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Intuition, creativity, emotion & nuanced understanding remains irreplaceable » Jan Mikolon

Updated: Oct 10, 2023

🔗 Connect with Jan on LinkedIn 🤳

Jan Mikolon is a Senior Advisory Data Scientist and co-technical Lead - at the Center for AI IBM and an External Ph.D. Candidate for Artificial Intelligence. In other words, the right man to talk to us about artificial intelligence in 2023. In addition to accepting the invitation to be a speaker at GoTech World 2023, he also answered a very interesting interview that we invite you to read down below if you want to understand AI better from a genuine and equitable perspective. 🤔

⚙️ Personal approach on AI & generative AI tools

At its essence, artificial intelligence is about machines mimicking human intelligence processes. Think of tasks that require human-like thought – visual perception, decision-making, or speech recognition. AI systems can be trained to handle these tasks, either through predefined rules or by learning from data. Now, when we talk about generative AI tools, we're diving into a specialized part of AI. Imagine an artist with a blank canvas; generative AI is somewhat similar. It creates or "generates" content, whether that's text, images, or even music. It's like having a virtual artist who's learned from countless paintings and can now paint its own masterpiece.

🧑🏻‍💻 The Basics

At its most basic, AI is the ability of a machine to perform tasks that typically require human intelligence. This could be anything from understanding language, recognizing patterns, making decisions, or even learning from experiences. It's not consciousness. Unlike humans, AI doesn't have feelings, emotions, or consciousness. It doesn't "think" or "understand" in the way humans do. It processes data and follows instructions based on its programming or training. It's not infallible.

Just because it's a machine doesn't mean it's always right. AI can make mistakes, especially if it's been fed inaccurate or biased data. It's not magic. While AI can seem miraculous at times due to its capabilities, it's grounded in mathematics, algorithms, and data. It's the result of years of research and development. It doesn't have intent. AI doesn't have desires, goals, or intentions. It operates based on its design and programming. So, when we say an AI "wants" to do something, it's a figure of speech. It doesn't "want" in the human sense.

📈 The benefits

AI has automated many routine tasks, allowing researchers and professionals to focus on more complex aspects of their work. This has boosted productivity in many sectors. In my personal work environment, AI is mainly used to increase the productivity of repetitive programming tasks. Developers can have their unit tests written by the AI to develop solutions faster. In addition, these systems are used as an internal knowledge base to find information faster.

🦾 AI vs Human

The first thing to clarify is that these AI tools are designed to augment human capabilities, not replace them. While ChatGPT can generate content or provide information based on its training, the human touch – intuition, creativity, emotion, and nuanced understanding – remains irreplaceable. One of the most valuable aspects of using tools like ChatGPT is the feedback loop they provide. As humans interact with the AI, the AI learns and refines its responses. Conversely, humans also learn how to better phrase their queries or inputs based on the AI's feedback. However, one concern is the potential over-reliance on AI. It's crucial to strike a balance and ensure that human judgment remains at the forefront. 

🤾🏻‍♂️ AI in real life

One of the standout examples in recent years, particularly in the realm of software development, is GitHub's CoPilot. It's an AI-powered code assistant that provides developers with code suggestions as they type. CoPilot is built on OpenAI's GPT-3 technology, one of the most advanced language models. This allows it to understand code context and provide relevant suggestions. This tool is used by many developers worldwide. Many experts believe that the usage of such tools leads to an efficiency boost of up to 55%.

📎 A glimpse into the future

The energy consumption of training large-scale AI models has been a concern. I anticipate significant advances in both model architectures and hardware tailored for AI that make them more energy efficient. This involves creating compact models without compromising performance and the rise of specialized AI chips and processors that consume less power. While many industries have already begun their AI journey, we'll see a deepening of AI integration.

Traditional sectors like agriculture, manufacturing, and even urban planning will see more AI-driven solutions, from precision agriculture using AI-driven insights to smart city solutions that leverage AI for optimization. While true AGI, an AI that can perform any intellectual task that a human can, might still be some distance away, we'll see incremental steps toward it. This means models that are more adaptable, can learn across a broader range of tasks, and require less specialized training.

As conclusion...

✍🏻 I'd love for everyone to walk away with a clear and balanced perspective on this. The core message is to embrace but with Awareness. In essence, don't fear AI; it's here to be a supportive tool, amplifying human potential. However, always be aware of its implications, ensuring its ethical and correct usage.

✍🏻 The future of AI in business is bright, but like any tool, its positive impact depends on how we choose to wield it.

🔗 Connect with Jan on LinkedIn 🤳

If you want to meet Jan Mikolon make sure to attend this year's GoTech World event on November 8-9.

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