Societal Implications of Generative AI Tutorial

The video explores generative AI, first presenting innovative applications like Google's DeepDream. It quickly addresses darker subjects, focusing on the ethical challenges of deepfakes, particularly their implications in terms of misinformation and national security. It also highlights issues of bias in AI, showing how training data can influence outcomes. A key point is the erosion of the concept of truth, where generative AI can sow distrust in the media and exacerbate social polarization. The video also acknowledges the opportunities offered by AI, while highlighting the tension between innovation and the need for regulation. It concludes by emphasizing the importance of education in navigating this complex landscape.

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résultat(s)
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Generative AI is at the heart of
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the new technological frontier,
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transforming our ways of seeing and creating.
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From the hallucinatory visual art
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of Mid Journey, Google's Deepdream,
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to the sophisticated melodies
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of Open a Is Muse Net,
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creative boundaries seem to be fading.
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However, with magic comes malice.
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Deepfakes blur the lines between real
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and fake, posing huge ethical problems.
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Deep fakes can be used to create fake
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videos or audios that feature public
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figures or politicians saying or
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doing things they never said or did.
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This can be used to spread false
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information or propaganda,
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manipulating public opinion.
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Deep fakes could be used to impersonate
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national leaders or military officers,
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creating videos that could trigger
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diplomatic crises or even conflicts.
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As deep fakes become more and
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more convincing,
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it becomes difficult for the public
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to discern the real from the fake.
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This could lead to a general erosion
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of trust in video and audio media
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where everything can be questioned.
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Artificial intelligence,
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including generative technologies
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like deepfakes,
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relies on data to learn and generalize.
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These data come from the real world,
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and just as our world has
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inequalities and stereotypes,
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these inequalities can
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be reflected in the data.
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When an AI is trained on biased data,
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it can incorporate and
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perpetuate these biases.
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Thus, if deep fakes are used to,
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say,
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generate voice or face samples
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that do not fairly represent
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the diversity of the real world,
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then the AI learning from these
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samples will itself be biased,
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leading to discriminatory systems.
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For example,
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an AI system for voice recognition
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that is trained primarily on male
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voices might struggle to recognize
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and correctly understand female
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voices or other voices that do not
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match the model it was trained on.
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With the spread of deepfakes,
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objective reality could be undermined.
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If everything can be falsified,
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the very concept of truth could be eroded,
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with profound consequences for society.
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As deepfakes become more sophisticated
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and indistinguishable from reality,
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the public could begin to
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systematically doubt the veracity
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of any video or audio content,
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whether it's news, documentaries,
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or interviews.
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This could lead to a society where people
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no longer believe what they see or hear,
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making the dissemination of reliable
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information extremely difficult.
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Media organizations might need to
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adopt new methods of validation
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and certification to prove the
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authenticity of their content.
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In a world where objective truth
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is questioned,
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individuals might turn more towards
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information sources that confirm
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their pre-existing beliefs,
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reinforcing echo chambers and polarization.
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Deep fakes could be used to support
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conspiracy theories and given that
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these videos would be difficult to refute,
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these theories could gain in
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popularity and influence.
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This could lead to increased
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fragmentation of society,
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with groups becoming more isolated
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and disagreeing on fundamental facts.
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Democracies rely on
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an informed electorate
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to function effectively.
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If citizens cannot discern the
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true from the false due to the
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proliferation of deep fakes,
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this could compromise the democratic process.
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Electoral campaigns could be
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marked by falsified videos
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intended to discredit opponents,
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and elections could be influenced
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by misleading information.
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Trust in democratic institutions
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could decrease,
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leading to political instability and
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a weakening of democratic values.
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How to legislate this
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rapidly evolving technology?
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The debate between innovation
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and regulation is more intense
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than ever. Historically,
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technology has often evolved faster
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than societies ability to regulate it.
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For example, when the Internet
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began to become popular, it took years
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before laws on privacy, copyright,
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or cybersecurity were established. With
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generative AI, the challenge is even greater
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because the technology has the potential
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to transform many aspects of our lives,
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from media to politics to the economy.
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The initial problems posed by the
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emergence of deepfakes were mainly
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ethical or related to misinformation.
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However, initially there was no clear
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regulation to penalize or frame the creation
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and dissemination of malicious deepfakes.
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Strict regulation can hinder innovation.
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If severe restrictions are imposed on
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research and development in generative AI,
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this could prevent potentially
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beneficial discoveries for society.
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On the other hand,
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overly strict regulation can push
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innovators to relocate their research to
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countries with more lenient legislation.
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Suppose regulation imposes a long
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and costly approval process for any
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new application of generative AI.
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This could discourage start-ups
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and individual innovators,
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thus favoring large companies
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with significant resources.
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The ideal balance between innovation and
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regulation requires close collaboration
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between policy makers, researchers,
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businesses, and civil society.
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It is essential to have open and
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inclusive discussions to understand
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the implications of technology and
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create regulation that protects
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society while fostering innovation.
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Some countries have begun to
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establish AI ethics committees,
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composed of experts from various fields
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to guide the creation of legislation.
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These committees examine the ethical,
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social and economic implications
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of AI and advise governments on
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how to frame the technology.
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Generative AI, despite its dangers,
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offers unprecedented opportunities.
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It can improve lives, create solutions,
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and enrich our culture.
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Guided by responsibility,
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ethics and collaboration,
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we can shape the future of
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generative AI for the good of all.
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Faced with these challenges,
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education is paramount.
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Solid training allows understanding
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the issues,
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adopting tools and asking informed questions.

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résultat(s)
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A IA generativa está no centro da
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a nova fronteira tecnológica,
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transformando as nossas formas de ver e criar.
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Da arte visual alucinatória
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de Mid Journey, Deepdream do Google,
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às melodias sofisticadas
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de Open a Is Muse Net,
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As fronteiras criativas parecem estar a desvanecer-se.
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No entanto, com a magia vem a malícia.
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Deepfakes borram as linhas entre o real
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e falsa, colocando enormes problemas éticos.
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Deep fakes podem ser usados para criar falsificações
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Vídeos ou áudios que apresentam público
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figuras ou políticos dizendo ou
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fazendo coisas que nunca disseram ou fizeram.
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Isso pode ser usado para espalhar falso
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informação ou propaganda,
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manipulação da opinião pública.
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Deep fakes podem ser usados para se passar por
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dirigentes nacionais ou oficiais militares,
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criação de vídeos que podem ser acionados
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crises diplomáticas ou mesmo conflitos.
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À medida que as deep fakes se tornam mais e
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mais convincente,
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torna-se difícil para o público
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discernir o real do falso.
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Isto poderia levar a uma erosão geral
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de confiança nos meios de vídeo e áudio
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onde tudo pode ser questionado.
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Inteligência artificial,
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incluindo tecnologias generativas
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como deepfakes,
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depende de dados para aprender e generalizar.
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Estes dados vêm do mundo real,
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e tal como o nosso mundo tem
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desigualdades e estereótipos,
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estas desigualdades podem
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refletir-se nos dados.
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Quando uma IA é treinada em dados tendenciosos,
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pode incorporar e
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perpetuar esses preconceitos.
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Assim, se as deep fakes estiverem acostumadas,
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diga,
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gerar amostras de voz ou rosto
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que não representam de forma justa
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a diversidade do mundo real,
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em seguida, a IA aprendendo com estes
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as amostras serão, elas próprias, tendenciosas,
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conduzindo a sistemas discriminatórios.
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Por exemplo
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um sistema de IA para reconhecimento de voz
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que é treinado principalmente em homens
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as vozes podem ter dificuldade em reconhecer
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e compreender corretamente o sexo feminino
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vozes ou outras vozes que não o fazem
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corresponder ao modelo em que foi treinado.
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Com a disseminação de deepfakes,
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a realidade objetiva poderia ser posta em causa.
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Se tudo puder ser falsificado,
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o próprio conceito de verdade poderia ser corroído,
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com profundas consequências para a sociedade.
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À medida que as deepfakes se tornam mais sofisticadas
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e indistinguível da realidade,
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o público poderia começar a
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duvidar sistematicamente da veracidade
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de qualquer conteúdo vídeo ou áudio,
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sejam notícias, documentários,
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ou entrevistas.
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Isto poderia conduzir a uma sociedade em que as pessoas
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já não acreditam no que vêem ou ouvem,
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tornar fiável a divulgação de
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informação extremamente difícil.
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As organizações de mídia podem precisar
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adotar novos métodos de validação
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e certificação para comprovar o
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autenticidade do seu conteúdo.
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Num mundo onde a verdade objetiva
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é questionado,
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os indivíduos podem voltar-se mais para
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fontes de informação que confirmam
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as suas convicções pré-existentes,
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reforço das câmaras de eco e polarização.
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Deep fakes podem ser usados para apoiar
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teorias da conspiração e dado que
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estes vídeos seriam difíceis de refutar,
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estas teorias poderiam ganhar em
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popularidade e influência.
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Isto pode levar a um aumento do
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fragmentação da sociedade,
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com grupos cada vez mais isolados
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e discordar de factos fundamentais.
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As democracias dependem de:
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um eleitorado informado
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para funcionar eficazmente.
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Se os cidadãos não conseguem discernir o
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verdadeiro do falso devido ao
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proliferação de deep fakes,
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Isto poderia comprometer o processo democrático.
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Campanhas eleitorais podem ser
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marcado por vídeos falsificados
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destinados a desacreditar os opositores,
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e as eleições podem ser influenciadas
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por informações enganosas.
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Confiança nas instituições democráticas
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podem diminuir,
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conduzindo à instabilidade política e
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um enfraquecimento dos valores democráticos.
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Como legislar
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tecnologia em rápida evolução?
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O debate entre inovação
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e a regulação é mais intensa
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do que nunca. Historicamente,
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A tecnologia evoluiu muitas vezes mais rapidamente
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do que a capacidade das sociedades de regulá-lo.
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Por exemplo, quando a Internet
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começou a tornar-se popular, demorou anos
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antes das leis sobre privacidade, direitos autorais,
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ou a cibersegurança. Com
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IA generativa, o desafio é ainda maior
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porque a tecnologia tem o potencial
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transformar muitos aspetos das nossas vidas,
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dos meios de comunicação social à política, passando pela economia.
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Os problemas iniciais colocados pelo
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surgimento de deepfakes foram principalmente
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ética ou relacionada à desinformação.
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No entanto, inicialmente não havia
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regulamento para penalizar ou enquadrar a criação
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e disseminação de deepfakes maliciosos.
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Uma regulamentação rigorosa pode entravar a inovação.
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Se forem impostas restrições severas a
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investigação e desenvolvimento em IA generativa,
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Isto poderia evitar potencialmente
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descobertas benéficas para a sociedade.
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Por outro lado
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uma regulamentação demasiado rigorosa pode empurrar
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inovadores a deslocalizarem a sua investigação para
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países com legislação mais branda.
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Suponhamos que a regulamentação impõe uma longa
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e dispendioso processo de aprovação para qualquer
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nova aplicação da IA generativa.
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Tal poderia desencorajar as empresas em fase de arranque
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e inovadores individuais,
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favorecendo assim as grandes empresas
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com recursos significativos.
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O equilíbrio ideal entre inovação e
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A regulamentação exige uma estreita colaboração
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entre decisores políticos, investigadores,
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as empresas e a sociedade civil.
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É essencial ter
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discussões inclusivas para entender
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as implicações da tecnologia e
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criar regulamentação que proteja
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ao mesmo tempo que fomenta a inovação.
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Alguns países começaram a
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criar comités de ética no domínio da IA,
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composto por especialistas de várias áreas
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orientar a criação de legislação.
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Estes comités examinam as questões éticas,
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implicações sociais e económicas
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de IA e aconselhar os governos sobre
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como enquadrar a tecnologia.
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IA generativa, apesar dos seus perigos,
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oferece oportunidades sem precedentes.
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Pode melhorar vidas, criar soluções,
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e enriquecer a nossa cultura.
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Guiados pela responsabilidade,
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ética e colaboração,
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podemos moldar o futuro da
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IA generativa para o bem de todos.
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Face a estes desafios,
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A educação é fundamental.
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Uma formação sólida permite a compreensão
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as questões,
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adotar ferramentas e fazer perguntas informadas.

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DiLeaP AI: THIS MIGHT BE HELPFUL

RELATED COURSES This tutorial is part of the following courses

Part 02 - Societal and Ethical Implications of Generative AI

Address the challenges and opportunities of generative AI, exploring its societal, ethical impacts, and implications on truth, risks, and trust in the digital era.

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