My Strategy for Learning AI: A Developer’s Guide Here are my findings so far:
Despite its impressive capabilities, AI is limited in several ways and fundamentally differs from human intelligence. AI models, including advanced ones like GPT-4, operate based on patterns in data they have been trained on, meaning they lack true understanding, consciousness, and emotional awareness. These models can only perform tasks they have been explicitly programmed for and may struggle with novel situations or ambiguous inputs outside their training data. Additionally, AI can be biased, reflecting prejudices present in the training datasets. Unlike humans, AI lacks common sense reasoning, the ability to independently learn from minimal data, and an understanding of context beyond its programming, which constrains its application and reliability in real-world scenarios.
To kickstart my AI development journey, I have utilized GitHub Copilot, Duet AI, and Gemini. GitHub Copilot and Duet AI provide AI-powered coding assistance, helping streamline the development process by suggesting code snippets and automating repetitive tasks. I’ve chosen Gemini as my primary tool for its ease of use on Google Cloud Platform (GCP), enabling seamless integration and deployment of AI models within the GCP ecosystem.
Current Limitations of Consumer AI for Language, Vision, and Sound Language Consumer AI for language processing, such as chatbots and virtual assistants, faces several limitations. These AI systems often struggle with understanding context, sarcasm, and nuanced human emotions, leading to misinterpretations and inappropriate responses. Additionally, they may fail to handle ambiguous queries or generate coherent responses in complex, multi-turn conversations. Language models can also produce biased or harmful content, as they learn from datasets that may contain biased information. Furthermore, these systems require significant computational resources, which can limit their accessibility and responsiveness in real-time applications.
Vision AI for computer vision, used in applications like facial recognition and object detection, also has notable constraints. Vision models can be fooled by changes in lighting, angles, or slight modifications to the objects they are trained to recognize. They often lack the ability to understand context, making them prone to errors in identifying objects in complex scenes. Additionally, these systems can exhibit biases, such as disproportionately misidentifying individuals from certain demographic groups. Real-time processing demands high computational power and advanced hardware, which can be a barrier for widespread consumer adoption.
Sound In the realm of sound, consumer AI applications like voice recognition and speech-to-text systems face challenges in accurately capturing and interpreting speech in noisy environments or from speakers with diverse accents and dialects. These systems can struggle with understanding colloquial language, slang, or rapid speech. Moreover, they often lack the ability to discern speaker intent, which can lead to errors in executing voice commands. The need for high-quality microphones and noise-canceling technology to improve accuracy can also limit the practicality and accessibility of these systems for everyday consumers.
I’ve been exploring the capabilities of AI in various creative and practical tasks. Using AI, I’ve managed to transcribe music, answer questions, draw art, and create MIDI files. While the technology has been helpful, I’ve found the results to be less than great. The music transcriptions often miss nuances, the answers to questions can sometimes lack depth, the AI-generated art doesn’t always match my vision, and the MIDI files require significant tweaking. Although it’s impressive what AI can do, there’s definitely room for improvement. What I did find useful was how quickly AI can find answers to the question within the question. The best application for this is https://www.perplexity.ai/ . OpenArt will generate interesting art and there is a free tier. Suno is fun for generating simple songs including the music and enables you to download and share. Gemini helped find quick coding methods. Much of this text was composed with the help of ChatGPT-4. I have not had a chance to test the midi software
Through step-by-step tutorials on Google Vertex AI , google Colaboratory , Gemini AI, Kaggle and Google Gen AI Startup School I have learned to launch and deploy small python ai Apps.
I have found the Vancouver AI meetup group and have been attending meetings monthly. There is an ecosystem that includes students, teachers, entrepreurs , developers , startup owners and others. It has been a great reasource.
Stay tuned for more granular details on my AI journey.