The BBC's Reith Lectures in 2022 featured Professor Stuart Russell from the University of California, Berkeley discussing artificial intelligence: its promises and its threats. Toby Walsh, Professor of AI at the University of NSW responds. He says autonomous vehicles have the potential to save 1,000 lives a year in Australia, people who currently die in road accidents as drivers drive whilst tired, drunk, or distracted. But autonomous war machines could wreak devastation. Toby Walsh says we need to stay in control and must not make ourselves redundant.

This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation. The thesis first prepares the readers with an overall overview of RL and key technical background in statistics and optimization. In each of the settings, the thesis motivates the problems to be studied, reviews the current literature, provides computationally efficient algorithms with provable efficiency guarantees, and concludes with future research directions. The thesis makes fundamental contributions to the three settings above, both algorithmically, theoretically, and empirically, while staying relevant to practical considerations.

This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The subsamples and their constituting data points are regarded as $\it{chromosome}$ and $\it{gene}$, respectively, in the terminology of genetic algorithm. Second, each pair of subsamples breeds two new subsamples, where each data point faces either $\it{crossover}$, $\it{mutation}$, or $\it{reproduction}$ with a certain probability. The dominant subsamples in terms of fitness values are inherited by the next generation. This process is repeated generation by generation and brings the sparse representation of kernel density estimator in its completion. We confirmed from simulation studies that the resulting estimator can perform better than other well-known density estimators.

On 30 July 2021, the Federal Court of Australia decided that AI systems can be inventors. In a word-first determination of Thaler v Commissioner of Patents,{[2021] FCA 879, ('Thaler')}, the Honourable Justice Beach found that AI systems can be the inventors on a patent application under Australian patent law. The decision has been appealed to the Full Bench of the Federal Court, which may decide to overrule it. For now, however, the decision is binding in Australia. Read on to find out what a patent is and an overview of the decision.

The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns. Past work has studied detection of current state-of-the-art models, but despite a developing threat landscape, there has been minimal analysis of the robustness of detection methods to adversarial attacks. To this end, we evaluate neural and non-neural approaches on their ability to detect computer-generated text, their robustness against text adversarial attacks, and the impact that successful adversarial attacks have on human judgement of text quality. We find that while statistical features underperform neural features, statistical features provide additional adversarial robustness that can be leveraged in ensemble detection models. In the process, we find that previously effective complex phrasal features for detection of computer-generated text hold little predictive power against contemporary generative models, and identify promising statistical features to use instead. Finally, we pioneer the usage of $\Delta$MAUVE as a proxy measure for human judgement of adversarial text quality.

Some of these meteorites fall in regions on Earth where fireball observatory networks are active, making it possible to record the trajectory of the fireball as it ablates material from the originating meteoroid. For some fireballs, this data can then be used to simulate both forward and backward in time to predict where the resulting meteorite landed on Earth and where the meteoroid originated in the solar system. Thus, recovering and analyzing these'orbital meteorites' with constrained, prior orbits provides an incredibly unique insight into the geology of the asteroid belt and the nature of mass transfer between the belt and the inner solar system. The Desert Fireball Network (DFN) (Bland et al. 2012; Howie et al. 2017) is one of many organizations (Oberst et al. 1998; Spurný et al. 2006; Trigo-Rodríguez et al. 2006; Olech et al. 2006; Colas et al. 2015; Devillepoix et al. 2020) that makes this possible.

Unleash the power of machine learning (ML) through hands-on learning and compete for prizes and glory. The AWS DeepRacer League is the world's first global autonomous racing competition driven by reinforcement learning; bringing together students, professionals, and enthusiasts from almost every continent. I'm Tomasz Ptak, a senior software engineer at Duco, an AWS Machine Learning Hero, an AWS DeepRacer competitor (named Breadcentric), a hobbyist baker, and a leader of the AWS Machine Learning Community on Slack, where we learn, race, and help each other start and grow our adventures in the cloud. It's my pleasure to unveil the exciting details of the upcoming 2022 AWS DeepRacer League season. It's a complete program that has helped over 175,000 individuals from over 700 businesses, educational institutions, and organizations begin their educational journey into machine learning through fun and rivalry.

Entrepreneurs are always looking for that next big idea to vault their business into the stratosphere. But just what is it that makes somebody want to start and grow their own company? It turns out it may be partly associated with a person's facial features, according to a team of researchers led by the University of Cyprus. They found that entrepreneurs chasing the prospect of becoming the next Sir Richard Branson or Elon Musk are more likely to have a symmetrical face and prominent cheekbones. However, although this makes them more likely to become an entrepreneur, it has no bearing on whether they will be a successful one, according to the researchers.

Read about how the Robotics industry is addressing key climate change issues highlighted by the IPCC's report in this editorial series. How can AI Prevent Fraud? Learn more about the potential of machine learning to help cut down on food waste. Read about how innovations in agricultural robotics are helping to reduce water waste with robotic irrigation. OcéanIA is providing hope for monitoring the effects of climate change on ocean systems.

Abbott approached Thaler about using the AI as the basis of the case and with a team of lawyers, all working pro bono, they filed patent applications in more than a dozen countries listing DABUS as the inventor of a beverage container it created. New Zealand's Assistant Commissioner of Patents rejected the initial application in January, ruling that the term "inventor" intrinsically refers to a natural person. Abbott said the test case was not about any sort of legal rights for machines, rather it was about trying to get a patent for "the inventive output from an AI" that lacks a traditional human inventor. Some firms were already using AI programmes to discover new drugs or to find ways to repurpose materials but the companies that many of the lawyers on the case represent wanted greater clarity on patent ownership before investing further, he said. The application was declined in Australia but later overturned by the Federal Court in 2021 which said the country's patent act had no specific provision excluding AI systems as inventors.