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6 Steps to AI Adoption: Benefits of LLMs & SLMs with Jerome Hardaway and Rob Ocel
Rob Ocel and Jerome Hardaway continue their series on AI adoption by exploring the world of AI, focusing on small language models (SLMs) and large language models (LLMs). They compare the unique capabilities of SLMs against the vast knowledge encompassed by LLMs, and highlight the transformative potential of AI in driving creativity, problem-solving, and user-centric design in technology. SLMs are designed to excel at specific tasks, offering faster processing and cost-effectiveness due to their open-source nature. These models have proven to be invaluable in sectors like finance, where data security is of utmost importance. By leveraging SLMs, organizations can enhance their security measures and protect sensitive information. Moreover, SLMs provide a stepping stone for engineers to adapt to new technologies and incorporate AI into their work, ultimately improving user experiences. On the other hand, LLMs encompass a wide range of knowledge, making them incredibly versatile. These models have the potential to transform industries by providing insights, predictions, and solutions to complex problems. With advancements in AI chip technology by tech giants like Apple, Nvidia, Google, and Meta, LLMs are becoming even more powerful and efficient. Evaluating AI models based on factors like stability and industry support is crucial to harnessing the full potential of LLMs. The conversation also addresses some ethical questions related to AI implementation. While AI brings numerous benefits, concerns about job displacement cannot be ignored. As AI continues to evolve, it is essential to strike a balance between automation and human involvement. Engineers must focus on improving AI sophistication and seamless integration into user interactions, ensuring that AI enhances human capabilities rather than replacing them. Additionally, ethical guidelines and regulations must be established to address potential biases and ensure responsible AI implementation. Download this episode here....
Apr 23, 2024
2 mins
How to Go from Software Engineer to AI Engineer with Shawn Wang
Shawn Wang discusses AI engineering’s potential impact on the software development labor market and developer experience. With the rise of artificial intelligence technologies, there is an increasing demand for professionals who can bridge the gap between AI and software engineering, and engineers are looking around for guidance on how to best learn AI. Shawn emphasizes the importance of acquiring core knowledge and skills in AI engineering, highlighting the need for a systematic approach to education and development. He envisions a future where AI engineering becomes a recognized profession, with specialized training programs and certifications. He further talks about mental models for approaching AI literacy, and skill building. Shawn also discusses the use of advanced AI technology in software development. He showcases tools like GPT Vision, which can automate tasks and generate code efficiently. While acknowledging the potential of AI in enhancing productivity, Shawn emphasizes the need to leverage AI tools to augment human capabilities rather than replace them. Download this episode here....
Apr 17, 2024
1 min