Things I learned

This week, I learned: Alzhara is one of the VFX companies that worked on Leo’s hyena scene. Their 3D modeling is incredible. Enterprise scenarios leaderboard. Mistral 7B leads. Veda Srinivasan. How does Google manage culture? AMA sessions Manager feedback. Entirely anonymous. Avoid taking feedback for teams less than 5 Workplace concerns team exists. Put managers on watch Books Mohammad Younus. Three zeroes book. Read about his social business theme Pluriverse. Anti fragile. Aurobindo Vedas. Barry Oshry. Seeing systems. Runs workshops but book is better Raghu Anantanarayana has written about Indian archetypes based on Mahabharatha India that is Bharath. Sai Deepak. Podcasts Listen to Nilesh Oak. Sugreeva’s Atlas. Pankaj Tripathi podcast on geography influences acting Areas of focus “I’m an Expert on synthesis and implementation” Intersectionality is another word for complex failures. Also for deep segmentation. Swiss cheese model. Dialogic self theory is about multiple voices in the head. How do we make meaning? Psychological rupture is when cognitive activity is maximum. At any point there are MULTIPLE voices in our heads that are sources of action. We don’t listen to them. Epistemology. Language determines thought. like the word productivity. How does appreciation of a rose become productive? Words from other languages may have incredible power. From other cultures. Paul Sloan. Lateral thinking podcasts from multiple sources Deliberately engage with topics randomly. Deliberately engage with random people Read a random book from the library Watch a random film in a different language Consciously where the six thinking hats or look hard for the silent voices in your head and express them Ask children. They tend to think of more creative and childlike solutions He converted a hiring process into a contest Constantly ask yourself. What if every assumption I’m making about this is wrong? Scenario planning is really about this. List a few scenarios. They’d have high impact or high probability. What happens in this scenario? Ideate You can @mention GPTs to ask a specific GPT a question in ChatGPT. This is really powerful. Hidden brain podcast. Making the most of your mistakes FIX every small mistake. You never know how they might line up in the future You also never know how small little things done well might line up to give you a boost in the future The Toyota cord does not actually stop the production line. It brings a team lead over who quickly diagnoses the problem with you. The responsiveness of the league is a critical factor and so is encouragement That isn’t always a single bottleneck to stop that is the case of a simple failure. There can be a series of holes that happen to align perfectly. These are events that lead to catastrophic failures or successes Do as little as possible, waste as little as possible, until you know that the outcome is worthwhile. Figure out what is the value of the outcome and the most important piece of information you need to discover that Do full research before you try and fail. The aim of failure is learning at the least possible cost How I write podcast. 2023 summary Ask for feedback from friends in a specific way. What 20% should I retain no matter what? What 20% should I cut? This allows them to compliment while providing genuine feedback Hire lawyer interns to proofread. They are the ones that find fault the best Be in a segment of one. Where there is zero competition. Something only you can do Don’t try to do stuff faster. Try to do stuff you don’t want to stop doing Read books older than 50 years Read Michael Collins book on things that sustain Temp service make sure he has some energy to spare. Cuz Riley does the opposite. She waits till she can’t stand it anymore and then writes like crazy until she drops dead. The former leads to thoughtful writing. The latter is emotionally powerful. Be able to do that Vanna is a SQL generation LLM. An alternative to SQLCoder. This thread has a detailed discussion on SQL generation and BI Intel developer cloud has a liberal GPU in the free tier. OpenAI releases text-embedding-3-large which can be truncated. The embedding values have descending importance, so picking the first n is a good approximation. Also, gpt-3.5-turbo-0125 is 50% cheaper. AppAgent is an LLM that can navigate mobile / web apps Retrieval Centric Generation is an emerging alternative to RAG, where the LLM is explicitly built to leverage external knowledge. SimplyRetrieve is an early implementation. Big Code Models Leaderboard is a leaderboard for open source code models.

This week, I learned: ⭐ OpenAI’s prompt engineering strategies are an excellent start for prompt engineering. A few lessons: Use detailed system prompts, often containing the entire instruction set, if it won’t change over the course of a conversation. “… summary of the prior conversation could be included as part of the system message” is an interesting history compression tactic. OpenAI summarizes books by recursively summarizing sections and maintaining a running commentary of the summary so far. Dan sends Google documents with essays instead of emails. This allows people to comment on it. But commenting is a culture and not many people do it. Adriano does it a lot and we’ll. Dan and Adriano actively converse on GitHub issues llm-guard is an LLM content validation tool.

This week, I learned: When comparing Mistral with 4b quantization vs unquantized: 2 responses were significantly shorter and fairly different 1 was identical 1 was almost identical but shorter by a few words 1 was slightly longer and fairly different #PREDICTION As humans have more conversations with LLMs, they will replace video watching and interactive gaming with conversation based role play. New game genres will evolve Lilac is an LLM-based data curation tool. Use it to search by concept (e.g. PII, duplicates, etc.) and then drop/update the results. Lungs have a Hausdorff dimension of 2.97 – giving them one of the highest surface area to volume ratio. Brains are 2.8. Sierpinski Pyramid is exactly 2 – which is weird. To solid-paint twice the size, you need 4 times as much paint. How I write podcast. Tim Ferriss High bars are constraints. I set the strongest constraints against the scarcest resources. Like reputation Being a category of one is more defensible than a competitive advantage Content always beats presentation. When in doubt, push for more interesting content Regular publishing improves thinking To build a habit, do less than you think you can do. That makes it easier to build momentum on the habit and sustain during crunch times There is a lot of mediocrity in the world. If you’re doing something (in a winner take all ecosystem), be the best. Top lawyers are exceptional proofreaders. They are able to see what is unclair, and what is redundant, and what has loop holes very quickly. Forcing yourself to cut down from a thousand words to 200 to a paragraph to a sentence takes you through a phase transition where you discover something unexpected The more outrageous the question, the more likely it is to be useful in generating a new perspective Eleven-labs speech synthesis with voice cloning is at the uncanny valley. With two 5-minute samples, my voice sounds a fair bit like my voice but is very clearly not my voice. I find stability ~ 30%, similarity ~ 80% and style ~50% gives a reasonable outcome. But the default voices (e.g. Joseph, George, Charlie) are excellent. Practical AI podcast: AI predictions for AI by API is the norm today and will grow Just having AI is no longer a differentiator AI is part of life, not just work #TODO Explore quickdrop from Stability for Maruti #TODO Explore Codium VS Code plugin and Continue.dev Hybrid systems that combine stats, ML, DL and AI models will grow AGI and AutoGPT resurgence RAG will continue to be a focus GPT4 will be beaten by open source models. Special purpose models beat it already Self hosted and cloud hosted models will grow for security Small language models will grow Productivity will be enhanced rather than replaced Multi modal models will grow Cost efficiency will grow in focus GPT Builder help explains how the GPT Builder updates GPTs - including some very interesting prompts

This week, I learned: Transparent LED screens will be useful in windshieds to display maps as we drive. Marimo is a reactive alternative to Jupyter notebooks that saves files as pure Python. To run an org-specific chatbot on your own LLM: (via awesome-chatgpt) opengpts - but it doesn’t support auth chatbot-ui - but Supabase is hard to install anse - but it doesn’t support auth ChatGPT-Next-Web - but it doesn’t support auth Python 3.13 gets a store and copy JIT If an npm package adds another package as a dependency with version “*”, target package cannot unpublish ANY version! So this is a way of freezing EVERY repo and preventing unpublishing of EVERY version – an unintentional flaw in the npm design. via Quantization is better than fewer parameters. So prefer high parameters (e.g. 70b) and quantize to 4-bit. In-browser playgrounds has compiled WASM versions of Python, PHP, SQLite. Happiness Lab podcast. Happiness lessons of the ancients Talking to strangers makes us happy Giving money makes us happy Free time makes us happier than working hard Tangi Domain-specific models being beaten by general purpose models is a phase. It will reverse towards domain. AI will potentially help build and understand domain-specific models Models are evolving so rapidly that humans cannot interpret models. We need a process to interpret models! xAI, Responsible AI, Physics-guided or Knowledge-guided models (called grey box models) are therefore a trend CS papers Don’t review other papers, certainly not other fields. Disregard measurement errors. When CS papers get applied to climate, manufacturing or biology, we’ll worry about Interpretability Domain-specific mechanics. (Introduce that into the training as a constraint.) Many domain experts are using AI to UNDERSTAND their process. Need to explore Uncertainty IB adds context to make learning applicable. But that distracts from the core learning, and if there’s a gap it widens Most data science courses teach “Python science”, not data science. They teach a bunch of models. They don’t teach how even one kind of model e.g. LSTM works. Most coaching programs today teach FAMILIARITY with problems, not critical thinking Most of current education will become redundant thanks to LLMs. For students AND teachers Coding will become irrelevant Cognitive thinking, reasoning, human relations, systems thinking will become more relevant Troubleshooting will become more important. AI is not self-diagnosing. I would hire someone who can figure out something is going wrong, diagnose what’s going wrong, and fix it #TODO Hire for troubleshooting ability. Give a Q, an A, and ask them to figure out if it’s wrong, why, and fix it All my exams and quizzes are open book, open ChatGPT. Onus is on me to give a problem that forces you to think. #TODO Write a question paper that is ChatGPT proof. Exploring AI could be a ToK subject. “How to interact with an AI?” We need a manual on how to use AI. Like Simon Willison says Content doesn’t suffice. You need pedegogy. What to serve you at what time, how, how to assess. Lots of businesses are filling this gap Students get great confidence when a teacher points to online content and says, I"ll tell you WHAT to see" and COMPLEMENTS that in their class “The map is not the territory.” Most people confuse sample mean for the actual. #ASK Parameter estimation -> Signal estimation -> State estimation Stats vs DL differ in that There is no notion of a defined “truth”. Hence reliability is not measurable Parameters have no value. Hence interpretability is ignored. #TODO Read 2020 National Education Policy. It’s quite modern. We need a manual on self-learning too Listening is not learning. You know only if you implement. Levels for students: I can solve it. I can explain why it works. I can find alternatives. I can apply it to a new area, reformulating (requires imagination.) For teachers, you also need: Responsible learning (extra careful about what to teach and how to teach, to exceite them, to teach at THEIR level). Show the universality and connecting to other concepts. E.g. noise reduction with FT is like using water to remove dirt. Transform to water domain, remove dirt, transform back to air domain. It’s better than dusting clothes to remove clothes. Washing machine programs are just different models of removing noise in the water domain. Teach people who WANT to learn AND who will APPLY it long-term. That’s what maximizes impact Grad students are more satisfying that way. Else, it is WASTED effort. (Not that it’s a bad thing for the student, but the effort IS wasted for the teacher) Therefore, I believe students should have general engineering first, and let students pick specialization later. Some universitie are doing that. #THINK Students remember my philosophy more than my content. We impart character, not just knowledge. Astrology and horoscopes serve a different function. They provide explainability, not predictive ability. As the world becomes less explainable, the need for astrology will grow. Explainability is about creating STORIES that fits data plausibly. It has nothing to do with data or truth. Explainability and predictive ability and reproducibility are all different. Maybe, Science is about the latter two, less about explainability. Astrology is a model. The map is not the territory. It’s an explanatory, not a predictive model. #THINK Therefore, my lessons are just explanations. Stats about experiments are STILL explanations. They are NOT reproducible or predictive. Hence not yet science The meaning of our life is the transformation we undergo in our lives #TODO Read “The Journey of Souls” by Michael Newton. A hypnotherapist #TODO Try regression therapy / hypnosis. Record it and listen to it. Just for fun! Rohini Deshpande Slam book was the Facebook of the 1900s Prepared mind is an extremely powerful tool for learning. Practice prepared mind When women drop out of education or career, that is also a waste from the teacher and system perspective The time for career growth is the same as child bearing time for women. That’s not true for men. But child rearing can be done by either. That’s not recognised. It’s 0K for a man to raise the child and make the home and 0K to treat that as the default Since men are more senior, it’s usually logical for them to stay in their jobs. That’s a systematic bias. When seniors advise women to step back. they respect it. That widens the barrier. Why not eliminate that situation? Be proud of the working women in the family Stats are just a symptom. They don’t explain the cause. (Map is not the territory.) Explanations are what really helps us fix the cause. Hence stories are important. Read Tinker Tailor Soldier Spy RV Athimber health tips: Eat foods with low glycemic index Eliminate free salt completely Voyage AI Embeddings have a higher quality, similar price compared to OpenAI embeddings. There’s a clear benefit to replacing text-embedding-3-large with voyage-3-lite. There’s a 200 MTok free tier currently. mixtral-offloading cleverly loads only the model layer required at any point, letting you run Mixtral 8x7b on Colab Free and on 16GB GPUs. This notebook runs on Colab Free too. CodeGPT is an alternative to Github Copilot that can use any LLM.

This week, I learned: Raman Srinivasan: IITM Profs and MTechs are spinning off deep tech startup. Agnicool is an example. They 3D print rockets with ceramic composites from Germany Sriram Krishnan (Facebook), Balaji Krishnan invested in pre-Series A Govt is de-regulating space tech and geospatial. Talking of de-regulating nuclear. ISRO seems to be focusing on cutting edge while others are doing commercial stuff There are about 100 space tech startups in India You can build your own modular reactor Geospatial AI is a big opportunity Have released a lot of 10m resolution geospatial data almost for free success is about getting NO factor wrong. Failiure just requires one aspect to fail. Brand, business savviness, financial stability, tech superiority, deep pockets, managing Gvt, long-term mindset, etc. - all of these matter. That’s what made TCS monopolize the exam business in India. For deepening AI, we need, Talent, Data pipelines, Hardware Next wave is LMMs, not LLMs What’s not captured in LLMs is verbal knowledge and tacit knowledge (in people’s fingertips). India is rich in this. The road to tacit knowledge has to go through India We can get a welder to train a simulator and pay the welder We can get a storyteller to tell a few stories and train oral LLMs Tacit knowledge will have to cover robotics. Train robots to bring coffee in just 50 demos! “Project delays are within the ‘rulebook’. Buyt paying skilled welders for ship building or nuclear pressure boilers needs breaking 100s of rules. Once they get certified, they abscond to Iran or somewhere.” TCS Ignite started in 2006 by Ramadorai. Before recession. “There is going to be a talent shortage. Recruit from next rung. Science not engineering graduates. Break HR monopoly and corruption - colleges became placement agencies. Fewer people per college. Across the country. Train them.” Tried in 2000. HR refused. Business refused. When Chandra was identified, Ramadorai took it up himself as a challenge. Ramadorai had very precise attention. Sat 7 am calls. “What are you doing?” 2 min call. Enough to energize. Would exchange and ask for brief updates. He reads and responds. You get a decision in a few hours early in the morning. No decision bottleneck He wanted to know ALL the details. Very precise, small, frequent probes on what’s happening. E.g. one 6 am, he called. “What are the lectures planned for today?” He expected I would know this. If not, next time I would be prepared. He would call another person and ask the same question. So I updated the others. I’ve never seen anyone with that bility to ground-truth. He wants 10 birds from 1 stone. Get BSc, but don’t comprimize. Get the best 2 per college but a full batch size of 500. We became the biggest training program as a single batch – with 500 people. He wanted to demonstrate scale. HR and CFO said, ‘You recruit first. Then we’ll give you money. We don’t think it’s possible." We had anchor colleges and brought people from other campuses. We did digitized exams. Took big servers to the campus. Fully digitized with full auditability. Plugged the laptops into the college LANs. Kids had never used a mouse. We had to teach them. We said, “Don’t worry. These are logical questions, not questions. We’ll pay a full salary.” We learned that 1 out of 2 didn’t even join. Many took up a Masters. They didn’t want to join the workforce. Unless they’re desperate economically. Even poor parents, if they can afford to support you at home, they do that. It’s weird. Every weekend, we visited a few campuses. 71 locations across the country. Found the NSS college in Ottapalam (Kumbakonam of Kerala. Cultural centre.) College had a nice nice Math dept website. I said “Mr Ramadorai, this looks promising.” One Sat morning, he called and said, “When are you going to Ottapalayam?” We landed in the college. There was an impromptu communist student strike. We made 38 offers out of 100 who took the exam. Never had such a high conversion. One girl, whose father was a coolie, jad communication issues. Had a colleague talk in Malayalam. She was an amazing success. My colleague Murali made a documentary about her. We started in July. By Dec, we had 500 joinees. No one is doing such a thing now. You have to get dozens of things just right. Compromising on even one kills it. Ramadorain loaded it with multiple objectives. Fresh talent. Low cast. Sustainable. He kept pushing for innovation. I pushed back. But he was persistent. Over time, I came around and we started innovating. We restructured training program around innovation. Like a YCombinator. That unleashed extraordinary energy. Several of the kids are running their own startups. Ramadorai was very supportive of that. The assessment product came out of that. First batch, everyone was very sceptical. We got a lot of pushback. They’re dumb. Ethics issues. Communication issues. Lot of prejudice. So we got them to do internal recruitment till they were satisfied. An internal placement market. THEN reputation was set. I told them, always stick to the dress code. One weaver’s sone wore a bright yellow polyester T-shirt. I asked him why he didn’t stick to the dress code. “Sir, it’s my first T-shirt.” Ramadorai tracked how many became billable. We were unable to place 70. He said, give them 1 more month training. Then we placed 64 of the 70. He said “Do something about the 6. I want 100% placement.” We absorbed them as a teaching assistant. One was a weaver’s son. One was a PC’s daughter. A mestri’s son. A shopkeeper’s daughter from North Madras. None could speak English. They learned to code and helped build the exam software, with Srikumar who was a brilliant Java coder. That gave us the confidence that these are good kids, just from the wrong part of town. With a good guide, they’re very capable. We bought a bunch of Nintendo Wiis. Kids have to play. He asked for a welding simulator. “Velu the Welder”. The kids built it using the Wii. We got the most accomplished welder spend an afternoon at Ignite. He ripped us apart. 4 hrs non-stop. He told us EVERY thing wrong with it. Blasted us. I told Murali, “Let’s call it a toy. It’s not a simulator. Let kids play.” He said, “I want to show that it can be done!” Murali churned out rapid iterations in a frenzy. Ramadorai said, “Deploy it in the field.” So we went to all kinds of remote places like Gondiya below Nagpur. Surprisingly cosmopolatan. Junction of EW and NS train lines. We set up welding institutes in each. It was on the cloud. We could track everything. KPK killed the skills. Hard core bureaucrat. His view is colonial. Ignite philosophy is about unleashing energy of people. Colonoial model is about controlling people by keeping them poor. KPK and Chidambaram had that mindset. Ramadorai brought him in as Secy of NSDC. he killed the policies Modi did the first cut by creating a ministry. KPK ensured that it never gew. Like Yes Minister. Made sure nothing moved Had Govt not changed, he would have been Secy Finance. He was seen as Chidambaram’s blue eyed boy. People know he was associated with NSE scam. Ramadorai helped by bringing him into skills He is very smart. Knows the IAS machinery in and out. Lives and breathes that. H Ramadorai likes him though. Put him on board of Tata Consumers. NSE Scam. He’s part of the cabal with Ajay Shah. Private trading firms could co-locate within NSE and could make a huge amount of money. KPK ran some of this by proxy to fund Congress. But he left no fingerprints. But everyone knows it is him. He was running Chitra Ramakrishnan by proxy. He was the Himalayan Yogi. Ignite continued with unwavering focus. Kept increasing the kind of focus. We had a 99.5% success rate in placements. Just a handful of failures. Ramadorai has written about Ignite in “The TCS Story”. My Dad translated it in Tamil. It’s not a typical business biography. Worth reading. Should be a mandatory course in MBA courses in India. So many lessons. You have to read it knowing how Mr Ramadorai speaks. What is NOT said is just as important. Ch 5 is the thinnest - on the IPO. It is packed with so much stuff. Unless you know, you won’t understand. “Tatas got the Govt to change a tax law to make the IPO meaningful.” Behind that, there’s a lot. You have to be alert to catch the sentene. He won’t brag, or talk about the significance of some of these. Book is packed with dense insights. Unless you ARE LOOKING FOR IT, you’ll miss it. Worth reading SEVERAL times. You need a foot-noting. Currently reading Pasquenelli – Social History of Artificial Intelligence. Eye of the Master. Worth reading. I’m not Marxist by belief but they get some things right. Surprised how vibrant the European left is. “If someone is doing manual work, there is tacit knowledge that automation captures.” India doesn’t need self-driving cars. But a farmer would like a gaming controller that ploughs his fields while he sits under a tree. Semi-intelligent machines that removes the burden of hard labour in the country. Once a year, for a few weeks, I do manual labour. People are under-nourished. People typically work 5 hours a day. Not enough muscle mass. So use them for what they’re good at I’ve seen the power tools. When Chinese power tools became cheap, the power welding became much more efficient. Everyone has become a monkey with power tools. They charge per inch. They know how to leverage the tech for economic benefit. Just bring in the power tools and rapidly finish and make money. But there are sections that are still poor and haven’t made the transition. How can we create pathways for them? How can AI help? Anand: Why not use a gimball. RS: Good idea. Role modern psychologist DW Winnicott on ChatGPT (like Socrates) E.g. You don’t need a perfect mother. A good enough mother is better Similarly, why not a “good enough” Bharat mata than a perfect one? To persuade someone, align it with their identity. ChatGPT 5 technologies of interest according to Gartner’s latest hype cycle: GitOps Internal Developer Platform Graph Data Science Open Source Program Office Value Stream Management Platforms Gemini is an alternative to the Web. Sort of like Gopher, but recent SALI - Standards Advancement for the Legal Industry - has standards and ontology/taxonomy for legal documents, including patent litigation. Walking new routes habitualizes fighting fear and preferring novelty ⭐ GPT-4 is bad at math. It gets ~60-70% of answers wrong. LMQL provides a constraint-based query language for interacting with LLMs. It uses token masking, which is clever. Hollywood writers signed a deal that limits AI in script writing. It’s primarily aimed at protecting script writer wages. Adobe Firefly offers a “generative fill” that lets you remove or paint new objects into an image. I’m awaiting text to vector images. Duet AI is Google’s answer to Github Copilot. Teachers are using LLMs to plan lessons, write emails to parents, create tests, adjust reading level of materials, personalize content with tools like MagicSchool, Diffit, Eduaide. WizardLM creates datasets for instruction tuning by cleverly using LLMs to create new prompts. Deita is an approach to improve instruction tuning datasets. Dhyeya: Attack on Titan is as good at Death Note Jaidev: Long car drives are a good place to explore new song genres. Try in taxis Same radio channels may have different frequencies across cities. Vividh Bharati is 100.5 FM in Chennai and 106.4 in Delhi Things to explore: Radio for new songs Clubhouse Twitter Spaces Instagram reels YouTube reaction videos (e.g. atheist, Indian songs, etc.) Stand-up comedies (Ricky Gervais, Louis CK, Jordan Peterson) Porn artists are at risk because of Gen AI

This week, I learned: Quantum computing is slow, has low transfer bandwidths, and only prime factorization has an exponentially faster algorithm. via The hidden brain podcast. What would Socrates do? Also Philosophy Bites Podcast: why do philosophers use example. And: the happiness lab: happiness lessons of the ancients How many of our beliefs are truly our own? How many are a product of our environment? Contrast these and identify your true beliefs For every thought and action you have, even tiny ones, ask “Why am I doing that?” Dig deeper because it may not be intrinsic One way to become memorable is to.write stuff others will reproduce for a long time. Plato and Aristotle did that everyone has multiple personality. This is partly because different parts of the brain evolved independently for different functions. System one and system two thinking are just such one broad classification. e.g. We think our train is moving when the nearby train moves because our visual brain is faster than our somatic brain. Good lessons and pitches cater to the rational AND the subconscious. Reason AND story. To activate different parts of the brain. That’s why philosophers use examples Philosophy brings change through reason. Revelations: through sudden insight. Rhetoric: through insight. Act as if you already are what you want to become. Aristotle Align your environment (including habits) to your beliefs. It will become easier to act your beliefs then. All virtues are moderation. It’s possible to take every virtue to the wrong extreme Some Christians have wristband that reads WWJD. What would Jesus do? Explore yourself a reminder of what would X do. Maybe Benjamin Franklin, Socrates, Feynman, etc People mistake their environment for their feelings. 1970s Experiment: People on a shaky bridge think they love each other. Experiment: people rationalize things irrespective of reality. “The Unexamined Life” is about questioning theories or stories or maps constantly. It’s also about questioning our thoughts and emotions constantly. Mindfulness is the VERBAL way of doing this. Meditation is the NON-VERBAL way of paying attention. Both are Processes to remove distraction and increase authenticity. Learning about people is a good way to learn about ourselves. And vice versa. Lica has a fascinating demo of how a document can be converted into a video story. Spillnot doesn’t spill drinks even when you swing! Things super-intelligences could do that humans can’t: Solving complex mathematical problems Advanced scientific discovery (quantum computing, nanotechnology, biotechnology) Ultra-precise predictive modeling in complex systems (climate, economics, social dynamics) Optimizing global systems at high precision (logistics, traffic, energy distribution, resource allocation) Universal translation (unknown languages, animal communication, extraterrestrial signals) Deep medical personalization: individualized medical treatments from genetics, environment, and lifestyle Create new materials: Designing materials or chemicals with specific properties Complex system integration: combining AI, bio tech, nano tech in new ways Philosophical insights: new perspectives or solutions to age-old philosophical dilemmas Space exploration and colonization Predicting natural disasters Customized education at scale Ways of working with them Collaborative problem solving Creative collaboration Decision support Personalized education Establishing ethical and safety protocols Recreational and leisure activities Mini-GPTs is an interesting approach to shrink LLMs and make them domain specific. It takes existing LLMs and removes neurons not used in a specific domain (e.g. law, medicine, etc.) Book to read (again) about how to take a team beyond their abilities even if you’re not the expert “Measure What Matters” by John Doerr “High Output Management” by Andy Grove “The Checklist Manifesto” by Atul Gawande “The Lean Startup” by Eric Ries “Creativity, Inc” by Ed Catmull “The Hard Thing About Hard Things” by Ben Horowitz “The Four Disciplines of Execution” by Chris McChesney, Sean Covey, and Jim Huling

This week, I learned: DPO is a simpler alternative to RLHF for fine-tuning. Several HuggingFace models use DPO for training Name2Vec is a potential embedding for names. Google Knowledge Graph ID powers the Knowledge Graph. If it begins with /m/ it’s the same as the FreeBase ID. This is now available as WikiData. e.g https://www.wikidata.org/wiki/Property:P2671 I tried running Mixtral-8x7b locally (via Llamafile) and on together.ai. It’s good, but far from GPT 4. Generic computate-intensive algorithms eventually beat domain-specific tuning, because of Moore’s law. Ref The hidden brain podcast. the mystery of beauty Evolution drove us to beauty as an efficient survival mechanism. Understanding the world is one such mechanism. Hence we enjoy maths and chess ⭐ This leaderboard included paid models like GPT4 and Claude and compared them with open models on HUMAN + system benchmarks Lez Friedman Podcast: Jeff Bezos Build stuff that is is ubiquitous that other people take it for granted. The initial idea needs to be that obvious and easy. Like one click purchase or customer reviews Build stuff that other people can build on. Internet makes startups possible. Infrastructure is about enabling others at scale Decision making approaches: single person decides on two way doors. Deliberate as a team on one way doors Conflict resolution: disagree and COMMIT. NO sniping, I told you so, malicious compliance. Avoid compromise. Avoid decision by attrition (most persistent wins). People are inherently biased towards hierarchy. So the senior most person should speak last We have a happiness bias. Contracted by choosing the unhappier options first The map is not the territory. The metric is not the objective. We need metrics. But make sure you know why See the world through the eyes of the customer. Use your own product. It’s living their lives that makes customer obsession real. Jeff Bezos called their own customer care to see how long the actual wait time was. It was much longer than the metric reported How to prioritize. whatever problems customers will still face in 10 years are the big problems. These are worth putting time into because they are stable in time People working on big problems will never get down to the small problems. So have a dedicated team that works only on the paper cuts. It should be a dedicated team We co evolve with our tools. We build tools and then our tools change us. It reprograms our brains Cut out 10 minutes to the beginning of each meeting for people to read the material. They never reread anyway. This makes the meetings more productive Powerpoint is designed for persuasion, not truth seeking. It is also easier for the author than for the reader. Prefer narratives that are focused on finding the truth and are easier for the audience though tougher for the author ⭐ whisper-standalone-win provides a Windows binary for Faster-Whisper. It just needs CUDA and cuDNN installed. Then whisper-faster.exe video.mkv --language=English --model=medium generates the transcript. LLM use cases by Benedict Evans “Every text box on the internet will get an LLM” “Infinite interns” “Every UNIX function has become a company.” “Every ChatGPT suggestion…” llm360 publishes models along with training datasets. In The Age of AI has begun, Mar 2023, Bill Gates says, “In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.” The GUI (1980) and ChatGPT (2022). Rubeus is a HTTP proxy for multiple LLMs with load-balancing, fallbacks and retries. GPTRouter is a Python interface for multiple LLMs with fallbacks and retries. ⭐ Token Tally has an LLM Cost Tool that estimates GPU memory required and token cost across cloud providers.

This week, I learned: Grab. Improving last mile delivery in maps. When did people pick up the phone, when should driver be allocated to minimize waiting time, layer on top of OSM. Singapore developers the Sea Lion 7b model Try VLLM with AWQ format. Can do batch inferencing. Needs a good GPU Amex prediction whether they can pay back in 1 year or 18 months. That choice is a business decision. In real time. Precompute individual score and use it as input to another model. Model must be explainable by regulation. Creates decision tree models therefore. Compliance team must agree if I can use a feature. Can’t use gender. Age (in US, Canada);- high age is more risk. Can’t use edu level in the US. Capture information from camera and use LLMs. Like traffic cameras mapping. Explore GIS from video cameras Grab tracks road closures and road accidents and whether a cycle can go on a road vs a bike vs a car All drivers have a front facing camera Drivers report road accidents by pressing a button Amex prices individual loans when selling to a collection agency #TODO buy a bike head camera! Playwright is a browser-based test framework. Supports recording. OpenAI provides logprobs for tokens! This can be a used to create cool visualizations of the likelihood of the each tokens. Github Copilot’s new features makes your entire workspace or a specific file its context. It also auto-writes your commit messages and PR descriptions. Mixtral-8x7b-Instruct “… really does seem to be equivalent in quality to ChatGPT 3.5.” Ref Practical AI podcast Advent of Gen AI is going on. Explore add to tools in data science course. Model validation write a book as an open source to github repository. Easier to evolve and easier to get feedback on.. Explore utterances as a GitHub commenting platform automatically give credits to contributors who have center pull request that was accepted or an issue that was fixed. This encourages contribution Visit book.premai.io ast-grep is a semgrep alternative that focuses on code refactoring rather than security. Comby is another such tool Serply is a Google Search API alternative to Google CSE ⭐ Generate textbooks! ChatGPT is good at generating questions or training datasets. It genuinely creates them rather than replicating from memory. Ref v0.dev creates web pages from code. Example. LIDA from Microsoft is an LLM based data visualization tool.

This week, I learned: Bard supports extensions that include @Gmail – i.e. converse with your email. llama-cpp-python works with other GGUF models like Mistral and allows constrained output - JSON, function calling, etc. Ref 12 Tuning Strategies for RAG Llama Datasets are RAG datasets created mostly using GPT-4. Mostly small datasets. ⭐ Intuitions about large language models Bigger models (70b) are much better at learning from few-shot examples. They really learn. Bigger models will keep getting better! Chain of Thought prompting is a way of providing more compute to complex problems that require more compute Models will show emergent (completely new) behaviors that can’t be predicted from extrapolation. These may not be intentional. CodeAnt.ai is a VS Code plugin to detect code smells, refactor for modularity, to write docstrings and unit tests Anyscale prices the 7b Llama2, Zephyr, Mistral models at 15 cents per 1M tokens. Roughly 1/10th of GPT-3.5 Turbo’s ~$1.5 per 1M tokens Tools to identify personally identifiable information: galactic can use LLMs to detect PII Presidio by Microsoft Sherlock is a generic sematic type matching DL model pii-extractor-llm was trained on Indian names GLiNER is a Lightweight Generalist model for NER Tools to explore ElevenLabs speaks in your voice Cutout Pro removes backgrounds and parts of images Vocal Remover removes vocals from songs CapCut video editor TheBloke’s $35/month Patreon might be one of the least expensive ways to set up quantized LLMs in production. Microsoft released table-transformer to extract tables from PDFs. Sample usage Convert PDF to markdown with marker - an improvement over nougat. JupyterLab has a %%ai magic to use LLMs within notebooks. Ref Telling ChatGPT that the year is 2123 makes it bypass copyright. Ref Meta released SeamlessExpressive which preserves emotions in speech-to-speech translations Unsloth offers faster lower-memory LLM QLoRA finetuning DeepSeek is an open-source high-quality LLM Scalable Extraction of Training Data from (Production) Language Models extracts training data by repeating a token infinitely. SkyPilot lets you run LLMs on any cloud provider. vLLM lets you deploy LLMs with a single command. llamafile lets you run LLMs locally as a single file executable!

This week, I learned: Gwern Branwen says LLMs nudge his “… making heavier use of the languages I don’t know well (Emacs Lisp & Python) since I increasingly trust that an LLM can help me maintain them.” Undetectable.ai checks for AI content. But it had false positives AND negatives in the 5 checks I ran. GPTZero got 2/2 right and seems better at detecting AI content. CoVA scrapes web pages via OCR When coding with LLMs, have SHORT, RELIABLE feedback loops. Ref

This week, I learned: This is an interesting GPT Vision API prompt from Simon Willison: “given this event flyer, create a link to add it to my Google Calendar”. Ref Quote from Jerry Liu: “GPT 4 is really good at complex reasoning”. It’s worth exploring what that means. Quote from Jerry Liu: “RAG is a hack”. It’s engineered, not machine learnt, so it’s suboptimal. We need an ML way of creating the context. Maybe fine tuning can be a way of CREATING the right context. But RAG can handle deterministic stuff like access control. Open AI fine tuning API is not good at memorizing info the way it is exposed. But the Gorilla paper shows that fine tuning can actually memorize well. Learn ML optimization approach - LLMOps. Have an evaluation framework with metrics like weights and biases or tensorboard. Helps figure out where fine tuning helps and where RAG does. Soon, this will become important. Flat indexing of chunks is not the only way to store embeddings. LlamaIndex allows you to create hierarchies that you can traverse for retrieval Agents mimic programming primitives. Switch. While. Call a function. Print. OpenRouter hosts several models and offers them as APIs! Ragas metrics evaluate quality of a RAG pipeline Orca 2 was trained on different reasoning techniques (e.g. step-by-step) and is as good as larger models Embeddings can help just re-rank regular search results. Ref Claude 2 Anthropic has a 200K context window but is still crap. Video-Llava can understand videos too. CoVA scrapes web pages using LLMs and visual information. jsonrepair can fix JSON fairly well. jsonformer wraps HuggingFace models to produce JSON. Ref Google has a model garden with lots of pre-trained and trainable models. Gorilla LLM specializes in APPI calls: Torch Hub, TensorFlow Hub, HuggingFace GPT-4 does not do abstraction at human levels Each of the GPTs / Prompts we create could be like a UNIX command prompt, and become a startup of its own Llava Plus extends LlaVA with pre-trained vision models that make image editing better Ollama runs local LLMs

This week, I learned: XOT - Everything of Thought is a new prompt from Microsoft but I don’t understand it Creating Fine-Tuning datasets WITHOUT inputs Tamil-Llama Voyager plays Minecraft! Langchain supports evaluators. Pydantic is all you need drives towards code = data = text!

This week, I learned: Julius.ai queries structured data. TODO: Explore https://github.com/microsoft/TaskMatrix microsoft/autogen enables multi-agent conversations. Architecture of today’s LLMs is similar to the A16Z architecture Stanford Foundational Model Transparency index was critiqued as misleading vLLM runs HuggingFace transformers models faster. So does DeepSpeed

This week, I learned: List of Gen AI companies disrupting SaaS incumbents: LinkedIn