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ARTIFICIAL INTELLIGENCE

An AI-native strategy firm

Global Advisors: a consulting leader in defining quantified strategy, decreasing uncertainty, improving decisions, achieving measureable results.

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We are hiring experienced top-tier strategy consultants

Quantified Strategy

Decreased uncertainty, improved decisions

Global Advisors is a leader in defining quantified strategies, decreasing uncertainty, improving decisions and achieving measureable results.

We specialise in providing highly-analytical data-driven recommendations in the face of significant uncertainty.

We utilise advanced predictive analytics to build robust strategies and enable our clients to make calculated decisions.

We support implementation of adaptive capability and capacity.

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Thoughts

Global Advisors’ Thoughts: Leading a deliberate life

Global Advisors’ Thoughts: Leading a deliberate life

By Marc Wilson
Marc is a partner at Global Advisors and based in Johannesburg, South Africa

Download this article at https://globaladvisors.biz/blog/2018/06/26/leading-a-deliberate-life/.

Picket fences. Family of four. Management position.

Mid-life crisis. Meaning. Purpose.

Someone once said that, “At 18, I had all the answers. At 35, I realised I didn’t know the question.”

Serendipity has a lot going for it. Many people might sail through life taking what comes and enjoying the moment. Others might be open to chance and have nothing go right for them.

Some people might strive to achieve, realise rare successes and be bitterly unhappy. Others might be driven and enjoy incredible success and fulfilment.

Perhaps the majority of us become beholden to the momentum of our lives.

We might study, start a career, marry, buy a dream house, have children, send them to a top school. Those steps make up components of many of our dreams. They are steps that may define each subsequent choice. As I discussed this with a friend recently, he remarked that few of these steps had been subject of deliberations in his life – increasingly these steps were the outcome of momentum. Each will shape every step he takes for the rest of his life. He would not have things any other way, but if he knew what he knows now, he might have been more deliberate about choice and consequence…..

Read more at https://globaladvisors.biz/blog/2018/06/26/leading-a-deliberate-life/

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Strategy Tools

PODCAST: Strategy Tools: Growth, Profit or Returns?

PODCAST: Strategy Tools: Growth, Profit or Returns?

Our Spotify podcast explores the relationship between Return on Net Assets (RONA) and growth, arguing that both are essential for shareholder value creation. The hosts contend that focusing solely on one metric can be detrimental, and propose a framework for evaluating business portfolios based on their RONA and growth profiles. This approach involves plotting business units on a “market-cap curve” to identify value-accretive and value-destructive segments.

The podcast also addresses the impact of economic downturns on portfolio management, suggesting strategies for both offensive and defensive approaches. The core argument is that companies should aim to achieve a balance between RONA and growth, acknowledging that both are essential for long-term shareholder value creation.

Read more from the original article – https://globaladvisors.biz/2020/08/04/strategy-tools-growth-profit-or-returns/

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Fast Facts

Selected News

Term: Model weights

Term: Model weights

“Model weights are the crucial numerical parameters learned during training that define a model’s internal knowledge, dictating how input data is transformed into outputs and enabling it to recognise patterns and make predictions.” – Model weights

Model weights represent the learnable numerical parameters within a neural network that determine how input data is processed to generate predictions, functioning similarly to synaptic strengths in a biological brain.1,2,4 These values control the influence of specific features on the output, such as edges in images or tokens in language models, through operations like matrix multiplications, convolutions, or weighted sums across layers.1,2,3 Initially randomised, weights are optimised during training via algorithms like gradient descent, which iteratively adjust them to minimise a loss function measuring the difference between predictions and actual targets.1,2,5

In practice, for a simple linear regression model expressed as y = wx + b, the weight w scales the input x to predict y, while b is the bias term.2 In complex architectures like convolutional neural networks (CNNs) or large language models (LLMs), weights include filters detecting textures and fully connected layers combining features, often numbering in billions.1,2,5 This enables tasks from image classification to real-time translation, with pre-trained weights facilitating transfer learning on custom datasets.1

Weights are distinct from biases, which add normalisation and extra characteristics to the weighted sum before activation functions, aiding forward and backward propagation.3,6 Protecting these parameters is vital, as they encode the model’s performance, robustness, and decision logic; unauthorised changes can lead to malfunction.5 In LLMs, weights boost emphasis on words or associations, shaping generative outputs.3

Key Theorist: Geoffrey Hinton

The preeminent theorist linked to model weights is **Geoffrey Hinton**, often called the ‘Godfather of Deep Learning’ for pioneering backpropagation and neural network training techniques that optimise these parameters.1,2 Hinton’s seminal 1986 paper with David Rumelhart and Ronald Williams popularised backpropagation, the cornerstone algorithm for adjusting weights layer-by-layer based on error gradients, revolutionising machine learning.2,4

Born in 1947 in Wimbledon, London, Hinton descends from a lineage of scientists: his great-great-grandfather George Boole invented Boolean logic, his grandfather Charles Howard Hinton coined ‘hyperspace’, and his great-uncle was logician Bertrand Russell. Initially studying experimental psychology at Cambridge (BA 1970), Hinton earned a PhD in AI from Edinburgh in 1978, focusing on Boltzmann machines-early stochastic neural networks with learnable weights. Disillusioned with symbolic AI, he championed connectionism, simulating brain-like learning via weights.

In the 1980s, amid the first AI winter, Hinton persisted at Carnegie Mellon and Toronto, developing restricted Boltzmann machines for unsupervised pre-training of weights, addressing vanishing gradients. His 2006 breakthrough with Alex Krizhevsky and Ilya Sutskever-training deep belief networks on ImageNet-proved deep nets with billions of weights could excel, sparking the deep learning revolution.1 At Google Brain (2013-2023), he advanced capsule networks and transformers indirectly influencing LLMs. Hinton quit Google in 2023, warning of AI risks, and won the 2018 Turing Award with Yann LeCun and Yoshua Bengio. His work directly underpins how modern models, including LLMs, learn weights to recognise patterns and predict outcomes.3,5

References

1. https://www.ultralytics.com/glossary/model-weights

2. https://www.tencentcloud.com/techpedia/132448

3. https://blog.metaphysic.ai/weights-in-machine-learning/

4. https://tedai-sanfrancisco.ted.com/glossary/weights/

5. https://alliancefortrustinai.org/how-model-weights-can-be-used-to-fine-tune-ai-models/

6. https://h2o.ai/wiki/weights-and-biases/

"Model weights are the crucial numerical parameters learned during training that define a model's internal knowledge, dictating how input data is transformed into outputs and enabling it to recognise patterns and make predictions." - Term: Model weights

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Consultants join our firm based on a fit with our values, culture and vision. They believe in and are excited by our differentiated approach. They realise that working on our clients’ most important projects is a privilege. While the problems we solve are strategic to clients, consultants recognise that solutions primarily require hard work – rigorous and thorough analysis, partnering with client team members to overcome political and emotional obstacles, and a large investment in knowledge development and self-growth.

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