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

Defining good Key Performance Indicators (KPI) is a fundamental step for each business. Having good data is irrelevant if we do not know how to use it to track how close we are to achieving our goals - https://vzocca.substack.com/p/harnessing-the-power-of-data-driven

“Quantum computers owe their power in part to a phenomenon known as superposition, which effectively allows them to simultaneously explore many options that a classical computer would need to consider individually. But it’s not as simple as performing multiple calculations at once to save time. Checking the result of a superposition of choices never reveals a superposition of outcomes — rather, you only ever obtain one of the possible outcomes, each of which has a different probability. Quantum algorithms rely on the fact that contributions to these probabilities can interfere with each other like waves on the surface of a pond, boosting the probability of getting the right answer while reducing the probability of every other outcome.” https://rb.gy/tfrqo

Alchemy, the Philosopher's Stone and the Ten Capital Sins of businesses. Is Data Science the new Philosopher's Stone? Have we finally learnt how to transform base metals (disorganised data) into gold (useful insights)? But how do we prevent falling into the trap of misusing this process or to waste our resources? Read about the Ten Capital Sins of businesses when it comes to organising and using the data they are paying to acquire, store and analyse - https://vzocca.substack.com/p/alchemy-the-philosophers-stone-and

As companies collect ever-larger amounts of data, they need robust data governance to manage it responsibly. Data governance provides the policies and procedures to oversee data acquisition, usage, and deployment. With proper governance, companies can unlock the value in their data while respecting privacy and security. Rather than simply accumulating data, organizations must have a data strategy aligned with business objectives and guided by governance principles. Effective data governance enables trust and extract maximum benefit from data assets. - https://open.substack.com/pub/vzocca/p/data-governance-what-is-it-and-why

How far are we from true, human level, AGI? Why current LLMs are not yet the solution. Rather than creating a model of the world, they create a synopsis - https://aisupremacy.substack.com/p/how-far-are-we-from-agi

Towards AGI. What is missing?
To achieve AGI it seems likely we will need one or more of the following:
1. Online non-greedy planning
2. A world model
3. Reinforcement Learning Agent
- https://mark-riedl.medium.com/toward-agi-what-is-missing-c2f0d878471a

“Optimization problems like route planning, supplier management, and financial portfolio management are places where quantum’s unique ability to quickly find the optimal solution by analyzing huge amounts of heterogeneous data would work well,” she said. “Classical computers get overwhelmed by exponential calculations when it comes to these enormous amounts of data…AI and machine learning algorithms are perfect candidates for quantum processing.” https://shorturl.at/yDU19

Is it accurate to claim that having more data inevitably leads to better outcomes?Can data be deemed useful unconditionally?Should businesses strive to amass as much data as they can, assuming its intrinsic value?In my view, data holds value exclusively when it can be purposefully employed; otherwise, it could potentially result in escalated expenditures. Businesses must exercise discretion when determining the data to gather and retain, aligning their choices with their precise business requirements; otherwise, they might inadvertently transform into mere data hoarders with no clear business purpose - https://vzocca.substack.com/p/collect-all-data-possible

Researchers are developing new brain-computer technology (BCI) that could one day allow stroke survivors like her to communicate more naturally through a digital avatar that resembles a person. The breakthrough technology was developed by researchers from UC San Francisco and UC Berkeley and decodes Ann's brain signals, turning them into text, speech and facial expressions for a digital avatar for the first time. The research is led by UCSF's Edward Chang, MD, chair of neurological surgery - https://www.youtube.com/watch?v=iTZ2N-HJbwA

“The technique relies on phonons, which are the sound equivalents of particles of light called photons. In the field of quantum mechanics, waves and particles are interchangeable. The research explores the use of phonons to store quantum information because it is relatively easy to build small devices capable of storing these mechanical waves.”

https://t.ly/1VQui

“ Layerwise learning for QNN training involves the gradual addition of individual circuit components during the training process. The circuit’s structure and associated parameters grow step-by-step during training. At the same time, we constrain randomization effects to specific parameter subsets throughout all training stages. This approach not only circumvents the issue of initiating training on a plateau but also diminishes the likelihood of accidentally reaching a plateau during the training process.” https://shorturl.at/qtV06

Data is not a monolithic asset. Data about a company’s customers, for example, can encompass aspects of their purchasing patterns, demographics, financial information, educational background, personal preferences, tastes, and numerous other attributes that define a person. All these distinctions naturally result in data fragmentation. Data integration involves the process of harmonising and combining data from diverse sources to use it for analysis, reporting, and decision-making.
The importance of data integration rests on various grounds:
1. Holistic View
2. Data Quality
3. Business Insights
4. Operational Efficiency
5. Enhanced Decision-Making
6. Data Governance
7. Agility and Innovation
8. Customer Experience
In our modern world, businesses' motto should not be the Roman "divide et impera" but rather "aggregate (your data) and conquer or divide and be conquered" - https://vzocca.substack.com/p/divide-and-be-conquered

Our conscious experience often seems like an accurate representation of reality, but it is actually a continuous process of making predictions and constructing our perception from within. Our senses provide the only access we have to the external world, but our perceptions are imperfect and shaped by our internal processes. Rather than passively receiving information from the outside, our brains actively generate and refine our conscious experience - https://aeon.co/videos/anil-seth-on-why-our-senses-are-fine-tuned-for-utility-not-for-reality

Geoffrey Hinton made headlines by leaving Google to openly discuss the risks associated with AI technology, a move not entirely surprising given the growing concerns in 2023 about AI's potential dangers. Hinton, often called the "Godfather of AI," emphasized that his critique was not directed at Google, his former employer, but rather a cautionary warning that AI could spiral out of control and harm humanity, especially with the rise of powerful language models like OpenAI's. In the first few weeks after he went public, he gave a number of interviews, including with WIRED’s own Will Knight - https://www.wired.com/story/plaintext-geoffrey-hinton-godfather-of-ai-future-ai/

Large language models, explained with a minimum of math and jargon - https://www.understandingai.org/p/large-language-models-explained-with

“In the last few years, classical machine learning models have made revolutionary strides in improving automated predictions. But when researchers tried using them to solve quantum problems, Preskill said, the models often got things right, but their accuracy was not guaranteed. Machine learning typically progresses via trial and error, so you’d need just the right kind of data — and a lot of it — to get useful information.” https://rb.gy/227ik