Collective Intelligence
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Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.
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Channel Posts
| 2 | https://blog.research.google/2024/01/responsible-ai-at-google-research-user.html | 175 |
| 3 | https://www.lesswrong.com/posts/YN7PHizHnxinLsKvy/sociallm-a-language-model-design-for-personalised-apps - ΠΈΠ΄Π΅Ρ ΠΊΠ°ΠΊ Π΄ΠΎΠΊΡΡΡΠΈΡΡ ΠΠ°ΠΌΠ±Ρ Ρ SSM Π±Π»ΠΎΠΊΠ°ΠΌΠΈ, ΡΡΠ΅Π½ΠΈΡΡΠ΅ΡΡΡ Π½Π° Π΄Π°Π½Π½ΡΡ
Π΄ΠΈΠ°Π»ΠΎΠ³ΠΎΠ², ΡΠ°ΡΠΎΠ², ΠΈ ΡΠΎΡΡΠΌΠΎΠ², ΡΡΠ΅ΠΊΠ°Π΅Ρ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠΌΠΈ SSM Π±Π»ΠΎΠΊΠ°ΠΌΠΈ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡ, Π½ΠΎ ΠΈ ΠΈΡΡΠΎΡΠΈΡ ΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΠΉ ΠΈ ΡΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΎΠ³ΠΎ, ΡΡΠΎ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΏΠΎΠΌΠ½ΠΈΡ ΠΏΡΠΎ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ±Π΅ΡΠ΅Π΄Π½ΠΈΠΊΠ°.
ΠΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠ΅ Π±ΠΈΠ·Π½Π΅Ρ-ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ:
- ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ½ΡΠ΅ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ, ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠ°Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ°, ΠΠ-ΡΡΠΈΡΠ΅Π»Ρ, ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΠ΅ Π·Π΄ΠΎΡΠΎΠ²ΡΠ΅, ΠΠ-Π΄ΡΡΠ³ Π°-Π»Ρ InflectionAI,
- ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Π΄Π»Ρ ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡΠΎΠ²
- ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Π΄Π»Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° ΠΊΠΎΠΌΠ°Π½Π΄Ρ, ΠΊΠ°ΠΊ ΠΎΠΏΠΈΡΠ°Π½ΠΎ ΡΡΡ: https://ojs.aaai.org/index.php/AAAI/article/view/25755
- ΠΈΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΠ²Π½ΡΠΉ ΡΡΠΎΡΠΈΡΠ΅Π»Π»ΠΈΠ½Π³
ΠΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠ΅ Π½Π°ΡΡΠ½ΡΠ΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ:
- Π‘ΠΎΡΠΈΠ°Π»ΡΠ½ΡΠ΅ Π½Π°ΡΠΊΠΈ, collective intelligence, AI safety (detection and prevention of collusion and deception) | 272 |
| 4 |  Π‘ΠΈΡΡΠ΅ΠΌΠ° ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π² Spotify π
Π Π°Π½Π΅Π΅ Ρ ΡΠΆΠ΅ Π½Π΅ΠΎΠ΄Π½ΠΎΠΊΡΠ°ΡΠ½ΠΎ ΠΏΠΈΡΠ°Π»Π° ΠΏΡΠΎ Spotify (ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ, Π³Π»ΠΎΠ±Π°Π»ΡΠ½Π°Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½Π°Ρ Π³ΡΡΠΏΠΏΠ°)
Π‘Π΅Π³ΠΎΠ΄Π½Ρ ΡΠ°ΡΡΠΊΠ°ΠΆΡ ΠΎ ΡΠΎΠΌ, ΠΊΠ°ΠΊ ΠΎΠ½ΠΈ ΡΠ΅Π°Π»ΠΈΠ·ΠΈΡΡΡΡ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ Π΄Π»Ρ ΡΠ°Π±ΠΎΡΡ Π½Π°Π΄ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π³Π»Π°Π²Π½ΠΎΠΉ ΡΡΡΠ°Π½ΠΈΡΡ/ΡΠΊΡΠ°Π½Π°.
ΠΠΠΠ£Π‘Π ΠΠΠ‘ΠΠΠ ΠΠΠΠΠ’Π:
1. ΠΠΎΠΌΠ°Π½Π΄Π° ΡΠΎΠΊΡΡΠΈΡΡΠ΅ΡΡΡ Π½Π° Π΄Π²ΡΡ
ΠΎΠ±Π»Π°ΡΡΡΡ
: ΡΠΊΠΎΡΠΎΡΡΡ Π·Π°ΠΏΡΡΠΊΠ° ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ² ΠΈ ΠΈΡ
ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ.
2. ΠΠ»Ρ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ° Home Config, ΡΡΠΎ Π²Π½ΡΡΡΠ΅Π½Π½ΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΈΠΏΠ° configuration-as-a-service. ΠΠ»Π°ΡΡΠΎΡΠΌΠ° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ βΠΈΠ³ΡΠ°ΡΡβ Ρ ΡΠΎΡΡΠΈΡΠΎΠ²ΠΊΠΎΠΉ, ΠΊΠΎΠ½ΡΠ΅Π½ΡΠΎΠΌ, ΠΊΠ°ΡΡΠΈΠ½ΠΊΠ°ΠΌΠΈ ΠΈ Π΄Ρ.
3. ΠΠ»Ρ Π·Π°ΠΏΡΡΠΊΠ° ΡΠ΅ΡΡΠ° Π² ΠΏΡΠΎΠ΄ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ° Spotify Experimentation Platform, Π²ΡΡΡΠΎΠ΅Π½Π½Π°Ρ Π²ΠΎ Π²ΡΡ ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌ ΠΏΡΠΎΠ΄ΡΠΊΡΠ°. Π’ΠΎ Π΅ΡΡΡ Π² Home config ΡΠΎΠ·Π΄Π°Π΅ΡΡΡ ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΡ Π΄Π»Ρ ΡΠ΅ΡΡΠ°, Π° Π΄Π°Π»ΡΡΠ΅ ΠΎΠ½ ΠΏΠΎΠΏΠ°Π΄Π°Π΅Ρ Π² EP Π΄Π»Ρ Π·Π°ΠΏΡΡΠΊΠ° Π² ΠΏΡΠΎΠ΄: ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ, Π³ΡΡΠΏΠΏΡ, ΠΌΠ΅ΡΡΠΈΠΊΠΈ ΠΈ Π°Π½Π°Π»ΠΈΠ· ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ².
4. ΠΠ»Ρ QA ΠΈ Π΄Π΅Π±Π°Π³Π³ΠΈΠ½Π³Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½Ρ Home QA. ΠΠ½ ΡΠΈΠΌΡΠ»ΠΈΡΡΠ΅Ρ Π·Π°ΠΏΡΠΎΡΡ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΠΊ Π³Π»Π°Π²Π½ΠΎΠΌΡ ΡΠΊΡΠ°Π½Ρ ΠΈ ΠΏΡΠΎΠ²Π΅ΡΡΠ΅Ρ ΡΠ°Π±ΠΎΡΡ Π·Π°ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°.
ΠΠΠΠΠΠΠΠ ΠΠ¦ΠΠ―:
ΠΠΆΠΈΠ΄Π°Π΅ΠΌΠΎ, Π²ΡΠ΅ ΠΊΠΎΠΌΠ°Π½Π΄Ρ Ρ
ΠΎΡΡΡ Π·Π°ΠΏΡΡΠΊΠ°ΡΡ ΡΠ΅ΡΡΡ Π½Π° Π³Π»Π°Π²Π½ΠΎΠΌ ΡΠΊΡΠ°Π½Π΅, ΠΏΠΎΡΡΠΎΠΌΡ Π²Π°ΠΆΠ½ΠΎ ΠΈΠΌΠ΅ΡΡ ΠΏΡΠΎΠ·ΡΠ°ΡΠ½ΡΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΡ ΠΈ ΠΎΠ±ΠΌΠ΅Π½ΠΈΠ²Π°ΡΡΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ΅ΠΉ. ΠΠ»Ρ ΡΡΠΎΠ³ΠΎ:
1. Spotify ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅Ρ Experimentation Tracker - Ρ
Π°Π± Π΄Π»Ρ Π²ΡΠ΅Ρ
ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠΉ ΠΎΡΡΠ°ΠΆΠ°Π΅Ρ Π΄Π΅ΡΠ°Π»ΠΈ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΡΠ΅ΡΡΠ° ΠΈ ΠΏΠΎΠΌΠΎΠ³Π°Π΅Ρ ΠΏΡΠΈΠΎΡΠΈΡΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΈΡ
Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅ΠΊΡΡΠΈΡ
Π±ΠΈΠ·Π½Π΅Ρ Π·Π°Π΄Π°Ρ.
2. Experiment Validation Assistant - ΡΠ΅ΡΠ²ΠΈΡ, ΠΊΠΎΡΠΎΡΡΠΉ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈ Π²Π°Π»ΠΈΠ΄ΠΈΡΡΠ΅Ρ Π²ΡΠ΅ A/B ΡΠ΅ΡΡΡ Π½Π° Π³Π»Π°Π²Π½ΠΎΠΉ ΠΏΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΠΌ ΡΠ°ΠΊΡΠΎΡΠ°ΠΌ ΠΈ Π΄Π°Π»Π΅Π΅ ΠΎΡΠΏΡΠ°Π²Π»ΡΠ΅Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π²Π°Π»ΠΈΠ΄Π°ΡΠΈΠΈ Π² Π²Π΅ΡΠΊΡ Π² Slack Ρ Π²ΡΠ²ΠΎΠ΄ΠΎΠΌ, ΠΎΠ±ΡΠ°ΡΠ½ΠΎΠΉ ΡΠ²ΡΠ·ΡΡ ΠΈ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΡΠΌΠΈ.
π ΠΠΎΠ»Π½Π°Ρ ΡΡΠ°ΡΡΡ.
#spotify | 254 |
| 5 | Symmetry-simplicity, broken-symmetry-complexity
Author: Krakauer, David C.
Source: Interface Focus; Vol.: 13; Article No.: 20220075; Issue: 3; DOI: 10.1098/rsfs.2022.0075; 14 April 2023
SFI Taxonomy: Information Theory, Machine Learning and Statistics
Abstract:
Complex phenomena are made possible when: (i) fundamental physical symmetries are broken and (ii) from the set of broken symmetries historically selected ground states are applied to performing mechanical work and storing adaptive information. Over the course of several decades Philip Anderson enumerated several key principles that can follow from broken symmetry in complex systems. These include emergence, frustrated random functions, autonomy and generalized rigidity. I describe these as the four Anderson Principles all of which are preconditions for the emergence of evolved function. I summarize these ideas and discuss briefly recent extensions that engage with the related concept of functional symmetry breaking, inclusive of information, computation and causality. | 208 |
| 6 | An adaptive bounded-confidence model of opinion dynamics on networks
Authors: Kan, Unchitta; Michelle Feng and Mason A. Porter
Source: Journal of Complex Networks; Vol.: 11; Issue: 1; Article No.: cnac055; DOI: 10.1093/comnet/cnac055; 30 December 2022
SFI Taxonomy: Social Networks
Abstract:
Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to study the spread of opinions on networks is by examining bounded-confidence models (BCMs), in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other nodes' opinions when they lie within some confidence bound of their own opinion. In this article, we extend the Deffuant-Weisbuch (DW) model, which is a well-known BCM, by examining the spread of opinions that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinions when they interact with neighbouring nodes and (2) break connections with neighbours based on an opinion tolerance threshold and then form new connections following the principle of homophily. This opinion tolerance threshold determines whether or not the opinions of adjacent nodes are sufficiently different to be viewed as 'discordant'. Using numerical simulations, we find that our adaptive DW model requires a larger confidence bound than a baseline DW model for the nodes of a network to achieve a consensus opinion. In one region of parameter space, we observe 'pseudo-consensus' steady states, in which there exist multiple subclusters of an opinion cluster with opinions that differ from each other by a small amount. In our simulations, we also examine the roles of early-time dynamics and nodes with initially moderate opinions for achieving consensus. Additionally, we explore the effects of coevolution on the convergence time of our BCM. | 161 |
| 7 | 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 description 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. | 125 |
| 8 | 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. | 143 |
| 9 | ΠΠ²ΡΠΎΠ³Π΅Π½Π΅ΡΠ°ΡΠΈΡ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠΎΠ²
ΠΠΎΠ²Π°Ρ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΡ ΠΎΡ Π³ΡΠ³Π» - Gemini - ΡΠΌΠ΅Π΅Ρ Π³Π΅Π½Π΅ΡΠΈΡΠΎΠ²Π°ΡΡ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΡ Π²Π½ΡΡΡΠΈ ΡΠ°ΡΠ° Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π·Π°Π΄Π°ΡΠΈ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ, Π²ΡΠ³Π»ΡΠ΄ΠΈΡ ΠΎΡΠ΅Π½Ρ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎ - ΠΊΠ°ΠΊ ΠΏΡΠΈΠΌΠ΅ΡΠ½ΠΎ ΠΈ ΠΎΠΏΠΈΡΡΠ²Π°Π»ΠΎΡΡ Π² ΡΡΠ°ΡΡΡΡ
ΠΎΠ± Π°Π²ΡΠΎΠ³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠΎΠ² Π² LLM.
ΠΠ½ΡΠ΅ΡΠ΅ΡΠ½ΠΎ ΡΠΊΠΎΠ»ΡΠΊΠΎ ΡΠ°ΠΌ Π²ΡΠ΅-ΡΠ°ΠΊΠΈ Π·Π°Π³ΠΎΡΠΎΠ²Π»Π΅Π½Π½ΡΡ
ΠΊΠ°Π½Π²Π°Ρ?
https://www.theverge.com/2023/12/6/23990466/google-gemini-llm-ai-model
Π£Π·Π½Π°Π» ΠΎΡ @cryptoEssay | 178 |
| 10 | https://ncase.me/trust/ | 134 |
| 11 | ΠΠ°ΡΡΠ° Π½Π°ΡΠΊΠΈ ΠΎ ΡΠ΅ΠΎΡΠΈΠΈ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΠΈ - ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π²Π΄ΠΎΡ
Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ Π² ΡΠ°Π±ΠΎΡΠ΅ ΡΠ΅ΡΠΏΠ°Ρ ΠΎΡΡΡΠ΄Π°
https://chad.is/getting-started-complexity-science/ | 145 |
| 12 | 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 | 149 |
| 13 | 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 | 175 |
| 14 | Ρ
ΠΌΠΌΠΌΠΌ
Π€ΡΠ½Π΄Π°ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠΉ ΡΠ΅ΡΠ΅ΡΡ ΠΎ "ΠΏΡΠΈΡΠΎΠ΄Π΅ Π΄Π°Π½Π½ΡΡ
" - The topology of data
ΠΠ»Ρ ΡΠΈΡΠΎΠΊΠΎΠΉ ΠΏΡΠ±Π»ΠΈΠΊΠΈ Π±ΡΠ΄Π΅Ρ ΠΎΡΠΊΡΡΡ 1 ΡΠ½Π²Π°ΡΡ
https://authors.library.caltech.edu/records/qa61x-ah042 | 179 |
| 15 | https://netflixtechblog.com/the-next-step-in-personalization-dynamic-sizzles-4dc4ce2011ef | 185 |
| 16 | Π ΠΌΡ Π½Π°ΠΏΠΈΡΠ°Π»ΠΈ Π½Π°ΡΡ ΠΏΠ΅ΡΠ²ΡΡ ΡΡΠ°ΡΡΡ Π½Π° Π₯Π°Π±Ρ!
ΠΠΎΡΠ²ΡΡΠΈΠ»ΠΈ Π΅Π΅ ΠΊΡΡΡΠΎΠΉ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ΅ RecTools ΠΎΡ ΠΊΠΎΠ»Π»Π΅Π³ ΠΈΠ· ΠΠ’Π‘. ΠΠ½ΡΡΡΠΈ:
βΆοΈΠ·Π° ΡΡΠΎ ΠΌΡ ΡΠ°ΠΊ Π»ΡΠ±ΠΈΠΌ ΡΡΡ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΡ;
βΆοΈΠ»ΠΈΠΊΠ±Π΅Π· ΠΏΠΎ ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌ ΡΠ΅ΠΊΡΠΈ-ΠΌΠΎΠ΄Π΅Π»ΡΠΌ (ItemKNN, ALS, SVD, Lightfm, DSSN);
βΆοΈΠΊΠ°ΠΊ Π³ΠΎΡΠΎΠ²ΠΈΡΡ Π΄Π°Π½Π½ΡΠ΅ ΠΈ Π·Π°ΠΏΡΡΠΊΠ°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π² Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ΅;
βΆοΈΠΊΠ°ΠΊ ΡΠ°ΡΡΡΠΈΡΡΠ²Π°ΡΡ ΠΌΠ΅ΡΡΠΈΠΊΠΈ;
βΆοΈΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ ΠΌΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ»Π΅Π·Π½ΡΡ
Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ².
ΠΡΠ΅Π½Ρ ΡΡΠ°ΡΠ°Π»ΠΈΡΡ, ΡΠ°ΠΊ ΡΡΠΎ ΠΆΠ΄Π΅ΠΌ Π²Π°ΡΠΈΡ
ΡΠ΅Π°ΠΊΡΠΈΠΉ!
π» #NN #train | 183 |
| 17 | https://business.adobe.com/blog/how-to/scale-up-with-hyper-personalization | 176 |
| 18 | ΠΡΠ½ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΠΎΠ»ΡΡΡΠ°ΠΌΠ° Π²ΡΡΡΡΠΏΠ» Ρ Π·Π°ΡΠ²Π»Π΅Π½ΠΈΠ΅ΠΌ ΠΎ ΡΠΎΠ·Π΄Π°Π½ΠΈΠΈ ΡΠ·ΡΠΊΠ° ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΎΠ²:
In a sense, whatβs 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/
ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΡΠ΅ΡΠΌΠΈΠ½ ruliad https://writings.stephenwolfram.com/2021/11/the-concept-of-the-ruliad/ (ΠΏΠ΅ΡΠ΅ΡΠΊΠ°Π· https://300.ya.ru/I91uS50c) | 2 333 |
| 19 | https://research.atspotify.com/2023/10/exploiting-sequential-music-preferences-via-optimisation-based-sequencing/ | 259 |
| 20 | https://research.atspotify.com/2023/10/llark-a-multimodal-foundation-model-for-music/ | 134 |
