Enablers for the agile topology optimization for businesses

1. Data, data, and more data: The rise of synthetic data into an already big data world

Synthetic data, simply put, is data artificially generated by an AI algorithm that has been trained on a real data set. The goal is to reproduce the statistical properties and patterns of the existing dataset by modeling its probability distribution and sampling it out.

The algorithm essentially creates new data that has all the same characteristics of the original data – leading to the same answer – but, crucially, it’s impossible for any of the original data to ever be reconstructed from either the algorithm or the synthetic data it has created.

So if we know the intended outcome (doughnut shape), we can leverage synthetic data to understand the features and their respective properties required for the outcome.

The major advantage with this emerging enabler for businesses is the security (no data privacy concerns), the speed and scalability that this offers to your research, and innovation capabilities. Simulating target outcomes is greatly enabled.

2. Developments in algorithmic power and explainability

Artificial Intelligence (AI) affords us a great opportunity to capture the value from the developments of the digital ecosystem. It is able to help us understand and predict future outcomes from heaps of data (real and/or synthetic) by applying the algorithmic power of machine learning – supervised, unsupervised, deep, and reinforcement learning.

AI is able to do this with speed, accuracy, and reliability but requires strong governance and ethical leadership. The precision when trying to land the topological effect is crucial. One family of AI models, neural networks, is notorious for high levels of accuracy that we’ve seen power recent developments of technologies such as Generative AI. I explain the phenomenon of these models to paint a picture of the powerful combination of AI and big data from existing and synthetic data.

Generative AI leverages Generative Adversarial Networks (GANs) - a type of neural network that can generate new data that is similar to a given data set. The idea is to train two neural networks, a generator, and a discriminator, in a game-like framework.

The generator takes random noise as input and generates new data that is similar to the training data set. The discriminator takes both real data from the training data set and generated data from the generator and tries to distinguish between them. The generator tries to fool the discriminator by generating data that is so realistic that the discriminator cannot tell it apart from real data.

The entire system can be trained with backpropagation. As the two networks play this game, they both improve over time. The generator gets better at generating realistic data, and the discriminator gets better at distinguishing between real and fake data. Eventually, the generator becomes so good that it can generate new data that is indistinguishable from the real data in the training data set. GANs have been used to generate realistic images, videos, music, speech, and text.

These innovations have also received a major boost on the explainability front. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) address this issue by proposing a new AI method that utilizes automated interpretability agents (AIA) built from pre-trained language models to autonomously experiment on and explain the behavior of neural networks.

This implies that the ability to explore the most advanced AI techniques with greater precision and explainability is unlocked going forward. Application for unorthodox revenue opportunities, cost optimization, customer experience improvement, employee productivity gains, and risk management will be enhanced to enable business growth and transformation.

3. Computing power and smart adoption

“Compute is going to be the currency of the future. Maybe the most precious commodity in the world” – Sam Altman, OpenAI CEO. This explains the reported plans by Microsoft and OpenAI to build a $100 billion data center set to power Artificial General Intelligence (AGI) “reality.” Nvidia has also revealed the Blackwell B200 GPU, the “world’s most powerful chip” for AI. These and other investments into AI-related advances such as nuclear energy and regulatory developments paint a bullish picture for the future of AI. This will drive the democratization of edge and quantum computing capabilities for most AI systems in the near future.

This will impact the training, fine-tuning, and performing inference on LLMs. Computing is an intensive task that involves the processing of massive datasets. This will power accelerated developments in AI. At a local level, the ability to adopt and customize the open source models using smaller, more efficient, and specialized models will yield the best of both worlds that support AI’s role in business transformation — powerful AI capabilities and better data and privacy control.

Level-set: What Exactly Do We Mean By Generative AI?

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