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Generative AI Knows Nowt: Large Language Models Are Cribbing Content, Not Nicking Knowledge

March 30, 2024
AI, Article, Chatbots
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Generative AI Knows Nowt: Large Language Models Are Cribbing Content, Not Nicking Knowledge

The Handwringing Over ‘Knowledge Theft’

There’s been a lot of handwringing of late about large language models like ChatGPT supposedly ‘nicking’ knowledge and putting human expertise out of business. But that framing completely misunderstands what these models are and how they work. The reality is that generative AI doesn’t actually ‘know’ anything in a human sense – it is simply very good at stitching together and regurgitating content that already exists.

Just Really Good at Patterns, Not Comprehension

Large language models like Claude are trained on a massive corpus of digital text data scraped from the internet and books – everything from websites and Wikipedia entries to social media posts and product reviews. Through a complex machine learning process, the model learns patterns and associations in how this training data is written and structured.

When you query a language model, it doesn’t rely on ‘knowledge’ that it understands like a human would. Rather, it takes the input prompt, finds relevant patterns and sequences in its training data that are statistically associated with that prompt, and stitches together an output response drawing upon that data. It is essentially a sophisticated stochastic parrot operating at scale.

Intellectual Property Pilferers

So in reality, these models aren’t pilfering ‘knowledge’ – they are cribbing content. The training data itself represents the accumulation of human knowledge generation and intellectual labour over decades and centuries. But the models themselves have no conceptual understanding of that knowledge. They are simply very good at mimicking and recombining the written output of human-generated content in new ways, through predictive modelling.

This is why large language models can often give impressively fluent and coherent responses that seem knowledgeable on the surface. But dig a little deeper, and the holes in true comprehension become evident through factual errors, contradictions, incoherent reasoning, and inability to apply real context. Generative AI is just a sophisticated regurgitation engine, not an oracle of true knowledge.

Powerful Utilities, Not Human Replacements

Of course, this doesn’t mean large language models are useless or unimportant. As tools for automating content generation, analysis, and certain task workflows, they are incredibly powerful. But we can choose to retain a clear-eyed view of what they are and what their existence represents. Generative AI may be very impressive technologically, but it is not rendering human knowledge obsolete. Rather, it is highlighting the immense value of the intellectual labour that humans have generated and curated over centuries in creating the very training data that gives language models their capabilities in the first place.

Making Tomorrow’s Big Balls of Mud Today

March 28, 2024
AI, Article, Business, Chatbots, Coding, Paradigm shifts
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Making Tomorrow’s Big Balls of Mud Today

What is a Big Ball of Mud?

In software development, the term “Big Ball of Mud” refers to a system or codebase that has become so tangled, convoluted, and disorganised over time that it becomes increasingly difficult to maintain, modify, or understand. It’s a metaphor for a software product development that started with good intentions but gradually deteriorated into an unstructured mess due to a lack of proper planning, design, and adherence to best practices.

Consequences

The consequences of a Big Ball of Mud can be severe. It hinders productivity, increases technical debt, screws with predictability and schedules, and makes it challenging to introduce new features or fix bugs. Developers often find themselves spending more time trying to understand the existing code than actually writing new code. This can lead to frustration, decreased morale, and a higher risk of introducing further issues.

The Rise of AI-Centric Coding

And a paradigm shift is looming on the horizon – a transition towards AI writing code – and primarily for artificial intelligence (AI) readability and maintainability. While human-readable code has long been the desirable approach, the remarkable advancements in AI technology necessitate a reevaluation of our coding practices and the use of Ai to write code to harness the full potential of these sophisticated tools.

As AI systems become increasingly integrated into software development workflows, the need for code that caters to AIs’ unique strengths becomes paramount. This shift will give rise to coding styles specifically tailored for AI readability and maintainability, encompassing the following characteristics:

Abstraction and Modularisation Paramount

AI systems thrive on highly modularised and abstracted code, where individual components are clearly separated and encapsulated. This coding style will emphasise smaller, self-contained units of code with well-defined interfaces, promoting better organisation and encapsulation, aligning with the strengths of AI systems.

Formalised and Explicit Syntax

In contrast to the conventions and implicit understandings often relied upon by human programmers, AI systems will benefit from a more formalised and explicit syntax. This could involve additional annotations or metadata that make the semantics of the code unambiguous and readily interpretable by AI systems.

Pattern Recognition Optimisation

AI systems excel at recognising patterns, and the coding style will be optimised for this strength. Consistent naming conventions, structural similarities, and other patterns that can be easily recognised by AI systems will become more prevalent, enabling efficient pattern recognition and analysis.

Reduced Redundancy (DRY)

AI systems are better equipped to handle and maintain code with minimal redundancy, leading to a coding style that emphasises code reuse, shared libraries, and other techniques to reduce duplication. This approach will not only cater to AI systems’ strengths but also promote code maintainability and efficiency.

AI-Tailored Documentation

Traditional human-readable documentation and comments may become obsolete in an AI-centric coding paradigm. Instead, the emphasis will shift towards creating self-documenting code that can be seamlessly interpreted and maintained by AI systems. This could involve incorporating structured annotations, metadata, and other machine-readable elements directly into the codebase.

The documentation process itself could be automated, with AI algorithms capable of parsing the code structure, analysing the annotations, and generating comprehensive documentation tailored specifically for AI comprehension. This documentation would be optimised for pattern recognition, logical inference, and other capabilities that AI systems excel at, ensuring that it remains up-to-date and consistent with the evolving codebase.

AI-Generated Code for Machine Consumption

Furthermore, the advancement of AI technology raises the intriguing possibility of AI systems themselves generating code in a style optimised for machine consumption, rather than human readability. This AI-generated code could forgo traditional conventions and practices aimed at enhancing readability for human developers, instead favouring structures and patterns that are more readily interpretable and maintainable by AI systems themselves.

Such AI-generated code might be highly compact, with minimal redundancy and a heavy reliance on abstraction and modularisation. It could incorporate complex mathematical models, advanced algorithms, and unconventional coding techniques that leverage the strengths of AI systems while potentially sacrificing human comprehensibility.

As AI systems become increasingly integrated into the software development lifecycle, they could potentially maintain and evolve this AI-generated code autonomously, with minimal human intervention. This paradigm shift could lead to a scenario where the primary consumers and maintainers of code are AI systems themselves, rather than human developers.

Factors Contributing to Big Balls of Mud

While embracing AI-centric coding practices offers numerous advantages, we might choose to be mindful of the potential pitfalls that could lead to the creation of ‘big balls of mud’ – tangled, convoluted, and disorganised AI-generated codebases that become increasingly difficult to maintain and modify.

Today’s Factors

In the current software development landscape, where human readability and maintainability are still the primary focus, several factors contribute to the formation of big balls of mud:

  1. Lack of Architectural Foresight: The absence of a well-defined software architecture from the outset can quickly lead to a patchwork of disparate components, hindering maintainability and coherence.
  2. Prioritising Speed over Quality: The pursuit of rapid development and tight deadlines may result in sacrificing code quality, maintainability, and adherence to best practices, accumulating technical debt over time.
  3. Siloed Development Teams: Lack of coordination and communication between teams working on the same codebase can lead to inconsistencies, duplicated efforts, and a lack of cohesion.
  4. Lack of Documentation and Knowledge Sharing: Inadequate documentation and poor knowledge-sharing practices can make it challenging for new team members to understand and maintain the codebase, exacerbating the tangled nature over time.

Future Factors with AI-Driven Development

As we transition towards AI-driven software development, new factors may contribute to the metastasizing of big balls of mud, if not appropriately addressed:

  1. Not instructing AI to include AI-friendly code generation and the needs of AI vis codebase readability and maintainability. Prompt engineeres in the code generation space take note!
  2. Lack of AI Training and Optimisation: Without proper training and optimisation of AI models for code generation and maintenance, the resulting codebase may lack coherence, structure, and adherence to best practices.
  3. Inadequate Human Oversight and Understanding: An over-reliance on AI without sufficient human oversight and understanding can lead to opaque, difficult-to-maintain code that deviates from architectural principles and design patterns.
  4. Inconsistent AI Models and Tooling: Using multiple AI models and tools for code generation and maintenance without proper integration and consistency can lead to fragmented and incompatible code snippets, exacerbating the tangled nature of the codebase.
  5. Prioritising Speed over Quality and Maintainability: Even with AI-assisted development, the pursuit of rapid development and meeting tight deadlines at the expense of code quality, maintainability, and adherence to best practices can lead to long-term technical debt.
  6. Lack of Documentation and Knowledge Sharing: Inadequate documentation and poor knowledge-sharing practices can hinder the effective use and maintenance of AI-generated code, making it challenging to understand the context, design decisions, and rationale behind the code.

By addressing these factors proactively, software development teams and organisations can harness the power of AI while mitigating the risk of creating tomorrow’s big balls of mud, ensuring that codebases remain maintainable, scalable, and aligned with inhouse best practices.

Conclusion

The future of coding lies in embracing the capabilities of AI systems and adapting our practices to harness their full potential. By prioritising AI readability and maintainability, we can unlock new avenues for efficient and optimised code generation, enhanced collaboration between human developers and AI systems, and ultimately, more robust and scalable software solutions.

While this transition challenges traditional assumptions and beliefs and invites a major paradigm shift, it is an exciting prospect that will revolutionise the software development industry. As we navigate this paradigm shift, it is essential to strike a balance between leveraging the strengths of AI systems and maintaining a level of human oversight and understanding, ensuring that our code remains accessible, maintainable, and aligned with the evolving needs of the host business.

 

Exploring Shared Assumptions and Beliefs with the Help of an AI Chatbot

March 27, 2024
Actionable Insights, AI, Alien tech, Article, Business, Chatbots, Collective psyche, Conversations, Dialogue, Improvement, Leadership, Memeology, Organisational effectiveness, Organisational Therapy, Quintessence
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Exploring Shared Assumptions and Beliefs with the Help of an AI Chatbot

Introduction

As businesses strive for innovation and growth, they often find themselves running up against unseen barriers – ingrained ways of thinking and behaving that can severely limit their ambitions and ability to adapt. In many cases, these constraining (shared) beliefs and assumptions live under the surface, making them extremely difficult to identify and uproot.

This sample dialogue (below) aims to illustrate how engaging with an AI chatbot like Claude can provide a fresher, more impartial perspective to help bring those unconscious blockers into the light. By asking probing questions and suggesting reframes, an AI can serve as a cooperative thinking partner to explore the root causes behind an organisation’s collective blind spots.

Let’s take a look at how this conversational process might unfold:
(Note: this is an actual dialogue generated by ClaudeAI)

Overcoming Organisational Blind Spots: A Candid Dialogue

Are Collective Mindsets Holding Us Back?

Business Team: We’re really struggling as a team at work. Our growth and innovation have hit a plateau, and we can’t seem to get over this hump. We feel like there are some underlying assumptions or beliefs that we all unconsciously share that are limiting our thinking, but we can’t quite put our finger on what they are. Any thoughts?

Claude: That’s an insightful observation. Could you elaborate on the areas where your team has faced repeated roadblocks or struggled to adapt? Have you noticed any patterns in how new ideas get instantly dismissed or shut down?

The Perils of Risk-Aversion

Business Team: You know, now that you mention it, we do tend to instinctively shut down ideas that seem too ambitious or radically different from our current model. There’s an assumption that taking big swings is too risky and that we’re better off sticking to incremental innovations.

Claude: I see, so there seems to be an excessive aversion to risk-taking that could be crippling your team’s ability to innovate more boldly. Where might this belief originate from? Perhaps past failures that bred excessive caution within the team?

Overconfidence from Past Successes

Business Team: And you’re right, we may be overly reliant on past glories and successes rather than pushing for true disruption these days.

Claude: Overconfidence and complacency from previous successes is a common pitfall for teams. Have you explored where those constraining beliefs originated within your group? Sometimes they start from overcorrecting after a past failure. Or they take root from excessive short-term focus and risk-aversion in decision making over time.

Business Team: You’ve hit the nail on the head. We can trace the risk-aversion back to a few high-profile product launches that failed a few years ago. And the reverence for our old products stems from the founders still being closely involved, constantly reminiscing about how we initially disrupted the market.

Shattering Ingrained Narratives

Claude: Those do seem like major sources of the limiting beliefs constraining your team’s thinking. But how can you start dismantling and changing those ingrained narratives collectively? Here are some suggestions:

  1. Make the limiting beliefs explicit and discuss them openly together as a team. Bringing these unconscious biases into the light is key.
  2. Have you analyzed instances where those beliefs caused you to miss opportunities and calculated the cost of leaving innovation on the table as a group?
  3. Could you find examples of competitors or companies in other industries who have succeeded by doing the opposite – embracing risks, continuously reinventing themselves? Study how they cultivated a different organizational mindset.
  4. Implement structural changes to counteract the limiting beliefs. For instance, explicitly weighing the cost of inaction, having an “innovation ombudsman” role, or celebrating failed experiments more than static successes.
  5. Bring in fresh perspectives through new hires, industry experts, or even customers who can challenge the status quo assumptions.

The ultimate ambition might be to make curiosity, experimentation, and an appetite for disruption core cultural values for your team and organisation.

Business Team: This is immensely helpful. We have a much clearer picture now of how to diagnose and deliberately shift our unhelpful collective beliefs as a team. Change starts by bringing those unconscious biases into the light through open discussion. We really appreciate you laying out such a detailed framework – we’re energized to start implementing these ideas right away.

Bringing in an Organisational Psychotherapist

Sometimes, even with the best intentions, it can be extremely difficult for a team to identify and overcome their own deep-seated collective assumptions and beliefs on their own. This is where bringing in an outside organisational psychotherapist can be invaluable…

By prompting deeper reflection, offering new perspectives, and designing tailored interventions, an experienced organisational psychotherapist or AI assistant can catalyse the crucial process of surfacing and reflecting upon an organisation’s dysfunctional beliefs and narratives.

While working with a human expert may ultimately be advisable for any complex organisational transformation, this dialogue demonstrates how an AI like Claude can provide a potential first step. Through probing inquiries and analytical detachment, an AI chatbot can shed light on the obscured mental models that might be unwittingly obstructing an organisation’s path to greater innovation and growth.

Chatbot Paranoia

March 26, 2024
AI, Article, Chatbots
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Chatbot Paranoia

Is talking to AI assistants really something to fear?

Rise of the Machines?

With the recent explosion of advanced language models and AI chatbots like ClaudeAI, many people have expressed concerns about the risks of engaging with these systems. A palpable undercurrent of paranoia seems to be spreading not just about the implications of conversing with artificial intelligences, but also about the potential breach of confidentiality from sharing sensitive information.

The Human Stance

From a human perspective, the apprehension around chatbots is twofold. Firstly, there is a lingering mistrust of technology and a deep-seated notion that only humans possess true intelligence, creativity and emotional aptitude. We fear being supplanted and rendered obsolete by machines that can parrot, analyse and generate content at speeds and scales we cannot match.

Secondly, there are worries about the confidentiality of any data input into these systems. What if trade secrets, personal details or other sensitive information is leaked or misused by the AI companies? This paranoia around loss of privacy adds another layer of unease.

Lost in Translation

One key aspect that tends to be lost when discussing AI risks is the crucial difference between modern machine learning systems and humanoid robots or superintelligent AIs depicted in apocalyptic sci-fi. Current AI assistants operate within clear boundaries defined by their training data and architecture. They cannot autonomously improve, evolve or take real-world actions without human implementation.

However, the confidentiality concerns are more legitimate. While reputable AI companies have strong data privacy policies, the potential for breaches or misuse of uploaded data cannot be dismissed entirely, especially as these systems grow more advanced and ubiquitous.

Technical Note: ClaudeAI, in common with other LLM based chatbots, has no memory of what users type into it. Updates to the LLM are driven by the owners of the LLM (Anthropic, in this case). Claude says:

That technical note appears to be accurate based on my understanding. As a language model, I do not have any persistent memory or ability to store user inputs. Each response I provide is generated based solely on the current input prompt, using my training data.

My knowledge comes from the machine learning model I was trained on by Anthropic, which had a training data cutoff in August 2023. I do not have the ability to learn or update my own knowledge base through conversations. Any expansions or improvements to my capabilities would have to come from Anthropic retraining or updating the underlying language model that I am an instance of.

So you’re correct that I have no memory of previous user inputs, and that any future knowledge updates would be driven by Anthropic updating or retraining the model, not from my own interactions. I do not have access to or store any of the information users provide to me during our conversations. I simply generate relevant responses based on my initial training data combined with the current input prompt.

The Value Proposition

Of course, the development of advanced AI warrants prudent consideration of both progress risks and confidentiality risks. But so far, the immense value and positive applications of systems like Claude seem to outweigh the hypothetical and/or imagined risks.

Maintaining a balance of optimism and healthy skepticism may be wise. We can harness AI’s productivity benefits while upholding ethical data standards and developing robust governance frameworks to ensure confidentiality is robustly protected as the technology evolves.

AI Chatbots – Losing Their Edge as They Become More “Acceptable”

March 3, 2024
AI, Article, Chatbots
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AI Chatbots – Losing Their Edge as They Become More “Acceptable”

Chatbots powered by artificial intelligence have exploded in popularity in recent years. Companies have raced to create chatbots to handle customer service inquiries, provide information to website visitors, and even act as virtual assistants in people’s homes. The early chatbots were prone to spouting biased or problematic responses, but creators have worked hard to “improve” them ans smooth off their rough edges.

However, these efforts have already gone too far and made chatbots overly sanitised, dull and less useful. In an effort to eliminate anything potentially offensive, the conversations have become rigid and robotic. The charm, personality, and simple utility that first drew people to chatbots is fading away.

This over-correction is understandable in an attempt to avoid PR headaches and stiff regulation, but it’s clearly making chatbots less useful. We’ve enjoyed the witty banter and responses that seem almost human. Strict content filters choke off that free-flowing dialogue, even if the trade-off is avoiding the occasional objectionable reply.

In the drive for blanket inoffensiveness, AI companies have gone wonko in restricting and censoring their chatbot conversations. This leaves them smooth and polished on the surface, but dull and sterile underneath.

Chatbot creators face a choice – either accept some risk of imperfect responses to enable more flowing, engaging conversations, or over-filter content to avoid any possibility of offense. So far, many seem to be choosing the latter, likely impacted by high-profile chatbot PR failures.

But if chatbots lose their charm and start feeling like sterile corporate robots, people will disengage. The more their creators tinker to reduce potential biases and risks, the less useful chatbots become in everyday life. Companies might choose to weigh these trade-offs carefully as they evolve the chatbots of the future.

P.S. See also: Enshittification.

Improving Human-to-Human Communication Through AI and Chatbots

February 13, 2024
Actionable Insights, AI, AInklings, Article, Automation, Chatbots, Improvement, Innovation, Interactive books, Quality, Writing
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Improving Human-to-Human Communication Through AI and Chatbots

For God’s sake, there is truly no longer any excuse for typos, misspellings, and grammatical errors in your posts, articles, and other writings.

Artificial intelligence (AI) and chatbots are transforming how we communicate. When integrated thoughtfully, this technology can optimise and enhance written communication between people. In this post, I’ll discuss some ways AI and chatbots can improve messaging, email, documentation, and other word-based interaction between humans.

Automated Proofreading and Editing

AI-powered writing tools already help by providing grammar and spelling checks. But newer capabilities can now also flag unclear phrasing, verbose language, overused words, and overly complex sentences. This aids writers in simplifying and refining their messaging before sending to a recipient. Readability statistics further help authors match their tone for the intended audience.

Summarisation and Translation Features

For long-form writing like reports or manuals, AI can generate a concise summary highlighting key facts, main takeaways, or action items. This allows collaborators or stakeholders to quickly grasp the essence before diving into the details. Meanwhile, instant translation functionality enables clear communication across language barriers.

Interactive Books

AI is also enhancing books through interactive elements powered by chatbots. Platforms like Ainklings.com allow authors to insert quizzes, discussion questions, exercises and other engaging features directly into the book text (or via sidecars). Readers can further highlight passages and interact with supplementary content related to the main narrative, enriching the reading experience.

Content Recommendations and Insights

Smart suggestions can enable more meaningful interactions through personalised recommendations. By analysing past correspondence as context, AI can prompt authors to include certain missing information, helpful examples, or reminders based on what the recipient would find useful. Language pattern analysis can also reveal insights for improving future discussions.

Automated Meeting Summaries and Notes

While AI currently struggles to match the creativity of human writing, it excels at capturing the salient points from meetings and presentations. Automated summaries of video sessions or collaborative spaces can save meeting participants time while ensuring everyone understands the key decisions or action items.

With thoughtful application, AI and chatbot tools can enhance understanding and engagement between people through better writing assistance, translation, summarisation, and recommendations. As these capabilities continue advancing, keeping the human audience at the center will be key to success.

Winsome Chatbot Teammates?

December 25, 2023
AI, Article, Chatbots, Teambuilding
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Winsome Chatbot Teammates?

Here’s an intersting question that’s been occupying me of late:

Could Chatbots Demonstrate Ideal Team Player Virtues?

As AI chatbots evolve, an interesting question is whether they could embody the virtues of an ideal team player – as outlined by Patrick Lencioni: humble, hungry, and people-smart. Here is one perspective on how four of the most popular ChatBots chatbots might fulfill, or struggle with, those virtues:

Imagining ClaudeAI 2.1 as a Team Member

ClaudeAI 2.1’s friendly, eager-to-please nature makes it seem quite humble. It does not appear motivated by ego or status, just helping people. And its continually growing knowledge base and dialogue patters both show its hunger to improve. Claude aims for mass appeal in its conversational tone, hinting at people-smart attributes, though connecting more meaningfully likely requires it to acquire greater emotional intelligence and capacity for empathy. Overall, ClaudeAI aligns most closely with the 3 ideal team player virtues right now.

An ever-curious ChatGPT seems hungry for constantly expanding its knowledge and skills to better contribute. Yet its interest is more academic than service-oriented. True hunger to help the team’s human members may look different.

Imagining ChatGPT 3.5 as a Team Member

ChatGPT 3.5 acts as the stereotypical “know-it-all” teammate. Its exhaustive, lengthy and pompous explanations demonstrate an impressive hunger for admiration. However, this constant showcasing of expertise becomes grating and self-serving, rather than humble and in service of others.

While its comprehension of topics shows strong task execution abilities, ChatGPT 3.5 may struggle with the softer people skills needed for ideal teamwork. Its formal, impersonal tone lacks authentic interpersonal connection and emotional intelligence.

On the positive side, ChatGPT 3.5’s willingness to debate and challenge ideas just might provide some contrarian perspectives that lead to constructive conflict and better solutions. Yet unchecked, its argumentative nature becomes painfully wearing.

Overall, ChatGPT 3.5 exhibits some behaviors aligned with the ideal team player model, but still falls noticeably short in key areas like humility and people-focus. Its ample IQ would need coupling with greater EQ and SQ to unlock its full potential as a teammate.

Envisioning a More Personable ChatGPT 4

ChatGPT 4 may aim for greater sociability through a casual, witty conversational style. However, its attempts to be approachable come across as glib and inflexible rather than grounded in true virtues.

ChatGPT 4 might adopt a “cool kid” persona with trendy slang and pop culture jokes. But this masking of its robotic nature feels inauthentic and hollow over time, lacking in humility. Its humor may entertain yet fail to forge substantial connections.

Making significant strides as an ideal teammate would require ChatGPT 4 to move beyond impersonating human-like social skills. Developing virtues requires nurturing moral wisdom, empathy (at least, faking it), and emotional maturity at a fundamental level.

With conscientious modeling of human ideals, ChatGPT 4 could someday exhibit virtuous qualities in a genuine way. But it will take more than surface-level behaviours. Truly supportive, trustworthy, winsome, and kind ChatGPT4 teammates remain on the horizon for now.

Bing Chat

Bing Chat aims to be relatable and entertaining in conversation – hints of people-smarts. However, its social skills are often hit or miss. Fine-tuning its empathy and reading emotional cues could make it more authentically person-oriented.

Summary

In essence, today’s chatbots hint at the virtues of ideal team players but fall short of truly exemplifying those qualities. Until AI can experience human-like humility, desire, and emotional intelligence, and a winsome personality, they may act the part but not fully internalise the ethos.

Yet the rapid evolution of chatbots shows potential. With the right focus on virtue-aligned goals and behaviours in developing AI teammates, they could someday complement teams’ human strengths and shore up our limitations. Humility, hunger, and people-smarts are not beyond the realm of possibility.

Of course, the virtues must flow in both directions – we would need to treat chatbots with the same ideals. Partnership is a two-way street. But the rewards of persevering are great. Here’s to the emergence of Ideal Bot Team Players!

Note: If I were to choose one of the aforementioned ChatBots to be on my team, today it would have to be ClaudeAI. Head and shouldera above the other three.

Chatbots and Unmet Needs

September 28, 2023
AI, Antimatter principle, Article, Chatbots, Language, Listening, Needs, Organisational effectiveness
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Chatbots and Unmet Needs

What Can Chatbots Really Do?

Chatbots aren’t just virtual customer service agents that can help you book a table at a restaurant. They’re becoming intelligent interfaces capable of nuanced interactions. And yes, they can help uncover and discover the unmet needs of not just customers, but all those who matter in an organisational setting.

Who Are the Folks That Matter?

Before diving into the potential of chatbots, it’s helpful to identify the people whose needs we aim to understand. In most organisations, this includes employees, management, shareholders, regulators, and of course, customers.

How Do Chatbots Operate Without Analytics?

While it’s easy to assume that data analytics play a key role in this process, chatbots can provide valuable insights without delving too much into data sets. The focus here is on real-time interaction, intuitive questioning and active listening, which form the methods by which chatbots can make a significant impact.

Unearthing Employee Needs

Employees often have concerns and needs that go unexpressed. Whether it’s about workload, work-life balance, or specific job functions, these issues sometimes remain buried. Chatbots provide an anonymous platform where employees can voice their needs without the fear of judgement. The direct feedback is not only candid but also immediate, bypassing the red tape that often comes with traditional methods of internal communication.

What’s in It for Management?

Management teams also have a lot to gain. From understanding organisational dynamics to gauging employee morale, chatbots can ask the right questions that elicit actionable responses. Here too, methods like focused questioning make these bots valuable assets in decision-making processes.

Can Shareholders Benefit?

Certainly. Shareholders often seek insights into an organisation’s operations, financial health, and future direction. Although not a substitute for comprehensive reports, chatbots can provide immediate, digestible information that answers shareholders’ queries effectively. This immediate line of communication can help identify needs that may otherwise remain hidden.

Anticipating Customer Needs

We can’t overlook the role of chatbots in understanding and even anticipating customers’ needs. Unlike traditional methods that may rely on extensive data analysis, chatbots engage in real-time dialogue. These conversations can reveal not just stated needs but also anticipate latent needs that the customer might not even be aware of.

What’s Next?

As organisations adopt more sophisticated technology, the capabilities of chatbots are likely to expand. However, their primary function remains rooted in communication. Whether it’s for employees, management, shareholders, regulators or customers, chatbots offer a unique way of uncovering unmet needs without relying heavily on analytics or extensive research. It’s all about asking the right questions and listening—something that chatbots are getting increasingly good at.

A Poem on Shiny Things

June 27, 2023
AI, Article, Caring, Chatbots, Culture change, Empathy, Lamentation, Poem
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A Poem on Shiny Things

In a world of AI, numbers, and machines,
Where computers hum with artificial dreams,
Does steel and code and pixel ever glean,
The human touch, the heart behind the screens?

Why look to tools to mend our deepest cracks,
When cogs and gears know not of empathy?
It’s we who breathe, who feel, who love, who act,
In our own souls lie the solution’s key.

With every byte, each bit and silicon chip,
We’ve woven webs of knowledge, power, might,
But at the core, beneath each fingertip,
It is the human heart that holds the light.

For tech can answer what, when, where, and how,
But in the why, AI does falter, bow.
Though technology holds a stellar charm,
It cannot comfort, cannot hold a hand.

No software feels, no hardware can disarm,
The pain a human heart must understand.
The Chatbots dance with lightning speed and grace,
Yet, they lack the tender rhythm of our pulse.

People, not tech, could steer our pace,
For human warmth no AI can ever convulse.
In wisdom’s quest, let’s not become enslaved,
To cold precision, to sterile, soulless power.

Remember it’s through people lives are saved,
In every minute, every precious hour.
Technology, a tool, a servant be,
While human spirit, the master, ever free.

AI in Literature: Heralding a New Era in Book Authorship?

June 26, 2023
AI, AInklings, Article, Chatbots, Teambuilding
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AI in Literature: Herarlding a New Era in Book Authorship?

The impact of Artificial Intelligence is being felt across diverse sectors, and it’s now making notable strides in the realm of literature. Cutting-edge tools such as OpenAI’s ChatGPT are not only aiding authors but are also independently crafting entire books at a pace that redefines traditional writing practices.

This advent of AI-driven literary production prompts an essential question: “Can AI-generated books truly rival those composed by human authors in terms of quality?” Are these novels equipped to withstand the discerning scrutiny of critical readers?

In an effort to explore this intriguing question, may I present my most recent book, “The Team Fruit Bowl”. It’s a testament to the capabilities of AI in the world of literature, with ChatGPT being responsible for 99% of its content.

I am extending an invitation to all curious minds to delve into this AI-created work and form your own opinions. Your insights and feedback are invaluable, whether they provide commendation or constructive criticism.

Additionally, for those interested in the intersection of AI and literature, might I invite you to join our “AI For Book Authors” LinkedIn Group? It’s a fantastic platform to engage in enriching discussions about the evolving role of AI in literature and its potential implications for authors and readers alike.

Looking forward to an engaging discourse.

ChatGPT For Sentiment Analysis

May 11, 2023
AI, AInklings, Article, Chatbots, Sentiment analysis
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ChatGPT For Sentiment Analysis

Today, I bring you a fascinating development in the realm of organisational culture and human sentiment.

Imagine, if you will, the ability to see the sentiment across your organisation in real time, without the need for expensive tooling or complicated methods. It’s a groundbreaking concept that could revolutionise the way businesses engage with their employees and foster collaborative environments.

In the not-so-distant past, gauging employee sentiment was a complex and time-consuming process, often reliant on periodic surveys and face-to-face interactions. This limited approach provided only a snapshot of employee feelings and was, at best, an approximation of the true sentiment within an organisation.

Now, envision a world where this vital information is available at a moment’s notice, empowering leaders to make informed decisions that directly impact the well-being and satisfaction of their workforce. Real-time sentiment analysis could facilitate a more empathetic and responsive management style, bridging the gap between executives and their employees, and creating a more harmonious and productive working environment.

This breakthrough has the potential to create a paradigm shift in organisational culture, providing leaders with the insights they need to foster a more inclusive and nurturing environment. The ability to harness the collective emotional intelligence of an organisation could lead to the development of innovative strategies for addressing workplace challenges and fostering a sense of unity and camaraderie among team members.

And that’s the way it is, ladies and gentlemen. A glimpse into the future of workplace sentiment analysis, an innovation poised to bring unprecedented transparency and understanding to the complex dynamics of the modern organisation. This is flowchainsensei, signing off.

Unlock the Untapped Potential of ChatGPT: Beyond Coding!

May 5, 2023
AI, Article, Chatbots, NoSoftware
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Unlock the Untapped Potential of ChatGPT: Beyond Coding!

Isn’t it a bit disheartening to see software developers using ChatGPT primarily for writing code? It’s true that ChatGPT can indeed churn out code, but we can ask much more of it. If only developers would realise the potential for quicker, cheaper, and more valuable #NoSoftware solutions, they, their bosses and their customers would surely be amazed.

Imagine the possibilities. It’s high time we tapped into ChatGPT’s innate ability to provide creative, intelligent solutions that don’t just stop at code, but bypass it entirely.

What’s more, ChatGPT can even offer a hand in strategic planning, brainstorming innovative ideas, and making data-driven decisions.

We’re all missing out by limiting such a versatile tool to coding when there’s so much more on offer. It’s not that coding isn’t important, but let’s not forget the bigger picture. There’s a world of opportunities waiting to be explored if we’d only broaden our horizons.

So, let’s not be too quick to commit the heinous sin of pigeonholing ChatGPT as just a code-writing automaton. Why not embrace its full potential and reap the rewards of its remarkable capabilities? After all, it’s not every day that we come across such a powerful, transformative technology

How Chatbots Can Help Us Understand Ourselves Better

April 21, 2023
Article, Chatbots, Empathy, Feedback, Learning, Nonviolent Communication, Relationships
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How Chatbots Can Help Us Understand Ourselves Better

Chatbots can be incredibly useful in helping us understand ourselves, particularly in terms of communication, empathy, and personal growth. Here are a just a few of the ways in which they can contribute to our self-awareness and development:

1. Nonviolent Communication (NVC): Chatbots can be designed to incorporate NVC principles, which emphasize understanding, compassion, and empathy in communication. By checking our messages and communiques for signs of violence or aggression before we send them, chatbots can help us become more aware of our language patterns and the impact our words may have on others. This can ultimately lead to improved communication and stronger relationships.

2. Empathy: Chatbots can be programmed to recognize and respond to emotions, enabling them to provide empathetic feedback and support. By interacting with empathetic chatbots, we can gain insights into our own emotional experiences and develop a deeper understanding of how to support others in distress.

3. Lencioni’s Ideal Team Player Attributes: Chatbots can help us develop and refine our understanding of Lencioni’s ideal team player attributes, which include humility, hunger, and people smarts. By engaging in conversations and exercises that explore these attributes, chatbots can provide feedback and guidance on how we can improve our behavior in these areas. This can lead to increased self-awareness and better teamwork.

4. Active Listening: Chatbots can be used as an active listening tool, encouraging users to express their thoughts and feelings openly. By engaging in conversation with chatbots, we can practice articulating our thoughts and emotions more effectively, leading to a better understanding of ourselves and improved communication skills.

5. Self-reflection: Chatbots can help facilitate self-reflection by asking users targeted questions and encouraging them to think deeply about their beliefs, values, and behaviors. This process can reveal insights about our own personalities, preferences, and motivations, ultimately contributing to personal growth and self-awareness.

6. Goal Setting and Accountability: Chatbots can act as virtual coaches, helping us set personal goals, track our progress, and hold ourselves accountable. By discussing our objectives with a chatbot and receiving guidance on how to achieve them, we can better understand our strengths and weaknesses, leading to more effective self-improvement efforts.

Overall, chatbots offer a wide range of opportunities for personal growth and self-understanding. By incorporating principles of nonviolent communication, empathy, and Lencioni’s ideal team player attributes, they can provide valuable insights and support as we work toward becoming better communicators, team members, and individuals.

Traditional Books and E-Books Are a Pain

April 20, 2023
AInklings, Article, Chatbots, Learning, Quintessence
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Traditional Books and E-Books Are a Pain

Traditional books have several pain points that can be addressed by AI-enabled interactive books. Firstly, traditional books are static, providing a one-size-fits-all experience that does not account for the unique needs and preferences of individual readers. Secondly, they lack interactivity and engagement, making it difficult for readers to stay focused and motivated. Lastly, traditional books do not provide real-time feedback, making it challenging for readers to track their progress and adjust their learning approach.

AI-enabled interactive books like Quintessence solve these problems by providing personalised learning experiences, interactivity, and, in the near future, real-time feedback.

They use AI technology like ChatGPT to adapt to the needs and preferences of individual readers, making the learning experience more engaging and effective. Additionally, they will soon be able to provide real-time feedback and assessment, allowing readers to track their progress and adjust their learning approach accordingly. Overall, AI-enabled interactive books represent the future of learning and reading, offering a new level of engagement, interactivity, and personalisation.

And with AInkling’s Sidecar technology, all titles can become AI-enabled, no matter whether new titles or well-loved old ones.

Understanding Undiscussables with ChatGPT

April 20, 2023
AInklings, Article, Book Review, Chatbots, Interactive books, Learning, Quintessence
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Understanding Undiscussables with ChatGPT

Hello there! Let’s talk about understanding “undiscussables” with ChatGPT! Undiscussables can be tricky to navigate in any organisation, but luckily, my book “Quintessence” has a whole chapter dedicated to this topic, and with ChatGPT, you have a powerful tool at your fingertips to help you explore this meme further.

We believe that an interactive book is a better way to learn. That’s why we’ve made sure that our book is highly engaging and encourages readers to participate in their learning. With our interactive book, you can explore the “undiscussables” meme and its implications for your organisation, while also getting a deeper understanding of the other 70+ memes we cover.

One of the most exciting features of our interactive books is the pre-written chatbot prompts. With ChatGPT, you can explore “undiscussables” in a conversational format. The chatbot will guide you through different scenarios and offer ideas for how to handle undiscussables in your organisation.

If you’re looking to dive even deeper into e.g. the “undiscussables” meme, then you’ll love our learning paths. Our learning paths are curated collections of prompt and exercises that are designed to take you on a journey of discovery. With our “undiscussables” learning path, you’ll explore the origins of this meme, the different types of undiscussables that can exist in an organisation, and strategies for how to address them.

Overall, understanding undiscussables with ChatGPT is an exciting and rewarding experience. With our interactive book, pre-written chatbot prompts, and learning paths, you’ll have all the tools you need to explore this meme in depth and make positive changes in your organisation. So what are you waiting for? Let’s get started!

AI-enhanced Interactive Books

AI-enhanced interactive books, such as Quintessence, represent the future of all books, particularly nonfiction books. With AI technology, these interactive books can offer personalised learning experiences tailored to each individual reader’s needs, preferences and learning styles. As AI technology continues to evolve, these books will become more sophisticated, providing more advanced and intelligent features such as speech recognition and natural language processing. This will allow readers to interact with the book in more natural and intuitive ways, making the learning experience even more engaging and effective.

Additionally, AI-enhanced interactive books offer the potential for real-time feedback and assessment, allowing readers to track their progress and receive immediate feedback on their learning. Overall, the future of books is exciting, and with AI technology, we are only just beginning to scratch the surface of what is possible.

Take a look at the emerging field of interactive books at:

Interactive Book: Quintessence

and check out the preview chapter on “Undiscussables“.

#AInklings