25,881 research outputs found
Conversational AI for Serving Fact-Checks
The purpose of this thesis was to create a conversational AI for serving fact-checks, using a collection of already existing fact-checking articles. The conversational AI uses a hybrid system, combining both a question answering agent, chitchat agent, and multiple non-AI based skills to perform the task.
The program created consists of a user interface, broker, and seven different skills. For the implementation multiple existing pre-trained deep learning models were used, where many are based on the Transformer architecture. Already fine-tuned versions of these models were used. The conversational AI can present fact-checking articles in multiple ways, fact-check a claim presented, and has some multi-turn capabilities.
The result is a functional conversational AI which is capable of serving fact-checks from a collection of fact-checking articles. Although the conversational AI is functional, there are several issues that should be addressed, and further work to be done
Persona-Coded Poly-Encoder: Persona-Guided Multi-Stream Conversational Sentence Scoring
Recent advances in machine learning and deep learning have led to the
widespread use of Conversational AI in many practical applications. However, it
is still very challenging to leverage auxiliary information that can provide
conversational context or personalized tuning to improve the quality of
conversations. For example, there has only been limited research on using an
individuals persona information to improve conversation quality, and even
state-of-the-art conversational AI techniques are unable to effectively
leverage signals from heterogeneous sources of auxiliary data, such as
multi-modal interaction data, demographics, SDOH data, etc. In this paper, we
present a novel Persona-Coded Poly-Encoder method that leverages persona
information in a multi-stream encoding scheme to improve the quality of
response generation for conversations. To show the efficacy of the proposed
method, we evaluate our method on two different persona-based conversational
datasets, and compared against two state-of-the-art methods. Our experimental
results and analysis demonstrate that our method can improve conversation
quality over the baseline method Poly-Encoder by 3.32% and 2.94% in terms of
BLEU score and HR@1, respectively. More significantly, our method offers a path
to better utilization of multi-modal data in conversational tasks. Lastly, our
study outlines several challenges and future research directions for advancing
personalized conversational AI technology.Comment: The 35th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI
Ten years after ImageNet: a 360° perspective on artificial intelligence
It is 10 years since neural networks made their spectacular
comeback. Prompted by this anniversary, we take a holistic
perspective on artificial intelligence (AI). Supervised learning for
cognitive tasks is effectively solved—provided we have enough
high-quality labelled data. However, deep neural network
models are not easily interpretable, and thus the debate between
blackbox and whitebox modelling has come to the fore. The rise
of attention networks, self-supervised learning, generative
modelling and graph neural networks has widened the
application space of AI. Deep learning has also propelled the
return of reinforcement learning as a core building block of
autonomous decision-making systems. The possible harms made
possible by new AI technologies have raised socio-technical
issues such as transparency, fairness and accountability. The
dominance of AI by Big Tech who control talent, computing
resources, and most importantly, data may lead to an extreme
AI divide. Despite the recent dramatic and unexpected success
in AI-driven conversational agents, progress in much-heralded
flagship projects like self-driving vehicles remains elusive. Care
must be taken to moderate the rhetoric surrounding the field
and align engineering progress with scientific principles
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning
The two fields of urban planning and artificial intelligence (AI) arose and
developed separately. However, there is now cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In the
present paper, we introduce the importance of urban planning from the
sustainability, living, economic, disaster, and environmental perspectives. We
review the fundamental concepts of urban planning and relate these concepts to
crucial open problems of machine learning, including adversarial learning,
generative neural networks, deep encoder-decoder networks, conversational AI,
and geospatial and temporal machine learning, thereby assaying how AI can
contribute to modern urban planning. Thus, a central problem is automated
land-use configuration, which is formulated as the generation of land uses and
building configuration for a target area from surrounding geospatial, human
mobility, social media, environment, and economic activities. Finally, we
delineate some implications of AI for urban planning and propose key research
areas at the intersection of both topics.Comment: TSAS Submissio
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI
We present a detailed implementation of five core principles for transparent and acccountable conversational AI, namely interpretability, inherent capability to explain, independent data, interactive learning, and inquisitiveness. This implementation is a dialogue manager called DAISY that serves as the core part of a conversational agent. We show how DAISY-based agents are trained with human-machine interaction, a process that also involves suggestions for generalization from the agent itself. Moreover, these agents are capable to provide a concise and clear explanation of the actions required to reach a conclusion. Deep neural networks (DNNs) are currently the de facto standard in conversational AI. We therefore formulate a comparison between DAISY-based agents and two methods that use DNNs, on two popular data sets involving multi-domain task-oriented dialogue. Specifically, we provide quantitative results related to entity retrieval and qualitative results in terms of the type of errors that may occur. The results show that DAISY-based agents achieve superior precision at the price of lower recall, an outcome that might be preferable in task-oriented settings. Ultimately, and especially in view of their high degree of interpretability, DAISY-based agents are a fundamentally different alternative to the currently popular DNN-based methods
Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions
yesThis paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution
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