信任
个展
2022年
《信任（Trust）》是一组作品，探索了信任这个概念的多重含义和影响。fuse*将信任从与金融、社交媒体和新闻来源三个重要社会机构相关的角度进行了分析。该展览分析了历史事件对社会信任水平的影响，以及这种关系如何在未来发展。
《信任》是由Artechouse委托并于2022年1月31日在纽约首次亮相。
《信任》被创作出来，给观众带来沉浸式体验，思考信任的存在或缺失如何改变我们个人和共享现实的感知。人际信任使个体能够建立一种集体信任感。同样，个人对系统失去信心可能导致共同失去互信。《信任》是fuse*在将技术与人类体验融合以引发共情方面持续研究的延伸。
该展览分为四个不同的装置展览：
ATH NY Scheme
沉浸式画廊的主要装置展览专注于信任作为一种复杂力量如何改变我们对现实的感知。体验包括三个章节（过去、现在和未来），通过一系列随着时间推移而演变的视听氛围来展现。
这种趋势是通过交织金融数据和密歇根大学提供的消费者情绪指数（ICS）的价值来重建的。
第一个章节是过去，由两个视觉层面组成，旨在评论媒体对我们现实观的影响能力。第一层通过重新处理从宏观视角（如城市和农村环境）到个体视角的点云来创建，其中人类的姿态代表了关系在个体建立中的基础作用。
信任、过去的桥梁、组合和选择
这些表现是通过不同的技术创建的。内部再现城市主要是通过摄影测量过程创建的，正如我们在之前的项目Treu中所研究的那样。早期的测试是通过深度学习技术进行单目深度估计，尝试从历史图像中推断出三维信息。此外，大片土地的航空描绘是通过美国地质调查局（USGS）提供的材料帮助创建的。这些内容已经被收集、操纵和重新处理在openFrameworks内生成最终的可视化效果。
第二层由人工创建的报纸文章组成，根据历史时期进行分类，这是一个经过训练以识别包含“信任”一词的报纸文章的算法的艺术结果，这些文章的日期介于1922年和2021年之间。我们使用关键字“trust”查询了news.google.com/newspapers的数据集，并仅使用免费资源过滤了结果。这样，我们得到了一个包含至少有一篇关于“信任”的文章的报纸页面数据集。根据概念和故事情节，我们将这个数据集分成了不同的时期，并使用这些文章训练了一个能够根据特定时期生成文章的神经网络。
新闻重要性如何影响现实的感知通过点云的波动来象征。现在的部分被设计成视觉上由一系列铭刻在电影上的时刻组成。负片图像与选择自2020年3月以来在疫情期间收集的500,000,000条消息中选择的750,000条推特一起滚动。所有选择的推特都包含“信任”一词，并通过情感分析算法进行了积极和消极标记的分类。
通过将ICS指数提取的月度信任数据和道琼斯指数的实时值相结合，算法选择了特定时刻最合适的情绪。
TRUST高 1
最终，未来由不同能力相互作用的平行时间线组成，根据预测的信任值及其向未来的演化而定。这些潜在的趋势是通过对金融数据和信心指数的历史系列进行预测性分析而得出的，我们从Treu.future景观002开始进行调查。
在整个安装过程中，软件分析与信心指数相关的金融数据组合，从而在未来生成无限的可能性演化。视听内容始终与此实时数据流相连，并描绘抽象的生成场景。在展示中的这部分，我们能够从二维航拍图像中提取三维模型，用点填充生成的网格，然后将新创建的点云输入我们专门设计的软件以开始整个动画过程。
未来高计划紧凑
TRUST [Atlas]
在西侧夹层中，访客能够体验和可视化引导整个装置群体的核心过程：通过一个循环地为未来100年绘制无限可能性的信任指数的递归神经网络（RNN）。除了展示人类不间断地尝试预测未来外，该装置还允许访客可视化过去信任趋势与一系列金融、社会和环境指标的相关性。通过展示过去100年的主要事件与指标，不同的相关性在历史系列、重大历史变革和信任感知之间浮现。
递归神经网络是一种特殊类型的网络，其中包括连接在一起的神经元，允许将先前的输出用作输入。它们在处理时间序列方面非常有用，其中数据按顺序进行分析。在这种特定情况下，我们使用了一种称为LSTM（长短期记忆）的RNN变体，这是一种为了解决标准RNN训练阶段遇到的问题而开发出来的架构，其无法保留数据的“记忆”。
为了更好地开展ICS（按月记录的值）的预测性分析，我们试图将ICS与其他经济参数联系起来；特别是道琼斯指数。然后，我们训练了一个LSTM来预测道琼斯指数的值，然后将预测值与相应的ICS指数相结合。然后，根据算法的预测，通过阅读历史数据库并受道琼斯指数的实时更新数据的影响，推断出可能的信心“场景”。
TRUST [未来档案]
这个视听装置是对主要预测算法不断生成的可能未来进行探索。夹层画廊作为一个档案馆，每一个想象中的未来都与一个视频配对，并与相关的信任预测一起展示出来。
通过分析RNN最后一次的信任预测，每个监视器显示一系列未来，根据它们与预测未来的相似程度进行排序。
TRUST [碎片]
这个实时装置将集体信任表示为小规模的基于个体信任的交互，这些交互随时发生。数据来自比特币区块链上发生的交易以及实时共享在推特上的情绪分析，根据积极和消极的内涵分割。这些数据被聚合起来，描绘出当下信任的状态。在[Fragments]中，fuse*反思了全球社区行为的揭示性质。
音频
Trust的声音部分根据主要装置的三个主要部分（过去、现在、未来）开发，通过音乐叙事展现了信任的趋势。整个作品的结构和构图建立在三部分结构（A-B-A）之上。在这些宏观部分中，我们找到一些小部分，这些部分在与视觉元素的紧密联系中发展和跟随彼此。我们利用多调性和积累来再现过去的部分，通过简单的声音单元随着时间的推移增加其复杂性和密度来再现信任的正负趋势；对于现在，我们通过节奏作曲和复节奏探索了事件的规律性和混乱性主题。最后，对于未来，之前部分中呈现的所有元素交替失去结构，重新创造一个不断衰退的模糊景观。
整个作品的声音材料通过实验录制的声学来源、打击乐、吉他、电贝斯，在不同的音频合成技术下进行了处理，重新创造了整个作品的各种音色元素。
制作：fuse*
艺术指导：Mattia Carretti、Luca Camellini
理念：Mattia Carretti、Luca Camellini、Matteo Salsi、Samuel Pietri
软件艺术家：Luca Camellini、Riccardo Bazzoni、Matteo Salsi、Samuel Pietri、Riccardo Bazzoni、Alessandro Mintrone
声音设计：Riccardo Bazzoni
后勤支持：Martina Reggiani
首映：Artechouse - 纽约
TRUST
SOLO EXHIBITION
2022
Trust is a body of works that explores the multiple meanings and implications of the concept of trust. fuse* breaks down trust as it relates to three major societal institutions: finance, social media, and news sources. The exhibit analyzes historical events’ impact on society’s level of trust up to the present moment and considers how this relationship might evolve in the future.
Trust was commissioned by Artechouse and premiered in New York City on January 31st 2022.
Trust has been created to give the audience an immersive experience to contemplate how the presence or absence of trust can shift the perception of our individual and shared realities. Interpersonal trust allows an individual to build a sense of collective trust. Likewise, a loss of one’s personal confidence in the system can lead to a collective loss of mutual trust. Trust is an expansion on fuse*’s continued research in merging technology and the human experience to elicit empathy.
The exhibition unfolds in four different installations:
ATH NY Scheme
The main installation in the immersion gallery focuses on the ability of trust as a complex force to modify our perception of reality. The experience is composed of three chapters (past, present and future) and is characterized by a succession of audiovisual atmospheres that evolve following the trend of trust over time.
This trend has been reconstructed by interweaving financial data and the value of the Index of Consumer Sentiment (ICS) provided by the University of Michigan.
The first chapter, the past, is composed by two visual layers, a comment on the capacity of the media’s ability to affect our view of reality. The first layer is created through the reprocessing of point clouds that range from macroscopic visions, such as urban and rural environments, to the individual perspective, where human gestures represent the fundamental role of relationships in building individual trust.past bridge combined opt
These representations are created with different techniques. Internal reproduction of cities are mostly created with photogrammetric processes as we started investigating in our previous project Treu. Earlier tests were carried out trying to extrapolate 3-dimensional information from historical images through deep-learning techniques performing monocular depth estimation. Moreover aerial depictions of large portions of lands are created with the help of material provided by the United States Geological Survey (abbreviated with USGS). This content has been collected and then manipulated and re-processed inside openFrameworks to generate the final visualization.past bridge
The second layer is composed of artificially created newspaper text – cataloged by historical period – is the artistic result of an algorithm trained to recognize newspaper articles containing the word “trust” dating between 1922 and 2021. We queried news.google.com/newspapers dataset with the keyword “trust” and filtered the results with only free resources. This way we obtained a dataset of newspaper pages where there is at least one article that speaks about “trust”. We splitted this dataset in different periods according to the concept and our storytelling and we used these articles to train a neural network able to generate articles based on the specific period.trust fake articles schema 3iThe perception of how the news’ magnitude can influence reality is symbolized by the fluctuations of the point clouds.The present is designed to be visually composed of a series of moments impressed on a film. The negative images scroll together with 750,000 tweets selected from 500,000,000 messages archived during the pandemic, starting in March 2020. All the selected tweets contain the word "trust" and have been categorized with a positive and negative label through a sentiment analysis algorithm.emotion negative filmBy combining the monthly trust data taken from the ICS index and the real time value of the Dow Jones index, the algorithm chooses which sentiment is the most appropriate for the specific moment. In each film frame, the system calculates an average sentiment of the 6 main emotions of the tweets included in that frame and visualizes these values on the film edge.
TRUST high 1
Finally, the future is composed of parallel timelines interacting together in different capacities according to the predicted trust value and its evolution into the future. The potential trends are the result of a predictive analysis carried out through a recurrent neural network trained on the basis of the historical series of financial data and confidence index as we started investigating in Treu.future landscape 002
During the entirety of the installation, the software analyzes the combination of financial data in relation to the index of confidence, thereby generating infinite possible evolutions of the installation in the future. The audiovisual content is always connected to this live data stream and it depicts abstract generative scenery. In this part of the show we were able to extract 3d models from bidimensional aerial images, populate the generated mesh with points and then feed the newly created pointcloud in our custom designed software for the whole animation process to begin.
future high scheme compact
TRUST [Atlas]
In the Mezzanine West the visitor is able to experience and visualize the core process that guides the entire group of installations: the continuous generation of possible futures by a recurrent neural network (RNN) that cyclically draws infinite possible evolutions of the trust index for the next 100 years. Together with showing the incessant human attempt to predict the future, the installation allows the visitor to visualize how the trend of trust in the past is intertwined with the tendency of a series of financial, social, and environmental indexes. Displaying the indexes together with the main events of the last 100 years, different correlations emerge between the historical series, the major historical transformations, and the perception of trust.Screenshot 739
Recurrent neural networks are a special kind of network that includes neurons connected together in a loop that allows previous outputs to be used as inputs. They are extremely useful for processing time series, in which data are analyzed sequentially. In this specific case,we used a variation of RNNs called LSTM (Long Short Term Memory), an architecture developed to overcome problems encountered during the training phase of standard RNNs, which cannot retain a ‘memory’ of data.
In order to better develop a predictive analysis of the ICS (a value recorded on monthly frequency) we attempted to relate the ICS with other economic parameters; in particular, the Dow Jones index. We then trained an LSTM to predict the value of the Dow Jones and then reconciled that predicted value with the corresponding ICS index. Potential confidence ‘scenarios’ are then inferred from the algorithm’s predictions by reading the historical database and influenced from the real-time updated data through the value of the Dow Jones.
TRUST [Futures Archives]
This audiovisual installation is an exploration of the possible futures unceasingly generated by the main prediction algorithm. The Mezzanine gallery serves as an archive where each imagined future is paired with a video and displayed together with the relative trust prediction.
By analyzing the last trust prediction made by the RNN, each of the six monitors displays a collection of futures, sorted according to their similarity with the predicted one.
Rykov 1107864
TRUST [Fragments]
This real-time installation represents collective trust by fragmenting it into small, individual trust-based interactions that occur at any given time. Data is drawn from transactions occurring on Bitcoin's blockchain and from the sentiment analysis of messages shared on twitter in real time, divided by positive and negative connotation. These data are aggregated to depict the state of trust of the immediate present. In [Fragments], fuse* reflects on the revealing nature of the behavior of the world-wide community. underneath
AUDIO
The sound component of Trust was developed following the three main parts of the main installation (Past, Present, Future) unveiling the trend of trust through a musical narrative. The whole structure and architecture of the composition are built on a ternary structure (A-B-A). Within these macro sections, we find smaller parts that develop and follow each other in strong connection with the visuals. We used polytonality and accumulation for the past, to recreate the positive and negative trend of trust with simple sound cells that stratify over time increasing their complexity and density; for the Present we explored the theme of the regularity and chaoticity of events through rhythmic compositions and polyrhythms. Finally, for the Future, all the elements presented in the previous sections alternate losing their structure, recreating an ambiguous landscape in continuous degradation.
Rykov 1108189
The timbral material of the entire composition was created through a first phase of experimentation by recording acoustic sources, percussion, guitars, electric basses and a second phase of manipulation of all the recorded material through different audio synthesis techniques, recreating the various timbral elements of the whole composition.
Production: fuse*
Art Direction: Mattia Carretti, Luca Camellini
Concept: Mattia Carretti, Luca Camellini, Matteo Salsi, Samuel Pietri
Software Artists: Luca Camellini, Riccardo Bazzoni, Matteo Salsi, Samuel Pietri, Riccardo Bazzoni, Alessandro Mintrone
Sound Design: Riccardo Bazzoni
Logistics: Martina Reggiani
Premiere: Artechouse - NYC
