Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the brain's structure to offer a powerful and efficient processing and learning model. In HDC, the data are encoded with long vectors, called hypervectors, typically with a length of 1K to 10K. The literature provides several encoding techniques to generate orthogonal or correlated hypervectors, depending on the intended application. The existing surveys in the literature often focus on the overall aspects of HDC systems, including system inputs, primary computations, and final outputs. However, this study takes a more specific approach. It zeroes in on the HDC system input and the generation of hypervectors, directly influencing the hypervector encoding process. This survey brings together various methods for hypervector generation from different studies and explores the limitations, challenges, and potential benefits they entail. Through a comprehensive exploration of this survey, readers will acquire a profound understanding of various encoding types in HDC and gain insights into the intricate process of hypervector generation for diverse applications.

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings.

Welcome to our July 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we report on RoboCup2023, congratulate ICML outstanding paper award winners, find out how machine learning can help in the study of supercooled liquids, and learn about a visually-grounded few-shot word learning method for low-resource languages. RoboCup2023 took place from 4-10 July in Bordeaux, and saw around 2500 participants, from 45 different countries participate in competitions, training sessions and a symposium. You can find out what the attendees got up to in our round-ups: Part 1 Part 2. Roberto Figueiredo took part in the kid-size soccer competition and is also a local representative for Junior Rescue Simulation. We spoke to Roberto about his RoboCup experience this year, and his progression from junior to major league.

Referential ambiguities arise in dialogue when a referring expression does not uniquely identify the intended referent for the addressee. Addressees usually detect such ambiguities immediately and work with the speaker to repair it using meta-communicative, Clarificational Exchanges (CE): a Clarification Request (CR) and a response. Here, we argue that the ability to generate and respond to CRs imposes specific constraints on the architecture and objective functions of multi-modal, visually grounded dialogue models. We use the SIMMC 2.0 dataset to evaluate the ability of different state-of-the-art model architectures to process CEs, with a metric that probes the contextual updates that arise from them in the model. We find that language-based models are able to encode simple multi-modal semantic information and process some CEs, excelling with those related to the dialogue history, whilst multi-modal models can use additional learning objectives to obtain disentangled object representations, which become crucial to handle complex referential ambiguities across modalities overall.

This year's International Conference on Machine Learning (ICML) is taking place in Honolulu, Hawai'i from 23-29 July. The winners of the outstanding paper awards for 2023 have now been announced. This paper introduces an interesting approach that aims to address the challenge of obtaining a learning rate free optimal bound for non-smooth stochastic convex optimization. The authors propose a novel method that overcomes the limitations imposed by traditional learning rate selection in optimizing such problems. This research makes a valuable and practical contribution to the field of optimization.

We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality of a machine-generated utterance, we leverage signals like user response length, sentiment and reaction of the future human utterances in the collected dialogue episodes. Our experiments use the publicly released deployment data from BlenderBot (Xu et al., 2023). Human evaluation indicates improvements in our new models over baseline responses; however, we find that some proxy signals can lead to more generations with undesirable properties as well. For example, optimizing for conversation length can lead to more controversial or unfriendly generations compared to the baseline, whereas optimizing for positive sentiment or reaction can decrease these behaviors.

The future has arrived in Bakersfield, and I'm not sure I'm ready for it. For nearly three hours, the conversation was nonstop at the home of Audrey and Ken Mattlin, who happen to live with several robots. There's ElliQ, who resembles a table lamp and speaks mainly to Audrey, 84, whom the robot refers to by a nickname. As in, "How did you sleep, Jelly Bean?" Goo-goo-eyed Astro looks like a short-handled vacuum cleaner with an electronic tablet for a face. He scoots around the house on wheels and follows people on command.

In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in many unstructured real-world settings, such a reward signal is unknown and humans cannot reliably craft a reward signal that correctly captures desired behavior. To solve tasks in such unstructured and open-ended environments, we present Demonstration-Inferred Preference Reinforcement Learning (DIP-RL), an algorithm that leverages human demonstrations in three distinct ways, including training an autoencoder, seeding reinforcement learning (RL) training batches with demonstration data, and inferring preferences over behaviors to learn a reward function to guide RL. We evaluate DIP-RL in a tree-chopping task in Minecraft. Results suggest that the method can guide an RL agent to learn a reward function that reflects human preferences and that DIP-RL performs competitively relative to baselines. DIP-RL is inspired by our previous work on combining demonstrations and pairwise preferences in Minecraft, which was awarded a research prize at the 2022 NeurIPS MineRL BASALT competition, Learning from Human Feedback in Minecraft. Example trajectory rollouts of DIP-RL and baselines are located at https://sites.google.com/view/dip-rl.

If you subscribe to Apple Arcade ($5, £5 or AU$8 a month), you can play this game at no additional charge, and without ads or in-app purchases, which is why this version is called "Stardew Valley Plus." This game was developed by ConcernedApe. It was nominated for a handful of awards in 2016 and won the Golden Joystick Awards's Breakthrough Award that same year. Stardew Valley opens with you leaving your office job and moving back to your grandfather's rundown farm with the hope of living a simpler life. But if you scratch beneath the surface you'll find that this game is anything but simple. Sure, you can stay on your land as you grow crops, raise animals and fix your home, but there's so much to do in Stardew Valley Plus.

Kevin Mitnick, whose pioneering antics tricking employees in the 1980s and 1990s into helping him steal software and services from big phone and tech companies made him the most celebrated U.S. hacker, has died at age 59. Mitnick died Sunday in Las Vegas after a 14-month battle with pancreatic cancer, said Stu Sjouwerman, CEO of the security training firm KnowBe4, where Mitnick was chief hacking officer. His colorful career--from student tinkerer to FBI-hunted fugitive, imprisoned felon and finally respected cybersecurity professional, public speaker and author tapped for advice by U.S. lawmakers and global corporations--mirrors the evolution of society's grasp of the nuances of computer hacking. Through Mitnick's professional trajectory, and what many consider the misplaced prosecutorial zeal that put him behind bars for nearly five years until 2000, the public has learned how to better distinguish serious computer crime from the mischievous troublemaking of youths hellbent on proving their hacking prowess. "He never hacked for money," said Sjouwerman, who became Mitnick's business partner in 2011.