RE-THINKING AI AT PANASONIC’S TOUGHBOOK INNOVATION FORUM

Likely due in no small part to popular Generative AI (GenAI) tools that were launched en-masse in 2022-3, AI has been thrust into the public consciousness in recent years. This has prompted an unprecedented wave of interest and experimentation in AI and machine learning (ML) solutions. 

However, while GenAI is hitting the headlines, AI has now matured to the degree where businesses are implementing specific AI solutions within workflows across almost every industry to solve specific problems – but this is somewhat lost in the generalisation of AI. 

For example, AI is now being used to explore how to use large language models (LLMs) to implement machine-based autonomy, enabling machines to communicate with each other and humans.

Reinforcement learning – one of the three principal ML paradigms – is also being used to educate machines on appropriate behaviours in any given environment to maximise output and rewards. Both of these problems use AI, but they are two very different applications of artificial intelligence which help to solve very different problems.

Putting the array of consumer devices such as chatbots and virtual assistants to one side, AI will first have the biggest business impact behind the scenes.

As with any sector that deals with sensitive data or personably identifiable information (PII), the adoption of AI will be cautious in nature and more gradual. LLMs are already being used to streamline the collection and documentation of data, and other administrative tasks that chip away at productivity, in a variety of sectors.

For example, in healthcare and telecare, AI will be used to analyse medical records and information from multiple sources quickly and efficiently, helping medical professionals to quickly identify the required care for patients, which can ultimately help to save lives, or detect early onset of diseases. 

Far from AI actually delivering the care, this is a prime example of AI technologies being used as a decision support tool, enabling humans to use vast amounts of data to derive actionable insights, and ultimately, decide what action to take.

AI adoption is accelerating and impacting a diverse range of sectors in very different ways. As we bear witness to the revolution, we should be wary of generalising and aware of the importance of establishing robust governance around AI use, enabling it to be safely leveraged to support people and processes.