Qualitative data is not distinguished explicitly here as such. The speaker focuses rather on the data's format (e.g. textual, sensor data, etc). The way it is described, it sounds like all data including textual data is raw material to be throw into the pot and fed into "Lucy" for analysis (although how exactly this analysis is/would be conducted is not detailed). The potential for data then comes not from the individual data points but from its sheer volume, which can then be analyzed across domains for surprising findings.
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Quote from Transcript:
"The data has huge varieties. It's not just textual data that people are typing, it's coming from sensors, it's coming from, it's coming from, from many connected devices, it's coming from radiological images, massive amounts of data being generated. And interesting, the data has varying degrees of veracity. It's not always accurate, it's not always true, we have to find ways of working with different kinds of data, different mediums of data, to in fact, extract valuable information. But one of the things that we'll see as we address the problems of Africa is the ability to relate that data together and to find patterns, and in fact, to find surprises. And as we begin to extract features, find patterns and find connections, we're going to find amazing things. And they will fall right into the bullseye of the kinds of things we're going to need to do in Africa."
This quote (see copy-pasted below) articulates why using machine learning is more robust... because "if you torture the data, for long enough, you can get it to confess to almost anything." The speaker justifies increased use of cognitive computing by stating that machines are better at the "discovery" portion because they can find the stories "that [are] trying to get out" of the data. So using machines for analysis are better than humans because they lack "any preconceptions or prejudices. That's discovery." 6 years later, the conversation in AI and machine learning has significantly shifted to acknowledge the inbuilt bias and prejudices that can be hard-coded into the technologies and softwares themselves (by the human beings that build these machines!).
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Quote from Transcript:
"And finally, what about discovery? So I'd like to take you back to your days, we've all had courses in statistics. And I, I don't know if you had the same experience, but I had pretty cranky instructors in statistics. And they told me that we should always form a hypothesis and then test the hypothesis. Don't jump the gun, don't start looking at the data before you've formed a hypothesis. That was nice. But then [inaudible name] came along. Professor [inaudible name] came along and said, well wait a minute. You know, if you torture the data, for long enough, you can get it to confess to almost anything. What about the story in that data that's trying to get out so what about exploring the data and looking at it before you have any preconceptions or prejudices. That's discovery. And in fact, cognitive computing is helping us to find out stories that are trying to escape from that data are harmonious. And in fact, we're finding in our research in cognitive computing, that amazing things are beginning to reveal themself to us by taking a very new and cognitive approach to information."
This quote (copy-pasted below) details why IBM decided not to use the name "Watson" which is how they have branded their super-computer around the world but when they bring the technology to Africa they call it "Lucy" after the earliest known human descendant, whose remains were discovered on the continent. It is unclear from this presentation what the technical difference is between "Lucy" and "Watson."
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Quote from Transcript:
"These problems are well stated, well known. Many of them are almost cliches, the problems are hard problems, the difficult to solve problems in Africa. So how are we going to address them? Well, project was...now why have we called this Lucy? Not far from here, about 3 million years ago, a woman walked upright. The name would be... Lucy, she's been called by the anthropological community, the archaeological community, Lucy. We don't know a lot about her but we strongly suspect and believe that we are all, we are all every one of us is related to her. And she lived in an environment...in a beautiful environment, which we all enjoy here in eastern Africa, in Rift Valley. And she lived in an environment and she managed, and she managed to be part of something that would become today's humans. So we chose the name Lucy, because Lucy reminds us that we are all connected. And we're all connected to our environment. And we're all connected to those hard problems, those problems of energy, and food and health. All the problems that Lucy had to deal with in her environment. That's why we call [inaudible] and what's Lucy going to do? Lucy is going to help us to marry together cognitive computing and problems of Africa."
This quote (copy-pasted below) argues that a technology solution like "Lucy" will help to understand cross-domain relationships. I think this is one of the most common benefits touted of Artificial Intelligence is that the machine can find new correlations that humans don't even know to start looking for. The "unexpected" findings are touted as a real benefit of doing this kind of big data analysis.
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Quote from Transcript:
"But most importantly, we will be [inaudible] combine information between domains. So we will understand, we'll begin to understand and discover the relationship between water in the cities or the relationship between water and agriculture. Perhaps we'll better understand the relationships between energy and traffic and congestion and loss of efficiency. Because of the congestion that we have on our roads. Or we'll understand the relationship between financial inclusion and does it actually create jobs? So we'll begin to understand these things. So we'll create and discover cross domain knowledge which will then be available to these clouds, and these server systems and remember, it [inaudible] full connectivity."
AO: This quote (see copy-pasted below) states that IBM (Research) believed that "many of the hardest problems in our world today, particularly in Africa, are problems of information." The speaker then goes on to articulate that there are not enough skilled personnel (doctors in his healthcare example) to serve the population of the continent. His supposed solution is that "Lucy" (their computing server) will just serve up information to other people in these facilities (he says midwives several times) and then those people will know what to do with that information and that will solve the problem. It is an incredibly naive and paternalistic perspective that reveals a lack of real understanding of the complexity of the issues on the ground and the long histories that have shaped the current issues. It also reveals a lack of understanding of what information is important and needed. Little of the information that may actual be helpful is currently even available online... a technical solution will not help with that and may in fact exacerbate the issue. How can a computer be expected to offer insight on something that has little to no information or "data" online? The speaker also (obviously) fails to articulate the real risks of misdiagnosis. Why are doctors in the US so adverse to patients coming in with "I found this on WebMD" and meanwhile IBM is selling exactly this to "Africa" under the guise that there are not enough well-trained doctors?
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Quote from transcript:
"...it was less than a year ago, in this auditorium, IBM announced that we were going to bring the biggest most comprehensive computer system and Watson, the Watson technology, which you'll hear about some more in a minute [inaudible]. We were going to bring that to Africa. Why would we bring that to Africa? We had a strong belief that many of the hardest problems in our world today, particularly in Africa, are problems of information [italics added], for example, problems of health care, we know that worldwide, about half of spending alone in health care, 40 or 50% is wasted. Why is it wasted? It's wasted on incorrect treatments, or treatments that don't work. Ok? In Africa, and the problem with healthcare, we have a problem of far insufficient number of trained physicians but we have many other people that can deliver care, ranging from a trained trained medical personnel and midwives, even family members, even the individuals themselves can understand about what they have what they should do, if they're feeling ill. So this is all about information. It's not entirely about information, of course, molecules are important, drugs, pharmaceuticals, understanding the mechanisms of diseases are very important. And information has a huge role to play. So what we'll be doing with Lucy. We will build and our goal is to build here in Kenya to serve all of Africa, a cognitive hub."
Most of the speaker's presentation is focused on the "technical" aspects of cognitive computing then he mentions "indigenous knowledge" in his last few minutes of closing (stating that there is need for cognitive computing... that "takes into account indigenous knowledge, tribal knowledge, and knowledge of Africa").
In the entire 20+ minute presentation, indigenous knowledge is not mentioned until the last minute of closing when the speaker throws in cognitive computing... that "takes into account indigenous knowledge, tribal knowledge, and knowledge of Africa." He does not elaborate on what any of those three terms mean (and why are they lumped together?) and uses them in a way that makes me squirm at his lack of political correctness. The rest of the presentation is focused on the "technical" aspects of cognitive computing and he mentions at several points "well let me get a little technical here...."
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Quote:
"So to wrap up the story, I think you've you've understood that these these techniques of cognitive computing and effort, they're really made for each other, but we need to solve them, we need to address them through an adaptive and a learning manner. That takes into account indigenous knowledge, tribal knowledge, and knowledge of Africa. What will work, what interdomain effects are important in Africa, and which ones are dominant and which ones are not. So we must integrate this cognition across these many domains, and we will discover relationships."
This research agenda is driven by profit and economic interests although the articulated narrative is about "solving Africa's grand challenges" which he states are "well-known." But then as he describes IBM's "solution," the program ("Lucy"), which will "combine that [student test score] data and do response curves, we'll finally learn what programs work and what programs don't work, particularly for challenges in Africa, like challenges of large class sizes."
So is the problem that we didn't know which edtech programs work for large class sizes or that the classes are too large to begin with? Are the classrooms where there are too many children able to afford the tablets that are going to be required to generate this data that he is puporting that "Lucy" will be able to analyze and use to shed new insight?
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Quote describing "Africa's problems" from the transcript:
"Now we're in Africa. Now we come to Africa, much has been said about all the grand challenges in Africa. The problems of cities, food, water and energy, and government and healthcare and [inaudible]. The challenges have been well documented, that if you haven't seen IBM's film about the challenges of Africa, in fact, many of the people here in the audience have been interviewed for that film, I urge you to look on YouTube and go and see that film. These problems are well stated, well known. Many of them are almost cliches, the problems are hard problems, the difficult to solve problems in Africa...."
The opening (copy-pasted below) from this IBM Research keynote talk by the CTO of Watson outlined why IBM has an optimistic view for "Africa" - because of projected growth in labor and capital, i.e. the two main factors of production in a capitalist economy. It is notable how the speaker collapses distinction between continent-wide scale vs nation-state at different points throughout his talk.
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"And Africa represents to us an incredible, very exciting set of opportunities. And that's for many reasons, okay, not the least of which is the African economy is expected to be about two and a half trillion dollars by next year, by the end of next year, which is bigger than countries like Brazil, Russia, India, Australia. So we have a remarkable economic base here. But another statistic, which I find very exciting...I worry about demographics around the world. And one of the great things about Africa is, you're still having babies, you're going to have 1.1 billion young people in Africa, more than India and China combined by 2040. These are remarkable numbers and this represents hope, and future for our planet, and huge set of opportunities."