	Machine	intelligence	
Machine	learning	
Expert	systems	
Bots	
Intelligent	agents	
Neural	networks	
Inference	engines	
Natural	language	processing	
Deep	learning	
Evolu+onary	algorithms	
Bayesian	networks	
Anything	‘clever’	J	
Alexa,	Siri,	Google,	Home	
Autonomous	cars	
Game	playing	–	chess,	Go	etc	
Chatbots	
Programma+c	adver+sing	&	marke+ng	
Predic+ve	analy+cs	
Voice	recogni+on	
Text	analy+cs	
Image	analy+cs	
Transla+on	so]ware
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Broad	Categories	of	AI	
•  Algorithms	
•  Expert	systems	
•  Heuris+cs	
•  Machine	learning	–	supervised	
•  Machine	learning	–	unsupervised	
•  Deep	learning
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Algorithms,	Rules,	Expert	Systems	
Strategy	for	playing	Noughts	and	Crosses	
1.  If	opponent	has	two	in	a	row,	take	the	remaining	square.	Else	
2.  If	a	move	"forks"	to	create	two	sets	of	two	in	a	row,	play	that	move.	Else	
3.  Take	the	centre	square	if	it	is	free.	Else	
4.  If	opponent	has	played	in	a	corner,	take	the	opposite	corner.	Else	
5.  Take	an	empty	corner	if	one	exists.	Else	
6.  Take	any	empty	square.	
	
This	used	to	be	the	main	way	to	do	AI,	but	now	‘machine	learning’	is	
more	prevalent.	
	
Many	MR	applica+ons	use/will	use	expert	systems,	e.g.	how	to	design	a	
project	and	reports	from	data
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Learning	Systems	
Supervised	Learning	
–  Given	a	set	of	inputs	and	outputs,	the	machine	learns	
to	to	generate	the	outputs	from	the	inputs	
•  For	example,	show	it	1000	coded	open-ended	comments	
and	ask	it	to	do	the	same	to	1	million	
Unsupervised	Learning	
–  The	machine	looks	for	paGerns	and	structures	in	the	
data	
•  Cluster	analysis	is	a	simple	example	of	this
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Deep	Learning	
•  Also	called	hierarchical	learning	
•  Uses	mul+ple	levels,	where	the	outputs	of	one	
level	feed	into	another.	
•  For	example,	
–  Teach	the	machine	to	play	chess,	give	it	some	ini+al	
strategies	(e.g.	based	on	real	games)	and	the	ability	to	
be	random	
–  Let	it	play	itself	over	and	over,	learning	the	best	
strategies
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
AI	and	Market	Research	
•  Chatbots	and	adap+ve	surveys	
–  Models	built	for	non-MR	use,	tweaked	for	MR	
•  Image,	text,	and	video	analy+cs	
–  Including	coding	and	biometrics	(e.g.	facial	coding)	
–  Supervised	learning,	either	from	MR	or	elsewhere	
•  Predic+ve	analy+cs,	aGribu+on	analy+cs,	mining	meaning	
–  Supervised	learning	and	unsupervised	learning	
•  Report	wri+ng	/	story	finding	
–  Expert	systems	and	unsupervised	learning	
•  Project	design	&	sample	management		
–  Expert	systems	ini+ally,	later	supervised	analy+cs
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
AI	and	MR	in	Summary	
1.  Most	things	that	are	‘clever’	will	be	called	AI	
2.  Most	developments	will	happen	outside	MR	and	then	be	
u+lised	–	e.g.	AWS	
3.  Machine	learning	will	focus	on	situa+ons	where	learning	
sets	exist	–	e.g.	coding	
4.  AI	will	allow	materials	that	have	tradi+onally	been	
analysed	with	qual	to	be	quan+ta+vely	analysed	
5.  Expert	systems	will	be	core	to	anything	that	does	not	
produce	large	training	sets	–	e.g.	project	design
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Thank	You!	
	
	
Follow	me	on	TwiGer	via	@RayPoynter	
Connect	on	LinkedIn	via	hGps://www.linkedin.com/in/raypoynter
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
Q	&	A	
BeGy	Adamou	
Research	Through	Gaming	
Ray	Poynter	
NewMR
An	Introduc+on	to	AI	and	Automa+on	
Ray	Poynter,	NewMR	
The Future of AI
& Automation
	
	
NewMR	2018	Sponsors	
Communica+on	
Silver	
Gold

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An Introduction to AI and Automation

  • 1. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation An Introduc+on to AI and Automa+on Ray Poynter NewMR
  • 2. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Agenda •  Automa+on •  Automa+on and market research •  Automa+on and jobs •  Ar+ficial intelligence •  Ar+ficial intelligence and market research •  The near future
  • 3. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Automa+on •  Glass blowing automated New York 1905 for boGle produc+on. •  BoGles per person up 600% •  Price per boGle fell to 8% of previous price •  Many skilled glass blowers lost their jobs •  Reduced costs and increased consistency led to new uses for glass boGles, including foods, drinks and medicines •  Massive increase in the use of glass, lots of new jobs, economic growth, health improvements (because food was safer)
  • 4. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation 4 Key Benefits of Automa+on 1.  Cheaper 2.  Faster 3.  Scalable 4.  BeGer
  • 5. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Cheaper Costs Fall Prices Fall Market Grows Or not!
  • 7. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Not every project delivers all four! 1.  Cheaper 2.  Faster 3.  Scalable 4.  BeGer
  • 8. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Automa+on & Market Research •  Herman Hollerith, US Census 1890 •  1970s and 80s –  Op+cal scanning of surveys, CATI, CAPI, SPSS, spreadsheets, desktop publishing, presenta+on & char+ng so]ware •  1990s & 2000s –  Online surveys, CAQDAS, DIY surveys, Online access panels, dashboards and reportals •  2010s –  ‘Shrink-wrapped’ research, chatbots, text & sen+ment analysis, image and video analysis, report automa+on, advanced analysis e.g. semio+cs and conjoint analysis
  • 10. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Automa+on and Jobs Automa+on has already cost hundreds of thousands of jobs in MR –  Interviewers, data punchers, call centre staff etc Automa+on will change exis+ng projects and jobs –  Faster, cheaper, more standardised, and needing fewer people hours –  Many jobs will go, and some new jobs will be created •  E.g. ‘clerical’ jobs will decline, storytelling and client success managers will grow If the market grows, there will be a growth in jobs –  Even in clerical jobs, but the +me spent on each project will be much shorter than before, so more projects will be handled and the role will be more Q&A, interpreta+on, sales etc.
  • 12. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation AI Ar+ficial Intelligence Machine intelligence Machine learning Expert systems Bots Intelligent agents Neural networks Inference engines Natural language processing Deep learning Evolu+onary algorithms Bayesian networks Anything ‘clever’ J Alexa, Siri, Google, Home Autonomous cars Game playing – chess, Go etc Chatbots Programma+c adver+sing & marke+ng Predic+ve analy+cs Voice recogni+on Text analy+cs Image analy+cs Transla+on so]ware
  • 13. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Broad Categories of AI •  Algorithms •  Expert systems •  Heuris+cs •  Machine learning – supervised •  Machine learning – unsupervised •  Deep learning
  • 14. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Algorithms, Rules, Expert Systems Strategy for playing Noughts and Crosses 1.  If opponent has two in a row, take the remaining square. Else 2.  If a move "forks" to create two sets of two in a row, play that move. Else 3.  Take the centre square if it is free. Else 4.  If opponent has played in a corner, take the opposite corner. Else 5.  Take an empty corner if one exists. Else 6.  Take any empty square. This used to be the main way to do AI, but now ‘machine learning’ is more prevalent. Many MR applica+ons use/will use expert systems, e.g. how to design a project and reports from data
  • 15. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Learning Systems Supervised Learning –  Given a set of inputs and outputs, the machine learns to to generate the outputs from the inputs •  For example, show it 1000 coded open-ended comments and ask it to do the same to 1 million Unsupervised Learning –  The machine looks for paGerns and structures in the data •  Cluster analysis is a simple example of this
  • 16. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Deep Learning •  Also called hierarchical learning •  Uses mul+ple levels, where the outputs of one level feed into another. •  For example, –  Teach the machine to play chess, give it some ini+al strategies (e.g. based on real games) and the ability to be random –  Let it play itself over and over, learning the best strategies
  • 17. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation AI and Market Research •  Chatbots and adap+ve surveys –  Models built for non-MR use, tweaked for MR •  Image, text, and video analy+cs –  Including coding and biometrics (e.g. facial coding) –  Supervised learning, either from MR or elsewhere •  Predic+ve analy+cs, aGribu+on analy+cs, mining meaning –  Supervised learning and unsupervised learning •  Report wri+ng / story finding –  Expert systems and unsupervised learning •  Project design & sample management –  Expert systems ini+ally, later supervised analy+cs
  • 18. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation AI and MR in Summary 1.  Most things that are ‘clever’ will be called AI 2.  Most developments will happen outside MR and then be u+lised – e.g. AWS 3.  Machine learning will focus on situa+ons where learning sets exist – e.g. coding 4.  AI will allow materials that have tradi+onally been analysed with qual to be quan+ta+vely analysed 5.  Expert systems will be core to anything that does not produce large training sets – e.g. project design
  • 19. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Thank You! Follow me on TwiGer via @RayPoynter Connect on LinkedIn via hGps://www.linkedin.com/in/raypoynter
  • 20. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation Q & A BeGy Adamou Research Through Gaming Ray Poynter NewMR
  • 21. An Introduc+on to AI and Automa+on Ray Poynter, NewMR The Future of AI & Automation NewMR 2018 Sponsors Communica+on Silver Gold