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Cognitive abilities, which include ideas like intentionality and consciousness, have long been viewed in Western philosophy as exclusive to the human realm. Intent is roundly considered justifiable only with minimum requirements for self-awareness or situational comprehension. However, such hard line views have softened gradually with modern enlightenment, and more of us are likely to accept that terms such as'agency', 'intelligence', and even'emotion' can apply for other species too. Even plants lean into sunlight in an intentional way; the identification of an intention doesn't have to arise from the plant to be true. Latterly their possibility has been extended even to artificial systems, which some find more acceptable, though a modern version of the privilege argument persists in a distinction between'simple' machinery and'complex' biology, which many believe still holds some principled leap in understanding. Ideological'blood-brain barriers', like these, continue to undermine efforts to form a rational causal explanation of intent, leading extremists to clutch at esoteric straws like quantum mechanics or complexity theory to account for perceived magic. In this note, I address another apparent schism that may shed light on these questions: the difference between process dynamics (the realm of physics) and interpretive semantics (the realm of linguistics and philosophy), and the suggestion that (deep down) intentionality might be a relatively simple phenomenon with an energetic explanation (as trust has been shown to be [9]). The recent acceptance of attention mechanisms in Large Language Models is related example [19, 22].

Semantic Spacetime (SST) is a discrete, graph theoretic'agent' representation of configurations and process phenomena, used for modelling scenarios that include knowledge representations, in the form of labelled directed graphs [1-4]. It enables both qualitative and quantitative interpretations of processes by combining physical and virtual concepts (from physics and information science) into a Promise Theoretic agent model [5]. Promise Theory principles emphasize the autonomy or locality of causal behaviour, so there are clear motivations for modelling phenomena in this way. As a graph theoretical structure, a Semantic Spacetime is a collection of nodes (agents) joined by links (channels for process information), both of which may have annotations and numerical values associated with them. A key application for Semantic Spacetime in artificial systems is to represent'knowledge' (in its simplified sense) and process structures, such as those normally associated with indexing methods or Semantic Webs, like the triple store approaches of the Resource Description Framework (RDF) [6].

The head of Australia's peak intelligence agency has warned that people like the Christchurch terrorist are being radicalised on social media, and artificial intelligence is likely to make it much worse. The director general of the Australian Security Intelligence Organisation (Asio), Mike Burgess, told a social media summit in Adelaide on Friday that social media is "both a goldmine and a cesspit" that creates communities and divides them, and the internet was "the world's most potent incubator of extremism". He said people were embracing anti-authority ideologies, conspiracy theories and diverse grievances, and while social media was not the sole driver, he said Asio considered it a "significant driver". "Social media allows extremist ideologies, conspiracies, dis- and misinformation to be shared at an unprecedented scale and speed," he said. He said radicalisation can now take days and weeks rather than months and years as it previously did, with the most likely perpetrator of a terrorist attack being a lone actor.

Brits largely endured frigid temperatures in September – but globally, the story was quite different. Last month was the second-hottest September on record, the EU's climate change programme has revealed. The global average air temperature for September 2024 was 61.1 F (16.17 C), which is 1.31 F (0.73 C) above the September average. What's more, it's just shy of the record set by September 2023 – 61.4 F (16.38 C). Worryingly, experts point to human-cased greenhouse gas emissions as the cause for this latest temperature'anomaly'.

Human communities have self-organizing properties that give rise to very specific natural grouping patterns, reflected in the Dunbar Number and its layered structure (a Dunbar Graph). Since work-groups are necessarily also social groups, we might expect the same principles to apply here as well. One factor likely to be important in limiting the size of groups is that conflicts typically escalate with the number of people involved. Here we analyse Wikipedia editing histories across a wide range of topics to show that there is an emergent coherence in the size of groups formed transiently to edit the content of subject texts, with two peaks averaging at around $N=8$ for the size corresponding to maximal contention, and at around $N=4$ as a regular team. These values are consistent with the observed sizes of conversational groups, as well as the hierarchical structuring of Dunbar graphs. We use the Promise Theory of trust to suggest a scaling law that may apply to all group distributions based on seeded attraction. In addition to providing further evidence that even natural communities of strangers are self-organising, the results have important implications for the governance of the Wikipedia commons and for the security of all online social platforms and associations.

We present a simple argument using Promise Theory and dimensional analysis for the Dunbar scaling hierarchy, supported by recent data from group formation in Wikipedia editing. We show how the assumption of a common priority seeds group alignment until the costs associated with attending to the group outweigh the benefits in a detailed balance scenario. Subject to partial efficiency of implementing promised intentions, we can reproduce a series of compatible rates that balance growth with entropy.

Deep artificial neural networks (DNNs) trained through backpropagation provide effective models of the mammalian visual system, accurately capturing the hierarchy of neural responses through primary visual cortex to inferior temporal cortex (IT). However, the ability of these networks to explain representations in higher cortical areas is relatively lacking and considerably less well researched. For example, DNNs have been less successful as a model of the egocentric to allocentric transformation embodied by circuits in retrosplenial and posterior parietal cortex. We describe a novel scene perception benchmark inspired by a hippocampal dependent task, designed to probe the ability of DNNs to transform scenes viewed from different egocentric perspectives. Using a network architecture inspired by the connectivity between temporal lobe structures and the hippocampus, we demonstrate that DNNs trained using a triplet loss can learn this task. Moreover, by enforcing a factorized latent space, we can split information propagation into "what" and "where" pathways, which we use to reconstruct the input. This allows us to beat the state-of-the-art for unsupervised object segmentation on the CATER and MOVi-A,B,C benchmarks.

In late 2018, Google AI researchers Anna Goldie and Azalia Mirhoseini got the go-ahead to test an elegant idea. Google had invented powerful computer chips called tensor processing units, or TPUs, to run machine learning algorithms inside its data centers--but, the pair wondered, what if AI software could help improve that same AI hardware? The project, later codenamed Morpheus, won support from Google's AI boss Jeff Dean and attracted interest from the company's chipmaking team. It focused on a step in chip design when engineers must decide how to physically arrange blocks of circuits on a chunk of silicon, a complex, months-long puzzle that helps determine a chip's performance. In June 2021, Goldie and Mirhoseini were lead authors on a paper in the journal Nature that claimed a technique called reinforcement learning could perform that step better than Google's own engineers, and do it in just a few hours.

OAKLAND, Calif., May 2 (Reuters) - Alphabet Inc's (GOOGL.O) Google said on Monday it had recently fired a senior engineering manager after colleagues, whose landmark research on artificial intelligence software he had been trying to discredit, accused him of harassing behavior. The dispute, which stems from efforts to automate chip design, threatens to undermine the reputation of Google's research in the academic community. It also could disrupt the flow of millions of dollars in government grants for research into AI and chips. Google's research unit has faced scrutiny since late 2020 after workers lodged open critiques about its handling of personnel complaints and publication practices. The new episode emerged after the scientific journal Nature in June published "A graph placement methodology for fast chip design," led by Google scientists Azalia Mirhoseini and Anna Goldie.