38 research outputs found
From Kepler to Newton: Explainable AI for Science Discovery
The Observation--Hypothesis--Prediction--Experimentation loop paradigm for
scientific research has been practiced by researchers for years towards
scientific discoveries. However, with data explosion in both mega-scale and
milli-scale scientific research, it has been sometimes very difficult to
manually analyze the data and propose new hypotheses to drive the cycle for
scientific discovery. In this paper, we discuss the role of Explainable AI in
scientific discovery process by demonstrating an Explainable AI-based paradigm
for science discovery. The key is to use Explainable AI to help derive data or
model interpretations, hypotheses, as well as scientific discoveries or
insights. We show how computational and data-intensive methodology -- together
with experimental and theoretical methodology -- can be seamlessly integrated
for scientific research. To demonstrate the AI-based science discovery process,
and to pay our respect to some of the greatest minds in human history, we show
how Kepler's laws of planetary motion and Newton's law of universal gravitation
can be rediscovered by (Explainable) AI based on Tycho Brahe's astronomical
observation data, whose works were leading the scientific revolution in the
16-17th century. This work also highlights the important role of Explainable AI
(as compared to Blackbox AI) in science discovery to help humans prevent or
better prepare for the possible technological singularity that may happen in
the future, since science is not only about the know how, but also the know
why.Comment: Presented at ICML-AI4Science 202
OpenAGI: When LLM Meets Domain Experts
Human intelligence has the remarkable ability to assemble basic skills into
complex ones so as to solve complex tasks. This ability is equally important
for Artificial Intelligence (AI), and thus, we assert that in addition to the
development of large, comprehensive intelligent models, it is equally crucial
to equip such models with the capability to harness various domain-specific
expert models for complex task-solving in the pursuit of Artificial General
Intelligence (AGI). Recent developments in Large Language Models (LLMs) have
demonstrated remarkable learning and reasoning abilities, making them promising
as a controller to select, synthesize, and execute external models to solve
complex tasks. In this project, we develop OpenAGI, an open-source AGI research
platform, specifically designed to offer complex, multi-step tasks and
accompanied by task-specific datasets, evaluation metrics, and a diverse range
of extensible models. OpenAGI formulates complex tasks as natural language
queries, serving as input to the LLM. The LLM subsequently selects,
synthesizes, and executes models provided by OpenAGI to address the task.
Furthermore, we propose a Reinforcement Learning from Task Feedback (RLTF)
mechanism, which uses the task-solving result as feedback to improve the LLM's
task-solving ability. Thus, the LLM is responsible for synthesizing various
external models for solving complex tasks, while RLTF provides feedback to
improve its task-solving ability, enabling a feedback loop for self-improving
AI. We believe that the paradigm of LLMs operating various expert models for
complex task-solving is a promising approach towards AGI. To facilitate the
community's long-term improvement and evaluation of AGI's ability, we
open-source the code, benchmark, and evaluation methods of the OpenAGI project
at https://github.com/agiresearch/OpenAGI.Comment: 18 pages, 6 figures, 7 table
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Modern recommender systems utilize users' historical behaviors to generate
personalized recommendations. However, these systems often lack user
controllability, leading to diminished user satisfaction and trust in the
systems. Acknowledging the recent advancements in explainable recommender
systems that enhance users' understanding of recommendation mechanisms, we
propose leveraging these advancements to improve user controllability. In this
paper, we present a user-controllable recommender system that seamlessly
integrates explainability and controllability within a unified framework. By
providing both retrospective and prospective explanations through
counterfactual reasoning, users can customize their control over the system by
interacting with these explanations.
Furthermore, we introduce and assess two attributes of controllability in
recommendation systems: the complexity of controllability and the accuracy of
controllability. Experimental evaluations on MovieLens and Yelp datasets
substantiate the effectiveness of our proposed framework. Additionally, our
experiments demonstrate that offering users control options can potentially
enhance recommendation accuracy in the future. Source code and data are
available at \url{https://github.com/chrisjtan/ucr}.Comment: Accepted for presentation at 26th European Conference on Artificial
Intelligence (ECAI2023
GenRec: Large Language Model for Generative Recommendation
In recent years, large language models (LLM) have emerged as powerful tools
for diverse natural language processing tasks. However, their potential for
recommender systems under the generative recommendation paradigm remains
relatively unexplored. This paper presents an innovative approach to
recommendation systems using large language models (LLMs) based on text data.
In this paper, we present a novel LLM for generative recommendation (GenRec)
that utilized the expressive power of LLM to directly generate the target item
to recommend, rather than calculating ranking score for each candidate item one
by one as in traditional discriminative recommendation. GenRec uses LLM's
understanding ability to interpret context, learn user preferences, and
generate relevant recommendation. Our proposed approach leverages the vast
knowledge encoded in large language models to accomplish recommendation tasks.
We first we formulate specialized prompts to enhance the ability of LLM to
comprehend recommendation tasks. Subsequently, we use these prompts to
fine-tune the LLaMA backbone LLM on a dataset of user-item interactions,
represented by textual data, to capture user preferences and item
characteristics. Our research underscores the potential of LLM-based generative
recommendation in revolutionizing the domain of recommendation systems and
offers a foundational framework for future explorations in this field. We
conduct extensive experiments on benchmark datasets, and the experiments shows
that our GenRec has significant better results on large dataset
Efficient DS-UWB MUD Algorithm Using Code Mapping and RVM
A hybrid multiuser detection (MUD) using code mapping and a wrong code recognition based on relevance vector machine (RVM) for direct sequence ultra wide band (DS-UWB) system is developed to cope with the multiple access interference (MAI) and the computational efficiency. A new MAI suppression mechanism is studied in the following steps: firstly, code mapping, an optimal decision function, is constructed and the output candidate code of the matched filter is mapped to a feature space by the function. In the feature space, simulation results show that the error codes caused by MAI and the single user mapped codes can be classified by a threshold which is related to SNR of the receiver. Then, on the base of code mapping, use RVM to distinguish the wrong codes from the right ones and finally correct them. Compared with the traditional MUD approaches, the proposed method can considerably improve the bit error ratio (BER) performance due to its special MAI suppression mechanism. Simulation results also show that the proposed method can approximately achieve the BER performance of optimal multiuser detection (OMD) and the computational complexity approximately equals the matched filter. Moreover, the proposed method is less sensitive to the number of users
Counterfactual Collaborative Reasoning
Causal reasoning and logical reasoning are two important types of reasoning
abilities for human intelligence. However, their relationship has not been
extensively explored under machine intelligence context. In this paper, we
explore how the two reasoning abilities can be jointly modeled to enhance both
accuracy and explainability of machine learning models. More specifically, by
integrating two important types of reasoning ability -- counterfactual
reasoning and (neural) logical reasoning -- we propose Counterfactual
Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to
improve the performance. In particular, we use recommender system as an example
to show how CCR alleviate data scarcity, improve accuracy and enhance
transparency. Technically, we leverage counterfactual reasoning to generate
"difficult" counterfactual training examples for data augmentation, which --
together with the original training examples -- can enhance the model
performance. Since the augmented data is model irrelevant, they can be used to
enhance any model, enabling the wide applicability of the technique. Besides,
most of the existing data augmentation methods focus on "implicit data
augmentation" over users' implicit feedback, while our framework conducts
"explicit data augmentation" over users explicit feedback based on
counterfactual logic reasoning. Experiments on three real-world datasets show
that CCR achieves better performance than non-augmented models and implicitly
augmented models, and also improves model transparency by generating
counterfactual explanations
Life history traits of low-toxicity alternative bisphenol S on Daphnia magna with short breeding cycles : A multigenerational study
Due to relatively lower toxicity, bisphenol S (BPS) has become an alternative to previously used bisphenol A. Nevertheless, the occurrence of BPS and its ecological impact have recently attracted increasing attentions because the toxicology effect of BPS with life cycle or multigenerational exposure on aquatic organisms remains questionable. Herein, Daphnia magna (D. magna) multigenerational bioassays spanning four generations (F0–F3) and single-generation recovery (F1 and F3) in clean water were used to investigate the ecotoxicology of variable chronic BPS exposure. For both assays, four kinds of life-history traits (i.e., survival, reproduction, growth and ecological behavior) were examined for each generation. After an 18-day exposure under concentration of 200 μg/L, the survival rate of D. magna was less than 15 % for the F2 generation, whereas all died for the F3 generation. With continuous exposure of four generations of D. magna at environmentally relevant concentrations of BPS (2 μg/L), inhibition of growth and development, prolonged sexual maturity, decreased offspring production and decreased swimming activity were observed for the F3 generation. In particular, it is difficult for D. magna to return to its normal level through a single-generation recovery in clean water in terms of reproductive function, ecological behavior and population health. Hence, multi-generational exposure to low concentrations of BPS can have adverse effects on population health of aquatic organisms with short breeding cycles, highlighting the necessity to assess the ecotoxicology of chronic BPS exposure for public health.publishedVersionPeer reviewe