This paper presents a comprehensive refutation of the so-called "blockchain trilemma," a widely cited but formally ungrounded claim asserting an inherent trade-off between decentralisation, security, and scalability in blockchain protocols. Through formal analysis, empirical evidence, and detailed critique of both methodology and terminology, we demonstrate that the trilemma rests on semantic equivocation, misuse of distributed systems theory, and a failure to define operational metrics. Particular focus is placed on the conflation of topological network analogies with protocol-level architecture, the mischaracterisation of Bitcoin's design--including the role of miners, SPV clients, and header-based verification--and the failure to ground claims in complexity-theoretic or adversarial models. By reconstructing Bitcoin as a deterministic, stateless distribution protocol governed by evidentiary trust, we show that scalability is not a trade-off but an engineering outcome. The paper concludes by identifying systemic issues in academic discourse and peer review that have allowed such fallacies to persist, and offers formal criteria for evaluating future claims in blockchain research.

Intuitive artificially intelligent entities such as Chat GPT bots will play key roles in tomorrow's Web3 and metaverse worlds It is said that the metaverse will be part of the next cultural evolution of humans, giving us unprecedented access to information, free communication, peer-to-peer trustless payments and contracts, and realise lifestyles that are unattainable for many in the real world. Although the metaverse is still a mostly unrealised concept, it is likely to be populated by AI entities that can interact with humans (making the metaverse more user-friendly), find and repair system bugs, and act as general caretakers. Recent years have seen giant steps forward in artificial intelligence (AI) research, and the emergence of products such as ChatGPT from the US AI company OpenAI are bringing AI technology to the masses. This article will look at the potential roles of AI in the metaverse, as well as the threats AI may bring and how they can be circumvented. First, we will introduce the concepts of the metaverse and Web3.

As we will now have to prepare ourselves for migration from Web 2.0 to Web 3.0, it's important to understand the tools and technologies that will play a central role in building a Web 3.0 ecosystem. Although this area is still evolving and will take some more time to come to a mature state, a few technologies have already paved their way into the Web3 world and have been embraced by the Web3 developer communities. Those technologies are Blockchain, some 3D design & modeling tools & platforms, AR/VR and a few other JavaScript & CSS-related packages, and of course AI & Machine Learning. Let's discuss them one by one in a bit more detail. One of the core philosophies driving Web3 is decentralisation, unlike Web2 which works in a centralised fashion.

Last year, I coined 2021 the Year of Digitalism as I foresaw the increase of corporate and governmental data surveillance. Unfortunately, it is safe to say that this has come true with Big Tech becoming more powerful than ever before and governments worldwide implementing Covid tracking apps. What also happened is that the Pandemic has been a strong catalyst for digital transformation in any sector and that the world is currently changing at lightning speed. There are economic changes such as increasing inflation rates, environmental disasters caused by climate change, social changes such as The Great Resignation, and a convergence of technologies that drives technological changes. Although the world has never changed so fast as in 2021, this year was also the most stable of all the years to come in this decade.

One regular Tuesday morning I was looking for new papers on edge Internet of Things networks, like we all do, and came across a new term called Federated Learning and immediately went on a quest to understand this technique. Federated Learning(FL) in its simplest form is a union between the vivacious worlds of Machine Learning and the Internet of Things. It is an ML-based solution that improves the functionality of edge devices in IoT networks. Edge devices in an Internet of Things network are the mobile devices that collect data and help in achieving the purpose of the network remotely instead of the traditionally connected nodes in a network. Our smartphones are the most common example of edge devices. According to the Google AI Blog from 2017- "Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on the device, decoupling the ability to do machine learning from the need to store the data in the cloud."

Abstract: The exponential growth of devices and data at the edges of the Internet is rising scalability and privacy concerns on approaches based exclusively on remote cloud platforms. Data gravity, a fundamental concept in Fog Computing, points towards decentralisation of computation for data analysis, as a viable alternative to address those concerns. Decentralising AI tasks on several cooperative devices means identifying the optimal set of locations or Collection Points (CP for short) to use, in the continuum between full centralisation (i.e., all data on a single device) and full decentralisation (i.e., data on source locations). We propose an analytical framework able to find the optimal operating point in this continuum, linking the accuracy of the learning task with the corresponding network and computational cost for moving data and running the distributed training at the CPs. We show through simulations that the model accurately predicts the optimal trade-off, quite often an intermediate point between full centralisation and full decentralisation, showing also a significant cost saving w.r.t.

It should come as no surprise that the marketing industry is riding a wave of change. In the wake of prolific data breaches and prominent scandals, consumers are demanding better protection of their personal information. In response, lawmakers are listening, introducing expansive data privacy regulations. Marketers have had to re-assess the way we collect, disseminate, and store sensitive information--learning to put privacy at the forefront and leaving behind the insecure and invasive methods of yesteryears. For now, the search is on for privacy-preserving solutions to be deployed at scale that will get marketers the data they need without infringing on the rights of users.

Ayasdi offers an enterprise-grade artificial intelligence platform that leverages big data to make intelligent business applications; for instance, Ayasdi has an application that powers parts of HSBC's anti-money laundering technology stack. Headquartered in California, Ayasdi has further offices in London with global expansion demanding a third office potentially coming to Singapore for 2018. Lots of stuff is going on with AI. Broadly speaking there are two major ways of thinking about problems addressable by AI today. One side is around perception based problems – self driving cars and virtual assistants – these rely on data such as imaging and sensing the environment.

Ayasdi offers an enterprise-grade artificial intelligence platform that leverages big data to make intelligent business applications; for instance, Ayasdi has an application that powers parts of HSBC's anti-money laundering technology stack. Headquartered in California, Ayasdi has further offices in London with global expansion demanding a third office potentially coming to Singapore for 2018. Lots of stuff is going on with AI. Broadly speaking there are two major ways of thinking about problems addressable by AI today. One side is around perception based problems – self driving cars and virtual assistants – these rely on data such as imaging and sensing the environment.