Mako AI Days 2024, DAY 1: "The current state of AI" - Piotr Ignaczak, CTO, MakoLab

In this insightful presentation, Piotr Ignaczak, CTO of MakoLab, explores the current state of artificial intelligence (AI), its applications, challenges, and future potential. The talk is part of an event designed to share knowledge, inspire innovation, and encourage collaboration among participants. Below are the key highlights and takeaways from the session:
🟡 AI is both an opportunity and a challenge, requiring careful consideration of its applications and implications.
🟡 Embedding organizational knowledge into AI models enhances their effectiveness and relevance.
🟡 Understanding the root causes of AI project failures is crucial for successful implementation.
🟡 The rapid advancements in AI technology demand a balance between innovation and sustainability.
📌 Key Highlights:
🟡 AI and Custom Software Development:
how MakoLab integrates AI into custom software solutions for clients, emphasizing the importance of leveraging AI to create impactful and meaningful outcomes.
🟡 Understanding the Current State of AI:
a multi-faceted perspective on AI, highlighting its rapid evolution and the need for organizations to adapt and apply it effectively.
🟡 Extracting Insights from Data:
identifying meaningful signals within vast amounts of data and using this understanding to make informed decisions.
🟡 Generative AI and Large Language Models:
the role of Transformer architecture in powering large language models like ChatGPT, showcasing their ability to generate text, maintain context, and perform creative tasks such as writing poetry.
🟡 Challenges in AI Projects:
common reasons for AI project failures and offers strategies to avoid these pitfalls, emphasizing the importance of embedding organizational knowledge into AI models.
🟡 Applications of AI Across Modalities:
examples of versatile AI uses in processing text, images, speech, video, and more, showcasing its potential across various domains.
🟡 Efficiency and Sustainability Concerns:
high computational demands of AI models, raising questions about their efficiency and the need for sustainable energy solutions to support AI infrastructure.
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