Data should not be approached as a monolithic asset, rather as a multi-faceted asset that comprises several dimensions. It is important for companies to move beyond the concept of big data volume as the only dimension to be interested in. In fact, big data has at least four important flavours, sometimes referred to as the four V's of data: volume, variety, veracity and velocity. Focusing on volume only can produce a negative linear impact on firm performance - https://vzocca.substack.com/p/how-to-handle-big-data

“In the research paper, published on February 8, 2023, in the journal Nature Communications, the scientists demonstrate how they have used a new and powerful technique, which they dub ‘UQ Connect’, to use electric field links to enable qubits to move from one quantum computing microchip module to another with unprecedented speed and precision. This allows chips to slot together like a jigsaw puzzle to make a more powerful quantum computer.” https://bit.ly/40UWkC2

One of the promising technologies for quantum computing makes use of superconducting circuits. Anton Potočnik, senior researcher in quantum computing at IMEC, says, "The energy states of superconducting qubits are relatively easy to control, and, throughout the years, researchers have been able to couple an increasing number of qubits together. This enables an ever-higher level of entanglement—which is one of the pillars of quantum computing. On top of that, research groups worldwide have demonstrated superconducting qubits with long coherence times (up to several 100 µs) and sufficiently high gate fidelities—two important benchmarks for quantum computation." https://bit.ly/3LHahy3

Insights come from understanding the data at a deep level (and this requires data variety) - https://open.substack.com/pub/vzocca/p/insights-come-from-understanding

The IBM Quantum Computer is built to take quantum computing out of the lab and into a commercial environment. Kids and adults alike can use this LEGO set to discover and learn about the composition of a Quantum Computer system while recreating a slice of a real-life Quantum computer data center used by quantum computing users in industry, academia, research and national labs. https://bit.ly/3pySodp

“The next generation of quantum devices requires high-coherence qubits that are less error-prone. Responding to this need, researchers at the AQT at Berkeley Lab, a state-of-the-art collaborative research laboratory, developed a blueprint for a novel quantum processor based on "fluxonium" qubits. Fluxonium qubits can outperform the most widely used superconducting qubits, offering a promising path toward fault-tolerant universal quantum computing.” https://bit.ly/3WpEeaH

You can find online material including slides, recordings, and a virtual machine for François Fleuret's deep-learning course.
This course is a thorough introduction to deep-learning, with examples in the PyTorch framework:
• machine learning objectives and main challenges,
• tensor operations,
• automatic differentiation, gradient descent,
• deep-learning specific techniques,
• generative, recurrent, attention models.

See material at https://fleuret.org/dlc/

OpenAI leaders call for regulation to prevent AI destroying humanity - https://www.theguardian.com/technology/2023/may/24/openai-leaders-call-regulation-prevent-ai-destroying-humanity

"This work validates the original hypothesis behind quantum annealing, coming full circle from some seminal experiments conducted in the 1990s" https://rb.gy/4xewc

“Scientists have long been interested in understanding how materials self-organize into complex structures, such as crystals. In the often-arcane world of quantum physics, this sort of self-organization of particles is seen in "density waves," where particles arrange themselves into a regular, repeating pattern or order; like a group of people with different colored shirts on standing in a line but in a pattern where no two people with the same color shirt stand next to each other.” https://rb.gy/epnim

“It was not clear, however, how to adapt classical methods of error correction to quantum computers. Quantum information cannot be copied; to correct errors, we need to collect information about them through measurement. The problem is, if you check the qubits, you can collapse their state—that is, you can destroy the quantum information encoded in them. Furthermore, besides having errors in flipped bits, in a quantum computer you also have errors in the phases of the waves describing the states of the qubits.” https://rb.gy/s506p

Should we have AI regulation or should we have a Far West type of internet news where anyone who is fast enough can just shoot whatever he/she likes? Some regulation is important, especially as we create new models which are still very dumb but quite powerful - https://vzocca.substack.com/p/when-a-man-with-a-45-meets-a-man

AI regulation is being discussed both among researchers as well as at the political level. But how did we get here? Whether you believe we are on the cusp of realising truly intelligent machines or we are still in the infancy of intelligent models, whether you believe we already have AGI or whether you think we have Machine Learning based on little more than statistical models, it is important to understand that we have started walking down a path from which we will not be able to come back and that we should start regulating it before it becomes too late, even if we are not there yet - https://vzocca.substack.com/p/a-short-history-of-artificial-intelligence

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner systematically goes through each and every page, scanning one square inch at a time. It would take you a long time to get through the whole book with that scanner, and most of that time would be wasted scanning empty space.
Such is the life of many an experimental physicist.

In particle experiments, detectors capture and analyze vast amounts of data, even though only a tiny fraction of it contains useful information. “In a photograph of, say, a bird flying in the sky, every pixel can be meaningful,”

But that’s starting to change. With a machine learning tool known as a sparse convolutional neural network (SCNN), researchers can focus on the relevant parts of their data and screen out the rest. Researchers have used these networks to vastly accelerate their ability to do real-time data analysis. https://rb.gy/d4czw

Topologists study the properties of general versions of shapes, called manifolds. Their animating goal is to classify them. In that effort, there are a few key distinctions. What exactly are manifolds, and what notion of sameness do we have in mind when we compare them? https://shorturl.at/DEFY6

Computer scientists can also use “teacher” systems to train another machine to complete a task. But just like with human learning, the student machine faces a dilemma of knowing when to follow the teacher and when to explore on its own. To this end, researchers from MIT and Technion, the Israel Institute of Technology, have developed an algorithm that automatically and independently determines when the student should mimic the teacher (known as imitation learning) and when it should instead learn through trial and error (known as reinforcement learning). https://shorturl.at/bOQX7

A model's performance decay can be due to different reasons, which is why it is important to update and retrain models consistently. Concept drift and model decay are not the same, and it is important to know the difference to understand the best way to keep the model’s accuracy - https://vzocca.substack.com/p/model-decay-vs-concept-drift-do-we

Want to learn more about Shapley values? What they are, how to use them, how to approximate them? Look no further - https://www.aidancooper.co.uk/approximating-shapley-values/

Five ways AI might destroy the world - https://www.theguardian.com/technology/2023/jul/07/five-ways-ai-might-destroy-the-world-everyone-on-earth-could-fall-over-dead-in-the-same-second

Party Planning Meets Graph Theory
To understand what a Ramsey number is, imagine you’re hosting a party.
How many people would you need to invite to guarantee that there will be a group of people who all know one another, or a group who are all strangers? You can encode this question in the language of graphs. Assign a vertex to each person. For n people, you get n vertices. Connect every pair of vertices with an edge. Color the edge red if the people in question know each other, and blue if they are strangers. https://rb.gy/qb2zr

“Quantum computing is a rapidly advancing field that has the potential to revolutionise the way we process and analyse data. In the realm of finance, quantum computing promises to provide powerful tools for financial modelling and risk assessment. However, it is not a straight path to success. This article will explore the benefits and harms of quantum computing and debate whether the financial sector will ever come to the point of requiring such a futuristic system.” https://shorturl.at/mpZ39