AI experts Camille Francois and Meredith Whittaker discuss how to break up Big Tech and build a safe and ethical AI.

While many of today's headline-grabbing artificial intelligence (AI) tools are designed in Silicon Valley, much of the labour that fuels the boom is based in the Global South, raising questions over who stands to gain from the technology and at what cost. Nobel Peace Prize laureate Maria Ressa and the director of the Digital Futures Lab, Urvashi Aneja, explore the impact AI is already having on communities in the Global South – from labour conditions to democracy and the environment – and why countries need to move beyond "catching up with the North" when deciding what role AI can and should play in their societies.

To celebrate International Women's Day, we take a look back over the past year and highlight some of the women we've interviewed, written about, chatted to, and featured on AIhub. Rose Nakasi is a Lecturer of Computer Science and a Research Scientist at the Makerere Artificial Intelligence Lab, in Makerere University, Uganda. She holds a PhD in Computer Science from Makerere University. Her research interests are in artificial intelligence and data science, and particularly in the use of these for developing improved automated tools and techniques for microscopy diagnosis of diseases like malaria in low-resourced but highly endemic settings. We spoke to Rose Nakasi about her work developing machine learning techniques to aid diagnosis of microscopically diagnosed diseases: Interview with Rose Nakasi: using machine learning and smartphones to help diagnose malaria.

This article is part 1 of our bias in AI series, an update to the original article in our Biotech Review of the year – issue 8. Read part 2 here. During the COVID-19 pandemic, the notion of different health outcomes for different populations has gained increased profile in the public consciousness, particularly in light of the varying effect of COVID-19 on different community groups. Varying outcomes can arise for a variety of reasons, one of which is bias (whether conscious or unconscious) in the healthcare system. But surely this isn't something that needs to be considered in relation to AI in health research, as AI systems are inanimate and can't display human faults…right? There is often a misconception that medical devices and AI systems can't produce biased results, as they work using logic and process, rather than being tainted by flawed assumptions based on human error or prejudice. However, ultimately it is humans that design medical devices, which are tested on human collected datasets.

Since the appearance of the first primates on earth around 55 million years ago, the brain evolution has progressed following a rather flat linear progression. Then, around 2 million years ago, while the evolutionary line leading to hominins finally became distinct and the Homo Habilis was walking its first steps, the growth rate of its cranial capacity suddenly began to increase exponentially from around 500cm3 to 1.330cm3 of the modern human brain. Along with its growth in size, brain kept increasing in the number of neurons it contained: from an estimated 40 to 50 billion neurons for Homo Habilis to the 86 billion of a modern adult human. And neurons are heavily involved in determining general information processing capacity (IPC), as reflected by general intelligence. The new'extra' portion of the brain that our ancestors gained, the neocortex, together with the high availability of neurons, is what makes us so special by giving us extraordinary cognitive abilities including feelings, language, thinking, planning, and personality.

Well done in kickstarting Azure Cognitive Services Emotions API. Remember that Emotions API(Project Oxford) is still in "Preview Stage" so not all your images are meant to work (Tried like 10 happiness emotion images and only 1 got processed). Emotion analysis is essential for all industries. We live in a world where emotions are changed instantly so if we analyze and take precautions before bad things happen, we can avoid dramas or even deaths. There used to be emotion reading in police departments.

How smart are you when it comes to the nuances of Artificial Intelligence (AI)? When you read about the future of AI, it can seem like there are a lot of buzzwords being thrown around in the media. Differentiating among AI, machine learning and deep learning technologies can be confusing, especially when terms are being used interchangeably. Let's begin by clearing things up with a few definitions. First, there is AI, which refers to intelligence exhibited by machines in the form of human cognitive functions like visual perception, speech recognition, decision-making, and language translation.

Machine learning and deep learning can be powerful toolkits for enterprise CIOs – but how can you tell which framework is best for your business? The prospect of deep learning doing all the heavy lifting when it comes to building a prediction model sounds very exciting. However, it poses significant challenges when it comes to organizational problem solving due to the following reasons. Cloud-based compute is usually the most feasible option, but may not sit well with companies that are very guarded about their data going off premise. On the other hand, on-premise servers and staff to run and maintain them are very costly.

Contact Center – the next generation of customer care products will be enabled by AI-enabled Bots to automate repetitive tasks and provide a user interface that is more "natural" to use (conversational UI). The products will most likely have ML capability to learn from the large data sets that we generate from previous interactions with end customers. Cisco Collaboration – we recently acquired Mindmeld, which will enable Cisco to deliver unique conversational interfaces for Cisco Collaboration products, changing the way users will interact with these applications in the future. Security – Cisco Stealthwatch uses NetFlow and AI-powered algorithms to provide visibility into the entire network to uncover anomalies and identify threats. Cisco Services – An example use case is within technical services where AI can enable faster search for relevant technical and contextual resources to resolve service requests and reduce MTTR.

In our earlier posts we've discussed, and proven, empathys' growing importance in artificial intelligence. The next questions to ask are, "How do we make AI empathetic?", "How do we build emotion into our AIs?" and "Can we ever make AI feel?" At Kairos, we believe the answer to the "how" question is in face analysis. Facial recognition allows software to identify and verify human faces while emotion analysis allows software to measure and read the emotions on those found faces. More importantly, facial recognition and emotion analysis looks at each user as an individual and captures their specific human data.