Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. In theory, we can understand and even predict human behaviour using that information. But there is a problem: one person may generate hundreds or thousands of words in a declaration, each sentence with its corresponding complexity. If you want to scale and analyze several hundreds, thousands or millions of people or declarations in a given geography, then the situation is unmanageable.

Almost two years ago, I paused thinking about the future of AI and drew down some "predictions" about where I thought the field was going. One of those forecasts concerned reaching a general intelligence in several years, not through a super powerful 100-layers deep learning algorithm, but rather through something called collective intelligence. However, except for very obvious applications (e.g., drones), I have not read or seen any big development in the field and I thus thought to dig a bit into that to check what is currently going on. As part of the AI Knowledge Map then, I will have a look here not only at Swarm Intelligence (SI) but more generally at Distributed AI, which also includes Agent-Based Modeling (ABM) and Multi-Agent Systems (MAS). Let's start from the broader classification.

In my over 40 years of conferences, symposia, and summits, the UN AI for Good Global Summit is top of my list on the most important, impactful, and engaging events ever. Bringing together a broad spectrum of stakeholders is critical to the development of a path for AI that reflects and positively impacts the UN Sustainable Development Goals. Technology, on its own, will not show the way. Through its visionary leadership, the ITU has started us on a critical journey. I look forward to subsequent events moving us to the ultimate destination.

If you thought your profession, trade or job is safe from the inevitable onslaught of AI and robots… from the consequences of automation… think again. Because surgeons surely will have to. Scientists have revealed a robotic drill that can cut the most sensitive brain surgery down from two hours to two and a half minutes. The machine, developed at the University of Utah, is being hailed as a potential breakthrough in survival for brain patients as the reduced time they spend in surgery will drastically cut the chances of infection. Researchers say can make one type of complex cranial surgery 50 times faster than standard procedures.

New Zealand is a leader in government use of artificial intelligence (AI). It is part of a global network of countries that use predictive algorithms in government decision making, for anything from the optimal scheduling of public hospital beds to whether an offender should be released from prison, based on their likelihood of reoffending, or the efficient processing of simple insurance claims. But the official use of AI algorithms in government has been in the spotlight in recent years. On the plus side, AI can enhance the accuracy, efficiency and fairness of day-to-day decision making. But concerns have also been expressed regarding transparency, meaningful human control, data protection and bias.

Artificial intelligence has ushered in a new era with more and more manual tasks being taken over by AI systems and robots. The technology has brought great changes and convenience but we should watch out for problems and negative effects it may trigger as it grows stronger, according to Robin Williams, director of the Institute for the Study of Science, Technology and Innovation at Edinburgh University. Williams thinks people's expectations of this new technology is sometimes a bit overheated, and they should pay attention to possible risks, he said at a pre-session of the Pujiang Innovation Forum. In the 1940s and 1950s, people had huge expectations about what AI could achieve, he said. In the UK in 1973, the British had a great passion for AI with huge sums invested in it.

Artificial intelligence (AI) has been mainly the passion of data science labs and development shops. Lately, however, the implications of its potential impact on business -- in the form of enhanced customer service, expanded intelligent capabilities, and even society at large -- have become clearer. That means the time has come for business leaders to not only understand the implications of AI, but also step up and lead the way. That's because with the great power of AI comes great responsibility. "While AI is quickly becoming a new tool in the CEO tool belt to drive revenues and profitability, it has also become clear that deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more important, to workers, individuals, and society as a whole," write Roger Burkhardt, Nicolas Hohn, and Chris Wigley, all with McKinsey.

Last week, Mona Lisa smiled. A big, wide smile, followed by what appeared to be a laugh and the silent mouthing of words that could only be an answer to the mystery that had beguiled her viewers for centuries. A great many people were unnerved. Mona's "living portrait," along with likenesses of Marilyn Monroe, Salvador Dali, and others, demonstrated the latest technology in deepfakes--seemingly realistic video or audio generated using machine learning. Developed by researchers at Samsung's AI lab in Moscow, the portraits display a new method to create credible videos from a single image.

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning presents us with a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. We outline fundamental machine learning methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that links data with modeling, experiments, and simulations. Machine learning provides a powerful information processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby allowing the DNN to abstain on confusing samples while continuing to learn and improve classification performance on the non-abstained samples. We show how such a deep abstaining classifier (DAC) can be used for robust learning in the presence of different types of label noise. In the case of structured or systematic label noise -- where noisy training labels or confusing examples are correlated with underlying features of the data-- training with abstention enables representation learning for features that are associated with unreliable labels. In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise. We provide analytical results on the loss function behavior that enable dynamic adaption of abstention rates based on learning progress during training. We demonstrate the utility of the deep abstaining classifier for various image classification tasks under different types of label noise; in the case of arbitrary label noise, we show significant improvements over previously published results on multiple image benchmarks.