Humans have a tendency to see 'human'-like qualities in objects around them. We name our cars, and talk to pets and even household appliances, as if they could understand us as other humans do. This behavior, called anthropomorphism, is also seeing traction in Machine Learning (ML), where human-like intelligence is claimed to be perceived in Large Language Models (LLMs). In this position paper, considering professional incentives, human biases, and general methodological setups, we discuss how the current search for Artificial General Intelligence (AGI) is a perfect storm for over-attributing human-like qualities to LLMs. In several experiments, we demonstrate that the discovery of human-interpretable patterns in latent spaces should not be a surprising outcome. Also in consideration of common AI portrayal in the media, we call for the academic community to exercise extra caution, and to be extra aware of principles of academic integrity, in interpreting and communicating about AI research outcomes.

Soft and lightweight grippers have greatly enhanced the performance of robotic manipulators in handling complex objects with varying shape, texture, and stiffness. However, the combination of universal grasping with passive sensing capabilities still presents challenges. To overcome this limitation, we introduce a fluidic soft gripper, named the ``Pac-Man'' gripper, based on the buckling of soft, thin hemispherical shells. Leveraging a single fluidic pressure input, the soft gripper can encapsulate slippery and delicate objects while passively providing information on this physical interaction. Guided by analytical, numerical, and experimental tools, we explore the novel grasping principle of this mechanics-based soft gripper. First, we characterize the buckling behavior of a free hemisphere as a function of its geometric parameters. Inspired by the free hemisphere's two-lobe mode shape ideal for grasping purposes, we demonstrate that the gripper can perform dexterous manipulation and gentle gripping of fragile objects in confined environments. Last, we prove the soft gripper's embedded capability of detecting contact, grasping, and release conditions during the interaction with an unknown object. This simple buckling-based soft gripper opens new avenues for the design of adaptive gripper morphologies with applications ranging from medical and agricultural robotics to space and underwater exploration.

This work introduces an asymptotic study of Hotelling-type tensor deflation in the presence of noise, in the regime of large tensor dimensions. Specifically, we consider a low-rank asymmetric tensor model of the form $\sum_{i=1}^r \beta_i{\mathcal{A}}_i + {\mathcal{W}}$ where $\beta_i\geq 0$ and the ${\mathcal{A}}_i$'s are unit-norm rank-one tensors such that $\left| \langle {\mathcal{A}}_i, {\mathcal{A}}_j \rangle \right| \in [0, 1]$ for $i\neq j$ and ${\mathcal{W}}$ is an additive noise term. Assuming that the dominant components are successively estimated from the noisy observation and subsequently subtracted, we leverage recent advances in random tensor theory in the regime of asymptotically large tensor dimensions to analytically characterize the estimated singular values and the alignment of estimated and true singular vectors at each step of the deflation procedure. Furthermore, this result can be used to construct estimators of the signal-to-noise ratios $\beta_i$ and the alignments between the estimated and true rank-1 signal components.

This paper studies the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise. Specifically, we provide a precise characterization of the large-dimensional performance of deflation in terms of the alignments of the vectors obtained by successive rank-1 approximation and of their estimated weights, assuming non-trivial (fixed) correlations among spike components. Our analysis allows an understanding of the deflation mechanism in the presence of noise and can be exploited for designing more efficient signal estimation methods.

There are synergies of research interests and industrial efforts in modeling fairness and correcting algorithmic bias in machine learning. In this paper, we present a scalable algorithm for spectral clustering (SC) with group fairness constraints. Group fairness is also known as statistical parity where in each cluster, each protected group is represented with the same proportion as in the entirety. While FairSC algorithm (Kleindessner et al., 2019) is able to find the fairer clustering, it is compromised by high costs due to the kernels of computing nullspaces and the square roots of dense matrices explicitly. We present a new formulation of underlying spectral computation by incorporating nullspace projection and Hotelling's deflation such that the resulting algorithm, called s-FairSC, only involves the sparse matrix-vector products and is able to fully exploit the sparsity of the fair SC model. The experimental results on the modified stochastic block model demonstrate that s-FairSC is comparable with FairSC in recovering fair clustering. Meanwhile, it is sped up by a factor of 12 for moderate model sizes. s-FairSC is further demonstrated to be scalable in the sense that the computational costs of s-FairSC only increase marginally compared to the SC without fairness constraints.

Leveraging on recent advances in random tensor theory, we consider in this paper a rank-$r$ asymmetric spiked tensor model of the form $\sum_{i=1}^r \beta_i A_i + W$ where $\beta_i\geq 0$ and the $A_i$'s are rank-one tensors such that $\langle A_i, A_j \rangle\in [0, 1]$ for $i\neq j$, based on which we provide an asymptotic study of Hotelling-type tensor deflation in the large dimensional regime. Specifically, our analysis characterizes the singular values and alignments at each step of the deflation procedure, for asymptotically large tensor dimensions. This can be used to construct consistent estimators of different quantities involved in the underlying problem, such as the signal-to-noise ratios $\beta_i$ or the alignments between the different signal components $\langle A_i, A_j \rangle$.

One of the most impressive developments in recent years has been the production of AI systems that can teach themselves to master the rules of a larger system. Notable successes have included experiments with chess and Starcraft. Given that self-teaching capability, it's tempting to think that computer-controlled systems should be able to teach themselves everything they need to know to operate. But it should be much easier with a simpler system, right? A group of researchers in Amsterdam attempted to take a very simple mobile robot and create a system that would learn to optimize its movement through a learn-by-doing process.

Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity with the ultimate goal of improving data interpretation. When moving from PCA to sPCA, there are a number of implications that the practitioner needs to be aware of. A relevant one is that scores and loadings in sPCA may not be orthogonal. For this reason, the traditional way of computing scores, residuals and variance explained that is used in the classical PCA cannot directly be applied to sPCA models. This also affects how sPCA components should be visualized. In this paper we illustrate this problem both theoretically and numerically using simulations for several state-of-the-art sPCA algorithms, and provide proper computation of the different elements mentioned. We show that sPCA approaches present disparate and limited performance when modeling noise-free, sparse data. In a follow-up paper, we discuss the theoretical properties that lead to this problem.

Technological innovation is moving at an ever-accelerating pace, and this comes with vast benefits and inevitable changes to our way of life. One downside is that machine learning and automation are already replacing jobs, and this will increase rapidly. It also has the potential to replace much of that income with Universal Basic Income (UBI), or government cash handouts to all adult citizens, perhaps starting with covering some element of taxes and rising in the range of $100,000/year per citizen within the next 20 years. Proponents of UBI include well-known figures such as Mark Zuckerberg, Richard Branson and Elon Musk. Musk stated last year that he believed job loss would be so severe due to automation that some form of UBI will be necessary to support our society.

Despite the longest growth run in nearly three decades, Japan's economic outlook remains far from robust as uncertainty abounds over wage growth and business investment. Under Abenomics, Prime Minister Shinzo Abe's program of radical monetary easing, fiscal spending and vows of structural reforms, the economy grew at an annualized rate of 0.5 percent in the October-December period, marking the eighth straight quarter of expansion. It slowed from a revised 2.2 percent increase in the previous quarter and was below the potential growth rate of around 1.0 percent. Many economists expect the economy to keep growing at a moderate pace this year, but the biggest wild card could be volatility in financial markets after the recent global stock market rout. The key question now is whether domestic demand -- private consumption and corporate spending -- can pick up further and help the world's third-largest economy sustain its recent growth momentum, economists said.