Exploring Innovation: Applications of Generative AI in Modern Commerce Video

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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1224 views

Generative AI
New

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Technology and innovation? No,

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No boundaries. Today, let's discover

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the new frontiers of generative

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AI and its transformative power

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in the field of commerce.

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Generative adversarial networks,

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better known as giants,

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play a crucial role in

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generating unprecedented data.

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How does it work?

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Gans use two neural networks, the generator,

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which creates images and the discriminator,

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which evaluates them.

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The generator produces an image,

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the discriminator evaluates it,

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and then the generator adjusts its

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parameters to improve the next one.

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This concurrent training leads

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to the creation of realistic

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virtual visual products.

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Companies use it to design new products,

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explore different designs,

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and preview products in various virtual

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environments before manufacturing.

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AI driven personalization

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is transforming commerce.

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By collecting and analyzing customer

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data such as purchase histories,

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browsing behaviours,

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and feedback,

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AI manages to understand consumer

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preferences and behaviours.

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It can then generate recommendations

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or create personalized products that closely

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match the tastes and needs of individuals.

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Thus, instead of targeting

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general market segments,

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companies can address each

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customer individually,

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improving satisfaction and loyalty.

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Beyond Gans, other technologies are

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emerging in the generative AI landscape.

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Take variational auto encoders,

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VAES, for example.

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VAES can generate new data like product

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designs or marketing strategies by capturing

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the statistical essence of training data.

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They are commonly used to generate

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innovative solutions by slightly

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transforming existing examples,

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allowing exploration of new ideas while

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remaining aligned with previous preferences.

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Additionally,

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reinforcement learning techniques can

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be employed to optimize strategies,

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learning iteratively from interactions and

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adapting strategies to maximize rewards

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such as customer conversions or sales.

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Now, let's look at concrete cases

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of generative AI use in commerce.

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Using generative AI,

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one can automatically generate

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product designs based on consumer data

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such as feedback and preferences,

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thus optimizing their offer to

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precisely meet market expectations.

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Another case used Gans to create virtual

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product images for their online catalogue,

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maximizing visual appeal.

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The integration of generative AI

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in commerce brings many benefits,

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but it also comes with challenges

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and criticisms.

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Ethical issues, for example,

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are at the forefront.

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How can we ensure that the data used

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to train AI models is free of bias?

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And how are the decisions generated

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by AI regulated or controlled?

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There is also the challenge of

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data quality and reliability.

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AI models are only as good as

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the data they are trained on,

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so ensuring data integrity

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and quality is paramount.

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Finally, technological

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acceptance and adaptation

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constitute a barrier requiring

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significant investments

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both financially and in skill development.

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Generative AI is emerging as an

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undeniable vector of innovation

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for the commerce of tomorrow.

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Let's envision this future together,

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merging technological advances

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and ethical principles to

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shape a future where technology

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enriches the human experience.

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Tecnologia e inovação? Não

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Sem fronteiras. Hoje, vamos descobrir

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As novas fronteiras da geração

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A IA e o seu poder transformador

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no domínio do comércio.

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Redes generativas adversárias,

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mais conhecidos como gigantes,

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desempenham um papel crucial na

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gerando dados sem precedentes.

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Como funciona?

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Gans usam duas redes neurais, o gerador,

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que cria imagens e o discriminador,

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que os avalia.

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O gerador produz uma imagem,

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o discriminador avalia-o,

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e, em seguida, o gerador ajusta o seu

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parâmetros para melhorar o próximo.

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Este treinamento simultâneo leva

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para a criação de realistas

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produtos visuais virtuais.

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As empresas utilizam-no para conceber novos produtos,

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explorar diferentes designs,

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e pré-visualização de produtos em vários virtuais

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ambientes antes da fabricação.

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Personalização orientada por IA

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está a transformar o comércio.

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Através da recolha e análise do cliente

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dados como históricos de compras,

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comportamentos de navegação,

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e feedback,

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IA consegue entender consumidor

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preferências e comportamentos.

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Pode, então, gerar recomendações

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ou criar produtos personalizados que se aproximem

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corresponder aos gostos e necessidades dos indivíduos.

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Assim, em vez de segmentar

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segmentos de mercado em geral,

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as empresas podem abordar cada

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cliente individualmente,

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melhorar a satisfação e a fidelização.

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Além de Gans, outras tecnologias são

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emergindo no cenário da IA generativa.

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Pegue codificadores automáticos variacionais,

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VAES, por exemplo.

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O VAES pode gerar novos dados como o produto

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designs ou estratégias de marketing por captura

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a essência estatística dos dados de formação.

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Eles são comumente usados para gerar

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soluções inovadoras da Slightly

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transformar exemplos existentes,

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permitindo a exploração de novas ideias, enquanto

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permanecer alinhado com as preferências anteriores.

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Além disso,

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As técnicas de aprendizagem por reforço podem

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ser empregado para otimizar estratégias,

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aprender iterativamente a partir de interações e

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adaptar estratégias para maximizar as recompensas

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como conversões de clientes ou vendas.

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Agora, vejamos casos concretos

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do uso generativo de IA no comércio.

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Usando IA generativa,

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pode-se gerar automaticamente

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Designs de produtos baseados em dados do consumidor

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tais como feedback e preferências,

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otimizando assim a sua oferta para

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atender com precisão às expectativas do mercado.

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Outro caso usou Gans para criar virtual

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imagens de produtos para o seu catálogo em linha,

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maximização do apelo visual.

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A integração da IA generativa

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no comércio traz muitos benefícios,

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mas também traz desafios

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e críticas.

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Questões éticas, por exemplo,

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estão na vanguarda.

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Como podemos garantir que os dados utilizados

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para treinar modelos de IA é livre de preconceitos?

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E como são geradas as decisões

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por IA regulada ou controlada?

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Há também o desafio de

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qualidade e fiabilidade dos dados.

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Os modelos de IA são tão bons quanto

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os dados sobre os quais recebem formação,

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garantindo assim a integridade dos dados

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e a qualidade é primordial.

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Por fim, a tecnologia

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aceitação e adaptação

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constituem um obstáculo que exige

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investimentos significativos

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tanto financeiramente como no desenvolvimento de competências.

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A IA generativa está a emergir como um

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vetor inegável de inovação

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para o comércio de amanhã.

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Vamos vislumbrar este futuro juntos,

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Fusão dos avanços tecnológicos

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e princípios éticos para

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moldar um futuro onde a tecnologia

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enriquece a experiência humana.

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RELATED COURSES This video is part of the following courses

Part 08 - Generative AI in Commerce

Discover how generative AI is revolutionizing commerce by personalizing customer experiences and optimizing market strategies, while presenting significant ethical challenges.

Your trainer for this course
Cours E-learning

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