Speech Transcript - Collective Intelligence and Team Learning (July 2014)

0:00:01	of the different kind of all preamble anyway
0:00:05	a little bit preamble we also know from that same study that
0:00:10	you know when you do you have high functioning teams
0:00:13	those schemes can generate the last and second you propagated other teams and you know
0:00:18	teams
0:00:19	we have a fair amount of data now in the
0:00:22	and the organisational behaviour literature more generally that teams can do a fair amount of
0:00:27	like areas learning from other teams so
0:00:30	really in here enhancing the quality of
0:00:33	teams at the team level can generate benefits for the plateau organisation
0:00:39	and finally and in some work that and doing that it will be describing today
0:00:43	but another line of work on the board and which includes michael o'leary a georgetown
0:00:48	i don't know with
0:00:49	if
0:00:51	are georgetown colleague here does michael but
0:00:55	we have been looking at multiple t membership and how you know really fat the
0:01:01	organisation scanned
0:01:03	strategically manage the assignment of people across scheme so that those ideas can cross pollen
0:01:08	eight across projects and they'll
0:01:11	that's all today that a lot of wanting and we talking about today at the
0:01:15	team level but i think that there are ways that we can read from that
0:01:19	the thinking about broader organisational by fax the of improving team collaboration
0:01:27	so without let me tell you a bit about the work within doing on collective
0:01:30	intelligence
0:01:32	in some gonna start with examples from the animal kingdom if in any of you
0:01:37	been familiar with some of the word my colleagues or i present you have to
0:01:41	do start here because the
0:01:43	because there isn't really fascinating example that i think that there are
0:01:47	a few observations we can generalize well
0:01:50	until recently if you had global collective intelligence than in even now
0:01:54	a lot of the are really hits you'll get in your search are examples from
0:01:59	the animal kingdom and in fact
0:02:01	we now have a collective intelligent conference
0:02:04	that's held every year or so at mit
0:02:08	and menu to people come present our people just study
0:02:12	these ideas any animals
0:02:14	because they're very they're really fascinating examples price to learn from in general i us
0:02:21	to it's for example are very you know simple creatures individually they don't have a
0:02:27	whole lotta
0:02:28	memory they don't have a whole lot of problem solving capacity but collectively because of
0:02:32	the effectiveness with which they coordinate
0:02:34	they can accomplish i huge range of things they can
0:02:38	build structures their fairly complex they can build bridges to traverse to rain that they
0:02:45	could and individually they can't hear e things
0:02:48	that are many multiple of their body way all because they have some very effective
0:02:53	mechanisms for coordinating individual after
0:02:56	into
0:02:57	we know one way in which these
0:03:02	examples don't generalized has to do with the range of things they try to accomplish
0:03:05	so
0:03:06	you know neither you know animal examples
0:03:09	there's a very limited
0:03:11	range of wave in which they're trying to coordinate
0:03:13	but they are hardwired to accomplish that coordination effectively sell and the an example
0:03:20	their fair amount trail today we
0:03:21	other in its follow the strength of the trail you know indicates to them how
0:03:27	many in the got along a particular path or you know whether or not it's
0:03:31	a good knoll the signal phase over time
0:03:34	and so
0:03:34	enables them to or me quite effectively in so we started to wonder if there
0:03:40	are groups
0:03:42	that i engage in a very high level high quality collaboration that consistent over time
0:03:49	and across domains
0:03:51	that essentially enables them to perform well at least in the range of things that
0:03:55	they're doing as a group
0:03:57	you know in that context in so
0:03:59	we know that there are some groups that perform at a very high level in
0:04:03	particular domain so this is a picture of the wealthiest chamber orchestra
0:04:08	there are world renowned chamber orchestra
0:04:12	that is also well known for being either a lesser conductor let they don't have
0:04:15	that
0:04:16	you know and appointee conductor they rotate model of shared leadership on the different pieces
0:04:22	that they play
0:04:23	so we know that or if yes excels and the music domain
0:04:27	but we wondered in our research is whether or not there is a measurable collective
0:04:32	intelligence teams that would differentiate your seventeens from others
0:04:37	and
0:04:38	and would give us insight into the prosody that they used to collaborate correlate effectively
0:04:43	over a wide range of situations and over time and can we use that information
0:04:48	actually project on which schemes are likely to perform well in the future so that
0:04:53	where we started this work
0:04:56	in starting this work we were really drawing on
0:05:00	ideas about general intelligence that originated with charles german
0:05:04	so
0:05:05	individual intelligent or i q as many
0:05:10	instruments refer to it you know originated with your men's work in the early twentieth
0:05:15	century he observe that school children who were good in one academic topic tended to
0:05:21	be also very good in and many other academic subjects
0:05:25	anything about
0:05:27	to where the empirically
0:05:29	and
0:05:30	in the va the really precursors a factor analysis which those who do research in
0:05:35	psychology and related domains are probably familiar with
0:05:39	but what he did of the isolated the underlying factor individual a factor of intelligent
0:05:46	orgy factor
0:05:47	and what we know that the chief that are that underlies performance in many domain
0:05:53	and this is the thing that individual like you task actually or task in the
0:05:58	level of g for an individual in so what we know from now decades and
0:06:05	actually over sentry of research after experiments initial work
0:06:10	is that individual intelligence
0:06:13	measured by task that can be administered in a short amount of time can predict
0:06:18	many outcomes over a broad range of domains over long periods of time so
0:06:24	if you give school children and intelligence tasks that not only projects how the performance
0:06:29	or
0:06:30	but also their career six that the probability with which to become an unwed teenage
0:06:37	period
0:06:38	i mean even their life expectancy so many domains over long periods of time
0:06:43	so we wondered if the same thing would be true for teams is there a
0:06:48	general collective intelligence factor or three factor to parallel a g fact there which day
0:06:54	just the degree to which
0:06:57	a just the quality of collaboration in coordination in a key and predict how well
0:07:01	they're likely to perform in the future
0:07:04	i
0:07:05	can you measure that their way to measure it can we predict which teams are
0:07:09	likely to perform well especially in advance of some fairly high speech projects their situations
0:07:14	may might put "'em" and i actually talked about this way to dump looks at
0:07:18	nasa who are interested in thinking about this with their groups of ask or not
0:07:22	because of the disastrous consequences
0:07:25	that they can experience if people
0:07:27	don't work well up on the space station and this is tell us something beyond
0:07:32	knowing individual intelligence so
0:07:34	in two we initiated this work a few years ago when we had talked about
0:07:38	intelligent teams it was as a function of the individual intelligence of numbers
0:07:43	and there is a lot well the research looking at you know what is the
0:07:46	relationship between average individual intelligence
0:07:49	indy performance and the
0:07:52	the answer is kind of complicated it depends on a lot of factors we wondered
0:07:58	of collective intelligence if we could measure a
0:08:00	we tell at something beyond individual intelligence with the is that a unique
0:08:05	capability that rim resided the group level that is the gauge of the effectiveness of
0:08:10	how the individuals work together
0:08:12	so we thought about doing that the research on methanol just briefly tell you about
0:08:18	our findings
0:08:20	we brought groups into the lab for several hours they worked on a whole range
0:08:25	of tasks we found strong evidence of an underlying collective intelligence factor
0:08:31	and that factor was a strong predictor of performance at a later point and end
0:08:36	time or more complex task multifaceted task in this case with the video games simulation
0:08:41	we have them deal collective intelligence with a much stronger predictor your performance
0:08:46	then the average i q or numbers or
0:08:50	many people ask what about the smartest person also but also to predict very much
0:08:54	about how the team performed especially compared to collective intelligence we replicated in this with
0:09:00	a broader with a different set of tasks a broader range of group sizes
0:09:05	i different outcome task and again found strong evidence of that
0:09:10	of a key factor
0:09:11	indian performance that predicted how these schemes
0:09:15	performed on this more complex task in much more cell
0:09:19	then the intelligence of individual members
0:09:22	and so just to kind of summarize the relative value of individual intelligence and collective
0:09:28	intelligence the dark bars are collective intelligence this is the pretty value of average individual
0:09:36	member intelligent and this is the value of
0:09:39	the maximum or the smartest person
0:09:42	in the group and so what we see is that the collective intelligence factor actually
0:09:47	gives us pretty good ability to predict which groups are likely to perform well in
0:09:52	the feature and in which all right
0:09:54	so
0:09:56	maybe more germane perhaps the thumb of the things will be talking about today we
0:10:00	also looked at that in the context of learning so we've now got nine and
0:10:05	several studies since our original paper command science a few years ago and looked at
0:10:10	it in a number of contexts predicting a number of different things over you no
0:10:15	longer ranges of time
0:10:17	and in found you know fairly consistent effects here where we're also interested in morning
0:10:23	in this particular task what we're getting at is what
0:10:28	some call implicit learning or implicitly coordination so this is the task where the group
0:10:33	play multiple rounds of again
0:10:35	where group members were paid on the basis of what they decided but also what
0:10:42	others in the group decided and these decisions were made with out communication so they
0:10:47	are on a computer
0:10:49	they're looking at a matrix
0:10:50	and they can i should of right a an example output of an example of
0:10:54	the matrix but basically
0:10:57	all the group members make a choice from the matrix and their p the on
0:11:02	the basis of the combination of choices that different group members make again without
0:11:07	communication and so
0:11:09	you know the start of in the first round after each round they find out
0:11:12	what their payoff wide and then they can make a choice for the next row
0:11:15	and basically what they're doing is they're trying to make
0:11:19	aghast about what others in the group are likely to choose based on you know
0:11:23	that outcomes that they're seeing with each round
0:11:26	itself
0:11:28	before they did if game we
0:11:30	gave them are caff battery to measure are their collective intelligence
0:11:34	and what's interesting about this is that in the first round
0:11:38	so the red line here are the groups that high collective intelligence the orange line
0:11:43	or yellow line or groups that were blowing collective intelligence
0:11:47	in what we see that the very first round groups that are high and low
0:11:50	collective intelligence don't really you know have exhibit very large differences in their performance in
0:11:58	fact if anything the high highly collectible intelligent groups are a little lower well as
0:12:02	you follow this red line you see that over the series of rounds
0:12:06	the highly collectively intelligent groups get better battery there's kind of this fairly steady climb
0:12:13	the and that significantly higher than where they began
0:12:17	overall
0:12:18	the members are much more money
0:12:20	then the group and the one
0:12:23	well collective intelligence groups where
0:12:25	it kinda jump around but they and that approximately where they began
0:12:30	and the are and significantly less money overall
0:12:34	in so in the economics literature these games as i mentioned are interpreted as gauging
0:12:39	implicit coordination and
0:12:42	and what we see is that there's something about the way that the groups that
0:12:45	are highly collective intelligence or functioning that enables them to sort of intensity what other
0:12:50	group members might be doing adjust their behaviour accordingly and perform better overall is the
0:12:55	you met
0:12:56	which we finally interesting
0:12:59	in another study this one was done in a classroom setting
0:13:03	again we gave teams beginning these routines that were
0:13:06	randomly assigned to work together as a group over the course of the semester we
0:13:11	gave them are collective intelligence battery at the beginning of the semester and then in
0:13:16	this particular course the teams had to take
0:13:19	i theories of exams together
0:13:21	and the first took the ml than individual and then without getting feedback on how
0:13:27	they done they did the same set of exam problems as a group
0:13:31	in this is part of the t f t vs learning if anybody's familiar with
0:13:36	that approach
0:13:37	it's two
0:13:39	again we measured collective intelligence the beginning of the term what we sat was that
0:13:43	the groups that were high collective intelligent shared steady improvement over the course of the
0:13:48	exams and the data together these are the teams choirs
0:13:51	a groups that were lower collective intelligence again not a not exactly upward trend but
0:13:57	without would be groups that will hire
0:13:59	but most interesting for us with even
0:14:03	how the group data relative to their best member so in this case of the
0:14:07	imagine a group the individual members to begin by themselves
0:14:10	and we were interested in how the group the relative to the student that in
0:14:14	the past an exam before the group get it together and what we see is
0:14:18	that and the groups that a high collective intelligence the group so we score significantly
0:14:23	better than their best number
0:14:25	whereas in the groups there alone collective intelligence their best members are just as good
0:14:29	as the members in the highly collectible intelligent teams but the group is not always
0:14:33	or better than that of course
0:14:35	in fact you know in the second exam a or
0:14:39	slightly worse
0:14:40	in so
0:14:41	there's something about the we that these groups that are high in collective intelligence or
0:14:45	coordinating that enables down to fill in the gaps of members knowledge so that they
0:14:49	together can actually give better an exam than any individual dead
0:14:54	on there are
0:14:55	for some reason that groups there alone collective intelligence this doesn't
0:15:00	so very told you that we try to predict collective intelligence on the basis of
0:15:05	individual intelligent and the correlation was very low so that kind of brings about the
0:15:11	question what does predict collective intelligence in so they were number of things that were
0:15:16	surprising that as we've been exploring
0:15:20	so the first is that it's not really directly predictable by the quality of relationships
0:15:25	that group members they're experiencing in the group and then we give invalidated scales of
0:15:32	things like good satisfaction and group cohesion and
0:15:36	of motivation et cetera and generally find no correlation between what group member say about
0:15:42	their experience and the matter collective intelligence of the group
0:15:45	this is not necessarily what they really the case that a group that deals more
0:15:49	group b is actually more collectively intelligent
0:15:52	in when i presented these results to a few different audiences in the military especially
0:15:57	the army in particular
0:15:59	they really wonder about the cohesion thing because there is a really strong belief there
0:16:03	that cohesion is the basis all other you know good performance variables and so this
0:16:10	research kind of call that are quite question
0:16:13	we've done a range of analyses on different personality managers
0:16:19	you know including the that
0:16:21	com and big five and related instruments of the main personality variables
0:16:28	it down no consistent relationship
0:16:30	with any sort of
0:16:32	purpose personality configurations in collective intelligence
0:16:37	one thing that we have found
0:16:39	you know fairly consistently and the relationship of the proportion of female in the group
0:16:45	in so
0:16:45	we recently kind of abrogated our data across several studies and so this represent hundreds
0:16:52	of groups
0:16:53	in what we find that when you're and so what we did we classify each
0:16:57	study on the basis of whether or not you know the corpus all male they
0:17:01	just had a single female
0:17:02	if it was majority male they had more than one female that it would be
0:17:05	in this point rather than in this at this point
0:17:08	if you know with half and half and then on over to a female and
0:17:12	what we feel that it is zero is the mean collective intelligence to me collective
0:17:19	intelligence is kind of a standard i score and so
0:17:22	zero is need
0:17:24	and here we use the is that it's when you get a majority female
0:17:28	or especially when you just have one now that groups more consistently reach
0:17:34	the highest level the collective intelligence when you get the all female
0:17:38	you see that the mean here draw the there are some benefit to diversity
0:17:43	but one that you know fevers female verses you know if you could be
0:17:50	so why we've continue to explore a little bit about why that might be and
0:17:55	you know all cover a few more results here let's start to give us a
0:17:59	little bit more insight
0:18:01	and so one of them have to do with inability that summer for the social
0:18:06	perceptive the sometimes i'm social intelligence
0:18:10	to in our study people completed a measure called the reading the mind in the
0:18:15	i have in this was originally developed by researchers of octave them and autism spectrum
0:18:22	disorders and so it involves looking at thirty six pictures of the eye region of
0:18:26	the and using
0:18:28	which of these options
0:18:31	you think represents what this person is thinking or feeling
0:18:35	in so
0:18:36	it turns out that if is very difficult for people who of art is ever
0:18:40	are not doesn't spectrum disorder
0:18:43	and it turns out that it also difficult for people and then normal and of
0:18:47	the continuum as well
0:18:49	and women on average or slightly better on this measure them and that was true
0:18:54	added into an all of our sample i mean it's true in the population overall
0:18:58	and this turns out to be highly predictable of collective intelligence the mean on this
0:19:03	scale
0:19:04	and so disprove this explained a lot of the f that not completely but a
0:19:09	lot of the in fact of the proportion of women in the group of collective
0:19:13	intelligence that you actually raise the average level of social perceptive
0:19:19	another compositional thing that we've looked at recently we start to look at various forms
0:19:25	of diversity
0:19:26	and so one form of diversity that
0:19:29	one of my graduate students is particularly interested in cognitive style diversity
0:19:34	and so what she finds of that this it's this moderate level of cognitive diversity
0:19:39	you see that there is curve a linear relationship but at the moderate level code
0:19:43	of cognitive diversity that seems to most enhance collective intelligence
0:19:48	and in similar for where recent study she's been looking a lot at you know
0:19:53	what exactly if that makes a group only moderately cognitively diverse and what you find
0:19:58	is that the existence of individuals you actually can
0:20:03	decline multiple different cognitive style
0:20:07	or one of the most important things that are present more often than is moderately
0:20:12	cognitively diverse groups so somebody you can take the perspective of
0:20:18	that is that what we're call it what the styles are called but just
0:20:21	to simplify for the moment you take mormon engineering perspective
0:20:25	but then also take more of an artistic perspective on a problem can help people
0:20:31	who are adapting only one cognitive style understand each other batter
0:20:36	and if is consistent with some other studies that are coming out on more recently
0:20:40	in the literature some other colleagues of mine have died
0:20:44	i'll looking at multicultural of them
0:20:47	p or people who are bilingual
0:20:50	and the role that a plane facilitating the group to their part have
0:20:54	in sort of helping bridge the gap between you know to people who anyways wouldn't
0:20:59	understand each other and they were finding that having some of these focus groups seems
0:21:04	to you know also enhance collective intelligence
0:21:09	so i've talked you about a set of things that you know we kind of
0:21:11	think of the screen compositional variables these are things that if you could screen people
0:21:16	in compose the ideal group
0:21:18	you know these might be things that you with pay attention to
0:21:22	but sometime you don't have that lingerie and so they're than other sort of things
0:21:26	that we've looked at
0:21:27	that have to do more with how the group actually behaves are interacts one there
0:21:31	together
0:21:33	it's the one set of findings really relates to group communication
0:21:37	so in our in our initial study we measured smoking behavior and patterns of speaking
0:21:44	and interrupting so our group members all war the gizmos called social badges they were
0:21:49	invaded by one of our collaborators at mit
0:21:53	see any pentland is written a lot about these in various contexts
0:21:57	but in our case we had them wearing i'm so that we could pick up
0:22:00	patterns of spoken language
0:22:02	and what we found is that
0:22:05	sort of more or distribution of speaking turns or
0:22:09	i was most strongly associated with high collective intelligence or in other words of somebody
0:22:15	was dominating the conversation and doing all of the talking kind of like i am
0:22:19	right now if this is how our whole interaction one that we will be very
0:22:25	close intelligent probably
0:22:28	and that's what we find a in our study
0:22:30	in we've also found it even with that's one studies that we dined with online
0:22:35	and so
0:22:37	we know that some studies where groups are interacting just by a chat
0:22:42	and we find that
0:22:44	you know total amount of communication and is predictive of collective intelligence but also equality
0:22:49	of communication
0:22:51	in this even generalize is over into now when we measure collective intelligence we use
0:22:57	a platform where we can keep track of
0:22:59	applying the answers to questions are battery and whose you know doing the work of
0:23:05	the tasks that we're having the group or and still we also can measure the
0:23:09	equality contribution to that in we all we find i think that or equal contribution
0:23:15	to the work of the group
0:23:17	is also associated with higher clock intelligence
0:23:23	in so then another piece that i'm gonna speech to on this is sort of
0:23:28	is that we talk about level communication but and it also how and what are
0:23:33	at communicating about in here we've been looking at levels of collaboration and integration
0:23:40	in still in a in a study that we published if you years ago now
0:23:46	we were giving groups basically care is
0:23:51	case scenario to solve in we compose these groups so that they either are included
0:23:56	some key pieces of expertise among them so experts in the topics that they needed
0:24:01	to analyzed in order to solve the case or they get in
0:24:06	but we also had i their spending time at the beginning of their work really
0:24:11	thinking through the aspects of the problem and how they should integrate the analyses that
0:24:16	they were gonna be doing individually or we get and we just let them kind
0:24:20	of start the way that they normally word
0:24:24	and what we found that when the group didn't have the expertise they needed
0:24:30	i didn't matter if we gave them some way of integrating this expertise you know
0:24:34	in this kind a higher quality weighted in how the information they needed to make
0:24:37	a release all the case
0:24:39	but more interesting was what happened when we did have the experts in the group
0:24:44	we have experts in the group but we didn't have them
0:24:47	spend time thinking about how to integrate their work the group actually perform significantly worse
0:24:52	than that they didn't have the expert
0:24:54	and what we found is that these group members work even more in isolation than
0:25:01	the groups that were lacking the expertise because they sort of this unit each person
0:25:05	kinda knew what they needed to do you do their partners together right in the
0:25:08	group would be fine so they didn't actually collaborate integrate
0:25:13	as much as the as the groups and the other condition however when the group
0:25:18	have the expertise in the and we gave them again a fairly simple
0:25:23	sort of starting exercise to think about how to integrate their work you know they
0:25:27	perform the basketball
0:25:29	in so it you don't really suggests that has that it's not about how you
0:25:33	compose the group of making sure you have the right people in it but what
0:25:36	they actually still when they're together
0:25:39	and when i tell people about some of these findings
0:25:42	i often dry analogies with madison
0:25:46	and i'm a big fan of a lot of what had to go one the
0:25:49	right in his but about the medical profession but one point he takes than the
0:25:55	others make it you know
0:25:57	in that it then they're doing a lot of research to try to come up
0:26:00	with bigger and better way to do things but in fact there are a lot
0:26:04	of really simple things that they are you know have huge impact in this trouble
0:26:08	is really just to get groups to do it to get people to do it
0:26:12	and so one really common intervention for example hospital if hand washing
0:26:17	right
0:26:18	we know that hand washing is one of the thing almost important things that
0:26:22	medical professionals can do this but to control the spread of the action however you
0:26:26	know it often doesn't happen in the then they have to do this big interventions
0:26:30	get people to last
0:26:32	and so another simple thing that
0:26:34	you know was tried a few years ago and had a huge tax is that
0:26:38	surgery chocolate and essentially it sounds very much like the type of thing we did
0:26:44	with our teams
0:26:45	be in the lab study
0:26:47	in this case they have a checklist again through the make sure the introduce themselves
0:26:51	they make sure they know what keys they're working on and you know where they're
0:26:55	doing their operation and they make sure they have all of the supplies that they
0:26:58	need it's that are tracked that are down the list and they found fairly large
0:27:04	for this fairly simple intervention because you know i'm sure many but support for stories
0:27:10	of people having the wrong one cutoff or whatever you know just because them fairly
0:27:14	key details or not from the beginning and sell everything is that you know what
0:27:19	our study suggest
0:27:21	well two things one is that i know from doing mathematically studies now then when
0:27:26	you bring a team into the room if you don't make them stop
0:27:29	and sorta
0:27:31	introduce themselves and use "'em" key things they don't
0:27:34	and it makes a huge difference in terms of how they operate as a team
0:27:37	and i think that's true and a lot of organisational settings as well and so
0:27:41	they're probably from fairly simple interventions that can be in a
0:27:45	the help teens achieve higher levels of integration
0:27:48	it's a while
0:27:49	we're still doing research to find even bigger fancier ways for them to achieve integration
0:27:53	and i think the implementation of the simple things well while but that
0:27:59	and then the final thing and this is like hot off the path in fact
0:28:03	we just present these results that a conference last week and my graduates didn't you
0:28:07	get the work on that's actually one an award for the poster
0:28:11	on this so you're one of the first groups that i'm gonna tell about it
0:28:16	but it really to trying to reduce as much as possible the intra groups that
0:28:23	of competition
0:28:25	and the effect that has for collective intelligence so in this particular study
0:28:31	we composed groups to include between zero in four when n
0:28:37	and then we had three different conditions we had sort of the condition where we
0:28:42	didn't do anything to manipulate status of group members
0:28:45	we can condition in which
0:28:48	we had them a lack the reader
0:28:51	and then after condition in which we had the molecular either but we told that
0:28:55	at the end of the
0:28:56	or later in the fashion name i have a chance to electoral a different year
0:29:00	if they want to deal
0:29:02	and what we found with that
0:29:04	that condition where the elected the we heard and they could change that meter is
0:29:08	what elicited the most
0:29:09	that its computation and so we later ask group members about their experiences that of
0:29:14	competition
0:29:15	in the group
0:29:16	and so what is in this graph is that if static computation on their measured
0:29:23	bubble of collective intelligence
0:29:25	as a function of having women are in the group
0:29:28	and what you see if that so that the solid line if the highest levels
0:29:32	of static competition
0:29:34	and basically at that of competition goes up
0:29:38	and the number of women in the group goes up
0:29:40	collective intelligence goes away down
0:29:44	and we know that's added competition is the highest in those conditions in which the
0:29:47	that the leader could change
0:29:50	whereas when status competition is low and especially as number of women in the group
0:29:57	go up paper for a whole lot at
0:30:00	and so we're still
0:30:03	looking at the nature of the information sharing the happen in the group you know
0:30:06	how that of competition was an active
0:30:10	a basically
0:30:11	you know kind of a really sense that i think people to the difference that
0:30:14	of the members of the group giving a sense that no one person could kind
0:30:19	of overtaken other in viewership of the group seems to have a really does more
0:30:23	facts for collective intelligence in groups and especially
0:30:28	as more women are included and
0:30:30	i be talking a lot recently i've been called by groups that are interested integrating
0:30:37	one and into their profession so you know engineering society vehicle society they're to that
0:30:46	seem to deal
0:30:47	especially focusing on that
0:30:50	and you know part of it is maybe when there is a lot of attention
0:30:54	test data in a particular profession women don't even want to be there to begin
0:30:59	what
0:31:00	and so this is something that you know with tax that a lot about is
0:31:04	you know to the degree that it's a very static on chat sort of setting
0:31:10	you know if that one of the things that is keeping one and from wanting
0:31:13	to stay
0:31:15	so
0:31:16	in with no certainly even if they use a on it doesn't help the quality
0:31:21	of the work of the groups that there
0:31:23	so another important feature not necessarily one that easy to resolve and reaching back to
0:31:28	the medical example with this is there in spades in terms of the status differences
0:31:34	that are very apparent in a lot of medical setting something that they reluctantly
0:31:38	trying to think about grapple with as well
0:31:42	so just by way of kind of summarising in wrapping up here
0:31:48	we now done you know dozens of studies looking at the existence of c factor
0:31:54	in crude and you know keep finding evidence of it in a wide variety of
0:32:00	settings so it's just underlying capability that captures the quality of collaboration in coordination in
0:32:05	a group
0:32:06	i that seems to be relatively stable absent some big intervention and
0:32:11	into et
0:32:12	and predicts future performance as well as group learning and the inability programmers to make
0:32:18	the most of what are the team to make the most of what individual members
0:32:22	now
0:32:23	and what we see what we look across the kind the factors that theme to
0:32:28	facilitate collective intelligence they all relate to
0:32:33	things that facilitate the transfer an integration of information
0:32:37	so people who are better at social perception or better able to pick up and
0:32:42	nonverbal cues in factor that into the way that they behave and how they
0:32:47	conduct group conversation
0:32:49	groups that are either low or moderate incarnated versa your have individuals in the group
0:32:55	who know how to read the different cues are the different perspectives of other members
0:33:00	giving more collectively intelligent
0:33:02	when we make sure everybody can be part of the conversation and relatively low
0:33:08	proportions we get more information from everybody more collect intelligence
0:33:14	interventions that enhance collaboration in integration are important and the status competition because as we
0:33:22	know some of the work of in the edmonton in others our business or
0:33:27	really looks that
0:33:28	you know how comfortable do all the different group members feel about speaking not in
0:33:33	a situation and if
0:33:34	that of competition high people are really paying attention is added then you probably a
0:33:39	certain people
0:33:40	we do not feel comfortable speaking up and that hurts the group
0:33:44	and that's very consistent what we found
0:33:48	so with that i'm going to close i
0:33:52	my domination of the conversation
0:33:55	and open it up i guess the questions a and other conversational
0:33:59	so now
0:34:02	hopefully o
0:34:06	i mean you can
0:34:08	any
0:34:11	i and then i
0:34:15	that and are higher than average and social perception so i suspect or okay
0:34:23	i
0:34:26	so
0:34:36	sure
0:34:38	that the great question
0:34:39	so we have you know so it's no longer the five or six hours in
0:34:44	the lab we've actually but for that you know had a big project of
0:34:49	creating it on my in a web based tool
0:34:52	where remembers login and so they can either be colocated are they can be at
0:34:57	different locations and a log and then
0:34:59	it gives them kind of a work study and in the you know facility that
0:35:04	they can use to communicate in the system kind of presents the problems that
0:35:09	and they can see each other's work as they work together kinda like google docks
0:35:14	if you've ever if not google docks exactly but it's that idea
0:35:18	and so and so that's what we used in so
0:35:20	the collective intelligence measures basically just had a the team perform across the different kinds
0:35:26	of problems we gave
0:35:28	and then go into a lot the problems that we gave
0:35:32	the groups but there are problems that
0:35:36	can all be done by us a little individual by themselves are really require different
0:35:40	kinds of collaboration across the group members so really gauge on the degree which they're
0:35:46	good at collaborating coordinating
0:35:49	i
0:35:57	i
0:35:59	that's interesting so that something we may be integrating into it at the moment the
0:36:04	way the system works of the day just have a limited amount of time to
0:36:07	work on each problem
0:36:09	and then we just take you know how well they perform a given that time
0:36:12	i'm in actually is well a lot of the standard i q task are structured
0:36:18	that way all the more of them are starting to also move toward the how
0:36:21	long to take approach to see if they can get a better a better estimate
0:36:26	that way
0:36:29	well
0:36:35	well as one okay well known for its right i well i l one
0:36:56	i star
0:36:59	a how i
0:37:07	so and making drying from analogy and i q task because of something that i
0:37:12	think a lot of people are familiar with
0:37:14	we really give them problems that don't require any particular domain expertise but are more
0:37:20	you know abstractions so that really what we're getting at is the group process verses
0:37:24	any particular content knowledge
0:37:29	actually i so same questions are you get a little bit different use and answered
0:37:37	it may be elaborate a little bit more and whether it only on a syllable
0:37:41	this what i think that i would have a actually the
0:37:44	i know it means for example we first apply prosody is that essentially and different
0:37:54	subject matter expertise is that what implies that
0:37:59	or is that something
0:38:01	so the kind of style diversity that we look at is beef and work still
0:38:10	you probably are familiar with the classic and educational distinction between people who are available
0:38:14	lasers of people who are visualise errors it more recent work and neuroscience the cognitive
0:38:21	psychology has for their distinguish different types of visualiser is there are some people who
0:38:25	are
0:38:26	object visualiser is their very good at they come up with very vivid mental images
0:38:31	to represent problems and kind of think about images holistic we in a tent were
0:38:38	quarry and sort of the visual arts a design
0:38:42	and their distinct emphatic terms kind of silly negative correlation
0:38:47	between the capabilities and of my calendars pop in your screen but
0:38:53	that there is a and negative correlation between the capabilities associated with that people who
0:38:58	are spatial visualiser is a really good a rotating objects and three dimensional space
0:39:03	and tend to be more represented in sort of engineering and that
0:39:08	professions and so
0:39:09	we use a manager that was developed by the cognitive psychologists you
0:39:14	identify this dimension and
0:39:16	and that classifies people a strong verbalize are strong but spatial visualiser is a strong
0:39:22	object visualise errors in there is a developing literature really
0:39:27	demonstrating how
0:39:28	people just engaging fundamentally different approaches to problem solving as a function of what their
0:39:34	dominant cognitive style as
0:39:38	and
0:39:39	you know so those are the dimension that we're using and so what we find
0:39:42	that diversity and groups create some problems although create some opportunities because those are associated
0:39:49	with different skills and so if you have people in the group we have the
0:39:52	skills obviously of the advantage of the scale but simply you know just talking together
0:39:56	about
0:39:57	how we wanna work together you know can be a little bit of a challenge
0:40:01	if it's a real eager brisk
0:40:07	i yes i scenes
0:40:12	brain clear
0:40:13	if you do i know if you higher
0:40:19	how he's and one
0:40:27	of course there are a well i schemes error bars
0:40:39	wow you please
0:40:44	and i hated this well this is with this study was the same
0:41:07	so no
0:41:10	well and so
0:41:12	so i be i should i have i'm familiar with seminar she's work on that
0:41:18	but i should look at what he
0:41:19	explored as in the explanation for why because
0:41:24	there's also this you know small but developing literature about the individuals were multi cultural
0:41:30	are multinational in the role that they can play
0:41:33	a disability not so i don't know if he has are people who are expat
0:41:39	do you can do the thinking and some care l
0:41:42	talk to everything and anything like that
0:41:45	well i e o
0:41:53	the question is a it's a well with the meeting area and
0:42:00	well
0:42:03	so you can
0:42:05	is it has been integration and
0:42:09	with initial work well i don't know and you met the some exercises duration
0:42:17	you look so well used or so
0:42:24	sure so i would say that it so to take the first question for so
0:42:28	cohesion is usually try to as
0:42:32	you know a concept that really to how people feel about the other people in
0:42:37	their group you know and so i know the latter the standard measures of cohesion
0:42:43	have to do with would you like to socialise with these people would you like
0:42:48	you know would you d
0:42:49	that is
0:42:50	these people left your group you have to do more with the interpersonal relationships with
0:42:56	integration is more of a cognitive
0:42:59	idea it's really more
0:43:01	do i see the connections between what i know in what i'm doing and what
0:43:05	you know in what you're doing
0:43:08	so that the distinction and under of that
0:43:12	make that are if i could be more if that doesn't
0:43:15	no i
0:43:17	and so
0:43:19	it so
0:43:20	in the exercises that we've use both in our lab stays but also i
0:43:25	what i suppose dark actually dead quite a bit work in the intelligence community and
0:43:29	that we were working with time analyses that were collaborating across agencies and so we
0:43:34	use similar a similar approach
0:43:36	but off then
0:43:38	in my case that a lot of the simple
0:43:42	has to do with having group members identify what their knowledge bases are
0:43:48	and then having the group together about what is the problem they're going to solve
0:43:53	the one of the parts of that problem and who would be passed to address
0:43:57	the different parts of the problem
0:44:00	and identifying what the overlaps are there so it's like okay you know brian and
0:44:04	i should be together about
0:44:07	international diversity in teams because you know we both know something about that i don't
0:44:12	know
0:44:13	the way you know i don't know about this but you know somebody else that
0:44:17	it just speaking systematically them at
0:44:21	a identify what numbers knowing be mapped onto the work that they're doing
0:44:25	in kind of a more explicit way
0:44:28	because otherwise what happens and i we thought it i observed a lot of exercising
0:44:32	the intelligence community before we started doing our work with the teens there
0:44:37	and you get to the end of the problem and they would discover
0:44:40	that there's this quiet woman in the corner who actually have the physics degree that
0:44:45	would have been really helpful for some of the stuff that we were looking at
0:44:49	and nobody in you know you know until the end and what was to eight
0:44:54	and that happens a lot and so i think just that simple step is you
0:45:00	know an initial
0:45:03	and initial interaction
0:45:08	i mean
0:45:11	as well
0:45:19	all right
0:45:28	and
0:45:32	as you were just going to do
0:45:40	so well
0:45:43	one
0:45:47	so i think you know it is really the path of like you know finding
0:45:50	out where f you have in how to put them together and what interesting so
0:45:56	you know that i remember that and that actually we did that would be intelligence
0:45:59	teens as well where you know if we were starting in echo
0:46:04	we have them spend five or ten minutes kare and interview each other
0:46:09	and then introduce your apartment or the group
0:46:12	and so that somebody brag about somebody held you know and how they're gonna be
0:46:18	really helpful and also you know create at least one you know more solid relationship
0:46:23	to start with nobody knows each other
0:46:26	and then let everybody else like you know you know what
0:46:31	what resources they have and the members
0:46:34	so i mean i again to downsample is if it is usually get really undermines
0:46:41	the ability of the team to really make use of what they have
0:46:47	well
0:46:50	you
0:46:54	we
0:47:04	one unsuccessful ones
0:47:07	one for people
0:47:10	we replace
0:47:13	right
0:47:15	used to
0:47:24	and
0:47:30	well
0:47:32	so we are starting to look at
0:47:35	at turn over
0:47:37	a bit in a different project that i didn't mention here
0:47:42	the
0:47:44	it depends on what the what the criteria of performance are
0:47:49	you know we find in some contexts where
0:47:52	you know what
0:47:54	generating you know deep insight or in our high levels of innovation
0:47:59	there is less harm from the
0:48:02	turnover but for the most power and you know i haven't come across anything at
0:48:07	that really suggests a great way to do deal with that i mean most of
0:48:11	the
0:48:12	the academic literature as well as you know the practical their of like you're mentioning
0:48:15	really just suggest that
0:48:19	you know turnover is
0:48:21	i had has a really deliver here if the fact
0:48:25	and when you
0:48:26	when you met your collective intelligence and eighteen am i think it can give you
0:48:30	actually an even better gauge of how much value you just way when you take
0:48:35	people out of the team or move them around
0:48:38	but unfortunately i mean i would love to come up a an organisation
0:48:42	who is dealing with that really well
0:48:45	because they have it
0:48:52	well is there i guess that is really
0:49:04	but the thing a more
0:49:09	at first
0:49:24	and
0:49:35	then
0:49:37	no
0:49:42	i
0:49:48	in
0:49:50	including
0:49:58	and you know
0:50:02	you know
0:50:03	i
0:50:07	all right
0:50:19	well
0:50:22	it's using the one
0:50:28	well i think i
0:50:35	well
0:50:38	no
0:50:43	i think it here
0:50:54	g
0:51:03	i
0:51:06	and
0:51:11	or not
0:51:23	i social
0:51:32	well as
0:51:39	we can work
0:51:51	i
0:52:00	there is thirty than another graduate student
0:52:05	is just completing and she with the my off as just the other day a
0:52:09	fading that he is finding a huge fat
0:52:13	of each diversity in this particular stamp all
0:52:17	and
0:52:18	you know trying to understand you know is that another way in which that is
0:52:22	is coming into you know the group or what's going on and so she's taking
0:52:27	into that i think you know it depends on the th adding you know the
0:52:33	degree to which differences in age will bring about those the fax
0:52:38	sometimes actually in more high tech settings it's not necessarily better to the older
0:52:43	in the it not necessarily the case that each diversity will automatically read the status
0:52:49	of a
0:52:50	but i do wanna definitely
0:52:53	you know i get
0:52:55	those something that i think with you know was saying about
0:53:00	you know readers
0:53:03	and what readers can do that can affect the
0:53:06	and i think definitely you know and some of the research again i'm at edmonton
0:53:12	is another person who's done some work on the
0:53:16	demonstrating that the way that the leader operate within the team like they're
0:53:20	there
0:53:24	appended feature
0:53:25	what different people think to invite their credit for them to admit when they don't
0:53:29	know things you know it's that arise seems to really make a difference in terms
0:53:34	of how much the status difference as
0:53:37	given the way of the group functioning so something that again you know
0:53:42	the median organisational culture around it but then there's something at the t mobile i
0:53:46	think they can be done trying to gated a little bit
0:53:53	and the is that
0:53:56	still
0:53:57	well once
0:53:59	also
0:54:02	in addition
0:54:09	you just
0:54:11	well the utterances
0:54:15	call this derivation
0:54:19	representation is integrated maybe
0:54:23	i guess of so
0:54:25	this became so literally research on
0:54:31	alright
0:54:36	i
0:54:38	so we haven't done study only think
0:54:45	well there is there is a study that we did this was with the
0:54:49	it was done in germany but with one of my collaborators we're they were looking
0:54:54	at collective intelligence and the groups where it was computer scientists and in other people
0:55:00	from other disciplines working on projects that were rated for
0:55:03	and i'm number of dimensions including innovation in we could find a strong
0:55:08	correlation between collective intelligence innovation
0:55:12	but also just you know theoretically
0:55:15	based on the very point two were making
0:55:17	other things that we know that we innovation and
0:55:20	learning
0:55:21	integration of expertise et cetera
0:55:25	coordination
0:55:28	our old things that lead innovation as well and so i think you know it's
0:55:32	it's just a small logical extension
0:55:35	that collective intelligence would
0:55:42	you can only do nothing so held
0:55:47	other than that and
0:55:51	impulse there working together or the
0:55:56	so we move or vision the you may or may be will in the u
0:56:06	to do you really uttered and when the more common well so how
0:56:17	you saw a copula can how can we will use the main issue
0:56:28	a role one syllable
0:56:34	all well
0:56:37	starting point
0:56:40	well known that is
0:56:47	and use them to you know what we do
0:57:04	how can we will
0:57:13	really
0:57:16	so but the and who wish
0:57:25	in fact another example that sometimes i hope and talks on the topic with comes
0:57:30	from support
0:57:32	and because of exactly words and you can have some very good players and the
0:57:37	team
0:57:38	you know
0:57:39	okay players great team you know what what's the different i would say that so
0:57:45	when a lot of our study
0:57:48	one that i was mentioning and some others
0:57:50	we measure collective intelligence pretty early on in the group slight if
0:57:55	and it's pretty predictable things that happen later
0:57:58	now we are not intervening to try to change the collective intelligence of the group
0:58:04	i mean that something that we're
0:58:05	you know trying to conduct some studies on vowel to see how that can be
0:58:09	done
0:58:10	but you know
0:58:12	what we know there's a whole you know kind a decade the literature i
0:58:18	group performance and norms and how
0:58:21	patterns get that very early in a group slight intend to be sustained like absent
0:58:26	some sort of intervention to change how that group is interacting
0:58:30	there are certain patterns that are sustained i mean in we viewed all the time
0:58:34	when i teach a class
0:58:37	people tend if anything c
0:58:38	the thing people talk all the time some people don't talk at all you know
0:58:43	and the atoms and like some sort of shaking things that rate as humans we
0:58:48	seem to be just wired a falling the patterns in follows patterns
0:58:52	fairly consistent rate
0:58:53	and so i would say that
0:58:56	in either you know the very do more diverse groups adding are in another group
0:59:00	standing often what makes the difference between a group that start slow and stays low
0:59:06	and one that gets better it's somebody something
0:59:10	trying to change there's patterns in get
0:59:13	people more bald as they get more comfortable
0:59:16	you know pull people out who are withdraw and you know kind of
0:59:21	quiet down the people who are dominating we know whatever the thing is that's going
0:59:25	on
0:59:26	and so you know i think
0:59:28	unlike individual intelligence we can't really change an individual's intelligence
0:59:32	sure of doing some sort of brain surgery
0:59:36	you know in collective intelligence you know i think there are lots of possibilities for
0:59:41	what we may be able to deal
0:59:48	and how
0:59:53	you see that
0:59:59	i he or
1:00:05	is more
1:00:08	at time
1:00:10	so we're actually right now in the middle of studying that questions that we spent
1:00:16	you know a couple years getting this web based tool designed that i mentioned earlier
1:00:21	and now we're in the middle this study where we you know where are trying
1:00:25	to measure collected collective intelligence and multiple point at multiple waiting time
1:00:31	the average we have so far suggests that at then an intervention it remains pretty
1:00:37	stable in our attributions are pretty much what i was just describing which is that
1:00:43	groups on the patterns and those patterns
1:00:45	you know that have than something shaking you know that kind of how they operate
1:00:50	and so you know things are fairly stable but we do still and again sometimes
1:00:55	the interventions there are fairly simple so my collaborator the one dimension sandy pentland humid
1:01:03	the social badges and then have also maybe things
1:01:08	which give groups realtime feedback about the distribution of their communication
1:01:15	and so they either have little
1:01:18	displays on the wall that are kind of indicate a you know who how much
1:01:22	each person is talking or they have there is a q one that i thought
1:01:26	where there is a ball there though the table is a display and there is
1:01:32	a ball in the centre and the group object is to
1:01:37	keep the ball in the centre but of one person a dominating the conversation like
1:01:40	right now the ball would flow for me
1:01:43	you know the then the with the kind of quiet down and let it you
1:01:47	know that make it a in the centre
1:01:50	and they find actually they look at you know performance that decision making kinda tasks
1:01:55	and they find that groups can work better when they get the speaker
1:01:59	about relative contribution
1:02:07	o two
1:02:12	i well
1:02:14	i k o k o nine or you know huh
1:02:26	but i
1:02:31	as i used it well i will use them as well
1:02:40	well you know i already know that users at last actually star
1:02:53	right you just see most
1:02:59	i mean you know i think that it's of my collaborator time alone at mit
1:03:07	whose you directs the centre for collective intelligence there
1:03:10	it's definitely interest in a nice idea that the organisation allowable
1:03:14	you know indefinitely the
1:03:17	you know possibilities or even you know multi organisational level where you know you can
1:03:23	setup collaborations a few well
1:03:26	that make you know the best use of what the individuals you know bring to
1:03:29	that collaboration you know even at that scale
1:03:37	in addition
1:03:39	all possible
1:03:49	which was a lot this is it's
1:03:56	one
1:03:58	i really with that but the researchers really controversy all
1:04:03	so i don't know the accuracies are baying i haven't you know i'm familiar with
1:04:07	the research in the book having read
1:04:10	you know that book
1:04:12	you know are repeated that work but
1:04:15	i would save to their number website now luminosity is one and you know there
1:04:21	are a number of others that both cognitive psychology researchers to
1:04:28	are interested in seeing if a can actually improve particular brain process the working memory
1:04:34	is often one of the key ones you know and
1:04:39	you know people's response speed you know how task they are attention is another you
1:04:45	know if we can improve this process it can we actually increased intelligence
1:04:50	and i would say at least studies that i'm familiar where
1:04:53	suggests that off and the
1:04:57	realm in which the effects
1:04:58	generalize is pretty limited
1:05:00	you know in so
1:05:03	on them a c d you can play these games it'll improve your memory your
1:05:08	attention and you're processing speed and generalized is to environments that are
1:05:13	you know those games are similar but it probably doesn't seem to generalize to you
1:05:18	know what you that do when you go back to your job and work with
1:05:22	your colleagues so
1:05:24	there's a lot of controversy
1:05:26	i would say that the most consistent added and comes from early intervention you know
1:05:32	and so that spend a lot of the support for head start programs and things
1:05:35	like that
1:05:36	in terms of enriching kids environments but
1:05:39	most of the evidence really suggest that those interventions have to happen early
1:05:45	so i o
1:05:49	in our is
1:05:54	well
1:06:00	where
1:06:02	you
1:06:04	but it is or so
1:06:09	so i
1:06:14	for me
1:06:19	if i
1:06:27	i
1:06:30	well as i say at all
1:06:37	i
1:06:41	where we're at her
1:06:44	so far
1:06:57	at a
1:07:06	it remember
1:07:09	dimensions
1:07:14	one is not sure that
1:07:32	i know there's huh
1:07:42	you haven't seen any of what content captured
1:07:45	okay
1:07:47	and with some
1:07:53	you are so i
1:07:58	i
1:08:02	i
1:08:08	unfortunately so you know there is a growing body of data there is a study
1:08:13	that came out and academic journal not too long ago looking at
1:08:17	after action reviews and there are facts for group learning and it was pretty convincing
1:08:22	evidence that it's definitely practise that is worth
1:08:26	doing for sure
1:08:29	if you're not only in terms of just you know
1:08:32	not making the mistakes you made here again but even you know
1:08:37	performance a novel domain so
1:08:39	you know just as a group we figure out things that we should just pay
1:08:43	attention to more broadly
1:08:45	so i would say that is definitely true i would also say that the organisation
1:08:52	there are few then i'm starting to get to know i don't think i can
1:08:56	name them yet but
1:08:58	consulting company where they also della turnover in people changing teams than that
1:09:04	it seems like
1:09:06	in and often differs something that
1:09:08	you know folks the total in a particular stress you can't really control but the
1:09:14	more broadly the information can be shared an integrated silence data
1:09:18	right in a document about this project
1:09:22	i am actually adding to expand the and involving document about projects of this kind
1:09:29	you know and that kind of creating more integrated knowledge than having a bunch of
1:09:33	individual reports where we you know here about how that project where and have to
1:09:38	do the integrating ourselves
1:09:40	but that involves quite a bit of
1:09:44	coordinated infrastructure and
1:09:47	openness of knowledge sharing for that to happen in a known a lot of organisations
1:09:51	that doesn't exist
1:09:53	but it works well and that kind in this consulting contracts that i studied
1:10:03	and it is in one
1:10:06	i guess the closest thing that a lot of people are familiar with is like
1:10:10	we could p d a right
1:10:12	so instead of
1:10:13	having documents that are in folders we key
1:10:17	and you know in several more working on projects of this time you know one
1:10:21	of our steps we you are team after action review and we all
1:10:25	work on expanding
1:10:27	the whiskey that is for projects of this kind
1:10:31	and it just sort of
1:10:32	you know how everybody is familiar with what other teams a put in there is
1:10:36	have to go there to have their own stuff
1:10:38	at that it added in this more integrated way
1:10:43	o
1:10:47	i met
1:10:58	it's to be used for
1:11:08	it is o g
1:11:26	o s
1:11:27	they are
1:11:31	i know
1:11:35	however i
1:11:39	yes
1:11:41	right
1:11:43	we wish
1:11:46	and
1:11:48	or it where i
1:11:56	there is there is no
1:12:02	you is
1:12:09	checked and
1:12:13	what about
1:12:17	huh
1:12:17	well i still
1:12:22	i will but also on line
1:12:27	as well
1:12:29	so
1:12:32	said issue
1:12:33	but
1:12:36	she
1:12:41	it is interesting there is used
1:12:48	so
1:12:52	you know
1:12:53	i was just
1:12:55	ryan example he's waves collaboration just remind me a growing number of collaboration for nine
1:13:02	have a you know involve a distributed collaborators which is why am i go to
1:13:07	meeting a lot but well have to take notes or do things in a double
1:13:11	dark as well so even note on our meeting you know in it
1:13:14	like okay so i'm gonna break down the you know we need is gonna do
1:13:18	x y z you know and so then by the end of the meeting you
1:13:21	know we have a set of collected notes that we can work you know work
1:13:26	off of so
1:13:28	i think that there is you know the technologies are enabling that's more so the
1:13:33	the quantities of static documents better in some folder have to go find that
1:13:37	you know or are starting to fade
1:13:44	okay thank you
1:13:47	right
1:13:52	well thank you i'm and enjoy talking with you about it and i hope it
1:13:57	with helpful or
1:13:59	you know that provoking for real and i look for the hearing more about what
1:14:02	you all the phi do
1:14:07	o
1:14:10	i m
1:14:14	i see i
1:14:18	okay
1:14:20	all right now mentioned it's not
1:14:22	this presentation
1:14:28	sure
1:14:29	between
1:14:33	okay alright makes
1:14:36	take care
1:14:41	here

Collective Intelligence and Team Learning (July 2014)

BBL

Dr. Anita Woolley, CMU