Anoop Gupta

I recently became aware of the term "Gish Gallop". I find that this concept can also be applied to a similar style of discussion, whereby a person dominates the conversation to control the narrative. The opposite style of discussion is where participants minimize words to convey the maximum quantity of useful information. Then, provide the maximum opportunity for participation by others. Learn to not gish gallop. https://lnkd.in/gYw4g_XE

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India is not just catching up in the AI race; we're innovating and leading in ways that are uniquely ours. The Economic Times recently highlighted how our approach to Generative AI goes beyond just Large Language Models (LLMs), addressing the specific challenges and opportunities within our diverse market. Rahul Agarwalla, Managing Partner at SenseAI Ventures, shared his invaluable insights into this AI revolution. His leadership and vision are truly driving innovation in the field, and it's an honour to be part of this journey with him. For anyone interested in the developments in AI and how India is making its mark on the global stage, this is a must-read.

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Mohit gives a good intro and overview of bufferbloat, codel, cobalt, and PIE: https://lnkd.in/gwQNjwFh

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Insightful explanation of how do go channels work behind the scenes. Its all about yielding the ptocessor for other goroutines. https://lnkd.in/gbyQ4yH9

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Human in the loop evals 🧑‍💻👀 → custom, automated evaluation models 🤖📈? https://lnkd.in/gruPtC8d Human in the loop (HITL) is a really popular technique for teams just getting started with LLMs and their LLMOps functions. Given your biggest concern is your model acting ‘not like a human’, it’s easy to see how sticking a human in the loop, to review and edit any potential mistakes or weirdness, can solve that problem. But it’s equally easy to see how challenging this can be to scale, and how it can eat at a lot of the cost-savings promised by automations. So how should teams approach balancing the security and certainty from human-in-the-loop, with an eye towards scaling? Over the weekend I got the chance to experiment with a few approaches to turning HITL datasets into their own custom evaluation models. I also think about how I would balance my human-agent evaluations alongside my model-driven evaluations, to set me up for long-term success. https://lnkd.in/gruPtC8d

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The analogy of tying data sets to a chair and extracting a confession is both vivid and apt. Data (not the one from Star Trek), much like a suspect in an interrogation room, holds many secrets. However, its revelations depend on the approach and techniques used to extract information. When handled with precision and insight, data can reveal intricate patterns, correlations, and trends. It can unveil consumer behaviors, market dynamics, and even predict future events or maybe write scripts for livestreams. You can call this AI / GenAi, we call it an advantage. For businesses we work with, this translates to strategic decisions, such as identifying untapped markets, niches, optimizing operations, and personalizing customer experiences. We spent a lot of time lately wrestling with the data we have accumulated when we livestream and some of that is now starting to show up as a tool. Here is the very start (jab kuch bhi nahi tha / when there was nothing), a long way to go from here. But what the heck! running startups is hard, small joys are needed Luke Thomas Chinmay Ektare Joyce Andrade Rosalyn Frayna #insights #data #

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Great discussion about "founder mode" with Shreyas Doshi. Well worth the watch/listen regardless of where you find yourself. Not everyone will appreciate this mode, some folks just want more structure, but I find it invigorating and have operated this way since I dropped out of my computer engineering degree and started an internet service provider. Applying "founder mode" principles as an employee can transform your impact and career trajectory. Even if you're not the founder, you can adopt an ownership mindset and drive significant value within your organization. Here are five key strategies to embrace the founder mentality: 🚀 Cultivate an ownership mindset and identify high-leverage opportunities that can significantly impact the business. 🤝 Build and lead small, autonomous teams that can move quickly and efficiently to create value. 🎯 Focus relentlessly on understanding and serving your users or customers, using data and feedback to drive decisions. 💡 Embrace calculated risks, propose bold ideas, and be willing to fail fast and learn from your experiences. 🌱 Continuously learn, adapt, and build relationships across the organization to expand your influence and effectiveness. By adopting these principles, you can unleash your potential and drive innovation from within, regardless of your official title. Are you ready to think like a founder and make a lasting impact? #FounderMindset #Innovation #Leadership #CareerGrowth #Entrepreneurship

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Sorry for the delay, but Joachim and I are back with, dare I say it, my favorite episode of Learn, Make, Learn yet! It's focused on the intersection of generative AI and product creation. This is a thorny topic and, fortunately, we're joined by Anmol Anubhai, who brings deep expertise in the field to our conversation. Her human-centered approach to the technology—one rooted in amplifying humans instead of replacing us—actually converted me from an AI-sceptic to a (cautious) AI-optimist. I hope you'll join us for a refreshingly grounded conversation on a topic that’s typically full of hype. You can find “Learn Make Learn” at any major podcast directory or listen at https://lnkd.in/gGv-tE6F And don’t hesitate to share your questions & perspectives here! #LearnMakeLearn #ProductMarketing #ProductManagement #ProductInnovation #GenAI #GenerativeAI

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I just bought a national subscription to American City Business Journals (ACBJ) so will be posting more articles from their network of 44 journals. The Puget Sound Business Journal was our primary source of regional business intelligence in the 1980s to early 90s, and it was instrumental for many younger growing companies, including Microsoft, Costco and Starbucks, among others. They are often the only pubs who cover important business news. When I filed my original core patent application in 2006, Google had 4 AI patents. In 2023, they had 1,870 AI patents. While there has been a massive spike in recent years due to the LLM hype-storm and Big Tech priority, what this really tells us is almost everyone was behind Google in AI -- and KYield frankly in our focus areas. It also tells us there is an LLM bubble. This article cautions on what occurred with the blockchain and the metaverse where there was a similar spike of investment and patents followed by a 70% bubble deflation. I expect that to occur in LLMs, but unlike the above mentioned -- AI has enormous value for consumers and businesses. We just need to plow the massive piles of noisy junk off the road so we can deliver the value. "Rather than obtaining patents with an eye toward suing competitors," Love said (Brian Love, Prof. of Law at Santa Clara University School of Law). "Silicon Valley companies tend to build portfolios for the purpose of deterring competitors from suing them." This is precisely our strategy at KYield. I decided not to pursue a portfolio of patent applications related to our Synthetic Genius Machine invention a couple of years ago for several reasons, including industrial espionage that ignores patent law (China is the leader but far from alone), the high costs associated with a complex IP portfolio, and other higher priorities. NM has some unique challenges in IP. Due to the national labs, there is plenty of IP, but there is precious little support for IP-related ventures as government dominates the economy, and government is an extremely poor early adopter. They not only have obstacles galore, they usually lack motivation or incentives. NM biz environment is primarily gov't contractors, mom & pops, a few mid-markets, & non-profits / quasi-gov't entities like healthcare and universities. The state VC fund is the only VC game in town of any size, and that's a political game. Otherwise, NM has a lot going for it... location, climate, affordability, science, infrastructure, but it is a challenged regional economy. There is no support like we experienced and provided in Seattle or SV. Even AZ offered a few options in the 90s. That said, there isn't any reason businesses can't thrive here -- it just requires a lot more energy and money to break through the early obstacles. We have quite a few now that are growing nicely. Several right here in Rio Rancho. Stay tuned.

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A good introduction to measuring quality of LLMs and GenAI apps via a combination of automated & human evaluations: https://lnkd.in/gYe7scGS. This is now an industry standard and most companies use some version of this approach for design time testing/evaluation and monitoring for quality/trust in production. Share any interesting learnings/experiences you may have.

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🌉 San Francisco's Surprising Comeback: From 'Doom Loop' to Renaissance? Recent data suggests that San Francisco, once trapped in a pandemic-induced 'doom loop', is showing strong signs of recovery. Here's what's changing: 📈 Positive Trends: - Population Growth: Net gains over the past two years, with domestic in-migration positive for the first time since 2012. - Housing Market: Median home sale prices up 1.2% YoY to $1,427,500 in June. - Job Growth: The Bay Area added 7,400 jobs between January and June. Downtown Revival: Foot traffic beyond typical work hours has recovered to over 90% of pre-pandemic levels. - Crime Reduction: Property crime down 32% and violent crime down 14% in Q1 2024 compared to Q1 2023. - AI Boom: The city is positioning itself as a hub for the AI revolution. 🚧 Ongoing Challenges: - High office vacancy rates - Homelessness and drug crisis - Lagging tourism and business travel 🔑 Key Initiatives: - "Vacant to Vibrant" program offering free rent to businesses in empty storefronts - Increased police patrols and encampment sweeping - Crackdown on open-air drug dealing 💡 Expert Insight: "The rising AI empire is giving the city a new edge. Funding, talents, and culture related to the AI revolution will help the city turn the corner." - Lu Chen, Senior Economist at Moody's Analytics 🤔 What's Your Take? Are these positive trends sustainable, or is it too early to declare a comeback? How can other cities learn from San Francisco's recovery strategies? What role will AI play in reshaping urban economies, particularly in tech hubs? https://lnkd.in/gCnPCBRU

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The term “AI Hallucination” describes instances where LLMs generate incorrect, misleading, or nonsensical results. However, recent discussions in psychology suggest that “confabulation” is a more accurate term (https://lnkd.in/gvYwBGCk). While hallucination implies a false sensory experience, confabulation refers to the creation of false memories—more akin to what AI does. Understanding these nuances helps us harness AI’s potential while mitigating its shortcomings. At GumGum, we thoughtfully evaluate state-of-the-art technology with rigorous quantitative testing and meticulous manual spot-checking before incorporating it into our existing workflows. This ensures our datasets are curated to remove anomalies and yield top-performing models.

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Wrote a post on how we're thinking about semantic modeling at Sigma, specifically focused on a GUI-based model, flexibility, integrations, and (of course) leveraging them for AI. Curious what folks think. Do these points resonate? Am I missing something important? Want to chat more about the topic in general? Let me know! https://lnkd.in/gnqtxnY3

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I enjoyed reading the report on AI skill adoption by The Burning Glass Institute. Here were some of my key takeaways from their recent report on AI Adoption on Why Companies Must Prioritize Employee Upskilling The rapid spread of AI skills across America is reshaping the job market at an unprecedented pace. Recent data reveals a compelling case for why companies need to invest in upskilling their workforce to remain competitive in this AI-driven landscape. Geographic Expansion of AI Skills The Generative AI Boom Generative AI has seen explosive growth, with demand increasing by nearly 100 times in the US over the last 18 months. This technology is diffusing faster and more broadly than previous AI waves, making it crucial for companies to equip their employees with these skills. Cross-Industry Adoption Generative AI is breaking out of the traditional tech sphere: Demand for generative AI skills in creative fields now surpasses that in IT and computer science. Marketing and public relations show higher demand for generative AI skills than science and research. .This cross-industry adoption means that companies in various sectors need to upskill their employees to leverage AI effectively. Democratization of AI Skills The rapid spread of generative AI skills suggests a potential transformation of these skills from niche expertise to essential workplace competencies. Companies that fail to upskill their workforce risk falling behind in productivity and innovation. By investing in employee upskilling, companies can: - Bridge the AI skills gap within their organization - Enhance productivity and innovation - Retain talent by providing growth opportunities - Stay competitive in an increasingly AI-driven market As AI continues to reshape the business landscape, companies that prioritize employee upskilling will be better positioned to thrive in the AI revolution. The time to act is now, as the pace of AI adoption shows no signs of slowing down. At Disco we are huge believers in the need for upskilling as the world of work undergoes this rapid transformation.

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Thrilled to support 💪 Perplexity return citations param - https://lnkd.in/g7kkztxX (+4 more updates 👇) ⚡️ Azure - return response headers ✅ Streaming - break out of infinite model loops 🎉 Instructor - fix completion_cost calc 👀 [PR] logging by API key - https://lnkd.in/gAG5YKQa

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Loved this insight on Risk from the esteemed Terence Tao In times of great uncertainty, the relative advantage of "playing it safe" diminishes because no option can completely eliminate risk. Paradoxically, in such situations, taking bold actions can become more rational, as decisions should align with the level of external risk in the system. This highlights that uncertainty fundamentally reshapes the balance between safe and bold choices, requiring a more nuanced approach to decision-making. Many decisions in life involve the tradeoff between risk and reward. Perhaps one has to choose between a "low risk, low reward" course of action that plays it safe but does not achieve any big wins, or a "high risk, high reward" choice that could potentially give greater benefits but also greater downsides. Risk management is a complex task, but it can initially be explored with very simplified models, always keeping in mind George Box's dictum that "all models are wrong, but some are useful." For this simple model, one can assume that each course of action comes with two numbers: the "reward," representing the average positive net gain, and the "risk," interpreted here as the standard deviation from the mean. A "safe" action might have a reward of 5 and a risk of 3, written as 5±3, while a "bold" action might have a reward of 9 and a risk of 10, written as 9±10. Given the general preference for risk aversion, one strategy is to minimize "value at risk"—the value one might lose in a bad scenario. For the safe action (5±3), the value at risk is 3-5 = -2, indicating that even in bad luck, a small gain is likely. For the bold action (9±10), the value at risk is 10-9 = 1, suggesting possible losses. This favors the safe action. When external shocks are introduced, the analysis changes. Suppose a fixed shock of -5 uniformly affects outcomes. While this lowers rewards, it does not alter relative decision-making. However, with uncertain shocks (e.g., -5±10), risk is recalculated using the law of additivity of variance. For uncorrelated risks, the safe action moves from 5±3 to 0±√(10²+3²)=0±10.44, while the bold action moves from 9±10 to 4±√(10²+10²)=4±14.14. This reduces the relative value of "playing it safe." In times of great uncertainty, no option remains truly safe. Paradoxically, bold actions can become more rational as personal risk tolerance aligns with systemic risk. However, this conclusion depends on the assumption that external shocks add to risks rather than multiply them. If risks are multiplied (e.g., converting 5±3 to 5±6), the safer option becomes preferable again. Behavioral economics highlights how human decisions deviate from models, as external shocks psychologically distort decisions. Nevertheless, there is a rational basis for altering choices when shocks themselves carry risk. The broader insight remains: uncertainty reshapes the value of safe versus bold decisions.

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If you are looking at the ongoing AI revolution as a trading opportunity and are enamored with Sam Altman, I have a contrarian point of view to offer. In the long run, Mark Z and Meta are going to eat up the entire AI space and it won't even be close. 1. Outside the US, WhatsApp is the primary way of doing business and p2p comms 2. Meta AI is integrated with WhatsApp and is already a very very good AI engine 3. Meta, through WhatsApp will have access to real conversations and ways of communication between people, something that other AI companies have to buy, after the fact, from platforms like reddit. Meta only has to turn on the spigot (with due disclosure and guardrails) to get this real time data. 4. 👆🏾means Meta will have a more current training data set than any of its competitors 5. In the long run, ease of use and familiarity with WhatsApp along with higher quality training data will allow Meta to leapfrog other AI players as the winner in this space. Do you believe differently? I'd love to hear your thoughts to refine my own. #ai #trading #stocks cc Gaurav Chopra - your inputs?

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