In a nondescript office building on the outskirts of Zaporizhzhia, Ukrainian soldiers have been honing what they believed will be a decisive weapon in their effort to repel the Russian invasion. Inside, the weapon glows from a dozen computer screens – a constantly updated portrayal of the evolving battlefield to the south. With one click on a menu, the map is populated with hordes of orange diamonds, showing Russian deployments. They reveal where tanks and artillery have been hidden, and intimate details of the units and the soldiers in them, gleaned from social media. Zooming in shows satellite imagery of the terrain in sharp detail.

Recently, an RTI filed by the Internet Freedom Foundation (IFF) revealed that the Delhi Police is using Facial recognition technology (FRT) to nab rioters in the capital city. This has caused an uproar as many members of the civil society raised concerns and called the Delhi Police's use of FRT'unethical' in the absence of a Data Protection Act in the country. The argument being made by them is national security should not come at the cost of privacy. Technology such as FRT has been controversial, and authorities leveraging such tech is definitely a concern. The RTI filed by IFF revealed that the procurement of the FRT by the Delhi Police was authorised as per a 2018 direction of the Delhi High Court in Sadhan Haldar v NCT of Delhi.

The adoption of robotics and artificial intelligence (AI) in Africa received a major boost after Uniccon Group, an Abuja-based tech startup, unveiled the continent's first humanoid robot. Omeife, the 1.8m female human-like robot, is African by design and has Igbo-like physical attributes. The battery-powered robot can speak Igbo, Yoruba, English, French, Swahili, Wazobia, Pidgin, Afrikaans and Arabic with native accents. Uniccon Group chief executive Chuks Ekwueme said: "Omeife also identifies objects and calculates positions and distances of objects." The launch of Omeife comes a few months after Abdul Malik Tejan-Sie, a South African-based Sierra Leonean innovator, presented a prototype of South Africa's first humanoid robot.

My friends in pharma may like this - the world's first documentary video hackathon covering the discovery of a novel medicine from the development of AI platform to using this AI platform to discover a novel target, generate novel molecule and take it all the way into the human clinical trials. The target was discovered using aging research and it may be the first aging-clock derived therapeutic. We started recording the footage in 2020 and generated over 160 hours worth of footage material, interviews, laboratory experiments, internal presentations, successes, failures, daily life of deeply committed scientists - all on tape. We are now offering 2 years of footage to the documentary video experts to take part in the global competition to tell the story and explain how novel medicines are made. We have a panel of celebrity judges and great prizes for the winners.

In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis. Typically, algorithms select individual collision events for preservation and store the complete experimental response. A relatively new alternative strategy is to additionally save a partial record for a larger subset of events, allowing for later specific analysis of a larger fraction of events. We propose a strategy that bridges these paradigms by compressing entire events for generic offline analysis but at a lower fidelity. An optimal-transport-based $\beta$ Variational Autoencoder (VAE) is used to automate the compression and the hyperparameter $\beta$ controls the compression fidelity. We introduce a new approach for multi-objective learning functions by simultaneously learning a VAE appropriate for all values of $\beta$ through parameterization. We present an example use case, a di-muon resonance search at the Large Hadron Collider (LHC), where we show that simulated data compressed by our $\beta$-VAE has enough fidelity to distinguish distinct signal morphologies.

A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to update and derive an interpretable expression for the digital twin. Two approaches for updating the digital twin are proposed. The first approach makes use of both the input and output information from a dynamical system, whereas the second approach utilizes output-only observations to update the digital twin. Both methods use a library of candidate functions representing certain physics to infer new perturbation terms in the existing digital twin model. In both cases, the resulting expressions of updated digital twins are identical, and in addition, the epistemic uncertainties are quantified. In the first approach, the regression problem is derived from a state-space model, whereas in the latter case, the output-only information is treated as a stochastic process. The concepts of It\^o calculus and Kramers-Moyal expansion are being utilized to derive the regression equation. The performance of the proposed approaches is demonstrated using highly nonlinear dynamical systems such as the crack-degradation problem. Numerical results demonstrated in this paper almost exactly identify the correct perturbation terms along with their associated parameters in the dynamical system. The probabilistic nature of the proposed approach also helps in quantifying the uncertainties associated with updated models. The proposed approaches provide an exact and explainable description of the perturbations in digital twin models, which can be directly used for better cyber-physical integration, long-term future predictions, degradation monitoring, and model-agnostic control.

Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics -- daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.

To this end, we propose a Neural Architecture DNN-powered solutions in numerous real-world applications. Search and Acceleration framework dubbed NASA, which However, the extensively used multiplications in DNNs enables automated multiplication-reduced DNN development dominate their energy consumption and have largely challenged and integrates a dedicated multiplication-reduced accelerator DNNs' achievable hardware efficiency, motivating for boosting DNNs' achievable efficiency. Specifically, multiplication-free DNNs that adopt hardware-friendly operators, NASA adopts neural architecture search (NAS) spaces that such as additions and bit-wise shifts, which require a augment the state-of-the-art one with hardware inspired smaller unit energy and area cost as compared to multiplications multiplication-free operators, such as shift and adder, armed [26]. In particular, pioneering works of multiplicationfree with a novel progressive pretrain strategy (PGP) together DNNs include (1) DeepShift [6] which proposes to adopt with customized training recipes to automatically search for merely shift layers for DNNs, (2) AdderNet [20] which advocates optimal multiplication-reduced DNNs; On top of that, NASA using adder layers to implement DNNs for trading the further develops a dedicated accelerator, which advocates a massive multiplications with lower-cost additions, and (3) chunk-based template and auto-mapper dedicated for NASA-ShiftAddNet [26] which combines both shift and adder layers NAS resulting DNNs to better leverage their algorithmic to construct DNNs for better trading-off the achievable properties for boosting hardware efficiency.

Two weeks ago, I gave a lecture setting out my current thoughts on AI safety, halfway through my year at OpenAI. I was asked to speak by UT Austin's Effective Altruist club. You can watch the lecture on YouTube here (I recommend 2x speed). The timing turned out to be weird, coming immediately after the worst disaster to hit the Effective Altruist movement in its history, as I acknowledged in the talk. I then spent 20 minutes taking questions. For those who (like me) prefer text over video, below I've produced an edited transcript, by starting with YouTube's automated transcript and then, well, editing it. Thank you so much for inviting me here. I do feel a little bit sheepish to be lecturing you about AI safety, as someone who's worked on this subject for all of five months. But this past spring, I accepted an extremely interesting opportunity to go on leave for a year to think about what theoretical computer science can do for AI safety. I'm doing this at OpenAI, which is one of the world's leading AI startups, based in San Francisco although I'm mostly working from Austin. Despite its name, OpenAI is famously not 100% open … so there are certain topics that I'm not allowed to talk about, like the capabilities of the very latest systems and whether or not they'll blow people's minds when released. By contrast, OpenAI is very happy for me to talk about AI safety: what it is and and what if anything can we do about it. So what I thought I'd do is to tell you a little bit about the specific projects that I've been working on at OpenAI, but also just, as an admitted newcomer, share some general thoughts about AI safety and how Effective Altruists might want to think about it. I'll try to leave plenty of time for discussion. Maybe I should mention that the thoughts that I'll tell you today are ones that, until last week, I had considered writing up for an essay contest run by something called the FTX Future Fund. Unfortunately, the FTX Future Fund no longer exists. It was founded by someone named Sam Bankman-Fried, whose a net worth went from 15 billion dollars to some negative number of dollars in the space of two days, in one of the biggest financial scandals in memory. This is obviously a calamity for the EA community, which had been counting on funding from this individual. I feel terrible about all the projects left in the lurch, to say nothing of FTX's customers. Let's start with this: raise your hand if you've tried GPT-3.

ChatGPT makes an irresistible first impression. It's got a devastating sense of humour, a stunning capacity for dead-on mimicry, and it can rhyme like nobody's business. Then there is its overwhelming reasonableness. When ChatGPT fails the Turing test, it's usually because it refuses to offer its own opinion on just about anything. When was the last time real people on the internet declined to tell you what they really think?