I wouldn't worry. There is a whole ecosystem of people who compare power/efficiency/functionality/cost.... Trust our technology. Eventually the cream will float to the surface. If he's smart he'll integrate a RISCV architecture with our IP. Everything else is shit. As oppionioned via the actions of the European space agency. And they re pretty smart.
Hey I'm not "worried" worried, if you know what I mean..
pub.towardsai.net
Further to @Frangipani post above and my previous post she kindly linked, I managed to find the abstract of the presso.Takashi Sato, Professor in the Graduate School of Informatics at Kyoto University (see @Fullmoonfever ’s post from August 2024), is also co-author of another (though similarly titled) paper describing research done with Akida: “Zero-Aware Regularization for Energy-Efficient Inference on Akida Neuromorphic Processor”, which happens to be presented at ISCAS (International Symposium on Circuits and Systems) 2025 in London tomorrow.
His two co-authors are fellow researchers from Kyoto University’s Graduate School of Informatics, but others than last year: PhD student Takehiro Habara and Associate Professor Hiromitsu Awano (who might actually be the son of Sato’s August 2024 co-author Hikaru Awano: https://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/215689/2/djohk00613.pdf, cf. Acknowledgment page VI: “Last but not least, I am truly grateful to my parents, Hikaru Awano and Akiko Awano for their support of my long student life.”)
View attachment 85398
View attachment 85399
View attachment 85401
Hiromitsu Awano
Affiliation Associate Professor, Graduate School of Informatics, Kyoto University Contact Room: S305, Research Bldg. No.9, Kyoto University Main Campus Mail: awano [at] i.kyoto-u.ac.jp awano [at] ist.osaka-u.ac.jp (still active) awano [at] vdec.u-tokyo.ac.jp (still active) awano [at]sites.google.com
View attachment 85400
First author Takehiro Habara is a PhD student at the “Kyoto University School of Platforms”, an interesting interdisciplinary PhD program. The Graduate School of Informatics he is affiliated with is one of the collaborating Graduate Schools.
He says about himself he is “researching low-power AI and creating handheld AI devices” and aims to build “a platform that enables advanced AI inference with low power consumption”, “an AI system that can be used anywhere and by anyone”.
View attachment 85404
People - 京都大学卓越大学院プログラム
京都大学は、2021年度から、博士課程「プラットフォーム学卓越大学院プログラム」を情報学研究科、農学研究科、医学研究科、防災研究所、公共政策大学院が連携して新たにスタートさせます。このプログラムは情報、通信技術を融合させた情報学と、情報やデータ創造し、価値創造を行う現場領域（農学、医学、防災等）および文系学術との系統的な連携によりプラットフォーム学を新たに創造し、この複数専攻領域からなるプラットフォーム学の知識と高度かつ独創的な基盤技術に関する研究力を取得できる教育プログラムを提供し、世界を牽引するプラットフォーム構築者を育てるものです。www.platforms.ceppings.kyoto-u.ac.jp
View attachment 85405
Translation courtesy of Google Lens:
View attachment 85406
View attachment 85407What Is Platform Science? - 京都大学卓越大学院プログラム
Kyoto University has opened a new PhD program of ʻKyoto University School of Platformsʼ in collaborawww.platforms.ceppings.kyoto-u.ac.jp
View attachment 85409
| Information for Paper ID 1590 | ||||||||||||||||||||
|
www.youtube.com
www.youtube.com
I can hear your friends at hot crapper calling you.
I have them on ignore right now.
I have them on ignore. The fun of teasing them to the extent they go into mindless rage rants has worn off.
️
pub.towardsai.net
That wasn’t meant for you
Published 3 days ago. Written by Adit Sheth, Senior Software Engineer at Microsoft.
View attachment 85758
Unlocking AI’s Next Wave: How Self-Improving Systems, Neuromorphic Chips, and Scientific AI are Redefining 2025
Adit Sheth
3 days ago
Generated by Microsoft Copilot
The year is 2025, and the world is not merely witnessing a technological shift; it’s experiencing a seismic redefinition of intelligence itself. Forget the fleeting hype cycles of yesteryear. The quiet hum of artificial intelligence has swelled into a thunderous roar, transforming industries, reimagining human-computer interaction, and forcing us to fundamentally reconsider what it means to think, to learn, to be. This isn’t just an upgrade; it’s a revolution, catapulting us beyond the generative models that captivated us just months ago into an era where AI is not just performing tasks, but autonomously enhancing its own capabilities, operating with the brain’s own whispered efficiency, and unlocking the universe’s deepest, most guarded secrets.
This article isn’t a dry technical report. It’s an invitation to explore the very frontier of innovation, a deep dive into the paradigm shifts at the heart of AI’s “next wave.” We’re talking about algorithms that learn to outsmart themselves, hardware that breathes like a biological brain, and models that speak the language of the cosmos. Buckle up. Welcome to 2025’s AI frontier — a landscape where intelligence self-evolves, conserves energy with breathtaking finesse, and accelerates scientific discovery with the precision of a cosmic clock.
The AI Renaissance: Beyond Hype to the Self-Evolving Frontier
For decades, the promise of Artificial Intelligence danced tantalizingly on the horizon, often retreating into the shadows of “AI winters.” But the current moment is different. Profoundly different. What distinguishes this AI Renaissance from all that came before isn’t just faster processors or bigger datasets; it’s a perfect storm of converging forces — the relentless march of computational power, the sheer tsunami of global data, and the algorithmic breakthroughs, epitomized by the transformative Transformer architecture, that didn’t just unlock Large Language Models (LLMs) but flung open the gates to far grander, more audacious ambitions.
By mid-2025, AI is no longer a nascent curiosity; it’s an indispensable, foundational layer, woven intricately into the fabric of global commerce, cutting-edge research, and our everyday lives. But the most electrifying development isn’t simply AI’s pervasive presence. It’s its burgeoning capacity for self-evolution. We are transitioning from AI that meticulously executes instructions to AI that proactively learns, adapts, and fundamentally improves itself. This profound shift is poised to accelerate innovation at a velocity previously unimaginable, enabling AI to conquer challenges of scale and complexity once considered firmly within the realm of speculative fiction. The age of self-optimizing intelligence has not just dawned; it is galloping into full stride.
The Ascent of Self-Improving AI: Intelligence That Learns to Learn
Imagine an intelligence that doesn’t just process information, but actively refines its own mind. For far too long, the meticulous art of improving an AI model remained a human-centric, often grueling, cycle of endless fine-tuning and manual iteration. Today, the very vanguard is defined by Self-Improving AI — systems endowed with the astonishing ability to autonomously monitor their own performance, diagnose their own flaws, generate new, targeted data (both synthetic and real), and even daringly refine their internal algorithms or fundamental architectures without constant human intervention. This is intelligence that doesn’t just learn from data; it learns how to learn better, initiating a relentless, accelerating spiral of intellectual ascent.
This revolutionary capability is underpinned by sophisticated, dynamic feedback loops that empower AI to become its own architect:
The ramifications of self-improving AI in 2025 are, quite frankly, profoundly staggering:
- Autonomous Learning Cycles: Picture AI agents engaged in a perpetual ballet of perception, decision, action, and then, crucially, self-evaluation. They assess their own outcomes with surgical precision, then dynamically rewrite elements of their decision-making logic or knowledge base for superior performance. In complex strategic games or hyper-realistic simulation environments, an AI can now play millions of rounds, pinpoint optimal strategies, and literally reprogram itself for victory.
- Reinforcement Learning with Self-Correction and Reflection: Building upon breakthroughs like Reinforcement Learning from Human Feedback (RLHF), cutting-edge techniques now allow AI systems to “reflect” on their past failures with a chilling clarity previously reserved for human introspection. They meticulously analyze precisely why a particular output was flawed, pinpoint subtle fallacies in their reasoning paths, and then autonomously generate new, targeted training examples or modify internal representations to prevent similar missteps. This concept, often termed “Recursive Self-Improvement” (RSI) or “self-healing AI,” isn’t just about iteration; it hints at a future where AI perpetually bootstraps its own intelligence, pushing the boundaries of its own cognitive capacity.
- Meta-Learning and AutoML for System Optimization: Beyond simply fine-tuning individual models, meta-learning enables AI to grasp the very principles of learning itself. This means an AI can become adept at rapidly adapting to entirely new tasks with minimal data, or even autonomously generate novel, more efficient machine learning algorithms specifically tailored to emerging problems. Modern Automated Machine Learning (AutoML) platforms are deeply integrating these meta-learning capabilities, allowing AI to autonomously design, optimize, and even deploy complex AI pipelines, from initial data preprocessing to final model integration. The result? A paradigm where AI actively participates, and even leads, in its own engineering. One exciting example of this can be seen in C3 AI’s advancements in multi-agent automation, showcasing how self-improving agents are tackling enterprise-scale challenges by refining their own workflows and reasoning. (Explore more on C3 AI’s “Agents Unleashed” here.)
Imagine an AI system orchestrating a global logistics network that doesn’t just learn from real-time traffic fluctuations, dynamic weather patterns, and unforeseen supply chain disruptions, but also self-revises its entire optimization algorithm to achieve efficiencies far beyond what even the most brilliant human experts could manually program. This isn’t distant futurism; this is the tangible, thrilling promise of self-improving AI, a true game-changer in humanity’s quest for intelligent autonomy. It marks a pivotal moment where AI transitions from a powerful tool to an active, evolving partner in its own progress.
- Unprecedented Autonomy and Resilience: Systems can now adapt to highly dynamic, unpredictable environments and novel situations in real-time, making them fundamentally more robust for mission-critical applications. Imagine autonomous vehicles that learn from every near-miss, refining their driving algorithms instantly; or dynamic infrastructure management systems that self-optimize in response to sudden demands; or next-gen cybersecurity platforms that don’t just detect threats, but autonomously engineer and deploy countermeasures against zero-day attacks. The system learns to fail forward, building resilience through continuous, relentless introspection.
- Exponential Development Cycles: AI is now, quite literally, accelerating its own evolution. As AI systems become more adept at identifying and fixing their own shortcomings, the very pace of innovation within the AI landscape itself is poised for an exponential surge. This could lead to breakthroughs emerging at a velocity previously deemed impossible, creating a virtuous cycle of accelerating intelligence.
- Radical Reduction in Human Intervention: While human oversight remains utterly crucial for alignment, ethical guardrails, and ultimate accountability, the need for constant, granular human intervention in optimization, debugging, and iteration decreases dramatically. This frees human engineers and researchers to focus on higher-level strategic challenges, abstract problem definition, and the profound ethical implications of guiding ever-smarter machines.
Neuromorphic Computing: Building Brain-Inspired, Energy-Efficient AI
As the computational and energy demands of large-scale AI — particularly the colossal LLMs and the resource-hungry self-improving systems — continue their meteoric rise, they cast a looming shadow: an undeniable bottleneck. This pressing challenge is precisely what Neuromorphic Computing steps forward to address, representing nothing less than a fundamental paradigm shift in how we design and build AI hardware. Drawing profound inspiration from the astonishing energy efficiency and parallel processing power of the human brain, neuromorphic chips bravely jettison the traditional von Neumann architecture, which, for decades, has inefficiently separated processing from memory, leading to constant, energy-intensive data movement.
Key principles defining this quiet revolution in silicon include:
The critical and rapidly escalating role of neuromorphic computing in 2025 cannot be overstated:
- In-Memory Computing (Processing-in-Memory): In stark contrast to conventional architectures, neuromorphic systems ingeniously co-locate processing units directly within or immediately adjacent to memory. This radical approach dramatically curtails the energy consumption associated with constantly shuttling data between distinct processing and storage components — the infamous “von Neumann bottleneck.” This architecture fundamentally mirrors the brain’s seamless, integrated computation and memory, operating with a fluidity unmatched by current digital systems.
- Event-Driven (Spiking Neural Networks — SNNs): Unlike typical deep learning models that process all inputs continuously, consuming power constantly, neuromorphic chips primarily operate on Spiking Neural Networks (SNNs). These artificial neurons “fire” (generate a computational event) only when a certain threshold of input is reached, mimicking the sparse, asynchronous, and incredibly efficient communication of biological neurons. This event-driven processing leads to extraordinarily low power consumption, as computations are performed only when genuinely necessary, minimizing idle energy drain. Imagine a light switch that only consumes power when it’s actively flipping.
- Intrinsic Parallelism and On-Chip Adaptability: Neuromorphic architectures are inherently massively parallel, allowing for millions of concurrent computations, much like the brain’s distributed processing. Furthermore, many neuromorphic designs are built for continuous, on-device learning and adaptation, making them uniquely suited for dynamic, real-world edge environments where constant cloud connectivity is impractical or impossible.
Leading the charge in this hardware revolution are innovators like Intel, with its groundbreaking Loihi series. Loihi 2, in particular, is pushing the boundaries of AI with its support for low-precision, event-driven computation, showing promising results for efficient LLM inference, demonstrating capabilities like real-time gesture recognition and pattern learning with vastly reduced power requirements. (Loihi 2 and its capabilities). IBM also continues its advancements in neuromorphic computing, with ongoing research pushing the boundaries of brain-inspired architectures. Meanwhile, companies like Brainchip are commercializing their Akida chip, a fully digital, event-based AI processor ideal for ultra-low power edge computing, demonstrating advanced capabilities in areas like event-based vision for autonomous vehicles and industrial automation. (See how Brainchip’s Akida is enabling breakthroughs in edge AI.). As these specialized processors mature and become more widely accessible, they promise to fundamentally reshape the hardware landscape of AI, driving us towards a future where intelligence is not just powerful, but also profoundly efficient, always-on, and truly pervasive.
- Addressing the Energy Crisis of AI: The monumental carbon footprint and staggering operational costs associated with training and running today’s colossal AI models are simply unsustainable. Neuromorphic chips offer a revolutionary path to orders of magnitude lower power consumption for demanding AI tasks, making large-scale AI deployment far more environmentally responsible and economically viable. This isn’t just an optimization; it’s an existential necessity for AI’s long-term, widespread scalability.
- Fueling the Edge AI Revolution: By enabling sophisticated AI to run directly on tiny, power-constrained devices — from next-generation wearables and smart sensors to agile drones and truly autonomous robotics — neuromorphic chips unleash the full potential of real-time, on-device intelligence. This dramatically reduces latency, enhances data privacy (as less sensitive data needs to be transmitted to the cloud), and facilitates always-on AI capabilities crucial for applications where consistent cloud connectivity isn’t feasible or desirable. Picture smart eyewear that provides real-time contextual awareness without draining its battery in minutes, or a drone performing complex environmental analysis on its own, far from any network.
- Opening New Frontiers in AI Application: This unprecedented energy efficiency and real-time processing ability enable novel AI applications that were previously confined to laboratories or supercomputers due to power constraints. Consider medical implants with embedded AI that continuously monitor biomarkers and adapt their function for years without external power, or vast smart city sensor networks that process complex visual and auditory data locally to manage traffic or detect anomalies without overwhelming central servers.
I was hoping Sean would get us some deals with other silicon valley companies. Wasn't his connection was the reason why he was the chosen one?
