OCCRP: Organized Crime Has a New Tool in Its Belts – Artificial Intelligence

Organized Crime and Corruption Reporting Project: Organized Crime Has a New Tool in Its Belts – Artificial Intelligence. “As new technologies offer a world of opportunities and benefits in many sectors, so too do they offer new avenues and for organized crime. It was true at the advent of the internet, and it’s true for the growing field of artificial intelligence and machine learning, according to a new joint report by Europol and the United Nations Interregional Crime and Justice Research Center.”

Machine learning for making machines: Applying visual search to mechanical parts (Purdue University)

Purdue University: Machine learning for making machines: Applying visual search to mechanical parts. “Computer vision researchers use machine learning to train computers in visually recognizing objects – but very few apply machine learning to mechanical parts such as gearboxes, bearings, brakes, clutches, motors, nuts, bolts and washers. A team of Purdue University mechanical engineers has created the first comprehensive open-source annotated database of more than 58,000 3D mechanical parts, designed to help researchers apply machine learning to those parts in actual machines.”

Scents of history: study hopes to recreate smells of old Europe (The Guardian)

The Guardian: Scents of history: study hopes to recreate smells of old Europe. “From the pungent scent of a cigar to the gentle fragrance of roses, smells can transport us to days gone by. Now researchers are hoping to harness the pongs of the past to do just that. Scientists, historians and experts in artificial intelligence across the UK and Europe have announced they are teaming up for a €2.8m project labelled ‘Odeuropa’ to identify and even recreate the aromas that would have assailed noses between the 16th and early 20th centuries.”

The Register: Bio-boffins devise potentially fast COVID-19 virus test kit out of a silicon wafer and machine-learning code

The Register: Bio-boffins devise potentially fast COVID-19 virus test kit out of a silicon wafer and machine-learning code. “Boffins have demonstrated that machine-learning algorithms may be able to help scientists identify viruses, and could even be used to develop more efficient tests for the presence of the COVID-19 coronavirus in the near future.”

University College London: Machine Learning Tool Developed To Detect Fake News Domains Upon Registration

University College London: Machine Learning Tool Developed To Detect Fake News Domains Upon Registration. “Academics at UCL and other institutions have collaborated to develop a machine learning tool that identifies new domains created to promote false information so that they can be stopped before the ‘fake news’ can be spread through social media and online channels.”

MIT News: System brings deep learning to “internet of things” devices

MIT News: System brings deep learning to “internet of things” devices. “Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the ‘internet of things’ (IoT).”

EurekAlert: How neural networks can help us gain a deeper understanding of financial markets

EurekAlert: How neural networks can help us gain a deeper understanding of financial markets. “A new research project at Aarhus University will use Bayesian Neural Networks to model, analyse and understand trading behaviour in the world of finance. The researchers behind the project expect the technology to revolutionise our understanding of financial data. For the first time ever, a team of artificial intelligence researchers and experts will use Bayesian Neural Networks (BNN), a kind of deep machine learning algorithm, to analyse, model and understand causal relationships within trading on global financial markets.”

Unite .ai: Researchers Develop New Tool to Fight Bias in Computer Vision

Unite .ai: Researchers Develop New Tool to Fight Bias in Computer Vision. “One of the recent issues that has emerged within the field of artificial intelligence (AI) is that of bias in computer vision. Many experts are now discovering bias within AI systems, leading to skewed results in various different applications, such as courtroom sentencing programs. There is a large effort going forward attempting to fix some of these issues, with the newest development coming from Princeton University. Researchers at the institution have created a new tool that is able to flag potential biases in images that are used to train AI systems.”

The Next Web: A beginner’s guide to the math that powers machine learning

The Next Web: A beginner’s guide to the math that powers machine learning. “At some point in your exploration and mastering of artificial intelligence, you’ll need to come to terms with the lengthy and complicated equations that adorn AI whitepapers and machine learning textbooks. In this post, I will introduce some of my favorite machine learning math resources. And while I don’t expect you to have fun with machine learning math, I will also try my best to give you some guidelines on how to make the journey a bit more pleasant.”

Machine learning takes on synthetic biology: algorithms can bioengineer cells for you (EurekAlert)

EurekAlert: Machine learning takes on synthetic biology: algorithms can bioengineer cells for you. “…scientists at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it; instead, with a limited set of training data, the algorithms are able to predict how changes in a cell’s DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal.”

Slate: Under the Gaze of Big Mother

Slate: Under the Gaze of Big Mother. “An artificial intelligence that can truly understand our behavior will be no better than us at dealing with humanity’s challenges. It’s not God in the machine. It’s just another flawed entity, doing its best with a given set of goals and circumstances. Right now we treat A.I.s like children, teaching them right from wrong. It could be that one day they’ll leapfrog us, and the children will become the parents. Most likely, our relationship with them will be as fraught as any intergenerational one. But what happens if parents never age, never grow senile, and never make room for new life? No matter how benevolent the caretaker, won’t that create a stagnant society?”

EurekAlert: How to make AI trustworthy

EurekAlert: How to make AI trustworthy. “One of the biggest impediments to adoption of new technologies is trust in AI. Now, a new tool developed by USC Viterbi Engineering researchers generates automatic indicators if data and predictions generated by AI algorithms are trustworthy. Their research paper, ‘There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks’ by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC Cyber Physical Systems Group, was featured in Frontiers in Artificial Intelligence.”

Phys .org: Machine-learning model finds SARS-COV-2 growing more infectious

Phys .org: Machine-learning model finds SARS-COV-2 growing more infectious. “The model, developed by lead researcher Guowei Wei, professor in the departments of Mathematics and Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein—a protein primarily responsible for facilitating infection—and found that five of the six known virus subtypes are now more infectious.”