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๐ข๐ญ ๐๐จ๐๐ฌ๐งโ๐ญ ๐ ๐๐ญ ๐๐๐ญ๐ญ๐๐ซ ๐ญ๐ก๐๐ง ๐ญ๐ก๐ข๐ฌ ๐ข๐ง ๐๐ฅ๐๐๐ค๐ฌ๐๐ฎ๐ซ๐ ๐ฟ Hokie-Pokey. Conference Crossover. The ACC Champs vs Big 12 Conference Champs ๐ฆ No. 6 Hokies vs No. 3 Cowboys ๐ค #ALLINALLTHETIME #Hokies ๐ฆ
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![This podcast is a special one. Based Show presents fiddy.dime - priv/acc ๐ฆก [founder of Paradigm Paradex ] We unpack the journey of building The Defi Super App The vision, the mission, the relentlessness, the execution. The man is a warrior ! Timestamps: 0:00 : Raw backstory & origin of the persona 2:19 : Attraction to fringe culture & diverse upbringing 4:24 : Gaming as first love 4:57 : Parents vs passion (traditional Indian household 7:02 : Music, DJing & creative experimentation 8:46 : Reality check โ passion vs financial reality in India 9:45 : Entrepreneurial family background 11:04 : Falling in love with finance (first MBA finance class) 12:31 : Curiosity for misunderstood & fringe systems 14:03 : First job in finance (quant โ trading desk) 17:16 : Bitcoin whitepaper moment 20:05 : Machine learning & exposure to new models 22:20 : Discovering broken systems in commodities trading 26:33 : Trading floor feels like a LAN party 28:43 : First automation breakthrough (15 min โ seconds) 30:45 : โYou should build a companyโ realization 31:18 : Immigrant founder constraint (visa risk) 33:22 : Forced delay becomes strategic advantage 34:40 : Biggest milestone โ overcoming immigration 37:43 : Founder psychology (war with your own mind) 39:03 : Paradigm growth & institutional dominance 41:01 : Why Paradex exists (dependencies kill companies) 42:54 : FTX collapse & darkest period 46:38 : Choosing the fight instead of quitting 51:30 : Transition from survival โ long-term vision 55:38 : DeFi super center framework (financial mega-cities analogy) 57:26 : Privacy as non-negotiable 59:54 : PRISM revealed (alpha moment) 1:02:50 : Toxic vs non-toxic flow explained 1:06:22 : Why ZK > L1 > Optimistic rollups 1:09:54 : Zero fees โ zero revenue 1:12:50 : Revenue increased after zero fees 1:16:09 : Real competition isnโt other DEXs (TradFi is the enemy) 1:20:05 : Infrastructure vs consumer apps (liquidity infrastructure thesis) 1:21:00 : โTrade anything, with anyone, settle instantlyโ A vision 1:22:48 : Why this vision is brutally hard & requires suffering 1:25:11 : Team size reveal 1:27:36 : Flat org structure (no managers, 2 meetings/week) 1:29:11 : Brutal hiring funnel (530 โ 6 โ 3 hires) 1:32:03 : Determination > pedigree (why degrees donโt matter) 1:34:32 : Family influence & fatherโs story 1:37:08 : Meritocracy, empathy & values 1:38:48 : Becoming a father & legacy mindset 1:40:46 : TGE delay, farming accusations & retention-first logic 1:43:51 : Tokenomics, alignment & governance pillars 1:47:54 : Biggest fear โ not reaching full potential 1:49:30 : Closing remarks & outro](https://image.24vids.com/tw-2011818134135804381/amplify_video_thumb/2011767032694685696/img/JCRL-ldiapS4YWXP.jpg)
![AGI? One day, but not yet. The only AI that works well right now is the one behind the screen [12-17]. But passing the Turing Test [9] behind a screen is easy compared to Real AI for real robots in the real world. No current AI-driven robot could be certified as a plumber [13-17]. Hence, the Turing Test isn't a good measure of intelligence (and neither is IQ). And AGI without mastery of the physical world is no AGI. Thatโs why I created the TUM CogBotLab for learning robots in 2004 [5], co-founded a company for AI in the physical world in 2014 [6], and had teams at TUM, IDSIA, and now KAUST work towards baby robots [4,10-11,18]. Such soft robots don't just slavishly imitate humans and they don't work by just downloading the web like LLMs/VLMs. No. Instead, they exploit the principles of Artificial Curiosity to improve their neural World Models (two terms I used back in 1990 [1-4]). These robots work with lots of sensors, but only weak actuators, such that they cannot easily harm themselves [18] when they collect useful data by devising and running their own self-invented experiments. Remarkably, since the 1970s, many have made fun of my old goal to build a self-improving AGI smarter than myself and then retire. Recently, however, many have finally started to take this seriously, and now some of them are suddenly TOO optimistic. These people are often blissfully unaware of the remaining challenges we have to solve to achieve Real AI. My 2024 TED talk [15] summarises some of that. REFERENCES (easy to find on the web): [1] J. Schmidhuber. Making the world differentiable: On using fully recurrent self-supervised neural networks (NNs) for dynamic reinforcement learning and planning in non-stationary environments. TR FKI-126-90, TUM, Feb 1990, revised Nov 1990. This paper also introduced artificial curiosity and intrinsic motivation through generative adversarial networks where a generator NN is fighting a predictor NN in a minimax game. [2] J. S. A possibility for implementing curiosity and boredom in model-building neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222-227. MIT Press/Bradford Books, 1991. Based on [1]. [3] J.S. AI Blog (2020). 1990: Planning & Reinforcement Learning with Recurrent World Models and Artificial Curiosity. Summarising aspects of [1][2] and lots of later papers including [7][8]. [4] J.S. AI Blog (2021): Artificial Curiosity & Creativity Since 1990. Summarising aspects of [1][2] and lots of later papers including [7][8]. [5] J.S. TU Munich CogBotLab for learning robots (2004-2009) [6] NNAISENSE, founded in 2014, for AI in the physical world [7] J.S. (2015). On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning (RL) Controllers and Recurrent Neural World Models. arXiv 1210.0118. Sec. 5.3 describes an RL prompt engineer which learns to query its model for abstract reasoning and planning and decision making. Today this is called "chain of thought." [8] J.S. (2018). One Big Net For Everything. arXiv 1802.08864. See also patent US11853886B2 and my DeepSeek tweet: DeepSeek uses elements of the 2015 reinforcement learning prompt engineer [7] and its 2018 refinement [8] which collapses the RL machine and world model of [7] into a single net. This uses my neural net distillation procedure of 1991: a distilled chain of thought system. [9] J.S. Turing Oversold. It's not Turing's fault, though. AI Blog (2021, was #1 on Hacker News) [10] J.S. Intelligente Roboter werden vom Leben fasziniert sein. (Intelligent robots will be fascinated by life.) F.A.Z., 2015 [11] J.S. at Falling Walls: The Past, Present and Future of Artificial Intelligence. Scientific American, Observations, 2017. [12] J.S. KI ist eine Riesenchance fรผr Deutschland. (AI is a huge chance for Germany.) F.A.Z., 2018 [13] H. Jones. J.S. Says His Life's Work Won't Lead To Dystopia. Forbes Magazine, 2023. [14] Interview with J.S. Jazzyear, Shanghai, 2024. [15] J.S. TED talk at TED AI Vienna (2024): Why 2042 will be a big year for AI. See the attached video clip. [16] J.S. Baut den KI-gesteuerten Allzweckroboter! (Build the AI-controlled all-purpose robot!) F.A.Z., 2024 [17] J.S. 1995-2025: The Decline of Germany & Japan vs US & China. Can All-Purpose Robots Fuel a Comeback? AI Blog, Jan 2025, based on [16]. [18] M. Alhakami, D. R. Ashley, J. Dunham, Y. Dai, F. Faccio, E. Feron, J. Schmidhuber. Towards an Extremely Robust Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms. Preprint arxiv 2404.08093, 2024.](https://image.24vids.com/tw-1925204946967801861/amplify_video_thumb/1925197727224250368/img/U-5TNxCzlWIlxyYf.jpg)