The New Scientist Book Club has various issues with Masud Husain's prize-winning popular science book about neurology The New Scientist Book Club stepped away from science fiction for our October read, turning to the winner of the Royal Society Trivedi Science Book Prize instead, serendipitously announced just in time for us to start on our next literary adventure. Six books had been up for the award, from Daniel Levitin's to Sadiah Qureshi's . Judges picked Masud Husain's and they praised it effusively, calling it "a beautiful exploration of how problems in the brain can cause people to lose their sense of self", and citing how these medical histories are "skilfully interwoven with Husain's personal story of moving to the UK as an immigrant in the 1960s, where he found himself grappling with his own sense of belonging". Sandra Knapp, chair of the judging panel for the 2025 Royal Society Trivedi Science Book Prize, explains why neurologist Masud Husain's collection of case studies is such an enlightening, compassionate book The first thing to say is: our book club members are much tougher judges than those on the panel for the Royal Society prize! While I think we were excited to get to grips with this book, and to venture into the world of non-fiction for a change, there were many issues that were raised and picked over by our readers. Let's tackle the positives first.

Husain claims that Darwin can uncover problems like missing data while suggesting solutions to problems in an AI training dataset, such as malformed or missing data. Darwin can also ostensibly deliver "explainable" model results that spotlight important aspects of a dataset, he says. On the cybersecurity side, SparkCognition offers DeepArmor, which leverages AI to attempt to mitigate executable-based cyberattacks. Meanwhile, the company's DeepNLP service automates workflows of unstructured data to simplify tasks like information retrieval, document classification, and analytics. SparkCognition's SparkPredict and Ensemble are AI-powered asset management and predictive maintenance platforms, built to detect suboptimal production yields and equipment failures proactively.

A spectrogram of the sound of a human voice, used by voice-recognition software. The idea of artificial intelligence (AI) -- systems so advanced they can mimic or outperform human cognition -- first came to prominence in 1950, when British computer scientist Alan Turing proposed an'imitation game' to assess whether a computer could fool humans into thinking they were communicating with another human. Soon after, researchers at Princeton University in New Jersey built MADALINE, the first artificial neural network applied to a real-world problem. Their system, modelled on the brain and nervous system, learnt to solve a maze through trial-and-error. Since then, the rise of AI has been enabled by exponentially faster and more powerful computers and large, complex data sets.

With all these new artificial intelligence use cases comes the daunting question of whether machines will force humans into obsolescence. The jury is still out: Some experts vehemently deny that artificial intelligence will automate so many jobs that millions of people find themselves unemployed, while other experts see it as a pressing problem. "The structure of the workforce is changing, but I don't think artificial intelligence is essentially replacing jobs," Rahnama said. "It allows us to really create a knowledge-based economy and leverage that to create better automation for a better form of life. It might be a little bit theoretical, but I think if you have to worry about artificial intelligence and robots replacing our jobs, it's probably algorithms replacing white-collar jobs such as business analysts, hedge fund managers and lawyers."

Futurist Ray Kurzweil famously predicted that "By 2029, computers will have emotional intelligence and be convincing as people." We don't know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they're conscious, does that mean they actually are? Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don't understand it?

As artificial intelligence systems take on more tasks and solve more problems, it's hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that "By 2029, computers will have emotional intelligence and be convincing as people." We don't know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they're conscious, does that mean they actually are?

Artificial intelligence (AI) is not a new concept. The modern field of AI came into existence in 1956, but it took decades of work to make significant progress toward developing an AI system and making it a technological reality. Today, AI and its commonly cited subset of machine learning are common, especially in the business world. Most of us interact with AI in some form or another daily, but the truth is there are vast applications of the technology, from the mundane to the breathtaking. As AI and machine learning further proliferate, they are becoming an imperative for businesses that want to maintain a competitive edge.

"Humans tend to overestimate technology in the short term but underestimate it in the long term," said Tom Foster, editor at large for Inc. magazine, during a panel he moderated on innovations in machine learning at South by Southwest (SXSW) in March. Artificial intelligence (AI) was a recurring theme across panels at SXSW 2018's Interactive Conference held in Austin, Texas. The topic was particularly popular in tracks titled "Intelligent Future" and "Startup & Tech Sectors." Many AI experts marveled at recent advances in the technology while pondering its future. "I've been working in AI for now more than 30 years and in the past eight years there are things that have occurred that I never thought would happen in my lifetime," said Adam Cheyer, co-founder of Viv Labs, during a discussion on innovations in AI.

Alarmist headlines abound about how AI and robots are stealing jobs from humans. The fears are spreading beyond concern over blue-collar jobs. Now there are greater worries that even white-collar jobs that require additional training and education will be replaced by artificial intelligence and AI-powered robots. Crunchbase News turned to AI expert, SparkCognition CEO and Founder Amir Husain, to get his thoughts on the matter. Austin-based AI startup SparkCognition has raised $56.5 million over the past year and $73.5 million since its inception in 2013.

This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling. Neural Networks exhibit continuous function approximator capabilities. Stochastic models allow direct specification of a model with known interaction between parameters to generate data. During the prediction phase, stochastic models generate a complete posterior distribution and produce probabilistic guarantees on the predictions. Thus BNNs are a unique combination of neural network and stochastic models with the stochastic model forming the core of this integration. BNNs can then produce probabilistic guarantees on it's predictions and also generate the distribution of parameters that it has learnt from the observations. That means, in the parameter space, one can deduce the nature and shape of the neural network's learnt parameters. These two characteristics makes them highly attractive to theoreticians as well as practitioners. Recently there has been a lot of activity in this area, with the advent of numerous probabilistic programming libraries such as: PyMC3, Edward, Stan etc. Further this area is rapidly gaining ground as a standard machine learning approach for numerous problems