Artificial intelligence seems to be taking over the world with systems that model pixels, words, and phonemes. The world is arguably made up, not of pixels, words, and phonemes but of entities (objects, things, including events) with properties and relations among them. Surely we should model these, not the perception or description of them. You might suspect that concentrating on modeling words and pixels is because all of the (valuable) data in the world is in terms of text and images. If you look into almost any company you will find their most valuable data is in spreadsheets, databases and other relational formats. These are not the form that are studied in introductory machine learning, but are full of product numbers, student numbers, transaction numbers and other identifiers that can't be interpreted naively as numbers. The field that studies this sort of data has various names including relational learning, statistical relational AI, and many others. This paper explains why relational learning is not taking over the world -- except in a few cases with restricted relations -- and what needs to be done to bring it to it's rightful prominence.

Elephants may be the only animals besides humans to come up with arbitrary names for each other, according to an analysis of recordings using machine learning. The analysis found that some calls from African savannah elephants (Loxodonta africana) seem to contain name-like components specific to certain individuals. What's more, those individuals know their names, responding more strongly than others do when calls addressed to them are played back on a speaker. "I had noticed from years back that when an elephant gave a contact rumble, within a group of elephants I would see one individual lift its head, listen and give an answer," says Joyce Poole at ElephantVoices, a small organisation that studies elephants and aims to protect them. "And the rest seemed to just ignore the elephant. So I did wonder whether the calls were being directed toward a specific individual."

Google originally unveiled its generative 3D AI system called Dream Fields in 2021, and now a new and improved version has arrived. Google's new next-generation artificial intelligence software designed to convert text into 3D generated images is called DreamFusion. So, how does this work? In a new proof-of-concept paper published to the pre-print server arXiv, researchers outlined that Dream Fusion, much like Dream Fields, uses a neural network called Neural Radiance Field (NeRF) that is designed to general novel views of complex 3D scenes using 2D datasets. However, DreamFusion has taken a different approach than Dream Fields, as explained by Google research scientist Ben Poole who wrote on Twitter that the team replaced OpenAI's CLIP technology that powered Dream Fields with Google's own AI model called Imagen. The 3D models seen above and below aren't as photo-realistic as what we've seen with Midjourney.

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In the middle of the 1980s, David Poole introduced a semantical, model-theoretic notion of specificity to the artificial-intelligence community. Since then it has found further applications in non-monotonic reasoning, in particularin defeasible reasoning. Poole's notion, however, turns out to be intricate and problematic,which -- as we show -- can be overcome to some extent by a closer approximation of the intuitive human concept of specificity. Besides the intuitive advantages of our novel specificity ordering over Poole's specificity relation in the classical examples of the literature, we also report some hard mathematical facts: Contrary to what was claimed before, we show that Poole's relation is not transitive. Our new notion of specificity is transitive and also monotonic w.r.t.

I've read the book Life 3.0 by physicist & AI philosopher Max Tegmark, where he sets out a series of possible scenarios and outcomes for humankind sharing the planet with artificial intelligence. Tegmark immediately shoots down any notion that we are likely to be victims of a robot-powered genocide, and claims the idea we would programme or allow a machine to have the potential to hate humans is preposterous - fuelled by Hollywood's obsession with the apocalypse. Actually, we have the power, now, to ensure that if AIs goals are properly aligned with ours from the start, so that it wants what we want, then there can never be a'falling out' between species. In other words, if AI does pose a threat - and in some of his scenarios it does - it will not come from The Matrix's marauding AIs, enslaving humanity and claiming, like Agent Smith, 'Human beings are a disease. You are a plague and we are the cure'.

HAL became operational in Urbana, Illinois, at the HAL Plant (the University of Illinois' Coordinated Science Laboratory, where the ILLIAC computers were built). The film says this occurred in 1992, while the book gives 1997 as HAL's birth year.[3] In 2001: A Space Odyssey (1968), HAL is initially considered a dependable member of the crew, maintaining ship functions and engaging genially with its human crew-mates on an equal footing. As a recreational activity, Frank Poole plays against HAL in a game of chess. In the film the artificial intelligence is shown to triumph easily.

Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. Often there is background taxonomic information (in terms of subclasses and subproperties) that should also be taken into account. We show that existing fully expressive (a.k.a. universal) models cannot provably respect subclass and subproperty information. We show that minimal modifications to an existing knowledge graph completion method enables injection of taxonomic information. Moreover, we prove that our model is fully expressive, assuming a lower-bound on the size of the embeddings. Experimental results on public knowledge graphs show that despite its simplicity our approach is surprisingly effective.

As artificial intelligence (AI) and virtual reality infuse everyday life, marketers are beginning to worry about what this means for their jobs. Will it really boil down to man versus machine? Or is it time to challenge the idea that AI is capable of doing everything humans can? According to market research firm WARC's "Toolkit 2017" report, 60% of those interviewed believed AI would be the most important technology of 2018, followed by chatbots and messenger apps. "AI saves money – after all, it doesn't need breaks or holidays – and it makes people nervous about the future," Daren Poole, global head of creative at Kantar's insights division, said at a recent event hosted by Kantar Millward Brown.

Who could turn down an invitation to an event that promised "a candid look at advertising in 2018" and undertook "to unpack the most talked about challenges and opportunities" in the field today"? It is, of course, a sign of my age that I was surprised that the invitation came from a Kantar Millward Brown. To have a research supplier reporting back on the Cannes Lions 2018, once the stronghold of creative agencies, is probably surprising only to someone who still has memories of struggling to stay awake while being subjected to hours of earnest monotone presentations of reams of statistics by market researchers. Of course, things have evolved, and Kantar Millward Brown now describes itself as "the world's leading experts in helping clients grow great brands". The website assures us that "we are constantly analysing, understanding and interpreting the world around us.