DataProphet is a South African, Cape Town based Consulting company that specialises in Machine Learning. I have had an opportunity to ask some questions from Bennie Leonard who is a Machine Learning Scientist at DataProphet. Thank you Bennie for the insights.
Q. I see you are a Machine Learning Scientist at DataProphet. Where did you study machine learning?
A. I studied computer science at the University of Pretoria. I specialised in optimisation algorithms, with a focus on swarm intelligence. Optimisation algorithms are a class of clever search methods and are often used to enable machines to learn from experience.
Q. Why did you choose machine learning as a career?
A. Programming computers to learn has been a field of interest for scientists for at least a few decades. However, over the last ten years or so, the field has gained substantial traction in real-world applications. Machine learning is widely used in the technology industry to perform a range of tasks, including product suggestions in online shopping, search prediction for online search engines, and even mastering difficult games like Go. Even so, there are still a huge number of businesses that are either unaware of the capabilities and potential benefits of machine learning, or struggle to understand how to apply machine learning to their unique business environments.
The enormous potential that machine learning and artificial intelligence has to offer, and the excitement of working in a very young and developing field, are what drove me into a career focussed on machine learning. At DataProphet we aim to understand and fill the gap between scientific advances in machine learning and the useful application thereof to individual businesses.
Q. What algorithms do you apply on your job as a machine learning specialist and why?
A. Different applications of machine learning often require unique combinations of algorithms to perform a given task. Our expertise at DataProphet ranges from relatively old (and commonly used) tree-based classification methods to the most recent developments in deep neural networks. Which specific algorithms to apply depends heavily on the scope and specifications of each individual project.
Q. We have professionals that are well known for their work in machine learning, who do you look up to?
A. There are many highly respected professionals in the field, but progress rarely comes without relying on the work of other scientists. Personally, I have deep admiration for the likes of Alan Turing, John von Neumann, and Ada Lovelace, who played crucial roles in laying the foundations for the science we build upon today.
Q. What is the adoption rate of machine learning in South Africa?
A. It’s hard to put a figure on the adoption rate of machine learning in South Africa. While it is definitely increasing, we often find that businesses are either overly optimistic, or overly skeptical when it comes to machine learning. There is still a lot to do in terms of educating people as to what the capabilities of machine learning are. With a better understanding of the technology, the adoption rate will likely increase faster.
Q. I had a presentation on machine learning where I gave an example of (FITZPATRICK,2015) article “Three Trends That Will Define the Next Horizon in Legal Research” where he talks about machine learning as one of the trends. The question that I got was around ethical issues when training these models. What is your take on that?
A. As with any technology, it is important to consider the ethical implications. Machine learning models are trained on the data that humans provide them with. In that sense, the algorithms are general-purpose algorithms. They will attempt to understand any data that is presented to them and the trained models can be applied in any way we wish to apply them. Throughout history (and still today) there are many examples of technology being used in unethical ways. It would be naive to think that machine learning is somehow immune to this possibility. Indeed, companies like Google’s Deep Mind and the non-profit OpenAI have already established ethics boards to guard against the unethical application of artificial intelligence, and rightly so.
However, the intent of scientific research is ultimately to expand and enrich human knowledge. That intelligence forms part of who we are is indisputable. And in our quest to truly understand intelligence, we will undoubtedly learn more about ourselves. So we are faced with two choices: we can either continue on this path to discover the true nature of intelligence, while being mindful of the potentially far-reaching ethical implications of what we might learn; or we can credulously decide that the risk is too great and be willfully ignorant about this mysterious quality we call intelligence, that is such a big part of who we are. We should all choose the former.