The last few months could easily be described as the “emergence of AI” finally entering the mainstream world and with it the floodgates opened bringing with it a series of exciting things. I have been involved with this area for some time and have provided comments on social media. I thought it might be better to write an article covering various observations in one place. I wrote this article to look at this in an optimistic way and accept the fact that these things are here to stay and focus on how they can be harnessed for good rather than cowering in fear or ignoring everything.
There are several important words that you will encounter in this world. A.I or what is called Artificial Intelligence is the ultimate goal of a program or group of programs that can not only behave like humans but also process and think like humans. That’s what we have today GPT or Generative Trained Transformer and LLM (Large Language Models). Then there is Reinforcement Learning From Human Feedback (RLHF) and a series of other keywords such as quantization, tokenization, neural networks, weights and bias in learning models, Natural language processing. Now I don’t want to dive into these terms any further because each term can be expanded to include an entire book or collection of books. I want to focus on how this is shaping our new world. if you’re new to the world but want to dive deeper into the technical side of the equation, I recommend starting with this article by Stephen Wolfram
Large vs. Large Language Models Search Engine
A real shift is occurring in the way we search for information from search engines to interacting with large language models like ChatGPT. Traditionally, the goal of search engines is to be as concise as possible, condensing your query to a few specific keywords to avoid diluting search results. With ChatGPT, we can elaborate more, providing more context to narrow down possible answers. This stems from fundamental differences in how we view these technologies.
Search engines are treated as tools – we direct them with precise instructions, while ChatGPT is seen as a system for understanding the language we use to communicate. A conversational style allows users to provide more details, which in turn helps the AI generate more precise answers. Additionally, search engines have been manipulated over time with SEO strategies, while AI systems have not been widely exploited in the same way. Lastly, while search engines present a variety of options, AI models provide immediate responses, therefore, we strive to ensure our input is as clear and comprehensive as possible to obtain the most accurate output.
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Education and Generative AI
One of the main areas where large language models can revolutionize operations is the world of academia. Rather than continuing to use conventional teaching methods, educators can harness the potential of tools like ChatGPT to enhance student learning. By encouraging students to explore topics using ChatGPT, teachers can change the dynamics of the traditional classroom. The first half of the class can involve students sharing their findings from AI-guided research, while the remaining time can be used to verify the accuracy of these findings, clarify doubts, and explain any misunderstandings.
What we need to understand is that although what I am advocating is learning in isolation, one thing I have realized over time is that people like to learn in public but prefer to make mistakes or be made aware of the mistakes they make in private. . It also hurts when a human catches your mistake a lot more than when a program finds your mistake, you fix it and move on. This LLM therefore provides an interesting safe place to play around making mistakes and learning from them.
It is important to understand that AI systems are not perfect – they do not have all the solutions and sometimes struggle to answer certain questions. This is the perfect opportunity for teachers to intervene, by asking thought-provoking questions to assess students’ understanding of a subject, not just their ability to remember facts. This kind of approach leverages the benefits of AI while acknowledging its shortcomings. It’s a balancing act – as the saying goes, AI will be a good tool but not a good one to use.
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Additionally, I would definitely recommend you to watch Sal Khan’s video talking about his adventures with the combination of AI and education.
Privacy and Generative AI
As I suggested above for students, this is an engaging playground for asking questions and getting answers without being judged. This will immediately interest people in using this system for several use cases, including discussing personal problems or trying to replace them as a therapist. Be aware I’m not advocating using AI/LLM to ask anything. You need to exercise caution. Use the system, don’t leak info where you end up being taken advantage of by the system. Interesting reads are these links here, here, this and this.
Another interesting use case I’ve seen people explore is opening their thoughts or notes to AI and trying to gain insights from it. It sounds like a good idea, but keep in mind that the data will be transferred to other parties although OpenAI has assured that the data provided through the API will not be used to train their models and they have provided the option to disable it through the interface, the community needs to exercise caution. What I always say is, “Once data is open, it is considered public.” So decide before you share your data with any public system.
I’m not saying we’re not leveraging AI, what I’m saying is it’s probably not the right time to leverage SaaS APIs to spread your data to others. There’s been a lot of work done to get this system working on individual machines, some of the efforts are listed here: and to list the top two attempts I’ve seen, but there’s a lot of activity going on in this area, so stay tuned for a more solution good and safe will be out soon. is an interesting subReddit to pay attention to because of its innovation and development.
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Rapid Engineering and a change in direction in how we think
I recently read an interesting article by Martin Fowler about his discussions with Xu Hao about the interesting observation that surface is a parallel between rapid engineering and the ability to document thoughts clearly. If we are adept at engineering effective cues, this will inherently increase our competence in clearly documenting our thinking, and vice versa.
This observation underscores the potential benefits of using large language models, namely the potential to improve documentation quality. This has the potential to address a long-standing challenge in the IT industry – the need for better documentation. The connection between rapid engineering and clear documentation of thinking, although not explicitly stated in Fowler’s article, becomes clear upon careful reflection and carries significant implications for IT practice.
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LLMs and what they tell us about the world
Finally, I came to some interesting realizations about LLMs and what this sudden explosion also revealed about how our world works. What matters most is the degree to which our world operates on repetition. The exam essentially tests rote learning and the ability to recall information. Furthermore, intelligence is often equated with the ability to convey information coherently. We are testing memory abilities, which is why computers always seem smarter.
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Large language models like ChatGPT give the illusion of intelligence because of the properties we as humans typically associate with intelligence. This includes eloquent sentences with minimal grammatical errors, words and phrases that seem to make sense, common but overlooked aspects of our environment, and the ability to quote eloquently without having to stick to the original word for word. In essence, the emergence of intelligence in AI largely depends on our perception of intelligence itself.
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I think it might be wiser to say that we all need to adjust our understanding of what we call intelligence, taking advantage of the array of tools and doors of technical innovation that these new technologies are opening up to us all and perhaps multiplying what intelligence brings. unique and “intelligent” human being. How we adapt to these changes and harness their potential will determine their effectiveness. Like all new technologies, the future is uncertain but full of potential.
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