Some believe it will present us with innovative solutions to a myriad of problems, others believe it will signal the demise of all mankind. In an effort to learn more about the current state of this exciting technology, and cut through the noise, I jumped at the opportunity to listen and learn from Galina Alperovich, senior machine learning researcher at Avast, during the Digital Transformation EXPO Europe, currently in its 15th year of congregating the most influential innovators and practitioners from around the globe. Avast uses a network comprised of hundreds of millions of sensors (their end-users) that funnel data into the company's A.I. system, in order to detect and defend against cyber threats. Read the interview with Galina Alperovich of Avast who answers basic questions of artificial intelligence on Cyber Defence Magazine.

RIO DE JANEIRO, BRAZIL – The government is progressing to govern the use of artificial intelligence in Brazil.

Speech is one of the most defining qualities of human beings. But for the 80 million adults and children in Europe living with speech impairments, according to figures from the Council of the European Union, even basic communication can be a challenge. Extensive European Commission studies have shown that such impairments can have major negative emotional and social impacts and can also get in the way of parents', teachers', and medical practitioners' ability to provide effective care. But Israeli research, backed by the EU's Horizon 2020 programme, is helping Europe's speech-impaired find their voice. "Mainstream speech recognition is developed with only'normal' speech in mind," says Danny Weissberg, CEO of Israeli startup Voiceitt.

Facial recognition technology became part of the fabric of life in China in 2019. Enabled by a vast network of cameras across the country, the government is using the technology for surveillance.

Investigation of human brain states through electroencephalograph (EEG) signals is a crucial step in human-machine communications. However, classifying and analyzing EEG signals are challenging due to their noisy, nonlinear and nonstationary nature. Current methodologies for analyzing these signals often fall short because they have several regularity assumptions baked in. This work provides an effective, flexible and noise-resilient scheme to analyze EEG by extracting pertinent information while abiding by the 3N (noisy, nonlinear and nonstationary) nature of data. We implement a topological tool, namely persistent homology, that tracks the evolution of topological features over time intervals and incorporates individual's expectations as prior knowledge by means of a Bayesian framework to compute posterior distributions. Relying on these posterior distributions, we apply Bayes factor classification to noisy EEG measurements. The performance of this Bayesian classification scheme is then compared with other existing methods for EEG signals.

Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper advocates for the training of surrogates that are consistent with the physical manifold -- i.e., predictions are always physically meaningful, and are cyclically consistent -- i.e., when the predictions of the surrogate, when passed through an independently trained inverse model give back the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, more resilient to sampling artifacts, and tend to be more data efficient. Using Inertial Confinement Fusion (ICF) as a test bed problem, we model a 1D semi-analytic numerical simulator and demonstrate the effectiveness of our approach. Code and data are available at https://github.com/rushilanirudh/macc/

This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice. Looking at these challenges through the lens of causality allows decisions about data collection, annotation procedures, and learning strategies to be made (and scrutinized) more transparently. We discuss how causal relationships between images and annotations can not only have profound effects on the performance of predictive models, but may even dictate which learning strategies should be considered in the first place. For example, we conclude that semi-supervision may be unsuitable for image segmentation---one of the possibly surprising insights from our causal analysis, which is illustrated with representative real-world examples of computer-aided diagnosis (skin lesion classification in dermatology) and radiotherapy (automated contouring of tumours). We highlight that being aware of and accounting for the causal relationships in medical imaging data is important for the safe development of machine learning and essential for regulation and responsible reporting. To facilitate this we provide step-by-step recommendations for future studies.

Episode Info: Episode 203 - Alex Bates discusses his book "Augmented Mind," and explores how augmenting humans, combining human intuition and artificial intelligence, will herald an unprecedented era of productivity and financial success. Alex performed DARPA-funded research in neural networks, human memory and computational diagnostics. He is the Managing Director at Neocortex Ventures a firm bringing artificial intelligence and machine learning to the forefront of the industrial market. Host, Kevin Craine Want to be a guest? Want to be a sponsor?

People need to be held accountable for the mistakes AI and algorithms make on their behalf, such as that seen in the government's robodebt scandal, according to Australian human rights commissioner Ed Santow. The proposal comes in a new discussion paper on the impact of new technologies on human rights in Australia, released by the commission on Tuesday. After the Australian government backed down on the use of automatic debt notices based on income averaging, and had legislation for its facial recognition system rejected by a government-dominated parliamentary committee, Santow said it was time to set some rules to govern how these new technologies are used. "Robodebt is just a prominent example of data science and government AI being used in decision-making," he said. All government use of AI should be enshrined in legislation, he said.

ANKARA – Turkey has dispatched a surveillance and reconnaissance drone to the breakaway north of ethnically divided island nation of Cyprus amid tensions over offshore oil and gas exploration, Turkey's state-run media said Monday. The Anadolu news agency said the Turkish-made Bayraktar TB2 drone took off from an airbase in Dalaman, Turkey, and touched down Monday at the airport in Gecitkala -- known as Lefkoniko in Greek, on Cyprus. Kudret Ozersay, foreign minister of the self-declared Turkish Cypriot state, told reporters Sunday that the Turkish deployment would be limited to unarmed drones as there was "no need" for armed ones. Earlier, Turkish Cypriot Prime Minister Ersin Tatar said there was an "urgent need" to address the security concerns of Turkey and the Turkish Cypriots in the eastern Mediterranean. It's unclear what the drones will be specifically tasked to do.