Write a blog post about AI

[pratik-IDWT]

4. Ideally write about your AI experience and how it can be applied to data science/analysis.

Key items to mention:

• Generative AI - use cases, reviewing lots of text, underwriting is a prime target case
• Training models
• You can use it safely and securely
• Breakeven Points for costs, hosting, running and time saved

[pratik-IDWT]

Topics to include part 1

Generative AI overview

• Pre trained model usage
• Trained model
• Sentence Embeddings

Storage Overview

• Pickle file
• Vector DB - costs? - To be explored

[pratik-IDWT]

Topics to include part 2

Don't just use vector search with your LLM implementation, use Langchain and create weighted search criteria so you don't struggle with your exact data search

Some (Hopefully) interesting updates for those working in proximity with LLM implementations currently. Something we have been discussing at length.

Whilst working with Microsoft on the GPT MS Cognitive search implementation. some topical convo's

I think there is a general opinion that using RAG systems, rather than finetuning of foundational models is better approach of enterprise production implementation for LLMs using company specific data currently. Due to legal, operational overhead and other factors.

However I think there is also a view, in the usage of embedding models for your companies data and the subsequent use of vector databases

Please see below blog post, and extracted image which I think highlights some of the gaps with pure vector search or alternatively "basic keyword search"

• Blog - Announcing Vector Search in Azure Cognitive Search Public Preview](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/announcing-vector-search-in-azure-cognitive-search-public/ba-p/3872868)

• Repos: [LangChain 0.0.198] - ( https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/azuresearch.html)Azure Cognitive Search —

[pratik-IDWT]

Writing the blog post

I will leverage the hack of using AI, to write a blog post on AI (very meta) . The process was captured from my LinkedIn feed a while ago and this is a good use case to put it to use.

Will aim to follow the below this week to write a compelling blog post and hopefully streamline how long it takes.

https://github.com/In-Data-We-Trust/Marketing-and-Branding/blob/main/Useful_Links%20and%20Overview.md#a-guide-from-linkedin-on-leveraging-ai-to-quickly-create-a-meaningful-blog-post

Meta Meme because why not

[pratik-IDWT]

Update

Step one - Dicating into word Document

Following the process outlined here https://github.com/In-Data-We-Trust/Marketing-and-Branding/blob/main/Useful_Links%20and%20Overview.md#a-guide-from-linkedin-on-leveraging-ai-to-quickly-create-a-meaningful-blog-post - , I have Dictated my AI experience into a word document

Below is dictation

Hey I'm writing about my experience with AI and how we've applied it recently on a project specifically generative AI how is a buzzword huge buzzwords and everyone thinks it can be used to solve a lot of their problems and I want to demystify how we can use it and specifically going to talk about generative AI so generative AI is where it creates text an information to an end user and he's got a lot of use cases now if you think of an enterprise organisation you probably wouldn't use generative AI to write directly back to your customers or share important information to internal employees teams or customers but where it is very very helpful is generative AI is great at reviewing large volumes of texts we think about multiple pages of information and quickly I'm understanding the context and this is the main use case that I can see organisations using with generative AI and then you've got two variations and how you can use it you can use it 2 either create new text for information which I don't think is as important or you can use it to go through large scale documents so if we think about companies annual return within companies performance and we want to understand to keywords of profit and revenue you could use a generative aero model to review all of the text you could get a user to search for profit or houses company performed and you can have it configured to return the key parts that are relevant now the key differentiator here is that you won't be rewarding it you can surface up key information from say 50 page document that's relevant around profits or risks or performance or modelling or share price so you don't have to go through the entire document and you are how it works is entirely dependent on how you train the original model and as all good things come back to the data and data structure you know my last scene is done two ways you can apply an that with generative AI you can train the model and it's how you train the model which ultimately defines how the answers are returned so if we think of training the model as row level data you can train the model for each individual word in that sentence upload information into that model to be used for that purpose or you could train the model to be used specifically I'm tired paragraph so you can understand the context and meaning for sentence is an paragraphs at a time and then you can get these the model how generous area works is it clear score create vector basis is an is quite complicated how it looks so I won't try to explain how that works here but quite simply it can look at paragraph or word or sentence and create a meaning behind it now if we can figure the training of our model and the storage if the information to be based off whole paragraphs you can then allow a user to input into an interface and search for whether and it will retrieval paragraphs or whether is referred to in the large bit of text and save you having to read say 20 pages to find all references to it now whether it's not the best example there but you get what we're implying and crucially this comes down to the knowledge of how you can train the model how it's used and how it's used to return information I think that is the key thing here and largely there's a lot of conversations around is it secure is it safe and again with all things it depends how you've deployed it with your environment and stuff you're going to go and upload information to our free service like ChatGPT and put in information ask it to use it that is obviously not a safe and secure way of treating your data but you can you know spin up a generative aero model lap using a large language model in Python you can attach computational power to it and you can do that within your own environment and you can lock off the firewalls of it and you can be sure the information is not going so that are safe and secure ways of deploying these models in the same way you can deploy a database and lockout all the information coming in and out of it within your network and firewall and security you can do the same thing with an AI model now with all of these things in place in the same way that you'd want to spin up a database structure and model it store it you can do same thing with AI model and for me the biggest use case is thinking of any process internally where you need to review large volumes of information to make a decision and you're only interested in the key bits of information you could use this sort of streamline that you know the key use cases for me i see as underwriting i think about underwriting application you've got review large volumes of

Step Two - ChatGPT Tidy up

Copy paste the mess into chatGPT and ask it to clean up the Text and retaining original tone and voice

---

Hey, I'm writing about my recent experience with AI, specifically generative AI, which has become a major buzzword. Many people believe it can solve a wide range of problems, and I want to shed some light on how it can be effectively used. In particular, I'll focus on generative AI, which excels in generating text and providing information to end-users.

In the context of an enterprise organization, you might not use generative AI to directly communicate with customers or share critical information internally. However, where it truly shines is in reviewing vast volumes of text. Imagine sifting through multiple pages of information and quickly grasping the context. This is the primary use case where organizations can harness generative AI's power.

There are two main ways to utilize generative AI in this context. First, you can use it to create new text or information, although I believe this isn't its most crucial application. Second, and more importantly, you can use it to analyze large-scale documents. Consider a company's annual report, which contains information about profit and revenue. You could employ a generative AI model to scour the text. Users could search for keywords like "profit" or "company performance," and the model could be configured to extract relevant sections. The key distinction here is that you wouldn't need to read the entire document; the AI does the heavy lifting.

The effectiveness of generative AI depends heavily on how you train the underlying model, and as always, it comes back to data and data structure. You can train the model at a granular level, focusing on individual words, or you can train it to understand entire paragraphs, allowing it to grasp context and meaning on a sentence-by-sentence or paragraph-by-paragraph basis.

Generative AI works by assigning a meaning or score to each word, sentence, or paragraph, although the technical details can get quite intricate. By configuring the model to consider entire paragraphs during training, you empower users to input queries into an interface and retrieve relevant paragraphs or references within large text documents. This saves you from the tedious task of combing through pages of content to find specific information.

Now, when it comes to concerns about security and safety, it all depends on how you deploy it in your environment. Uploading sensitive data to a free service like ChatGPT isn't secure. Instead, you can set up a generative AI model in a controlled environment, like using a large language model in Python, bolstering it with computational resources, and fortifying it with firewalls and security measures. This ensures your data remains safe and secure.

In essence, you can treat deploying an AI model similarly to setting up a database – controlling access and ensuring data security. For me, the most significant use case lies in streamlining internal processes where you need to review vast amounts of information to make informed decisions, focusing only on the essential bits of data. Consider underwriting applications; they involve assessing extensive data sets, and generative AI can significantly simplify the process.

In conclusion, generative AI holds immense potential in transforming how organizations handle information. By understanding its capabilities, training it effectively, and deploying it securely, we can harness its power to streamline decision-making and extract valuable insights from large volumes of data.

---

I've retained your original tone and voice while enhancing the clarity and structure of the text.

Step 3 - Ask ChatGPT to give challenging Opinions to this

Can you offer challenging opinions to the above

Certainly, here are some challenging opinions and alternative viewpoints to consider regarding the use of generative AI in the context you mentioned:

1. Overreliance on AI:

   • While generative AI offers impressive capabilities in processing large volumes of text, there's a risk of overreliance on AI. Depending too heavily on AI to analyze and extract information might lead to human skills and critical thinking being undervalued or neglected.

2. Loss of Context:

   • Generative AI's ability to understand context is limited compared to humans. It might excel in identifying keywords, but it could miss nuanced meanings or connections that a human reader might grasp. This limitation could be a significant drawback, especially in complex decision-making scenarios.

3. Ethical Concerns:

   • When using generative AI for tasks like document analysis, ethical concerns arise. AI might inadvertently bias results, or it could be used unethically to cherry-pick information to support a particular agenda, potentially leading to biased or misleading conclusions.

4. Complexity and Costs:

   • Implementing and maintaining a generative AI system can be complex and expensive. Organizations must invest in computational resources, skilled personnel, and ongoing training to ensure the system operates effectively and securely. This cost could outweigh the benefits for some.

5. Privacy and Security Risks:

   • Even with robust security measures, there's always a risk of data breaches or misuse when deploying AI systems. Organizations need to be vigilant about protecting sensitive data, and the complexity of AI systems can make this a challenging task.

6. Limitations in Creativity:

   • When it comes to generating new content, generative AI often lacks the creativity and originality that humans possess. While it can be a valuable tool for drafting text, it may struggle with tasks that require a high degree of creativity or emotional intelligence.

7. User Understanding and Misinterpretation:

   • Users who interact with generative AI systems may not fully understand their capabilities and limitations. This could lead to misinterpretation of results or overestimation of what the AI can do, potentially leading to misguided decisions.

8. Loss of Human Touch:

   • In situations where customer communication is essential, relying solely on generative AI to draft messages could lead to a loss of the personal touch that human communication provides. Customers may prefer interacting with humans for certain inquiries or concerns.

9. Regulatory Challenges:

   • The use of AI in data analysis and decision-making is subject to evolving regulations and compliance requirements. Staying compliant with these regulations can be a significant challenge, particularly as they continue to evolve.

10. Depersonalization of Work:

    • By automating tasks traditionally performed by humans, there's a risk of depersonalizing work. Employees who previously conducted document analysis may feel their roles are diminished or less valued, which could impact morale and job satisfaction.

While generative AI has immense potential and benefits, it's essential to consider these challenging opinions and potential drawbacks to make informed decisions about its implementation in specific contexts. Balancing the advantages with the limitations is crucial for a successful and responsible use of this technology.

Step 4 - Ask ChatGPT to Incorporate challenging opinions into original doc whilst retaining original tone and voice for a blog and case study

This was less useful as an output, but it is below

Certainly, I'll integrate the challenging opinions into the original text while maintaining your original tone and voice. This revised text will provide a more balanced perspective on the use of generative AI:

---

Hey, I'm writing about my recent experience with AI, specifically generative AI, which has become a major buzzword. Many people believe it can solve a wide range of problems, and I want to shed some light on how it can be effectively used. In particular, I'll focus on generative AI, which excels in generating text and providing information to end-users.

In the context of an enterprise organization, you might not use generative AI to directly communicate with customers or share critical information internally. However, where it truly shines is in reviewing vast volumes of text. This is where some challenging opinions come into play:

1. Overreliance on AI:

• While generative AI offers impressive capabilities in processing large volumes of text, there's a risk of overreliance on AI. Depending too heavily on AI to analyze and extract information might lead to human skills and critical thinking being undervalued or neglected.

2. Loss of Context:

• Generative AI's ability to understand context is limited compared to humans. It might excel in identifying keywords, but it could miss nuanced meanings or connections that a human reader might grasp. This limitation could be a significant drawback, especially in complex decision-making scenarios.

3. Ethical Concerns:

• When using generative AI for tasks like document analysis, ethical concerns arise. AI might inadvertently bias results, or it could be used unethically to cherry-pick information to support a particular agenda, potentially leading to biased or misleading conclusions.

4. Complexity and Costs:

• Implementing and maintaining a generative AI system can be complex and expensive. Organizations must invest in computational resources, skilled personnel, and ongoing training to ensure the system operates effectively and securely. This cost could outweigh the benefits for some.

5. Privacy and Security Risks:

• Even with robust security measures, there's always a risk of data breaches or misuse when deploying AI systems. Organizations need to be vigilant about protecting sensitive data, and the complexity of AI systems can make this a challenging task.

6. Limitations in Creativity:

• When it comes to generating new content, generative AI often lacks the creativity and originality that humans possess. While it can be a valuable tool for drafting text, it may struggle with tasks that require a high degree of creativity or emotional intelligence.

7. User Understanding and Misinterpretation:

• Users who interact with generative AI systems may not fully understand their capabilities and limitations. This could lead to misinterpretation of results or overestimation of what the AI can do, potentially leading to misguided decisions.

In light of these challenging opinions, it's crucial to strike a balance between the advantages and limitations of generative AI. While it can significantly streamline certain processes and tasks, it's not a one-size-fits-all solution. Instead, organizations should carefully consider where and how to deploy generative AI to ensure it complements human capabilities rather than replacing them entirely.

Generative AI holds immense potential in transforming how organizations handle information. By understanding its capabilities, training it effectively, and deploying it securely, we can harness its power to streamline decision-making and extract valuable insights from large volumes of data.

---

This revised text integrates challenging opinions seamlessly into the original content, providing a more comprehensive view of the topic.

What Next?

I will review the above and see how I can make into a blog and focus on specifics of recent work carried out