A single man searching for love in Miami is confused. On the one hand, he received a barrage of criticism online for asking to split the bill on a first date with a girl he met on Tinder, an online dating app. On the other hand, he thinks – with modern-day feminism strongly in place in 2024 – women want equality and all that comes with it. The single man, who goes by "Water Boy" (@TheWaterBoy) on TikTok, posted a video recording of the date on TikTok. Users posting retellings of their bad dates are commonplace on the platform.

Aligning large language models with human objectives is paramount, yet common approaches including RLHF suffer from unstable and resource-intensive training. In response to this challenge, we introduce ARGS, Alignment as Reward-Guided Search, a novel framework that integrates alignment into the decoding process, eliminating the need for expensive RL training. By adjusting the model's probabilistic predictions using a reward signal, ARGS generates texts with semantic diversity while being aligned with human preferences, offering a promising and flexible solution for aligning language models. Notably, ARGS demonstrates consistent enhancements in average reward compared to baselines across diverse alignment tasks and various model dimensions. For example, under the same greedy-based decoding strategy, our method improves the average reward by 19.56% relative to the baseline and secures a preference or tie score of 64.33% in GPT-4 evaluation. We believe that our framework, emphasizing decoding-time alignment, paves the way for more responsive language models in the future. Code is publicly available at: \url{https://github.com/deeplearning-wisc/args}.

Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss.

Robot dog uses sensors, cameras and artificial intelligence to perceive and navigate surroundings. Agnieszka Pilat is not your typical artist. She doesn't use brushes, pencils or even her own hands to create her artwork. Pilat, who was born in Poland and now lives in the U.S., spent months teaching three of these four-legged machines named Basia, Vanya and Bunny to hold a paintbrush in their "mouths" and move them across a large canvas, turning the paint into abstract art. They use sensors, cameras and artificial intelligence to perceive and navigate their surroundings.

To extend the limited scope of autonomy used in prior missions for operation in distant and complex environments, there is a need to further develop and mature autonomy that jointly reasons over multiple subsystems, which we term system-level autonomy. System-level autonomy establishes situational awareness that resolves conflicting information across subsystems, which may necessitate the refinement and interconnection of the underlying spacecraft and environment onboard models. However, with a limited understanding of the assumptions and tradeoffs of modeling to arbitrary extents, designing onboard models to support system-level capabilities presents a significant challenge. In this paper, we provide a detailed analysis of the increasing levels of model fidelity for several key spacecraft subsystems, with the goal of informing future spacecraft functional- and system-level autonomy algorithms and the physics-based simulators on which they are validated. We do not argue for the adoption of a particular fidelity class of models but, instead, highlight the potential tradeoffs and opportunities associated with the use of models for onboard autonomy and in physics-based simulators at various fidelity levels. We ground our analysis in the context of deep space exploration of small bodies, an emerging frontier for autonomous spacecraft operation in space, where the choice of models employed onboard the spacecraft may determine mission success. We conduct our experiments in the Multi-Spacecraft Concept and Autonomy Tool (MuSCAT), a software suite for developing spacecraft autonomy algorithms.

In 1960, Herbert Simon, who went on to win both the Nobel Prize for economics and the Turing Award for computer science, wrote in his book The New Science of Management Decision that "machines will be capable, within 20 years, of doing any work that a man can do." History is filled with exuberant technological predictions that have failed to materialize. Within the field of artificial intelligence, the brashest predictions have concerned the arrival of systems that can perform any task a human can, often referred to as artificial general intelligence, or AGI. So when Shane Legg, Google DeepMind's co-founder and chief AGI scientist, estimates that there's a 50% chance that AGI will be developed by 2028, it might be tempting to write him off as another AI pioneer who hasn't learnt the lessons of history. Still, AI is certainly progressing rapidly.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This episode we're chatting with Adam Braus about natural stupidity, natural intelligence, misericordianism and avoidable misery, the drowning child thought experiment, natural state of morality, Donald Trump bot, Asimov's rules, human instincts, the positive outcomes of AI and more… Adam Braus is a professor and polymath professional, author, and expert in the fields of ethics, education, and organizational management. He is a writer, speaker, teacher, podcaster, coach, and consultant. He lives in San Francisco, California. You can subscribe to his weekly podcast, find links to his books, or contact him via his website.

Black Eyed Peas member Will.i.am is taking another step into the future, partnering with an AI to co-host a radio show. Will.i.am is set to debut "Will.i.am Presents the FYI Show" Jan. 25 on Sirius XM radio, a new weekly show co-hosted by the musician and "the first ever AI co-host on the SiriusXM platform, qd.pi [pronounced cutie pi]," per a press release for the show. In an interview with The Hollywood Reporter, Will.i.am. He continued, "I'm ultra-freaking colorful and expressive. And that combination, we ain't seen in the history of freaking broadcasts anywhere."

One notable application is the semantic typographic logo, which symbolizes a unique identity in a concise yet informative manner. Due to its expressiveness and memorability [7], semantic typographic logo has been widely used as visual signatures for individuals [28], brand logos with commercial values [15, 20], and symbols for significant events and city promotions [3, 43]. However, crafting a semantic typographic logo presents a formidable challenge, requiring seamless blending of typeface and imagery while preserving readability. Experienced designers often rely on professional software like Adobe Illustrator to manually adjust the outline of the typeface to incorporate specific imagery, which is a time-consuming and error-prone process. They often experiment with different strokes or letters of typeface and various imageries to find a visually appealing and memorable representation, intensifying the lengthy process. This requires creative thinking, practical skills, and the ability to persist through continuous trial and error.

In the era of big data and rapidly evolving information systems, efficient and accurate data retrieval has become increasingly crucial. Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (graph DBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data. The usage of neural embedding storage and complex neural logical query answering provides NGDBs with generalization ability. When the graph is incomplete, by extracting latent patterns and representations, neural graph databases can fill gaps in the graph structure, revealing hidden relationships and enabling accurate query answering. Nevertheless, this capability comes with inherent trade-offs, as it introduces additional privacy risks to the database. Malicious attackers can infer more sensitive information in the database using well-designed combinatorial queries, such as by comparing the answer sets of where Turing Award winners born before 1950 and after 1940 lived, the living places of Turing Award winner Hinton are probably exposed, although the living places may have been deleted in the training due to the privacy concerns. In this work, inspired by the privacy protection in graph embeddings, we propose a privacy-preserving neural graph database (P-NGDB) to alleviate the risks of privacy leakage in NGDBs. We introduce adversarial training techniques in the training stage to force the NGDBs to generate indistinguishable answers when queried with private information, enhancing the difficulty of inferring sensitive information through combinations of multiple innocuous queries. Extensive experiment results on three datasets show that P-NGDB can effectively protect private information in the graph database while delivering high-quality public answers responses to queries.