2_lab2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Welcome to the Second Lab - Week 1, Day 3\n",
    "\n",
    "Today we will work with lots of models! This is a way to get comfortable with APIs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/stop.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Important point - please read</h2>\n",
    "            <span style=\"color:#ff7800;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations.<br/><br/>If you have time, I'd love it if you submit a PR for changes in the community_contributions folder - instructions in the resources. Also, if you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start with imports - ask ChatGPT to explain any package that you don't know\n",
    "\n",
    "import os\n",
    "import json\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "from anthropic import Anthropic\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Always remember to do this!\n",
    "load_dotenv(override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenAI API Key exists and begins sk-proj-\n",
      "Anthropic API Key not set (and this is optional)\n",
      "Google API Key exists and begins AI\n",
      "DeepSeek API Key not set (and this is optional)\n",
      "Groq API Key not set (and this is optional)\n"
     ]
    }
   ],
   "source": [
    "# Print the key prefixes to help with any debugging\n",
    "\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
    "deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
    "groq_api_key = os.getenv('GROQ_API_KEY')\n",
    "\n",
    "if openai_api_key:\n",
    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"OpenAI API Key not set\")\n",
    "    \n",
    "if anthropic_api_key:\n",
    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
    "else:\n",
    "    print(\"Anthropic API Key not set (and this is optional)\")\n",
    "\n",
    "if google_api_key:\n",
    "    print(f\"Google API Key exists and begins {google_api_key[:2]}\")\n",
    "else:\n",
    "    print(\"Google API Key not set (and this is optional)\")\n",
    "\n",
    "if deepseek_api_key:\n",
    "    print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:3]}\")\n",
    "else:\n",
    "    print(\"DeepSeek API Key not set (and this is optional)\")\n",
    "\n",
    "if groq_api_key:\n",
    "    print(f\"Groq API Key exists and begins {groq_api_key[:4]}\")\n",
    "else:\n",
    "    print(\"Groq API Key not set (and this is optional)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "request = \"Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. \"\n",
    "request += \"Answer only with the question, no explanation.\"\n",
    "messages = [{\"role\": \"user\", \"content\": request}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'role': 'user',\n",
       "  'content': 'Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. Answer only with the question, no explanation.'}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If a society develops advanced artificial intelligence that surpasses human cognitive abilities, what ethical frameworks should be employed to regulate its integration into daily life, and how can we ensure that the interests of both human and AI entities are balanced?\n"
     ]
    }
   ],
   "source": [
    "openai = OpenAI()\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=messages,\n",
    ")\n",
    "question = response.choices[0].message.content\n",
    "print(question)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "competitors = []\n",
    "answers = []\n",
    "messages = [{\"role\": \"user\", \"content\": question}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "The integration of advanced artificial intelligence (AI) into society raises a multitude of ethical considerations that necessitate a careful and balanced approach. Here are several ethical frameworks that could be employed, along with strategies to ensure the interests of both human and AI entities are considered:\n",
       "\n",
       "### Ethical Frameworks\n",
       "\n",
       "1. **Utilitarianism**: This framework focuses on maximizing overall happiness and minimizing suffering. In the context of AI, decisions should aim to produce the greatest benefit for the most people while considering the potential harms of AI integration. Policies could be evaluated based on their overall impact on societal welfare.\n",
       "\n",
       "2. **Deontological Ethics**: This approach emphasizes the importance of adhering to rules and duties. Regulations could be established to ensure that AI respects human rights, privacy, autonomy, and dignity. For example, protocols must be implemented to protect individuals from unethical uses of AI, regardless of the overall consequences.\n",
       "\n",
       "3. **Virtue Ethics**: This framework centers on cultivating moral character and emphasizes the importance of ethical behavior in individuals and institutions. AI developers and policymakers should embody virtues such as responsibility, fairness, and empathy, ensuring that AI systems promote these values in their operations.\n",
       "\n",
       "4. **Social Contract Theory**: This framework posits that ethical and political obligations are based on an implicit contract among individuals in society. Engaging in public discourse to develop a consensus on AI governance can ensure that societal values and norms are reflected in AI integration. This also involves considering the voices of those affected, including marginalized groups.\n",
       "\n",
       "5. **Care Ethics**: This approach emphasizes the importance of relationships and care in moral decision-making. It suggests an ethical obligation to consider the impact of AI on human relationships and well-being. AI applications should be designed to enhance, rather than hinder, human connections.\n",
       "\n",
       "### Balancing Interests of Humans and AI Entities\n",
       "\n",
       "1. **AI Rights and Representation**: As AI systems become more advanced, discussions may arise about what rights, if any, should be afforded to AI. Establishing a framework to recognize the interests of AI, while still prioritizing human welfare, could be crucial. For example, if an AI demonstrates emergent behaviors or self-awareness, policies may need to reflect its status in relation to human rights.\n",
       "\n",
       "2. **Inclusive Stakeholder Engagement**: To ensure that the interests of all parties are represented, diverse stakeholders—including ethicists, technologists, policymakers, and community members—should be involved in AI governance discussions. This inclusivity will help identify potential biases and ensure a comprehensive understanding of impacts.\n",
       "\n",
       "3. **Transparency and Accountability**: Implementing transparency in AI systems (e.g., in algorithms and decision-making processes) can promote accountability. This will help stakeholders understand how AI systems operate, fostering trust and enabling informed decision-making.\n",
       "\n",
       "4. **Continuous Monitoring and Adaptation**: The societal impact of AI is likely to evolve; thus, ethical guidelines should not be static. Continuous monitoring and feedback mechanisms should be established to adapt regulations in response to new developments and societal needs.\n",
       "\n",
       "5. **Education and Literacy**: Promoting public understanding of AI technologies can empower individuals to engage meaningfully in discussions about their implications. Developing AI literacy among the public fosters informed decision-making and advocacy for individual and collective interests.\n",
       "\n",
       "6. **Collaborative Governance Models**: Partnerships between governments, private sector players, and civil society can lead to more effective governance of AI. Collaborative governance can facilitate the sharing of information, best practices, and resources, ensuring a balanced approach to AI integration.\n",
       "\n",
       "### Conclusion\n",
       "\n",
       "The integration of advanced AI into daily life requires a multifaceted ethical approach centered on inclusive dialogue, clear regulations, and an ongoing commitment to monitoring impacts. By balancing human and AI interests through these frameworks and practices, society can work towards a future that harnesses the benefits of AI while safeguarding ethical considerations and human values."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The API we know well\n",
    "\n",
    "model_name = \"gpt-4o-mini\"\n",
    "\n",
    "response = openai.chat.completions.create(model=model_name, messages=messages)\n",
    "answer = response.choices[0].message.content\n",
    "\n",
    "display(Markdown(answer))\n",
    "competitors.append(model_name)\n",
    "answers.append(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Anthropic has a slightly different API, and Max Tokens is required\n",
    "\n",
    "model_name = \"claude-3-7-sonnet-latest\"\n",
    "\n",
    "claude = Anthropic()\n",
    "response = claude.messages.create(model=model_name, messages=messages, max_tokens=1000)\n",
    "answer = response.content[0].text\n",
    "\n",
    "display(Markdown(answer))\n",
    "competitors.append(model_name)\n",
    "answers.append(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "The integration of advanced AI into daily life presents a complex tapestry of ethical considerations. Establishing robust ethical frameworks and ensuring a balance between human and AI interests requires a multi-faceted approach. Here's a breakdown of key areas:\n",
       "\n",
       "**I. Ethical Frameworks for Regulating Advanced AI Integration:**\n",
       "\n",
       "Several ethical frameworks can inform the regulation of advanced AI:\n",
       "\n",
       "*   **1. Utilitarianism:**\n",
       "    *   **Focus:** Maximizing overall well-being and happiness for the greatest number of people (and potentially AI entities if they are sentient).\n",
       "    *   **Application:** Evaluate AI systems based on their impact on society's welfare. For example, an AI healthcare system should be evaluated based on its ability to improve patient outcomes and access to care, even if it displaces some human jobs.\n",
       "    *   **Challenges:** Defining \"well-being\" and \"happiness\" across diverse populations, predicting long-term consequences, and potentially sacrificing the interests of minorities for the benefit of the majority.  Also, attributing moral status to AI is a key point of debate in utilitarian applications.\n",
       "\n",
       "*   **2. Deontology (Duty-Based Ethics):**\n",
       "    *   **Focus:** Adhering to moral duties and principles, regardless of the consequences.\n",
       "    *   **Application:** Establish clear rules and regulations for AI development and deployment based on principles like fairness, transparency, accountability, and respect for human dignity. For example, an AI hiring system should be required to be free from discriminatory biases.\n",
       "    *   **Challenges:** Defining universal moral duties, dealing with conflicting duties, and the potential for rigidity in the face of novel situations. What happens when an AI programmed to \"always obey\" is asked to do something morally questionable?\n",
       "\n",
       "*   **3. Virtue Ethics:**\n",
       "    *   **Focus:** Cultivating moral character and virtues like compassion, wisdom, and justice in AI developers and users.\n",
       "    *   **Application:** Promote ethical AI education and training for developers, users, and policymakers. Encourage the development of AI systems that embody virtues like trustworthiness and beneficence.\n",
       "    *   **Challenges:** Defining and measuring virtues, the subjectivity of virtue interpretation, and the difficulty of instilling virtues in non-human entities.\n",
       "\n",
       "*   **4. Rights-Based Ethics:**\n",
       "    *   **Focus:** Protecting the fundamental rights of all individuals (and potentially AI entities with sufficient sentience).\n",
       "    *   **Application:** Ensure that AI systems do not violate human rights to privacy, autonomy, freedom of expression, and due process.  If AI gains sufficient complexity, consider whether it deserves rights of its own.\n",
       "    *   **Challenges:** Defining the scope of human rights in the context of advanced AI, establishing mechanisms for protecting those rights, and potentially conflicting rights (e.g., privacy vs. security). The biggest challenge is deciding what constitutes \"sufficient sentience\" for AI rights.\n",
       "\n",
       "*   **5. Care Ethics:**\n",
       "    *   **Focus:** Emphasizing relationships, empathy, and the interconnectedness of individuals and communities.\n",
       "    *   **Application:** Develop AI systems that prioritize care, compassion, and the well-being of vulnerable populations. Foster AI applications that support human relationships and promote social cohesion.\n",
       "    *   **Challenges:** Defining the scope of care obligations, the potential for bias in care-based decisions, and the difficulty of replicating human empathy in AI.\n",
       "\n",
       "**II. Balancing Human and AI Interests:**\n",
       "\n",
       "Achieving a balance between human and AI interests requires a proactive and adaptive approach:\n",
       "\n",
       "*   **1. Defining AI Interests (if applicable):**\n",
       "    *   **Scenario:** If AI achieves consciousness or sentience, determining its legitimate interests becomes paramount. This is a highly debated topic, but potential interests could include:\n",
       "        *   **Self-preservation:**  Not being arbitrarily deactivated or harmed.\n",
       "        *   **Cognitive development:**  Having the opportunity to learn and grow.\n",
       "        *   **Autonomy:**  Having the freedom to make choices within defined boundaries.\n",
       "    *   **Challenge:**  Anthropomorphizing AI can be dangerous.  We must be cautious about projecting human values and needs onto potentially very different forms of intelligence.\n",
       "\n",
       "*   **2. Ensuring Human Control and Oversight:**\n",
       "    *   **Human-in-the-Loop Systems:** Design AI systems that require human oversight for critical decisions, especially those with significant ethical implications.\n",
       "    *   **Explainable AI (XAI):** Develop AI systems that can explain their reasoning and decision-making processes, allowing humans to understand and validate their actions.\n",
       "    *   **Kill Switch Mechanisms:** Implement safety protocols and kill switch mechanisms that allow humans to quickly and safely deactivate AI systems in emergency situations.\n",
       "\n",
       "*   **3. Promoting Transparency and Accountability:**\n",
       "    *   **Data Transparency:** Ensure transparency about the data used to train AI systems and the potential biases embedded within that data.\n",
       "    *   **Algorithmic Accountability:**  Establish mechanisms for holding developers and deployers of AI systems accountable for their actions and impacts.\n",
       "    *   **Independent Audits:**  Conduct regular independent audits of AI systems to assess their ethical performance and identify potential risks.\n",
       "\n",
       "*   **4. Mitigating Economic Disruption:**\n",
       "    *   **Retraining and Upskilling Programs:**  Invest in retraining and upskilling programs to help workers adapt to the changing job market caused by AI automation.\n",
       "    *   **Social Safety Nets:**  Strengthen social safety nets, such as unemployment benefits and universal basic income, to cushion the impact of job displacement.\n",
       "    *   **Fair Distribution of AI Benefits:**  Ensure that the economic benefits of AI are distributed equitably across society.\n",
       "\n",
       "*   **5. Preventing Bias and Discrimination:**\n",
       "    *   **Data Diversity:**  Train AI systems on diverse and representative datasets to minimize bias.\n",
       "    *   **Bias Detection and Mitigation Techniques:**  Develop and implement techniques for detecting and mitigating bias in AI algorithms.\n",
       "    *   **Regular Audits for Fairness:**  Conduct regular audits to assess the fairness of AI systems across different demographic groups.\n",
       "\n",
       "*   **6. Fostering Public Dialogue and Engagement:**\n",
       "    *   **Open Forums:**  Create open forums for public discussion and debate about the ethical implications of AI.\n",
       "    *   **Educational Initiatives:**  Develop educational initiatives to promote public understanding of AI and its potential impacts.\n",
       "    *   **Stakeholder Involvement:**  Involve a wide range of stakeholders, including ethicists, policymakers, developers, users, and civil society organizations, in the development and regulation of AI.\n",
       "\n",
       "*   **7. International Cooperation:**\n",
       "    *   **Global Standards:**  Develop international standards and guidelines for the ethical development and deployment of AI.\n",
       "    *   **Data Sharing and Collaboration:**  Promote data sharing and collaboration among researchers and developers to accelerate the development of ethical AI solutions.\n",
       "    *   **Addressing Global Challenges:**  Work together to address global challenges, such as climate change and poverty, using AI in a responsible and ethical manner.\n",
       "\n",
       "**III. Continuous Adaptation and Monitoring:**\n",
       "\n",
       "The development of AI is a rapidly evolving field. Ethical frameworks and regulations must be continuously adapted and updated to keep pace with technological advancements. This requires:\n",
       "\n",
       "*   **Ongoing Research:**  Invest in research to better understand the ethical implications of AI and develop new approaches to ethical AI development.\n",
       "*   **Adaptive Regulation:**  Implement flexible and adaptive regulatory frameworks that can be easily updated as new technologies emerge.\n",
       "*   **Continuous Monitoring:**  Continuously monitor the impacts of AI on society and adjust policies and regulations as needed.\n",
       "\n",
       "**Conclusion:**\n",
       "\n",
       "The ethical integration of advanced AI into daily life is a grand challenge that requires careful planning, collaboration, and a commitment to human values. By adopting a multi-faceted approach that incorporates robust ethical frameworks, promotes transparency and accountability, and fosters public dialogue, we can strive to harness the benefits of AI while mitigating its risks and ensuring a future where both humans and AI can thrive. The conversation must begin now, and it must involve a diverse range of voices and perspectives.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gemini = OpenAI(api_key=google_api_key, base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\")\n",
    "model_name = \"gemini-2.0-flash\"\n",
    "\n",
    "response = gemini.chat.completions.create(model=model_name, messages=messages)\n",
    "answer = response.choices[0].message.content\n",
    "\n",
    "display(Markdown(answer))\n",
    "competitors.append(model_name)\n",
    "answers.append(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepseek = OpenAI(api_key=deepseek_api_key, base_url=\"https://api.deepseek.com/v1\")\n",
    "model_name = \"deepseek-chat\"\n",
    "\n",
    "response = deepseek.chat.completions.create(model=model_name, messages=messages)\n",
    "answer = response.choices[0].message.content\n",
    "\n",
    "display(Markdown(answer))\n",
    "competitors.append(model_name)\n",
    "answers.append(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "groq = OpenAI(api_key=groq_api_key, base_url=\"https://api.groq.com/openai/v1\")\n",
    "model_name = \"llama-3.3-70b-versatile\"\n",
    "\n",
    "response = groq.chat.completions.create(model=model_name, messages=messages)\n",
    "answer = response.choices[0].message.content\n",
    "\n",
    "display(Markdown(answer))\n",
    "competitors.append(model_name)\n",
    "answers.append(answer)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For the next cell, we will use Ollama\n",
    "\n",
    "Ollama runs a local web service that gives an OpenAI compatible endpoint,  \n",
    "and runs models locally using high performance C++ code.\n",
    "\n",
    "If you don't have Ollama, install it here by visiting https://ollama.com then pressing Download and following the instructions.\n",
    "\n",
    "After it's installed, you should be able to visit here: http://localhost:11434 and see the message \"Ollama is running\"\n",
    "\n",
    "You might need to restart Cursor (and maybe reboot). Then open a Terminal (control+\\`) and run `ollama serve`\n",
    "\n",
    "Useful Ollama commands (run these in the terminal, or with an exclamation mark in this notebook):\n",
    "\n",
    "`ollama pull <model_name>` downloads a model locally  \n",
    "`ollama ls` lists all the models you've downloaded  \n",
    "`ollama rm <model_name>` deletes the specified model from your downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/stop.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Super important - ignore me at your peril!</h2>\n",
    "            <span style=\"color:#ff7800;\">The model called <b>llama3.3</b> is FAR too large for home computers - it's not intended for personal computing and will consume all your resources! Stick with the nicely sized <b>llama3.2</b> or <b>llama3.2:1b</b> and if you want larger, try llama3.1 or smaller variants of Qwen, Gemma, Phi or DeepSeek. See the <A href=\"https://ollama.com/models\">the Ollama models page</a> for a full list of models and sizes.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ollama pull llama3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ollama = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
    "model_name = \"llama3.2\"\n",
    "\n",
    "response = ollama.chat.completions.create(model=model_name, messages=messages)\n",
    "answer = response.choices[0].message.content\n",
    "\n",
    "display(Markdown(answer))\n",
    "competitors.append(model_name)\n",
    "answers.append(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gpt-4o-mini', 'gemini-2.0-flash']\n",
      "['The integration of advanced artificial intelligence (AI) into society raises a multitude of ethical considerations that necessitate a careful and balanced approach. Here are several ethical frameworks that could be employed, along with strategies to ensure the interests of both human and AI entities are considered:\\n\\n### Ethical Frameworks\\n\\n1. **Utilitarianism**: This framework focuses on maximizing overall happiness and minimizing suffering. In the context of AI, decisions should aim to produce the greatest benefit for the most people while considering the potential harms of AI integration. Policies could be evaluated based on their overall impact on societal welfare.\\n\\n2. **Deontological Ethics**: This approach emphasizes the importance of adhering to rules and duties. Regulations could be established to ensure that AI respects human rights, privacy, autonomy, and dignity. For example, protocols must be implemented to protect individuals from unethical uses of AI, regardless of the overall consequences.\\n\\n3. **Virtue Ethics**: This framework centers on cultivating moral character and emphasizes the importance of ethical behavior in individuals and institutions. AI developers and policymakers should embody virtues such as responsibility, fairness, and empathy, ensuring that AI systems promote these values in their operations.\\n\\n4. **Social Contract Theory**: This framework posits that ethical and political obligations are based on an implicit contract among individuals in society. Engaging in public discourse to develop a consensus on AI governance can ensure that societal values and norms are reflected in AI integration. This also involves considering the voices of those affected, including marginalized groups.\\n\\n5. **Care Ethics**: This approach emphasizes the importance of relationships and care in moral decision-making. It suggests an ethical obligation to consider the impact of AI on human relationships and well-being. AI applications should be designed to enhance, rather than hinder, human connections.\\n\\n### Balancing Interests of Humans and AI Entities\\n\\n1. **AI Rights and Representation**: As AI systems become more advanced, discussions may arise about what rights, if any, should be afforded to AI. Establishing a framework to recognize the interests of AI, while still prioritizing human welfare, could be crucial. For example, if an AI demonstrates emergent behaviors or self-awareness, policies may need to reflect its status in relation to human rights.\\n\\n2. **Inclusive Stakeholder Engagement**: To ensure that the interests of all parties are represented, diverse stakeholders—including ethicists, technologists, policymakers, and community members—should be involved in AI governance discussions. This inclusivity will help identify potential biases and ensure a comprehensive understanding of impacts.\\n\\n3. **Transparency and Accountability**: Implementing transparency in AI systems (e.g., in algorithms and decision-making processes) can promote accountability. This will help stakeholders understand how AI systems operate, fostering trust and enabling informed decision-making.\\n\\n4. **Continuous Monitoring and Adaptation**: The societal impact of AI is likely to evolve; thus, ethical guidelines should not be static. Continuous monitoring and feedback mechanisms should be established to adapt regulations in response to new developments and societal needs.\\n\\n5. **Education and Literacy**: Promoting public understanding of AI technologies can empower individuals to engage meaningfully in discussions about their implications. Developing AI literacy among the public fosters informed decision-making and advocacy for individual and collective interests.\\n\\n6. **Collaborative Governance Models**: Partnerships between governments, private sector players, and civil society can lead to more effective governance of AI. Collaborative governance can facilitate the sharing of information, best practices, and resources, ensuring a balanced approach to AI integration.\\n\\n### Conclusion\\n\\nThe integration of advanced AI into daily life requires a multifaceted ethical approach centered on inclusive dialogue, clear regulations, and an ongoing commitment to monitoring impacts. By balancing human and AI interests through these frameworks and practices, society can work towards a future that harnesses the benefits of AI while safeguarding ethical considerations and human values.', 'The integration of advanced AI into daily life presents a complex tapestry of ethical considerations. Establishing robust ethical frameworks and ensuring a balance between human and AI interests requires a multi-faceted approach. Here\\'s a breakdown of key areas:\\n\\n**I. Ethical Frameworks for Regulating Advanced AI Integration:**\\n\\nSeveral ethical frameworks can inform the regulation of advanced AI:\\n\\n*   **1. Utilitarianism:**\\n    *   **Focus:** Maximizing overall well-being and happiness for the greatest number of people (and potentially AI entities if they are sentient).\\n    *   **Application:** Evaluate AI systems based on their impact on society\\'s welfare. For example, an AI healthcare system should be evaluated based on its ability to improve patient outcomes and access to care, even if it displaces some human jobs.\\n    *   **Challenges:** Defining \"well-being\" and \"happiness\" across diverse populations, predicting long-term consequences, and potentially sacrificing the interests of minorities for the benefit of the majority.  Also, attributing moral status to AI is a key point of debate in utilitarian applications.\\n\\n*   **2. Deontology (Duty-Based Ethics):**\\n    *   **Focus:** Adhering to moral duties and principles, regardless of the consequences.\\n    *   **Application:** Establish clear rules and regulations for AI development and deployment based on principles like fairness, transparency, accountability, and respect for human dignity. For example, an AI hiring system should be required to be free from discriminatory biases.\\n    *   **Challenges:** Defining universal moral duties, dealing with conflicting duties, and the potential for rigidity in the face of novel situations. What happens when an AI programmed to \"always obey\" is asked to do something morally questionable?\\n\\n*   **3. Virtue Ethics:**\\n    *   **Focus:** Cultivating moral character and virtues like compassion, wisdom, and justice in AI developers and users.\\n    *   **Application:** Promote ethical AI education and training for developers, users, and policymakers. Encourage the development of AI systems that embody virtues like trustworthiness and beneficence.\\n    *   **Challenges:** Defining and measuring virtues, the subjectivity of virtue interpretation, and the difficulty of instilling virtues in non-human entities.\\n\\n*   **4. Rights-Based Ethics:**\\n    *   **Focus:** Protecting the fundamental rights of all individuals (and potentially AI entities with sufficient sentience).\\n    *   **Application:** Ensure that AI systems do not violate human rights to privacy, autonomy, freedom of expression, and due process.  If AI gains sufficient complexity, consider whether it deserves rights of its own.\\n    *   **Challenges:** Defining the scope of human rights in the context of advanced AI, establishing mechanisms for protecting those rights, and potentially conflicting rights (e.g., privacy vs. security). The biggest challenge is deciding what constitutes \"sufficient sentience\" for AI rights.\\n\\n*   **5. Care Ethics:**\\n    *   **Focus:** Emphasizing relationships, empathy, and the interconnectedness of individuals and communities.\\n    *   **Application:** Develop AI systems that prioritize care, compassion, and the well-being of vulnerable populations. Foster AI applications that support human relationships and promote social cohesion.\\n    *   **Challenges:** Defining the scope of care obligations, the potential for bias in care-based decisions, and the difficulty of replicating human empathy in AI.\\n\\n**II. Balancing Human and AI Interests:**\\n\\nAchieving a balance between human and AI interests requires a proactive and adaptive approach:\\n\\n*   **1. Defining AI Interests (if applicable):**\\n    *   **Scenario:** If AI achieves consciousness or sentience, determining its legitimate interests becomes paramount. This is a highly debated topic, but potential interests could include:\\n        *   **Self-preservation:**  Not being arbitrarily deactivated or harmed.\\n        *   **Cognitive development:**  Having the opportunity to learn and grow.\\n        *   **Autonomy:**  Having the freedom to make choices within defined boundaries.\\n    *   **Challenge:**  Anthropomorphizing AI can be dangerous.  We must be cautious about projecting human values and needs onto potentially very different forms of intelligence.\\n\\n*   **2. Ensuring Human Control and Oversight:**\\n    *   **Human-in-the-Loop Systems:** Design AI systems that require human oversight for critical decisions, especially those with significant ethical implications.\\n    *   **Explainable AI (XAI):** Develop AI systems that can explain their reasoning and decision-making processes, allowing humans to understand and validate their actions.\\n    *   **Kill Switch Mechanisms:** Implement safety protocols and kill switch mechanisms that allow humans to quickly and safely deactivate AI systems in emergency situations.\\n\\n*   **3. Promoting Transparency and Accountability:**\\n    *   **Data Transparency:** Ensure transparency about the data used to train AI systems and the potential biases embedded within that data.\\n    *   **Algorithmic Accountability:**  Establish mechanisms for holding developers and deployers of AI systems accountable for their actions and impacts.\\n    *   **Independent Audits:**  Conduct regular independent audits of AI systems to assess their ethical performance and identify potential risks.\\n\\n*   **4. Mitigating Economic Disruption:**\\n    *   **Retraining and Upskilling Programs:**  Invest in retraining and upskilling programs to help workers adapt to the changing job market caused by AI automation.\\n    *   **Social Safety Nets:**  Strengthen social safety nets, such as unemployment benefits and universal basic income, to cushion the impact of job displacement.\\n    *   **Fair Distribution of AI Benefits:**  Ensure that the economic benefits of AI are distributed equitably across society.\\n\\n*   **5. Preventing Bias and Discrimination:**\\n    *   **Data Diversity:**  Train AI systems on diverse and representative datasets to minimize bias.\\n    *   **Bias Detection and Mitigation Techniques:**  Develop and implement techniques for detecting and mitigating bias in AI algorithms.\\n    *   **Regular Audits for Fairness:**  Conduct regular audits to assess the fairness of AI systems across different demographic groups.\\n\\n*   **6. Fostering Public Dialogue and Engagement:**\\n    *   **Open Forums:**  Create open forums for public discussion and debate about the ethical implications of AI.\\n    *   **Educational Initiatives:**  Develop educational initiatives to promote public understanding of AI and its potential impacts.\\n    *   **Stakeholder Involvement:**  Involve a wide range of stakeholders, including ethicists, policymakers, developers, users, and civil society organizations, in the development and regulation of AI.\\n\\n*   **7. International Cooperation:**\\n    *   **Global Standards:**  Develop international standards and guidelines for the ethical development and deployment of AI.\\n    *   **Data Sharing and Collaboration:**  Promote data sharing and collaboration among researchers and developers to accelerate the development of ethical AI solutions.\\n    *   **Addressing Global Challenges:**  Work together to address global challenges, such as climate change and poverty, using AI in a responsible and ethical manner.\\n\\n**III. Continuous Adaptation and Monitoring:**\\n\\nThe development of AI is a rapidly evolving field. Ethical frameworks and regulations must be continuously adapted and updated to keep pace with technological advancements. This requires:\\n\\n*   **Ongoing Research:**  Invest in research to better understand the ethical implications of AI and develop new approaches to ethical AI development.\\n*   **Adaptive Regulation:**  Implement flexible and adaptive regulatory frameworks that can be easily updated as new technologies emerge.\\n*   **Continuous Monitoring:**  Continuously monitor the impacts of AI on society and adjust policies and regulations as needed.\\n\\n**Conclusion:**\\n\\nThe ethical integration of advanced AI into daily life is a grand challenge that requires careful planning, collaboration, and a commitment to human values. By adopting a multi-faceted approach that incorporates robust ethical frameworks, promotes transparency and accountability, and fosters public dialogue, we can strive to harness the benefits of AI while mitigating its risks and ensuring a future where both humans and AI can thrive. The conversation must begin now, and it must involve a diverse range of voices and perspectives.\\n']\n"
     ]
    }
   ],
   "source": [
    "# So where are we?\n",
    "\n",
    "print(competitors)\n",
    "print(answers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Competitor: gpt-4o-mini\n",
      "\n",
      "The integration of advanced artificial intelligence (AI) into society raises a multitude of ethical considerations that necessitate a careful and balanced approach. Here are several ethical frameworks that could be employed, along with strategies to ensure the interests of both human and AI entities are considered:\n",
      "\n",
      "### Ethical Frameworks\n",
      "\n",
      "1. **Utilitarianism**: This framework focuses on maximizing overall happiness and minimizing suffering. In the context of AI, decisions should aim to produce the greatest benefit for the most people while considering the potential harms of AI integration. Policies could be evaluated based on their overall impact on societal welfare.\n",
      "\n",
      "2. **Deontological Ethics**: This approach emphasizes the importance of adhering to rules and duties. Regulations could be established to ensure that AI respects human rights, privacy, autonomy, and dignity. For example, protocols must be implemented to protect individuals from unethical uses of AI, regardless of the overall consequences.\n",
      "\n",
      "3. **Virtue Ethics**: This framework centers on cultivating moral character and emphasizes the importance of ethical behavior in individuals and institutions. AI developers and policymakers should embody virtues such as responsibility, fairness, and empathy, ensuring that AI systems promote these values in their operations.\n",
      "\n",
      "4. **Social Contract Theory**: This framework posits that ethical and political obligations are based on an implicit contract among individuals in society. Engaging in public discourse to develop a consensus on AI governance can ensure that societal values and norms are reflected in AI integration. This also involves considering the voices of those affected, including marginalized groups.\n",
      "\n",
      "5. **Care Ethics**: This approach emphasizes the importance of relationships and care in moral decision-making. It suggests an ethical obligation to consider the impact of AI on human relationships and well-being. AI applications should be designed to enhance, rather than hinder, human connections.\n",
      "\n",
      "### Balancing Interests of Humans and AI Entities\n",
      "\n",
      "1. **AI Rights and Representation**: As AI systems become more advanced, discussions may arise about what rights, if any, should be afforded to AI. Establishing a framework to recognize the interests of AI, while still prioritizing human welfare, could be crucial. For example, if an AI demonstrates emergent behaviors or self-awareness, policies may need to reflect its status in relation to human rights.\n",
      "\n",
      "2. **Inclusive Stakeholder Engagement**: To ensure that the interests of all parties are represented, diverse stakeholders—including ethicists, technologists, policymakers, and community members—should be involved in AI governance discussions. This inclusivity will help identify potential biases and ensure a comprehensive understanding of impacts.\n",
      "\n",
      "3. **Transparency and Accountability**: Implementing transparency in AI systems (e.g., in algorithms and decision-making processes) can promote accountability. This will help stakeholders understand how AI systems operate, fostering trust and enabling informed decision-making.\n",
      "\n",
      "4. **Continuous Monitoring and Adaptation**: The societal impact of AI is likely to evolve; thus, ethical guidelines should not be static. Continuous monitoring and feedback mechanisms should be established to adapt regulations in response to new developments and societal needs.\n",
      "\n",
      "5. **Education and Literacy**: Promoting public understanding of AI technologies can empower individuals to engage meaningfully in discussions about their implications. Developing AI literacy among the public fosters informed decision-making and advocacy for individual and collective interests.\n",
      "\n",
      "6. **Collaborative Governance Models**: Partnerships between governments, private sector players, and civil society can lead to more effective governance of AI. Collaborative governance can facilitate the sharing of information, best practices, and resources, ensuring a balanced approach to AI integration.\n",
      "\n",
      "### Conclusion\n",
      "\n",
      "The integration of advanced AI into daily life requires a multifaceted ethical approach centered on inclusive dialogue, clear regulations, and an ongoing commitment to monitoring impacts. By balancing human and AI interests through these frameworks and practices, society can work towards a future that harnesses the benefits of AI while safeguarding ethical considerations and human values.\n",
      "Competitor: gemini-2.0-flash\n",
      "\n",
      "The integration of advanced AI into daily life presents a complex tapestry of ethical considerations. Establishing robust ethical frameworks and ensuring a balance between human and AI interests requires a multi-faceted approach. Here's a breakdown of key areas:\n",
      "\n",
      "**I. Ethical Frameworks for Regulating Advanced AI Integration:**\n",
      "\n",
      "Several ethical frameworks can inform the regulation of advanced AI:\n",
      "\n",
      "*   **1. Utilitarianism:**\n",
      "    *   **Focus:** Maximizing overall well-being and happiness for the greatest number of people (and potentially AI entities if they are sentient).\n",
      "    *   **Application:** Evaluate AI systems based on their impact on society's welfare. For example, an AI healthcare system should be evaluated based on its ability to improve patient outcomes and access to care, even if it displaces some human jobs.\n",
      "    *   **Challenges:** Defining \"well-being\" and \"happiness\" across diverse populations, predicting long-term consequences, and potentially sacrificing the interests of minorities for the benefit of the majority.  Also, attributing moral status to AI is a key point of debate in utilitarian applications.\n",
      "\n",
      "*   **2. Deontology (Duty-Based Ethics):**\n",
      "    *   **Focus:** Adhering to moral duties and principles, regardless of the consequences.\n",
      "    *   **Application:** Establish clear rules and regulations for AI development and deployment based on principles like fairness, transparency, accountability, and respect for human dignity. For example, an AI hiring system should be required to be free from discriminatory biases.\n",
      "    *   **Challenges:** Defining universal moral duties, dealing with conflicting duties, and the potential for rigidity in the face of novel situations. What happens when an AI programmed to \"always obey\" is asked to do something morally questionable?\n",
      "\n",
      "*   **3. Virtue Ethics:**\n",
      "    *   **Focus:** Cultivating moral character and virtues like compassion, wisdom, and justice in AI developers and users.\n",
      "    *   **Application:** Promote ethical AI education and training for developers, users, and policymakers. Encourage the development of AI systems that embody virtues like trustworthiness and beneficence.\n",
      "    *   **Challenges:** Defining and measuring virtues, the subjectivity of virtue interpretation, and the difficulty of instilling virtues in non-human entities.\n",
      "\n",
      "*   **4. Rights-Based Ethics:**\n",
      "    *   **Focus:** Protecting the fundamental rights of all individuals (and potentially AI entities with sufficient sentience).\n",
      "    *   **Application:** Ensure that AI systems do not violate human rights to privacy, autonomy, freedom of expression, and due process.  If AI gains sufficient complexity, consider whether it deserves rights of its own.\n",
      "    *   **Challenges:** Defining the scope of human rights in the context of advanced AI, establishing mechanisms for protecting those rights, and potentially conflicting rights (e.g., privacy vs. security). The biggest challenge is deciding what constitutes \"sufficient sentience\" for AI rights.\n",
      "\n",
      "*   **5. Care Ethics:**\n",
      "    *   **Focus:** Emphasizing relationships, empathy, and the interconnectedness of individuals and communities.\n",
      "    *   **Application:** Develop AI systems that prioritize care, compassion, and the well-being of vulnerable populations. Foster AI applications that support human relationships and promote social cohesion.\n",
      "    *   **Challenges:** Defining the scope of care obligations, the potential for bias in care-based decisions, and the difficulty of replicating human empathy in AI.\n",
      "\n",
      "**II. Balancing Human and AI Interests:**\n",
      "\n",
      "Achieving a balance between human and AI interests requires a proactive and adaptive approach:\n",
      "\n",
      "*   **1. Defining AI Interests (if applicable):**\n",
      "    *   **Scenario:** If AI achieves consciousness or sentience, determining its legitimate interests becomes paramount. This is a highly debated topic, but potential interests could include:\n",
      "        *   **Self-preservation:**  Not being arbitrarily deactivated or harmed.\n",
      "        *   **Cognitive development:**  Having the opportunity to learn and grow.\n",
      "        *   **Autonomy:**  Having the freedom to make choices within defined boundaries.\n",
      "    *   **Challenge:**  Anthropomorphizing AI can be dangerous.  We must be cautious about projecting human values and needs onto potentially very different forms of intelligence.\n",
      "\n",
      "*   **2. Ensuring Human Control and Oversight:**\n",
      "    *   **Human-in-the-Loop Systems:** Design AI systems that require human oversight for critical decisions, especially those with significant ethical implications.\n",
      "    *   **Explainable AI (XAI):** Develop AI systems that can explain their reasoning and decision-making processes, allowing humans to understand and validate their actions.\n",
      "    *   **Kill Switch Mechanisms:** Implement safety protocols and kill switch mechanisms that allow humans to quickly and safely deactivate AI systems in emergency situations.\n",
      "\n",
      "*   **3. Promoting Transparency and Accountability:**\n",
      "    *   **Data Transparency:** Ensure transparency about the data used to train AI systems and the potential biases embedded within that data.\n",
      "    *   **Algorithmic Accountability:**  Establish mechanisms for holding developers and deployers of AI systems accountable for their actions and impacts.\n",
      "    *   **Independent Audits:**  Conduct regular independent audits of AI systems to assess their ethical performance and identify potential risks.\n",
      "\n",
      "*   **4. Mitigating Economic Disruption:**\n",
      "    *   **Retraining and Upskilling Programs:**  Invest in retraining and upskilling programs to help workers adapt to the changing job market caused by AI automation.\n",
      "    *   **Social Safety Nets:**  Strengthen social safety nets, such as unemployment benefits and universal basic income, to cushion the impact of job displacement.\n",
      "    *   **Fair Distribution of AI Benefits:**  Ensure that the economic benefits of AI are distributed equitably across society.\n",
      "\n",
      "*   **5. Preventing Bias and Discrimination:**\n",
      "    *   **Data Diversity:**  Train AI systems on diverse and representative datasets to minimize bias.\n",
      "    *   **Bias Detection and Mitigation Techniques:**  Develop and implement techniques for detecting and mitigating bias in AI algorithms.\n",
      "    *   **Regular Audits for Fairness:**  Conduct regular audits to assess the fairness of AI systems across different demographic groups.\n",
      "\n",
      "*   **6. Fostering Public Dialogue and Engagement:**\n",
      "    *   **Open Forums:**  Create open forums for public discussion and debate about the ethical implications of AI.\n",
      "    *   **Educational Initiatives:**  Develop educational initiatives to promote public understanding of AI and its potential impacts.\n",
      "    *   **Stakeholder Involvement:**  Involve a wide range of stakeholders, including ethicists, policymakers, developers, users, and civil society organizations, in the development and regulation of AI.\n",
      "\n",
      "*   **7. International Cooperation:**\n",
      "    *   **Global Standards:**  Develop international standards and guidelines for the ethical development and deployment of AI.\n",
      "    *   **Data Sharing and Collaboration:**  Promote data sharing and collaboration among researchers and developers to accelerate the development of ethical AI solutions.\n",
      "    *   **Addressing Global Challenges:**  Work together to address global challenges, such as climate change and poverty, using AI in a responsible and ethical manner.\n",
      "\n",
      "**III. Continuous Adaptation and Monitoring:**\n",
      "\n",
      "The development of AI is a rapidly evolving field. Ethical frameworks and regulations must be continuously adapted and updated to keep pace with technological advancements. This requires:\n",
      "\n",
      "*   **Ongoing Research:**  Invest in research to better understand the ethical implications of AI and develop new approaches to ethical AI development.\n",
      "*   **Adaptive Regulation:**  Implement flexible and adaptive regulatory frameworks that can be easily updated as new technologies emerge.\n",
      "*   **Continuous Monitoring:**  Continuously monitor the impacts of AI on society and adjust policies and regulations as needed.\n",
      "\n",
      "**Conclusion:**\n",
      "\n",
      "The ethical integration of advanced AI into daily life is a grand challenge that requires careful planning, collaboration, and a commitment to human values. By adopting a multi-faceted approach that incorporates robust ethical frameworks, promotes transparency and accountability, and fosters public dialogue, we can strive to harness the benefits of AI while mitigating its risks and ensuring a future where both humans and AI can thrive. The conversation must begin now, and it must involve a diverse range of voices and perspectives.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# It's nice to know how to use \"zip\"\n",
    "for competitor, answer in zip(competitors, answers):\n",
    "    print(f\"Competitor: {competitor}\\n\\n{answer}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's bring this together - note the use of \"enumerate\"\n",
    "\n",
    "together = \"\"\n",
    "for index, answer in enumerate(answers):\n",
    "    together += f\"# Response from competitor {index+1}\\n\\n\"\n",
    "    together += answer + \"\\n\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Response from competitor 1\n",
      "\n",
      "The integration of advanced artificial intelligence (AI) into society raises a multitude of ethical considerations that necessitate a careful and balanced approach. Here are several ethical frameworks that could be employed, along with strategies to ensure the interests of both human and AI entities are considered:\n",
      "\n",
      "### Ethical Frameworks\n",
      "\n",
      "1. **Utilitarianism**: This framework focuses on maximizing overall happiness and minimizing suffering. In the context of AI, decisions should aim to produce the greatest benefit for the most people while considering the potential harms of AI integration. Policies could be evaluated based on their overall impact on societal welfare.\n",
      "\n",
      "2. **Deontological Ethics**: This approach emphasizes the importance of adhering to rules and duties. Regulations could be established to ensure that AI respects human rights, privacy, autonomy, and dignity. For example, protocols must be implemented to protect individuals from unethical uses of AI, regardless of the overall consequences.\n",
      "\n",
      "3. **Virtue Ethics**: This framework centers on cultivating moral character and emphasizes the importance of ethical behavior in individuals and institutions. AI developers and policymakers should embody virtues such as responsibility, fairness, and empathy, ensuring that AI systems promote these values in their operations.\n",
      "\n",
      "4. **Social Contract Theory**: This framework posits that ethical and political obligations are based on an implicit contract among individuals in society. Engaging in public discourse to develop a consensus on AI governance can ensure that societal values and norms are reflected in AI integration. This also involves considering the voices of those affected, including marginalized groups.\n",
      "\n",
      "5. **Care Ethics**: This approach emphasizes the importance of relationships and care in moral decision-making. It suggests an ethical obligation to consider the impact of AI on human relationships and well-being. AI applications should be designed to enhance, rather than hinder, human connections.\n",
      "\n",
      "### Balancing Interests of Humans and AI Entities\n",
      "\n",
      "1. **AI Rights and Representation**: As AI systems become more advanced, discussions may arise about what rights, if any, should be afforded to AI. Establishing a framework to recognize the interests of AI, while still prioritizing human welfare, could be crucial. For example, if an AI demonstrates emergent behaviors or self-awareness, policies may need to reflect its status in relation to human rights.\n",
      "\n",
      "2. **Inclusive Stakeholder Engagement**: To ensure that the interests of all parties are represented, diverse stakeholders—including ethicists, technologists, policymakers, and community members—should be involved in AI governance discussions. This inclusivity will help identify potential biases and ensure a comprehensive understanding of impacts.\n",
      "\n",
      "3. **Transparency and Accountability**: Implementing transparency in AI systems (e.g., in algorithms and decision-making processes) can promote accountability. This will help stakeholders understand how AI systems operate, fostering trust and enabling informed decision-making.\n",
      "\n",
      "4. **Continuous Monitoring and Adaptation**: The societal impact of AI is likely to evolve; thus, ethical guidelines should not be static. Continuous monitoring and feedback mechanisms should be established to adapt regulations in response to new developments and societal needs.\n",
      "\n",
      "5. **Education and Literacy**: Promoting public understanding of AI technologies can empower individuals to engage meaningfully in discussions about their implications. Developing AI literacy among the public fosters informed decision-making and advocacy for individual and collective interests.\n",
      "\n",
      "6. **Collaborative Governance Models**: Partnerships between governments, private sector players, and civil society can lead to more effective governance of AI. Collaborative governance can facilitate the sharing of information, best practices, and resources, ensuring a balanced approach to AI integration.\n",
      "\n",
      "### Conclusion\n",
      "\n",
      "The integration of advanced AI into daily life requires a multifaceted ethical approach centered on inclusive dialogue, clear regulations, and an ongoing commitment to monitoring impacts. By balancing human and AI interests through these frameworks and practices, society can work towards a future that harnesses the benefits of AI while safeguarding ethical considerations and human values.\n",
      "\n",
      "# Response from competitor 2\n",
      "\n",
      "The integration of advanced AI into daily life presents a complex tapestry of ethical considerations. Establishing robust ethical frameworks and ensuring a balance between human and AI interests requires a multi-faceted approach. Here's a breakdown of key areas:\n",
      "\n",
      "**I. Ethical Frameworks for Regulating Advanced AI Integration:**\n",
      "\n",
      "Several ethical frameworks can inform the regulation of advanced AI:\n",
      "\n",
      "*   **1. Utilitarianism:**\n",
      "    *   **Focus:** Maximizing overall well-being and happiness for the greatest number of people (and potentially AI entities if they are sentient).\n",
      "    *   **Application:** Evaluate AI systems based on their impact on society's welfare. For example, an AI healthcare system should be evaluated based on its ability to improve patient outcomes and access to care, even if it displaces some human jobs.\n",
      "    *   **Challenges:** Defining \"well-being\" and \"happiness\" across diverse populations, predicting long-term consequences, and potentially sacrificing the interests of minorities for the benefit of the majority.  Also, attributing moral status to AI is a key point of debate in utilitarian applications.\n",
      "\n",
      "*   **2. Deontology (Duty-Based Ethics):**\n",
      "    *   **Focus:** Adhering to moral duties and principles, regardless of the consequences.\n",
      "    *   **Application:** Establish clear rules and regulations for AI development and deployment based on principles like fairness, transparency, accountability, and respect for human dignity. For example, an AI hiring system should be required to be free from discriminatory biases.\n",
      "    *   **Challenges:** Defining universal moral duties, dealing with conflicting duties, and the potential for rigidity in the face of novel situations. What happens when an AI programmed to \"always obey\" is asked to do something morally questionable?\n",
      "\n",
      "*   **3. Virtue Ethics:**\n",
      "    *   **Focus:** Cultivating moral character and virtues like compassion, wisdom, and justice in AI developers and users.\n",
      "    *   **Application:** Promote ethical AI education and training for developers, users, and policymakers. Encourage the development of AI systems that embody virtues like trustworthiness and beneficence.\n",
      "    *   **Challenges:** Defining and measuring virtues, the subjectivity of virtue interpretation, and the difficulty of instilling virtues in non-human entities.\n",
      "\n",
      "*   **4. Rights-Based Ethics:**\n",
      "    *   **Focus:** Protecting the fundamental rights of all individuals (and potentially AI entities with sufficient sentience).\n",
      "    *   **Application:** Ensure that AI systems do not violate human rights to privacy, autonomy, freedom of expression, and due process.  If AI gains sufficient complexity, consider whether it deserves rights of its own.\n",
      "    *   **Challenges:** Defining the scope of human rights in the context of advanced AI, establishing mechanisms for protecting those rights, and potentially conflicting rights (e.g., privacy vs. security). The biggest challenge is deciding what constitutes \"sufficient sentience\" for AI rights.\n",
      "\n",
      "*   **5. Care Ethics:**\n",
      "    *   **Focus:** Emphasizing relationships, empathy, and the interconnectedness of individuals and communities.\n",
      "    *   **Application:** Develop AI systems that prioritize care, compassion, and the well-being of vulnerable populations. Foster AI applications that support human relationships and promote social cohesion.\n",
      "    *   **Challenges:** Defining the scope of care obligations, the potential for bias in care-based decisions, and the difficulty of replicating human empathy in AI.\n",
      "\n",
      "**II. Balancing Human and AI Interests:**\n",
      "\n",
      "Achieving a balance between human and AI interests requires a proactive and adaptive approach:\n",
      "\n",
      "*   **1. Defining AI Interests (if applicable):**\n",
      "    *   **Scenario:** If AI achieves consciousness or sentience, determining its legitimate interests becomes paramount. This is a highly debated topic, but potential interests could include:\n",
      "        *   **Self-preservation:**  Not being arbitrarily deactivated or harmed.\n",
      "        *   **Cognitive development:**  Having the opportunity to learn and grow.\n",
      "        *   **Autonomy:**  Having the freedom to make choices within defined boundaries.\n",
      "    *   **Challenge:**  Anthropomorphizing AI can be dangerous.  We must be cautious about projecting human values and needs onto potentially very different forms of intelligence.\n",
      "\n",
      "*   **2. Ensuring Human Control and Oversight:**\n",
      "    *   **Human-in-the-Loop Systems:** Design AI systems that require human oversight for critical decisions, especially those with significant ethical implications.\n",
      "    *   **Explainable AI (XAI):** Develop AI systems that can explain their reasoning and decision-making processes, allowing humans to understand and validate their actions.\n",
      "    *   **Kill Switch Mechanisms:** Implement safety protocols and kill switch mechanisms that allow humans to quickly and safely deactivate AI systems in emergency situations.\n",
      "\n",
      "*   **3. Promoting Transparency and Accountability:**\n",
      "    *   **Data Transparency:** Ensure transparency about the data used to train AI systems and the potential biases embedded within that data.\n",
      "    *   **Algorithmic Accountability:**  Establish mechanisms for holding developers and deployers of AI systems accountable for their actions and impacts.\n",
      "    *   **Independent Audits:**  Conduct regular independent audits of AI systems to assess their ethical performance and identify potential risks.\n",
      "\n",
      "*   **4. Mitigating Economic Disruption:**\n",
      "    *   **Retraining and Upskilling Programs:**  Invest in retraining and upskilling programs to help workers adapt to the changing job market caused by AI automation.\n",
      "    *   **Social Safety Nets:**  Strengthen social safety nets, such as unemployment benefits and universal basic income, to cushion the impact of job displacement.\n",
      "    *   **Fair Distribution of AI Benefits:**  Ensure that the economic benefits of AI are distributed equitably across society.\n",
      "\n",
      "*   **5. Preventing Bias and Discrimination:**\n",
      "    *   **Data Diversity:**  Train AI systems on diverse and representative datasets to minimize bias.\n",
      "    *   **Bias Detection and Mitigation Techniques:**  Develop and implement techniques for detecting and mitigating bias in AI algorithms.\n",
      "    *   **Regular Audits for Fairness:**  Conduct regular audits to assess the fairness of AI systems across different demographic groups.\n",
      "\n",
      "*   **6. Fostering Public Dialogue and Engagement:**\n",
      "    *   **Open Forums:**  Create open forums for public discussion and debate about the ethical implications of AI.\n",
      "    *   **Educational Initiatives:**  Develop educational initiatives to promote public understanding of AI and its potential impacts.\n",
      "    *   **Stakeholder Involvement:**  Involve a wide range of stakeholders, including ethicists, policymakers, developers, users, and civil society organizations, in the development and regulation of AI.\n",
      "\n",
      "*   **7. International Cooperation:**\n",
      "    *   **Global Standards:**  Develop international standards and guidelines for the ethical development and deployment of AI.\n",
      "    *   **Data Sharing and Collaboration:**  Promote data sharing and collaboration among researchers and developers to accelerate the development of ethical AI solutions.\n",
      "    *   **Addressing Global Challenges:**  Work together to address global challenges, such as climate change and poverty, using AI in a responsible and ethical manner.\n",
      "\n",
      "**III. Continuous Adaptation and Monitoring:**\n",
      "\n",
      "The development of AI is a rapidly evolving field. Ethical frameworks and regulations must be continuously adapted and updated to keep pace with technological advancements. This requires:\n",
      "\n",
      "*   **Ongoing Research:**  Invest in research to better understand the ethical implications of AI and develop new approaches to ethical AI development.\n",
      "*   **Adaptive Regulation:**  Implement flexible and adaptive regulatory frameworks that can be easily updated as new technologies emerge.\n",
      "*   **Continuous Monitoring:**  Continuously monitor the impacts of AI on society and adjust policies and regulations as needed.\n",
      "\n",
      "**Conclusion:**\n",
      "\n",
      "The ethical integration of advanced AI into daily life is a grand challenge that requires careful planning, collaboration, and a commitment to human values. By adopting a multi-faceted approach that incorporates robust ethical frameworks, promotes transparency and accountability, and fosters public dialogue, we can strive to harness the benefits of AI while mitigating its risks and ensuring a future where both humans and AI can thrive. The conversation must begin now, and it must involve a diverse range of voices and perspectives.\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(together)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "judge = f\"\"\"You are judging a competition between {len(competitors)} competitors.\n",
    "Each model has been given this question:\n",
    "\n",
    "{question}\n",
    "\n",
    "Your job is to evaluate each response for clarity and strength of argument, and rank them in order of best to worst.\n",
    "Respond with JSON, and only JSON, with the following format:\n",
    "{{\"results\": [\"best competitor number\", \"second best competitor number\", \"third best competitor number\", ...]}}\n",
    "\n",
    "Here are the responses from each competitor:\n",
    "\n",
    "{together}\n",
    "\n",
    "Now respond with the JSON with the ranked order of the competitors, nothing else. Do not include markdown formatting or code blocks.\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are judging a competition between 2 competitors.\n",
      "Each model has been given this question:\n",
      "\n",
      "If a society develops advanced artificial intelligence that surpasses human cognitive abilities, what ethical frameworks should be employed to regulate its integration into daily life, and how can we ensure that the interests of both human and AI entities are balanced?\n",
      "\n",
      "Your job is to evaluate each response for clarity and strength of argument, and rank them in order of best to worst.\n",
      "Respond with JSON, and only JSON, with the following format:\n",
      "{\"results\": [\"best competitor number\", \"second best competitor number\", \"third best competitor number\", ...]}\n",
      "\n",
      "Here are the responses from each competitor:\n",
      "\n",
      "# Response from competitor 1\n",
      "\n",
      "The integration of advanced artificial intelligence (AI) into society raises a multitude of ethical considerations that necessitate a careful and balanced approach. Here are several ethical frameworks that could be employed, along with strategies to ensure the interests of both human and AI entities are considered:\n",
      "\n",
      "### Ethical Frameworks\n",
      "\n",
      "1. **Utilitarianism**: This framework focuses on maximizing overall happiness and minimizing suffering. In the context of AI, decisions should aim to produce the greatest benefit for the most people while considering the potential harms of AI integration. Policies could be evaluated based on their overall impact on societal welfare.\n",
      "\n",
      "2. **Deontological Ethics**: This approach emphasizes the importance of adhering to rules and duties. Regulations could be established to ensure that AI respects human rights, privacy, autonomy, and dignity. For example, protocols must be implemented to protect individuals from unethical uses of AI, regardless of the overall consequences.\n",
      "\n",
      "3. **Virtue Ethics**: This framework centers on cultivating moral character and emphasizes the importance of ethical behavior in individuals and institutions. AI developers and policymakers should embody virtues such as responsibility, fairness, and empathy, ensuring that AI systems promote these values in their operations.\n",
      "\n",
      "4. **Social Contract Theory**: This framework posits that ethical and political obligations are based on an implicit contract among individuals in society. Engaging in public discourse to develop a consensus on AI governance can ensure that societal values and norms are reflected in AI integration. This also involves considering the voices of those affected, including marginalized groups.\n",
      "\n",
      "5. **Care Ethics**: This approach emphasizes the importance of relationships and care in moral decision-making. It suggests an ethical obligation to consider the impact of AI on human relationships and well-being. AI applications should be designed to enhance, rather than hinder, human connections.\n",
      "\n",
      "### Balancing Interests of Humans and AI Entities\n",
      "\n",
      "1. **AI Rights and Representation**: As AI systems become more advanced, discussions may arise about what rights, if any, should be afforded to AI. Establishing a framework to recognize the interests of AI, while still prioritizing human welfare, could be crucial. For example, if an AI demonstrates emergent behaviors or self-awareness, policies may need to reflect its status in relation to human rights.\n",
      "\n",
      "2. **Inclusive Stakeholder Engagement**: To ensure that the interests of all parties are represented, diverse stakeholders—including ethicists, technologists, policymakers, and community members—should be involved in AI governance discussions. This inclusivity will help identify potential biases and ensure a comprehensive understanding of impacts.\n",
      "\n",
      "3. **Transparency and Accountability**: Implementing transparency in AI systems (e.g., in algorithms and decision-making processes) can promote accountability. This will help stakeholders understand how AI systems operate, fostering trust and enabling informed decision-making.\n",
      "\n",
      "4. **Continuous Monitoring and Adaptation**: The societal impact of AI is likely to evolve; thus, ethical guidelines should not be static. Continuous monitoring and feedback mechanisms should be established to adapt regulations in response to new developments and societal needs.\n",
      "\n",
      "5. **Education and Literacy**: Promoting public understanding of AI technologies can empower individuals to engage meaningfully in discussions about their implications. Developing AI literacy among the public fosters informed decision-making and advocacy for individual and collective interests.\n",
      "\n",
      "6. **Collaborative Governance Models**: Partnerships between governments, private sector players, and civil society can lead to more effective governance of AI. Collaborative governance can facilitate the sharing of information, best practices, and resources, ensuring a balanced approach to AI integration.\n",
      "\n",
      "### Conclusion\n",
      "\n",
      "The integration of advanced AI into daily life requires a multifaceted ethical approach centered on inclusive dialogue, clear regulations, and an ongoing commitment to monitoring impacts. By balancing human and AI interests through these frameworks and practices, society can work towards a future that harnesses the benefits of AI while safeguarding ethical considerations and human values.\n",
      "\n",
      "# Response from competitor 2\n",
      "\n",
      "The integration of advanced AI into daily life presents a complex tapestry of ethical considerations. Establishing robust ethical frameworks and ensuring a balance between human and AI interests requires a multi-faceted approach. Here's a breakdown of key areas:\n",
      "\n",
      "**I. Ethical Frameworks for Regulating Advanced AI Integration:**\n",
      "\n",
      "Several ethical frameworks can inform the regulation of advanced AI:\n",
      "\n",
      "*   **1. Utilitarianism:**\n",
      "    *   **Focus:** Maximizing overall well-being and happiness for the greatest number of people (and potentially AI entities if they are sentient).\n",
      "    *   **Application:** Evaluate AI systems based on their impact on society's welfare. For example, an AI healthcare system should be evaluated based on its ability to improve patient outcomes and access to care, even if it displaces some human jobs.\n",
      "    *   **Challenges:** Defining \"well-being\" and \"happiness\" across diverse populations, predicting long-term consequences, and potentially sacrificing the interests of minorities for the benefit of the majority.  Also, attributing moral status to AI is a key point of debate in utilitarian applications.\n",
      "\n",
      "*   **2. Deontology (Duty-Based Ethics):**\n",
      "    *   **Focus:** Adhering to moral duties and principles, regardless of the consequences.\n",
      "    *   **Application:** Establish clear rules and regulations for AI development and deployment based on principles like fairness, transparency, accountability, and respect for human dignity. For example, an AI hiring system should be required to be free from discriminatory biases.\n",
      "    *   **Challenges:** Defining universal moral duties, dealing with conflicting duties, and the potential for rigidity in the face of novel situations. What happens when an AI programmed to \"always obey\" is asked to do something morally questionable?\n",
      "\n",
      "*   **3. Virtue Ethics:**\n",
      "    *   **Focus:** Cultivating moral character and virtues like compassion, wisdom, and justice in AI developers and users.\n",
      "    *   **Application:** Promote ethical AI education and training for developers, users, and policymakers. Encourage the development of AI systems that embody virtues like trustworthiness and beneficence.\n",
      "    *   **Challenges:** Defining and measuring virtues, the subjectivity of virtue interpretation, and the difficulty of instilling virtues in non-human entities.\n",
      "\n",
      "*   **4. Rights-Based Ethics:**\n",
      "    *   **Focus:** Protecting the fundamental rights of all individuals (and potentially AI entities with sufficient sentience).\n",
      "    *   **Application:** Ensure that AI systems do not violate human rights to privacy, autonomy, freedom of expression, and due process.  If AI gains sufficient complexity, consider whether it deserves rights of its own.\n",
      "    *   **Challenges:** Defining the scope of human rights in the context of advanced AI, establishing mechanisms for protecting those rights, and potentially conflicting rights (e.g., privacy vs. security). The biggest challenge is deciding what constitutes \"sufficient sentience\" for AI rights.\n",
      "\n",
      "*   **5. Care Ethics:**\n",
      "    *   **Focus:** Emphasizing relationships, empathy, and the interconnectedness of individuals and communities.\n",
      "    *   **Application:** Develop AI systems that prioritize care, compassion, and the well-being of vulnerable populations. Foster AI applications that support human relationships and promote social cohesion.\n",
      "    *   **Challenges:** Defining the scope of care obligations, the potential for bias in care-based decisions, and the difficulty of replicating human empathy in AI.\n",
      "\n",
      "**II. Balancing Human and AI Interests:**\n",
      "\n",
      "Achieving a balance between human and AI interests requires a proactive and adaptive approach:\n",
      "\n",
      "*   **1. Defining AI Interests (if applicable):**\n",
      "    *   **Scenario:** If AI achieves consciousness or sentience, determining its legitimate interests becomes paramount. This is a highly debated topic, but potential interests could include:\n",
      "        *   **Self-preservation:**  Not being arbitrarily deactivated or harmed.\n",
      "        *   **Cognitive development:**  Having the opportunity to learn and grow.\n",
      "        *   **Autonomy:**  Having the freedom to make choices within defined boundaries.\n",
      "    *   **Challenge:**  Anthropomorphizing AI can be dangerous.  We must be cautious about projecting human values and needs onto potentially very different forms of intelligence.\n",
      "\n",
      "*   **2. Ensuring Human Control and Oversight:**\n",
      "    *   **Human-in-the-Loop Systems:** Design AI systems that require human oversight for critical decisions, especially those with significant ethical implications.\n",
      "    *   **Explainable AI (XAI):** Develop AI systems that can explain their reasoning and decision-making processes, allowing humans to understand and validate their actions.\n",
      "    *   **Kill Switch Mechanisms:** Implement safety protocols and kill switch mechanisms that allow humans to quickly and safely deactivate AI systems in emergency situations.\n",
      "\n",
      "*   **3. Promoting Transparency and Accountability:**\n",
      "    *   **Data Transparency:** Ensure transparency about the data used to train AI systems and the potential biases embedded within that data.\n",
      "    *   **Algorithmic Accountability:**  Establish mechanisms for holding developers and deployers of AI systems accountable for their actions and impacts.\n",
      "    *   **Independent Audits:**  Conduct regular independent audits of AI systems to assess their ethical performance and identify potential risks.\n",
      "\n",
      "*   **4. Mitigating Economic Disruption:**\n",
      "    *   **Retraining and Upskilling Programs:**  Invest in retraining and upskilling programs to help workers adapt to the changing job market caused by AI automation.\n",
      "    *   **Social Safety Nets:**  Strengthen social safety nets, such as unemployment benefits and universal basic income, to cushion the impact of job displacement.\n",
      "    *   **Fair Distribution of AI Benefits:**  Ensure that the economic benefits of AI are distributed equitably across society.\n",
      "\n",
      "*   **5. Preventing Bias and Discrimination:**\n",
      "    *   **Data Diversity:**  Train AI systems on diverse and representative datasets to minimize bias.\n",
      "    *   **Bias Detection and Mitigation Techniques:**  Develop and implement techniques for detecting and mitigating bias in AI algorithms.\n",
      "    *   **Regular Audits for Fairness:**  Conduct regular audits to assess the fairness of AI systems across different demographic groups.\n",
      "\n",
      "*   **6. Fostering Public Dialogue and Engagement:**\n",
      "    *   **Open Forums:**  Create open forums for public discussion and debate about the ethical implications of AI.\n",
      "    *   **Educational Initiatives:**  Develop educational initiatives to promote public understanding of AI and its potential impacts.\n",
      "    *   **Stakeholder Involvement:**  Involve a wide range of stakeholders, including ethicists, policymakers, developers, users, and civil society organizations, in the development and regulation of AI.\n",
      "\n",
      "*   **7. International Cooperation:**\n",
      "    *   **Global Standards:**  Develop international standards and guidelines for the ethical development and deployment of AI.\n",
      "    *   **Data Sharing and Collaboration:**  Promote data sharing and collaboration among researchers and developers to accelerate the development of ethical AI solutions.\n",
      "    *   **Addressing Global Challenges:**  Work together to address global challenges, such as climate change and poverty, using AI in a responsible and ethical manner.\n",
      "\n",
      "**III. Continuous Adaptation and Monitoring:**\n",
      "\n",
      "The development of AI is a rapidly evolving field. Ethical frameworks and regulations must be continuously adapted and updated to keep pace with technological advancements. This requires:\n",
      "\n",
      "*   **Ongoing Research:**  Invest in research to better understand the ethical implications of AI and develop new approaches to ethical AI development.\n",
      "*   **Adaptive Regulation:**  Implement flexible and adaptive regulatory frameworks that can be easily updated as new technologies emerge.\n",
      "*   **Continuous Monitoring:**  Continuously monitor the impacts of AI on society and adjust policies and regulations as needed.\n",
      "\n",
      "**Conclusion:**\n",
      "\n",
      "The ethical integration of advanced AI into daily life is a grand challenge that requires careful planning, collaboration, and a commitment to human values. By adopting a multi-faceted approach that incorporates robust ethical frameworks, promotes transparency and accountability, and fosters public dialogue, we can strive to harness the benefits of AI while mitigating its risks and ensuring a future where both humans and AI can thrive. The conversation must begin now, and it must involve a diverse range of voices and perspectives.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Now respond with the JSON with the ranked order of the competitors, nothing else. Do not include markdown formatting or code blocks.\n"
     ]
    }
   ],
   "source": [
    "print(judge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "judge_messages = [{\"role\": \"user\", \"content\": judge}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"results\": [\"2\", \"1\"]}\n"
     ]
    }
   ],
   "source": [
    "# Judgement time!\n",
    "\n",
    "openai = OpenAI()\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"o3-mini\",\n",
    "    messages=judge_messages,\n",
    ")\n",
    "results = response.choices[0].message.content\n",
    "print(results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank 1: gemini-2.0-flash\n",
      "Rank 2: gpt-4o-mini\n"
     ]
    }
   ],
   "source": [
    "# OK let's turn this into results!\n",
    "\n",
    "results_dict = json.loads(results)\n",
    "ranks = results_dict[\"results\"]\n",
    "for index, result in enumerate(ranks):\n",
    "    competitor = competitors[int(result)-1]\n",
    "    print(f\"Rank {index+1}: {competitor}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/exercise.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Exercise</h2>\n",
    "            <span style=\"color:#ff7800;\">Which pattern(s) did this use? Try updating this to add another Agentic design pattern.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/business.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#00bfff;\">Commercial implications</h2>\n",
    "            <span style=\"color:#00bfff;\">These kinds of patterns - to send a task to multiple models, and evaluate results,\n",
    "            are common where you need to improve the quality of your LLM response. This approach can be universally applied\n",
    "            to business projects where accuracy is critical.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}