Recent Publications
TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese
Large language models (LLMs) have significantly advanced natural language processing, but their progress has yet to be
equal across languages. While most LLMs are trained in high-resource languages like English, multilingual models generally
underperform monolingual ones. Additionally, aspects of their multilingual foundation sometimes restrict the byproducts
they produce, like computational demands and licensing regimes. In this study, we document the development of
open-foundation models tailored for use in low-resource settings, their limitations, and their benefits. This is the
TeenyTinyLlama
pair: two compact models for Brazilian Portuguese text generation. We release them under the permissive Apache 2.0 license
on
GitHub and
Hugging Face for community use and further development.
Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance
The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth
numerous benefits and conveniences. However, this expansion has also provoked ethical concerns, such as privacy breaches,
algorithmic discrimination, security and reliability issues, transparency, and other unintended consequences.
To determine whether a global consensus exists regarding the ethical principles that should govern AI applications
and to contribute to the formation of future regulations, this paper conducts a meta-analysis of 200 governance
policies and ethical guidelines for AI usage published by public bodies, academic institutions, private companies,
and civil society organizations worldwide.
We identified at least 17 resonating principles prevalent in the policies and guidelines of our dataset, released as an
open-source database and tool. We present the limitations of performing a global scale analysis study paired with a
critical analysis of our findings, presenting areas of consensus that should be incorporated into future
regulatory efforts.
Risks of Using Facial Recognition Technologies in Public Security Applications
Counterfactual Analysis by Algorithmic Complexity: A metric between possible worlds
Counterfactuals have become an important area of interdisciplinary interest, especially in logic, philosophy of language, epistemology,
metaphysics, psychology, decision theory, and even artificial intelligence. In this study, we propose a new form of analysis for
counterfactuals: analysis by algorithmic complexity, inspired by Lewis-Stalnaker's Possible Worlds Semantics.
Engaging in a dialogue with literature this study will seek to bring new insights and tools to the debate, so that the object of interest,
counterfactuals, may be understood in an intuitively plausible way, and a philosophically justifiable manner, aligned with the way we
usually think about counterfactual propositions and our imaginative reasoning.
On the efficiency of ethics as a governing tool for artificial intelligence
Meta-analyses of the AI Ethics research field point to convergence on certain ethical principles that supposedly govern
the AI industry. However, little is known aboutthe effectiveness of this form of "Ethics."" In this paper, we would like
to conducta critical analysis of the current state of AI Ethics and suggest that this form ofgovernance based on principled
ethical guidelines is not sufficient to norm theAI industry and its developers. We believe that drastic changes are necessary,
both in the training processes of professionals in the fields related to the development of software and intelligent systems
and in the increased regulation ofthese professionals and their industry.
To this end, we suggest that law shouldbenefit from recent contributions from bioethics, to make the contributions of AI ethics
to governance explicit in legal terms.
Progress in the Federal Senate 2022 PL 21/20 - PL 5051/19 - PL 872/21
Good AI for the Present of Humanity Democratizing AI Governance
Singularity and Coordination Problems: Pandemic Lessons from 2020
Metanormativity: Solving questions of moral and empirical uncertainty
How can someone reconcile the desire to eat meat, and a tendency toward vegetarian ideals? How should we reconcile
contradictory moral values? How can we aggregate different moral theories? How individual preferences can be
fairly aggregated to represent a will, norm, or social decision? Conflict resolution and preference aggregation
are tasks that intrigue philosophers, economists, sociologists, decision theorists, and many other scholars,
being a rich interdisciplinary area for research. When trying to solve questions about moral uncertainty a
meta understanding of the concept of normativity can help us to develop strategies to deal with norms themselves.
2 nd-order normativity, or norms about norms, is a hierarchical way to think about how to combine many
different normative structures and preferences into a single coherent decision. That is what metanormativity
is all about, a way to answer: what should we do when we don't know what to do? In this study, we will review
a decision-making strategy dealing with moral uncertainty, Maximization of Expected Choice-Worthiness.
Given the similarity to this metanormative strategy to expected utility theory, we will also show that it is possible
to integrate both models to address decision-making problems in situations of empirical and moral uncertainty.