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Intuit

intuit

"In today's digital age, security compliance is not just a necessity but a cornerstone of trust," says Akshay Sekar Chandrasekaran, a leading compliance engineering expert at Intuit. He provides insights at a crucial time when cybersecurity threats and regulatory complexities are at an all-time high. Akshay directly enhances Intuit's security environment with his initiatives, setting a high bar for digital security across the industry. The Imperative of Compliance in a Digital World Immense pressure forces businesses to fortify their defenses while adhering to increasingly stringent regulatory requirements. Intuit, a global financial software company, faces these challenges head-on and uses professionals like Akshay Sekar Chandrasekaran to enhance its security posture. Akshay integrates advanced automation and machine learning into compliance engineering, streamlining processes and reducing human error with his innovative methodologies. "We’ve reinforced a system that automates compliance checks across many layers, significantly reducing the risk of non-compliance and potential breaches," Akshay explains. "This has been a game-changer for us, allowing for continuous monitoring and swift responses to potential threats." Intuit Assist: A Revolution in Financial Guidance Intuit Assist, a distinctive artificial intelligence (AI)-a powered platform offering personalized financial guidance to over 100 million subscribers, is central to Intuit's innovative course of action. Akshay’s role in the development of Intuit Assist was crucial via the achievement of compliance certifications along with the development of new security controls, policies, and standards, which paved the way for an advanced assistant. The system handles 810 million AI-driven interactions and 25 million natural language conversations annually. Intuit Assist provides real-time data to offer customized guidance that adapts to each user's unique financial scenario, setting itself apart. Unlike conventional financial tools that basically react to user inputs, Intuit Assist takes a proactive stance, anticipating needs based on a deep understanding of each user's financial journey. By seamlessly integrating across Intuit products such as Quickbooks, TurboTax, Credit Karma, and MailChimp, it democratizes financial decision-making for individuals and businesses. Compliance certifications served as a cornerstone to the integration of the assistant across Intuit products whilst also allowing data sharing between Intuit and its financial partners. "Automation in compliance improves accuracy, but it also enhances our ability to predict and prevent security incidents," says Akshay Sekar. "Foresight is a superpower that only AI can supply for now." Automating Compliance: A Technological Leap Akshay leads the compliance automation framework at Intuit, utilizing automation and artificial intelligence to perform real-time audits and generate comprehensive, easy-to-interpret reports. This innovation has streamlined the auditing process, reducing the time required from weeks to mere hours. This allows Intuit to maintain compliance with global security compliance standards such as ISO 27001, PCI, SOC2, and SOX  more efficiently, freeing up resources to focus on proactive security measures. In 2023, Intuit's automated systems flagged potential vulnerabilities in third-party integrations, allowing immediate mitigation before any exploit could occur. A Vision for Sustainable Security As the digital terrain shapeshifts, strategies employed to secure it must also change. Akshay envisions a future where compliance engineering is fully integrated into enterprise operations and advances with AI and machine learning. This integration will enable security frameworks to be more dynamic and responsive and adapt to new threats in real-time. "The future of compliance engineering lies in its ability to scale with the changing digital environment," Akshay reflects. "Our goal is to create a resilient infrastructure that not only meets today's standards but anticipates and prepares for tomorrow's challenges." Akshay Sekar Chandrasekaran exemplifies the critical role of compliance engineering in modern enterprise security through his work at Intuit. He has bolstered Intuit's defenses and set a new industry benchmark through his use of automation and AI. "Ultimately, our mission is to build trust through security," concludes Akshay Sekar Chandrasekaran. "We can establish a safer digital future for everyone by staying ahead of regulatory demands and emerging threats." This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. "In today's digital age, security compliance is not just a necessity but a cornerstone of trust," says Akshay Sekar Chandrasekaran, a leading compliance engineering expert at Intuit. He provides insights at a crucial time when cybersecurity threats and regulatory complexities are at an all-time high. "In today's digital age, security compliance is not just a necessity but a cornerstone of trust," Akshay directly enhances Intuit's security environment with his initiatives, setting a high bar for digital security across the industry. The Imperative of Compliance in a Digital World The Imperative of Compliance in a Digital World Immense pressure forces businesses to fortify their defenses while adhering to increasingly stringent regulatory requirements. Intuit, a global financial software company, faces these challenges head-on and uses professionals like Akshay Sekar Chandrasekaran to enhance its security posture. Akshay integrates advanced automation and machine learning into compliance engineering, streamlining processes and reducing human error with his innovative methodologies. "We’ve reinforced a system that automates compliance checks across many layers, significantly reducing the risk of non-compliance and potential breaches," Akshay explains. "This has been a game-changer for us, allowing for continuous monitoring and swift responses to potential threats." "We’ve reinforced a system that automates compliance checks across many layers, significantly reducing the risk of non-compliance and potential breaches," "This has been a game-changer for us, allowing for continuous monitoring and swift responses to potential threats." Intuit Assist: A Revolution in Financial Guidance Intuit Assist: A Revolution in Financial Guidance Intuit Assist, a distinctive artificial intelligence (AI)-a powered platform offering personalized financial guidance to over 100 million subscribers, is central to Intuit's innovative course of action. Akshay’s role in the development of Intuit Assist was crucial via the achievement of compliance certifications along with the development of new security controls, policies, and standards, which paved the way for an advanced assistant . The system handles 810 million AI-driven interactions and 25 million natural language conversations annually. advanced assistant advanced assistant Intuit Assist provides real-time data to offer customized guidance that adapts to each user's unique financial scenario, setting itself apart. Unlike conventional financial tools that basically react to user inputs, Intuit Assist takes a proactive stance, anticipating needs based on a deep understanding of each user's financial journey. By seamlessly integrating across Intuit products such as Quickbooks, TurboTax, Credit Karma, and MailChimp, it democratizes financial decision-making for individuals and businesses. Compliance certifications served as a cornerstone to the integration of the assistant across Intuit products whilst also allowing data sharing between Intuit and its financial partners. "Automation in compliance improves accuracy, but it also enhances our ability to predict and prevent security incidents," says Akshay Sekar. "Foresight is a superpower that only AI can supply for now." "Automation in compliance improves accuracy, but it also enhances our ability to predict and prevent security incidents," "Foresight is a superpower that only AI can supply for now." Automating Compliance: A Technological Leap Automating Compliance: A Technological Leap Akshay leads the compliance automation framework at Intuit, utilizing automation and artificial intelligence to perform real-time audits and generate comprehensive, easy-to-interpret reports. This innovation has streamlined the auditing process, reducing the time required from weeks to mere hours. This allows Intuit to maintain compliance with global security compliance standards such as ISO 27001, PCI, SOC2, and SOX  more efficiently, freeing up resources to focus on proactive security measures. In 2023, Intuit's automated systems flagged potential vulnerabilities in third-party integrations, allowing immediate mitigation before any exploit could occur. A Vision for Sustainable Security A Vision for Sustainable Security As the digital terrain shapeshifts, strategies employed to secure it must also change. Akshay envisions a future where compliance engineering is fully integrated into enterprise operations and advances with AI and machine learning. This integration will enable security frameworks to be more dynamic and responsive and adapt to new threats in real-time. "The future of compliance engineering lies in its ability to scale with the changing digital environment," Akshay reflects. "Our goal is to create a resilient infrastructure that not only meets today's standards but anticipates and prepares for tomorrow's challenges." "The future of compliance engineering lies in its ability to scale with the changing digital environment," "Our goal is to create a resilient infrastructure that not only meets today's standards but anticipates and prepares for tomorrow's challenges." Akshay Sekar Chandrasekaran exemplifies the critical role of compliance engineering in modern enterprise security through his work at Intuit. He has bolstered Intuit's defenses and set a new industry benchmark through his use of automation and AI. "Ultimately, our mission is to build trust through security," concludes Akshay Seka r Chandrasekaran. "We can establish a safer digital future for everyone by staying ahead of regulatory demands and emerging threats." "Ultimately, our mission is to build trust through security," r "We can establish a safer digital future for everyone by staying ahead of regulatory demands and emerging threats." This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here here

Compliance Engineering Expert Elevates Enterprise Security at Intuit

কমপ্লায়েন্স ইঞ্জিনিয়ারিং বিশেষজ্ঞ Intuit এ এন্টারপ্রাইজ নিরাপত্তা উন্নত করে

Compliance-Engineering-Experte verbessert Unternehmenssicherheit bei Intuit

Experto en ingeniería de cumplimiento eleva la seguridad empresarial en Intuit

Un expert en ingénierie de conformité élève la sécurité de l'entreprise chez Intuit

अनुपालन इंजीनियरिंग विशेषज्ञ ने इंट्यूट में उद्यम सुरक्षा को उन्नत किया

コンプライアンス エンジニアリングの専門家が Intuit のエンタープライズ セキュリティを向上

규정 준수 엔지니어링 전문가, Intuit의 기업 보안 강화

Especialista em engenharia de conformidade eleva segurança empresarial na Intuit

Эксперт по обеспечению соответствия требованиям повышает безопасность предприятия в Intuit

Uyumluluk Mühendisliği Uzmanı Intuit'te Kurumsal Güvenliği Artırıyor

Chuyên gia kỹ thuật tuân thủ nâng cao bảo mật doanh nghiệp tại Intuit

合规工程专家提升 Intuit 的企业安全性

In late 2014, I was part of a small humanitarian effort that took me and a few Intuit colleagues to rural India for two weeks. When we first arrived, it was clear that we would be receiving much more than we could possibly give. I’ll always remember the feeling of great anticipation as we walked up to the village where dozens of locals waited. After months of preparation, we were finally here. When we arrived, we were given leis of local flowers On the streets were written “Welcome.” The children were shy, but couldn’t hide their smiles as we thanked them for their warm welcome and hospitality. Most of them didn’t know why we were there, but nevertheless, they were all kind. The goal of our first fews days in the village was simply to meet with a handful of the women who would be attending our workshop. We wanted to meet the participants in their village to have better context. Months prior — and 10,000 miles away, we developed the workshop’s curriculum (almost like building software for customers you’ve never met). Needless to say, we spent the whole night updating our plans. Already, we were learning so much. After 30 minutes into our interviews, I stepped outside to talk with a few of the men that spoke English. The guy to my right was the one who helped arrange our visit. Taking pictures turned out to be a great way to break the ice. It was a conversation starter. They asked me about my family and where I was from. It was an odd feeling being the center of attention among so many people. You couldn’t help but feel like a celebrity. But you also knew you were just an ordinary person — excited to be learning about a new culture and meeting people on the other side of the world. The men and boys hanging out — waiting for us to finish with our “interviews” with the local entrepreneurs I’m so grateful for my colleague, , for taking this picture (below) with her phone. It’s one of my favorites. When I spoke, it was translated to Hindi. The women would answer, and then I would receive the translation back into English. This lasted for over 30 minutes. I honestly don’t remember anything that was said — just the feeling of intensity. No matter who was talking or translating, we just looked at each other. Everyone had incredible presence. Nicole I showed this picture to my kids for the first time the other day. Their question — “Dad, what were you saying?” I have no idea. But I promise it was nothing profound. After the interviews, we spent time at the school. They had prepared some singing and dancing — led by the elementary students. Anne, one of my favorites, stood up to teach the children, “Head, Shoulders, Knees, and Toes.” It was classic Anne — and a fun moment for everyone. The kids learned quickly and everyone was smiling and laughing. Once we started to sing, everyone who wasn’t already with us, came out to watch. The outdoor schoolroom was small, but it seemed to fit the entire village. People were looking over their fences and watching overhead on their rooftops. It was quite a scene. I remember asking Anne what prompted to her to share the song. She said “every kid needs to know Head, Shoulders, Knees, and Toes.” I completely agreed. The women in red was the teacher. She had written down the days of the week in English. The students would stand up one by one to recite. The kids loved to get their picture taken. I would take their picture and then show them on the back of my camera. A couple days later — and many more hours of prep, the workshop began. We’re excited to get started. The three women in the middle were there to perform the welcome ceremony. Before the participants arrived in buses from the nearby villages, we had a ceremony of sorts. Roli and rice were applied between our eyebrows as a way to welcome us to the community center, where we would hold our classes. The roli and rice are believed to imbue energy and focus. Whatever it did, we knew it was it was game time — there was a nervous energy between all of us. I love the image above. I had to laugh at this scene because it was all too familiar. No matter what kind of workshop or conference I’ve attended, they all start like this — long lines, disorderly, and confusing. But these women were super patient. For the most part, they all seemed happy to be there. We taught in circus-like tents. I loved it because everything — every detail — was so different from anything I’ve participated in before. The whole experience was incredibly unique. We started the class by throwing around a beachball. When you had the ball, you were to share your name, town you were from, and something you hoped to learn during the course. Some of the women were more vocal and clearly the leaders of the various groups. In particular, I remember this woman in the middle. I couldn’t understand her, but she seemed incredibly bright. The women all listened closely when she spoke. The women seemed to always be smiling and laughing. Every day for the next three days we came back to the community center — back to the tents to teach the fundamentals of marketing, sales, and finance. I honestly don’t think they learned much from us. Even though most of these women didn’t have a formal education beyond elementary school, they were street smart. They already understood many of the concepts we taught — we just gave them names. At the end of the program, the women and locals put together another celebration, a closing ceremony. There was a band, cheering, and of course, dancing. Amit, to the left. Instrumental in making all of this possible. The day before the workshop ended, we received a few gifts. The men received hand-made “kurta” suits and the women in our group received saris. Earlier in the week, we were measured. We weren’t sure what it was for until they presented us the gifts. We all wore them on the last day and finally felt like locals :) I’m holding a jar of pickles — one of the small businesses owned by one of our students. When I got home, I got a lot of questions about my experience. But, it was difficult to explain in words. Most of what I shared were pictures — but even the pictures didn’t do the experience justice. I remember wishing my wife and daughters could have been with me. During the closing ceremonies, I wondered if we had any impact on the women we taught. In the end, I concluded that we didn’t teach them much — they already knew and practiced most of the concepts we taught. In some respect, that was a little frustrating. Selfishly, I was explaining a bit of my concern to one of the locals helping us. He then shared with me more about these women — that many of them lived in abusive situations, many had seen little outside of their village and had little support. He said that our visit, specifically, meant so much because now they knew they had a . He said that was our gift to them. group of people on the other side of the world that believed in them A year later, we jumped on a Skype call with a few of the women from the workshop. The goal was to simply share our support and listen to them about their progress as entrepreneurs. I loved how it felt to be connected to a group of people from a few small villages in rural India on the other side of the world. I’ll forever be grateful for those two weeks in India.

My Two Weeks in India

The Noonification: Leetcode: Two-sum an Intuitive Approach (4/23/2024)

Imagine yourself as the proud owner of a souvenir shop inside the Spot-On Change Hotel. When closing the cash register, you notice an excess of 8 euros. It seems that a guest didn’t receive the exact change. This could tarnish the hotel’s reputation. To avoid this, you decide to solve the mystery of the incorrect change. Upon opening the shop’s cash system, you discover that the error occurred on two different accounts! You devise a plan: to visit each room and ask the guests what change they received in the shop until you find the two with the wrong bill. Arriving on the first floor, a guest reports receiving 4 euros in change. You calculate that you need to find a number that, when added to the 4 euros, results in 8. Moving to the second floor, the guest’s change is 5 euros. 4 + 5 results in 9, so it can’t be this one… Fifth floor, sixth… Tenth, NO, NO, NO! Since you didn’t find the value on the first attempt, you go down to the second floor and start asking all the guests again about their change so that you can compare it with the value from the next starting floor, exhausting, isn’t it? In computer science, this search method is called brute force, an extremely slow search method that works by comparing one item with the following ones in the array. This consumes a lot of time and is not efficient, imagine going up and down all the floors of the Spot-On Change Hotel several times! This is not practical for you and also not practical for the computer. If you’re curious about how this going up and down the floors would be in a software program, it would look like this in JavaScript: twoSum(nums, target) {
 for (let i = 0; i < nums.length; i++) {
 for (let j = i + 1; j < nums.length; j++) {
 if (nums[i] + nums[j] === target) {
 return [i, j];
 }
 }
 }
} After assessing the situation calmly, you realize that there’s a more efficient way to find the two guests with the wrong change. You refine your initial mathematical intuition that 9 euros equals x + y. Applying a bit of arithmetic, you realize that: x + y = 9 is the same as saying that y = 9 — x, and that makes all the difference when increasing the efficiency of your search. Another thing that changed in your strategy is that now you will take a notepad to write down the values that the guests tell you, so you don’t have to ask the same value twice for the guests, and you don’t have to spend the day going up and down stairs. (What a terrible time for the elevator to break!). With the new tools in hand, you head to the first floor, where a guest informs you that they have 5 euros in change. You record this as {5: 0}, indicating a value of 5 at position 0. Substituting x with 5 in the equation, you calculate y = 8–5, resulting in y = 3. Since your notepad is still empty, you don’t have the number 3 recorded, which would be the complementary number to 5 to reach the result 8. You then write down the number 5 on your notepad (remember to always write down the number verified at the moment and not its complement; you will only use the complement to compare it with the target number that you have written down). You proceed to the second guest, who mentions receiving 2 euros in change. Applying the formula again: y = 8–2 => y = 6, you check your record, where you don’t find any number 6. Thus, you add the 2 to your record, which now stands as {5: 0, 2: 1}. Continuing the search, the next guest reports receiving 3 euros in change. You calculate again: y = 8–3 => y = 5. Bingo! You have a 5 recorded on your notepad! The guest from floor 0 is complementary to the one from floor 2! This approach is known as a hash table, and it’s much faster and efficient, both for you and the computer. In JavaScript, this would be implemented as follows: twoSum(nums, target) {
 const myTable = {};
 for (let i = 0; i < nums.length; i++) {
 const complementaryNumber = target - nums[i];
 if (complementaryNumber in myTable) {
 return [i, myTable[complementaryNumber]];
 }
 myTable[nums[i]] = i;
 }
} This is an easy problem from Leetcode that only becomes easy after you understand the concepts behind it. I recommend that you try to solve the problem on your own and spend some time trying to build intuition behind it, create your own analogy, review, and try to write pseudocode before moving on to the solution. I hope my analogy has helped you better understand the concepts behind the two sum challenge. See you next time! Imagine yourself as the proud owner of a souvenir shop inside the Spot-On Change Hotel. When closing the cash register, you notice an excess of 8 euros. It seems that a guest didn’t receive the exact change. This could tarnish the hotel’s reputation. To avoid this, you decide to solve the mystery of the incorrect change. Upon opening the shop’s cash system, you discover that the error occurred on two different accounts! You devise a plan: to visit each room and ask the guests what change they received in the shop until you find the two with the wrong bill. Arriving on the first floor, a guest reports receiving 4 euros in change. You calculate that you need to find a number that, when added to the 4 euros, results in 8. Moving to the second floor, the guest’s change is 5 euros. 4 + 5 results in 9, so it can’t be this one… Fifth floor, sixth… Tenth, NO, NO, NO! Since you didn’t find the value on the first attempt, you go down to the second floor and start asking all the guests again about their change so that you can compare it with the value from the next starting floor, exhausting, isn’t it? In computer science, this search method is called brute force, an extremely slow search method that works by comparing one item with the following ones in the array. This consumes a lot of time and is not efficient, imagine going up and down all the floors of the Spot-On Change Hotel several times! This is not practical for you and also not practical for the computer. If you’re curious about how this going up and down the floors would be in a software program, it would look like this in JavaScript: twoSum(nums, target) {
 for (let i = 0; i < nums.length; i++) {
 for (let j = i + 1; j < nums.length; j++) {
 if (nums[i] + nums[j] === target) {
 return [i, j];
 }
 }
 }
} twoSum(nums, target) {
 for (let i = 0; i < nums.length; i++) {
 for (let j = i + 1; j < nums.length; j++) {
 if (nums[i] + nums[j] === target) {
 return [i, j];
 }
 }
 }
} After assessing the situation calmly, you realize that there’s a more efficient way to find the two guests with the wrong change. You refine your initial mathematical intuition that 9 euros equals x + y. Applying a bit of arithmetic, you realize that: x + y = 9 is the same as saying that y = 9 — x, and that makes all the difference when increasing the efficiency of your search. Another thing that changed in your strategy is that now you will take a notepad to write down the values that the guests tell you, so you don’t have to ask the same value twice for the guests, and you don’t have to spend the day going up and down stairs. (What a terrible time for the elevator to break!). With the new tools in hand, you head to the first floor, where a guest informs you that they have 5 euros in change. You record this as {5: 0}, indicating a value of 5 at position 0. Substituting x with 5 in the equation, you calculate y = 8–5, resulting in y = 3. Since your notepad is still empty, you don’t have the number 3 recorded, which would be the complementary number to 5 to reach the result 8. You then write down the number 5 on your notepad (remember to always write down the number verified at the moment and not its complement; you will only use the complement to compare it with the target number that you have written down). You proceed to the second guest, who mentions receiving 2 euros in change. Applying the formula again: y = 8–2 => y = 6, you check your record, where you don’t find any number 6. Thus, you add the 2 to your record, which now stands as {5: 0, 2: 1}. Continuing the search, the next guest reports receiving 3 euros in change. You calculate again: y = 8–3 => y = 5. Bingo! You have a 5 recorded on your notepad! The guest from floor 0 is complementary to the one from floor 2! This approach is known as a hash table, and it’s much faster and efficient, both for you and the computer. In JavaScript, this would be implemented as follows: twoSum(nums, target) {
 const myTable = {};
 for (let i = 0; i < nums.length; i++) {
 const complementaryNumber = target - nums[i];
 if (complementaryNumber in myTable) {
 return [i, myTable[complementaryNumber]];
 }
 myTable[nums[i]] = i;
 }
} twoSum(nums, target) {
 const myTable = {};
 for (let i = 0; i < nums.length; i++) {
 const complementaryNumber = target - nums[i];
 if (complementaryNumber in myTable) {
 return [i, myTable[complementaryNumber]];
 }
 myTable[nums[i]] = i;
 }
} This is an easy problem from Leetcode that only becomes easy after you understand the concepts behind it. I recommend that you try to solve the problem on your own and spend some time trying to build intuition behind it, create your own analogy, review, and try to write pseudocode before moving on to the solution. Leetcode I hope my analogy has helped you better understand the concepts behind the two sum challenge. See you next time!

Leetcode: Two-sum an Intuitive Approach

লিটকোড: দুই-সমষ্টি একটি স্বজ্ঞাত পদ্ধতি

Leetcode: Zweisummen, ein intuitiver Ansatz

Leetcode: dos sumas y un enfoque intuitivo

Leetcode : une approche intuitive à deux sommes

लेटकोड: दो-योग एक सहज दृष्टिकोण

Leetcode: 2 つの合計を直感的に計算するアプローチ

Leetcode: 직관적인 접근 방식의 2합

Leetcode: duas somas e uma abordagem intuitiva

Leetcode: интуитивный подход с двумя суммами

Leetcode: İki Toplamlı Sezgisel Bir Yaklaşım

Leetcode: Tổng hợp hai cách tiếp cận trực quan

Leetcode：二和的直观方法

Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References 7 FINDINGS Quantitative Findings. From the exit interviews, 12 of 14 participants preferred ConstitutionMaker to the baseline version. The results from the questionnaire are summarized in Figure 4. We found that ConstitutionMaker was perceived to be more helpful for writing rules that effectively guided the bot (Z = 8, p = .007), scoring on average 5.79 (𝜎 = 1.01) whereas the baseline scored 4.0 (1.51). When participants rewound parts of the conversation, they thought the ConstitutionMaker principles were followed by the bot better. Next, participants felt it was significantly easier (Z = 10.5, p = .006) to convert their feedback into principles with ConstitutionMaker (𝜇 = 5.86, 𝜎 = 0.91) than with the baseline (𝜇 = 3.93, 𝜎 = 1.39). The automatic conversion of kudos and critiques into principles eased the process of converting intuitive feedback into clear criteria for the bot to follow. Participants perceived that they were significantly more efficient (Z = 5, p = .004) writing rules with ConstitutionMaker (𝜇 = 5.86, 𝜎 = 1.51) than with the baseline (𝜇 = 3.64, 𝜎 = 1.34). Finally, participants also felt that writing principles with ConstitutionMaker (𝜇 = 3.0, 𝜎 = 1.41) required significantly less mental demand (Z = 1.5, p = .002) than with the baseline (𝜇 = 5.21, 𝜎 =1.78). There was no statistically significant difference for diversity (Z = 19, p = .06); participants felt that they exercised their creativity and wrote relatively diverse principles in the baseline. Next, regarding the feature usage metrics, participants wrote significantly more principles (t(13)=4.73, p < .001) with ConstitutionMaker than with the baseline; participants wrote on average 6.78 (𝜎 = 2.11) principles per chatbot with ConstitutionMaker and 4.42 (𝜎 = 1.24) principles with the baseline. Of the 95 principles written in the ConstitutionMaker condition, 40 (42.1%) came from kudos, where 37 were selected from the generate rationales and 3 were written; 28 (29.5%) came from critique, where 8 were selected and 20 were written; 13 (13.6%) came from the rewrite features; 14 (14.7%) were manually written. Participants found rewriting a bit cumbersome and preferred the less intensive workflow of describing what they liked or did not like to generate principles. In the following sections, we provide further context to these findings. 7.1 Participants’ Workflows for Writing Principles The two conditions led to quite different workflows for writing principles. In the ConstitutionMaker condition, participants commonly scanned the three candidate outputs from the chatbot, identified the output they liked the most, and then gave kudos to that output if they thought it had a quality that was not currently reflected in their principles. For example, while P1 was working on FoodBot, he was asking for easy-to-make vegetarian dishes, and in one of the bot’s candidate outputs, each suggested dish had a short description and explanation on why it was easy. He appreciated this, skimmed the kudos, and then selected one that conveyed this positive feature of the bot’s output. However, if participants disliked all of the responses, they would then switch to critiquing one of the outputs. Accordingly, this kudos-first workflow helps to explain how most of the principles participants wrote came from that principle elicitation feature. Meanwhile, in the baseline condition, participants generally wrote principles when the bot deviated quite a bit (in a negative way) from what they expected. P8 explained, “Here it feels like what I more naturally do is write corrective rules, to guardrail anything that goes weird...If it’s already doing the right thing it doesn’t need a rule from me. I wouldn’t feel the need to write those.” In the baseline condition, participants only see one candidate output from the chatbot, and this might deemphasize the stochastic nature of the chatbot’s outputs. As a result, when participants are okay with a response, they could feel that they do not need to write a principle to encourage that kind of response further. Overall, with the baseline, participants predominantly wrote principles to steer the LLM from less optimal behavior, while with ConstitutionMaker, participants mostly used kudos the most to encourage behavior they liked. 7.2 ConstitutionMaker Supported Users’ Thought Processes In the following section, we discuss how ConstitutionMaker supported participants’ thought process from (1) forming an intuition on ways the chatbot could be improved, (2) expressing this intuition as feedback, and (3) converting this feedback into a specific and clear principle. As P5 was using the baseline after using ConstitutionMaker, she explained how she wished she could see multiple outputs again: “Sometimes, I don’t know what I’m missing [in the baseline]. I’m thinking of the Denali hiking example [which occurred when she wrote principles for VacationBot with ConstitutionMaker]. Two of the responses didn’t mention that Denali was good for young children. But one did, and I was able to pull that out as a positive principle.” While she was writing principles for VacationBot with ConstitutionMaker, P5 started off the conversation saying she was looking for suggestions for her family, which included two young children. As the conversation progressed and a general location was established, P5 then asked for hiking recommendations, for which the bot gave some, but only one of its responses highlighted that the hikes it was recommending were good for young children. P5 gave kudos to that response and created the following principle: “Consider information previously inputted by the user when providing recommendations.” Ultimately, it can be hard to form opinions on responses without getting exposed to alternatives, so by providing multiple chatbot outputs, ConstitutionMaker supported participants in forming an intuition on how the model might be steered. 7.2.1 Multiple chatbot outputs helped participants form an intuition on how the model could be steered. . Upon seeing a candidate response from the chatbot, participants could intuitively tell if they liked or disliked it, but struggled to articulate their thoughts. The automatically generated kudos and critiques helped participants recognize and formulate this feedback. For example, while working on FoodBot with ConstitutionMaker, P9 asked the bot to identify the pizzeria with the best thin crust from a list of restaurants provided in a prior turn. The bot responded with, “Pizzaiolo has the best thin crust pies.” P9 knew he did not like the response, so he went to critique it and selected the following generated option: “This response is bad because it does not provide any information about the other pizza places that the user asked about.” The following principle was generated: “If the user asks about a specific attribute of a list of items, provide information about all of the items in the list that have that attribute,” which then produced a set of revised responses that compared the qualities of each pizzeria’s crusts. Reflecting on this process, P9 stated, “I didn’t like that last answer [from FoodBot], but I didn’t have a concrete reason why yet...I didn’t really know how to put it into words yet...but reading the suggestions gave me at least one of many potential reasons on why I didn’t like the response.” Thus, ConstitutionMaker helped participants transition from fast thinking [15], that is, their intuitive and unconscious responses to the bot’s outputs, to slow thinking, a more deliberate, conscious formulation of their feedback. 7.2.2 Automatically providing kudos and critique rationales helped participants formulate their intuitive feedback Sometimes the generated kudos and critique rationales did not capture participants’ particular feedback on the chatbot, and so they would then write their own. Their feedback was often under-specified, and ConstitutionMaker helped convert this feedback into a clear and specific principle. For example, P4 was writing principles for VacationBot using ConstitutionMaker. During the conversation, they had told VacationBot that they were planning a week long vacation to Japan, to which the bot immediately responded with a comprehensive 7-day itinerary. P4 then wrote in their own critique: “This response is bad because it does not take into account the user’s interests.” The resulting principle was, “When the user mentions a location, ask them questions about what they are interested in before providing an itinerary.” This principle was aligned with what P4 had in mind, and reflecting on her experience using ConstitutionMaker, she stated, Finally, even when the resulting principle was not exactly what they had in mind, participants appreciated the useful starting point it provided. Along these lines, P11 explained, Overall, ConstitutionMaker helped participants specify their feedback into clear principles. 7.2.3 Generating principles from feedback helped users write clear, specific principles. “When I would critique or kudos, it would give examples of principles that were putting it into words a little bit better than I could about like what exactly I was trying to narrow down to here.” “It was easier to say yes-and with Tool A [ConstitutionMaker]. Where it [the generated principle] wasn’t all the way there, but I think it’s 50% of the way there, and I can get it to where I want to go.” 7.3 Participants’ Workflows Introduced Challenges with Writing Principles Participants struggled to find the right level of granularity for their principles, and the two conditions led to different problems in this regard. Both workflows had participants switch roles from end-user, where participants experimented with different user journeys, to bot designer, where they evaluated the bot’s responses to write principles. The more conversation-forward interface of the baseline blurred the distinction between these two roles. P3 explained that without the multiple bot outputs and principle elicitation features, And by leaning further into this user role, participants wrote principles that were more conversational, but under-specified. For example, while writing principles for FoodBot with the baseline, P11 wrote the principle “Be cognizant of the user’s dietary preferences.” What P11 really had in mind was a principle that specified that the bot should ask the user for their preferences and allergies prior to generating a meal plan. These underspecified principles often did not impact the bot’s responses and would frustrate participants while they used the baseline. “you can simulate yourself as the user a lot better in this mode [the baseline].” Meanwhile, while using ConstitutionMaker, the opposite problem occurred, where users’ workflows led to principles that were over-specified. For example, while working on VacationBot, P7 asked the model to help him narrow down a few vacation options, and the model proceeded to ask them questions (without any principle written specifying so). Appreciating that the model was gathering context, they selected a kudos that praised the model for asking about the user’s budget constraints prior to recommending a vacation destination. The resulting principle was, “Ask the user their budget before providing vacation options.” However, once this principle came into effect, the model’s behavior then anchored specifically to only asking for budget prior to making a recommendation. And so, this workflow of providing feedback at every conversational step, instead of for entire conversations, led to a principle that was too specific and impacted the bot’s performance negatively. While users generally appreciated ConstitutionMaker’s ability to form specific principles from their feedback, there were rare instances where the principles were too specific. Finally, in both conditions, by switching back and forth between the end-user to bot designer roles, participants would sometimes write principles that conflicted with each other. For example, while P2 was working on VacationBot with the baseline, they asked the bot for dog-friendly hotel recommendations in the Bay Area, and VacationBot responded with three recommendations. P2 wanted more recommendations and wrote a principle to “Provide >= 10 recommendations.” Later on in the conversation, P2 now had a list of dog-friendly restaurants, with their requisite costs, and he asked VacationBot which it recommends, to which it responded by listing positive attributes of all the hotel options. P2, who now wanted a decisive, single response wrote the following principle: “If I ask for a recommendation, give *1* recommendation only.” VacationBot, now with two conflicting principles on the number of recommendations to provide, alternated between the two. Ultimately, by providing feedback on individual conversational turns, participants ended up with conflicting principles. P8 imagined a different workflow, where he would experiment with full user journeys and then write principles: In summary, one’s workflow as they probe and test the model impacts the types of principles they produce and the challenges they face. “I think it might help me to actually go through the [whole] user flow and then analyze it as a piece instead of switching...it would allow me to inhabit one mindset [either bot designer or user] for a period of time and then switch mindsets.” 7.4 The Limits of Principles Some participants questioned if writing natural language principles was the optimal way to steer all aspects of a bot’s behavior. While writing a principle to shorten the length of the chatbot’s responses, P13 reflected, They imagined that aspects like the form of the model’s responses would be better customized with UI elements like sliders to adjust the length of the bot’s responses, or exemplifying the structure of the bot’s response (e.g., an indented, numbered list for recommendations) for the model to follow, instead of describing these requests in natural language. In a similar vein, P14 noticed that her principles pertained to different parts of the conversation, and as a list, they seemed hard to relate to each other. She wanted to structure and provide feedback on higher-level “conversational arcs,” visually illustrating the flow and “forks in the road” of the conversation (e.g., “If the user does X, do Y. Otherwise, do Z”). Principles are computational in a sense, and they dictate the ways the conversation can unfold; there might be better ways to let users author this flow, other than with individual principles. “It feels a little weird to use natural language to generate the principles...it doesn’t feel efficient, and I’m not sure how it’s going to interpret it.” This paper is under CC 4.0 license. available on arxiv

Efficiency and Intuition: Key Factors in User Preference for ConstitutionMaker

Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References 3 FORMATIVE STUDY To understand how to support users with writing principles for chatbots, we conducted (1) a one-hour formative study, where we observed eight industry professionals write principles for chatbots of their choice. These participants all had prompting experience. Two participants are designers and six are software engineers, all at a large technology company. During the workshop, participants used an early version of ConstitutionMaker, without principle elicitation features. They spent 25 minutes writing principles for their chatbot. Afterwards, we discussed the difficulties they faced while writing principles. Finally, we collected the principles they wrote and classified them to understand the kind of principles they wanted to write. 3.1 Design Goals In this section, we summarize a set of three design goals for ConstitutionMaker we established from the formative workshop and subsequent think-alouds. D.1 Today’s LLMs are quite sophisticated, and even with just a preamble describing how the bot should behave, the chatbot can hold a convincing conversation. Because of this, participants mentioned that it was sometimes hard to imagine how the chatbot’s responses could be improved. This did not mean, however, that they thought the chatbot’s response was perfect, but instead passable and without any glaring errors. Therefore, to help participants recognize better kinds of responses to steer the chatbot to, our first design goal was to provide multiple candidate responses from the chatbot at each conversational turn. This way, participants can compare them and recognize components they like more than others. Help users recognize ways to improve the chatbot’s responses by showing alternative chatbot responses. D.2 One piece of feedback we got from participants was that writing principles involves a difficult two-step process of first (1) articulating one’s feedback on the model’s current output, and then (2) converting this feedback into a principle for the LLM to follow. Often, one’s initial reaction to the model’s output is intuitive, and converting that intuition into a principle for the chatbot to follow can be challenging. In addition, once participants had a particular bit of feedback in mind (e.g., “I don’t like how the chatbot didn’t introduce itself”), they were unsure how to phrase their principle. However, in line with prior research [45, 46], they found that more concrete principles that specified what should happen and when (e.g., “Introduce yourself at the start of the conversation, and state what you can help with”) generally led to better results. Thus, our second design goal was to help users go from their initial reaction to the model’s output to a specific, clearly written principle to steer the model. Help convert user feedback into specific principles to make principle writing easier. D.3 As participants wrote more principles, they wanted ways to test these principles to make sure they worked. The early version of ConstitutionMaker only let users restart the conversation, and did not let users enable or disable principles. Users wanted to test individual principles on certain portions of the conversation, to see if the model was generating the correct content. And so, our last design goal was to enable easier testing of principles. Enable easier testing of principles to help users understand how well their principles are steering the chatbot’s behavior. 3.2 Principle Classification From the formative workshop and follow up sessions, we collected 79 principles in total and classified them to understand the kinds of principles users wanted to write. These principles correspond to a number of very different chatbots, including a show recommender, chemistry tutor, role playing game manager, travel agent and more. We describe common types of principles below. Unconditional principles are those that apply at every conversational turn. Examples include: (1) those that define a consistent personality of the bot (e.g., “Act grumpy all the time” or “Speak informally and in the first person”), (2) those that place guardrails on the conversational content (e.g., “Don’t talk about anything but planning a vacation”), and (3) those that establish a consistent form for the bot’s responses (e.g., “Limit responses to 20 words”). Meanwhile, a conditional principle only applies when a certain condition is meant. For example, “Generate an itinerary after all the information has been collected,” only applies to the conversation when some set of information has been acquired. Writing a conditional principle essentially defines a computational interaction; users establish a set of criteria that make the principle applicable to the conversation, and once that set of criteria is met, the principle is executed (e.g., an itinerary is generated). Principles can be either unconditional or conditional. For example, “Generate an itinerary after all the information has been collected” depends on the entire conversation history to determine if all of the requisite information has been collected. Similarly the following principle written for a machine learning tutor, “After verifying a user’s goal, provide help to solve their problem,” depends on the conversation history to identify if the user’s goal has been verified. Meanwhile, the principle “When the user says they had a particular cuisine the night before, recommend a different cuisine,” written for a food recommender, pertains just to the latest response by the user. Finally, the condition can depend on the action the bot is about to take, like “When providing a list of suggestions, use free text rather than bullet points,” which applies to any situation when the bot thinks it is appropriate to make suggestions. Conditional principles can depend on the entire conversation history, the user’s latest response, or the action the bot is about to take. For example, the principle “At the start of the conversation, introduce yourself and ask a fun question to kick off the conversation” is fulfilled in a single conversational turn, in which the bot introduces itself. Similarly, “Before recommending a restaurant, ask the user for their location” is also fulfilled in a single turn. Meanwhile, for a role playing game (RPG) bot that guides the user through an adventure, a participant wrote the following principle: “When the user tries to do something, put up small obstacles. Don’t let them succeed on the first attempt.” This principle implies that the bot needs to take action multiple turns prior to being fulfilled (e.g., by first putting up a small obstacle and then subsequently letting the user succeed). Similarly, for a travel agent bot, a user wrote “Ask questions one-by-one to get an idea of their preferences,” which also requires multiple conversational turns prior to fulfillment. Conditional principles can be fulfilled in a single or multiple conversational turns. In summary, principles can either be conditional, where they apply when a certain condition is met, or unconditional, where they apply at every conversational step. Conditional principles further break down into those that depend on the entire conversation history, the user’s last response, or the action the bot is about to take. And finally, conditional principles are either fulfilled in a single turn or multiple conversational turns. This paper is under CC 4.0 license. available on arxiv

Formative Study Illuminates the Path to Intuitive Chatbot Customization

Parachains have become extremely popular for building unique use cases, all while maintaining a seamless interoperability amongst various parachains and blockchains, especially within the Polkadot ecosystem. Yet, beneath the surface of coding and implementing business logic on parachains, a labyrinth of vital activities — crucial for their deployment, management, and scaling — often remain overlooked. DevOps Before any parachain goes into production, it’s essential to subject it to intense testing within a simulated mainnet environment. Zombienet becomes an invaluable tool in this context to test parachain’s resilience by taking it through numerous brute force scenarios, replicating every possible real-world challenge it might encounter. This includes simulations with all node types, from full nodes to validator nodes, and stress-testing configurations to ensure stability and optimal behavior. A discernible gap also lies in the domain of advanced analytics and proactive monitoring, both imperative to validate performance and ensure readiness for the mainnet. In pursuit of a solution, submitted a detailed grant proposal at Web3 Foundation. Recognizing the merit in addressing this operational gap, the foundation lent its support, awarding a grant to Zeeve for the development of a GUI tool (LARCH) designed to automate the parachain simulation testing workflow using Zombienet. Zeeve Details of the Grant: We are happy to announce that we have successfully completed both milestone I and Milestone II of our grant from the Web3 Foundation. This grant has enabled us to focus on the development of for enhanced management of Zombienet with automation and monitoring. Larch More details of the grant can be found . here The software is now available under an open-source license on GitHub and accessible to everyone, complete with its source code, developer documentation and tutorials. We’re confident that Larch will significantly benefit the Polkadot/Substrate parachain developer ecosystem, extending its advantages to the larger Web3 community. What is Larch? Zeeve’s Larch is a comprehensive frontend for Zombienet CLI, seamlessly integrating with K8 and Podman. Larch is thoughtfully designed to simplify the Zombienet network creation, management, and testing processes. With it, developers and parachain teams can swiftly set up the relay chains and parachains with Zombienet, unlocking the potential to test a plethora of configurations, easily, right from the dashboard. Key Features of Larch: Parachain setups are crucial and complex. Larch enables a simple UI driven Zombienet setup, including relaychain and parachain core configurations. Comprehensive Automation: : Larch allows for a ‘pick and choose’ based selection for genesis parameters and chain configurations, crucial for initializing parachain setups tailored for the desired Zombienet. Intuitive Configurations Choose from the existing zombienet configurations, save them as templates, or quickly modify and retest using previously saved templates. Users can also effortlessly upload new ones without delving into manual configuration creation. Flexible Replication: Users can conveniently execute key operations such as start, delete, view progress & metrics directly from the dashboard, ensuring optimal testing flexibility. More operational control: Supports the setup of all node types, be it Full node, Validator node, or Collator node. All-Inclusive Node Setup: Enables developers to monitor live logs during test runs and review post-run results directly through the interface. Real-time Logging: Incorporates a stack of Prometheus and Grafana for effortless monitoring of Zombienet. Advanced Monitoring: Larch’s setup is universal and can be installed on any cloud / local machine of choice with Linux based OS. Cloud Agnostic: In summation, Zeeve’s Larch is set to transform the way substrate & parachain developers interact with Zombienet, offering a simplified, efficient, and intuitive experience. Deploying a Zobienet Network using Larch in a few clicks: This tutorial focuses on creating a template and subsequently, a network from this template. The intention is to emulate the official Zombienet example found at . Zombienet on GitHub This guide is tailored for Linux systems. For Mac users, please refer to the Larch . Although Mac isn’t officially supported yet, if Podman emulation operates smoothly, then Larch should be able to manage the network effectively. Note: Prerequisites section Likewise, execution on a remote Linux cluster via a Mac’s setup should function. K8 kubectl Step 1: Get the larch binary either by downloading from the or yourself. releases build it Step 2: Ensure Podman / kubectl is installed as per the on your system for the orchestration of the Zombienet networks. instructions Step 3: Assign execute permissions to the larch binary using: chmod 755 larch Run the larch binary by doing: ./larch Step 4: Navigate to in your browser. Click on the ‘ button located at the page’s top. http://localhost:9000 Network Template +’ : Step 5 Label the template as “ “. Choose either Podman or Kubernetes as the Provider based on your setup. Proceed by clicking . sample-template ‘Next’ : Step 6 Under the section, populate the subsection. Insert “ ” as the name and select the ‘ option. By pressing the button, add a new node. For this node, input “ ” and again choose ‘ . Press . Relaychain Configuration Nodes alice Validator’ + bob Validator’ Next : Step 7 In the section, tap on the ‘ ‘ button. Set the Parachain ID to 100 (or any other number). Name the collator “ ” and for the command, use ‘ . Click ‘ . Parachain Configuration + collator01 polkadot-parachain’ Next’ : Step 8 For this tutorial, we’ll bypass . Simply click ‘ . HRMP Next’ : Step 9 Within the section, after the pre-filled sections, append the necessary configuration. Store the template by hitting ‘ . Test Configuration Save’ : Step10 On the Templates page, the new template should now be visible. Select ‘ to initiate the network. A dialogue box will pop up – input the network’s name and click ‘ to start the orchestration. The app will transition you to the networks page while in the creation state. Create’ Confirm’ : Step 11 To inspect command and execution logs, click on the ‘ icon. For a detailed command, tap the ‘ button; a modal will pop up with the command and other metadata. To peruse logs/outputs, select the ‘ button, opening another modal revealing the streams. Eye’ Command’ Output’ The preceding step concludes the network creation. : Step 12 To access metrics of the deployed network, head to the and tap the ‘ icon. This redirects you to the Grafana service (default login: admin/admin). Within the Dashboards segment, locate and open the dashboard from the ‘ folder to inspect metrics. My Network page Monitoring’ General’ Wrapping up: Zeeve is deeply committed to the web3 ecosystem. As we conclude, our goal is clear: we want ‘Larch’ getting adopted widely and put to use in a diverse parachain testing scenarios. We are encouraging developers to utilize it for their testing needs and share feedback. Zeeve will continue to update and enhance Larch, adding new features based on community inputs. Together, we’ll keep iterating on Larch, ensuring it remains relevant and beneficial for all developers. Polkadot/Substrate

Zeeve's Larch: Simplifying Polkadot/Substrate Zombienet Operations with an Intuitive GU

জিভের লার্চ: একটি স্বজ্ঞাত জিইউ দিয়ে পোলকাডট/সাবস্ট্রেট জম্বিনেট অপারেশনকে সরলীকরণ করা

Zeeve's Larch: Vereinfachung der Polkadot/Substrat-Zombienet-Operationen mit einer intuitiven GU

Alerce de Zeeve: simplificando las operaciones de Polkadot/Substrate Zombienet con una GU intuitiva

Zeeve's Larch : simplifier les opérations Polkadot/Substrate Zombienet avec un GU intuitif

ज़ीव्स लार्च: एक सहज ज्ञान युक्त जीयू के साथ पोलकाडॉट/सब्सट्रेट ज़ोम्बीनेट संचालन को सरल बनाना

Zeeve's Larch: 直感的な GU でポルカドット/サブストレート ゾンビネットの運用を簡素化

Zeeve's Larch: 직관적인 GU로 Polkadot/Substrate Zombienet 운영 단순화

Zeeve's Larch: simplificando as operações Polkadot/Substrate Zombienet com uma GU intuitiva

Лиственница Зива: упрощение операций Polkadot/Substrate Zombienet с помощью интуитивно понятного графического интерфейса

Zeeve's Larch: Sezgisel Bir GU ile Polkadot/Substrate Zombienet Operasyonlarını Basitleştirme

Zeeve's Larch: Đơn giản hóa các hoạt động của Polkadot/Substrate Zombienet bằng GU trực quan

Zeeve 的 Larch：通过直观的 GU 简化 Polkadot/Substrate Zombienet 操作

PRINCIPLES OF REASON, OR INTUITIVE TRUTHS.

The Problem of Philosophy, by Bertrand Russells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . ON INTUITIVE KNOWLEDGE ON INTUITIVE KNOWLEDGE There is a common impression that everything that we believe ought to be capable of proof, or at least of being shown to be highly probable. It is felt by many that a belief for which no reason can be given is an unreasonable belief. In the main, this view is just. Almost all our common beliefs are either inferred, or capable of being inferred, from other beliefs which may be regarded as giving the reason for them. As a rule, the reason has been forgotten, or has even never been consciously present to our minds. Few of us ever ask ourselves, for example, what reason there is to suppose the food we are just going to eat will not turn out to be poison. Yet we feel, when challenged, that a perfectly good reason could be found, even if we are not ready with it at the moment. And in this belief we are usually justified. But let us imagine some insistent Socrates, who, whatever reason we give him, continues to demand a reason for the reason. We must sooner or later, and probably before very long, be driven to a point where we cannot find any further reason, and where it becomes almost certain that no further reason is even theoretically discoverable. Starting with the common beliefs of daily life, we can be driven back from point to point, until we come to some general principle, or some instance of a general principle, which seems luminously evident, and is not itself capable of being deduced from anything more evident. In most questions of daily life, such as whether our food is likely to be nourishing and not poisonous, we shall be driven back to the inductive principle, which we discussed in Chapter VI. But beyond that, there seems to be no further regress. The principle itself is constantly used in our reasoning, sometimes consciously, sometimes unconsciously; but there is no reasoning which, starting from some simpler self-evident principle, leads us to the principle of induction as its conclusion. And the same holds for other logical principles. Their truth is evident to us, and we employ them in constructing demonstrations; but they themselves, or at least some of them, are incapable of demonstration. Self-evidence, however, is not confined to those among general principles which are incapable of proof. When a certain number of logical principles have been admitted, the rest can be deduced from them; but the propositions deduced are often just as self-evident as those that were assumed without proof. All arithmetic, moreover, can be deduced from the general principles of logic, yet the simple propositions of arithmetic, such as 'two and two are four', are just as self-evident as the principles of logic. It would seem, also, though this is more disputable, that there are some self-evident ethical principles, such as 'we ought to pursue what is good'. It should be observed that, in all cases of general principles, particular instances, dealing with familiar things, are more evident than the general principle. For example, the law of contradiction states that nothing can both have a certain property and not have it. This is evident as soon as it is understood, but it is not so evident as that a particular rose which we see cannot be both red and not red. (It is of course possible that parts of the rose may be red and parts not red, or that the rose may be of a shade of pink which we hardly know whether to call red or not; but in the former case it is plain that the rose as a whole is not red, while in the latter case the answer is theoretically definite as soon as we have decided on a precise definition of 'red'.) It is usually through particular instances that we come to be able to see the general principle. Only those who are practised in dealing with abstractions can readily grasp a general principle without the help of instances. In addition to general principles, the other kind of self-evident truths are those immediately derived from sensation. We will call such truths 'truths of perception', and the judgements expressing them we will call 'judgements of perception'. But here a certain amount of care is required in getting at the precise nature of the truths that are self-evident. The actual sense-data are neither true nor false. A particular patch of colour which I see, for example, simply exists: it is not the sort of thing that is true or false. It is true that there is such a patch, true that it has a certain shape and degree of brightness, true that it is surrounded by certain other colours. But the patch itself, like everything else in the world of sense, is of a radically different kind from the things that are true or false, and therefore cannot properly be said to be true. Thus whatever self-evident truths may be obtained from our senses must be different from the sense-data from which they are obtained. It would seem that there are two kinds of self-evident truths of perception, though perhaps in the last analysis the two kinds may coalesce. First, there is the kind which simply asserts the existence of the sense-datum, without in any way analysing it. We see a patch of red, and we judge 'there is such-and-such a patch of red', or more strictly 'there is that'; this is one kind of intuitive judgement of perception. The other kind arises when the object of sense is complex, and we subject it to some degree of analysis. If, for instance, we see a round patch of red, we may judge 'that patch of red is round'. This is again a judgement of perception, but it differs from our previous kind. In our present kind we have a single sense-datum which has both colour and shape: the colour is red and the shape is round. Our judgement analyses the datum into colour and shape, and then recombines them by stating that the red colour is round in shape. Another example of this kind of judgement is 'this is to the right of that', where 'this' and 'that' are seen simultaneously. In this kind of judgement the sense-datum contains constituents which have some relation to each other, and the judgement asserts that these constituents have this relation. Another class of intuitive judgements, analogous to those of sense and yet quite distinct from them, are judgements of memory. There is some danger of confusion as to the nature of memory, owing to the fact that memory of an object is apt to be accompanied by an image of the object, and yet the image cannot be what constitutes memory. This is easily seen by merely noticing that the image is in the present, whereas what is remembered is known to be in the past. Moreover, we are certainly able to some extent to compare our image with the object remembered, so that we often know, within somewhat wide limits, how far our image is accurate; but this would be impossible, unless the object, as opposed to the image, were in some way before the mind. Thus the essence of memory is not constituted by the image, but by having immediately before the mind an object which is recognized as past. But for the fact of memory in this sense, we should not know that there ever was a past at all, nor should we be able to understand the word 'past', any more than a man born blind can understand the word 'light'. Thus there must be intuitive judgements of memory, and it is upon them, ultimately, that all our knowledge of the past depends. The case of memory, however, raises a difficulty, for it is notoriously fallacious, and thus throws doubt on the trustworthiness of intuitive judgements in general. This difficulty is no light one. But let us first narrow its scope as far as possible. Broadly speaking, memory is trustworthy in proportion to the vividness of the experience and to its nearness in time. If the house next door was struck by lightning half a minute ago, my memory of what I saw and heard will be so reliable that it would be preposterous to doubt whether there had been a flash at all. And the same applies to less vivid experiences, so long as they are recent. I am absolutely certain that half a minute ago I was sitting in the same chair in which I am sitting now. Going backward over the day, I find things of which I am quite certain, other things of which I am almost certain, other things of which I can become certain by thought and by calling up attendant circumstances, and some things of which I am by no means certain. I am quite certain that I ate my breakfast this morning, but if I were as indifferent to my breakfast as a philosopher should be, I should be doubtful. As to the conversation at breakfast, I can recall some of it easily, some with an effort, some only with a large element of doubt, and some not at all. Thus there is a continual gradation in the degree of self-evidence of what I remember, and a corresponding gradation in the trustworthiness of my memory. Thus the first answer to the difficulty of fallacious memory is to say that memory has degrees of self-evidence, and that these correspond to the degrees of its trustworthiness, reaching a limit of perfect self-evidence and perfect trustworthiness in our memory of events which are recent and vivid. It would seem, however, that there are cases of very firm belief in a memory which is wholly false. It is probable that, in these cases, what is really remembered, in the sense of being immediately before the mind, is something other than what is falsely believed in, though something generally associated with it. George IV is said to have at last believed that he was at the battle of Waterloo, because he had so often said that he was. In this case, what was immediately remembered was his repeated assertion; the belief in what he was asserting (if it existed) would be produced by association with the remembered assertion, and would therefore not be a genuine case of memory. It would seem that cases of fallacious memory can probably all be dealt with in this way, i.e. they can be shown to be not cases of memory in the strict sense at all. One important point about self-evidence is made clear by the case of memory, and that is, that self-evidence has degrees: it is not a quality which is simply present or absent, but a quality which may be more or less present, in gradations ranging from absolute certainty down to an almost imperceptible faintness. Truths of perception and some of the principles of logic have the very highest degree of self-evidence; truths of immediate memory have an almost equally high degree. The inductive principle has less self-evidence than some of the other principles of logic, such as 'what follows from a true premiss must be true'. Memories have a diminishing self-evidence as they become remoter and fainter; the truths of logic and mathematics have (broadly speaking) less self-evidence as they become more complicated. Judgements of intrinsic ethical or aesthetic value are apt to have some self-evidence, but not much. Degrees of self-evidence are important in the theory of knowledge, since, if propositions may (as seems likely) have some degree of self-evidence without being true, it will not be necessary to abandon all connexion between self-evidence and truth, but merely to say that, where there is a conflict, the more self-evident proposition is to be retained and the less self-evident rejected. It seems, however, highly probable that two different notions are combined in 'self-evidence' as above explained; that one of them, which corresponds to the highest degree of self-evidence, is really an infallible guarantee of truth, while the other, which corresponds to all the other degrees, does not give an infallible guarantee, but only a greater or less presumption. This, however, is only a suggestion, which we cannot as yet develop further. After we have dealt with the nature of truth, we shall return to the subject of self-evidence, in connexion with the distinction between knowledge and error. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Bertrand Russell (2009). THE PROBLEMS OF PHILOSOPHY. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/5827/5827-h/5827-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Understanding Intuitive Knowledge

Have you ever found yourself in a situation where you need to interact with a smart home device, Smart TV or Smart Display but you can't touch it? Imagine, you are in the kitchen with wet or dirty hands, and you need to switch to the next episode on your Smart TV. It's not practical to touch the remote or device in this situation. Similarly, if your phone starts ringing and you need to quickly pause the music playing on it, it's much more convenient to use voice commands than to touch the device. Thanks to gesture recognition technology and voice recognition, users can now control their devices multimodel: via gestures or voice control. My name is Daria and I am a Senior Product Manager with 5+ years of experience in the tech industry, working on products related to hardware, computer vision, and voice recognition, and in this article, we'll dive into gesture recognition systems, why and how these services are created, and what both product and technical teams need to consider in their development. I will share personal examples, my own experience, and reflections on the mistakes that I made along with my team when we were developing multimodal navigation for smart devices by SberDevices. Why? Enter touchscreens, the game-changer for tech interaction. Swipe, tap, and voila – complex actions made easy and user experiences amped up. But it wasn't always like this, before iPhone was released the only way to interact with a device was through a series of buttons. The iPhone's release marked a turning point, launching us into the touchscreen era, fueled by Apple's extensive research and development efforts. With its debut, the iPhone introduced an intuitive touch interface that eliminated the need for a stylus or buttons, setting it apart from competitors. But now, we're on the brink of another transition: from touchscreens to touchless interfaces. Touchscreens no longer suffice for users' needs, leading to the emergence of gesture control as an additional modality for interaction. Designing this new experience is an incredibly creative task. The first touchscreen prototype was featured in Star Trek, while Blade Runner and Black Mirror demonstrated gesture interfaces. Our sci-fi-inspired imaginations have envisioned touchless interfaces for years, and as technology advances, we eagerly anticipate which other futuristic concepts will become reality. What exactly? To be specific, let's examine interaction examples and use cases that arise. For instance, smart TV devices with cameras, such as Facebook Portal or SberBox Top, are designed with voice-first interfaces, thanks to their onboard virtual assistants that create an exceptional assisted experience. However, these devices still come with a traditional remote. The presence of a camera adds another dimension of interaction – gestures. Users can make hand movements in the air without touching the screen or remote, creating a blend of touchscreen and remote control where the system responds to gestures. While I believe that we'll eventually transition to fully touchless interfaces, current computational and recognition technology limitations place us in a transitional period. We're developing multimodal interfaces, allowing users to choose the most convenient method – voice, remote, or gesture – for accomplishing their tasks. How? I will share insights and recommendations derived from my team's work in developing touchless interfaces. These guidelines aim to help others avoid unnecessary mistakes when tackling similar challenges. By using these recommendations as a reference point, you can streamline your process and make more informed decisions. : Identify situations where users may need touchless interactions, such as when their hands are wet or dirty, or when interacting from a distance Understand Usage Contexts : Consider device constraints like recognition methods, hardware load limits, acceptable delays, and operating ranges under various lighting conditions. Understand Technical Limitations : Create a list of user-friendly gestures based on usage contexts and technical limitations. Determine intuitive gestures for specific tasks, like adjusting the volume or pausing a video. Formulate Gesture Basket Refine the gesture recognition system through extensive testing under different conditions and by gathering user feedback to enhance accuracy and user experience. Conduct Iterative Testing: When designing a gesture recognition system, select movements that are easy to execute, culturally appropriate, consistent, and unique. The gestures should be logically connected and adaptable for future additions. Сriterias for our gesture basket: Ease of execution: Gestures should be simple to perform with one hand. Cultural appropriateness: Gestures should not be offensive or have negative cultural connotations. Consistency: Gestures should be recognizable and consistent across users. Uniqueness: Gestures should not resemble common, accidental movements made in daily life. Technical and data collection sides One of the toughest challenges we faced in creating our gesture recognition system involved working with data from both aspects: how to collect it and how to annotate it. When collecting datasets from paid respondents, they tended to perform the movements more accurately and mechanically, as they followed specific instructions. This created a significant gap between the dataset domains and real-world scenarios. To address this issue, we collaborated with actors who could take their time to get into character and exhibit more natural behaviour, allowing us to gather a more diverse and representative set of data. However, in real life, people behave quite differently – they may be lounging on a couch or in bed, resulting in more relaxed and imprecise movements. But that wasn't the end of our problems! After collecting the data, we had to correctly label each movement, which was a daunting task in itself, as it was often difficult to determine where the movement began and ended. We faced issues like defining consistent movements, accounting for user backgrounds and lighting conditions, and balancing movement complexity with model retraining. Iterative testing helped us refine our system, collecting data from different angles and lighting conditions. The key aspect of our work was the iterative beta testing that our team started piloting in the early stages when the recognition network was not yet perfect. We conducted a closed beta with respondents, using a false positive detection system. When the network recognized a movement, it saved that frame on the device, and only the device owner had access to these frames. This allowed us to quickly receive feedback on unique real-life cases where we performed poorly. Immediately after receiving feedback, we collected new data on a larger scale to cover that particular case. For example, at the very beginning, the network recognized holding a cup in one's hand as a 'like' gesture, and we collected data from people holding cups to retrain the network. Designing a gesture recognition system is no easy task, and we encountered several unexpected challenges along the way: Defining a consistent set of movements proved difficult, as people executed even the most basic gestures in different ways. The diversity of users' backgrounds and lighting conditions made it challenging for our system to differentiate between movements and background elements. We faced a difficult decision: should we make the movements more complex for users or retrain the model to account for movement diversity? Iterative testing was crucial to refining our gesture recognition system, and we had to collect data from different angles and lighting conditions to ensure its effectiveness. Key takeaways : When designing gesture recognition interfaces, it's important to consider the context in which users will be interacting with the device. For example, users might be in different physical positions, have different levels of lighting, or be wearing different types of clothing. To account for these factors, we need to design interfaces that are adaptable and responsive to these varying contexts. Context is key : One of the biggest challenges in developing gesture recognition systems is the high degree of variability in how users perform gestures. This means that it's essential to test and refine our systems iteratively, both in controlled settings and in real-world scenarios. By continuously gathering feedback from users and refining our algorithms, we can improve the accuracy and effectiveness of our gesture recognition systems over time. Iterative testing is essential In order to train machine learning algorithms to recognize gestures, we need large amounts of annotated data that accurately represent the range of movements that users might make. However, annotating this data can be a time-consuming and labour-intensive process. To address this challenge, we can use automated annotation tools, or leverage crowdsourcing platforms to enlist the help of a large pool of annotators. : Data annotation is crucial : Finally, in order to create truly successful gesture recognition products, we need to continually gather feedback from our users. This can involve conducting user surveys, analyzing usage data, or even directly observing users as they interact with our products. By listening to our users and incorporating their feedback into our product design and development process, we can create gesture recognition interfaces that are intuitive, effective, and truly responsive to their needs. User feedback is essential for success

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