And an awesome one,' says Pro Jank Footy developer David Ashby (left) with comedian Broden Kelly at the Great Northern Hotel in Carlton North. And an awesome one,' says Pro Jank Footy developer David Ashby (left) with comedian Broden Kelly at the Great Northern Hotel in Carlton North. 'People in Japan thought we made footy up': Pro Jank Footy, the video game taking Australian rules to the world T ry explaining Australian rules football to anyone outside Australia, and "it sounds like you're making it up," says David Ashby, the multi-hyphenate comedic mind behind deeply silly spoofs such as Italian Spider-Man and Danger 5. "Eighteen players a side, running around on a cricket oval with a rugby ball that has to be bounced every so often and gets kicked between posts - like, what the fuck is happening?" Two years ago at a fantasy footy barbecue, Ashby had a brainwave: why not make a video game where Australia's strange national sport becomes even stranger the more you play? Pro Jank Footy reimagines the sport as a high-octane fever dream, where every time your opponent kicks a goal, you get to change one rule so the game gets increasingly janky (and hilariously stupid). Your opponents turn into seagulls.

Engineering methodologies predominantly revolve around established principles of decomposition and recomposition. These principles involve partitioning inputs and outputs at the component level, ensuring that the properties of individual components are preserved upon composition. However, this view does not transfer well to intelligent systems, particularly when addressing the scaling of intelligence as a system property. Our prior research contends that the engineering of general intelligence necessitates a fresh set of overarching systems principles. As a result, we introduced the "core and periphery" principles, a novel conceptual framework rooted in abstract systems theory and the Law of Requisite Variety. In this paper, we assert that these abstract concepts hold practical significance. Through empirical evidence, we illustrate their applicability to both biological and artificial intelligence systems, bridging abstract theory with real-world implementations. Then, we expand on our previous theoretical framework by mathematically defining core-dominant vs periphery-dominant systems.

If the pickled bodies, partial skeletons and stuffed carcasses that fill museums seem a little, well, quiet, fear not. In the latest coup for artificial intelligence, dead animals are to receive a new lease of life to share their stories – and even their experiences of the afterlife. More than a dozen exhibits, ranging from an American cockroach and the remnants of a dodo, to a stuffed red panda and a fin whale skeleton, will be granted the gift of conversation on Tuesday for a month-long project at Cambridge University's Museum of Zoology. Equipped with personalities and accents, the dead creatures and models can converse by voice or text through visitors' mobile phones. The technology allows the animals to describe their time on Earth and the challenges they faced, in the hope of reversing apathy towards the biodiversity crisis.

Our fascination with intelligent machines goes back to ancient times with the mythical automaton Talos, Aristotle's mode of mechanical thought (syllogism) and Heron of Alexandria's mechanical machines. However, the quest for Artificial General Intelligence (AGI) has been troubled with repeated failures. Recently, there has been a shift towards bio-inspired software and hardware, but their singular design focus makes them inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? A careful examination of computation in biological systems suggests that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is key to building AGI.

Researchers at America's Pacific Northwest National Laboratory (PNNL) are developing machine learning techniques to help the Feds crack down on potentially rogue nuclear weapons. Suffice to say, it's generally illegal for any individual or group to own a nuclear weapon, certainly in the United States. Yes, there are the five officially recognized nuclear-armed nations – France, Russia, China, the UK, and the US – whose governments have a stash of these devices. And there are countries that have signed the United Nations' Treaty on the Prohibition of Nuclear Weapons, meaning they've promised not to "develop, test, produce, acquire, possess, stockpile, use or threaten to use" these gadgets. So if anyone has a nuke in their possession, it's because they are a country in the official nuclear-armed club, they are a government that's produced its own nukes, a terrorist who stole, bought, or somehow built one themselves, or some other sketchy scenario, in America's eyes at least.

The Coalition for Critical Technology (CCT) penned a letter opposing the publication of research called "A Deep Neural Network Model to Predict Criminality Using Image Processing." At the time of publication the letter has more than 1,000 signatures from researchers, practitioners, academics, and others. According to a press release from Harrisburg University, the paper is slated for publication in a book series from Springer Publishing, and the letter urges readers to demand that Springer pull the paper and condemn the use of criminal justice statistics to predict criminality. The use of algorithms in predictive policing is a fraught subject. As the CCT letter elaborates, criminal justice data is notoriously flawed.

Rachel Ashby is the Senior Principal Product Marketing Manager for Nuance Core Technologies, automatic speech recognition, text-to-speech and transcription engine, and Nuance APIs, Tooling and Analytics. Before joining Nuance, Ashby worked in various worldwide marketing and sales positions at IBM, including driving IBM Cloud marketing strategy, development and execution for global multi-million-dollar campaigns. As an Associate Partner in IBM Global Services, she worked closely with some of IBM's largest Fortune 100 clients to plan and deliver successful software deployments. Ashby has over 20 years of experience in the high-tech industry. Eduardo is the Director of User Experience within Nuance's Technology Advancement Group (TAG).

What are the best practices for creating artificial intelligence? It's a question posed by the "partnership on AI" formed by major American technology firms. The goal of the partnership, which includes Google, IBM, Microsoft and Facebook, is to "conduct research, recommend best practices, and publish research under an open license (sic) in areas such as ethics, fairness and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology." Now, a clarification of terms: AI and robots are different. I should know: I wrote a series of novels about self-replicating humanoid robots.

THE question I want to discuss is whether a mechanical chess player can outplay its designer (1). I don't say "beat" its designer; I say "outplay." I want to set aside all mechanical brains that beat their designer by sheer brute power of analysis. If the designer is a mediocre player, who can see only three moves ahead, let the machine be restricted until it, too, can see only three moves ahead. Let us assume that the machine cannot analyze the position right out and that it must make judgments.

Transactions of the Ninth Conference March 20-21, 1952, Macy Foundation, New York, N. Y.