The document discusses the integration of AI functionalities into Flutter applications, emphasizing their benefits such as enhanced user experience, increased efficiency, and improved accessibility. It outlines key AI functionalities including image and voice recognition, natural language processing, and provides a case study on semantic search. The document also highlights challenges and considerations for successful AI implementation in Flutter apps.

1. Integrating AI
Functionalities in
your Flutter App
Mobile Track

2. Who am I?
Senior Flutter Developer, Jesta IS
CTO and Cofounder, Fertitude.co
7+ years experience as a
Developer.
Founder, Code Clan Nigeria.
Instructor, Teacher.

3. Agenda
1. Why AI in Flutter apps?
2. Key AI Functionalities for Flutter
3. Case Studies - Let’s build.
4. Challenges and Considerations
5. Q and A.

4. Why Flutter?

5. Flutter is an open-source UI software development
toolkit created by Google, known for its ability to
efficiently build natively compiled applications for
mobile, web, and desktop from a single codebase.

6. Why AI in Flutter
app?

7. Why AI in Flutter app?
Integrating AI into Flutter apps offers
benefits such as enhanced user
experience through personalization and
predictive analytics, increased efficiency
via automation and data analysis,
improved accessibility for users with
disabilities, and a competitive edge in the
market with cutting-edge features.
1. Enhanced User Experience
2. Competitive Advantage
3. Automation
4. Improved Accessibility
5. Data Driven insights

8. AI Functionalities in
Flutter

9. Al Functionalities in Flutter
1. Image Recognition and Processing.
- Using packages like TensorFlow Lite or ML Kit
1. Natural Language Processing
- Implementing chatbots or sentiment analysis.
1. Voice Recognition and Synthesis
- Integrating with packages like Speech to Text and Text to
Speech.

10. Case Study
(Semantic search)

11. Semantic Search
Semantic search is a method of retrieving
information from a database or the
internet that aims to understand the intent
and contextual meaning of a user's query,
rather than just matching keywords.
In semantic search, the system attempts
to comprehend the user's query in the
context of the content it's searching
through. This involves understanding the
relationships between words, the meaning
of the query, and the meaning of the
documents being searched.

12. Semantic Search
Source: https://www.pinecone.io/learn/search-with-pinecone/

13. Tools

14. Steps for Semantic Search
1. Take data from the data warehouse(PDF) and generate vector
embeddings using an AI model - Open AI Embeddings.
2. Save those embeddings in Pinecone.
3. Embed queries using the same AI model to create a “query
vector.”
4. Search through Pinecone using the query embedding, and
receive ranked results based on semantic similarity.

15. OpenAI’s text embeddings
measure the relatedness of
text strings

16. Vector Embeddings
Vector embeddings are like special codes that computers use to
understand words and their meanings. These codes help
computers figure out which words are similar and how they are
connected. For example, they can understand that "cat" and
"dog" are related because they are both animals. These special
codes make it easier for computers to understand and work with
language.

17. "The key to artificial
intelligence has always
been the representation." -
Jeff Hawkins

18. Let’s see
some code

19. Challenges and
considerations

20. Some Considerations
1. Start with a well-defined problem
2. Choose the right AI model
3. Optimize for mobile devices
4. Continuously update and improve
5. Can it scale?

21. Resources
https://langchaindart.com/
https://youtube.com/temicodes

22. Thanks for listening