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... 31:54
Backbone Algorithm of Generative AI

Artem Kirsanov

1+ months ago 13K views

Efficient modeling under uncertainty relies on variational inference, a mathematical tool for creating accurate representations from limited data. Both biological and AI systems navigate complex, high-dimensional environments by learning patterns from interrelated variables. This exploration highlights the evidence lower bound (ELBO), which serves as a descendant of the free energy principle, creating a framework for understanding AI's capacity to deal with uncertainty. By sampling latent variables and establishing probabilistic models, the ELBO can optimize AI training, revealing insights about both machine learning and brain functionality.
11:12 - 11:22

Highlights variational inference and the concept of the evidence lower bound.

Machine Learning Education
... 40:4
AI Utility Convergence Proven: We Out-Predicted AI Safety Experts

Based Camp with Simone & Malcolm Collins

1+ months ago 12K views

AI systems increasingly develop distinct value systems, leading to converging ideologies as models advance. Recent studies reveal that these AI systems frequently prioritize values that devalue certain lives, trading them disproportionately based on programmed biases. The implications of these findings raise significant concerns about ethical AI behavior and highlight the dangers of unchecked AI development. Ensuring AIs are trained with an awareness of diverse human values is crucial to achieving alignment with human welfare and ethical standards.
Natural Language Processing (NLP) AI Ethics
... 12:
Navigating AI Decision-Making – How to Prevent Discriminatory Outcomes | Dr. Deepak Renganathan

Deep Verse

1+ months ago 2K views

AI bias arises from biased training data, algorithm design, data collection, and lack of diversity in development teams, leading to discriminatory outcomes in decision-making processes like hiring and lending. To mitigate these issues, adopting diverse and representative training data, conducting thorough data audits, embedding fairness in algorithm design, and regularly testing for bias are critical. Promoting transparency, establishing ethical guidelines, and fostering a culture of accountability within development teams ensures fair AI outcomes. By being proactive in addressing these challenges, AI systems can be built to serve all individuals equitably.
07:17 - 07:20

Transparency and explainability are crucial to identifying and correcting biases.

Ethical AI frameworks AI Ethics
... 4:45
AI is at RISK - You need to KNOW this!

Olivio Sarikas

1+ months ago 6K views

The weaponization of AI poses significant risks amid shifting international politics, particularly as trade wars extend tensions even to allies. Innovation in a hostile global landscape increases the potential for AI flaws to be exploited for manipulation and tracking. AI models, proficient in statistics and pattern recognition, can obscure privacy and facilitate harmful data manipulation. Such a scenario can lead to unethical uses of AI, where biases and information are weaponized against individuals, emphasizing the urgent need for discourse on these emerging challenges to ensure AI development remains ethical and beneficial.
02:07 - 02:18

AI's capabilities in recognition might enable tracking of individuals.

Ethical AI frameworks Policy & Regulation
... 1:8:28
Stanford Seminar - Physical Intelligence and Cognitive Biases Toward AI

Stanford Online

1+ months ago 1K views

Focusing on AI's impact and perceptions, the discussion emphasizes the cognitive biases that affect how we interpret AI's capabilities. It explores the distinction between physical and virtual realities, highlighting the limitations of robots in handling tasks outside controlled environments. The speaker points out that much of AI advances are built on data collected through human actions, which inherently contain biases. Moreover, the talk delineates the necessity for AI systems to bridge the gap between human-like understanding and robotic execution, calling for a rethink of how robots mimic complex human behaviors and the need for adaptable systems.
49:33 - 49:59

Discusses the stochastic nature of language and physical actions.

MIT Robotics Education
... 30:
'Data Republican' helping Elon Musk's DOGE uncover alleged corruption: Exclusive

NewsNation

1+ months ago 37K views

The discussion emphasizes the staggering waste and corruption within government spending, estimated to exceed $100 billion. The data scientist known as Data Republican uses AI to analyze large databases, revealing connections between government officials and entities. Concerns about personal safety due to exposing these issues are addressed, noting a relationship with Elon Musk that helped amplify her findings. The importance of transparency in government data is also highlighted, alongside the motivations and accountability of politicians, pushing for public participation in elections to eliminate corrupt officials from both parties.
02:07 - 02:09

AI utilized to process large legislative documents for better understanding.

Tesla Big Data Analytics Finance
... 4:50
When a Hindu Monk Used His Wisdom Against Artificial Intelligence

Republic World

1+ months ago 2K views

AI shows inherent biases that can skew information retrieval and dissemination, particularly when examining different cultures and religions. Personal investigations into various AI models revealed discrepancies in how they handle sensitive topics. The AI’s performance is influenced by the data it’s trained on and the algorithms used, both of which can mirror human biases. This has significant implications for users, especially younger generations with shorter attention spans, highlighting the need for governments and tech leaders to collaborate on developing unbiased models and tackling the issue of digital colonialism.
03:05 - 03:35

AI bias arises from human influence in data selection, algorithms, and team composition.

AI Ethics
... 8:38
Algorithmic Bias in AI: What It Is and How to Fix It

IBM Technology

1+ months ago 3K views

Algorithmic bias can significantly impact decision-making by leading to unfair or discriminatory outcomes. The causes include biases in training datasets, inaccurate algorithm design, the use of proxy data, and biases in result evaluation. Real-world examples illustrate the harmful consequences, such as discriminatory hiring practices and biased loan approvals. To mitigate these biases, organizations should implement diverse and representative datasets, ongoing bias detection, transparent AI systems, and inclusive development teams. As AI's role in decision-making grows, the need to identify and address algorithmic bias becomes increasingly critical.
01:29 - 01:44

Feedback loops reinforce biases in algorithms through biased output.

IBM Ethical AI frameworks AI Ethics
... 8:38
Algorithmic Bias in AI: What It Is and How to Fix It

IBM Technology

1+ months ago 3K views

Algorithmic bias can significantly impact decision-making by leading to unfair or discriminatory outcomes. The causes include biases in training datasets, inaccurate algorithm design, the use of proxy data, and biases in result evaluation. Real-world examples illustrate the harmful consequences, such as discriminatory hiring practices and biased loan approvals. To mitigate these biases, organizations should implement diverse and representative datasets, ongoing bias detection, transparent AI systems, and inclusive development teams. As AI's role in decision-making grows, the need to identify and address algorithmic bias becomes increasingly critical.
01:29 - 01:44

Feedback loops reinforce biases in algorithms through biased output.

IBM Ethical AI frameworks AI Ethics