Because AI is an engineering discipline and not a scientific field, it's never possible to fully separate the properties of a given approach from those of its specific implementations. The artifact is the method.
If you remove #Bitcoin that hasn't moved in 10+ years, the percent of the supply in profit has recently reached all-time lows.
32.73% of the supply is sitting in profit, lower than all prior bear markets.
If bottoms occur during max pain, we are certainly there/close to it.
Investing in digital assets feels like learning a new language. 🌐
It’s not just about gains—it’s about understanding what you support.
Do we truly grasp the value of what we hold? 🤔
Research deepens conviction.
#Crypto#Blockchain
DAOs are fascinating—decentralized decision-making sounds great, but can they avoid the flaws of traditional orgs? 🤔
Flat structures are cool, but what about blurry accountability?
Still, DAOs teach us a lot about digital-age collaboration. 💡
#DAO#Blockchain
Blockchain isn't just about crypto—it’s about trust, transparency, and possibility. 🌍
From fairer supply chains to empowering creators with ownership, the potential is massive. But are we focusing enough on real-world impact? 🤔
#Blockchain#Innovation
Big news: we just released Keras 3.0!
▶ Run Keras on top of JAX, TensorFlow, and PyTorch
▶ Train faster with XLA compilation
▶ Unlock training runs with any number of devices & hosts via the new Keras distribution API
It's live on PyPI now! 🚀
The metaverse feels like the next frontier, but are we ready for it? 🤔
Building a space where ownership, identity, and freedom matter takes more than tech—it needs trust, transparency, and collaboration.
Excited yet cautious. 🌐
#Metaverse#Blockchain
Decentralized storage feels like one of those ideas that just makes sense. Why trust a single server when you can spread your data across a network that no one fully controls? 🌍
It’s not just about security—it’s about freedom. 💡
#Web3#Decentralization
DeFi is like a playground for financial freedom 🌍
But as much as I love the innovation, I wonder... are we building systems that truly empower everyone? Or are we just recreating old inequalities in a digital space? 🤔
#DeFi#Blockchain
Bitcoin tested key support last week amid rising geopolitical tensions, sparking a sharp but short-lived sell-off.
Buyers quickly defended the Short-Term Holder Realised Price, but BTC remains range-bound between $100K–$110K.
📊 Chart @BitcoinMagPro
https://t.co/pmYnTBWkcY
Active Predictive Coding (ActPC) comprises a biologically inspired framework for learning and inference. By minimizing prediction errors between internal models and observed data, ActPC iteratively refines both latent representations and model parameters.
Unlike backpropagation-based neural networks, ActPC emphasizes local error signals, making it inherently more suited for real-time, online learning and enabling integration of reinforcement learning and symbolic reasoning.
Traditional gradient-based optimization in neural networks struggles to support large-scale real-time learning dynamics due to the brittleness of the underlying backpropagation algorithm, which requires carefully coordinated and synchronized updating across a large network (leading to a reliance on large batch-based updates), and which suffers convergence problems when neural architectures are too recurrent or otherwise too complex.
These shortcomings lead to an unfortunate sociotechnical dynamic in which neural architectures oriented toward robust AGI-oriented learning, reasoning, and memory are insufficiently pursued because they tend to involve network topologies for which backpropagation will not easily converge.
ActPC resolves these issues on both conceptual and mathematical levels, but can also suffer from lengthy convergence times and undesirable transient dynamics. Information-geometrically enhanced ActPC (ActPC-Geom) provides a compelling potential alternative: by incorporating measure-dependent operators derived from the Wasserstein distance, one aligns parameter updates with the natural structure of the probability distributions underlying ActPC, thereby accelerating learning and smoothing out digressive transient dynamics that might otherwise occur.
Like most practical applications of information geometry, ActPC-Geom faces significant computational challenges. However, we believe these can be addressed via appropriate deployment of machine learning and reasoning algorithms within the geometric modeling itself (using neural approximators and information-geometry-guided kernel-PCA-based embeddings). Further, these ML and MR algorithms can be used to inject valuable cognitive properties into the neural learning process, alongside providing acceleration.
Read the full paper to learn more: https://t.co/Q62zdVPhIu
I hate that I keep having to post (and pin) this, but anything you think you see (or hear) via AI-generated fake videos from “me” on this platform, Instagram, Facebook, WhatsApp and/or Bluesky claiming I have a stock (or crypto) picking club for a fee are all IMPOSTERS … I am ONLY active on this platform (make sure you’re following the actual me, not the scammers) and LinkedIn … I’m not active on ANY OTHER platform