Exploring Innovation: Applications of Generative AI in Modern Commerce Tutorial

Discover the fascinating world of generative AI and its revolutionary impact on contemporary commerce. This video introduces you to the main concepts and applications of emerging technologies, such as Generative Adversarial Networks (GANs) and Reinforcement Learning. Illustrated with tangible and concrete case studies. Admire how these AI tools generate innovative product designs and advanced customer personalization. We will also explore the challenges and ethical dilemmas of implementing these technologies. Offering a comprehensive overview of the intersection between technological innovation and business strategy in the digital age. Join us in this exploration of the next step in the evolution of commerce.

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Objectifs :

This document aims to explore the transformative power of generative AI in commerce, detailing its mechanisms, applications, benefits, and challenges. It seeks to provide a comprehensive understanding of how generative AI technologies, such as GANs and VAEs, are reshaping the landscape of product design and customer personalization.

Chapitres :

  1. Introduction to Generative AI in Commerce
    Generative AI is revolutionizing the field of commerce by pushing the boundaries of technology and innovation. This document delves into the new frontiers of generative AI, particularly focusing on its transformative capabilities and the role of generative adversarial networks (GANs) in generating unprecedented data.
  2. Understanding Generative Adversarial Networks (GANs)
    GANs consist of two neural networks: the generator and the discriminator. The generator creates images, while the discriminator evaluates them. This process involves the generator producing an image, which the discriminator assesses, leading to adjustments in the generator's parameters for improved output. This concurrent training results in the creation of realistic virtual products, enabling companies to design and preview products in various virtual environments before manufacturing.
  3. AI-Driven Personalization in Commerce
    AI is transforming commerce through personalization by analyzing customer data, including purchase histories and browsing behaviors. This analysis allows AI to understand consumer preferences, generating tailored recommendations and creating personalized products that align closely with individual tastes. This shift from targeting general market segments to addressing individual customers enhances satisfaction and loyalty.
  4. Emerging Technologies in Generative AI
    Beyond GANs, other technologies like Variational Autoencoders (VAEs) are emerging in the generative AI landscape. VAEs can generate new data, such as product designs or marketing strategies, by capturing the statistical essence of training data. They facilitate the exploration of innovative solutions while remaining aligned with existing preferences. Additionally, reinforcement learning techniques can optimize strategies by learning from interactions and adapting to maximize rewards, such as customer conversions.
  5. Concrete Applications of Generative AI in Commerce
    Generative AI is being utilized in various practical applications within commerce. For instance, it can automatically generate product designs based on consumer feedback and preferences, optimizing offerings to meet market expectations. Another application involves using GANs to create virtual product images for online catalogs, enhancing visual appeal and customer engagement.
  6. Challenges and Ethical Considerations
    While the integration of generative AI in commerce offers numerous benefits, it also presents challenges and ethical concerns. Key issues include ensuring that the data used to train AI models is free from bias and regulating the decisions generated by AI. Data quality and reliability are crucial, as AI models depend on the integrity of the data they are trained on. Furthermore, technological acceptance and adaptation require significant investments in both finances and skill development.
  7. Conclusion: The Future of Generative AI in Commerce
    Generative AI is emerging as a significant vector of innovation for the future of commerce. By merging technological advancements with ethical principles, we can envision a future where technology enriches the human experience. The journey towards this future involves addressing the challenges and harnessing the potential of generative AI to create a more personalized and efficient commercial landscape.

FAQ :

What are Generative Adversarial Networks (GANs)?

Generative Adversarial Networks (GANs) are a type of machine learning model that consists of two neural networks, the generator and the discriminator, which work together to create realistic data by competing against each other.

How does AI-driven personalization work in commerce?

AI-driven personalization works by collecting and analyzing customer data, such as purchase histories and browsing behaviors, to understand consumer preferences. This allows companies to generate tailored recommendations and create personalized products that meet individual needs.

What are Variational Autoencoders (VAEs) used for?

Variational Autoencoders (VAEs) are used to generate new data, such as product designs or marketing strategies, by capturing the statistical essence of training data. They help in exploring new ideas while remaining aligned with existing preferences.

What challenges does generative AI face in commerce?

Generative AI faces several challenges, including ethical issues related to data bias, the need for high-quality data, and the requirement for significant investments in technology and skill development for successful implementation.

How can generative AI improve product design?

Generative AI can improve product design by automatically generating designs based on consumer feedback and preferences, allowing companies to optimize their offerings to better meet market expectations.

Quelques cas d'usages :

Automated Product Design Generation

Companies can use generative AI to automatically create product designs based on consumer data, such as feedback and preferences. This approach helps optimize product offerings to align closely with market demands.

Virtual Product Image Creation

Retailers can leverage GANs to generate virtual product images for their online catalogs. This enhances visual appeal and allows customers to better visualize products before making a purchase.

Personalized Marketing Strategies

Businesses can utilize VAEs to develop innovative marketing strategies by analyzing existing data and generating new ideas that resonate with their target audience, thus improving engagement and conversion rates.

Optimizing Customer Conversion Rates

By employing reinforcement learning techniques, companies can iteratively optimize their marketing strategies based on customer interactions, maximizing conversions and sales through data-driven decision-making.

Glossaire :

Generative Adversarial Networks (GANs)

A class of machine learning frameworks where two neural networks, the generator and the discriminator, compete against each other to create realistic data. The generator creates images, while the discriminator evaluates them, leading to improved outputs over time.

Neural Networks

Computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process data and learn patterns through training.

AI-driven Personalization

The use of artificial intelligence to tailor products and services to individual consumer preferences by analyzing data such as purchase history and browsing behavior.

Variational Autoencoders (VAEs)

A type of generative model that learns to encode input data into a compressed representation and then decode it back to generate new data, often used for creating innovative solutions based on existing examples.

Reinforcement Learning

A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards, often used to optimize strategies in various applications.

Data Integrity

The accuracy and consistency of data over its lifecycle, crucial for ensuring the reliability of AI models.

Ethical Issues in AI

Concerns related to the moral implications of AI technologies, including bias in data, decision-making transparency, and the impact of AI on society.

résultat(s)
00:00:05
Technology and innovation? No,
00:00:07
No boundaries. Today, let's discover
00:00:09
the new frontiers of generative
00:00:11
AI and its transformative power
00:00:13
in the field of commerce.
00:00:15
Generative adversarial networks,
00:00:16
better known as giants,
00:00:19
play a crucial role in
00:00:21
generating unprecedented data.
00:00:23
How does it work?
00:00:24
Gans use two neural networks, the generator,
00:00:27
which creates images and the discriminator,
00:00:29
which evaluates them.
00:00:31
The generator produces an image,
00:00:33
the discriminator evaluates it,
00:00:34
and then the generator adjusts its
00:00:37
parameters to improve the next one.
00:00:39
This concurrent training leads
00:00:40
to the creation of realistic
00:00:42
virtual visual products.
00:00:44
Companies use it to design new products,
00:00:46
explore different designs,
00:00:48
and preview products in various virtual
00:00:51
environments before manufacturing.
00:00:53
AI driven personalization
00:00:54
is transforming commerce.
00:00:56
By collecting and analyzing customer
00:00:58
data such as purchase histories,
00:01:00
browsing behaviours,
00:01:01
and feedback,
00:01:02
AI manages to understand consumer
00:01:04
preferences and behaviours.
00:01:06
It can then generate recommendations
00:01:07
or create personalized products that closely
00:01:10
match the tastes and needs of individuals.
00:01:12
Thus, instead of targeting
00:01:14
general market segments,
00:01:15
companies can address each
00:01:17
customer individually,
00:01:18
improving satisfaction and loyalty.
00:01:20
Beyond Gans, other technologies are
00:01:23
emerging in the generative AI landscape.
00:01:25
Take variational auto encoders,
00:01:28
VAES, for example.
00:01:30
VAES can generate new data like product
00:01:33
designs or marketing strategies by capturing
00:01:35
the statistical essence of training data.
00:01:38
They are commonly used to generate
00:01:40
innovative solutions by slightly
00:01:42
transforming existing examples,
00:01:43
allowing exploration of new ideas while
00:01:46
remaining aligned with previous preferences.
00:01:49
Additionally,
00:01:49
reinforcement learning techniques can
00:01:51
be employed to optimize strategies,
00:01:53
learning iteratively from interactions and
00:01:56
adapting strategies to maximize rewards
00:01:58
such as customer conversions or sales.
00:02:01
Now, let's look at concrete cases
00:02:03
of generative AI use in commerce.
00:02:06
Using generative AI,
00:02:07
one can automatically generate
00:02:09
product designs based on consumer data
00:02:11
such as feedback and preferences,
00:02:13
thus optimizing their offer to
00:02:16
precisely meet market expectations.
00:02:18
Another case used Gans to create virtual
00:02:21
product images for their online catalogue,
00:02:24
maximizing visual appeal.
00:02:25
The integration of generative AI
00:02:27
in commerce brings many benefits,
00:02:29
but it also comes with challenges
00:02:32
and criticisms.
00:02:32
Ethical issues, for example,
00:02:34
are at the forefront.
00:02:36
How can we ensure that the data used
00:02:38
to train AI models is free of bias?
00:02:40
And how are the decisions generated
00:02:42
by AI regulated or controlled?
00:02:45
There is also the challenge of
00:02:47
data quality and reliability.
00:02:48
AI models are only as good as
00:02:50
the data they are trained on,
00:02:51
so ensuring data integrity
00:02:53
and quality is paramount.
00:02:55
Finally, technological
00:02:56
acceptance and adaptation
00:02:58
constitute a barrier requiring
00:03:00
significant investments
00:03:02
both financially and in skill development.
00:03:05
Generative AI is emerging as an
00:03:08
undeniable vector of innovation
00:03:10
for the commerce of tomorrow.
00:03:12
Let's envision this future together,
00:03:13
merging technological advances
00:03:15
and ethical principles to
00:03:17
shape a future where technology
00:03:18
enriches the human experience.

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résultat(s)
00:00:05
Tecnologia e inovação? Não
00:00:07
Sem fronteiras. Hoje, vamos descobrir
00:00:09
As novas fronteiras da geração
00:00:11
A IA e o seu poder transformador
00:00:13
no domínio do comércio.
00:00:15
Redes generativas adversárias,
00:00:16
mais conhecidos como gigantes,
00:00:19
desempenham um papel crucial na
00:00:21
gerando dados sem precedentes.
00:00:23
Como funciona?
00:00:24
Gans usam duas redes neurais, o gerador,
00:00:27
que cria imagens e o discriminador,
00:00:29
que os avalia.
00:00:31
O gerador produz uma imagem,
00:00:33
o discriminador avalia-o,
00:00:34
e, em seguida, o gerador ajusta o seu
00:00:37
parâmetros para melhorar o próximo.
00:00:39
Este treinamento simultâneo leva
00:00:40
para a criação de realistas
00:00:42
produtos visuais virtuais.
00:00:44
As empresas utilizam-no para conceber novos produtos,
00:00:46
explorar diferentes designs,
00:00:48
e pré-visualização de produtos em vários virtuais
00:00:51
ambientes antes da fabricação.
00:00:53
Personalização orientada por IA
00:00:54
está a transformar o comércio.
00:00:56
Através da recolha e análise do cliente
00:00:58
dados como históricos de compras,
00:01:00
comportamentos de navegação,
00:01:01
e feedback,
00:01:02
IA consegue entender consumidor
00:01:04
preferências e comportamentos.
00:01:06
Pode, então, gerar recomendações
00:01:07
ou criar produtos personalizados que se aproximem
00:01:10
corresponder aos gostos e necessidades dos indivíduos.
00:01:12
Assim, em vez de segmentar
00:01:14
segmentos de mercado em geral,
00:01:15
as empresas podem abordar cada
00:01:17
cliente individualmente,
00:01:18
melhorar a satisfação e a fidelização.
00:01:20
Além de Gans, outras tecnologias são
00:01:23
emergindo no cenário da IA generativa.
00:01:25
Pegue codificadores automáticos variacionais,
00:01:28
VAES, por exemplo.
00:01:30
O VAES pode gerar novos dados como o produto
00:01:33
designs ou estratégias de marketing por captura
00:01:35
a essência estatística dos dados de formação.
00:01:38
Eles são comumente usados para gerar
00:01:40
soluções inovadoras da Slightly
00:01:42
transformar exemplos existentes,
00:01:43
permitindo a exploração de novas ideias, enquanto
00:01:46
permanecer alinhado com as preferências anteriores.
00:01:49
Além disso,
00:01:49
As técnicas de aprendizagem por reforço podem
00:01:51
ser empregado para otimizar estratégias,
00:01:53
aprender iterativamente a partir de interações e
00:01:56
adaptar estratégias para maximizar as recompensas
00:01:58
como conversões de clientes ou vendas.
00:02:01
Agora, vejamos casos concretos
00:02:03
do uso generativo de IA no comércio.
00:02:06
Usando IA generativa,
00:02:07
pode-se gerar automaticamente
00:02:09
Designs de produtos baseados em dados do consumidor
00:02:11
tais como feedback e preferências,
00:02:13
otimizando assim a sua oferta para
00:02:16
atender com precisão às expectativas do mercado.
00:02:18
Outro caso usou Gans para criar virtual
00:02:21
imagens de produtos para o seu catálogo em linha,
00:02:24
maximização do apelo visual.
00:02:25
A integração da IA generativa
00:02:27
no comércio traz muitos benefícios,
00:02:29
mas também traz desafios
00:02:32
e críticas.
00:02:32
Questões éticas, por exemplo,
00:02:34
estão na vanguarda.
00:02:36
Como podemos garantir que os dados utilizados
00:02:38
para treinar modelos de IA é livre de preconceitos?
00:02:40
E como são geradas as decisões
00:02:42
por IA regulada ou controlada?
00:02:45
Há também o desafio de
00:02:47
qualidade e fiabilidade dos dados.
00:02:48
Os modelos de IA são tão bons quanto
00:02:50
os dados sobre os quais recebem formação,
00:02:51
garantindo assim a integridade dos dados
00:02:53
e a qualidade é primordial.
00:02:55
Por fim, a tecnologia
00:02:56
aceitação e adaptação
00:02:58
constituem um obstáculo que exige
00:03:00
investimentos significativos
00:03:02
tanto financeiramente como no desenvolvimento de competências.
00:03:05
A IA generativa está a emergir como um
00:03:08
vetor inegável de inovação
00:03:10
para o comércio de amanhã.
00:03:12
Vamos vislumbrar este futuro juntos,
00:03:13
Fusão dos avanços tecnológicos
00:03:15
e princípios éticos para
00:03:17
moldar um futuro onde a tecnologia
00:03:18
enriquece a experiência humana.

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résultat(s)
00:00:05
Tecnologia e innovazione? No
00:00:07
Senza confini. Oggi, scopriamo
00:00:09
le nuove frontiere del generativo
00:00:11
L'IA e il suo potere trasformativo
00:00:13
nel campo del commercio.
00:00:15
Reti antagoniste generative,
00:00:16
meglio conosciuti come giganti,
00:00:19
svolgono un ruolo cruciale in
00:00:21
generazione di dati senza precedenti.
00:00:23
Come funziona?
00:00:24
I GAN utilizzano due reti neurali, il generatore,
00:00:27
che crea le immagini e il discriminatore,
00:00:29
che le valuta.
00:00:31
Il generatore produce un'immagine,
00:00:33
il discriminatore la valuta,
00:00:34
e poi il generatore regola il suo
00:00:37
parametri per migliorare quello successivo.
00:00:39
Questa formazione simultanea porta
00:00:40
alla creazione di qualcosa di realistico
00:00:42
prodotti visivi virtuali.
00:00:44
Le aziende lo utilizzano per progettare nuovi prodotti,
00:00:46
esplora diversi design,
00:00:48
e visualizza in anteprima i prodotti in vari ambienti virtuali
00:00:51
ambienti prima della produzione.
00:00:53
personalizzazione basata sull'intelligenza artificiale
00:00:54
sta trasformando il commercio.
00:00:56
Raccogliendo e analizzando i clienti
00:00:58
dati come la cronologia degli acquisti,
00:01:00
comportamenti di navigazione,
00:01:01
e feedback,
00:01:02
L'intelligenza artificiale riesce a comprendere il consumatore
00:01:04
preferenze e comportamenti.
00:01:06
Può quindi generare raccomandazioni
00:01:07
o crea prodotti personalizzati così da vicino
00:01:10
soddisfare i gusti e le esigenze delle persone.
00:01:12
Quindi, invece di mirare
00:01:14
segmenti di mercato generali,
00:01:15
le aziende possono rivolgersi a ciascuno
00:01:17
cliente individualmente,
00:01:18
miglioramento della soddisfazione e della fidelizzazione.
00:01:20
Oltre ai Gans, altre tecnologie sono
00:01:23
emergenti nel panorama dell'IA generativa.
00:01:25
Prendiamo gli encoder automatici variazionali,
00:01:28
VAES, ad esempio.
00:01:30
VAES può generare nuovi dati come il prodotto
00:01:33
progetti o strategie di marketing acquisendo
00:01:35
l'essenza statistica dei dati di formazione.
00:01:38
Sono comunemente usati per generare
00:01:40
soluzioni innovative di poco
00:01:42
trasformando gli esempi esistenti,
00:01:43
permettendo l'esplorazione di nuove idee mentre
00:01:46
rimanendo in linea con le preferenze precedenti.
00:01:49
Inoltre,
00:01:49
le tecniche di apprendimento per rinforzo possono
00:01:51
essere impiegati per ottimizzare le strategie,
00:01:53
imparando in modo iterativo dalle interazioni e
00:01:56
adattare le strategie per massimizzare i premi
00:01:58
come le conversioni o le vendite dei clienti.
00:02:01
Ora, diamo un'occhiata a casi concreti
00:02:03
dell'uso generativo dell'IA nel commercio.
00:02:06
Utilizzando l'IA generativa,
00:02:07
si può generare automaticamente
00:02:09
progetti di prodotti basati sui dati dei consumatori
00:02:11
come feedback e preferenze,
00:02:13
ottimizzando così la loro offerta a
00:02:16
soddisfare con precisione le aspettative del mercato.
00:02:18
Un altro caso ha utilizzato Gans per creare ambienti virtuali
00:02:21
immagini dei prodotti per il loro catalogo online,
00:02:24
massimizzare l'appeal visivo.
00:02:25
L'integrazione dell'IA generativa
00:02:27
nel commercio porta molti vantaggi,
00:02:29
ma comporta anche delle sfide
00:02:32
e critiche.
00:02:32
Questioni etiche, ad esempio
00:02:34
sono in prima linea.
00:02:36
Come possiamo garantire che i dati utilizzati
00:02:38
addestrare modelli di intelligenza artificiale è privo di pregiudizi?
00:02:40
E come vengono generate le decisioni
00:02:42
dall'IA regolamentata o controllata?
00:02:45
C'è anche la sfida di
00:02:47
qualità e affidabilità dei dati.
00:02:48
I modelli di intelligenza artificiale sono validi solo quanto
00:02:50
i dati su cui vengono addestrati,
00:02:51
garantendo così l'integrità dei dati
00:02:53
e la qualità è fondamentale.
00:02:55
Infine, tecnologico
00:02:56
accettazione e adattamento
00:02:58
costituiscono una barriera che richiede
00:03:00
investimenti significativi
00:03:02
sia finanziariamente che nello sviluppo delle competenze.
00:03:05
L'IA generativa sta emergendo come
00:03:08
innegabile vettore di innovazione
00:03:10
per il commercio di domani.
00:03:12
Immaginiamo questo futuro insieme,
00:03:13
fusione dei progressi tecnologici
00:03:15
e principi etici per
00:03:17
plasmare un futuro in cui la tecnologia
00:03:18
arricchisce l'esperienza umana.

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résultat(s)
00:00:05
Технологии и инновации? Нет,
00:00:07
Нет границ. Сегодня давайте узнаем
00:00:09
новые горизонты генеративного
00:00:11
Искусственный интеллект и его преобразующая сила
00:00:13
в сфере торговли.
00:00:15
Генеративные состязательные сети,
00:00:16
более известные как гиганты,
00:00:19
играют решающую роль в
00:00:21
генерирование беспрецедентных данных.
00:00:23
Как это работает?
00:00:24
Ганы используют две нейронные сети, генератор,
00:00:27
который создает изображения и дискриминатор,
00:00:29
который оценивает их.
00:00:31
Генератор создает изображение,
00:00:33
дискриминатор оценивает его,
00:00:34
а затем генератор настраивает свой
00:00:37
параметры для улучшения следующего.
00:00:39
Это параллельное обучение приводит
00:00:40
к созданию реалистичного
00:00:42
виртуальные визуальные продукты.
00:00:44
Компании используют его для разработки новых продуктов,
00:00:46
изучите различные дизайны,
00:00:48
и просматривайте продукты в различных виртуальных форматах
00:00:51
среды перед производством.
00:00:53
Персонализация на основе искусственного интеллекта
00:00:54
трансформирует коммерцию.
00:00:56
Собирая и анализируя клиентов
00:00:58
такие данные, как истории покупок,
00:01:00
поведение при просмотре веб-страниц,
00:01:01
и обратная связь,
00:01:02
Искусственному интеллекту удается понять потребителя
00:01:04
предпочтения и поведение.
00:01:06
Затем оно может генерировать рекомендации
00:01:07
или тщательно создавайте персонализированные продукты
00:01:10
соответствуют вкусам и потребностям людей.
00:01:12
Таким образом, вместо таргетинга
00:01:14
общие сегменты рынка,
00:01:15
компании могут обратиться к каждому
00:01:17
клиент в индивидуальном порядке,
00:01:18
повышение удовлетворенности и лояльности.
00:01:20
Помимо Gans, есть и другие технологии
00:01:23
появляются в мире генеративного искусственного интеллекта.
00:01:25
Возьмите вариационные автоэнкодеры,
00:01:28
Например, VAES.
00:01:30
VAES может генерировать новые данные, такие как продукт
00:01:33
проекты или маркетинговые стратегии путем сбора
00:01:35
статистическая сущность обучающих данных.
00:01:38
Они обычно используются для генерации
00:01:40
немного инновационные решения
00:01:42
преобразование существующих примеров,
00:01:43
позволяя исследовать новые идеи, одновременно
00:01:46
оставаясь в соответствии с предыдущими предпочтениями.
00:01:49
Кроме того,
00:01:49
Методы обучения с подкреплением могут
00:01:51
использоваться для оптимизации стратегий,
00:01:53
итеративное обучение на основе взаимодействий и
00:01:56
адаптация стратегий для максимизации вознаграждений
00:01:58
таких как конверсия клиентов или продажи.
00:02:01
Теперь давайте рассмотрим конкретные случаи
00:02:03
использования генеративного искусственного интеллекта в торговле.
00:02:06
Использование генеративного ИИ,
00:02:07
можно автоматически генерировать
00:02:09
дизайн продукции на основе данных потребителей
00:02:11
такие как отзывы и предпочтения,
00:02:13
таким образом оптимизируя свое предложение до
00:02:16
точно соответствовать ожиданиям рынка.
00:02:18
В другом случае Gans использовался для создания виртуальных
00:02:21
изображения продуктов для их онлайн-каталога,
00:02:24
максимизация визуальной привлекательности.
00:02:25
Интеграция генеративного искусственного интеллекта
00:02:27
в торговле приносит много преимуществ,
00:02:29
но это также сопряжено с проблемами
00:02:32
и критика.
00:02:32
Например, этические вопросы
00:02:34
находятся на переднем крае.
00:02:36
Как мы можем гарантировать, что используемые данные
00:02:38
обучать модели искусственного интеллекта можно без предвзятости?
00:02:40
И как принимаются решения
00:02:42
ИИ регулирует или контролирует?
00:02:45
Существует также проблема
00:02:47
качество и надежность данных.
00:02:48
Модели искусственного интеллекта хороши лишь в том случае, если
00:02:50
данные, на которых они обучаются,
00:02:51
таким образом, обеспечение целостности данных
00:02:53
и качество имеет первостепенное значение.
00:02:55
Наконец, технологический
00:02:56
принятие и адаптация
00:02:58
представляют собой барьер, требующий
00:03:00
значительные инвестиции
00:03:02
как в финансовом отношении, так и в развитие навыков.
00:03:05
Генеративный искусственный интеллект развивается как
00:03:08
неоспоримый вектор инноваций
00:03:10
для торговли завтрашнего дня.
00:03:12
Давайте вместе представим себе это будущее,
00:03:13
объединение технологических достижений
00:03:15
и этические принципы к
00:03:17
формировать будущее, в котором технологии
00:03:18
обогащает человеческий опыт.

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