It’s about exponential efficiency and improvement
Gartner recently described generative AI as one of the most disruptive technologies ever: “Generative AI will progress rapidly in both scientific discovery and technology commercialization. Generative AI [adoption] is high, because exploration of generative AI methods is growing and proving itself in a wide range of industries, including life sciences, healthcare, manufacturing, material science, media, entertainment, automotive, aerospace, defense and energy.”
McKinsey predicts that generative AI “could add the equivalent of $2.6 trillion to $4.4 trillion annually across 63 use cases we analyzed. By comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.” The applications are almost limitless.
The impact of generative AI
It kind of sounds like 1989, when Tim Berners-Lee wrote “Information Management: A Proposal,” laying out the structure and theory of the web. That invention, of course, changed our business and personal lives so dramatically, it’s impossible to imagine life without it.
Generative AI is about to do the same. Traditional AI has already transformed businesses with the superpower to ingest vast data sets, identify salient patterns, and make decisions and predictions based on that data.
Now large language models can present LLM data in a more naturalistic, human-sounding fashion. Along with progress in transformer-driven deep neural networks, LLM data models have paved the way for generative AI platforms, including ChatGPT, Bing Chat, Bard, LLaMA, and DALL-E. These technologies are unique – they also learn patterns from the input training data but have the additional capability to generate new data with similar characteristics as the training set.
Efficiency and improvement
This content “generation” is the magic. The optimization loop of generative AI delivers amazing efficiencies when detailed data is supplied. This enables AI to identify complex patterns that are either too large for a person to digest or so unrecognizable that they wouldn't see them. Generative AI’s detailed pattern recognition creates significant efficiencies in processes, with results as detailed as the dataset we provide. And as generative AI, LLM data and training models continue to gain capabilities, efficiencies grow.
Even beyond efficiencies, the AI optimization loop delivers instructional improvement much, much better and much, much faster. The time saved with efficiencies can be leveraged for improvement, as AI becomes a bespoke teacher catering to your personal learning style. This exponential improvement means you could design a safer EV with a longer range or learn to play the piano like Beethoven.
While there are justifiable concerns about the potential misuse of generative AI, including Intellectual property and deepfakes, the possibilities for good are overwhelming. For example, in a recent Wired article, Kingston University researcher Oded Ben-Tal, pointed out that generative AI shouldn’t be about stealing a composer’s IP. It’s a tool more like turntables — when artists discovered they could use turntables to scratch records and sample their sounds, they created whole new genres. It also has the potential to significantly reduce biases in creating creative assets. By using machine learning algorithms, generative AI can analyze vast amounts of data and generate new content that is free from human biases. This technology can help to eliminate the unconscious biases that often creep into the creative process, such as gender, race, and cultural stereotypes. Additionally, generative AI can create content that is inclusive and representative of diverse perspectives, which can help to promote greater equity and enrich life for all.
Like the internet, generative AI will transform our businesses — and our lives. Soon, it will be impossible to imagine life without it.
