…aims to find solutions to continent’s challenges
The cost of AI is getting cheaper in terms of computation power and in terms of tools. Each new tool/library is helping machine learning developers to spend less time on prediction problems.
Google’s TensorFlow, AutoML, or even scikit can be shown as examples for this purpose. We can also show the increased usage of GPU computing as an illustration of the cost reduction in AI.
The forecast for sales for the next quarter of a company is an obvious prediction problem, but developing an autonomous vehicle was not a prediction problem a decade ago. Cost reduction in AI is changing our way of thinking, which means we started thinking of various problems as a prediction problem.
We were already using autonomous vehicles in controlled environments like factories, where the vehicle could be programmed by using ‘if-else’ programming conditions.
Changing the mindset and looking to this as a prediction problem helped engineers to develop autonomous cars, which can be used in the wild.
Here is how it worked, basically; an engineer taught an AI what a human would do in various conditions, and this enabled the generation of onboard software that allows drivers to use cars for thousands of miles instead of tiring out after a few hundred.
AI learns what a human would do and can start predicting what it should do.
Instructively, Internet technology giant Google has officially opened its Artificial Intelligence (AI) Research Centre in Ghana with high hopes of finding solutions to Africa’s problems.
Artificial intelligence is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. It helps find solutions to real-world problems. It can help people focus on what is relevant and open up new ways to solve problems in almost every imaginable field such as AI helping pathologists to spot cancer cells on slides, advising farmers on how to address problems with their crops and helping manufacturers detect equipment breakdowns.
Google is optimistic the lab in the West African country – the first in Africa – will transform lives by coming up with bespoke solutions for the continent’s problems including natural disasters.
“Africa has many challenges where the use of AI could be beneficial sometimes even more than in other places,” Head of Google AI Accra, Moustapha Cisse, explained, during the official opening of the centre in Ghana’s capital Accra.
Machine Learning researchers and software engineers run the AI centre to populate the system with local AI content. Google is collaborating with stakeholders such as universities and start-ups to enhance AI development on the continent.
Cisse believes it is an opportunity for local researchers and education institutions on the continent to collectively address the challenges facing the continent.
“Being here in attracting an international team of researchers and engineers allow us to raise awareness so that policy makers will understand better the importance of this technology and I hope they will invest more in AI education across Africa and also promote its application and its effective use in different areas.
“These opportunities and awareness is the first step. Now we are doing the research here and we are looking forward to collaborate with other researchers working across Africa to tackle some of these ambitious and tough challenges and hopefully make differences,” the Senegalese AI expert said.
Cisse is positive Africa stands to benefit most from Artificial Intelligence while calling on the youth to take up opportunities in the technology space.
“I strongly believe that AI has a bright future in Africa,” he said, adding, “Africa has a lot to give to AI and AI has a lot to give to Africa. We are the youngest continent on this planet. It’s also the fastest growing population and the challenges are huge and AI has a huge role to play. This type of technology will help accelerate various sciences that are relevant. I believe the future of AI is mainly here in Africa.”
Google as a global company, has been sponsoring a lot of young people for their degrees and masters to help develop a new generation of AI developers.
The AI lab in Accra has about 10 people coming from more than 12 different countries including Senegal, Uganda, USA, Israel, Nigeria, Ireland, Canada and UK. Other Google AI centres are based in Paris, Zurich, Tokyo, Beijing, Montreal, Toronto, Seattle, Cambridge/Boston, Tel Aviv/Haifa, New York and San Franciso.
The Accra office will join similar AI research centres in countries like Paris, Tel Aviv, and, of course, San Francisco.
The centre will among others inform policies and capitalize on the potential use of cases in education among others.
Google is expected to also collaborate with local universities and other research organisations to deploy AI in resolving challenges in the healthcare sector, among others.
The goal is to advance the frontiers of this science so we expect to have a scientific impact through collaborations with different institutions working on local challenges, by applying the technology to agriculture, health and to other areas.
The modern definition of artificial intelligence (or AI) is “the study and design of intelligent agents” where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.
John McCarthy, who coined the term in 1956, defines it as “the science and engineering of making intelligent machines.”
Other names for the field have been proposed, such as computational intelligence, synthetic intelligence or computational rationality.
The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates.
AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic.
AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.
Computational intelligence Computational intelligence involves iterative development or learning (e.g., parameter tuning in connectionist systems).
Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing.
Subjects in computational intelligence as defined by IEEE Computational Intelligence Society mainly include: Neural networks: trainable systems with very strong pattern recognition capabilities.
Fuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems; capable of working with concepts such as ‘hot’, ‘cold’, ‘warm’ and ‘boiling’.
Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem.
These methods most notably divide into evolutionary algorithms (e.g., genetic algorithms) and swarm intelligence (e.g., ant algorithms).
With hybrid intelligent systems, attempts are made to combine these two groups.
Expert inference rules can be generated through neural network or production rules from statistical learning such as in ACT-R or CLARION.
It is thought that the human brain uses multiple techniques to both formulate and cross-check results.
Thus, systems integration is seen as promising and perhaps necessary for true AI, especially the integration of symbolic and connectionist models.
