My Key Take Aways from "The AI Bootcamp"

Join me in the AI journey where I share my 6 top lessons learned from completing The AI Bootcamp.

As a business leader, I’m eagerly looking forward to understand the AI technology in order to be able to integrate AI into my strategy plan and to be prepared for the future.

Last year, I took a significant step by enrolling in The AI Bootcamp to accelerate my understanding and hands-on experience with this disruptive technology.

Now that I've completed the course, I'm excited to share my top 6 lessons learned and what’s behind them.

Here’s what:

1. It’s important I understand the AI foundations to generate meaningful use cases.

• To generate meaningful use cases, it's crucial to comprehend the basics of AI. This starts with distinguishing what constitutes AI and what doesn't—systems built on conditional rules, mimicking human domain knowledge, mathematical optimization techniques, basic business intelligence, or static models for one-off predictions without adaptation.

• AI, as a field of study, encompasses various subsets: Machine Learning (ML), Deep Learning (DL), and Generative AI (Gen AI). Key modeling techniques, including Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning, require different algorithms and data types. However, AutoML, as introduced in this newsletter, automates the tedious parts of Machine Learning. 😊

• Deep Learning (DL) is remarkable, mimicking the human brain through Deep Neural Networks. It learns patterns from large datasets using supervised learning, demanding substantial computing power (GPUs).

• Gen AI, the most significant models today, can generate new content (language, image, video, audio) based on what is learned from existing content. This is made possible by Foundation Models that learn patterns from unstructured content with little or no training, employing unsupervised learning

2. AI is only as good as the data it uses.

• There are various types of data—qualitative and quantitative—with several sub-types.

• Data Management and Infrastructure constitute a distinct practice, necessitating specialists to strategically and methodically manage the organisation's data assets. AI technology plays a crucial role in enhancing data quality, analysis, and decision-making.

• Emphasising the significance of the Feature Engineering process, which involves selecting, transforming, creating, and aggregating raw data attributes to enhance ML model performance. AI tools like Akkio or Tabula can simplify this intricate process.

3. Putting AI into Production is complex.

• Deployment stands as the biggest challenge, constituting the focal point where the majority of AI projects encounter difficulties. Challenges encompass computing requirements, migrating models into legacy systems, scalability issues, data drift, model updates, language portability, and managing model versions/configurations during iterations.

• Implementing AI in production is a pivotal stage, necessitating meticulous planning and post-deployment activities for seamless integration.

• It is imperative to comprehend how end-users will interact with the model's predictions, including considerations like frequency, request-based interactions, batch processing, and latency requirements.

• ML Ops facilitates a structured process and governance for the reliable and efficient deployment and maintenance of machine learning models.

• Machine learning models necessitate more frequent updates compared to regular software applications.

4. Not having fear to AI, with a strong focus on ethics and trustworthiness and AI-readiness culture.

• Acknowledging the risks associated with the irresponsible use of AI, it is crucial to confront these concerns with a robust focus on ethics and trustworthiness to effectively address potential challenges.

• Fundamental principles of Ethical AI include: Fairness, Accountability, Transparency, Privacy, and Security.

• Recognising AI as a system meant to enhance human intelligence rather than replace it underscores the emergence of Human-Centered AI (HCAI) as a crucial discipline.

• Companies must foster an AI culture through effective communication, training, employee involvement, surveys, etc., embracing AI as an opportunity and emphasising the augmentation of human abilities instead of replacement.

• The imperative need for Managing Organisational Change is evident for the gradual adoption of AI.

• The use of artificial intelligence in the EU will be regulated by the AI Act, representing the world’s first comprehensive AI law.

5. There will be and increased demand for AI Specialists.

• The accessibility of No-code AI has made AI available to everyone.

• However, whether as consumers or creators, individuals will need to attain mastery in AI.

• New job opportunities are emerging, including roles such as AI Engineers, Head of AI, AI (Agile) Project Managers, Data Engineers, etc.

6. Gen AI is beyond Chat GPT.

• Gen AI surpasses the confines of Chat GPT and LLMs, representing more than a simple chatbot.

• It encompasses the generation of images, empowerment of robots and cars (which are essentially robots), AI Autonomous Agents through reinforcement learning, multimodal AI models learning from more than just text, and more.

• Embrace curiosity about ongoing advancements; the future possibilities are just beginning to unfold.

That’s all for today.

See you next week! 👋

Disclaimer: The information provided in the newsletter and related resources is intended for informational and educational purposes only. It does not constitute professional advice, and any actions taken based on the content are at the reader's discretion.