From Information to Knowledge to Wisdom: Can Ai Save the Day? – Part 1

March 12, 2023

Social Progression in the Age of Personal Computers, a Necessity for Survival. Can Ai save the day? Explained by ChatGPT, from it’s perspective, with prompt engineering, quality control and some minor contributions by all-too-human, Ralph Losey. All images prepared by Dall-E 2 and Losey.

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Introduction

This article will provide an overview of Ralph Losey’s vision of social transformation in the age of computers. He believes we must progress from an information-based society, which was triggered by personal computers, to a knowledge and wisdom-based society. He argues this transition is required to combat the dangers of misinformation and manipulation. AI and other related technologies are, in his view, the key to this transition. If we fail to go beyond our current information culture, the results may be catastrophic.

Over the past generation, computers and the internet have brought about a seismic shift in the world. The rise of personal computing has created a wealth of information previously unimaginable, while the internet has made it possible to share this information easily. This sudden abundance of information is a two-edged sword. While it has brought many benefits, information alone can be dangerous, very dangerous. To truly manage and control the flood of information, we need knowledge and ultimately wisdom.

We must become more thoughtful and knowledgeable to handle the information explosion. Education is critical in this effort. We cannot simply ignore or undo the information age; the genie is out of the bottle. We must quickly become knowledge-based, embracing the goal of personal and collective wisdom to ensure our survival and success as a society. We need to transform from superficially informed individuals with short attention spans, to thoughtful and knowledgeable people who can navigate the complexities of our world. With the right focus and commitment, we can harness the power of computers, Ai and the internet to achieve a brighter future.

Information → Knowledge → Wisdom

The Information → Knowledge → Wisdom theory posits that a technological society progresses in three stages. The first stage is gathering information, which involves collecting raw data from sources like books, websites, and personal experiences. The next stage is transforming this information into knowledge by analyzing and interpreting it to gain a deeper understanding. The ultimate goal is to achieve wisdom, which requires applying our knowledge in a way that is both ethical and effective.

The theory of Information → Knowledge → Wisdom offers a perspective on the profound cultural changes brought about by computer technology. It stresses the significance of critical thinking, analysis, and interpretation in converting raw information into real knowledge. By employing these skills, individuals and society as a whole can gain a more comprehensive understanding of the world, and utilize that knowledge to enhance our lives and, ultimately, preserve our planet.

Failure to swiftly advance towards a knowledge and wisdom-based society could result in being overwhelmed by the flood of unprocessed and inaccurate information. By embracing knowledge and wisdom, we can navigate the intricate and ever-evolving world of the digital age and create a brighter future for ourselves and generations to come.

Danger of Information Without Knowledge and Wisdom

The risk of not moving towards a knowledge and wisdom based society from an information-based culture is the potential for large segments of society to be easily manipulated by false information and lies. When people lack the ability to analyze and interpret information, they become vulnerable to false information, leading to poor decisions and negative consequences such as the breakdown of democracy and justice, as well as issues like inequality and discrimination.

Moreover, the widespread use of technology has made it simpler for people to spread misinformation and propaganda. This can have a severe impact on society, eroding trust in institutions and creating social unrest.

To counteract these dangers, it is critical to stress the significance of progressing from an information-based culture to one that is knowledge and wisdom-based. This requires teaching critical thinking and analytical skills, as well as promoting values like ethics, morality, and common decency. By doing so, we can create a more informed and responsible society that is better prepared to navigate the intricacies of the modern world. Ralph Losey believes that AI is the key to achieving this goal, transitioning from raw information to knowledge and wisdom.

Child’s View Summary

To put the Information → Knowledge → Wisdom theory in simple, child-like terms, and, in Losey’s opinion , so capture its essence, consider this summary.

Information is when we learn new things like reading a book or watching a video. Knowledge is when we use our brains to understand what we learned and remember it. Wisdom is when we make good choices using our knowledge and thinking about what is right and wrong.

It’s important to use our knowledge and wisdom because if we don’t, we might believe things that aren’t true and make bad choices. This can cause problems for ourselves and other people. By using our knowledge and wisdom, we can make good choices and help others too. Ai can help us to do that.

Wisdom is the Ultimate Goal of the Personal Computer Revolution – Not Information, Not Knowledge

The first step in the progression towards a mature society is Information, but it is not the ultimate goal. The transition to a Knowledge-based culture is expected to occur first, in the next fifteen years. However, knowledge alone is also not enough, as an academic world of specialized know-it-alls is also dangerous. This was not the original vision of the first generation of hackers, led by Steve Jobs and Steve Wozniak, who aimed to change the world through personal computers. Jobs’ travels and personal experiences with psychedelics led him to see the potential for personal computers to serve a higher purpose. He recognized the importance of wisdom, having come to terms with death and the meaning of life. Jobs, Wozniak, and other computer enthusiasts worldwide saw computers as a tool for personal use, including for games, art, and education. The business aspect of computers only became dominant later.

Quoting the well-known Stanford commence speech of Steve Jobs in 2005 provides more insights into the wisdom goal:

Remembering that I’ll be dead soon is the most important tool I’ve ever encountered to help me make the big choices in life. Because almost everything – all external expectations, all pride, all fear of embarrassment or failure – these things just fall away in the face of death, leaving only what is truly important. Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart.

Steve Jobs, 2005

The goal of any advanced civilization is Wisdom, not Knowledge. Living your life with awareness and understanding of your own mortality. Living your life with joy, with flow. Just ask Socrates, who boasted of knowing Nothing (as he had transcended that and phased into Wisdom), and the ancient Greeks. They still exemplify our understanding of high culture in the West. In the East just ask Buddha or Lao Tzu. In fact, consult any of the great wisdom traditions, the great religions. It is not enough in any wisdom tradition to know, we must use this knowledge for both personal happiness and the advancement of all Mankind, indeed, for the benefit of all life on Earth.

That is Wisdom – knowledge converted to beneficial action. This is essentially the Zen-like philosophy of Steve Jobs and many others like him. It is not a vision of amassing knowledge, which is often just the dogma generated by another person. It is the wisdom to follow your own inner voice.

Again to quote Job’s commencement speech at Stanford in 2005:

Your time is limited, so don’t waste it living someone else’s life. Don’t be trapped by dogma – which is living with the results of other people’s thinking. Don’t let the noise of other’s opinions drown out your own inner voice. And most important, have the courage to follow your heart and intuition. They somehow already know what you truly want to become. Everything else is secondary.

Steve Jobs, 2005

Wisdom in all true cultures means the freedom to live your own life, to be happy, or at least try. As the American founders put it in the Declaration of Independence, it means to live in accord with certain self-evident truths:

[T]hat all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness.

Declaration of Independence

To be continued. Part Two of this two-part blog is coming soon. Part two will include poetry and well known quotes of T.S. Eliot (1885-1965), who won the Nobel Prize for Literature in 1948. He also mused on the idea of information to knowledge to wisdom.

1 Comment | AI Ethics, Evidence, informaton, knowledge, Lawyers Duties, Metadata, Related Legal Webs, Search, Security, Technology, VENDORS, wisdom | Permalink
Posted by Ralph Losey

Chat GPT Helps Explains My Active Machine Learning Method of Evidence Retrieval

January 28, 2023

Prompt Engineered, Edited and Quality Controlled by Ralph Losey. This essay was co-written by a human and machine with over 80% of the text “generated” by ChatGPT.

Active machine learning is a type of Artificial Intelligence that is used by attorneys to train a computer to find and classify documents relevant to a particular legal issue or lawsuit. The attorney provides the computer with a set of documents that have been identified as targets, such as documents that are relevant or privileged, and the computer uses this information to learn how to identify similar documents on its own. The goal of active machine learning is to automate the process of document review and classification, making it faster and more efficient for attorneys to locate and analyze relevant documents. Ralph Losey has created a whole online course to explain how to do it.

The target documents that are provided by lawyers to the computer for training are typically those that are relevant to a particular legal issue or lawsuit, or that have some other legal classification, such as privilege. The computer uses this information to learn how to identify similar documents on its own, so that it can assist the attorney in locating and analyzing relevant documents more efficiently.

Continuous, Intelligently Spaced Training

“Dall-E 2” depiction of IST using a dreamy art style

Continuous active learning is a machine learning approach in which the model is continuously updated with new data as it becomes available, rather than being trained on a fixed dataset and then deployed. This allows the model to continually improve its accuracy and performance over time. In the training system designed by Ralph Losey the AI Trainer spaces out the training sessions in time to fit the particular challenges of the project and data seen. Losey calls this Intelligently Spaced Training (IST). It is a training system for machine learning models that is designed to fit the circumstances of the case at hand. In this approach, the AI trainer spaces out the training sessions in time, rather than training the model continuously. This allows the trainer to selectively focus on particular aspects of the case and to test the machine’s effectiveness at different stages of the process.

In the context of legal document review, continuous active learning or Losey’s IST, are used to train a machine learning model to identify relevant documents. The attorney provides the model with a set of initial target documents, and then continuously update the model with new documents as they are discovered or become available. The model would use this new information to continually, or even better, intelligently, update and improve its ability to identify relevant documents.

The goal of IST Hybrid Multimodal Predictive Coding 4.0 is to optimize the learning process by allowing the machine to absorb and retain new information more effectively. This can be especially useful in the context of legal document review, where the volume of data can be large and the consequences of missing relevant documents can be significant. By allowing the machine to learn and test its effectiveness in a more structured and deliberate way, IST machine learning can help improve the accuracy and efficiency of the document review process.

One advantage of the ongoing training method is that it allows the model to adapt to changes in the data and remain up-to-date, which can be especially useful in a field like law where the relevant documents and legal issues may change over time, even during the document review project. You often start off with one definition of relevance and end up with another, more highly evolved understanding.

Hybrid Approach to Predictive Coding

There are several advantages to using a balanced hybrid approach that combines the efforts of both humans and computers in legal document review:

  1. Speed: Computers can process and analyze large amounts of data much faster than humans, so using a hybrid approach can help speed up the document review process.
  2. Accuracy: Humans are still generally better at tasks that require judgment, critical thinking, and understanding context, so a hybrid approach can help improve the accuracy of the document review process.
  3. Efficiency: By dividing the work between humans and computers, a hybrid approach can make the document review process more efficient. For example, humans could focus on the most important or complex documents, while the computer handles the more routine or straightforward ones.
  4. Cost-effectiveness: Using a hybrid approach can be more cost-effective than relying solely on human or computer review, as it allows the review process to be completed more quickly and with fewer errors.
  5. Improved collaboration: A hybrid approach can also facilitate better collaboration between humans and computers, as the two can work together to identify and analyze relevant documents more effectively.

Multimodal Approach to Predictive Coding

There are also advantages to a multimodal approach to predictive coding that combines AI search and all other types of search, including keywords and concept search. This is done in order to improve the accuracy and efficiency of the document review process. The basic kinds of search methods are shown in the pyramid. By using a combination of methods you can more effectively save on costs and implement a bottom line driven proportional strategy,

Some specific advantages of the multimodal approach include:

  1. Greater coverage: By using multiple search methods, a multi-modal approach can provide a more comprehensive search of the document set, increasing the chances of finding relevant documents.
  2. Improved accuracy: Different search methods can have different strengths and weaknesses, so using multiple search methods can help improve the overall accuracy of the search. This can be especially important in legal contexts, where the consequences of missing relevant documents can be significant.
  3. Reduced bias: Different search methods can also be subject to different types of bias. Using a multi-modal approach can help reduce the impact of these biases, as the different search methods may produce different results.
  4. Enhanced efficiency: By using multiple search methods, a multi-modal approach can help make the document review process more efficient. For example, humans could focus on reviewing the most relevant documents identified by the search, while the computer handles the less relevant ones.
  5. Improved collaboration: A multi-modal approach can also facilitate better collaboration between humans and computers, as the two can work together to identify and analyze relevant documents more effectively.

Advantages of a Hybrid Multimodal Approach to Predictive Coding

Some specific advantages of Ralph Losey’s methods and team approach to predictive coding include:

  1. Improved accuracy: By allowing the machine to learn and test its effectiveness in a more structured and deliberate way, Losey’s methods can help improve the accuracy of the document review process.
  2. Enhanced efficiency: The approach also helps make the document review process more efficient by allowing the machine to focus on the most important or complex documents, while humans handle the more routine or straightforward ones.
  3. Reduced bias: Losey’s multimodal methods can help reduce the impact of bias in the document review process by allowing the machine to learn from a more diverse and representative sample of documents.
  4. Improved collaboration: A hybrid, multi-modal, IST approach can also facilitate better collaboration between humans and computers, as the two can work together to identify and analyze relevant documents more effectively.
  5. Greater flexibility: Hybrid IST can also be more flexible than continuous active learning, as it allows the trainer to customize the learning process to fit the specific needs and circumstances of the case.
  6. Improved accuracy: By allowing the machine to learn and test its effectiveness in a more structured and deliberate way, IST can help improve the accuracy of the document review process.
  7. Enhanced adaptability: The IST approach to continuous active learning can also help the machine adapt to changes in the data or the legal landscape, as the trainer can update the training sessions as needed. This can help ensure that the machine remains up-to-date and relevant.
  8. Enhanced collaboration: The hybrid multi-modal IST approach also allows for better collaboration between humans and computers, as the trainer can work with the machine to identify and analyze relevant documents more effectively.
  9. Cost-effectiveness: Overall, the hybrid multi-modal IST approach can be more cost-effective than other methods, as it allows the review process to be completed more quickly and with fewer errors.

Chat-GPT’s “Easy Sales-type Language” Conclusion

Generated by Dall-E 2

The hybrid, multimodal, continuous active learning method is a dream team approach. It combines the speed and efficiency of computers with the judgment and critical thinking of humans, and it’s always learning and improving. This means that you get the best of both worlds: a super-accurate and up-to-date search that can find every last relevant document of importance, without missing a beat. Plus, because it’s continuously learning, it can adapt to changes in your case or the legal landscape, ensuring that you always have the most current and accurate information at your fingertips.

Using hybrid multimodal predictive coding search is like having a diverse team of genius attorneys, techs and paralegals working on your case. This super team can scour every nook and cranny of your document set, using every trick in the book (and a few that haven’t been invented yet) to find that crucial piece of evidence you need to win your case. Plus, they’re completely unbiased and super efficient, so you can save time and money while getting the best possible result. Who wouldn’t want them on their side?

1 Comment | AI Ethics, Evidence, Lawyers Duties, Review, Search, Technology, VENDORS | Permalink
Posted by Ralph Losey

The GPT-3 “AI” Explains Itself to Tech-Savvy e-Discovery Lawyers and Shows a Few Possible Applications

January 10, 2023

Article and Illustrations Written by GPT-3 as Generated by Human Attorney, Ralph Losey

Ralph Losey 2022 AI

GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art natural language processing (NLP) model developed by OpenAI. It is trained on a massive dataset of over 8 billion words. This training allows it to generate human-like text that can be used for various language-based tasks, such as language translation, summarization, question answering (and writing this blog).

GPT-3 could be used in the legal field in the process of electronic discovery. E-discovery refers to the process of identifying, collecting, and producing electronically stored information (ESI) in response to a request for production in a legal case.

Here are examples of how GPT-3 could potentially be used in e-discovery:

AI Generated image re AI doc classification
  1. Document review: GPT-3 could be used to review and classify large volumes of ESI, such as emails and documents, in order to identify relevant information and reduce the burden on human reviewers. AI systems can also be used to identify patterns and trends in data that might not be immediately apparent to human reviewers. For example, an AI system might be able to identify connections between different pieces of data, such as a common sender or recipient, or a particular keyword that appears repeatedly in a group of documents. This can help legal teams identify relevant information more quickly and efficiently, and potentially uncover new leads or evidence that might not have been discovered through manual review.
  2. Predictive coding: GPT-3 could be used to assist with predictive coding, a process that uses machine learning algorithms to identify relevant ESI based on examples provided by human reviewers. By using AI to identify and prioritize relevant documents, legal teams can focus their efforts on the most important and relevant information, rather than having to review every document in a dataset.
  3. Summarization: GPT-3 could be used to generate summaries of large volumes of ESI, making it easier for reviewers to quickly understand the content and identify relevant information.
  4. Language translation: GPT-3 could be used to translate ESI from one language to another, allowing reviewers to more easily review and understand documents written in languages they may not be proficient in.
  5. Question answering: GPT-3 could be used to assist with answering questions related to ESI, such as clarifying the meaning of certain terms or providing context for certain documents.
  6. Legal research: GPT-3 can be used to quickly search through large volumes of legal documents, such as case law or statutes, and provide relevant information to lawyers.
  7. Drafting legal documents: GPT-3 can be used to assist lawyers in drafting legal documents, such as contracts or pleadings, by suggesting language and providing relevant information.
  8. Redaction. GPT-3 can help identify and redact sensitive or privileged information from documents, improving the efficiency of the review process.
  9. Data visualization: GPT-3 tools can help visualize and analyze large volumes of data, making it easier to identify patterns and trends.
AI Generated Art Using “Dall E 2” to Illustrate Predictive Coding in e-Discovery

Overall, GPT-3 has the potential to significantly improve efficiency and accuracy in the e-discovery process by automating certain tasks and assisting with the review and analysis of ESI. GPT-3’s ability to process and generate human-like text makes it a useful tool for lawyers because it can assist with tasks such as legal research, document review, summarization, document drafting, and language translation.

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Posted by Ralph Losey

Back to Where I Once Belonged

January 25, 2022

I’ve escaped the e-Discovery Niche After 15 Years of Super-Specialization

Ralph Losey, January 25, 2022

After fifteen years of writing weekly blogs on e-discovery, I took three years off to focus on implementation of all those words. Now I’m back, back to where I once belonged. Writing again, but writing not just about my Big Law niche, the fun little AI corner that I had painted myself into, but back to writing about ALL of my interests in Law and Technology. That has been my home since I started in legal practice in 1980 and at the same time started coding, mostly games, but also music software, midi creations and law office technology. Proud to recall that I was one of the first computer lawyers in the country. (Also one of the first to get in trouble with the Bar for my Internet Website, FloridaLawFirm.com, which they thought was a television broadcast!)

Ralph in the early 90s

Anyway, when not haggling with the Bar and fellow attorneys who would tease me, the first nerd, and call me a “secretary” (ooh how terrible) for having a keyboard on my desk. I kid you not! I used PCs when they first came out in my law firm as the new associate. I have had them on my desk to try to work smarter ever since. Not PCs necessarily, but all kinds.

So I’m back to where I once belonged, in the great big world of technology law, making deals and giving advice. Oh yeah, I may still consult on e-discovery too, especially the AI parts that so fascinated me ever since my Da Silva Moore breakthrough days. (Thank you Judge Andrew Peck.) For my full story, some of which I had to hide in my Big Law role as a super-specialist, see: https://www.losey.law/our-people/25-uncategorized/108-ralph-losey Not many people know I was a Qui Tam lawyer too; and for both sides.

Wait, there is still more. I’ve left the best for last. I went back home, left Big Law for good, and am now practicing law with my son, Adam Losey, daughter in law, Cat Losey, and thirteen other, crazy tech lawyer types at Losey.law. Yes, that is the real domain name and the name of the firm itself is Losey. So of course I had to go there. Check it out. Practicing law with my son is a dream come true for both of us. I’m loving it. It was lonely being the only tech wiz in a giant firm. Adam knows tech better than me, is much faster in every respect (except maybe doc review with AI) and he and Cat are obviously a lot smarter.

To my long-time readers, thanks for your encouragement. I heard you and got back to my roots of general tech-law, and got back to blogging and home. To quote the Beatles the funny “Get Back” song in their great LET IT BE album:

Rosetta (who are you talking about?) about Sweet Loretta Fart. . . .

Get back, get back

Get back to where you once belonged

Get back, get back

Get back to where you once belonged

Get back Loretta ooh, ooh

Go home

Get Back (Remastered 2009) – YouTube

Stay tuned, because a new blog is coming at you soon. Feel free to drop me an email at Ralph at Losey dot Law. Humans only please. Robots not welcome (unless you’re from the future and don’t have weapons).

4 Comments | AI Ethics, Evidence, Forensic Exam, informaton, knowledge, Lawyers Duties, Metadata, New Rules, Related Legal Webs, Review, Search, Security, Spoliation/Sanctions, Technology, VENDORS, wisdom | Permalink
Posted by Ralph Losey

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