Search Talks

At first sight, it seems that almost any QFT, seen as a low-energy effective field theory (EFT), can be coupled to gravity -- simply add an Einstein-Hilbert term, change all derivatives by covariant ones, and you're done. However, a growing body of evidence coming from String Theory compactifications, general properties of black hole evaporation, and rigorous results in holography, suggests that the opposite is true. We can work out the hidden constraints that quantum gravity consistency imposes in the matter sector systematically, an approach that receives the colourful name of "The Swampland Program". I will briefly review the program, its motivation, current results, and long-term goals.

I will review implications of the AdS/CFT correspondence for quantum gravity, paying particular attention to holographic RG flows and the processing of information. Deformed hyperbolic geometries correspond to boundary field theory RG flows. Conversely, as I will exemplify using the recent approach of discrete holography, boundary RG flows may be used to reconstruct the bulk spacetime, for instance via tensor networks. I will discuss recent approaches towards including gravity dynamics into this reconstruction process. --- The presenter will be joining via Zoom for this talk.

I will briefly review the key concepts underlying the Effective Field Theory of General Relativity, and give a couple of examples of how it works. Then I will describe seven lessons which can be extracted from the theory. Finally I discuss some of the limitations of the EFT framework.

The performance of neural networks like large language models (LLMs) is governed by "scaling laws": the error of the network, averaged across the whole dataset, drops as a power law in the number of network parameters and the amount of data the network was trained on. While the mean error drops smoothly and predictably, scaled up LLMs seem to have qualitatively different (emergent) capabilities than smaller versions when one evaluates them at specific tasks. So how does scaling change what neural networks learn? We propose the "quantization model" of neural scaling, where smooth power laws in mean loss are understood as averaging over many small discrete jumps in network performance. Inspired by Max Planck's assumption in 1900 that energy is quantized, we make the assumption that the knowledge or skills that networks must learn are quantized, coming in discrete chunks which we call "quanta". In our model, neural networks can be understand as being implicitly a large number of modules, and scaling simply adds modules to the network. In this talk, I will discuss evidence for and against this hypothesis, its implications for interpretability and for further scaling, and how it fits in with a broader vision for a "science of deep learning".

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Zoom link https://pitp.zoom.us/j/93886741739?pwd=NzJrcTBNS2xEUUhXajgyak94LzVvdz09

The rich EM phenomenology in the seconds, minutes, and hours just before, during, and after a compact object merger encodes the magnetization of the binary components, the nature of the post-merger remnant, the neutron star equation of state, the free neutron abundance, and a wide array of other compelling physics. Unfortunately, the requirement to search, find, and classify an electromagnetic counterpart within the large GW localization regions before targeted follow-up with sensitive instruments can begin, excludes access to these earliest times, even for the most well localized GW sources. The ability to promptly localize a GW source to within the field-of-view of a narrow-field sensitive facility, would enable extraordinary science. I will discuss the science cases that require extremely early time observations, and the coordination, instruments, and analyses necessary to achieve it. These include gamma-ray imaging, novel data analysis techniques, pre-merger GW detection, faster space telescopes, and new experiments.

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Zoom link https://pitp.zoom.us/j/96186614241?pwd=R0xpT0dDZVZzek5RT0x4Q1c3Z1RuUT09