Keys to Success, Pitfalls to Avoid, and Best Practices Tutorial

We dive here into the heart of Generative Artificial Intelligence, this revolutionary technology that shapes the digital landscape. We start by exploring the essential foundations of its success, highlighting the importance of data quality, model choice, and required resources. Then, we address the challenges faced illustrated by poignant examples of errors and limitations highlighting potential biases and ethical dilemmas. Finally, the video concludes with a series of practical recommendations, offering valuable advice for successfully integrating generative AI into various projects. This immersion offers a balanced perspective on the immense potential and inherent challenges of generative AI.

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Objectifs :

This document aims to provide a comprehensive understanding of generative artificial intelligence, its foundational elements, challenges, and best practices for effective integration into business strategies.

Chapitres :

  1. Introduction to Generative Artificial Intelligence
    Generative artificial intelligence (AI) is at the forefront of the digital revolution, capable of creating content from scratch. As we navigate this uncharted territory, it is essential to explore the secrets behind its success and understand the foundational pillars that support this technology.
  2. The Pillars of Generative AI
    To harness the power of generative AI, we must recognize its key components: - **Data Quality**: The fuel of AI. Accurate and diverse data is crucial; without it, even the best models can fail. - **Model Selection**: Choosing the right model is essential, as each has its strengths and weaknesses. - **Hardware Resources**: Robust hardware is necessary to support the computational demands of AI. - **Competent Team**: A skilled team is vital for training and guiding the AI models effectively.
  3. Case Study: OpenAI's GPT-4
    OpenAI's GPT-4 serves as a prime example of generative AI's capabilities. With billions of parameters powered by petabytes of data, it has revolutionized text generation. However, its success is attributed not only to its architecture but also to the expertise of the team behind it, which ensures proper training and guidance.
  4. Challenges of Generative AI
    Despite its potential, generative AI faces several challenges: - **Data Bias**: Poorly prepared data can lead to biases in AI outputs. - **Model Misconfiguration**: Incorrectly configured models can waste resources or produce inaccurate results. - **Integration Issues**: Without thoughtful integration, generative AI can disrupt operations rather than enhance them. For instance, there have been instances where AI generated offensive or discriminatory content due to biases in the training data, leading to serious ethical and social repercussions.
  5. Best Practices for Effective Integration
    To maximize the benefits of generative AI while minimizing risks, organizations should adopt best practices: 1. **Rigorous Data Collection and Preparation**: Ensure data is accurate and diverse. 2. **Model Selection**: Choose the right model for specific tasks. 3. **Result Validation**: Carefully validate AI outputs to ensure accuracy. 4. **Strategic Integration**: Incorporate AI into a broader strategy, considering ethical implications.
  6. Visionary Companies Leading the Way
    Companies like NVIDIA exemplify how to effectively adopt generative AI. They enhance their offerings while remaining aware of the technology's limitations and responsibilities. By leveraging the right tools, best practices, and a clear vision, generative AI can become a powerful ally in the quest for innovation.
  7. Conclusion
    In conclusion, generative AI holds immense potential for innovation. By understanding its foundational elements, recognizing the challenges, and implementing best practices, we can embark on an exciting adventure that harnesses the power of this transformative technology.

FAQ :

What is generative artificial intelligence?

Generative artificial intelligence refers to AI systems that can create new content, such as text, images, or music, by learning from existing data patterns.

Why is data quality important for AI?

Data quality is essential because accurate and diverse data fuels AI models. Poor data can lead to incorrect results and biases in AI outputs.

What are the challenges of generative AI?

Challenges include biases from poorly prepared data, resource wastage from misconfigured models, and the potential for generating offensive content if not integrated thoughtfully.

How can we mitigate biases in AI?

Mitigating biases involves rigorous data collection and preparation, careful model selection, and validating results to ensure fairness and accuracy.

What role does NVIDIA play in generative AI?

NVIDIA is a leading technology company that has adopted generative AI to enhance its offerings while being mindful of the ethical implications and responsibilities associated with its use.

What best practices should be followed when using generative AI?

Best practices include ensuring high data quality, selecting the appropriate model, validating results, and integrating AI into a broader strategy that considers ethical implications.

Quelques cas d'usages :

Content Creation for Marketing

Generative AI can be used by marketing teams to create engaging content, such as blog posts and social media updates, quickly and efficiently, improving productivity and creativity.

Automated Customer Support

Companies can implement generative AI to develop chatbots that provide instant responses to customer inquiries, enhancing customer service and reducing response times.

Personalized Learning Experiences

Educational institutions can leverage generative AI to create customized learning materials and assessments tailored to individual student needs, improving learning outcomes.

Game Development

Game developers can use generative AI to create dynamic narratives and character dialogues, enriching the gaming experience and reducing development time.

Data Analysis and Reporting

Businesses can utilize generative AI to automate the generation of reports and insights from large datasets, enhancing decision-making processes and operational efficiency.

Glossaire :

Generative Artificial Intelligence

A type of AI that can create content from scratch, such as text, images, or music, by learning patterns from existing data.

Data Quality

The accuracy, completeness, and reliability of data, which is crucial for the performance of AI models.

Model

A mathematical representation of a process used by AI to make predictions or generate content. Different models have unique strengths and weaknesses.

OpenAI's GPT-4

A state-of-the-art generative AI model developed by OpenAI, known for its ability to generate human-like text based on vast amounts of data.

Bias

A systematic error in data or algorithms that can lead to unfair or prejudiced outcomes, often arising from poorly prepared training data.

Ethical Implications

The moral considerations and potential consequences of using AI technologies, particularly regarding fairness, accountability, and transparency.

NVIDIA

A technology company known for its contributions to AI and graphics processing, which has adopted generative AI to enhance its products while addressing ethical concerns.

résultat(s)
00:00:05
Generative artificial intelligence is at
00:00:07
the forefront of the digital revolution,
00:00:09
creating content from scratch.
00:00:11
But how do we navigate this
00:00:13
uncharted territory? Together?
00:00:15
Let's explore the secrets of
00:00:17
its success before diving in.
00:00:19
It's crucial to understand the
00:00:21
pillars that support this technology.
00:00:25
Data quality is the fuel of AI.
00:00:30
Without accurate and diverse data,
00:00:32
even the best model could fail.
00:00:34
Then the choice of the model is
00:00:37
just as essential, as each model
00:00:39
has its strengths and weaknesses.
00:00:41
Add to this robust hardware
00:00:43
resources and a competent team and
00:00:45
you have the recipe for success.
00:00:47
Take Open AI's GPT 4 as an example.
00:00:53
With its billions of parameters
00:00:54
powered by petabytes of data,
00:00:56
it has revolutionized text generation.
00:00:58
But without a team of experts
00:01:00
to train and guide it,
00:01:02
it wouldn't have reached such heights.
00:01:05
However, every coin has two sides.
00:01:10
Generative AI is not without challenges.
00:01:13
Poorly prepared data can induce biases.
00:01:15
Poorly configured models can waste
00:01:18
resources or produce incorrect results.
00:01:20
And without thoughtful integration,
00:01:21
generative AI can disrupt more than help.
00:01:24
For instance, we've seen AI generate
00:01:27
offensive or discriminatory content
00:01:29
due to biases in training data.
00:01:31
These errors can have serious
00:01:33
ethical and social repercussions,
00:01:35
but there is hope.
00:01:37
By adopting best practices,
00:01:38
we can make the most of this technology.
00:01:42
Starts with rigorous data
00:01:44
collection and preparation,
00:01:45
choosing the right model for the right job.
00:01:49
Carefully validating the
00:01:51
results and most importantly,
00:01:52
integrating AI into an overall strategy
00:01:55
considering ethical implications.
00:01:56
Visionary companies like NVIDIA
00:01:58
have already shown the way,
00:02:00
adopting generative AI to enhance their
00:02:02
offerings while remaining aware of
00:02:04
its limitations and responsibilities.
00:02:06
With the right tools,
00:02:08
best practices, and a clear vision,
00:02:11
generative AI can be a powerful
00:02:13
ally in our quest for innovation.
00:02:18
Let's embark together on
00:02:20
this exciting adventure.

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résultat(s)
00:00:05
A inteligência artificial generativa está em
00:00:07
a vanguarda da revolução digital,
00:00:09
Criação de conteúdo a partir do zero.
00:00:11
Mas como navegar por isso?
00:00:13
território desconhecido? Juntos?
00:00:15
Vamos explorar os segredos de
00:00:17
seu sucesso antes de mergulhar.
00:00:19
É crucial compreender o
00:00:21
pilares que suportam esta tecnologia.
00:00:25
A qualidade dos dados é o combustível da IA.
00:00:30
Sem dados precisos e diversos,
00:00:32
mesmo o melhor modelo poderia falhar.
00:00:34
Em seguida, a escolha do modelo é
00:00:37
tão essencial quanto cada modelo
00:00:39
tem os seus pontos fortes e fracos.
00:00:41
Adicione a este hardware robusto
00:00:43
recursos e uma equipa competente e
00:00:45
Você tem a receita para o sucesso.
00:00:47
Tomemos como exemplo o GPT 4 da Open AI.
00:00:53
Com seus bilhões de parâmetros
00:00:54
alimentado por petabytes de dados,
00:00:56
revolucionou a geração de texto.
00:00:58
Mas sem uma equipa de especialistas
00:01:00
treiná-lo e orientá-lo,
00:01:02
não teria atingido tais alturas.
00:01:05
No entanto, cada moeda tem duas faces.
00:01:10
A IA generativa não está isenta de desafios.
00:01:13
Dados mal preparados podem induzir vieses.
00:01:15
Modelos mal configurados podem desperdiçar
00:01:18
recursos ou produzir resultados incorretos.
00:01:20
E sem uma integração ponderada,
00:01:21
A IA generativa pode atrapalhar mais do que ajudar.
00:01:24
Por exemplo, vimos a IA gerar
00:01:27
conteúdo ofensivo ou discriminatório
00:01:29
devido a enviesamentos nos dados de treino.
00:01:31
Estes erros podem ter graves
00:01:33
repercussões éticas e sociais,
00:01:35
Mas há esperança.
00:01:37
Ao adotar as melhores práticas,
00:01:38
Podemos tirar o máximo partido desta tecnologia.
00:01:42
Começa com dados rigorosos
00:01:44
recolha e preparação,
00:01:45
escolher o modelo certo para o trabalho certo.
00:01:49
Validação cuidadosa da seringa
00:01:51
resultados e, mais importante,
00:01:52
integrar a IA numa estratégia global
00:01:55
considerando implicações éticas.
00:01:56
Empresas visionárias como a NVIDIA
00:01:58
já mostraram o caminho,
00:02:00
adotando IA generativa para melhorar a sua
00:02:02
ofertas, mantendo-se ciente de
00:02:04
suas limitações e responsabilidades.
00:02:06
Com as ferramentas certas,
00:02:08
melhores práticas e uma visão clara,
00:02:11
A IA generativa pode ser uma poderosa
00:02:13
aliado na nossa busca pela inovação.
00:02:18
Vamos embarcar juntos
00:02:20
esta aventura emocionante.

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résultat(s)
00:00:05
L'intelligenza artificiale generativa è a
00:00:07
all'avanguardia della rivoluzione digitale,
00:00:09
creazione di contenuti da zero.
00:00:11
Ma come affrontiamo questa situazione
00:00:13
territorio inesplorato? Insieme?
00:00:15
Esploriamo i segreti di
00:00:17
il suo successo prima di immergerti.
00:00:19
È fondamentale capire il
00:00:21
pilastri che supportano questa tecnologia.
00:00:25
La qualità dei dati è il carburante dell'IA.
00:00:30
Senza dati accurati e diversificati,
00:00:32
anche il modello migliore potrebbe fallire.
00:00:34
Quindi la scelta del modello è
00:00:37
altrettanto essenziale, come ogni modello
00:00:39
ha i suoi punti di forza e di debolezza.
00:00:41
Aggiungete a questo hardware robusto
00:00:43
risorse e un team competente e
00:00:45
hai la ricetta per il successo.
00:00:47
Prendiamo come esempio il GPT 4 di Open AI.
00:00:53
Con i suoi miliardi di parametri
00:00:54
alimentato da petabyte di dati,
00:00:56
ha rivoluzionato la generazione di testo.
00:00:58
Ma senza un team di esperti
00:01:00
per addestrarlo e guidarlo,
00:01:02
non avrebbe raggiunto tali altezze.
00:01:05
Tuttavia, ogni moneta ha due facce.
00:01:10
L'IA generativa non è priva di sfide.
00:01:13
Dati scarsamente preparati possono indurre pregiudizi.
00:01:15
I modelli mal configurati possono essere uno spreco
00:01:18
risorse o producono risultati errati.
00:01:20
E senza un'integrazione ponderata,
00:01:21
L'IA generativa può rivoluzionare più che aiutare.
00:01:24
Ad esempio, abbiamo visto l'IA generare
00:01:27
contenuti offensivi o discriminatori
00:01:29
a causa di distorsioni nei dati di formazione.
00:01:31
Questi errori possono essere gravi
00:01:33
ripercussioni etiche e sociali,
00:01:35
ma c'è speranza.
00:01:37
Adottando le migliori pratiche,
00:01:38
possiamo sfruttare al meglio questa tecnologia.
00:01:42
Inizia con dati rigorosi
00:01:44
raccolta e preparazione,
00:01:45
scegliere il modello giusto per il lavoro giusto.
00:01:49
Convalidare attentamente il
00:01:51
risultati e, soprattutto,
00:01:52
integrare l'intelligenza artificiale in una strategia globale
00:01:55
considerando le implicazioni etiche.
00:01:56
Aziende visionarie come NVIDIA
00:01:58
hanno già indicato la strada,
00:02:00
adottando l'intelligenza artificiale generativa per potenziare i propri
00:02:02
offerte rimanendo consapevoli di
00:02:04
i suoi limiti e responsabilità.
00:02:06
Con gli strumenti giusti,
00:02:08
le migliori pratiche e una visione chiara,
00:02:11
l'intelligenza artificiale generativa può essere potente
00:02:13
alleato nella nostra ricerca di innovazione.
00:02:18
Imbarchiamoci insieme
00:02:20
questa emozionante avventura.

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résultat(s)
00:00:05
Генеративный искусственный интеллект находится в
00:00:07
на переднем крае цифровой революции,
00:00:09
создание контента с нуля.
00:00:11
Но как нам в этом разобраться
00:00:13
неизведанная территория? Вместе?
00:00:15
Давайте исследуем секреты
00:00:17
его успех перед тем, как погрузиться в воду.
00:00:19
Очень важно понять
00:00:21
опоры, лежащие в основе этой технологии.
00:00:25
Качество данных — это топливо искусственного интеллекта.
00:00:30
Без точных и разнообразных данных
00:00:32
даже самая лучшая модель может выйти из строя.
00:00:34
Тогда выбор модели
00:00:37
так же важно, как и каждая модель
00:00:39
имеет свои сильные и слабые стороны.
00:00:41
Добавьте к этому надежное оборудование
00:00:43
ресурсы и компетентную команду и
00:00:45
у вас есть рецепт успеха.
00:00:47
В качестве примера возьмем GPT 4 от Open AI.
00:00:53
С миллиардами параметров
00:00:54
работает на петабайтах данных,
00:00:56
он произвел революцию в генерации текста.
00:00:58
Но без команды экспертов
00:01:00
обучать и направлять его,
00:01:02
оно бы не достигло таких высот.
00:01:05
Однако у каждой монеты есть две стороны.
00:01:10
Генеративный искусственный интеллект не лишен проблем.
00:01:13
Плохо подготовленные данные могут вызвать искажения.
00:01:15
Плохо сконфигурированные модели могут привести к потере
00:01:18
ресурсы или давать неверные результаты.
00:01:20
И без продуманной интеграции
00:01:21
Генеративный искусственный интеллект может не только помочь, но и помешать.
00:01:24
Например, мы видели, как искусственный интеллект генерирует
00:01:27
оскорбительный или дискриминационный контент
00:01:29
из-за искажений в данных обучения.
00:01:31
Эти ошибки могут иметь серьезные последствия
00:01:33
этические и социальные последствия,
00:01:35
но надежда есть.
00:01:37
Внедряя передовую практику,
00:01:38
мы можем максимально использовать эту технологию.
00:01:42
Начнем со точных данных
00:01:44
сбор и подготовка,
00:01:45
выбор подходящей модели для подходящей работы.
00:01:49
Тщательная проверка
00:01:51
результаты и, самое главное,
00:01:52
интеграция искусственного интеллекта в общую стратегию
00:01:55
учет этических последствий.
00:01:56
Такие дальновидные компании, как NVIDIA
00:01:58
уже показали путь,
00:02:00
применяя генеративный искусственный интеллект для улучшения своих
00:02:02
предложения, оставаясь в курсе
00:02:04
его ограничения и обязанности.
00:02:06
При наличии подходящих инструментов,
00:02:08
лучшие практики и четкое видение,
00:02:11
генеративный искусственный интеллект может быть мощным
00:02:13
союзник в нашем стремлении к инновациям.
00:02:18
Давайте вместе приступим к
00:02:20
это захватывающее приключение.

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résultat(s)
00:00:05
La inteligencia artificial generativa está en
00:00:07
a la vanguardia de la revolución digital,
00:00:09
crear contenido desde cero.
00:00:11
Pero, ¿cómo manejamos esto
00:00:13
¿territorio inexplorado? ¿Juntos?
00:00:15
Exploremos los secretos de
00:00:17
su éxito antes de sumergirse.
00:00:19
Es crucial entender el
00:00:21
pilares que sustentan esta tecnología.
00:00:25
La calidad de los datos es el combustible de la IA.
00:00:30
Sin datos precisos y diversos,
00:00:32
incluso el mejor modelo podría fallar.
00:00:34
Entonces la elección del modelo es
00:00:37
tan esencial como cada modelo
00:00:39
tiene sus puntos fuertes y débiles.
00:00:41
Agregue a esto un hardware robusto
00:00:43
recursos y un equipo competente y
00:00:45
tienes la receta del éxito.
00:00:47
Tomemos como ejemplo el GPT 4 de Open AI.
00:00:53
Con sus miles de millones de parámetros
00:00:54
alimentado por petabytes de datos,
00:00:56
ha revolucionado la generación de texto.
00:00:58
Pero sin un equipo de expertos
00:01:00
para entrenarlo y guiarlo,
00:01:02
no habría alcanzado tales alturas.
00:01:05
Sin embargo, cada moneda tiene dos caras.
00:01:10
La IA generativa no está exenta de desafíos.
00:01:13
Los datos mal preparados pueden inducir sesgos.
00:01:15
Los modelos mal configurados pueden ser un desperdicio
00:01:18
recursos o producen resultados incorrectos.
00:01:20
Y sin una integración cuidadosa,
00:01:21
La IA generativa puede perturbar más que ayudar.
00:01:24
Por ejemplo, hemos visto a la IA generar
00:01:27
contenido ofensivo o discriminatorio
00:01:29
debido a sesgos en los datos de entrenamiento.
00:01:31
Estos errores pueden ser graves
00:01:33
repercusiones éticas y sociales,
00:01:35
pero hay esperanza.
00:01:37
Al adoptar las mejores prácticas,
00:01:38
podemos aprovechar al máximo esta tecnología.
00:01:42
Comienza con datos rigurosos
00:01:44
recopilación y preparación,
00:01:45
elegir el modelo correcto para el trabajo correcto.
00:01:49
Validando cuidadosamente el
00:01:51
resultados y, lo que es más importante,
00:01:52
integrar la IA en una estrategia global
00:01:55
teniendo en cuenta las implicaciones éticas.
00:01:56
Empresas visionarias como NVIDIA
00:01:58
ya han mostrado el camino,
00:02:00
adoptando la IA generativa para mejorar sus
00:02:02
ofertas sin dejar de estar al tanto de
00:02:04
sus limitaciones y responsabilidades.
00:02:06
Con las herramientas adecuadas,
00:02:08
mejores prácticas y una visión clara,
00:02:11
La IA generativa puede ser poderosa
00:02:13
aliada en nuestra búsqueda de innovación.
00:02:18
Emprendamos juntos
00:02:20
esta emocionante aventura.

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résultat(s)
00:00:05
Generatieve kunstmatige intelligentie is
00:00:07
de voorhoede van de digitale revolutie,
00:00:09
vanuit het niets inhoud creëren.
00:00:11
Maar hoe gaan we hiermee om?
00:00:13
onbekend terrein? Samen?
00:00:15
Laten we de geheimen van
00:00:17
het is een succes voordat je erin duikt.
00:00:19
Het is cruciaal om inzicht te krijgen in de
00:00:21
pijlers die deze technologie ondersteunen.
00:00:25
Datakwaliteit is de brandstof van AI.
00:00:30
Zonder nauwkeurige en diverse gegevens
00:00:32
zelfs het beste model zou kunnen falen.
00:00:34
Dan is de keuze van het model
00:00:37
net zo essentieel als elk model
00:00:39
heeft zijn sterke en zwakke punten.
00:00:41
Voeg daar robuuste hardware aan toe
00:00:43
middelen en een bekwaam team en
00:00:45
je hebt het recept voor succes.
00:00:47
Neem GPT 4 van Open AI als voorbeeld.
00:00:53
Met zijn miljarden parameters
00:00:54
aangedreven door petabytes aan data,
00:00:56
het heeft een revolutie teweeggebracht in het genereren van tekst.
00:00:58
Maar zonder een team van experts
00:01:00
om het te trainen en te begeleiden,
00:01:02
het zou niet zulke hoogten hebben bereikt.
00:01:05
Elke munt heeft echter twee kanten.
00:01:10
Generatieve AI is niet zonder uitdagingen.
00:01:13
Slecht voorbereide gegevens kunnen leiden tot vooroordelen.
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Slecht geconfigureerde modellen kunnen verspild worden
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middelen of produceer onjuiste resultaten.
00:01:20
En zonder doordachte integratie
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generatieve AI kan meer verstoren dan helpen.
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We hebben bijvoorbeeld AI zien genereren
00:01:27
beledigende of discriminerende inhoud
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vanwege vooroordelen in trainingsgegevens.
00:01:31
Deze fouten kunnen ernstig zijn
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ethische en sociale gevolgen,
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maar er is hoop.
00:01:37
Door de beste praktijken toe te passen,
00:01:38
we kunnen het beste uit deze technologie halen.
00:01:42
Begint met rigoureuze gegevens
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inzameling en bereiding,
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het kiezen van het juiste model voor de juiste taak.
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Zorgvuldig valideren van de
00:01:51
resultaten en vooral
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AI integreren in een algemene strategie
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rekening houdend met ethische implicaties.
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Visionaire bedrijven zoals NVIDIA
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hebben al de weg gewezen,
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het toepassen van generatieve AI om hun
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aanbiedingen terwijl u op de hoogte blijft van
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haar beperkingen en verantwoordelijkheden.
00:02:06
Met het juiste gereedschap
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beste praktijken en een duidelijke visie,
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generatieve AI kan krachtig zijn
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bondgenoot in onze zoektocht naar innovatie.
00:02:18
Laten we samen aan de slag gaan
00:02:20
dit spannende avontuur.

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résultat(s)
00:00:05
Generatywna sztuczna inteligencja jest w
00:00:07
czołówka rewolucji cyfrowej,
00:00:09
Tworzenie treści od podstaw.
00:00:11
Ale jak poruszać się po tym
00:00:13
Niezbadane terytorium? Razem?
00:00:15
Odkryjmy tajemnice
00:00:17
jego sukces przed nurkowaniem.
00:00:19
Ważne jest, aby zrozumieć
00:00:21
filary wspierające tę technologię.
00:00:25
Jakość danych jest paliwem sztucznej inteligencji.
00:00:30
Bez dokładnych i różnorodnych danych,
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Nawet najlepszy model może zawieść.
00:00:34
Wtedy wybór modelu jest
00:00:37
tak samo istotny, jak każdy model
00:00:39
ma swoje mocne i słabe strony.
00:00:41
Dodaj do tego solidnego sprzętu
00:00:43
zasoby i kompetentny zespół oraz
00:00:45
Masz przepis na sukces.
00:00:47
Weźmy jako przykład GPT 4 Open AI.
00:00:53
Z miliardami parametrów
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zasilane przez petabajty danych,
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zrewolucjonizował generowanie tekstu.
00:00:58
Ale bez zespołu ekspertów
00:01:00
trenować i prowadzić go,
00:01:02
Nie osiągnęłaby takiej wysokości.
00:01:05
Jednak każda moneta ma dwie strony.
00:01:10
Generatywna sztuczna inteligencja nie jest pozbawiona wyzwań.
00:01:13
Źle przygotowane dane mogą wywoływać uprzedzenia.
00:01:15
Źle skonfigurowane modele mogą marnować
00:01:18
zasoby lub dają nieprawidłowe wyniki.
00:01:20
I bez przemyślanej integracji,
00:01:21
generatywna sztuczna inteligencja może zakłócać więcej niż pomóc.
00:01:24
Na przykład widzieliśmy generowanie sztucznej inteligencji
00:01:27
treści obraźliwe lub dyskryminujące
00:01:29
z powodu uprzedzeń w danych szkoleniowych.
00:01:31
Błędy te mogą być poważne
00:01:33
konsekwencje etyczne i społeczne,
00:01:35
Ale jest nadzieja.
00:01:37
Przyjmując najlepsze praktyki,
00:01:38
Możemy w pełni wykorzystać tę technologię.
00:01:42
Zaczyna się od rygorystycznych danych
00:01:44
zbieranie i przygotowanie,
00:01:45
wybór odpowiedniego modelu do odpowiedniej pracy.
00:01:49
Ostrożna walidacja
00:01:51
wyniki, a co najważniejsze,
00:01:52
Integracja sztucznej inteligencji do ogólnej strategii
00:01:55
rozważając konsekwencje etyczne.
00:01:56
Wizjonerskie firmy, takie jak NVIDIA
00:01:58
już pokazali drogę,
00:02:00
przyjęcie generatywnej sztucznej inteligencji w celu ulepszenia ich
00:02:02
Oferty, pozostając świadomym
00:02:04
jego ograniczenia i obowiązki.
00:02:06
z odpowiednimi narzędziami,
00:02:08
najlepsze praktyki i jasna wizja,
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generatywna sztuczna inteligencja może być potężna
00:02:13
Sojusznik w naszym dążeniu do innowacji.
00:02:18
Wyruszmy razem
00:02:20
Ta ekscytująca przygoda.

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résultat(s)
00:00:05
A generatív mesterséges intelligencia itt van
00:00:07
a digitális forradalom élvonalát,
00:00:09
tartalom létrehozása a semmiből.
00:00:11
De hogyan navigálhatunk ebben
00:00:13
feltérképezetlen terület? Együtt?
00:00:15
Fedezze fel a titkait
00:00:17
sikerét, mielőtt belemerülne.
00:00:19
Fontos megérteni a
00:00:21
Oszlopok, amelyek támogatják ezt a technológiát.
00:00:25
Az adatminőség az AI üzemanyaga.
00:00:30
Pontos és változatos adatok nélkül,
00:00:32
Még a legjobb modell is kudarcot vallhat.
00:00:34
Ezután a modell kiválasztása
00:00:37
ugyanolyan fontos, mint minden modell
00:00:39
vannak erősségei és gyengeségei.
00:00:41
Adja hozzá ehhez a robusztus hardverhez
00:00:43
erőforrások és hozzáértő csapat és
00:00:45
Megvan a siker receptje.
00:00:47
Vegyük példaként az Open AI GPT 4-et.
00:00:53
Több milliárd paraméterével
00:00:54
petabájt adattal működtethető,
00:00:56
forradalmasította a szöveggenerálást.
00:00:58
De szakértői csapat nélkül
00:01:00
kiképzésére és irányítására,
00:01:02
nem érte volna el ilyen magasságot.
00:01:05
Minden érmének azonban két oldala van.
00:01:10
A generatív AI nem mentes kihívások nélkül.
00:01:13
A rosszul előkészített adatok elfogultságot válthatnak ki.
00:01:15
A rosszul konfigurált modellek pazarolhatnak
00:01:18
erőforrásokat vagy helytelen eredményeket hoz.
00:01:20
Átgondolt integráció nélkül,
00:01:21
A generatív AI többet zavarhat, mint segíthet.
00:01:24
Például láttuk, hogy AI generál
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sértő vagy megkülönböztető tartalom
00:01:29
a képzési adatok elfogultsága miatt.
00:01:31
Ezek a hibák komolyak lehetnek
00:01:33
etikai és társadalmi következmények,
00:01:35
De van remény.
00:01:37
A bevált gyakorlatok elfogadásával,
00:01:38
A legtöbbet hozhatjuk ki ebből a technológiából.
00:01:42
Szigorú adatokkal kezdődik
00:01:44
gyűjtés és előkészítés,
00:01:45
a megfelelő modell kiválasztása a megfelelő munkához.
00:01:49
Gondosan érvényesítjük a
00:01:51
Az eredmények, és ami a legfontosabb,
00:01:52
Az AI integrálása egy átfogó stratégiába
00:01:55
Etikai következmények figyelembevételével.
00:01:56
Vizionális cégek, mint az NVIDIA
00:01:58
már megmutatták az utat,
00:02:00
generatív mesterséges intelligencia alkalmazása a fejlesztés érdekében
00:02:02
ajánlatok, miközben tisztában maradnak
00:02:04
korlátai és felelősségei.
00:02:06
Megfelelő eszközökkel,
00:02:08
legjobb gyakorlatok és egyértelmű elképzelés,
00:02:11
A generatív AI erőteljes lehet
00:02:13
Szövetséges az innováció iránti törekvésünkben.
00:02:18
Induljunk együtt
00:02:20
Ez az izgalmas kaland.

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Mandarine AI: CE QUI POURRAIT VOUS INTÉRESSER

RELATED COURSES This tutorial is part of the following courses

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Part 03 - Case Studies and Analysis

Discover the revolutionary potential of generative AI through diverse and concrete case studies illustrating its impact on various jobs with...

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