SmileSIM

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Fortune Smiles On Mitya

A very good friend of mine has heard me tell many a tale of trips in which I have been given free drinks, gratis hotel and car rental upgrades and various freebies. However, without experiencing my stories first hand, he always suspected there was a degree of embellishment involved to make my stories a bit more entertaining. travel We eventually had the opportunity to together and I was able to demonstrate the power of a little respect and an authentic smile. travel It’s Kool To Be Kind We began our trip sitting in the dreary, uncomfortable airport terminal, with ample time before our flight. Bored, I explored the terminal and located the airline’s club lounge. Despite not being a member, I entered and immediately knew where I was going to spend the next couple of hours. Sitting behind the receptionist desk, in a chair that was two sizes too small for him was, Don, a large Hawaiian with a hearty smile. He was clearly uninterested in the paperback book in his oversized hands and he seemed open to chatting. After some brief pleasantries, I asked him what it cost to join the club and if they offered a one-day pass. He laughed and apologetically said, “It’s something like $500 a year. We don’t have day rates, but I can let you slide this time if you just want to check it out.” I thanked him and hurried back to grab my buddy. After a leisurely time in the airline club, we boarded our flight and met Kelly, a young surfer dude in his early twenties who was our attendant for the next five hours. Once everyone was settled in their seats, I asked him where he lived in Hawaii, where he surfed, what sort of board he used, etc. As soon as we were airborne, I asked Kelly if he could hook me up with a Mai Tai. He laughed and asked if I wanted one of his “special creations.” My reply, without hesitation, “Why of course.” He returned and waited for me to sample the drink. I took a large swallow and sincerely told him, “You are an artist. You should call this ‘Kelly’s Creation.” I had several more of Kelly’s Creations before we landed, all at no charge. We bid Kelly farewell, hoping we’d see him again on our return flight and headed toward the car rental agency. It was very hot and the woman at the counter did not look happy. I approached her with a broad smile and asked how her day was going. She replied, “It’s about to get worse. All the flights from the mainland are landing.” She clearly wasn’t happy. After a bit of joking around, she begrudgingly grinned and eventually was laughing at my corny humor. She waived the fee for my friend to be an authorized driver and she gave us a gratis upgrade to a slightly nicer vehicle. So What Entrepreneurs are likewise well served to seek out and cultivate transitory relationships with service employees whom self-important professionals generally overlook, such as: waitresses, car rental agents, flight attendants and call-center agents. Such folks encounter a horde of unhappy, disgruntled and downright surly travelers each and every day. Thus, it is easy to differentiate yourself by simply smiling and showing these hard-working people the courtesy they deserve. Greeting someone by genuinely asking, “Wow. How can you look so cool under pressure when you are so busy?” will often be rewarded with a smile and a special deal. Making A Connection Make an authentic connection with a friendly, sincere smile. Then engage the person you are approaching as a peer, a courtesy many service workers seldom experience from most of their harried, self-absorbed customers. The interaction should always be based on a genuine expression of interest. For instance, after a long day of travel, I approached the night clerk at a hotel at about 1:00 in the morning. He was clearly studying a textbook and I genuinely admired that he had a night job while attending college. Rather than ignoring his book or asking him a superficial question like, “What’s your major” or “Where do you go to school?” I asked him, “What are your dreams once you graduate?” He hesitated, clearly debating if he should give me a thoughtful, honest answer or if he should remain in transaction mode and give me a cursory, glib reply. He opted for honesty and told me that he wanted to someday open a veterinary clinic because he loved animals. We had a pleasant conversation, I was given a free upgrade without even asking and we parted with me sincerely wishing him the best. I didn’t even realized he had upgraded me until I arrived in my multi-room suite. I wasn’t thrilled with the suite, which I only spent a couple waking hours in. What made my evening was the personal connection we briefly shared. The Real Reward If you are rolling your eyes , I will let you in on a — the real reward is not saving a few dollars. secret The foundation upon which such personal connections are built is the levity and kindness infused into otherwise tedious situations. Traveling becomes less mundane and a lot more joyful when you create a genuine rapport with the folks you meet along the way. The small, tangible freebies that sometimes result from such interactions are a nice bonus, but the real payoff is making brief, yet meaningful connections you will make with some awesome people with whom you share this planet. Follow John’s startup-oriented Twitter feed here: @johngreathouse

The Secret To Getting Free Drinks, Upgrades And Genuine Smiles When You Travel

How To Priority Matrix 😈 Stop Using Product Roadmaps And Use THIS Instead

First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change Over the last five years, the dominant narrative surrounding data infrastructure has emphasized the importance of companies owning and leveraging their data as they acquire as much information about their users and customers as possible. Due to mounting privacy regulations, they need to gather the data themselves instead of relying on third-party data operators, such as ad networks or platforms like Google, Meta, and Amazon. Companies have fallen in line with this narrative and made the shift. But, in the battle for the best data, is first-party really better? Not by itself, but it could be with a little help from vectors, frameworks like RAG, and open-source foundation models like Llama 3. The Push for First-Party Data The argument for first-party data generally goes like this: companies need to become better stewards of data acquisition and management amid growing calls for data privacy. Consumers increasingly want to know who is hanging onto their personal information, how they got it, why they have it, and what is being done with it– and they generally don’t like the answers to those questions. Access and Delete requests are rapidly increasing, and the data privacy landscape is changing quickly. Companies are finding it hard enough to figure out their own privacy management processes; they don’t want to have to worry about third parties' as well. But the shift to first-party is not all about privacy. There is also the notion that third-party data will lose value as we move towards the proverbial cookieless future. Companies can’t get the same granular details they once could so why should they devote budget to a service that delivers less than it used to? And then there is the perpetual concern that the big platforms and ad networks will make unexpected changes. For example, they could change their algorithms, restrict access to certain types of data, or alter advertising policies in a manner that could harm a business’ performance with little or no notice. Being dependent on another company’s practices leaves one vulnerable. Companies feel stuck because they’ve already invested so much time, money, and resources into their data strategy. In this light, the push to take back control of data seems essential. But is it practical? The First-Data Problems No One is Talking About Early results for companies taking a gamble on first-party data have not met expectations. We’re seeing case after case of consumer companies that made the shift suffering. Farfetch, Allbirds, and  SmileDirectClub are just a few examples. Would better, more strategic use of first-party data turn the tide on the rising customer acquisition costs? Still, the current reliance on first-party data– and the practices to extract that data– is a commonality across several companies that are experiencing tougher times today. It’s noticeable enough to make analysts, VCs, and marketers themselves ask if they were wrong to prioritize first-party data. The downsides of first-party data, as it is currently acquired and leveraged, are often considered to be underestimating the disappearance of device IDs, changing IP addresses, consumer adoption of fake emails, and ad blockers. While true, much more significant issues are at play. First, there is a massive talent gap. Big Tech and the platform companies snag the best. They have the most to offer, making it difficult for up-and-coming consumer companies to compete for the data scientists and ML talent needed to make sense of the information being collected and analyzed. Without truly exceptional people, companies have struggled. There is also a real tooling problem. The offerings available to companies are nowhere near equivalent to the tools Big Tech companies boast internally (which can be a factor in the talent gap). Tooling makes a difference, and most companies simply can’t compete right now. Finally, there is a volume challenge. Big Tech and the ad networks have mountains of data, as these companies pool and anonymize hundreds of billions of data points to make their models run effectively. By contrast, if a company has only its data to work with, there is just not enough for ML to work as promised. While these problems seem serious, is it time to give up on the potential– and the need for– first-party data? No way! The Power of Vectors The biggest issue hindering first-party data is how companies are trying to access it. Up to this point, businesses have taken an Old World approach. Models must be built from scratch to extract the value from data that businesses require. This takes time, money, and most of all talent; it depends on how good your ML engineers and data scientists are. However, as discussed above, there is not enough available talent to make this approach work better than leveraging third-party data. The lack of talent creates the bottleneck. However, this does not mean we need to give up on first-party data. We just need to change how we approach it. In the New World that is possible today, vectors and vector embeddings are the key. Vectors are generic mathematical objects that can represent data points' features or attributes while embedding models analyze patterns in data to generate these information-packed, meaningful representations learned from data; they capture the semantic relationships. Vector embeddings are the format that can encode everything you know about a user or a customer and make that information accessible to an analytics system, or leverageable to personalize the user experience, or even to catch fraud. There are so many possibilities. Vectors are poised to drive a sea change because they can power analytics in a fundamentally different way. Retrieval-augmented generation (RAG) is generating a lot of buzz right now for all it can enable, but vector embeddings are what make RAG useful. They are a central component of the framework that helps with context, responses, integration of retrieval and fine-tuning of models. Generating high-quality vectors and querying them correctly is a critical task that enables any RAG system to actually work. There are other frameworks, but RAG is particularly well-suited for a first-party data revolution. Sounds great. Let’s all figure out how to use vectors and vector embeddings. This is not the total answer. The issues like limited data sets and tooling remain. All is not yet wrapped up in a pretty bow, but I believe it soon will be. Because open-source, pre-trained foundation models like Meta’s Llama 2, which will give way to the more robust Llama 3 in July, can level the playing field. The issue of not having enough data volume compared to BigTech gets mitigated. By using an open source model pre-trained on large and diverse datasets, that model has a certain level of knowledge and understanding built in. Companies simply need to fine-tune Llama 2 (or Llama 3) on their specific domain or task with their data. This eases the bottleneck because in many cases, you no longer have to train a model from scratch. This may sound like an oversimplification, given that Llama helps companies deal with text, but most of the data companies run on is not text. The structured data companies run on need to be integrated into this process. For example, the user behavioural events which form a large percentage of first-party data typically, are not suitable to be processed by any LLM. This is changing so companies should be ready as new multimodal solutions emerge. Similarly, tooling is still lacking, but there is a lot of attention on the space, so great strides are being taken. It’s coming along! With the biggest issues fundamentally addressed, first-party data hype is back, baby! Companies don’t have to worry about third parties violating privacy, nor do they have to depend on Big Tech to help them know their customers. Expect to see first-party data explode this year as companies finally take full advantage– especially with Llama 3 at the ready. For all of its promise, perhaps the greatest potential for Llama 3 will be to actually solve the first-party data problem once and for all. First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change Over the last five years, the dominant narrative surrounding data infrastructure has emphasized the importance of companies owning and leveraging their data as they acquire as much information about their users and customers as possible. Due to mounting privacy regulations, they need to gather the data themselves instead of relying on third-party data operators, such as ad networks or platforms like Google, Meta , and Amazon . Companies have fallen in line with this narrative and made the shift. Google, Meta Amazon But, in the battle for the best data, is first-party really better? Not by itself, but it could be with a little help from vectors, frameworks like RAG, and open-source foundation models like Llama 3 . Llama 3 The Push for First-Party Data The argument for first-party data generally goes like this: companies need to become better stewards of data acquisition and management amid growing calls for data privacy . Consumers increasingly want to know who is hanging onto their personal information, how they got it, why they have it, and what is being done with it– and they generally don’t like the answers to those questions. Access and Delete requests are rapidly increasing, and the data privacy landscape is changing quickly. Companies are finding it hard enough to figure out their own privacy management processes; they don’t want to have to worry about third parties' as well. first-party data data privacy Access Access Delete Delete But the shift to first-party is not all about privacy. There is also the notion that third-party data will lose value as we move towards the proverbial cookieless future. Companies can’t get the same granular details they once could so why should they devote budget to a service that delivers less than it used to? And then there is the perpetual concern that the big platforms and ad networks will make unexpected changes. For example, they could change their algorithms, restrict access to certain types of data, or alter advertising policies in a manner that could harm a business’ performance with little or no notice. Being dependent on another company’s practices leaves one vulnerable. Companies feel stuck because they’ve already invested so much time, money, and resources into their data strategy. In this light, the push to take back control of data seems essential. But is it practical? The First-Data Problems No One is Talking About Early results for companies taking a gamble on first-party data have not met expectations. We’re seeing case after case of consumer companies that made the shift suffering. Farfetch , Allbirds , and SmileDirectClub are just a few examples. Would better, more strategic use of first-party data turn the tide on the rising customer acquisition costs? Farfetch Farfetch Allbirds Allbirds SmileDirectClub SmileDirectClub Still, the current reliance on first-party data– and the practices to extract that data– is a commonality across several companies that are experiencing tougher times today. It’s noticeable enough to make analysts, VCs, and marketers themselves ask if they were wrong to prioritize first-party data. The downsides of first-party data, as it is currently acquired and leveraged, are often considered to be underestimating the disappearance of device IDs, changing IP addresses, consumer adoption of fake emails, and ad blockers. While true, much more significant issues are at play. First, there is a massive talent gap. Big Tech and the platform companies snag the best. They have the most to offer, making it difficult for up-and-coming consumer companies to compete for the data scientists and ML talent needed to make sense of the information being collected and analyzed. Without truly exceptional people, companies have struggled. There is also a real tooling problem. The offerings available to companies are nowhere near equivalent to the tools Big Tech companies boast internally (which can be a factor in the talent gap). Tooling makes a difference, and most companies simply can’t compete right now. Finally, there is a volume challenge. Big Tech and the ad networks have mountains of data, as these companies pool and anonymize hundreds of billions of data points to make their models run effectively. By contrast, if a company has only its data to work with, there is just not enough for ML to work as promised. While these problems seem serious, is it time to give up on the potential– and the need for– first-party data? No way! The Power of Vectors The biggest issue hindering first-party data is how companies are trying to access it. Up to this point, businesses have taken an Old World approach. Models must be built from scratch to extract the value from data that businesses require. This takes time, money, and most of all talent; it depends on how good your ML engineers and data scientists are. However, as discussed above, there is not enough available talent to make this approach work better than leveraging third-party data. The lack of talent creates the bottleneck. However, this does not mean we need to give up on first-party data. We just need to change how we approach it. In the New World that is possible today, vectors and vector embeddings are the key. Vectors are generic mathematical objects that can represent data points' features or attributes while embedding models analyze patterns in data to generate these information-packed, meaningful representations learned from data; they capture the semantic relationships. Vector embeddings are the format that can encode everything you know about a user or a customer and make that information accessible to an analytics system, or leverageable to personalize the user experience, or even to catch fraud. There are so many possibilities. Vectors are poised to drive a sea change because they can power analytics in a fundamentally different way. Retrieval-augmented generation (RAG) is generating a lot of buzz right now for all it can enable, but vector embeddings are what make RAG useful. They are a central component of the framework that helps with context, responses, integration of retrieval and fine-tuning of models. Generating high-quality vectors and querying them correctly is a critical task that enables any RAG system to actually work . There are other frameworks, but RAG is particularly well-suited for a first-party data revolution. to actually work Sounds great. Let’s all figure out how to use vectors and vector embeddings. This is not the total answer. The issues like limited data sets and tooling remain. All is not yet wrapped up in a pretty bow, but I believe it soon will be. Because open-source, pre-trained foundation models like Meta’s Llama 2, which will give way to the more robust Llama 3 in July, can level the playing field. The issue of not having enough data volume compared to BigTech gets mitigated. By using an open source model pre-trained on large and diverse datasets, that model has a certain level of knowledge and understanding built in. Companies simply need to fine-tune Llama 2 (or Llama 3) on their specific domain or task with their data. This eases the bottleneck because in many cases, you no longer have to train a model from scratch. This may sound like an oversimplification, given that Llama helps companies deal with text, but most of the data companies run on is not text. The structured data companies run on need to be integrated into this process. For example, the user behavioural events which form a large percentage of first-party data typically, are not suitable to be processed by any LLM. This is changing so companies should be ready as new multimodal solutions emerge. Similarly, tooling is still lacking, but there is a lot of attention on the space, so great strides are being taken. It’s coming along! With the biggest issues fundamentally addressed, first-party data hype is back, baby! Companies don’t have to worry about third parties violating privacy, nor do they have to depend on Big Tech to help them know their customers. Expect to see first-party data explode this year as companies finally take full advantage– especially with Llama 3 at the ready. For all of its promise, perhaps the greatest potential for Llama 3 will be to actually solve the first-party data problem once and for all.

How Vectors, RAG and Llama 3 Are Changing First-Party Data

কিভাবে ভেক্টর, রাগ এবং লামা 3 প্রথম পক্ষের ডেটা পরিবর্তন করছে

Wie Vectors, Rag und Llama 3 First-Party-Daten verändern

Cómo Vectors, Rag y Llama 3 están cambiando los datos propios

Comment Vectors, Rag et Llama 3 modifient les données propriétaires

वेक्टर्स, रैग और लामा 3 किस तरह से फर्स्ट-पार्टी डेटा को बदल रहे हैं

Vectors、Rag、Llama 3 がファーストパーティ データをどのように変えているのか

Vector, Rag 및 Llama 3가 자사 데이터를 어떻게 변화시키는가

Como Vectors, Rag e Llama 3 estão mudando os dados primários

Как Vectors, Rag и Llama 3 меняют собственные данные

Vektörler, Rag ve Llama 3 Birinci Taraf Verilerini Nasıl Değiştiriyor?

Cách Vectors, Rag và Llama 3 thay đổi dữ liệu của bên thứ nhất

Vectors、Rag 和 Llama 3 如何改变第一方数据

Silicon Valley: With Swords & Shields

Authors:
(1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Abstract This research delves into the comparative advantages of Generative AI chatbots (GenAIbots) - ChatGPT, Bing Chat, Bard, and Claude - in the context of Chemistry education, framed within a constructivist perspective. Our primary objective was to identify which of these four AI tools is more effective for enhancing Chemistry learning. Employing a single-case study approach, we scrutinised interaction logs between the AI systems and a simulated student persona during Chemistry learning simulations, incorporating Content Analysis methodology to delve deeper into the discourse. Our findings underscore these tools' potential as "agents-to-think-with", enhancing critical thinking, problem-solving, comprehension, creativity, and tailored learning. Especially noteworthy is their ability to stimulate learners through Socratic-like questioning, aligning with constructionist principles. The research emphasises the pivotal role of prompt crafting to coax desired responses from GenAIbots, engendering iterative reflections. It also highlights the need for robust educator training to infuse these technologies into educational settings. Conclusively, while ChatGPT, Bing Chat, Bard, and Claude are poised to enrich Chemistry education by fostering dynamic, inclusive learning experiences, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in-depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Keywords: Chemistry education, ChatGPT, Bing Chat, Bard, Claude, Artificial Intelligence in Education, agents-to-think-with Introduction Chemistry, a core Science Education subject, clarifies matter's properties and transformations, thus critically shaping our daily lives (Dunlop et al., 2020). However, Chemistry is also a complex and dynamic field that necessitates a profound grasp of fundamental concepts and principles, and it is sometimes difficult for students to associate real-life circumstances with abstract chemistry concepts (Dewi et al., 2021). Research in Chemistry Education seeks effective strategies to alleviate these learning difficulties (Permatasari et al., 2022; Timilsena et al., 2022; Tümay, 2016). Timilsena et al. (2022) identified difficulties in understanding the abstract nature of chemical reactions and factors such as inadequate teaching materials and curriculum complexity, emphasising the need for effective teaching strategies and tools. Tümay (2016) discussed students' struggles in understanding fundamental chemistry concepts and emphasised the importance of addressing misconceptions and learning difficulties. Dewi et al. (2021) underscored the need for critical thinking skills and the integration of digital technology to enhance the quality of Chemistry education for Generation Z students. Dunlop et al. (2020) proposed introducing philosophical dialogue in higher education to address undergraduate Chemistry students' challenges, suggesting that it can stimulate new ways of thinking. In 2023, Castro Nascimento and Pimentel undertook a study to evaluate the proficiency of the ChatGPT model by having it respond to five distinct tasks across various subfields of chemistry (Castro Nascimento & Pimentel, 2023). These tasks included converting compound names to their SMILES chemical representation and vice versa, procuring information on the octanol-water partition coefficient of chemical compounds, extracting structural information on coordination compounds, determining the water solubility of polymers, and identifying the molecular point groups of simple molecular compounds. The unsatisfactory outcomes highlighted potential limitations in the model's ability to adequately address these specific chemistry-related queries. Notably, the suboptimal performance observed might be attributed to utilising an antiquated version of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). In a separate study conducted in the same year, Leon and Vidhani (2023) explored the reliability of ChatGPT responses within the framework of an introductory college-level Chemistry course. Their findings indicated a significant reliability concern, with ChatGPT failing to secure a score above 37%. Such a performance implies that learners relying on this tool for study support would predominantly receive incorrect responses, with the variability of the tool leading to different responses for individual learners. Although the authors didn't specify which version of ChatGPT they utilised in their study, their reference to "ChatGPT's free original version" and the citation of Floridi and Chiriatti's (2020) research on ChatGPT-3 hint at the likelihood that they employed this version. Additionally, Pimentel et al. (2023) delved into the efficacy of ChatGPT versions 3 and 4 in responding to intricate questions spanning six topics in Chemistry. While they concluded that both versions were currently inadequate in addressing the nuances of complex topics, they also observed noteworthy advancements from ChatGPT-3 to ChatGPT-4. Such progress signals promising potential for the tool to aid scientists in future literature reviews, surveys, and educational endeavours. Generative AI-powered chatbots (GenAIbots), including ChatGPT, Bing Chat, Bard, and Claude, have been introduced as innovative solutions to persistent challenges in Chemistry education (Baidoo-Anu & Owusu Ansah, 2023; Taylor et al., 2022). Their emergence offers a dynamic, inclusive educational environment, transforming how complex concepts are conveyed and understood. These GenAIbots simplify intricate topics, promote self-reflection, engage users in stimulating dialogues, facilitate personalised learning, and enhance critical thinking, collaboration, and cognitive development (Okonkwo & Ade-Ibijola, 2021). As a result, they have played a pivotal role in revolutionising Chemistry education. In the discourse on agency, traditional theories, such as the ones of Anscombe and Davidson, attribute actions to entities based on representational mental states encompassing desires, beliefs, and intentions. However, alternative perspectives, drawing from the works of Heider and Simmel, Dennett, Davidson, and Barandiaran et al., challenge this representation dependent view, suggesting potential agency without such mental representations (Schlosser, 2019). When considering GenAIbots within these frameworks, the entity's ability to respond based on training contrasts with its lack of self-awareness, intentions, and proactive behaviour— characteristics central to the agency. Consequently, while dominant philosophical and cognitive models would not grant ChatGPT agency, more expansive interpretations, as offered by these cited thinkers, could accept it. Building on this foundation and inspired by Melanie Swan's suggestion, we have expanded upon Papert's (1980) concept of "objects-to-think-with" to introduce “agents-to-thinkwith.” This positions GenAIbots as integral participants in the educational path, echoing Turkle's (1984) concept of "metacognitive machines," in the sense that they assist in enhancing one's awareness of their own cognitive processes, i.e., "thinking about thinking" as described by Flavell (1976). In Latour's (1991) perspective, we can perceive GenAIbots as "hybrids," which blur the boundaries between humans and non-human entities. Such a perspective further aligns with Swan's (2015) vision of a future where augmented humans and AI collaborate and thrive symbiotically. While GenAIbots posit themselves as powerful metacognitive educational tools, promoting critical thinking, problem-solving, and deep comprehension of concepts, it's imperative to acknowledge their limitations. For instance, there's the possibility of generating nonsensical or inaccurate content, as OpenAI (2023) highlighted. Nevertheless, the unique ability of GenAIbots to provide instant feedback, introduce diverse perspectives, and foster an interactive engagement with complex ideas solidifies their position as influential agents-to-think-with in the realm of Chemistry education. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Authors: Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Abstract and Introduction Materials And Methods Materials And Methods Results and Analyses Results and Analyses Prompts and generated texts Prompts and generated texts Conceptualizing chemical reactions Conceptualizing chemical reactions Deepening on understanding of chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about combustion Question about a graph of gases turning into water over time Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Deepening on the concept of mole Question about changing of state Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about plasma, a state of matter Question about chemical bondings Question about chemical bondings Question about illustration of chemical bonds Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Question about the essence of the type of chemical bonding Further analysis Further analysis Conclusions Conclusions Limitations of the study and possible future studies Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Author Contributions, Conflicts of interest, Acknowledgements, and References Abstract This research delves into the comparative advantages of Generative AI chatbots (GenAIbots) - ChatGPT, Bing Chat, Bard, and Claude - in the context of Chemistry education, framed within a constructivist perspective. Our primary objective was to identify which of these four AI tools is more effective for enhancing Chemistry learning. Employing a single-case study approach, we scrutinised interaction logs between the AI systems and a simulated student persona during Chemistry learning simulations, incorporating Content Analysis methodology to delve deeper into the discourse. Our findings underscore these tools' potential as "agents-to-think-with", enhancing critical thinking, problem-solving, comprehension, creativity, and tailored learning. Especially noteworthy is their ability to stimulate learners through Socratic-like questioning, aligning with constructionist principles. The research emphasises the pivotal role of prompt crafting to coax desired responses from GenAIbots, engendering iterative reflections. It also highlights the need for robust educator training to infuse these technologies into educational settings. Conclusively, while ChatGPT, Bing Chat, Bard, and Claude are poised to enrich Chemistry education by fostering dynamic, inclusive learning experiences, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in-depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Keywords: Chemistry education, ChatGPT, Bing Chat, Bard, Claude, Artificial Intelligence in Education, agents-to-think-with Keywords: Introduction Chemistry, a core Science Education subject, clarifies matter's properties and transformations, thus critically shaping our daily lives (Dunlop et al., 2020). However, Chemistry is also a complex and dynamic field that necessitates a profound grasp of fundamental concepts and principles, and it is sometimes difficult for students to associate real-life circumstances with abstract chemistry concepts (Dewi et al., 2021). Research in Chemistry Education seeks effective strategies to alleviate these learning difficulties (Permatasari et al., 2022; Timilsena et al., 2022; Tümay, 2016). Timilsena et al. (2022) identified difficulties in understanding the abstract nature of chemical reactions and factors such as inadequate teaching materials and curriculum complexity, emphasising the need for effective teaching strategies and tools. Tümay (2016) discussed students' struggles in understanding fundamental chemistry concepts and emphasised the importance of addressing misconceptions and learning difficulties. Dewi et al. (2021) underscored the need for critical thinking skills and the integration of digital technology to enhance the quality of Chemistry education for Generation Z students. Dunlop et al. (2020) proposed introducing philosophical dialogue in higher education to address undergraduate Chemistry students' challenges, suggesting that it can stimulate new ways of thinking. In 2023, Castro Nascimento and Pimentel undertook a study to evaluate the proficiency of the ChatGPT model by having it respond to five distinct tasks across various subfields of chemistry (Castro Nascimento & Pimentel, 2023). These tasks included converting compound names to their SMILES chemical representation and vice versa, procuring information on the octanol-water partition coefficient of chemical compounds, extracting structural information on coordination compounds, determining the water solubility of polymers, and identifying the molecular point groups of simple molecular compounds. The unsatisfactory outcomes highlighted potential limitations in the model's ability to adequately address these specific chemistry-related queries. Notably, the suboptimal performance observed might be attributed to utilising an antiquated version of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). In a separate study conducted in the same year, Leon and Vidhani (2023) explored the reliability of ChatGPT responses within the framework of an introductory college-level Chemistry course. Their findings indicated a significant reliability concern, with ChatGPT failing to secure a score above 37%. Such a performance implies that learners relying on this tool for study support would predominantly receive incorrect responses, with the variability of the tool leading to different responses for individual learners. Although the authors didn't specify which version of ChatGPT they utilised in their study, their reference to "ChatGPT's free original version" and the citation of Floridi and Chiriatti's (2020) research on ChatGPT-3 hint at the likelihood that they employed this version. Additionally, Pimentel et al. (2023) delved into the efficacy of ChatGPT versions 3 and 4 in responding to intricate questions spanning six topics in Chemistry. While they concluded that both versions were currently inadequate in addressing the nuances of complex topics, they also observed noteworthy advancements from ChatGPT-3 to ChatGPT-4. Such progress signals promising potential for the tool to aid scientists in future literature reviews, surveys, and educational endeavours. Generative AI-powered chatbots (GenAIbots), including ChatGPT, Bing Chat, Bard, and Claude, have been introduced as innovative solutions to persistent challenges in Chemistry education (Baidoo-Anu & Owusu Ansah, 2023; Taylor et al., 2022). Their emergence offers a dynamic, inclusive educational environment, transforming how complex concepts are conveyed and understood. These GenAIbots simplify intricate topics, promote self-reflection, engage users in stimulating dialogues, facilitate personalised learning, and enhance critical thinking, collaboration, and cognitive development (Okonkwo & Ade-Ibijola, 2021). As a result, they have played a pivotal role in revolutionising Chemistry education. In the discourse on agency, traditional theories, such as the ones of Anscombe and Davidson, attribute actions to entities based on representational mental states encompassing desires, beliefs, and intentions. However, alternative perspectives, drawing from the works of Heider and Simmel, Dennett, Davidson, and Barandiaran et al., challenge this representation dependent view, suggesting potential agency without such mental representations (Schlosser, 2019). When considering GenAIbots within these frameworks, the entity's ability to respond based on training contrasts with its lack of self-awareness, intentions, and proactive behaviour— characteristics central to the agency. Consequently, while dominant philosophical and cognitive models would not grant ChatGPT agency, more expansive interpretations, as offered by these cited thinkers, could accept it. Building on this foundation and inspired by Melanie Swan's suggestion, we have expanded upon Papert's (1980) concept of "objects-to-think-with" to introduce “agents-to-thinkwith.” This positions GenAIbots as integral participants in the educational path, echoing Turkle's (1984) concept of "metacognitive machines," in the sense that they assist in enhancing one's awareness of their own cognitive processes, i.e., "thinking about thinking" as described by Flavell (1976). In Latour's (1991) perspective, we can perceive GenAIbots as "hybrids," which blur the boundaries between humans and non-human entities. Such a perspective further aligns with Swan's (2015) vision of a future where augmented humans and AI collaborate and thrive symbiotically. While GenAIbots posit themselves as powerful metacognitive educational tools, promoting critical thinking, problem-solving, and deep comprehension of concepts, it's imperative to acknowledge their limitations. For instance, there's the possibility of generating nonsensical or inaccurate content, as OpenAI (2023) highlighted. Nevertheless, the unique ability of GenAIbots to provide instant feedback, introduce diverse perspectives, and foster an interactive engagement with complex ideas solidifies their position as influential agents-to-think-with in the realm of Chemistry education. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Enhancing Chemistry Learning with ChatGPT, Bing Chat, Bard, and Claude as Agents-to-Think-With

ChatGPT, Bing Chat, Bard, এবং Claude-এর সাথে এজেন্ট-টু-থিঙ্ক-এর মাধ্যমে রসায়ন শেখার উন্নতি করা

Verbesserung des Chemielernens mit ChatGPT, Bing Chat, Bard und Claude als Denkagenten

Mejora del aprendizaje de química con ChatGPT, Bing Chat, Bard y Claude como agentes con los que pensar

Améliorer l'apprentissage de la chimie avec ChatGPT, Bing Chat, Bard et Claude en tant qu'agents avec lesquels penser

चैटजीपीटी, बिंग चैट, बार्ड और क्लाउड के साथ रसायन विज्ञान सीखने को बेहतर बनाना, एजेंट-टू-थिंक-विथ

ChatGPT、Bing Chat、Bard、Claude をエージェントとして利用して化学学習を強化する

ChatGPT, Bing Chat, Bard 및 Claude를 생각하는 에이전트로 사용하여 화학 학습 강화

Aprimorando o aprendizado de química com ChatGPT, Bing Chat, Bard e Claude como agentes para pensar

Улучшение изучения химии с помощью ChatGPT, Bing Chat, Bard и Claude в качестве агентов для размышлений

Birlikte Düşünülecek Aracılar Olarak ChatGPT, Bing Chat, Bard ve Claude ile Kimya Öğrenimini Geliştirme

Nâng cao việc học hóa học với ChatGPT, Bing Chat, Bard và Claude với tư cách là Đại lý để suy nghĩ

使用 ChatGPT、Bing Chat、Bard 和 Claude 作为思考代理来增强化学学习

Heads, I kill him. Tails — well, Tails and he not only understands who wants him dead, he will learn how little he knows of real pain. I’ll teach him, though. When I’m done, there will be nothing about it beyond his experience and there will come a moment when he breaks and begs me to finish him, prays like a supplicant to me, his Almighty, singular judge of his redemption and sole voice of forgiveness. I’ll play deaf because I’m not paid to listen. Fuck. Heads again. Goddamn it. Another boring Friday night. Three minutes of focus, three seconds to jab the guy and plunge Father Paul’s secret recipe into his ass, and then what am I supposed to do for the rest of the night? What was all the training for if I can’t make use of it? “The toss allows God to decide,” the cunning old priest would say. “Rejoice in the blessing endowed on you to be God’s Will enabled.” I don’t get it. These bastards deserved to die in the worst ways possible, but God has never let a Tails spin into my palm. It’s like He’s fucking with me, like it’s a fucking joke to Him to wind me up and then relish the frustration and disappointment when that fucking coin smiles back at me. Whatever. I’m here, he’s here and between the crowd and the music, no one will hear him scream. Might as well be in space. Hoppin’ and boppin’ through the dancers, I could be invisible for all the attention I get. He, on the other hand, exudes so much charisma that even women with partners are half-turned to watch him from the corner of their eyes. Maybe becoming a serial kidnapper-rapist-murderer wasn’t entirely a matter of choice. Maybe he’s only using the gifts God gave him, just like I’m using mine, but I doubt I’m as happy with my lot as he is — or has been — with his. No complaints on my part, though. The money’s pretty good, I travel the world in First Class and though my green Vatican City passport may not have quite the get-out-of-jail-free status of the light blue United Nations one, I don’t attract national security bureaus like they do. The moments between songs allow me to close unnoticed, my syringe ready in its sheath under my left cuff. An instant after the pulsing bass returns, I stab and then pretend to trip, moving bent over to disguise my route. No need to confirm his death, so sure am I in the evil priest’s alchemical expertise, and then my Friday night boredom is redeemed via text message — double headers are rare enough events that I would exhaust my list with less than five fingers. My mission window is short and I must first change and dispose of these clothes, so I waste no time waiting for an Uber and flag the first taxi in the rank outside the club. I have never questioned a target and I don’t now, but I can’t honestly wrap my mind around what a pubescent girl could have done to deserve me. It’s late enough she’s probably in bed, asleep, but that makes no never mind to me. Five minutes after striding through the hotel’s front entrance, having greeted the doorman and smiled at the night-shift manager, I exit by a side door dressed in running gear, my syringe nestled in my sock. The taxi ride uptown is quick, and I climb out beside a park. I limber up until the cab is out of sight and then jog without urgency towards the target address. This late, all the neighbourhood businesses I pass are closed — coffee shops, upscale boutiques, salons, even a small insurance office. Slowing as I arrive ‘home,’ I pause and stretch on the pavement out front, using my movements to gather my tools. With tired purpose, I climb the front stairs. Two breaths and I’m shutting the front door behind me in practiced silence. My mission demands less illumination, so I turn the lights off as I come to them. Keeping to the shadows, I ignore the ground floor and head upstairs. A small part of me is grateful for the wall-to-wall as I step onto the landing, but a greater part smiles at the sound of pop music emerging from an alcove. Tossing my coin as instructed, giving God the final word, my breath catches. Tails. What the fuck? Tails again. And a third time. So. Two things occur to me. I cannot torture her here and I need a way to move her somewhere safe. An image brightens in my head — the sign in the window of the insurance agent listed it was closed all weekend. It will do in a pinch. I need a suitcase. Back down the stairs, ignoring the main floor once again, because suitcases are stored in the basement. I choose the largest and retrace my steps to the girl’s room. None of the lights have been turned back on, so there is no call for collateral victims. One final check. Tails. So be it. Opening the door enough to slowly reach in and quench the main light, I use her confusion to dart across to where she lies on her stomach lost in her iPad. A rap on her temple with a folded knuckle is enough to steal her awareness, and the suitcase is ample. On a whim, I toss in her iPad. Here, then, is the riskiest part. A man in running gear does not often haul a large suitcase through the night, but once more pretending purpose suffices to protect me. Trixie Thorn would otherwise have grown into a great beauty were it not for my orders, but I tie her to the table in the staff kitchen as securely as I always do. I push a piece of torn tea towel into her mouth and secure it with tape. The one thing I don’t do is cut away her pyjamas. Nudity is a great psychological weapon but I just can’t do it. Even armed with the remaining piece of towel doused with ammonia from under the sink, I hesitate to wake her. I cannot say why. So I study her. It’s pointless, but perhaps her iPad can tell me something. Lead image by on ZSun Fu Unsplash

Heads & Tails: A Tale of Chance, Faith and Morality - Part 1

মাথা ও পুচ্ছ: সম্ভাবনা, বিশ্বাস এবং নৈতিকতার গল্প - পার্ট 1

Kopf und Zahl: Eine Geschichte von Zufall, Glauben und Moral – Teil 1

Cara y Cruz: Una historia de azar, fe y moralidad - Parte 1

Pile et Face : Une histoire de hasard, de foi et de moralité - Partie 1

हेड्स एंड टेल्स: संयोग, विश्वास और नैतिकता की कहानी - भाग 1

表と裏：偶然、信仰、道徳の物語 - パート 1

앞면과 뒷면: 우연, 신앙, 도덕에 관한 이야기 - 1부

Cara e Coroa: Uma História de Acaso, Fé e Moralidade - Parte 1

Орел и решка: История о шансе, вере и морали. Часть 1

Yazı ve Yazı: Bir Şans, İnanç ve Ahlak Hikayesi - Bölüm 1

Đầu & Đuôi: Câu chuyện về cơ hội, niềm tin và đạo đức - Phần 1

正面与反面：机遇、信仰与道德的故事（上）

Sylvia: 100 Days

This interview series celebrates all the phenomenal . Congratulations! Well-Deserved, Champion.The HackerNoon community is excited to learn more about you! Startups of the Year Winners share the link to your winning city award here [example: ] https://hackernoon.com/startups/north-america/north-america-toronto-canada What does it mean for you to win this title? To us, it highlights that people recognize the need for a VPN in the privacy space that takes privacy seriously - and that you can do this while still maintaining a sense of humor. We’ve been working tirelessly to ensure connectivity in many challenging regions lately. Be it politics, censorship, or dictatorial aggression – we will power through with smiles on our faces. What sets you apart from the competition? I briefly mentioned our sense of humor, and that really does have an impact on all aspects of Windscribe. I think that shows. We get a ton of feedback from people saying that they love our VPN not just because it works well; it’s because of how we communicate with our community. We have the largest VPN community at discord.gg/VPN. We get fan mail from people around the world saying how certain jokes made their day - it’s great, our user base is so awesome. It makes us more approachable and that allows us to cater to their needs better than anyone else. We genuinely love this business and what it does for others. What do you love about your team, and why are you the ones to solve this problem? Our team is awesome: super smart people, from around the world, that get along and are genuinely concerned with privacy. Obviously, there are a lot of VPN companies out there, but we don’t cut corners, we don’t waste money on advertising or affiliate marketing, and we have rigid ethical standards (sometimes to the detriment of our revenue, but to the relief of our consciousness). We have the largest physical network out of any of our competitors. We are entirely self-funded so no one can force us to abandon our ethics. We are open about our ownership and our location - basically, there is a face behind the company, instead of a series of hidden shell companies. We spend a lot on talent, infrastructure, and researching the best solutions to online privacy issues. We take our mission very seriously, but we don’t take ourselves seriously. If you weren’t building your startup, what would you be doing? Eating chili cheese dogs and letting shit slide? Enjoying having a good posture, unburdened by sitting and typing for 11 hours a day? Yelling at the moon for not being the sun? Teaching my dog how to drive? There is so much I could be doing. At the moment, how do you measure success? What are your core metrics? We have a ton of internal metrics. Speed, reliability, and ability for quick turnaround on new features. To roll out fixes, find solutions for censorship, and ensure we respond to any issue within hours. Tell us how your startup is changing the world. At the risk of sounding like we are pitching to a bunch of out-of-touch San Francisco VCs - we are changing the world by helping people who are isolated in countries with restricted internet the means to stay connected and informed about the world around them. We help journalists, protestors, and activists to champion their cause. We provide a means of communication between loved ones. We allow those in countries with only state-approved media a glimpse at the truth of issues that affect them from sources they may not traditionally have access to. We fight for an open internet and privacy for all. We’re also kind of funny, so there’s that, too. How do you or your company intend to embrace the responsibility of this title in 2024? I think if we’re able to maintain and improve on the level of service and reliability of our network, we’ll be able to make Windscribers proud. We often have cat-and-mouse-like antics with those who attempt to censor their citizens. They will often try to find a way to block us which requires us to then find ways to circumvent their censorship. Luckily our circumvent censorship feature, something we’re very proud of, works incredibly well to this extent. Our team knows what to look for, how to adapt, and how to overcome these challenges. What goals are you looking forward to accomplishing in 2024? We want to continue to grow both of our companies, Windscribe and Control D, internally and externally. It feels genuinely great knowing that we can help more and more people access information in traditionally “closed-off” parts of the world and manage their knowledge on their own terms - and do so with an international team of people who care about it just as much, and in some cases, even more than I can (some of our employees live in heavily censored countries). Pretty cliché answer, I know - we just want to keep growing. Which trend(s) are you most excited about in 2024? Share your reasons. Broccoli-style haircuts. Their rise in popularity has given us much to laugh at. But we will be much more excited once that trend dies like the man-bun trend before it. What will come after? Who knows. In the realm of technology, the list is really endless: rocketry and space exploration, AI, increasingly more useful and easier-to-access online educational content… there is a lot of interesting technology being worked on - so we are permanently excited to see what awaits the world. 2023 had been another crazy year, especially in tech, with layoffs and the Generative AI takeover! Which trend are you most concerned about? Be as brief or as detailed as you like. Honestly, AI has been really helpful to our teams. We have adopted it to help teams do more in less time — it eases the burden/annoyance of repetitive and lower-priority tasks, giving everyone more time to work on engaging problems. As for the layoffs, we haven’t had to lay anyone off, which is a blessing. We know it really sucks being let go from a company, but at the same time, this market has allowed us to scoop up a ton of really high-level talent. We have been hiring like crazy, and are still hiring more. Come and apply! Share your biggest success so far and/or your biggest failure so far. We have a company filled with very smart people who all get along to work on problems that affect people directly (censorship, access to information, etc). The team is our biggest success. We also are close to having 69 million users, which is numerologically important to us because funny sex number. My dog didn’t poop in the office this week either. Really, it’s a string of one success after another. Biggest failure? Can’t remember, overshadowed by too much success (see previous sentence). Any words of wisdom you’d like to share with us? At a time when the world is increasing its interconnectivity, it is becoming harder and harder to safeguard one’s own privacy. The main word of “wisdom” (I’ll leave it to you to decide whether it is wise), is that everyone should pay close attention to their online privacy and protect it fiercely, as it is becoming a commodity for companies, governments, and various groups to exploit citizens, and profit off of them both monetarily and psychologically. Take your privacy seriously because no one else will. Other than that… never judge a group of people by the way their government acts. Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. See our global winners announcement . here Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners will get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

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