Artificial intelligence (AI) is changing society as profoundly as the steam engine and electricity have done. But unlike past technological revolutions, the AI revolution offers a unique chance to improve lives without opening up and exacerbating global inequalities. That will require widening of the locations where AI is done. The vast majority of experts are in North America, Europe and Asia. Africa, in particular, is barely represented.

Artificial Intelligence tools are rapidly changing how financial institutions operate, manage data, and interact with customers. The revolution brought by the AI – a blend of three advanced technologies: machine learning, natural language processing and cognitive computing – has huge implications for the financial services industry in Nigeria. According to Microsoft Nigeria Country Manager, Mr Akin Banuso, with the use of modern tools like Microsoft's Azure Machine Learning platform, Financial Service Providers can crunch large volumes of data faster and more accurately, which considerably lessens time-to-market to deliver products and services. "The AI has the potential to advance nearly every field of human endeavour and address countless societal challenges. This is why we are investing in not only making the technology more accessible, but also building capacity in the use of machine learning concepts to address analytical gaps in financial inclusion and other areas," Banuso says.

PlantVillage, a research and development project, based at Penn State University, is beginning to bring artificial intelligence to these smaller farms. Scientists at PlantVillage, in collaboration with international organizations, local farm extension programs and engineers at Google, is working to tailor A.I. technology for farmers in Tanzania who have inexpensive smartphones. The initial focus is on cassava, a hearty crop that can survive droughts and barren soil. But plant disease and pests can reduce crop yields by 40 percent or more. PlantVillage and International Institute of Tropical Agriculture have developed a simple A.I. assistant, called Nuru ("light" in Swahili). Wave the phone over a plant leaf, and the software diagnoses the disease or pest blight and suggests low-tech treatments.

Across various domains--such as health, education, and housing--improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthcare, labor market outcomes, or how votes are accounted for in a democratic society. Problems that can have an out-sized impact on individuals whose opportunities have historically been limited often pose conceptual and technical challenges, requiring insights from many disciplines. Conversely, the lack of interdisciplinary approach can leave these urgent needs unaddressed and can even exacerbate underlying socioeconomic inequalities. To realize the opportunities in these domains, we need to correctly set objectives and reason about human behavior and actions. Doing so requires a deep grounding in the field of interest and collaboration with domain experts who understand the societal implications and feasibility of proposed solutions. These insights can play an instrumental role in proposing algorithmically-informed policies. In this article, we describe the Mechanism Design for Social Good (MD4SG) research agenda, which involves using insights from algorithms, optimization, and mechanism design to improve access to opportunity. The MD4SG research community takes an interdisciplinary, multi-stakeholder approach to improve societal welfare. We discuss three exciting research avenues within MD4SG related to improving access to opportunity in the developing world, labor markets and discrimination, and housing. For each of these, we showcase ongoing work, underline new directions, and discuss potential for implementing existing work in practice.

The results of a routine check-up at Oregon Zoo in the US has caused a stir online when X-ray images of some of its animals were shared to Twitter. As part of the procedure the animals are photographed and scanned to ensure they are in good health. The eerie images reveal that the tail of a beaver has a bone reaching all the way to the tip and an X-ray of a snake reveals the mesmerising vertebrae that curve and stretch throughout the deadly ball python. Other images show the difference between birds (a screech owl) and flying mammals (a flying fox) and a hedgehog with trapped gas in its stomach. The Rodrigues flying fox (pictured) is actually a species of bat called Pteropus rodricensis and is only found in the wild on Rodrigues, an island in the Indian Ocean belonging to Mauritius.

It is designed to plug in to Metacorps, an experimental web app for annotating metaphor. As Metacorps users annotate metaphors, the system will use user annotations as training data. When the system is confident, it will suggest an identification and an annotation. Once approved by the user, this becomes more training data. This naturally allows for transfer learning, where the system can, with some known degree of reliability, classify one class of metaphor after only being trained on another class of metaphor. For example, in our metaphorical violence project, metaphors may be classified by the network they were observed on, the grammatical subject or object of the violence metaphor, or the violent word used (hit, attack, beat, etc.).

At 25, Ms Annabelle Kwok has already made a name for herself. Two years ago, she made waves when she co-founded SmartCow, an artificial intelligence (AI) company that came up with an electronic board that could run various AI software. Last year, Ms Kwok left SmartCow and started NeuralBay, a company that focuses on detection and recognition software related to humans, objects and text, and offers AI-driven solutions for multinational corporations. Her clients include an aviation corporation, an automation industry company and chocolate company Ferrero. Ms Kwok traces her interest in tech to a box of Lego bricks with electric cables called Lego Mindstorms, which her parents, who work in banking, bought for her when she was in primary school.

Every discussion about artificial intelligence seems to alternate between utopia and dystopia. Some believe that the productivity unleashed through automation will lift up all of society, creating a world of superabundance and more meaningful work, while others see robots taking our jobs and an acceleration of trends favoring capital over labor. In fact, in an article in Harvard Business Review, Accenture's Mark Knickrehm describes five distinct schools of thought, ranging from both extremes to various shades of gray in between. He suggests that leaders need to reinvent operating models, redefine jobs and include employees in the process of transformation. Yet that's easier said than done.

Delivery drones are real and they're operating on a national level, but they're not dropping off impulse purchases, and some of the most important applications are not in the United States. Zipline, a Bay Area startup, inked a deal with the government of Rwanda in 2016 and now uses small, autonomous planes to deliver medical supplies, and in particular blood, to rural communities across the African country. "It's a pretty cool paradigm shift for people who think all technological revolution is going on in US, and it'll trickle down to poor countries," says Zipline CEO, Keller Rinaudo, presenting his vision for drone deliveries on stage at the WIRED25 summit in San Francisco on Monday. "This is the opposite of that." Amazon created an internet-wide buzz when it announced it wanted to start delivering online shopping via drone, in a 60 Minutes interview in 2013.

A Ghanaian teenager, Mustapha Diyaol Haqq, created an AI system that is able to predict breast cancer. Ahmadiyya Senior High School in Kumasi, says he taught himself to code and got himself working as a coding instructor and a mentor in Ghana Code Club. The AI model makes use of breast cancer test results from breast fine needle aspiration test to classify breast cancer tumour I for malignant (cancerous) and O for benign (Not cancerous). Mustapha believes that over time with the use of larger Ghanaian data, models can be created with higher accuracy and used for real case scenarios. His model was tested on the machine learning repository by the University of California.