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Giordano B Ferreira
PhD | Artificial Intelligence
Giordano B Ferreira is an Artificial Intelligence Specialist at NeoSpace AI, based in São Paulo, Brazil. He holds a PhD in Artificial Intelligence and has a strong background in computer science, particularly in machine learning and generative AI.
Professional Background
Giordano has been actively involved in exploring advancements in generative AI and has developed proof-of-concept projects to better understand this technology. He has also assisted others in launching their AI applications, focusing on the operational aspects of AI models, such as prompt management and performance evaluation. His work includes the development of Hyla, a platform designed to support prompt engineers and project leaders in deploying generative AI applications more effectively.12
Research Contributions
In addition to his role at NeoSpace AI, Giordano has contributed to academic research in the field of artificial intelligence. His work has been published and cited in various academic platforms, showcasing his expertise in areas such as agent-based modeling and artificial life.34
Contact and Networking
Giordano is active on LinkedIn under the username giordanobsf, where he shares insights on AI advancements and engages with the community.2
Highlights
Uma das coisas que tem me interessado em modelos de IA generativa é sobre formas alternativas do usuário interagir diretamente com os modelos além dos prompts.
Não me leve a mal, não acho que os prompts deixarão de existir. A linguagem natural é nativa do ser-humano, e poder gerar conteúdo à partir de um texto é mágico. Mas novas formas de interface com esses modelos são necessárias para um maior controle sobre o resultado.
Por exemplo, imagine que você gere uma imagem de um elefante com chapéu utilizando um modelo como Stable Diffusion ou Dall-E. Você gostou bastante do elefante e do cenário, mas o chapéu não ficou legal. Você pode criar um novo prompt que irá gerar uma imagem com um chapéu mais interessante, mas você poderá perder o restante da imagem que você gostou. Agora imagine que você tenha alguns botões nos quais você consiga alterar somente o chapéu da imagem, fazendo com que ele se transforme em um sombrero ou mude a cor dele. Na minha opinião, isso abriria uma nova gama de possibilidades e controle sobre esses modelos.
Ontem demos um novo passo em direção de maior entendimento e controle desses modelos. Pesquisadores do time de interpretabilidade da Anthropic publicaram um artigo sobre uma nova forma de identificar e controlar conceitos dentro de um modelo de linguagem de tamanho significativo.
Os autores utilizaram a técnica de "dictionary learning" para identificar milhões de conceitos dentro dos pesos da rede neural. Basicamente, foram identificadas quais partes da rede neural são responsáveis por gerar conceitos como "San Francisco" ou "immunology".
Dessa forma, os autores foram capazes de modificar as respostas do modelo levando em consideração aquele conceito. A imagem abaixo mostra um exemplo do que acontece ao aumentar a ativação do conceito "Golden Gate Bridge". Quando se pergunta ao modelo original qual a sua forma física, ele responde que não tem de fato uma forma física pois é uma inteligência artificial. Porém, ao fazer uma modificação no modelo para amplificar o conceito, o modelo passa a responder como se fosse a própria Golden Gate Bridge.
Além de novas formas de interação com os modelos, este trabalho poderá ser utilizado para aumentar a segurança dos modelos, impossibilitando que determinados conceitos sejam ativados pelos usuários.
O resumo das descobertas e o artigo publicado podem ser encontrados nesse link: https://lnkd.in/dyHDBQzg
#ai #interpretability #llm
Você já ouviu falar de fusão de modelos de linguagem (model merging)? A ideia é poder criar um novo modelo com capacidades de outros models distintos, sem a necessidade de treinar o modelo novamente com dados específicos. Por exemplo, seria possível criar um modelo capaz de resolver problemas matemáticos em Português somente agrupando um modelo treinado com textos em Português com outro treinado a resolver problemas matemáticos.
Por ser uma técnica simples e fácil de experimentar, a Open LLM Leaderboard do Hugging Face está cheia de modelos criados à partir do merge de outros modelos.
Brinquei um pouco com a biblioteca mergekit (a principal biblioteca para model merging) e escrevi um artigo detalhando o que é esta técnica e como você pode começar a criar os seus próprios modelos.
Ainda vejo dificuldades em ver aplicabilidade da técnica em produção, mas estou animado para ver os próximos avanços.
Have you ever heard of language model merging? The idea is to be able to create a new model with the capabilities of other distinct models, without fine-tuning the model again with specific data. For example, it would be possible to create a model capable of solving mathematical problems in Portuguese by simply combining a model trained with texts in Portuguese with another trained to solve mathematical problems.
Due to the simplicity and ease to experiment with, the Open LLM Leaderboard from Hugging Face is full of models created from the merge of other models.
The last couple days I played around a bit with the mergekit library (the main library for model merging) and wrote an article detailing what this technique is and how you can start creating your own models.
However, I still see difficulties in applying this technique in production. I am excited to see the next advances though.
#llm #generativeai #artificialintelligence
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