How Simulation Transforms Industries

How Innovation Works

In his book “How Innovation Works”, Matt Ridley debunks the myth of the creative genius. The same challenges arise across industries and economies, leading numerous companies to seek solutions. The winners aren’t those who get there first, but those who see the full potential of a product.

Innovation is most often close to market, solving small problems and unblocking bottlenecks. If you face a problem then others do too, and if there is no market solution you create one. Most bottlenecks in small businesses have been faced by others and relieving them requires a mindset shift to change the ways of working.

A handful of companies create knowledge and turn it into products. NVIDIA is an example, developing software services to enable clients to solve problems on its hardware. Its business model is partnering with industry leaders to ensure the services are valuable.

At CES last week, NVIDIA CEO Jenson Huang name checked Meta for transforming enterprises. Its Llama open source models are foundational for the AI reinventing corporations and NVIDIA hopes to do the same for robotics.

Adapting Llama models is too expensive for small businesses, but off-the-shelf tools will provide the means to redesign workflows. Last week we looked at some ways this is happening. Today we’ll explore the more substantial changes already underway.

Real-time Decision Making

The best business idea is meeting a customer’s needs. Scaling that idea requires interpreting feedback. To do this, managers want to be making data-driven decisions.

Right now, data-driven means using historic behaviour to project future demand. AI changes that across a range of industries. Data will be real-time and predicted, allowing companies to make informed decisions about customer demand, cash flow generation and product performance. Processes will be recreated from ground level.

Synthetic data holds the key to this transformation. Tech leaders talk about running out of data, but what they mean is there is not enough useful data to train models. AI generates relevant data by making minor adjustments to real world text, audio and video. What will companies do with this data?

Scaling for Simulation

Last week Huang talked about two new ways of scaling intelligence beyond training data. The first uses AI to critique AI in a process known as reinforcement learning and the second is reasoning. This means scenario testing solutions at the speed of machine thought, which requires the massive compute NIVIDA’s hardware is designed to handle.

As an example, Huang says robots need three models – one to train, one to deploy and one to simulate. That third one is where the transformation occurs.

Training equips robots with core skills, while the decision about what to do next uses real-time scenario testing. At a junction, a car can simulate outcomes and take the best course of action. This on-the-job training will be what allows driverless cars to function in crowded cities.

Expensive heavy trucks are being retrofitted as autonomous vehicles monitored through digital twins and remotely controlled. Humanoid robots working production lines are a few years away, but they can haul rubbish into skips and load vehicles in the factory yard. What’s missing is enough video of people doing this. Humans create this using virtual reality and AI generates multiple versions with slight variations for comprehensive training datasets.

This idea is not limited to robots. Synthetic data is used to model protein behaviour for drug discovery, control traffic flow on roads and in stadiums, and test customer service agents for bias. Simulation allows real-time adjustments to behaviour and, for example, may be how automated medical assistants develop their bedside manner.

A New Internet

It’s a common misconception that Microsoft missed mobile. On the contrary, it attempted to shoehorn Windows onto mobile, but this required too much memory and was abandoned in 2017. 99% of cellular phones use either Apple iOS or open source Android, which were purpose built for mobile.

Generative AI promises a new operating system for the internet. This will choose and perform actions at a moment in time, obliterating the idea of traditional applications. It may also restore the internet to its original conception as a decentralised communication network, before Google, cloud service providers and the large telcos took control of traffic.

A related use case is a decentralised energy grid. Renewables and storage are turning energy into a technology industry, where prices fall through time. An AI operated grid will balance load and provide energy independence to small communities.

Wearables may be the entry point for a GenAI operating system. Meta has shown with Llama that it has no intention of being trapped by a competitor as it is with iOS. Meta Quest has its own operating system built on Android and optimised for virtual reality.

At the moment, GenAI is built into operating systems, for example with Windows Copilot. Meanwhile NVIDIA customises Linux into DGX OS for AI workloads on its hardware. The need for low latency and energy efficiency to run AI at device level, may be the trigger for the next generation internet.

Questions to Ask and Answer

1. Does my business lack data in critical areas?

2. How might real-time decision making change my industry?

3. Are we dependent on technology that generative AI renders obsolete?

When you are ready there are three ways I can help:

1. Schedule a call to talk with me and my partners about your AI needs.

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