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.

Machines might scare policymakers from Brussels to Washington, but artificial intelligence could yield a significant developmental dividend in the developing world. In African markets, the technology behind Alexa and Siri can be harnessed to diagnose illness or address traffic gridlock. One of the most transformative applications of artificial intelligence (AI) is in financial technology, where global investment has risen 38% over the last year. Machine learning, whereby algorithms make predictions and improve based on large amounts of data, is often relegated to the realm technologists and the elite; but for the two billion unbanked adults worldwide, this technology could light a path out of poverty by helping traditional lenders approve loans using hundreds of non-traditional data points. AI has the capacity to add value at the individual, small business, and the large corporate level alike across Africa.

Society's operating system needs an upgrade. The model we have been using is simply not up to the challenges of the Fourth Industrial Revolution. A new era is unfolding at breakneck speed. It has huge potential to address some of the world's most critical challenges, from food security, to reducing congestion in big cities, to increasing energy efficiency, to accelerating cures to the most intractable diseases. But it also raises a host of social and governance issues that need addressing.

SiFive does a quarterly series of tech talks, not necessarily directly to do with SiFive or even RISC-V. For example, last quarter it was Paul Kocher (and if you don't know that name, you need to go and read my post about that talk Paul Kocher: Differential Power Analysis and Spectre). This quarter it was Krste Asanović on Accelerating AI: Past, Present, and Future. This post will cover the past. The present and future have to wait (good title for a movie?).

Artificial intelligence and machine learning saw a significant spike of attention in the past few years – whether it's through partnerships, acquisitions, or in-house developments. The largest financial institutions in the US have been involved in one way or another in bringing artificial intelligence into operations and customer-facing functions. A recent study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) by MEDICI Team found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor. Some of the most prominent banks in this space across regions are Bank of America, OCBC, ABN Amro, YES BANK, etc. While front-office applications have certainly seen a higher intensity, scope, and adoption, the AI strategy in the US banking industry, in reality, is far more diverse.

Artificial intelligence can help insurance companies and third-party entities to process insurance claims. It can verify data, check for errors and fraud, or find correlations and trends. We count on insurance to be there for us when we need it, and like anything else, it's a system that can be overused and even abused. Artificial intelligence is taking on an important role in the prevention of inaccurate healthcare claims and in innovative claims management. At H2OWorld 2017, in Mountain View, CA, speaker Adam Sullivan of Change Healthcare explained the process of using machine learning to verify healthcare claims data, denied claims, and erroneous payments for hospitals/providers, as well as to predict procedures.