Willingness to Delegate to Digital Assistants -- интересный концепт для "умных" продуктов

https://dl.acm.org/doi/10.1145/3544549.3585763

Цифровой геноцид ревью: Перспективы HCI
Next Steps in Human-Computer Integration

Важно полностью понимать и совместно формировать технологии, где пользователь и технология вместе образуют тесно связанную систему в более широком физическом, цифровом и социальном контексте - с этих строк начинается манифест Next Steps in Human-Computer Integration огромного коллектива авторов, среди которых достаточно большое число имен, которых я читал в статьях и рецензиях.

Наступает эпоха «многих машин для многих пользователей», современная эра повсеместных вычислений, которая смещает фокус HCI с вопроса «Как мы взаимодействуем с компьютерами?» к «Как интегрированы люди и компьютеры?»

Область человеко-компьютерной интеграции охватывает четыре тематические области: (1) технологии, совместимые с человеком; (2) Влияние интеграции на идентичность и поведение; (3) Человеческая интеграция и общество; и (4) Проектирование интегрированного взаимодействия.

Большое число вычислений большого числа устройств создает принципиально иное отношения в социуме, наборов правил поведения,связанных с устройствами. Вторым челленджем является более глубокая интеграция в тело и психику устройств. В последние годы появились такие достижения, как эпидермальная электроника и интерактивный текстиль, в которых используется гибкая и растягивающаяся электроника, обеспечивающая более сильное слияние с человеческим телом.

Для UX это отдельный вызов - авторы подчеркивают, что тут будет много проектирования неявного взаимодействия

Основатель Вольфрама выступил с заявлением о создании языка описания концептов/идей/гипотез:
In a sense, what’s happening is that Wolfram Language shifts from concentrating on mechanics to concentrating on conceptualization.

https://www.ted.com/talks/stephen_wolfram_how_to_think_computationally_about_ai_the_universe_and_everything

https://writings.stephenwolfram.com/2023/10/how-to-think-computationally-about-ai-the-universe-and-everything/

Базовое понятие, которое лежит в основе "языка идей" - рулиада (ruliad)
https://writings.stephenwolfram.com/2021/11/the-concept-of-the-ruliad/

хмммм
Фундаментальный ресерч о "природе данных" - The topology of data

Для широкой публики будет открыт 1 января 2024

https://authors.library.caltech.edu/records/qa61x-ah042

Compared to machine learning, causal inference allows us to build a robust framework that controls for confounders in order to estimate the true incremental impact to members

https://netflixtechblog.com/a-survey-of-causal-inference-applications-at-netflix-b62d25175e6f

Illustrating power using the example of flipping a coin 100 times and calculating the fraction of heads. The black and red dashed lines show, respectively, the distribution of outcomes assuming the probability of heads is 50% (null hypothesis) and 64% (specific value of the alternative hypothesis). Here, the power against this alternative is 80% (red shading).

https://netflixtechblog.com/interpreting-a-b-test-results-false-negatives-and-power-6943995cf3a8

Карта науки о теории сложности - последние вдохновления в работе черпаю отсюда

https://www.art-sciencefactory.com/complexity-map_feb09.html

THE EVOLUTION OF TRUST: в интерактивном формате изучаем основы теории игр, стратегий поведения и точки роста для социума.

https://ncase.me/trust/

Автогенерация интерфейсов

Новая нейросеть от гугл - Gemini - умеет генерировать интерфейсы внутри чата в зависимости от задачи пользователя, выглядит очень перспективно - как примерно и описывалось в статьях об автогенерации интерфейсов в LLM.

Интересно сколько там все-таки заготовленных канвас?
https://www.theverge.com/2023/12/6/23990466/google-gemini-llm-ai-model


Узнал от @cryptoEssay

Does big data serve policy? Not without context. An experiment with in silico social science

Authors: Graziul, Chris; Belikov, Alexander; Ishanu Chattopadyay; Ziwen Chen; Hongbo Fang; Anuraag Girdhar; Xiaoshuang Jua; P. M. Krafft; Max Kleiman-Weiner; Candice Lewis; Chen Liang; John Muchovej; Alejandro Vietos; Meg Young and James Evans
Source: Computational and Mathematical Organizational Theory; Vol.: 29; Issue: 1; Pp.: 188-219;

DOI: 10.1007/s10588-022-09362-3; March 2023
SFI Taxonomy: Models, Tools, and Scientific Visualization (Human Social Dynamics)

Abstract:
The DARPA Ground Truth project sought to evaluate social science by constructing four varied simulated social worlds with hidden causality and unleashed teams of scientists to collect data, discover their causal structure, predict their future, and prescribe policies to create desired outcomes. This large-scale, long-term experiment of in silico social science, about which the ground truth of simulated worlds was known, but not by us, reveals the limits of contemporary quantitative social science methodology. First, problem solving without a shared ontology-in which many world characteristics remain existentially uncertain-poses strong limits to quantitative analysis even when scientists share a common task, and suggests how they could become insurmountable without it. Second, data labels biased the associations our analysts made and assumptions they employed, often away from the simulated causal processes those labels signified, suggesting limits on the degree to which analytic concepts developed in one domain may port to others. Third, the current standard for computational social science publication is a demonstration of novel causes, but this limits the relevance of models to solve problems and propose policies that benefit from the simpler and less surprising answers associated with most important causes, or the combination of all causes. Fourth, most singular quantitative methods applied on their own did not help to solve most analytical challenges, and we explored a range of established and emerging methods, including probabilistic programming, deep neural networks, systems of predictive probabilistic finite state machines, and more to achieve plausible solutions. However, despite these limitations common to the current practice of computational social science, we find on the positive side that even imperfect knowledge can be sufficient to identify robust prediction if a more pluralistic approach is applied. Applying competing approaches by distinct subteams, including at one point the vast TopCoder.comglobal community of problem solvers, enabled discovery of many aspects of the relevant structure underlying worlds that singular methods could not. Together, these lessons suggest how different a policy-oriented computational social science would be than the computational social science we have inherited. Computational social science that serves policy would need to endure more failure, sustain more diversity, maintain more uncertainty, and allow for more complexity than current institutions support.

Economics in nouns and verbs

Author: Arthur, W. Brian
Source: Journal of Economic Behavior & Organization; Vol.: 205; Pp.: 638-647;

DOI: 10.1016/j.jebo.2022.10.036; January 2023

SFI Taxonomy: Models, Tools, and Scientific Visualization (Economics)

Abstract:
Standard economic theory uses mathematics as its main means of understanding, and this brings clarity of reasoning and logical power. But there is a drawback: algebraic mathematics restricts economic modeling to what can be expressed only in quantitative nouns, and this forces theory to leave out matters to do with process, formation, adjustment, and creation-matters to do with nonequilibrium. For these we need a different means of understanding, one that allows verbs as well as nouns. Algorithmic expression is such a means. It allows verbs-processes-as well as nouns-objects and quantities. It allows fuller denoscription in economics, and can include heterogeneity of agents, actions as well as objects, and realistic models of behavior in ill-defined situations. The world that algorithms reveal is action-based as well as object-based, organic, possibly ever-changing, and not fully knowable. But it is strangely and wonderfully alive.