Transcript of the opening speech by Adam Habib at the 1st Deep Learning Indaba, 11 September 2017 (Video).
When I was invited, I didn’t think I was going to come and speak about my vision for a Deep Learning Indaba. I was told come and look pretty, say a few words of welcome and then you can go away. So that’s a new one from the organisers. I don’t have anything deep to say, but what I do want, is to say a few words of welcome to everyone. It’s a wonderful pleasure to have you all here. I’m told that when we originally started this plan, that it would involve 50 students. The organisers had said to me that they were looking at having 50 individuals coming to be part of this deep learning indaba. And now it’s grown to this large. I’m told that it’s the largest of this kind of deep learning indaba in the world. And that it involves students, practitioners, startups, corporates, and it should be the kind of thing that we would want to encourage. I’ve got some speaking notes here, but I’m not going to do that, I’m going to tell you about a trip I just had.
I’ve just landed from China, and did a visit of probably the top 9 Chinese universities. We were a group of academics from Wits university, and our visit was about exploring the potential possibility of partnerships. That’s what we wanted to do. But when I went there, I walked out of these meetings with a deep sense of foreboding. Because I was utterly astonished at the amount of resources that the Chinese government was putting into research at its universities. People have a sense of China with 1.4bn people, where you can’t walk in the streets, and that’s true. But when you walk in its universities, you see institutions of wealth and space. And the amount of resources going into research is truly astonishing. In a lot of ways, I constantly said on this visit to people, this is a government planning to run an empire. And you could see this in the investments they were making in higher- education, and the investments they were making in research. But what truly astonished me was the amount of research, and the amount of deep money that was going into deep learning and machine learning. It’s in every single institution I went to. Not only were there departments that were specialising in producing people to be trained in these area. Not only were there researchers at the cutting edge of machine learning. But what also astonished me, is they had a series of innovation labs that were simultaneously taking what was being taught in the classroom, taking the research that was happening, and trying to produce a series of technological outputs that would change the face of how we live in the 21st century. Truly astonishing.
I’ve seen similar things. in the United states, I’ve walked on the Google campus, I’ve been at American institutions, I’ve see in it in Europe. But the scale of it in China truly astonished me. Now why the sense of foreboding? The sense of foreboding because I walked out feeling utterly depressed that the African continent is once again being left out of the equation. And that we are so focussed, correctly I think, on correcting the historical deficits of our past that we forget that there are a new series of deficits that are going to happen in our future. You can fix all of the historical disparities of the last 350 years, but if we don’t keep an eye on the future, we will again be left out of the global economy. And that’s what frightened the hell out of me.
What frightened the hell out of me is being in the midst of a government whose playing with highly changing cabinets and leaders every second month. But with no idea of the challenge and the transformations that are truly being unleashed in our globe. And unless we get our act together, unless as Africans we have the courage to come together and say that we are going to prepare for the 21st century, we are going to be at the cutting edge of machine learning. Unless we have the courage to do that, we are again going to be left out of the 21st century.
That was my fear. And of course the thing that struck me as I was coming here, is that I was going to open up a Deep Learning Indaba on the first day I arrived in South Africa. And so as I walked in this morning in the science stadium that sense of foreboding didn’t entirely disappear. But it lightened a little. Because it suggests that there are others who are thinking in a similar vein. It suggests that their are others in our country who recognised that we’ve got to come together very urgently. By the way we can’t do this as Wits University. We can’t do it as corporates. We cannot do it as science councils. We can only do it if we breach the institutional boundaries that have defined us for so long. Unless we have the capacity to come together beyond companies, beyond startups, beyond universities, and beyond governments. Unless we have the courage to come together beyond those institutional boundaries we will never be able to compete. One thing that the Chinese have, is that they can instruct their companies to operate. The can instruct their universities to operate. That is one of the advantages of a communist economy. There are other disadvantages. But one of the advantages is that you can move quick once government has made its decision. One of the most astonishing things about the communist party in china is their utter pragmatism. They are utterly pragmatic.
I went to they and said to them, how many university students do you have in this university? And they said 50,000. And I said how many residences do you have in this university? And the president looked at me befuddled, and said 50,000. And I said you’ve got an entire bed for every student. He said, yes, don’t you! They recognised that if you want to actually build a university, if you want to create an equitable economy, if you want to ensure that all of your people who come from very disparate class and racial and ethnic backgrounds, if they are going to have equal conditions, you’ve got to create them. So what do they do, they create residences from everyone one of their students, and every one of them live there. And in one broad stroke they’ve equalised the playing field. That’s the astonishing thing.
That’s not going to happen here. We are going to have to operate despite what happens in our society. We are going to have to come together beyond institutional boundaries. We are going to have to come together beyond all the challenges we are facing. And so this is particularly important that we have colleagues from all over, from the startups, from the private sector, from the public sector, from the science councils, from multiple universities, from Wits, because it is exactly the kind of audience you need to pull to start getting us to the cutting edge of learning around artificial intelligence, machine learning and all of the kind of associated research and technologies that are required.
I will say this, the other thing that you are going to require, is youth. Why do you need youth? Because young people are prepared to take risks. By the way I know, I’ve been through 2 years of protests with them. They are prepared to take risks. But I want to deploy those risks in a manner that will change our world. And frankly, whether you like it or not, our world is changing. And whether you like it or not, machine learning is here to stay. And the fundamental question is it is fundamentally going to change our world. And we’ve got to learn to adapt it. We must learn to evolve with it, and we must learn to transform it in a manner that is of benefit to the world itself. And that’s why it’s so important that we have the best brains, and the most youthful energies dedicated to this task.
Here’s the other reason why I think we should be moved. And this is a message to the like of IBMs, because I know they are here, and to the Facebooks and amazons and the Googles. You want to have the African voices in your conversation. Because however important machine learning is, the fundamental issue is the questions you ask. The fundamental questions are the questions that are posed for those machines to be deployed to answer. And those questions, are fundamentally contextually driven.
You can do uber in the middle of San Francisco. But how you configure uber in the middle of San Francisco is fundamentally different to how you do it in the Kenyan highlands. And that question comes from your contextual grounding. In my notes they say, we have to point out the dangers of marginalisation of African persons from the deep learning community. I think it’s absolutely true. We have to point out that the marginalisation of women from the deep learning community, it’s absolutely true. Because they bring from their own experience, from their own contextually-driven circumstances, a series of questions and lenses through approach questions that are different.
As much as that is important, the African continent itself cannot be marginalised. If you are born in this continent, if you confront the challenges of this continent, if you simply drive from home to work on this continent. You confront a series of challenges that are distinctive. And it is precisely that distinctiveness that allows you to ask questions in a different light. And it is precisely when you ask questions in a different light that you can deploy solutions that are fundamentally different. Now that doesn’t mean that you go though some autarchic research agenda which says that we reject the world. The trick is to take the combined scientific knowledge of the world, and deploy it to our circumstances. But as we deploy it to our circumstances, we innovate and we contribute to the world.
That’s what this is about. That’s how you become world class. You don’t become world class by imitating the other. You will always be a poor imitation of the other. You become world class by taking the corpus of scientific knowledge, deploying it to your context which is unique and specific, and through that uniqueness and specificity you innovate and you give back to the global technological and academic community.
I had a dinner a year or two ago with google executives. And they said to me at that point we’ve come to 2bn of the world, the other 4bn is going to be much harder. Because the kids of acts they require, the kinds of digital solutions they need to have, are very, very different. And in that context, Africa is so fundamental.
See the great thing about South Africa is that it’s an incredible social laboratory. Thabo Mbeki used to say, ‘ we are two worlds in one’. And he mentioned it as a burden, and it is. BUt in this context it is a social laboratory for learning. Because the blokes in Sandton could operate in New York city or London as easily, but the blokes in Graskop could operate in Burundi. We have those two worlds in one. And those two contextual realities, pose questions and challenges in a way that doesn’t exist on many other parts of the world.
Being here, having this community of innovators, pioneers, is absolutely important because you are starting to think through the importance of deep learning but ensuring that Africa isn’t marginalised once again from a new technological revolution. So that’s what you are here for.
In the next 5 to 6 days, you are going to learn. You are going to be asking questions. You are going to be debating. You are going to be confronting. If Wits is anything, you are going to be arguing like hell. But the idea is, out of those processes a sense of learning happens, a sense of innovations happen, that we begin to start answering all the kinds of issues, and we begin to undertake the kinds of technological revolutions that will change, not only South Africa, not only Africa, but that will allow us to make contribution on a more equitable footing on the world itself.
And so what I want to say to all of the players here, Wits university wants to be a partner to this. We are public university by the way, we are not here to make profit, whatever people think actually. We are here to ensure that we create an enabling environment for innovation to happen. And that’s what we want to do. So if the success and innovation and technology emerge out of these processes that land up into startups emerging, by any one of you; if it lands up in technologies being developed that ultimately get taken up by Google or IBM or Facebook or Twitter or Amazon or any of those institutions, Alibaba, Tencent, it’s ok, because what we want to ensure is that African questions are at the forefront of machine learning. That our contextual realities are also being thought through in the context of machine learning. And that our questions are at the very forefront of the kinds of issues, because our questions, are going to be relevant for much of our world.
And so that’s what we are here for. And I’m sure this is not going to be the first, I’m sure that is is going to be one of many. And I want to say to the organisers, that this university’s facilities are there for you. Our academics are there to assist, our legal facilities are there to enable you to translate whatever needs to happen into whatever outcomes. We’ve got, not very far from here, an innovation hub called Tshimologong. Just 2 blocks from here. We set it up about 18 months ago to explore a kind of innovation hub for digitised learning. We have to move to a second phase in that, in VR and AR. And again we are their in partnership with IBM, and many others: Microsoft, Telkom, MMI, provincial government, municipal government, etc. Those facilities are there for your innovation. They are there to be used. They are there to be engaged. So our innovation hub, our University facilities, please utilise them in any way that can be of assistance in creating an African ecosystem of deep machine learning. That’s what we want to do.
And so, I want to say welcome to Wits University. It’s really an astonishing premises by the way. In these corridors Nelson Mandela walked. In these corridors Robert Sobukwe walked. We produce 70% of the CEOs on the JSE. We produce much of the technology that enables deep level mining in many of the mines in this continent. This is a truly astonishing space. I often say, my colleagues in sociology don’t like it, that Wits University produces the most billionaires on the African continent. And then what it does it produces the most activists who fight those billionaire’s on the continent. We are a broker from all sides. And so what we want to do is be a broker for machine learning.
We want to make sure that we create and enable the students to emerge that participate in this industry. We want to ensure that the CEOs that come of our system participate in that. We want to ensure that the activists who emerge to fight those CEOs to protect the securities and privacy of the millions of citizens of our globe also come out of here. We are a broker. We want to enable an ecosystem for your learning, for your innovation, for your interrogation, for your experimentation. So welcome to Wits university, I wish you a fantastic 6 days. Welcome to the guests from other parts of the world. Please enjoy this intellectual stimulation, but I hope you’ll take some time and sample the delights of Johannesburg and South Africa and the continent, while you are here. Thank you very, very much.