InteliGente

InteliGente is a groundbreaking initiative dedicated to integrating AI in education, fostering equity, and customizing learning experiences for diverse student needs. By empowering students to develop AI solutions for societal good, we're advancing education, democratizing AI, and addressing disparities in technology access, particularly in regions like the Global South. InteliGente has been validated at the São Paulo State College of Technology since 2021. In a short period, the project has shown a substantial impact, ranging from innovative projects to awards.

Technological advancements in Artificial Intelligence (AI) have benefited our society and economy. However, AI development has been concentrated in technologically advanced countries, often overlooking the unique needs and challenges faced by less developed regions, such as the Global South.

SDG 10: Reduced Inequalities — By focusing on the development of AI in regions that are typically underrepresented in technological advancements, this initiative aligns with targets such as 10.1, 10.2 and 10.3. However, within the initiative, there are solutions that address several SDGs. Furthermore, InteliGente aims to increase the positive impact of AI on sustainable development by empowering students with practical applications of AI for social good and ethical knowledge. InteliGente aligns with regional needs and SDGs by addressing local challenges through culturally sensitive, community-driven AI projects, focusing on health, education, and equality, ensuring impactful and sustainable development.

No, the proposal is an educational initiative utilizing AI, not an AI solution or API itself. It focuses on teaching students to develop AI projects, potentially using foundational models. The innovation lies in applying AI to real-world problems, enhancing learning and societal impact. Furthermore, InteliGente teaches participants several guidelines proposed in the literature as a methodology, such as “How to develop and use AI responsibly”, “AI for all”, “AI4People — Good AI Society”, “Ethics of AI (UNESCO)”, among others. We also adopt the principles of Data-Centric AI (DCAI), putting data at the heart of an AI system development process. We also applied the FAIR data principles, guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of data, aiding scientific advancement and promoting.

InteliGente is integrated with the AutoAI-Pandemics hub (http://autoaipandemics.icmc.usp.br/) and the AI4PEP network (https://ai4pep.org/), which is an international partnership covering 16 nations of the Global South, including five African countries, four Asian countries, four from the Middle East and North Africa, and three from Latin America.

To ensure an ongoing and accessible opportunity for participation, registrations for the project are open every semester. This allows for the formation of at least two new groups annually, providing a constant window for interested parties. This regular approach to registration ensures that more students can benefit from the initiative, aligning with the goal of promoting inclusive and continuous education. Thus, upon joining, students have the opportunity to conduct a project/study over up to 18 months in teams. To develop critical thinking, the project will use active methodologies, such as Problem-Based Learning (PBL). Essentially, the project proposes three phases, each lasting six months. This division ensures that students are engaged in a complete cycle of learning, research, and extension, providing a comprehensive educational experience, forming not only qualified professionals but also citizens prepared to contribute positively to society. In summary, in the first phase (Teaching), students are prepared and instructed to understand basic concepts of AI and its responsible and conscious use through study materials, along with discussions with a mediator and peers. At this stage, students are encouraged to identify relevant problems in society that can be solved with AI, such as the Sustainable Development Goals. With the problem selected, another stage of studies and discussions about the theme begins, aiming to refine the focus of the subject and develop the students' skills in argumentation, communication, knowledge, planning, organization, and group work. At the end of this first stage, the group must present answers to various questions, in addition to responding to questions posed by the mediator and peers. In the second phase (Research), the groups dedicate most of their time to studying the implementation of their solutions, learning objectives, and the initial prototyping of the project, with weekly/biweekly discussions with the mediator and peers. This stage promotes a deeper immersion in research, encouraging students to explore and apply theoretical knowledge in practice, as well as developing skills in analysis and problem-solving. In the third phase (Extension), the groups finalize the implementation of the proposed solution and prepare a general presentation of the project, including storytelling and an extended summary/report. At this point, the groups are also encouraged to participate in idea competitions, events, conferences, and community activities to promote the project and connect with other professionals in the field, encouraging networking. Finally, the groups are expected to make the solutions freely available to the public. If the group wishes to start a startup, the students are guided to study theories on how to expand such innovations, seeking to understand which factors influence the adoption of new ideas, such as relative advantage, compatibility, complexity, trialability, and observability.

A list of results and projects can be viewed in the Gallery and News section.

For a better understanding, we have an article published at the following link: InteliGente (Former BioFatecou) - Article.

However, you can also contact us via email: rpbonidia@gmail.com.

The proposal's profitability lies in forming socially conscious citizens, valuing societal impact and ethical education over financial gain, thus redefining success through community betterment and responsible AI use.

16-[Link][Book] CARVALHO, A. C. P. L. F.; MENEZES, A. G.; BONIDIA, R. P. Ciência de Dados - Fundamentos e Aplicações. 1. ed. LTC - Grupo Gen, 2024. v. 1. 376p.

15-[Link][Conference] BONIDIA, Robson Parmezan; CARVALHO, André Carlos Ponce de Leon Ferreira de. BioAutoML: Democratizing Machine Learning in Life Sciences. In: PRÊMIO ARTUR ZIVIANI - CONCURSO DE TESES E DISSERTAÇÕES (DOUTORADO) - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 85-90. ISSN 2763-8987.

14-[Link][Conference] FLORENTINO, Bruno R.; BONIDIA, Robson P.; CARVALHO, André C. P. L. F. de. Breaking Barriers: Democratizing Machine Learning for RNA-Protein Interaction Prediction in Life Sciences. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 7-12. ISSN 2763-8987.

13-[Link][IF 2022: 6.000] Florentino, B. R., Bonidia, R. P., Sanches, N. H., da Rocha, U. N., & de Carvalho, A. C. BioPrediction-RPI: Democratizing the Prediction of Interaction Between Non-Coding RNA and Protein with End-to-End Machine Learning. Computational and Structural Biotechnology Journal, 2024.

12-[Link][IF 2022: 4.100] AVILA SANTOS, ANDERSON P.; DE ALMEIDA, BRENO L. S.; BONIDIA, ROBSON P.; STADLER, PETER F.; STEFANIC, POLONCA; MANDIC-MULEC, INES; ROCHA, ULISSES; SANCHES, DANILO S.; DE CARVALHO, ANDRÉ C.P.L.F. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. Rna Biology, v. 21, p. 1-12, 2024.

11-[Link][Conference] MEDEIROS, A. C. B.; FRANCISCON, J. F. V.; LONGO, J. B. P.; MORAES, M. V. R.; MACEDO, M. O.; BONIDIA, R. P. Grace: Sistema de Recomendação de Currículos com Inteligência Artificial. In: Workshop de Trabalhos de Alunos da Graduação (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 2023, Belo Horizonte - MG. Workshop de Trabalhos de Alunos da Graduação (WTAG), 2023. p. 8-14.

10-[Link][Conference] BERNARDES, D. R.; FRANCISCON, J. F. V.; ARAUJO, F. R.; OLIVEIRA, M. P.; BONIDIA, R. P. Predizendo os Vencedores dos Playoffs: Um Estudo de Caso com Aprendizado de Máquina em Partidas de Futebol Americano. In: Workshop de Trabalhos de Alunos da Graduação (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 2023, Belo Horizonte - MG. Workshop de Trabalhos de Alunos da Graduação (WTAG), 2023. p. 22-28.

9-[Link][Conference] CARDOZO, W. L.; PRADO, R. C.; BONIDIA, ROBSON P. Águeda: Uma Inteligência Artificial para Detecção Precoce do Câncer de Mama. In: 23 Congresso Nacional de Iniciação Científica - CONIC-SEMESP, 2023, Online. Anais CONIC-SEMESP, 2023. p. 1-6.

8-[Link][Conference] BONIDIA, R. P.; SANTOS, A. P. A.; ALMEIDA, B. L. S.; STADLER, P.; ROCHA, U. N.; SANCHES, D. S.; CARVALHO, A. C. P. L. F. BioAutoML: End-to-End Machine Learning Package for Life Sciences. In: 10th FEMS Congress of European Microbiologists, 2023, Hamburg - Germany. 10th FEMS Congress of European Microbiologists, 2023.

7-[Link][Conference] FLORENTINO, B. R.; SANCHES, N. H.; BONIDIA, R. P.; DE CARVALHO, ANDRÉ C. P. L. F. BioPrediction: Democratizing Machine Learning in the Study of Molecular Interactions. In: XX Encontro Nacional de Inteligência Artificial e Computacional, 2023, Belo Horizonte - MG. Anais do XX Encontro Nacional de Inteligência Artificial e Computacional, 2023. p. 525-539.

6-[Link][IF 2021: 13.994] BONIDIA, ROBSON P; SANTOS, ANDERSON P AVILA; DE ALMEIDA, BRENO L S; STADLER, PETER F; DA ROCHA, ULISSES N; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F. BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria. Briefings in Bioinformatics, v. 1, p. 1-13, 2022.

5-[Link][IF 2021: 2.738] BONIDIA, ROBSON P; SANTOS, ANDERSON P AVILA; DE ALMEIDA, BRENO L S; STADLER, PETER F; DA ROCHA, ULISSES N; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F. Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy. Entropy, v. 24, p. 1398, 2022.

4-[Link][IF 2021: 13.994] BONIDIA, ROBSON P; DOMINGUES, DOUGLAS S; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F. MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors. Briefings in Bioinformatics, v. 1, p. 1-10, 2022.

3-[Link][IF 2020: 11.622] BONIDIA, ROBSON P; SAMPAIO, LUCAS D H; DOMINGUES, DOUGLAS S; PASCHOAL, ALEXANDRE R; LOPES, FABRÍCIO M; DE CARVALHO, ANDRÉ C P L F; SANCHES, DANILO S. Feature extraction approaches for biological sequences: a comparative study of mathematical features. Briefings in Bioinformatics, v. 00, p. 1-20, 2021.

2-[Link][IF 2020: 16.971]ALKHNBASHI, OMER S; MITROFANOV, ALEXANDER; BONIDIA, ROBSON; RADEN, MARTIN; TRAN, VAN DINH; EGGENHOFER, FLORIAN; SHAH, SHIRAZ A; ÖZTÜRK, EKREM; PADILHA, VICTOR A; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F; BACKOFEN, ROLF. CRISPRloci:comprehensive and accurate annotation of CRISPR-Cas systems. NUCLEIC ACIDS RESEARCH, v. 1, p. gkab456, 2021.

1-[Link][IF 2019: 3.745] BONIDIA, ROBSON P.; MACHIDA, JAQUELINE SAYURI; NEGRI, TATIANNE C.; ALVES, WONDER A. L.; KASHIWABARA, ANDRE Y.; DOMINGUES, DOUGLAS S.; DE CARVALHO, ANDRE C.P.L.F.; PASCHOAL, ALEXANDRE R.; SANCHES, DANILO S. A Novel Decomposing Model with Evolutionary Algorithms for Feature Selection in Long Non-Coding RNAs. IEEE Access, v. 1, p. 1-15, 2020.

Over the years, our project has achieved significant visibility and engagement through direct interactions, awards, media coverage, and social media outreach. We estimate our total reach as follows:

• Direct Impact: 82,224+ individuals, who have directly engaged with our articles, pages, solutions, books, games, or participated in educational programs and events.
• Indirect Impact: 466,717+ individuals, including those reached through media coverage, social media impressions, and the influence of awards and recognitions. This includes:
  
  
  • 32,000 people reached through awards and recognitions (based on major awards reaching 2,000 people each, and smaller awards reaching 500 people each).
  • 150,000 people reached through media coverage (based on major news outlets reaching 5,000 people each, and smaller outlets reaching 1,000 people each).
  • 284,717 social media impressions, reflecting high visibility of our content across all platforms.
• Total Estimated Impact: 548,941+ individuals, combining both direct and indirect engagement with our project.

This estimate reflects a cautious and conservative approach to calculating reach, ensuring that the numbers are based on tangible data from website analytics, social media platforms, award announcements, and media reports (see https://www.similarweb.com/).

We are proud to announce that InteliGente has delivered over 30,000 hours of learning and engagement through our books, games, projects, and online content. Here is a detailed breakdown of how this number was calculated:

• Books (Physical and Digital):
  
  • Physical Books: 1,050 copies sold; 376 pages/book; partial reading (25% of the book) estimated at 50% and full read 50% = 4,112 hours.
  • E-Books: 325 copies sold; 568 pages/book; partial reading (25% of the book) estimated at 50% and full read 50% = 1,923 hours.
  • Total: 6,035 hours.
• Pages Accessed on MinhaBiblioteca Platform:
  
  • Total pages accessed: 7,844; 1 minute/page = 131 hours.
• Students Engaged in Projects:
  
  • 100+ students; 8 months/student; 4 hours/week = 12,800 hours.
• Games (InteliGenteCards):
  
  • 500 people impacted; 30 minutes/player = 250 hours.
• Website and Article Accesses:
  
  • Web Page: 30,000; 25 seconds/visit (mean - Google Analytics) = 125 hours.
  • Scientific Articles: 42,832; 15 minutes/visit (Considering that reading a scientific article typically takes much more time, it is challenging to accurately estimate how many readers fully engage with the entire content) = 10,708 hours.
  • Total: 10,833 hours.

Grand Total: Combining all these categories, we estimate 30,049 hours of learning and engagement delivered through InteliGente.