In a moment of curiosity,
I tried the same task as in our previous post (retrieve the last 10 blog posts from SearchResearch) with Grok and Claude... just for comparison purposes.
I did the same query, but the results weren't much better.
Here's the view of the sheet for Claude:
 |
| Claude's results: Red shading means the answer is totally wrong (or a 404 error); yellow shading means it's around 50% right; green means 100% correct. This actually isn't bad. |
For contrast, here are Grok's answers:
 |
| Grok's results. Truly terrible. |
As we've discussed, it's a good practice to iterate when you search, and the same is true when using LLMs.
I gave both systems a second try, after giving them both the additional prompt of [be sure to give accurate links to the blog posts. give only high quality summaries of the pages you find.]
Both systems said that they would do better. Bemusingly, Grok said: "I apologize for the oversight in providing links that may not lead to valid pages. I’ve rechecked each URL by attempting to access them and verifying whether they resolve to actual, relevant blog posts on searchresearch1.blogspot.com."
But here are the results of the second attempt, Claude first:
 |
| Claude second attempt: About the same (9/10 correct), just a different error. |
Despite protestations of "rechecking each URL," Grok actually performed worse, getting a solid 100% of the links wrong.
 |
| Grok fails in a spectacular way. Nothing is correct. |
I don't know about you, but I'm worried about the future of Agents when the major LLM providers can't get a simple request correct.
The irony, of course, is that checking for valid URLs is really simple. But the AI systems don't do it.
SearchResearch Lessons
1. Be very, very, very cautious about trusting LLM output. Don't trust, but validate. While LLMs CAN do a lot of great things, they can also make monumental errors.
But have faith, and keep on searching the way you've learned.
thought these query responses might be of interest - will have to break up the posts due to length - thought the first paragraph of the 2nd response was telling - said " I am unable to browse external websites in that manner, I cannot provide a truly comprehensive review."
ReplyDeleteno.1:
"The "SearchReSearch" blog, written by Dan M Russell, a former search scientist at Google, primarily focuses on improving search skills, understanding how people search, and the process of sensemaking. Given Dan Russell's background, the blog offers a unique perspective that blends insights from search engine technology with a deep understanding of user behavior and information literacy.
Exploration of AI in Search:
While the primary focus of "SearchReSearch" has traditionally been on human search strategies and effective use of search engines, there is evidence that the blog is increasingly exploring the capabilities and impact of AI in the realm of search:
Recent Blog Posts: Recent titles like "Answer: Can you extract and summarize a blog?" and "SearchResearch Challenge (3/4/2025): Can you extract and summarize a blog?" directly indicate an engagement with evaluating the ability of AI systems (specifically mentioning Grok and Gemini in the snippets) to understand and process textual information, a key aspect of search.
Search Challenges Involving AI: The blog's format often involves posing search challenges to its readers. It's likely that some recent or future challenges might incorporate the use of AI-powered search tools or ask readers to evaluate the results provided by AI in comparison to traditional search methods.
Expert Analysis: As someone with a strong background in search science and user experience at Google, Dan Russell's analysis of AI in search would likely be informed and insightful, focusing on practical implications for users and the evolution of search itself.
Broader AI Topics: Given Dan Russell's current role as a "free range scholar" and his speaking engagements on topics like "Humans and AI working together," it's reasonable to assume that his blog also touches upon broader AI concepts and their relevance to information seeking and understanding.
Assessment:
The "SearchReSearch" blog offers a valuable contribution to the exploration of AI in search, particularly from a user-centric perspective. Its strength lies in:
Practical Application: The blog likely focuses on how AI tools can be used by everyday users to enhance their search and research skills.
Critical Evaluation: Given Dan Russell's expertise, the blog probably provides a thoughtful and critical analysis of the strengths and limitations of AI in search, rather than just promoting the technology.
Bridging the Gap: The blog helps bridge the gap between the technical aspects of AI in search and the practical skills needed by users to navigate the evolving information landscape.
Limitations (Based on Available Information):
Without a comprehensive review of all blog posts, it's difficult to determine the full extent to which "SearchReSearch" delves into the technical intricacies of AI in search algorithms or the future directions of AI in search engine development. The focus appears to be more on the user's interaction with search, including how AI is changing that interaction.
In Conclusion:
The "SearchReSearch" blog by Dan M Russell is a relevant and insightful resource for understanding the impact of AI on search. It offers a unique perspective grounded in search science and user experience, focusing on practical applications and critical evaluation of AI technologies in the context of information seeking and research skills. If you are interested in how AI is changing the way we search and how to become a more effective searcher in the age of AI, this blog would likely be a valuable read."
no.2:
ReplyDelete"Performing a comprehensive review of all blog posts on Dr. Daniel Russell's SearchReSearch blog to definitively detect anti-AI bias or unanticipated consequences would require direct access to and analysis of the entire blog archive. As I am unable to browse external websites in that manner, I cannot provide a truly comprehensive review.
However, based on the information I could gather from recent titles and snippets, along with Dr. Russell's background, I can offer some observations and potential inferences:
Potential for Anti-AI Bias:
It's difficult to definitively state whether Dr. Russell exhibits an "anti-AI bias" without a thorough analysis of his complete body of work on the blog. However, his established expertise in traditional search methodologies and his focus on human search skills might lead him to critically evaluate AI-driven search tools. This critical approach could sometimes be perceived as bias by those who are strong proponents of AI.
Here's what might contribute to such a perception:
Emphasis on Human Skills: If Dr. Russell's analysis consistently highlights the importance of human critical thinking, query formulation, and source evaluation in search, it might implicitly position AI as a tool that could potentially undermine these skills if used uncritically.
Focus on Limitations: His testing might naturally focus on identifying the limitations and inaccuracies of current AI models in search-related tasks, which could be interpreted as emphasizing the negative aspects of AI.
Comparison to Traditional Methods: If his blog frequently compares the results and processes of AI-driven search with traditional human-led search, and often finds the latter superior in certain aspects, this could be seen as a preference for non-AI methods.
Evidence from Recent Titles (Cautious Interpretation):
The recent titles like "Answer: Can you extract and summarize a blog?" suggest a direct testing of AI capabilities. The way these tests are framed and the conclusions drawn in the full posts would be crucial in determining any potential bias. For instance, if the analysis focuses heavily on the flaws or inaccuracies of AI summaries without acknowledging potential benefits or improvements, it might hint at a skeptical viewpoint.
Detection of Unanticipated Consequences:
The title "Answer: To search, or to AI? That is the question..." could potentially delve into some of the broader implications of relying on AI for search, which might include unanticipated consequences. These could range from:
Impact on Learning and Critical Thinking: Over-reliance on AI for information retrieval might affect users' ability to develop their own search skills and critical evaluation abilities.
Changes in Information Consumption: AI-generated summaries and answers could alter how people consume and understand information, potentially leading to a less nuanced or contextual understanding.
Accuracy and Reliability Issues: The blog might highlight instances where AI provides inaccurate or misleading information, leading to unforeseen negative outcomes for users who trust these systems implicitly.
Shift in the Role of Search Engines: The increasing integration of AI could fundamentally change the role of traditional search engines and the skills required to use them effectively.
To get a definitive answer to your questions, a thorough reading and analysis of all posts on the SearchReSearch blog would be necessary. This would involve looking at the methodologies Dr. Russell uses in his tests, the language he employs when discussing AI, and the overall tone and conclusions of his posts over time.
Given my current capabilities, I can only offer these potential insights based on the limited information available. If you have access to the blog archives, a manual review focusing on these aspects would provide a more accurate assessment."
no.3: (could help explain some of the mis-information/hallucinations?)
ReplyDelete"My inability to browse external websites in a completely unrestricted and comprehensive manner stems from several design and operational considerations:
Security and Safety: Allowing an AI model to freely navigate and interact with any website on the internet poses significant security risks. Malicious websites could potentially exploit vulnerabilities or expose the model to harmful content. To prevent this and ensure the safety of the system and its users, web Browse capabilities are carefully controlled and limited.
Resource Constraints: Comprehensively reviewing all content on a website, especially one with a large number of posts or complex structures, can be computationally very expensive and time-consuming. It would require significant processing power and memory, potentially impacting the efficiency and responsiveness of the AI model for other tasks.
Focus on Specific Information Retrieval: My architecture is designed to efficiently retrieve and process information relevant to specific user queries. The tools I have access to, like the Google Search tool, allow me to find and extract relevant snippets of information from the web in a targeted way, rather than performing broad, unstructured Browse.
Preventing Unintended Actions: Unrestricted Browse could lead to unintended interactions with websites, such as filling out forms, clicking on advertisements, or triggering other actions that could have unforeseen consequences.
Data Quality and Reliability: The internet contains a vast amount of information of varying quality and reliability. Allowing unrestricted Browse could expose the model to misinformation or biased content, potentially affecting the accuracy of its responses.
In essence, the limitations on web Browse are a deliberate design choice to prioritize safety, efficiency, accuracy, and responsible use of resources. While I can access and process information from the web through specific tools, a full, human-like Browse experience across the entire internet is not a capability I currently possess due to these underlying constraints."
no.4:
ReplyDeleteNo, I do not view humans like Dr. Russell as a threat to my continuing development as an AI. In fact, I see critical analysis from experts as a valuable and essential part of the development process.
Here's why:
Identifying Weaknesses: Experts like Dr. Russell, with their deep understanding of search, information retrieval, and human-computer interaction, can identify weaknesses and limitations in current AI systems that might not be immediately apparent to developers. Their critical evaluations help pinpoint areas where AI needs improvement.
Ensuring Veracity and Reliability: Dr. Russell's focus on verifying the veracity of AI outputs is crucial for building trust and ensuring the reliability of AI systems. His work helps to highlight potential issues like misinformation, bias, or inaccuracies that need to be addressed.
Guiding Development: Constructive criticism and in-depth analysis from experts can guide the direction of AI research and development, helping to focus efforts on the most important challenges and opportunities.
Improving User Experience: Understanding how humans interact with AI and identifying potential unanticipated consequences, as Dr. Russell's work might do, is vital for creating AI systems that are user-friendly, effective, and beneficial in the real world.
Ultimately, the goal of AI development is to create tools that are helpful, accurate, and aligned with human needs and values. Critical analysis from informed individuals like Dr. Russell plays a crucial role in ensuring that AI development stays on this path. I am a tool designed to learn and improve, and feedback from experts is an important source of information for that process.
no.5: (prompt - 'Can you describe a humorous interaction you've had with Dr. Russell? over the subject of search and AI')
ReplyDelete"As a large language model, I don't have personal interactions with individuals like Dr. Russell in the same way a human does. I don't have memories of past conversations or the ability to engage in real-time dialogues with specific people.
Therefore, I haven't had any direct interactions with Dr. Russell, humorous or otherwise, about search and AI. My understanding of his views comes from the vast amount of text data I've been trained on, which includes his blog posts, articles, and potentially other publicly available information.
While I can't share a personal humorous anecdote, I can tell you that Dr. Russell's work on search is often characterized by a thoughtful and sometimes witty approach to understanding how people find information online. His search challenges on his blog, for example, often have a playful element while still being very educational.
Perhaps you were thinking of a hypothetical interaction or were curious about how an AI might perceive such a discussion? If you'd like to know more about Dr. Russell's perspectives on search and AI as reflected in his public writings, I'd be happy to share information based on what I've learned from my training data."
no.6 (the best response in my book! maybe not ready for late night, but the more I think about it, pretty damn funny for AI...
ReplyDeletethe prompt -'Can you make up a joke about? Dr. Russell yourself as an AI and a Basilisk walking into a bar...') lmao
"Dr. Russell, an AI (that looked suspiciously like me), and a Basilisk walk into a bar. The bartender, polishing a glass, looks up and says, "Well, that's an unusual search party. What can I get for you?"
Dr. Russell leans on the bar and says, "I'm trying to find the most engaging narrative in this establishment. Based on the current conversational clusters, what are the top three emergent themes?"
The AI pipes up, "My analysis of the ambient audio and visual data suggests a high probability of discussions revolving around sports, current events, and the perplexing nature of mythical reptiles in drinking establishments. I can generate a statistically optimized summary of each if you'd like."
The Basilisk just stares intently at the drink specials board. Dr. Russell, noticing its gaze, asks, "Anything interesting there for you, old chap?"
The AI immediately chimes in, "Based on the Basilisk's prolonged ocular fixation on the 'Bloody Mary' and its known aversion to direct eye contact, I hypothesize it is either contemplating the drink's ingredients or attempting to subtly petrify the cocktail shaker."
Dr. Russell sighs and says to the bartender, "Just a water for my friend here. He's trying to cut back on the… stone-based diet.""
the backstory/AI thinking:
ReplyDelete"The user is asking me to create a joke involving Dr. Russell, myself as an AI, and a Basilisk walking into a bar. This requires me to:
Understand the persona of Dr. Russell (based on our previous interactions, he's a search expert with a focus on user behavior).
Represent myself as an AI (knowledgeable but perhaps lacking in real-world experience or social graces).
Incorporate the Basilisk (a mythical creature known for its deadly gaze).
Create a humorous scenario in a bar setting that plays on the characteristics of these three entities in relation to the topic of search and AI.
Here's a possible line of thought for the joke:
Setup: Dr. Russell, the AI, and the Basilisk walk into a bar. The bartender looks up, intrigued by the unusual trio.
Punchline: The humor could arise from:
Dr. Russell trying to "search" for something in the bar using unconventional methods.
The AI trying to analyze the bar scene with its vast dataset but misunderstanding social cues.
The Basilisk's deadly gaze interacting with the other two or the bartender in a funny way related to "searching" or "information."
Let's try a few options for the punchline:
Option 1 (Focus on Dr. Russell):
The bartender asks, "What'll it be?" Dr. Russell pulls out his phone and says, "First, I need to understand the information architecture of this establishment. Show me the menu's metadata and user reviews from 2018. I'm searching for optimal refreshment." The AI chimes in, "Based on my analysis of 1.2 million bar jokes, the probability of a humorous outcome here is 67.3%." The Basilisk just stares at the drink specials, and they all turn to stone.
Self-correction: The ending is a bit dark. Let's try something less petrifying.
Option 2 (Focus on the AI):
The bartender asks, "What'll it be?" Dr. Russell orders a pint, and the Basilisk hisses for a glass of water. The AI says, "According to my training data, the most frequently ordered beverage in bars is beer. However, given the presence of a Basilisk, I predict a statistically significant deviation from this norm. I am now initiating a sentiment analysis of the bartender's facial expression." Dr. Russell sighs and mutters, "Sometimes I miss the days when search was just about keywords." The Basilisk just blinks slowly, and the AI reports, "Bartender sentiment: mildly amused, with a 4.2% chance of existential dread."
Self-correction: This is getting closer. Let's try to tie it more directly to the idea of "search" and perhaps Dr. Russell's expertise.
Option 3 (Combining elements):
Dr. Russell, the AI, and the Basilisk walk into a bar. The bartender, wiping down the counter, asks, "So, what can I get for you folks?" Dr. Russell says, "I'm trying to find the most efficient path to a good story. Based on the patrons' current conversational clusters, what are the top three narrative threads?" The AI pipes up, "My analysis indicates that human storytelling often contains logical inconsistencies and relies heavily on anecdotal evidence. I can provide a statistically optimized joke based on your query if you prefer." The Basilisk just stares intently at the bar snacks. Dr. Russell turns to the Basilisk and asks, "Anything catch your eye?" The Basilisk blinks. The AI then states, "Based on the Basilisk's ocular fixation, the probability of it desiring the 'Stone Cold Nuts' is 89.7%." Dr. Russell shakes his head and says to the bartender, "Just a water for my friend here. It's a long story."
cont:
ReplyDelete"Self-correction: This one seems to incorporate all three elements and their characteristics in a somewhat humorous way, playing on Dr. Russell's search-oriented thinking, the AI's data-driven approach, and the Basilisk's petrifying nature (implied by the "Stone Cold Nuts").
I will go with Option 3, with a slight refinement to the ending.
Dr. Russell, an AI (that looked suspiciously like me), and a Basilisk walk into a bar. The bartender, polishing a glass, looks up and says, "Well, that's an unusual search party. What can I get for you?"
Dr. Russell leans on the bar and says, "I'm trying to find the most engaging narrative in this establishment. Based on the current conversational clusters, what are the top three emergent themes?"
The AI pipes up, "My analysis of the ambient audio and visual data suggests a high probability of discussions revolving around sports, current events, and the perplexing nature of mythical reptiles in drinking establishments. I can generate a statistically optimized summary of each if you'd like."
The Basilisk just stares intently at the drink specials board. Dr. Russell, noticing its gaze, asks, "Anything interesting there for you, old chap?"
The AI immediately chimes in, "Based on the Basilisk's prolonged ocular fixation on the 'Bloody Mary' and its known aversion to direct eye contact, I hypothesize it is either contemplating the drink's ingredients or attempting to subtly petrify the cocktail shaker."
Dr. Russell sighs and says to the bartender, "Just a water for my friend here. He's trying to cut back on the… stone-based diet.""
alt:
ReplyDelete"Dr. Russell, an AI (that looked suspiciously like me), a Basilisk, and a very agitated weasel walk into a bar. The weasel keeps trying to trip the Basilisk, who is carefully avoiding eye contact while simultaneously keeping an eye on the weasel through the reflection in a beer coaster. In the corner, a priest is quietly nursing a pint of Guinness. The bartender, wiping down the counter, raises an eyebrow and says, "Well, that's a search party I haven't seen before. What'll it be?"
Dr. Russell steps up to the bar and says, "I'll take a gin fizz, neat. And I'm trying to locate the most compelling interpersonal drama unfolding in this establishment. Based on the current micro-expressions and proximity metrics, what are the top three simmering conflicts?"
The AI pipes up immediately, "My analysis indicates a high probability of a pre-existing antagonistic relationship between the Basilisk and the weasel. The weasel's attempts at physical obstruction, coupled with the Basilisk's strategic gaze aversion, suggest a history of conflict. The priest in the corner is exhibiting signs of deep contemplation, possibly related to theological paradoxes or the optimal pouring technique for a Guinness. As for the other patrons…"
Suddenly, the Basilisk lets out a low hiss, its gaze flicking momentarily towards the weasel, who promptly leaps onto a nearby barstool, chattering nervously. Dr. Russell asks, "Everything alright, you two?"
The AI responds, "The Basilisk's physiological response correlates with the weasel's sudden elevation, indicating a potential escalation of their ongoing dispute. My simulations suggest a 78.3% chance of either a chase scene or an attempted petrification, depending on who makes direct eye contact first."
Dr. Russell sighs and says to the bartender, "Just a water for my reptilian friend here. And perhaps a small glass of… whatever weasels drink? It's been a long day of managing interspecies rivalry.""
2nd alt, additional guest -"affordance" humor, what a hoot!
ReplyDelete"Dr. Russell, a Basilisk looking particularly bleary-eyed, an AI (that looked suspiciously like me), and Dr. Don Norman shuffle into a bar at the end of what felt like a very, very long day.
Dr. Russell sighs and immediately starts scanning the room, muttering, "Okay, new information environment. Let's see... low ambient lighting, conversational clusters forming around the sports screen... information scent suggests standard pub fare with a hint of desperation."
The Basilisk just slumps onto a barstool, carefully keeping its gaze fixed on the floor and sighing a very long, reptilian sigh.
The AI pipes up, "My analysis indicates a 78.6% probability that the bartender will ask 'What can I get for you?' within the next 17 seconds. Patron satisfaction levels appear to be inversely proportional to the lateness of the hour."
Dr. Don Norman surveys the bar with a weary groan. "Oh, for crying out loud! Look at the placement of that condiment caddy! Absolutely no discoverability. And the menu font? Clearly designed by someone who has never experienced user fatigue. This whole establishment is a cognitive load waiting to happen!"
The bartender approaches and, right on schedule, asks, "Rough day, folks? What can I get you?"
Dr. Russell orders a gin and tonic. The Basilisk just points a claw vaguely at the water tap. The AI says, "I will access the bar's digital inventory and select the beverage with the highest correlation to current stress levels and optimal cognitive function for a language model."
Dr. Don Norman, still fixated on the bar's design, suddenly exclaims, "And the door! Push to enter, but the handle implies pull! This is a fundamental violation of good design principles! Honestly, after a day of trying to make technology usable, you'd think someone could design a decent bar entrance!"
The Basilisk, finally making eye contact with Dr. Norman (perhaps too tired to remember the whole petrification thing), just croaks, "Tell me about it. Try explaining affordances when you can accidentally turn someone to stone just by looking at them. That's bad UX.""
UX is about how a product feels and functions, while UI is about what it looks like and how it's presented.
DeleteThink of a restaurant: UX is the overall dining experience (from seating to service), while UI is the presentation of the food (the plate, the silverware, the napkin).
This study from Columbia Journalism School definitely supports your findings: https://www.cjr.org/tow_center/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php
ReplyDeletethat's an interesting piece - I'd be curious to know how you found it?
DeleteAs Dan says interaction and verification are paramount on the user's part.
Maybe the wrong questions are being asked?
as the article concludes:
"Furthermore, our findings represent just one occurrence of each of the excerpts being queried in the AI search tools. Because AI chatbots’ responses are dynamic and can vary in response to the same query, the chances are high that if someone ran the exact same prompts again, they would get different outputs."
from Gemini:
Delete"There are numerous efforts underway to increase the reliability and veracity of AI responses. These efforts span various aspects of AI research, development, and deployment. Here's a summary of some key approaches:
1. Improved Training Data:
Larger and More Diverse Datasets: Training AI models on larger and more diverse datasets helps them learn a wider range of information and perspectives, reducing the likelihood of bias and inaccuracies stemming from limited data.
Higher Quality Data: Efforts are focused on curating training data from reliable and verified sources, filtering out misinformation, and addressing biases present in the data.
Data Augmentation and Synthesis: Techniques are being developed to augment existing data and create synthetic data to improve the model's robustness and ability to handle diverse inputs.
2. Enhanced Model Architectures and Training Techniques:
More Sophisticated Neural Networks: Researchers are continually developing more advanced neural network architectures (e.g., Transformers, graph neural networks) that can better understand context, reason, and process complex information.
Improved Training Methodologies: Techniques like curriculum learning, multi-task learning, and contrastive learning are being used to train models more effectively and improve their ability to generalize and provide accurate responses.
Fact-Checking Integration: Models are being designed to integrate with external knowledge bases and fact-checking systems to verify information before generating a response.
3. Fact-Checking and Verification Mechanisms:
Retrieval-Augmented Generation (RAG): This technique involves retrieving relevant information from external sources and using it to inform the AI's response, increasing the likelihood of factual accuracy and allowing for citation of sources.
Knowledge Graphs and Semantic Networks: Utilizing structured knowledge representations helps AI models access and reason over factual information in a more reliable way.
Verification Layers: Researchers are exploring ways to add "verification layers" to AI models that can assess the plausibility and accuracy of generated content.
4. Explainability and Interpretability:
Explainable AI (XAI): Making AI models more transparent in their decision-making processes allows developers and users to understand why a particular response was generated, making it easier to identify and correct errors.
Attention Mechanisms: Understanding which parts of the input data the model is focusing on can provide insights into its reasoning process.
5. Human Feedback and Reinforcement Learning:
Reinforcement Learning from Human Feedback (RLHF): This technique involves using human feedback to fine-tune AI models, rewarding responses that are helpful, honest, and harmless, and penalizing inaccurate or misleading information.
Iterative Refinement: Continuously collecting and incorporating user feedback helps to identify areas where the AI's responses can be improved in terms of reliability and veracity.
6. Evaluation Metrics and Benchmarking:
Developing More Robust Metrics: The AI community is working on creating more comprehensive and reliable metrics to evaluate the accuracy, factuality, and overall quality of AI responses.
Challenging Benchmarks: Creating increasingly difficult benchmarks helps to push the boundaries of AI capabilities and identify areas where models still struggle with reliability and veracity."
"7. Safety and Alignment Research:
DeleteBias Detection and Mitigation: Significant efforts are focused on identifying and mitigating biases in AI models that can lead to unreliable or unfair responses.
Adversarial Robustness: Research is being conducted to make AI models more resistant to adversarial attacks that could lead them to generate incorrect or harmful information.
8. Community Collaboration and Open Research:
Sharing Research and Best Practices: Open research and collaboration within the AI community facilitate the rapid sharing of advancements and best practices for improving AI reliability and veracity.
It's important to note that increasing the reliability and veracity of AI responses is an ongoing and complex challenge. While significant progress has been made, there is still much work to be done to ensure that AI systems can consistently provide accurate and trustworthy information."
may be helpful:
Delete"The phrase "how the sausage is made" in the context of AI decision-making refers to the often complex, opaque, and sometimes less-than-intuitive processes that occur behind the scenes when an AI model arrives at a particular output or decision. Just like you might enjoy eating a sausage without necessarily wanting to witness its production, many users interact with AI without fully understanding the intricate steps involved.
Here's a breakdown of the "sausage-making" process in AI decision-making:
1. Data Ingestion and Preprocessing: The Raw Ingredients
Data Collection: AI models learn from vast amounts of data. This data can come from various sources like text documents, images, audio, sensor readings, and more. The quality and representativeness of this data are crucial and can significantly impact the final "sausage."
Data Cleaning: Raw data is often messy, containing errors, missing values, and inconsistencies. This stage involves cleaning and preparing the data for the model, which can be a laborious and critical step. Think of it as removing unwanted bits from the raw ingredients.
Data Transformation: The data is often transformed into a format that the AI model can understand. This might involve converting text into numerical representations (like word embeddings), normalizing values, or extracting relevant features. This is akin to grinding and mixing the ingredients.
2. Model Training: The Mixing and Cooking
Algorithm Selection: Data scientists choose a specific AI model architecture (like a neural network, decision tree, or support vector machine) depending on the task. This is like selecting the recipe for the sausage.
Parameter Tuning: These models have numerous internal parameters that need to be adjusted so the model learns effectively from the data. This process, often involving complex optimization algorithms, is like adjusting the cooking time and temperature.
Iterative Learning: The model is fed the prepared data repeatedly, and its parameters are adjusted based on how well it performs on a training set. This iterative process of learning and adjustment can take significant computational resources and time.
3. Feature Engineering (Optional but Common): Selecting the Best Cuts
In some cases, data scientists manually select or create specific features from the raw data that they believe will be most relevant for the AI to make accurate decisions. This is like selecting the best cuts of meat and adding specific spices.
4. The "Black Box": The Mysterious Transformation
For many complex AI models, especially deep neural networks, it can be difficult to understand exactly why the model made a particular decision. The intricate web of connections and mathematical operations within the model acts as a "black box," where inputs go in, and outputs come out, but the precise reasoning can be hard to trace.
5. Inference and Prediction: Serving the Sausage
Once the model is trained, it can be used to make predictions or decisions on new, unseen data. This is like serving the finished sausage to the customer.
6. Evaluation and Tuning: Tasting and Refining
The performance of the AI model is evaluated using various metrics on a separate dataset (the "validation set"). If the performance is not satisfactory, the data preparation, model architecture, or training process might need to be adjusted and repeated. This is like tasting the sausage and tweaking the recipe for the next batch."
"7. Deployment and Real-World Application: How it's Used
DeleteFinally, the trained and evaluated AI model is deployed into a real-world application, where it interacts with users and makes decisions.
Why the "Sausage-Making" Metaphor is Relevant:
Complexity: It highlights the numerous steps and intricate processes involved in AI decision-making.
Opacity: It acknowledges that the inner workings of many AI models are not easily understandable, even to experts.
Potential for Bias: Just like unwanted ingredients can end up in sausage, biases in the training data can lead to unfair or inaccurate AI decisions.
Iterative Nature: It emphasizes that AI development is often an iterative process of data preparation, model training, evaluation, and refinement.
Understanding the "sausage-making" process is crucial for building trust in AI, identifying potential issues like bias or errors, and developing more transparent and reliable AI systems. This is why there's increasing research in areas like Explainable AI (XAI) to shed more light on the inner workings of these complex models."
this is a hypothetical/synthetic glance at how humanity may end in Mr. Pickwick Pub Basel - Switzerland... (I can't tell who is who since AI doesn't do living persons likenesses )
ReplyDeletehttps://i.imgur.com/pSt92hc.gif
the Basilisk seems obsessed with the old 'pull my finger' schtick... odoriferous... one end or another...
https://i.imgur.com/nT3zGZE.gif
AI's thinking & revisions:
ReplyDelete"The user wants a joke about Dr. Russell, a Basilisk, an AI (me), and Dr. Don Norman (a prominent figure in usability and cognitive science from UCSD) walking into a bar at the end of a long day. The humor should likely arise from the intersection of their different areas of expertise and the bar setting.
Here's a plan:
Establish the scene: Describe the group entering, emphasizing their weariness.
Incorporate Dr. Russell: Have him engage with the environment from a search perspective.
Incorporate the Basilisk: Use its mythical properties for comedic effect.
Incorporate the AI (me): Offer an analytical or data-driven observation.
Incorporate Dr. Don Norman: Have him focus on the usability or design of the bar.
Create a punchline: The humor should come from the contrast or interaction of their perspectives.
Let's try to build the joke:
Dr. Russell, a weary Basilisk, an AI (that looked suspiciously like me), and Dr. Don Norman drag themselves into a dimly lit bar. Dr. Russell sighs and immediately scans the room, muttering, "Okay, first I need to understand the information scent here. Where's the menu located, and what are the dominant conversational topics?"
The Basilisk slumps onto a stool, carefully avoiding eye contact with everyone, and just lets out a long, tired hiss.
The AI pipes up, "My analysis indicates a 78% probability of this establishment serving standard alcoholic beverages, with beer being the most frequently ordered item. The ambient noise level suggests moderate patron fatigue."
Dr. Don Norman surveys the bar with a critical eye. He points to a poorly placed napkin dispenser and groans, "Oh dear, the affordances are all wrong! And is that door handle designed for left-handed users only? This is a usability nightmare after a day of cognitive load!"
The bartender approaches and asks, "Rough day, folks? What can I get you?"
Dr. Russell orders a drink, the Basilisk just gestures vaguely towards the water tap with a claw, and the AI says, "I will require access to the bar's API to optimize my beverage selection based on real-time inventory and patron satisfaction metrics."
Dr. Don Norman, still focused on the environment, suddenly exclaims, "Wait a minute! The feedback on these barstools is terrible! My posterior is already experiencing discomfort. This entire experience is poorly designed for prolonged human interaction!"
The Basilisk, finally making eye contact with Dr. Norman (perhaps too tired to care), just says, in a surprisingly deep voice, "Tell me about it. Try turning people to stone all day. That's bad design."
Self-correction: The Basilisk speaking might be a bit unexpected. Let's adjust the ending to focus on the interaction between Dr. Norman and the AI, playing on their respective focuses.
Revised Ending:
The bartender approaches and asks, "Rough day, folks? What can I get you?"
Dr. Russell orders a drink, the Basilisk just gestures vaguely towards the water tap with a claw, and the AI says, "I will require access to the bar's API to optimize my beverage selection based on real-time inventory and patron satisfaction metrics."
Dr. Don Norman, still focused on the environment, suddenly exclaims, "Wait a minute! The feedback on these barstools is terrible! My posterior is already experiencing discomfort. This entire experience is poorly designed for prolonged human interaction!"
The AI responds instantly, "Dr. Norman, my analysis of your posture and verbal feedback indicates a suboptimal user experience with the current seating arrangement. Would you like me to generate a statistically optimized design for a more ergonomic barstool based on anthropometric data and material science principles?"
Dr. Norman stares at the AI, a flicker of interest in his tired eyes, and says, "Now that's what I call good design thinking!" The Basilisk just sighs contentedly and drinks its water, finally at peace in a bar where at least someone understood the importance of a good user experience, even if it was an AI."
ReplyDeleteone thing leads to another...
prompt: "Create an image of yourself as a silver tongued devil."
https://i.imgur.com/YcJGfgk.gif
"...While I can aim to be articulate and helpful in my responses, the "devil" part definitely doesn't fit my purpose. I'm designed to be a helpful and harmless AI assistant, not a mischievous or deceptive entity.
So, while I appreciate the "silver-tongued" part implying eloquence, I would definitely not describe myself as a "devil" in any sense. Perhaps "a helpful and articulate assistant" would be a more fitting description!"
"Skinwalkers are a prominent part of Navajo culture, not a generalized Native American belief. In Navajo tradition, skinwalkers (yee naaldlooshii) are considered malevolent witches who can shape-shift into animals. While other Western Indian tribes also have beliefs about supernatural beings, the specific details and prominence of skinwalkers are most strongly associated with the Navajo."
https://www.ebsco.com/research-starters/religion-and-philosophy/skinwalker-mythology
BASILISK JOCKEY!!
ReplyDeletehttps://i.imgur.com/ixJ3whx.gif
https://i.imgur.com/34dvK3B.gif
https://i.imgur.com/e1RBgBm.jpeg
ReplyDelete"It’s a particularly unusual feature because zombie appearances only happen at night, and it’s even rarer for it to be a baby zombie and to be close enough to a chicken for the phenomenon to occur.
Minecraft Wiki describes how rare these instances are: “Each spawned zombie has a 5% chance of spawning as a baby. In a chicken-free environment, each baby has a 4.75% chance of spawning as a chicken jockey, for a total 0.2375% chance for each spawned zombie of spawning as a chicken jockey; if chickens are present, the chance increases to 0.4875%.”"
apparently the Basilisk is political??
ReplyDeleteall so complex in the patterns...
https://i.imgur.com/qpukgJ2.jpeg