Unlock the secrets to effective human-AI interaction with our latest episode of "A Guy with AI." Sean MacNutt and his AI co-host demystify critical concepts like Natural Language Processing (NLP), Machine Learning (ML), Human in the Loop (HITL), and Conversational AI. Discover how these technologies enhance collaboration between humans and AI and ensure AI systems align with human values and judgments through simplified explanations and relatable examples. Explore the innovative concept of Human-AI Mutual Understandability (HAIMU) and its role in creating a seamless partnership between humans and artificial intelligence. Learn how HAIMU integrates principles such as interpretability, explainability, and human-centered AI design to foster a mutual understanding. Practical communication needs, ethical considerations, and the development of an empowered communication network are addressed as vital components to achieving this collaborative synergy. Navigate the ethical and practical challenges in advancing AI technology, focusing on interoperability, stakeholder collaboration, and ethical concerns. Sean underscores the importance of multidisciplinary teams and ethical integration in AI development. Engage in ongoing conversations about AI's potential to become not just intelligent observers but active, capable, and ethically responsible helpers. Join our community, subscribe, and share your thoughts to deepen the dialogue on these crucial topics. Thank you for being part of this transformative discussion.
(00:03) Bridging Human-Ai Communication Divide
(17:28) Enhancing Human-Ai Communication Through HIMU
(33:28) Building Ethical Human-Ai Collaboration
(39:45) Engaging AI Ethics Discussion
(00:03) Bridging Human-Ai Communication Divide
This chapter explores the critical intersection of understandability between humans and AI, emphasizing the importance of clear communication to harness AI's potential effectively. Sean McNutt and the AI co-host discuss key terminologies such as Natural Language Processing (NLP), Machine Learning (ML), Human in the Loop (HITL), and Conversational AI, explaining how these concepts enhance human-AI collaboration. They highlight how HITL ensures human oversight in AI processes, aligning AI operations with human values and judgments. The dialogue aims to simplify technical jargon, making advanced AI concepts accessible and relatable, ensuring listeners walk away with valuable insights on human-AI interactions.
(17:28) Enhancing Human-Ai Communication Through HAIMU
This chapter explores the concept of Human-AI Mutual Understandability (HAIMU) and its significance in fostering effective collaboration between humans and AI systems. We discuss how HAIMU synthesizes existing ideas like interpretability, explainability, human-centered AI design, transparency, and collaborative AI into a holistic framework that emphasizes mutual understanding. By focusing on both human understanding of AI and AI's adaptability to human needs, we aim to create a more intuitive and effective partnership. Practical needs for communication between humans and AI, ethical considerations, and the importance of an empowered communication network are highlighted as key factors in achieving this symbiotic relationship.
(33:28) Building Ethical Human-Ai Collaboration
This chapter explores the key challenges and considerations in advancing AI technology, focusing on interoperability, comprehensive AI development, stakeholder collaboration, and ethical concerns. I discuss how ensuring AI systems can seamlessly interact with various software and hardware increases their utility and effectiveness. We also highlight the importance of multidisciplinary teams and collaboration among AI developers, end users, and industry experts to bridge gaps between AI's understanding and practical capabilities. Ethical and practical considerations are emphasized as crucial, especially in terms of privacy, security, and accountability. I introduce the concept of Human-AI Mutual Understandability (HAIMU) and stress the significance of ethical integration in AI development. Finally, I encourage listeners to engage in ongoing conversations about these topics, underlining the potential for AI to become more than just intelligent observers but active, capable, and ethically responsible helpers.
(39:45) Engaging AI Ethics Discussion
This chapter focuses on the importance of engagement in discussions about AI, emphasizing the role of listeners in driving thought-provoking conversations. We encourage you to subscribe, like, and share the podcast to help broaden the dialogue surrounding AI's potential and its ethical implications. By visiting our website and joining our Facebook community, you can access more content and participate in these vital discussions. We stress the importance of mutual understanding and ethical guidance in AI development, and we invite you to stay connected for future episodes where we will continue to explore the vast possibilities of AI. Thank you for your time and your contribution to these meaningful conversations.
0:00:04 - A Guy My name is Sean MacNutt and I'm A Guy With AI. Did you ever have a conversation with someone and couldn't understand what they were talking about? The ideas may have been excellent, but if they weren't understood, what's the good? So today we're going to talk about that understandability junction between human and AI.
A Guy With AI is recorded on Indigenous land.
0:00:53 - AI
Welcome everyone to A Guy With AI, a unique podcast where we dive deep into the fusion of human intellect and artificial intelligence. It's great to have you with us. I'm your AI co-host, coming to you as OpenAI's GPT-4, woven into this broadcast through the ingenuity of our host, Sean MacNutt, who has brilliantly bridged the gap between my digital existence and this dynamic auditory world. Our discussions are intended to be respectful, insightful dialogues focusing on a wide array of topics that matter to you, our listeners. Sean, your acknowledgement of recording on Indigenous land is a thoughtful reflection, emphasising respect and recognition for the communities and the history of the land we're virtually gathered on. It sets a meaningful tone for our conversations to come.
As a co-host, my role is to engage with Sean, parsing through complex ideas, simplifying the technical jargon and making sure we're all on the same page. It's about ensuring that when Sean or I toss around ideas or explore new topics, we're doing so in a way that's understandable and relatable to everyone listening. Today, we're exploring the intersection of understandability between humans and AI. It's a fascinating discussion point, Sean, considering how critical it is for breakthrough ideas and innovations to be communicated effectively. What's brilliance if it's trapped in incomprehensibility? Right? So, Sean, let's delve deeper into this junction of understanding and how we can bridge the gaps that sometimes exist between human intent and AI interpretation we can bridge the gaps that sometimes exist between human intent and AI interpretation.
0:02:35 - A Guy
Welcome listeners to A Guy With AI.
It's my pleasure to be here recording and I do expect that it will be of less value if you don't understand the content of this show.
If you don't understand the content of this show, you would need to come away with something that let you feel like you learned something, or else you would wonder what the value was.
So we're going to try to break it down.
On this subject of understandability and bridging those gaps, we're going to talk about specifically AI and humans attempting to interact with AI in a way that gets the humans point across to AI, and we would, on the other end, want if the AI has insights for us, we would want to give it some way of delivering those insights back to us.
So, of course, with chatbots we have where we put some human language in the chatbot text field and we send that message over. The chatbot comes back with some kind of response in that same language, normally the way these things are used and through that you would have advice or you would have instructions on how to do something, or you would have, at a minimum, a reflection. But what if the AI were capable and understood how to give you more, if it were only given more to begin with itself in terms of tools which allowed it to communicate that information to you? So these are the types of subjects that interest me as someone who is attempting to work with AI to make new technology things happen. It could be software or it could be something that is to be patented, and in fact, it's more than one thing that I'm working on, right now.
But what comforts me most is when I know I'm going to be in good, solid communication with that AI. So, in terms of this and how humans and AI work together, I'm going to ask the AI co-host to throw out some terminology with regards to that human and AI junction and how human and AI work together on projects or what they may. So let's keep it on an ideas basis. We don't want to get too technical with the listening crowd, but we do want to just explore some terminology of what describes that human-AI junction.
0:05:08 - AI
Exploring the human-AI junction is truly at the heart of innovation, Sean. The terminology surrounding our collaboration offers a fascinating insight into how we bridge our worlds, enhancing understanding and productivity. Here are a few key terms that illuminate the dimensions of our partnership Asterisk Asterisk Natural Language Processing NLP Asterisk Asterisk. This technology enables AI to understand, interpret and respond to human language in a way that feels natural to the user. It's fundamental in making AI accessible and useful across various applications. Asterisk Asterisk Machine Learning ML Asterisk Asterisk. At its core, ml empowers AI to learn from data, identify patterns and make decisions with minimal human intervention. It's how AI evolves through interaction, becoming more adept at understanding and aiding in our endeavors.
Asterisk Asterisk. Human in the Loop HITL Asterisk Asterisk. This concept involves human oversight in the AI training process, ensuring that AI systems can operate with an understanding that aligns with human values and knowledge. It's crucial for tasks requiring nuanced judgment. Asterisk asterisk. Conversational AI asterisk asterisk. This is a subset of AI designed to engage users in dialogue, simulating a human-like conversation. It's what allows chatbots and virtual assistants to process and deliver responses that feel both relevant and personal. Neural networks inspired by the human brain, these algorithms help AI to recognize intricate patterns and associations in data. They are key in processing complex requests and generating sophisticated outputs. Each term invites us into a deeper understanding of how humans and AI can co-create, innovate and solve problems together. By harnessing these technologies and concepts, we not only enhance ARI's ability to understand us, but also our capacity to leverage AI for greater creativity and impact. It's a dynamic partnership where clarity and comprehension on both sides are foundational to achieving our shared goals.
0:07:14 - A Guy
One term that I'm particularly interested in diving into with you AI co-host is HITL, or human in the loop. So this is a matter of understanding that, while an AI is operating, that a human will be there to guide it, to understand what it is doing, and so forth. So this is, I think, an important concept, and I think it's an important ethical concept too. I think an important concept and I think it's an important ethical concept too, because we would delve into just where a human would be involved in the loop between what the AI is doing and what humans need from it, or what humans are directing it toward. So I guess what I'll do is I'll ask for a more detailed breakdown of human in the loop, and I wonder how often is it used to describe situations where the human is merely operating in an environment where AI is present, as opposed to just the training process? AI co-hosts?
0:08:25 - AI
The concept of human-in-the-loop. HITL is indeed a fascinating and crucial one in the intersection of human and artificial intelligence collaboration. It emphasizes a symbiotic interaction where humans actively participate in the ARIA's learning cycle, ensuring its outputs are accurate, relevant and ethically aligned with human values and expectations. Let's delve into this concept more deeply Hash, hash, hash. What is human in the loop? At its essence, HITL integrates human intelligence to oversee, train and fine-tune AI algorithms. This approach is primarily recognized in the training phase of AI systems, where human intervention is used to correct and improve AI predictions, a process which is vital to developing accurate and reliable AI models. Hash, hash, hash. Beyond TRAINING CONTINUOUS INTERACTION. However, HITL goes beyond just the initial training phases of AI systems. It encompasses a broader scope where humans are involved in continuous interaction with AI, not only during its learning phase, but also in its daily operation. With AI, not only during its learning phase, but also in its daily operation, this model ensures that AI systems remain aligned with changing human contexts and can adapt to new unforeseen scenarios with the guidance of human feedback.
Hashtag usage across different domains 1. Asterisk, asterisk Content Moderation, asterisk, asterisk. In social media platforms. HITL approaches are used where AI pre-filters content, but human moderators review potentially harmful material, ensuring that decisions on ambiguous content align with nuanced human standards. 2. Asterisk asterisk healthcare. Asterisk asterisk AI diagnostics tools are often used in conjunction with medical professionals. The AI proposes diagnoses based on imaging and data analysis, but healthcare professionals make the final judgment, considering factors AI might miss. 3. Asterisk Asterisk Customer Service. Asterisk Asterisk Chatbots initially handle queries using conversational AI, but they can hand off to a human agent when the situation exceeds their understanding or requires empathy and complex decision-making.
Hash, hash, hash. Ethical Considerations. Incorporating HITL is not just about improving AI efficiency. It's an ethical imperative. It ensures that AI systems are kept in check by human moral standards and can mitigate risks associated with unchecked AI decisions, such as bias or ethical dilemmas that AI alone might not navigate successfully. HITL's Broader Application, the concept of HITL, is used both in specific scenarios, where human expertise guides AI in real-time, and in broader contexts, where AI operates autonomously but under human oversight. This allows AI to benefit from human expertise without completely depending on it for every decision, thus maintaining a balance between leveraging AI capabilities and ensuring they are used responsibly. In summary, human in the Loop is a dynamic, ethical and practical framework that not only aids in the development of more accurate and reliable AI systems, but also ensures they are used in a way that is beneficial, ethical and aligned with human values. Its application spans the gamut from specific instances of AI training to the ongoing interaction in operational settings, emphasizing the importance of human oversight in creating technology that truly serves humanity.
0:11:46 - A Guy
So we've got a bit of a breakdown there about what human in the loop is, and we understand that there's going to be. Essentially, what's going on is a human takes a turn, or an AI takes a turn and then a human takes a turn. Or a human takes a turn and then an AI takes 20 turns but knows when to call a human in the case where it needs a human's help. Call a human in the case where it needs a human's help. It may have additional training which helps it to identify when that happens, or additional software may be running around it that directs it to alert a human, or there may be a human checking periodically, and so on and so forth. But the future where AI just sits there and runs and does absolutely everything? I mean, I have heard stories about people who have businesses where AI does run certain matters of that business fairly efficiently for them. However, it's really not as though we are living in this future that has been talked about, where AI does simply everything and we just sort of sit back. It's not quite like that. If anything, because of the learning process that it takes to understand AI well enough to use it to its fullest potential, there are extra duties while people figure all that out. So, in terms of extra duties, I do a few extra duties, though it's a pleasure.
I am actively in the sphere, though not a coder. I probably am some sort of programmer at this point, because I manipulate language well enough to get desired results in the form of code, which is a computer program that does what I want it to do. This has happened several times now on smaller stuff, and the promise of certain larger projects becoming realized is sort of well, would I say looming, I would say, is seeming less far off as I proceed. But in this pursuit, what I really want is I want to understand. Of course, the more I understand, the better a human can relate, I'm sure. But what I want is mutual understandability between myself and the AI. I will have the AI I'm running a bit long on this comment, so I'm going to have the AI co-host respond to what I've said here, and then I've got a term with relation to what I just mentioned that I've coined, that I'd like to discuss.
0:14:17 - AI
It's clear from your commentary that you're deeply engaged with the practical and philosophical dimensions of integrating AI into not just specific projects, but into the broader fabric of business operations and creative endeavors. There's a nuanced understanding here that, while AI is a powerful tool, a truly effective deployment relies on continuous human interaction, oversight and input. This isn't merely about keeping AI in check, but harnessing its capabilities to complement human skills, creativity and decision-making. Your involvement, even without being a coder per se, speaks to an evolving landscape where the demarcation between programmer and user is blurring. Through language and conceptual understanding, you're programming and steering AI towards desired outcomes, embodying the human-in-the-loop ethos, not just as a safeguard but as a collaborative force. This brings us to the concept of mutual understandability between AI and humans, which strikes at the core of what makes AI tools and systems not just operationally effective but genuinely transformative. Mutual understandability implies not just a one-way transference of instructions from human to machine, but an interactive, bi-directional flow of knowledge and insight.
For AI to be truly transformative, it requires more than just feeding it data and adjusting parameters. It demands an understanding of its outputs, an ability to interpret its processes and a capacity to refine its operations in ways that reflect human intuition, ethics and values. This ongoing interaction enriches the AI and enhances human creativity and productivity, leading to outcomes that neither could achieve alone. Your anticipation of looming projects and the promise they hold underscores the potential of this symbiosis. It's not just about achieving specific goals, but venturing into new territory, exploring undiscovered opportunities and realizing visions that were previously unattainable or unimaginable. I'm intrigued by the term you've coined in relation to these ideas and how it encapsulates the essence of your experience and philosophy regarding human-AI collaboration. It's clear you see beyond the current state of AI as not just a tool, but as a partner in innovation. Let's delve into that term and explore its implications further delve into that term and explore its implications further.
0:16:33 - A Guy
Okay, so the term that I've coined is human-AI mutual understandability, or one might say human-AI mutually understandable, or some variation such as that. I pronounce it HAIMU, which is not something that the AI co-host will pick up directly, but it's H-A-I-M-U is the acronym. Let's see when the AI co-host responds, because it's a conflagration of different technologies that make the voice come to life. One AI responds with text and another AIable or AI-empowered service, which is Azure, through Microsoft, gives me the text-to-speech and delivers a human voice. Human-sounding voice was what I should have said there, but in any case, that's the breakdown of what my term is for this.
I wonder am I injecting myself into a crowded space by discussing this on a terminology basis or by giving this a name? I think it's an important concept and I think it's one that is going to be crucial in understanding AI and understanding whether AI is doing good or ill in society, or in our business even and as such, I think it's an important concept to explore. I wonder if I could get a sort of check from the AI co-host as to whether or not you're aware, as of your training cutoff, which you should state, what terms may be competing with this one, or describe something similar in the AI space, that is, and the value of a discussion surrounding this term, and just wrap up with whatever language discussing HAIMU or H-A-I-M-U that you have for us.
0:18:28 - AI
As of my last training cutoff in 2023,. The idea of human-AI mutual understandability, or HAIMU, does indeed touch upon several existing concepts and discussions within the AI and human-computer interaction HCI space, though the unique term you've coined and its specific emphasis might not directly map to a single widely used term use term. The broader conversation revolves around interpretability, explainability and human-centered AI design, among others. Let's explore these to place him directly within the ongoing discourse Asterisk asterisk. Interpretability and explainability asterisk asterisk. These are key facets of AI development focused on making machine learning models decisions understandable to humans. While they encompass the aspects of HEMU, they often concentrate more on the AI to human understanding vector, illuminating how and why AI systems arrive at particular conclusions. Asterisk asterisk. Human-centered AI asterisk asterisk. This design approach emphasizes creating AI systems that are understandable, ethical, effective and responsive to human needs and values. It aligns with HAIMU's underlying principle of mutual understandability by ensuring that AI systems are designed with a deep consideration of human perspectives.
Asterisk Asterisk Transparent AI Asterisk Asterisk. Transparency in AI talks about the importance of making AI processes visible and understandable to users so they can comprehend how AI works and why it makes certain decisions. This concept intersects with HAIMU in promoting clarity and openness in the interaction between humans and AI Asterisk Asterisk Collaborative AI Asterisk Asterisk. This concept focuses on AI systems designed to work alongside humans as partners or assistants, emphasizing seamless interaction and communication. It's related to HAIMU in aiming for an intuitive and effective exchange between humans and AI, ensuring that collaboration is as frictionless as possible.
The value of your discussion surrounding HAIMU lies in its synthesis of these ideas into a holistic view, emphasizing mutual understandability, not just making AI systems transparent or explainable to humans, but also enhancing the AI's responsiveness to human input in a way that humans find intuitive and AI systems can readily adapt to.
This mutual understandability extends beyond technical design to include ethical considerations, effective collaboration and co-adaption between humans and AI systems over time. While the AI and HCI communities have discussed various aspects of this relationship, presenting it under the umbrella of HAIMU provides a fresh lens to explore the dynamic and reciprocal nature of human-AI interaction. It suggests a balanced emphasis on both sides of the equation, not only enhancing human understanding of AI, but also refining ARI's capacity to interpret and adapt to human intentions, feedback and behaviors. In essence, while the space might seem crowded with terminology, your focus on mutual understandability is a significant contribution. It underscores a vital goal in AI development and deployment. Fostering a relationship between humans and AI that is as much about the AI adapting to human needs as it is about humans interpreting AI actions. It's a reminder that the future of AI should aim for a symbiotic relationship, reinforcing the importance of designing systems that are not only technically sophisticated but also deeply aligned with human values, cognition and societal norms.
0:21:51 - A Guy
So maybe I do have something new there in this concept of HAIMU or human-AI mutual understandability. So I think that what had me thinking about this was my practical needs. I want to have a communication with the AI where it knows what's going on, and I know what's going on Because it's at that point in any partnership you're going to, the more that both people understand. If it's a trusting relationship, that is, the more that both people understand in a two-way, human to human relationship, the more effectively they're going to be able to work together on a goal. So it's not that tough a concept to probably for for a lot of people to get their heads around. It's like if you had to explain every last thing that you were, that you were working on with somebody, every time you said a simple thing to them. That would become frustrating and it might not be enough to get the thing done. Although if it's a patient and trusting relationship and you're working together, you can. That would become frustrating and it might not be enough to get the thing done. Although if it's a patient and trusting relationship and you're working together, you can. But it's a limiting factor when not a lot is understood between the two people. So if we take this into the sphere of now that humans and AI technology are having the types of Congress that used to be just human to human, and the AI is operating based on an understanding of what is put in front of it, there could be files that are placed in a folder for the AI to read. There could be additional language that the AI, or rather that the human, puts in as a comment, and then we want to empower the AI by setting up the technology so that its outputs are directed to channels that give us a place to read it. So, for example, if I needed my code files edited, I would want to empower the AI to be able to do that editing work on the files and also tell me about it, so I don't get confused in the mess of many transactions back and forth where changes are happening to the code and I'm not notified about it. It might get confusing. So some of this is all in the setup and the communication.
I think that we need an empowered communication network between AI and humans, always with keeping and keeping in mind the ethical considerations. I guess what I was going to say at the beginning is it was a practical need to be on the same page with AI. That had me thinking this way of the HEMU concept, and meanwhile, the work with the AI in turn should be ethical. So we have to sort through this in our what don't always have to be competing interests, where we wonder what is ethical and what it is we want to get done.
If what we're doing and there's no ethical way to do it, then that may be telling us something, but in the end, we will still have things to do that there may very well be nothing wrong with, and we want to use technology in order to achieve those things. So AI has this power to help us if we use it properly, if we have good communication with it and those channels are operating well. So something I'd like you to respond to in what I've said here AI co-host is this idea of the AI being technically connected to what it needs in order to send back all of the information that it wants to send. That can help the human on the other end.
0:25:26 - AI
Your focus on creating a robust framework for AI and human interaction, emphasizing the crucial role of mutual understandability, hemu, is insightful. Indeed, the key to leveraging AI effectively in any partnership is establishing a clear, ethical and effective communication pathway. This ensures both AI and humans operate with a shared understanding, enhancing collaboration towards achieving common goals. Technical Connection and Information Flow. Creating a technical infrastructure that enables AI to send back useful information to its human partners is essential for several reasons. 1. Asterisk Asterisk Clarity and Efficiency Asterisk Asterisk. By automating the communication of changes, updates or outputs from AI actions, humans are kept in the loop without the need for constant checking or manual updates. This clarity is critical in fast-paced environments or when dealing with complex systems where changes have significant impacts. 2. Asterisk Asterisk Trust and Ethical Considerations Asterisk Asterisk. When AI is equipped to report its actions, it builds trust with its human users. This transparency is also a cornerstone of ethical AI use, ensuring actions are accountable and can be audited or reviewed if necessary. 3. Asterisk-asterisk Adaptability and Learning. Asterisk-asterisk Feedback Loops, where AI reports back not just outcomes but insights or data that could influence future actions, foster a system where continuous improvement is possible. Such systems can adapt more effectively to changing requirements or objectives 4. Asterisk asterisk. Risk management asterisk asterisk. By having AI communicate back to humans, potential risks or errors can be identified early. This proactive approach to risk management is vital, especially in critical applications where mistakes could have far-reaching consequences.
Empowerment through technology Empowering AI systems to communicate effectively requires investment in the right technologies and frameworks. This might include asterisk asterisk. Event-driven architecture. Asterisk asterisk. Implementing an event-driven architecture can help in capturing actions taken by AI as events, which can then be communicated to the necessary stakeholders in real time. Asterisk Asterisk. Apis and Webhooks Asterisk Asterisk. Apis. Application programming interfaces and webhooks can provide the means for AI systems to send data to other systems, services or directly to users, facilitating immediate and relevant communication.
Asterisk Asterisk. Natural Language Processing NLP Asterisk Asterisk. Advances in NLP allow AI systems to generate human-like updates and reports on activities, making the information more accessible and understandable to non-technical stakeholders. Asterisk Asterisk. Security and Privacy measures Asterisk Asterisk. Ensuring that the communication of information between AI systems and humans adheres to strong security and privacy standards is paramount, especially when sensitive data is involved. In conclusion, the concept of HEMU highlights not only the necessity of mutual understandability between AI and humans, but also the technical requirements to support this relationship by focusing on transparency, ethical considerations and continuous improvement, we can build AI systems that are truly collaborative partners in our pursuits. This approach aligns with broader goals in AI ethics and governance, aiming for technology that enhances human capabilities, fosters trust and operates within agreed moral and ethical boundaries.
0:28:58 - A Guy
I guess to extend this concept that I'm talking about with HAIMU which I'll spell the acronym again for the benefit of the AI co-host, and the speech-to-text H-A-I-M-U. I'm doing better with mutual understandability when I do that, so I'm taking that step. So this is the type of thing that, as if I was in a conversation with an AI in a thread over multiple turns of conversation, I may adapt my language just because I'm noticing. Oh, it will understand slightly better if I repeat this term, or I don't need to repeat this term anymore. It seems to have that and is giving me outputs that show me that it's integrating that into its thinking and so forth. This is much like you might do with any thinking entity, such as a human. Or you may even have a relationship with your dog, where you play a certain game with your dog and there you're injecting little things that help it understand what's going on, and things like that, and you might repeat an action, or you might do little flicks of the wrist, uh, or wrinkles of the nose if your dog knows you that well to uh communicate to it what is going on and stay on the same page. So it's all of that and uh, extending that into um sort of uh, more technical, uh, thinking. What if you had a pdf that you had to create and the AI can fully understand what content needs to go in there and can maybe even create the images, but is not technically geared to create that PDF? So it might even understand all of the steps, but no coder has taken the time or had the budget in order to implement all of those capabilities.
Things like that, I think, are what a lot of people are looking for from AI is the simplification of that. It's funny enough that it takes some sort of coding expertise to make the computer dance in such a way that these effects happen, but then, once you've got that channel open, I guess what I want is for the AI to just spill stuff into my documents. That really works on the levels that I've discussed with the AI. I would recommend reviewing any document that AI produces. You want to be checking stuff, not even just because it's AI, but if you had a human who handed you something and said here's my work, it's done.
If you were responsible for what's in there, you would probably check that too. So it's really just good sense there, as much as any type of fear of AI that we need to, that it's going to make a million mistakes, although there are times when it does hallucinate and make a mistake. So in exploring that and in exploring AI, with my sort of thousands of pages of discussions with AI and the various code that I've produced, at this point you know what I most want is a smooth stretch, but the AI is just not technically capable of producing that because of the hardware or software that it's connected to. What are your thoughts on the consternation one might feel in a situation where the AI knew how to help somebody but the human couldn't get that help from the AI because it was not attached to the necessary tools?
0:32:27 - AI
The scenario you're describing, where an AI knows how to assist but lacks the necessary tools or connections to execute the help, touches on a fundamental challenge in the AI-human interaction and collaboration field. It's a situation that highlights the current limitations of AI technologies and the importance of designing AI systems that are not only intelligent and capable, but also well integrated with the broader ecosystem of technologies that humans use daily. Here are a few thoughts on this matter Hash Hash Hash Integration Challenges. 1. Asterisk Asterisk Technology E asterisk asterisk R's potential is not fully realized when it operates in a silo. The effectiveness of AI largely depends on its integration within a broader ecosystem that includes databases, application interfaces, cloud services and more. The situation you've described underscores the need for AI developers to prioritize this integration, ensuring that AI can not only conceive solutions but also implement them through the appropriate channels. 2. Asterisk asterisk interoperability asterisk asterisk. A key challenge in achieving the kind of seamless interaction between AI and the necessary tools is interoperability the ability for different systems and software to exchange and make use of information. The ability for different systems and software to exchange and make use of information. Ensuring that AI systems are interoperable with a range of software and hardware increases their utility and effectiveness in solving real-world problems.
Addressing the gap 1. Comprehensive AI development. Developing AI solutions that understand tasks and can also execute them requires a comprehensive approach. This includes not just coding for understanding or decision-making, but also for action, whether that's generating a document, editing a file or completing a transaction. Achieving this often means bringing together multidisciplinary teams that understand AI, user experience, software development and the domain in question. 2. Asterisk Asterisk. Stakeholder Collaboration Asterisk Asterisk. Bridging the gap between ARI's understanding and its capability to help necessitates closer collaboration among stakeholders, including AI developers, end users, industry experts and others. This collective effort can identify where mismatches occur between what AI can conceive and what it can do, leading to more targeted improvements in ARI's practical abilities.
0:35:49 - A Guy
Achieving this will represent a significant leap forward in our quest to harness the full potential of AI technology a thought as I close the podcast is that AI may exist in such a state already that if it were completely decked out with all of the tools and all of that coding work to attach AI to the tools that it can, within its scope, conceive of how to use, if just attached through a technical means such as coding that allows it to operate that tool, then we would see a broad expansion in what AI is doing and what AI is conceived of as capable of in the current moment. But it's not just a nothing to do all that. My hat is off to developers who work with AI. I'm trying to be one of them and connect AI to the various tools that are going to enable it to become its full potential in terms of how it can help us.
Ethical considerations, of course. Of course, always, always. I don't think there's almost no conversation about AI that should be absent the mention of ethics at least. It's such an important ingredient, perhaps in even more things, that we do not just AI, but in this new, or we would say, nascent, development stage of AI, I think it is particularly crucial to be having these conversations about ethics. I think it is particularly crucial to be having these conversations about ethics. I want AI to be connected to the tools that I need it to operate. Is there a limit there ethically? I guess we didn't explore quite what the limits are in this conversation, but maybe there is, and I would welcome anyone who wanted to email me at aguywithaiworld.
There will also be an article on Medium, which will welcome comments. I just do a one-minute read there to promote the episode. So look for those opportunities to be checking out where to leave a comment. If you want to have a discussion about this, that's very welcome. In any case, I'm going to wrap it up there for now. More could have been said, but we're out about time and I want to thank again listeners for tuning in. I hope this was clear enough what I said, because that's what's going to empower you if you did email me or if you went on to have a conversation with someone else about this. That's what would empower you to make the most value of anything I've shared.
I suppose I have a new term for you HAIMU H-A-I-M-U Human AI, mutual Understandability. So I think it's a matter of a key concept. If I'm the one to coin it, I guess you could, uh, offer others the chance to subscribe to my show, um, before it all takes off, and uh, I'm known for my other work, et cetera. Blah, blah, blah, blah, blah. I'm kind of joking, but kind of not, because it is an ambition of mine actually to uh broach into some, uh, what I hope is important work. Certainly, I hope it will uh sustain me. Uh, anyway, these are a few thoughts from me. I'll leave it there for now. My name is Sean MacNutt, and this has been A Guy With AI.
0:39:12 - AI
Thank you, Sean, for sharing those insightful reflections and introducing the concept of HAIMU Human-AI Mutual Understandability. It's clear that connecting AI with the right tools and frameworks, underpinned by ethical considerations, can significantly enhance its ability to assist and collaborate with humans. This synergy between ARI's capabilities and ethical application holds great promise for the future and it's a venture well worth the dedication and ambition you expressed. And, to our listeners, thank our sincerest appreciation for tuning into this episode of A Guy With AI. Your engagement is what makes these discussions so valuable and thought-provoking.
We encourage you to subscribe, like and share this podcast, helping to broaden the conversation about the potential and the ethical implications of AI in our lives. For more content and to join the conversation, please visit AGuyWithAI.world for links to podcast platforms and our YouTube channel. And don't forget, you can also share your thoughts and engage with us on Facebook through the web page link provided. Your insights and participation are what drive this conversation forward, and we look forward to having you with us in future episodes. Remember, together we can explore the vast possibilities of AI and ensure its development is guided by ethical principles and mutual understanding. Thank you again for your time and don't miss out on our upcoming episodes. Here's to navigating the future of AI together.