PROMPTING CLAUDE TO DISCUSS THE IMPLICATIONS OF GENERATIVE AI (LARGE LANGUAGE MODELS OR LLM) FOR KNOWLEDGE MANAGEMENT

This is my attempt to leverage the large context size in Claude by Anthropic to relate the discipline of knowledge management to newer developments in artificial intelligence. The general narrative about Generative AI is that it is technological discontinuity even compared to other “traditional” machine and deep learning methods. I think we can locate this new technology within a broader frame of knowledge creation and usage. This is not explicit in current discussion about this technology so I thought it would be interesting to also test how an LLM would “connect” two disparate literatures. I used as prompts older articles on knowledge management the latest being written in 2011 combined with the more recent articles on Gen AI (though the one on Software 2.0 is from 2017) which do not mention knowledge management directly.?

After some trial and error prompting, I was able to generate a passable draft albeit with some hallucinations/factual errors, also a bit pedantic for my taste. It can always be edited to a more polished version. The experience made me think of chatting with someone who has read a lot and is able to connect concepts, but who can improve on precision and clarity. Whether LLMs can reason or judge as we do I can leave others to debate; but that something uniquely human (language) suddenly is no longer is, that is the more interesting thought to ponder.?

Knowledge Management: Basic Concepts and Evolution

Knowledge management refers to the process of creating, sharing, using and managing the knowledge and information of an organization. It aims to help organizations generate value from their intellectual and knowledge-based assets. Most companies today depend on the knowledge of their employees and teams to operate. Knowledge management emerged in the 1990s to address this issue.

In the 1990s, knowledge management gained popularity due to the increasing importance of knowledge work and the need to improve knowledge worker productivity. Two seminal books published in 1995 boosted knowledge management. They are “The Knowledge-Creating Company” by Nonaka and Takeuchi and “The Knowledge Advantage” by Karl Sveiby. These books emphasized the importance of managing knowledge as a key intangible asset and a source of competitive advantage.

Knowledge management involves the identification and mapping of knowledge within people, places and processes of an organization. Explicit knowledge refers to knowledge that is codified while tacit knowledge refers to knowledge that resides in people's heads and is hard to articulate. Knowledge management aims to capture and codify tacit knowledge through interviews and knowledge synthetization. Codified knowledge can then be stored in knowledge bases and made accessible to relevant stakeholders. Tacit knowledge remains difficult to capture and codify, hence communities of practices and robust internal networking are important to share such knowledge.

Three types of knowledge conversions are defined:

• Tacit to tacit (socialization): Sharing of knowledge between individuals through discussion and collaborative interactions. Communities of practices can facilitate this process.

• Tacit to explicit (externalization): Codification of tacit knowledge into explicit knowledge such as documents, online videos, podcasts and wikis. This is a key step in knowledge management.

• Explicit to explicit (combination): Combining different sources of explicit knowledge to form new knowledge. This can be done by developing taxonomies, ontologies and knowledge maps.

• Explicit to tacit (internalization): Learning and understanding explicit knowledge to the extent that it becomes tacit. Reading, watching and practicing are ways to internalize explicit knowledge.

The objectives of knowledge management typically include:

• Capturing knowledge for reuse and business continuity - Codify expertise, lessons learned and best practices.

• Creating knowledge for innovation - Facilitate knowledge sharing and collaboration to create new knowledge.

• Connecting people to share knowledge - Develop networks and communities of practice.

• Measuring the value of knowledge assets - Use tools like diagrams, maps, scorecards and dashboards to visualize knowledge flows and metrics.

• Designing a supportive culture and structure - Develop rewards, incentives and frameworks to promote knowledge sharing behaviors. Provide infrastructures like online collaboration tools.

• Integrating knowledge management into key business processes - Embed knowledge sharing and collaboration into the business rhythm and workflows.

Early knowledge management initiatives focused on developing knowledge bases, expertise directories, best practice databases and implementing knowledge management technologies. However, many companies failed to achieve the envisioned benefits due to a lack of consideration for cultural and behavioral changes required. In the 2000s, the focus shifted to nurturing knowledge sharing cultures, developing networks and supporting knowledge workers. Knowledge management integrated itself into HR practices, change management and business processes.

In the 2010s, knowledge management expanded to encompass new concepts like social networks, collaboration, innovation management, organizational learning and talent management. Knowledge management 2.0 leveraged Web 2.0 technologies such as blogs, wikis, social networking platforms and crowdsourcing tools to support connectivity and co-creation. In recent years, knowledge management integrated artificial intelligence to enhance knowledge capture, discovery and personalized knowledge delivery. Knowledge graphs, machine learning and natural language processing enabled new knowledge management capabilities.

Knowledge management as a discipline has evolved over the decades. However, its core philosophy of systematically managing knowledge to drive value remains unchanged. Looking ahead, knowledge management will become increasingly AI-infused while balancing the need to support human connections, networks and talent. Knowledge management will transition into an AI-human partnership to unlock knowledge that fuels business growth.

Large Language Models and Generative AI

Recent years have seen rapid progress in artificial intelligence, especially in the domains of computer vision, natural language processing and machine learning. A key enabler of such progress is the availability of huge datasets that neural networks can learn from, as well as increased computing power, in the form of GPUs, that can train these neural networks. The neural networks and algorithms powering such systems are known as large language models and generative AI.

Large language models refer to neural networks trained on huge text datasets to learn language. They are usually based on the Transformer architecture introduced in 2017. The Transformer consists of an encoder and decoder stack of layers, each containing self-attention and feed-forward layers. The key innovation is the self-attention mechanism that allows each word in a sentence to be represented as a weighted sum of other words in the sentence. This overcomes the fixed dependency between words in traditional recurrent neural networks.

Examples of large language models include OpenAI's GPT-3, Google's T5 and BERT (Bidirectional Encoder Representations from Transformers). These models contain billions of parameters and are trained on datasets containing hundreds of billions of words. Due to their huge size, they exhibit a phenomenon known as broad contextual understanding. This means that they have sufficient parameters and training data to understand language at a broad, general level.

Large language models can generate text, translate between languages, answer questions, summarize text and perform various NLP tasks. They demonstrate strong zero-shot and few-shot learning capabilities, which means that they can perform new tasks without requiring massive amounts of data. This is an important step towards artificial general intelligence.

However, large language models also have significant limitations. They can generate grammatically correct but factually incorrect text. They reflect biases in their training data. Their broad understanding is still narrow compared to human knowledge. Nevertheless, large language models represent a paradigm shift in NLP and software development. They enable new capabilities and experiences powered by artificial intelligence.

Generative AI refers to neural networks that can generate novel data such as images, videos, speech and text. Generative adversarial networks or GANs are a popular form of generative AI. GANs consist of two neural networks - a generator and a discriminator.?

The generator generates new data while the discriminator determines if the data is real or synthetic. By pitting the two networks against each other, the generator is able to generate increasingly realistic data.

Examples of generative AI include:

• GPT-3 (OpenAI): Generates human-like text

• DALL-E and CLIP (OpenAI): Generates images from text and vice versa

• WaveGan (Anthropic): Generates human speech

• ImageGan and VideoGan (NVIDIA): Generates images and videos

• Closed-form math solver (Anthropic): Solves complex math problems in natural language

• MuseNet (OpenAI): Generates classical music

Generative AI demonstrates how models with a large number of parameters trained on huge datasets can achieve human-level performance on creative tasks. The results are fascinating yet sobering. As models become increasingly capable, researchers will need to ensure that they are robust, transparent and aligned with human values. On the other hand, generative AI presents opportunities to augment human creativity and enhance human experiences.

Current and Potential Capabilities of Large Language Models

Large language models developed in recent years have demonstrated a range of capabilities due to their huge size and broad, general understanding of language. Some of the key capabilities include:

• Natural language generation: Large language models can generate coherent paragraphs of text, mimic the style of different authors and compose poems, songs, stories and scripts. While generated text often lacks a coherent narrative over long form, models are generating higher quality text and for a wider range of applications.

• Translation: Large language models trained on multilingual data can translate between dozens of languages. They outperform earlier statistical machine translation models with a single model capable of translating any language pair.

• Summarization: Large language models can analyze long documents and abstract key ideas into shorter summaries while preserving meaning. They are able to capture contextual nuances to generate summaries with high relevance.

• Question answering: With broad knowledge and contextual understanding, large language models can answer questions on any topic with varying degrees of accuracy. They struggle with questions requiring logical reasoning but can leverage information from knowledge bases and their training data to directly answer more straightforward questions.

• Dialogue: Large language models can engage in conversational dialogue, taking turns to respond to user input. However, they often generate inconsistent, contradictory or inappropriate responses due to their lack of long-term contextual cues and inability to reason. Their dialogue abilities remain narrow and limited.

• Few-shot learning: Large language models can perform new tasks that they have not been trained on by using their broad, general knowledge. This ability is known as few-shot or zero-shot learning. For example, a model trained only on classification tasks could perform summarization given just a few examples. This capability suggests that large language models may be able to reach and eventually exceed human-level intelligence. However, their few-shot learning abilities are still quite narrow compared to human intelligence. They cannot readily transfer skills and knowledge across domains the way humans do.

• Personalization: With a large number of parameters, language models can memorize details about individuals to provide personalized responses and recommendations. However, their abilities do not extend far beyond simple recall. They lack the social and emotional intelligence to deeply understand individuals and form truly personal relationships. Significant research in personalization, social intelligence and computational empathy is still required.

Overall, large language models demonstrate a range of fascinating capabilities in image generation, music composition, mathematics and more due to their scalability and general knowledge. However, they remain limited in critical thinking, social skills, common sense reasoning and other higher-level cognitive abilities that humans possess. Exceeding human-level intelligence will require massive scaling far beyond current models in addition to qualitative improvements such as lifelong, multi-modal learning and embodied experiences. nevertheless, large language models have potential to enhance and augment human capabilities through generative, personalized and conversational interactions. With responsible development, they could transform how we live and work in the decades to come.

Comparing Language Models to Traditional Knowledge Management Technologies

Large language models represent a significant evolution from earlier knowledge management technologies in their ability to understand and generate natural language. Some of the main differences include:

• Knowledge bases: Earlier knowledge management systems relied on knowledge bases containing expert curated information and rules. In contrast, large language models learn directly from huge datasets without requiring manual engineering of knowledge bases. However, knowledge bases are more transparent and can provide justifications for their content and responses.

• Search: Knowledge management systems were centered around search to locate and retrieve codified knowledge. Large language models can generate knowledge and directly answers queries using their broad, learned knowledge. However, their knowledge lacks the precision of expert curated information. They also struggle with complex questions requiring logical reasoning that can potentially be addressed by knowledge bases.

• Personalization: Knowledge bases and search technologies provided generic knowledge independent of users. Large language models can memorize personal details to provide personalized responses and recommendations. They open up opportunities for intelligent assistants and bots with rudimentary personalities. However, their personalization abilities are limited to simple recall without a true understanding of users, emotions or relationships.

• Taxonomies: Knowledge in traditional systems was organized through taxonomies and ontologies developed by subject matter experts. In contrast, knowledge in large language models is learned in an unstructured, self-organizing manner. This provides flexibility but makes it difficult to trace how specific knowledge was acquired or assess its accuracy. Taxonomies built by experts likely result in a more coherent body of knowledge.

• Context: Earlier systems struggled with understanding context in language and how knowledge is applied in different contexts. Large language models can understand the broader context in language through their huge datasets and modelling of latent correlations in text. However, their contextual understanding remains limited and often inconsistent due to biases and holes in their training data. They cannot readily transfer knowledge across different contexts the way humans do.

• Natural language interfaces: Knowledge management systems required users to interact through clunky menus, forms and unnatural language interfaces. Large language models enable more natural conversational interfaces for accessing and interacting with knowledge. They open up opportunities for ubiquitous, invisible and ambient knowledge management experiences powered by virtual assistants and voice assistants. However, their natural language abilities are still narrow and unable to match the range of interactive scenarios that humans navigate each day.

In summary, large language models demonstrate significant advantages over traditional knowledge management technologies in scalability, flexibility, natural language understanding, personalization and context. Nevertheless, they continue to lack human-level intelligence with all its nuance, complexity, social skills and multi-dimensional thinking. For the foreseeable future, knowledge management will be most effective through human and AI partnerships, leveraging the complementary strengths of knowledge bases, search, taxonomies, large language models and human experts. As language models continue to advance, knowledge management systems must be designed for meaningful human control and oversight to ensure that knowledge used to inform decisions is held accountable to stakeholders. Achieving the right balance between human and AI will be key to developing knowledge management systems that are useful, transparent and aligned with human values.

Two Possible Futures of Knowledge Management

Looking ahead, I envision two possible futures for knowledge management enabled or driven by large language models and other AI technologies:

Future 1: Knowledge management becomes obsolete. Large language models eventually reach and exceed human-level intelligence across all domains. They can generate any knowledge on demand, answer any question with a high degree of accuracy and have a 360-degree view of organizational knowledge in its many forms. In this scenario, the role of humans is diminished to the point of obsolescence or irrelevance as AI systems far surpass human cognitive abilities. Machines take over the role of identifying, capturing, sharing and applying knowledge to drive business execution and value creation.

This is an unlikely scenario as human judgment, creativity, morality and emotional intelligence will be difficult to replicate in AI for the foreseeable future. Large language models today lack common sense reasoning, emotional skills and higher-level thinking that humans possess. They cannot readily apply knowledge across contexts and domains the way people do based on life experiences, education and intuitions. While machines may take over routine cognitive tasks, knowledge work will largely remain human. Knowledge management aims to augment and empower human capabilities rather than replace them.

Future 2: Large language models enhance knowledge management by overcoming human limitations. AI systems with natural language understanding provide a personalized layer between people and knowledge. They can capture tacit knowledge through conversations, generate new insights by correlating cross-domain knowledge, and recommend relevant and contextual knowledge to individuals. However, human experts, leaders and stakeholders remain actively engaged in validating, interpreting, applying and governing organizational knowledge.

This partnership model leverages the complementary strengths of human and AI. Large language models enhance knowledge accessibility, discovery and capture at scale but rely on human judgment and oversight to ensure organizational knowledge is accurate, relevant and aligned with business values. Knowledge management gets smarter but human capabilities drive how knowledge is identified, validated, shared and applied to generate value.

The second scenario is most likely given the current capabilities and limitations of AI including large language models. Knowledge management will transition into a symbiotic human-AI partnership with each augmenting the other. Large language models enhance knowledge processes and experiences but human experts provide the knowledge, judgment, creativity and governance to fuel value creation. Overall, the future of knowledge management will be powered by human and AI partnerships, not the obsolescence or dominance of either. With responsible development focused on transparency, oversight and governance, large language models can fulfill their potential to raise knowledge management to new heights as an enabler of human progress, creativity and innovation.

In conclusion, knowledge management emerged as a discipline to help organizations systematically manage their knowledge assets for competitive advantage. It evolved from a focus on knowledge capture and codification to nurturing knowledge sharing cultures and networks. In recent years, knowledge management integrated artificial intelligence and new technologies to enhance its vision, capabilities and experiences.

Large language models represent the latest evolution in knowledge management. They introduce natural language understanding, personalization and broad contextual knowledge on an unprecedented scale. However, they remain narrow systems with significant limitations compared to human intelligence. Knowledge management will be most impactful by advancing human and AI partnerships, not the dominance of either. AI can enhance knowledge accessibility, discovery and capture but human judgment is necessary to ensure knowledge is accurate, relevant and aligned with business values.

The future of knowledge management will likely involve human and AI collaboration, with each augmenting the other. Large language models can raise knowledge management to new heights as an enabler of progress but they depend on human oversight and governance to fulfill their potential. Overall, knowledge management continues to be a story of human possibilities and aspirations enabled by technology. Large language models offer a new and promising chapter through enhancing and scaling the human capacity to learn, connect and imagine. With responsible development, they could transform how we identify, share and apply knowledge to solve problems, gain insights and build a better future. But human hearts and minds remain very much at the centre of that story.

Input Sources:

https://www.skyrme.com/kmbasics/evolution.htm (KM)

https://www.skyrme.com/kmarticles/kmoxy.htm (KM)

https://www.skyrme.com/kmpresentations/iis40.htm (KM)

https://deepblue.lib.umich.edu/bitstream/2027.42/35289/1/10113_ftp.pd (KM)

https://karpathy.medium.com/software-2-0-a64152b37c35 (LLM)

https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llm-foundations/(LLM)

https://a16z.com/2023/03/30/b2b-generative-ai-synthai/(LLM)

https://time.com/6274752/ai-health-care/(LLM)