In South Africa, there are drones monitoring weeds; in Mauritius, there are computers crunching health data for better outcomes for patients; and in Nairobi, surveillance systems impose a modicum of order on the chaotic traffic. The bright new future of artificial intelligence in Africa is part of the bright new future of the continent as a whole, advocates say. "One thing is clear: Africans have a goldmine at our fingertips. A rapidly growing population of 1.4 billion people, 70% under the age of 30, combined with huge growth in AI investments, creates a potent recipe … We will not sit back and wait for the rest of the world to reap our rewards," wrote Mahamudu Bawumia, the vice-president of Ghana and head of the government's economic management team, in the Guardian earlier this year. Growing alarm about the threats posed by uncontrolled innovation in artificial intelligence has prompted global leaders to hold the first ever safety summit.

Using Artificial Intelligence, corporations can see new patterns in their data and maintain a competitive edge. Blending these AI technologies into business strategy and operations is the subject of a newly published book. Using Artificial Intelligence, corporations can see new patterns in their data and maintain a competitive edge. Blending these AI technologies into business strategy and operations is the subject of a newly published book. Deploying an Artificial Intelligence (AI) in a corporate business can be a costly endeavour.

South African organisations yesterday committed to being responsible in their use of artificial intelligence (AI) technologies. Stakeholders yesterday participated in a virtual event – AI Dialogue South Africa – which culminated in the signing of the expression of interest (EOI) that advocates for responsible AI. The AI Dialogue South Africa is spearheaded by Convergence Partners, Accenture, University of Johannesburg, Digital Council Africa and Sun & Shield Technologies. In a statement, the organisations say much like a microcosm of our socio-economic context, the AI landscape in SA is uneven and burdened with regulatory challenges. They add that if not addressed, these challenges could give more power to those who already control AI systems, evoking concerns about power dynamics and how the role of humans will be redefined.

Rational decision making in its linguistic description means making logical decisions. In essence, a rational agent optimally processes all relevant information to achieve its goal. Rationality has two elements and these are the use of relevant information and the efficient processing of such information. In reality, relevant information is incomplete, imperfect and the processing engine, which is a brain for humans, is suboptimal. Humans are risk averse rather than utility maximizers. In the real world, problems are predominantly non-convex and this makes the idea of rational decision-making fundamentally unachievable and Herbert Simon called this bounded rationality. There is a trade-off between the amount of information used for decision-making and the complexity of the decision model used. This explores whether machine rationality is subjective and concludes that indeed it is.

This paper studies whether rationality can be computed. Rationality is defined as the use of complete information, which is processed with a perfect biological or physical brain, in an optimized fashion. To compute rationality one needs to quantify how complete is the information, how perfect is the physical or biological brain and how optimized is the entire decision making system. The rationality of a model (i.e. physical or biological brain) is measured by the expected accuracy of the model. The rationality of the optimization procedure is measured as the ratio of the achieved objective (i.e. utility) to the global objective. The overall rationality of a decision is measured as the product of the rationality of the model and the rationality of the optimization procedure. The conclusion reached is that rationality can be computed for convex optimization problems.

Thispaper studies the question on whether machines can be rational. It observes the existing reasons why humans are not rational which is due to imperfect and limited information, limited and inconsistent processing power through the brain and the inability to optimize decisions and achieve maximum utility. It studies whether these limitations of humans are transferred to the limitations of machines. The conclusion reached is that even though machines are not rational advances in technological developments make these machines more rational. It also concludes that machines can be more rational than humans. Introduction Oneof the most interesting concepts invented by humans is rationality (Anand, 1993; Marwala, 2014&2015).

Artificial intelligence (AI) is changing the face of economic theory and soon this could change the way you invest in the markets. This is according to Vice-Chancellor of the University of Johannesburg Professor Tshilidzi Marwala, whose research into the use of AI to make calculations in economic theory found that, where it was used, it significantly reduced the number of purchases and sales made in markets, but made each one more effective, with less risk. The theory of demand and supply, Marwala said, was one of the factors that influenced prices and the value of goods and services. Using artificial intelligence, Marwala's research found that AI engineers were able to build an individual demand curve to be able to individually price goods and service. On the internet, websites like Amazon make it possible to find two prices of a book within five minutes of each other." Marwala's work was premised on the fact that humans are intrinsically bad at making economic decisions, because they are only able to process a limited amount of information at a time, despite unlimited access to data. "When companies are listed on the stock market, their net worth is calculated and part of the company is offered to the public to buy shares of that company … One additional element that comes about as a result of publicly trading shares is that the price of the stock can end up not reflecting the intrinsic value of the shares.

This paper proposes a new neuro-rough model for modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Markov Chain Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62% as opposed to 58% obtained from a Bayesian formulated rough set model trained using Markov chain Monte Carlo method and 62% obtained from a Bayesian formulated multi-layered perceptron (MLP) model trained using hybrid Monte. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model. Keywords: Neuro-rough model, multi-layered perceptron, Bayesian, HIV modelling Introduction The role of machine learning is to be able to make predictions given a set of inputs.

In the fault classification process there are various stages involved and these are: data extraction, data processing, data analysis and fault classification. Data extraction process involves the choice of data to be extracted and the method of extraction. Data that have been used for fault classification include strains concentration in structures and vibration data where strain gauges and accelerometers are used respectively [1]. In this paper vibration data processed using modal analysis are used for fault classification. In the data processing stage the measured vibration data need to be processed. This is mainly due to the fact that the measured vibration data, which are in the time domain, are difficult to use in raw form.

This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.