Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I was 21 when I first watched Spike Jonze's 2013 sci-fi romance Her in theaters in New York City--a then–fresh college graduate teeming with the potent and deluded optimism that came with being a very broke and online millennial hoping to change the world. Her sparked some of my first reflections about whether tech innovation is inherently good or bad for society, and helped validate my early moral quandaries and panic at the time. I was graduating at the first turn of a recovering recession (mainly due to big tech investments in digital and social media) and securing my first full-time role as an online reporter. Though I was eager and rosy, a quiet, worried voice also began growing inside of me. Me, my job, my realities, were entirely dependent on tech--mainly Facebook content dissemination and programmatic turnkey digital ads--and I was not sure these huge tech investments by our broligarchical founding fathers would lead us anywhere good.

It might sound like a far-fetched plot from dystopian science fiction, but novel research could soon allow parents to grow their baby in a bag. Just like the 2023 film The Pod Generation, artificial wombs could support an infant from conception to birth without any need for pregnancy. Although most of the population remains sceptical of this revolutionary change to motherhood, Gen Z seems ready to embrace the technology with open arms. In a survey conducted by religious issues think tank, Theos, 42 per cent of people aged 18-24 said they would support'growing a foetus entirely outside of a woman's body'. In the first large-scale survey of its kind, as part of its Motherhood vs The Machine podcast, Theos asked 2,292 people for their views on artificial wombs.

Accurate trajectory tracking is an essential characteristic for the safe navigation of a quadrotor in cluttered or disturbed environments. In this paper, we present in detail two state-of-the-art model-based control frameworks for trajectory tracking: the Linear Model Predictive Controller (LMPC) and the Nonlinear Model Predictive Controller (NMPC). Additionally, the kinematic and dynamic models of the quadrotor are comprehensively described. Finally, a simulation system is implemented to verify feasibility, demonstrating the effectiveness of both controllers.

Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge.

Calibration is an essential key in machine leaning. Semi Unsupervised Calibration through Prior Adaptation (SUCPA) is a calibration algorithm used in (but not limited to) large-scale language models defined by a {system of first-order difference equation. The map derived by this system} has the peculiarity of being non-hyperbolic {with a non-bounded set of non-isolated fixed points}. In this work, we prove several convergence properties of this algorithm from the perspective of dynamical systems. For a binary classification problem, it can be shown that the algorithm always converges, {more precisely, the map is globally asymptotically stable, and the orbits converge} to a single line of fixed points. Finally, we perform numerical experiments on real-world application to support the presented results. Experiment codes are available online.

With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider a general, but realistic, scenario in which a predictive model is learned from (potentially biased) training data, and model predictions are assessed post-hoc for fairness by some auditing method. We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to the prediction stage. Unlike prior work, we evaluate the downstream impacts of data biases quantitatively rather than qualitatively and prove theoretical guarantees for detection. Under reasonable assumptions, we quantify how the amount of bias in the model predictions varies as a function of the amount of differential sampling bias in the data, and at what point this bias becomes provably detectable by the auditor. Through experiments on two criminal justice datasets -- the well-known COMPAS dataset and historical data from NYPD's stop and frisk policy -- we demonstrate that the theoretical results hold in practice even when our assumptions are relaxed.

The ultimate performance of machine learning algorithms for classification tasks is usually measured in terms of the empirical error probability (or accuracy) based on a testing dataset. Whereas, these algorithms are optimized through the minimization of a typically different--more convenient--loss function based on a training set. For classification tasks, this loss function is often the negative log-loss that leads to the well-known cross-entropy risk which is typically better behaved (from a numerical perspective) than the error probability. Conventional studies on the generalization error do not usually take into account the underlying mismatch between losses at training and testing phases. In this work, we introduce an analysis based on point-wise PAC approach over the generalization gap considering the mismatch of testing based on the accuracy metric and training on the negative log-loss. We label this analysis PACMAN. Building on the fact that the mentioned mismatch can be written as a likelihood ratio, concentration inequalities can be used to provide some insights for the generalization problem in terms of some point-wise PAC bounds depending on some meaningful information-theoretic quantities. An analysis of the obtained bounds and a comparison with available results in the literature are also provided.

Let me tell you what it's like to be an astrobiologist. I painted a white picket fence this summer. It was a task I'd set myself without realizing what a long-winded and frustrating process it would be. But eventually that endless scraping, priming, painting, and maneuvering settled into something therapeutic, even meditative. I'd paint the apex--dab, dab--run down the narrow sides, coat the smooth front, shuffle along, repeat. All the while acutely aware of being surrounded by the churn of summer in the northern hemisphere of a living planet.

A Building Automation System (BAS) has functions of monitoring and controlling the operation of all building sub-systems such as HVAC (Heating-Ventilation, Air-conditioning Control), electric consumption management, fire alarm control, security and access control, and appliance switching control. In the BAS, almost operations are automatically performed at the control centre, the building security therefore must be strictly protected. In the traditional system, the security is usually ensured by a number of cameras installed at fixed positions and it may results in a limited vision. To overcome this disadvantage, our paper presents a novel security system in which a mobile robot is used as a patrol. The robot is equipped with fuzzy-based algorithms to allow it to avoid the obstacles in an unknown environment as well as other necessary mechanisms demanded for its patrol mission. The experiment results show that the system satisfies the requirements for the objective of monitoring and securing the building.

Cambridge, United Kingdom – Theo, a 12-year-old boy who is blind, is seated at a table in a crowded kitchen on a gray and drippy mid-December day. A headband that houses cameras, a depth sensor and speakers rings his sandy-brown hair. He swivels his head left and right until the camera in the front of the headband points at the nose of a person on the far side of a counter. Theo hears a bump sound followed by the name "Martin" through the headband's speakers, which are positioned above his ears. "It took me like five seconds to get you, Martin," Theo says, his head and body fixed in the direction of Martin Grayson, a senior research software development engineer with Microsoft's research lab in Cambridge.