"I don't think Homo sapiens-type people will exist in 10 or 20 years' time," Getnet Assefa, 31, speculates as he gazes into the reconstructed eye sockets of Lucy, one of the oldest and most famous hominid skeletons known, at the National Museum of Ethiopia. "Slowly the biological species will disappear and then we will become a fully synthetic species," Assefa says. "I believe [we] can inspire the Ethiopian youth to actually get really engaged in AI and feel like it's their thing." "Perception, memory, emotion, intelligence, dreams -- everything that we value now -- will not be there," he adds. Assefa is a computer scientist, a futurist, and a utopian -- but a pragmatic one at that. He is founder and chief executive of iCog, the first artificial intelligence (AI) lab in Ethiopia, and a stone's throw from the home of Lucy.

Black swans are the ultimate outliers. They have the ability to surprise and disrupt the status quo. I was recently in Shenzhen and was permitted access to Huawei's campus. I know I didn't see it all, but I saw enough to get me thinking. I had heard lots of stories, but reality was even more interesting. Seeing and talking to the people gave me new insights. I'd heard that in China, tech employees worked 10 hours straight a day. The offices, campus and the university (yes, a University where all employees study) are perhaps even more modern and inviting than many I've seen in the United States.

The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation datasets. In this work we investigate the relevance of recently introduced bilinear residual neural network representations, which mimic numerical integration schemes such as Runge-Kutta, for the forecasting and assimilation of geophysical fields from satellite-derived remote sensing data. As a case-study, we consider satellite-derived Sea Surface Temperature time series off South Africa, which involves intense and complex upper ocean dynamics. Our numerical experiments demonstrate that the proposed patch-level neural-network-based representations outperform other data-driven models, including analog schemes, both in terms of forecasting and missing data interpolation performance with a relative gain up to 50\% for highly dynamic areas.

Robustness to adversarial perturbation has become an extremely important criterion for applications of machine learning in security-sensitive domains such as spam detection [25], fraud detection [6], criminal justice [3], malware detection [13], and financial markets [27]. Systematic methods for generating adversarial examples by small perturbations of original input data, also known as "attack," have been developed to operationalize this criterion and to drive the development of more robust learning systems [4, 26, 7]. Most of the work in this area has focused on differentiable models with continuous input spaces [26, 7, 14, 14]. In this setting, the proposed attack strategies add a gradient-based perturbation to the original input. It has been shown that such perturbations can result in a dramatic decrease in the predictive accuracy of the model. Thus this line of research has demonstrated the vulnerability of deep neural networks to adversarial examples in tasks like image classification and speech recognition. We focus instead on adversarial attacks on models with discrete input data, such as text data, where each feature of an input sample has a categorical domain. While gradient-based approaches are not directly applicable to this setting, variations of gradient-based approaches have been shown effective in differentiable models. For example, Li et al. [15] proposed to locate the top features with the largest gradient magnitude of their embedding, and Papernot et al. [20] proposed to modify randomly selected features of an input through perturbing each feature by signs of the gradient, and project them onto the closest vector in the embedding space.

According to data published by Grand View Research, the global artificial intelligence market size is projected to reach $35.87 billion by 2025, while growing at a CAGR of 57.2 percent. The report indicates that rapid improvements in fast information storage capacity, high computing power, and parallelization are some of the factors that are contributing to the rapid innovations of robotics and artificial intelligence technology. AI becomes prominent in end-use industries such as automotive and healthcare. In addition, there is strong demand for understanding and analyzing visual contents and gaining meaningful insights, which is also expected to provide strength to the market over the forecast period. A report by the McKinsey Global Institute surveyed companies about their use of AI.

As I'm passionate about shaping a better future in the Smart Technology and specifically to help transform Africa through Artificial Intelligence (AI), Big Data & Analytics, Internet of Things (IoT), and Blockchain technologies, it was a privilege to participate as invited AI expert at the AI for Good Global Summit in Geneva, Switzerland on 15-17 May 2018 as well as the AI: Current Policy Reflections and Future Strategies on 18 May 2018 at the United Nations. As mentioned here as well as this post, this was also an opportunity to represent the Machine Intelligence Institute of Africa (MIIA), Cortex Logic (as one of the sponsors) and the African perspective on the use of these technologies with respect to the United Nations' Sustainable Development Goals. In this post I would like to share some links, feedback, perspectives and outcomes of the AI for Good Global Summit. I also share my presentation on Health, Water, Smart Education & Smart Technology Services for African Smart Cities. In a separate post, I'll do the same for the AI: Current Policy Reflections and Future Strategies conference.

The demand of metals by modern technology has been shifting from common base metals to a variety of minor metals, such as cobalt or indium. The industrial importance and limited geological availability of some minor metals have led to them being considered more "critical," and there is a growing interest in such critical metals and their producing companies. In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), to predict the stock trend of major critical metal producers. Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors," each advisor dealing with a particular economic sector. Then through ensembles of weak classifiers, each advisor produces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a misclassification error of 32% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296%, or an excess return of 130% within one year. In addition, the feature set selected by dynABE also suggests potentially influential factors to metal criticality, because stock prices of major producers influence metal production. Therefore, not only does this research propose a novel framework for specialized stock trend prediction, it also provides domain insights into dynamic features that potentially influence metal criticality.

At Google's campus in Mountain View, California, executives are trying to assuage thousands of employees protesting a contract with the Pentagon's flagship artificial-intelligence initiative, Project Maven. Thousands of miles away, algorithms trained under Project Maven--which includes companies other than Google--are helping war fighters identify potential ISIS targets in video from drones. The controversy around Silicon Valley's cooperation with the military may intensify in coming months as Project Maven expands into new areas, including developing tools to more efficiently search captured hard drives. Funding for the project roughly doubled this year, to $131 million. Now the Pentagon is planning a new Joint Artificial Intelligence Center to serve all US military and intelligence agencies that may be modeled on Project Maven.

The theory of situated cognition postulates that language is inseparable from its physical context--words, phrases, and sentences must be learned in the context of the objects or concepts to which they refer. Yet, statistical language models are trained on words alone. This makes it impossible for language models to connect to the real world--the world described in the sentences presented to the model. In this paper, we examine the generalization ability of neural language models trained with a visual context. A multimodal connectionist language architecture based on the Differential State Framework is proposed, which outperforms its equivalent trained on language alone, even when no visual context is available at test time. Superior performance for language models trained with a visual context is robust across different languages and models.

With the many, many uses of AI, we're seeing an increase in researchers, scientists, organisations and start-ups of all kinds looking at ways we can leverage this technology for good. Whilst'high-technology' has become synonymous with high wages, and high investment, there are loads of projects out there applying this technology to poverty reduction. Harnessing the power of AI to help the most desperate in our society is a fantastic way to use it. So, how is this being done? Recognising the causes of poverty is key in looking at how to tackle the problems using technologies.