Insights · June 17th, 2024
Back in 1995 I was a keen student studying for a BSc Applied Psychology and Computing. I had opted to specialize in organizational psychology, linguistics and a rather nascent field of study into artificial intelligence (AI). These were the days of lower power computers, limited data infrastructures and the academic idea that human-level ‘intelligence’ – still a wildly ill-defined term – could have far reaching effects.
Today, we find ourselves in an all-consuming hype bubble on the promise of efficient and automated futures with Artificial Intelligence (AI). But, how did we find ourselves in this situation?
A not-so-brief history of AI developments
In the 1990s – beyond the AI winters – we saw the early promises, foundations and data Innovations.
In 1997 we saw IBM’s Deep Blue Defeats Garry Kasparov – Deep Blue, a chess-playing computer, famously defeated world chess champion Garry Kasparov in a six-game match. This victory marked a significant milestone in AI, demonstrating that machines could outperform humans in complex, strategic games.
At the same time we saw the birth and acceleration of the Internet and generation of new online business models, platforms, vast amounts of data, laying the groundwork for data-driven AI models.
Then we entered the 2000s with vastly improving data infrastructure and compute power meeting more powerful machine learning and sensor-based capabilities.
In 2002 we saw the dawn of the first autonomous car, developed by the Stanford Racing Team, completed a 7-mile drive on a California highway. This was an early step towards the development of autonomous vehicles. Then around 2006, one of the ‘Godfathers of AI’ Geoffrey Hinton, and his colleagues, revived interest in neural networks with the introduction of deep learning, leveraging multiple layers to improve the learning process. This breakthrough addressed many limitations of previous AI models.
The stories were fairly academic and interesting. The world was not really tuned into all of these developments.
Then in the 2010s we gained pace with a number of notable milestones and the beginning of the hype as development teams collided with marketing functions to say – THE FUTURE IS HERE!
- 2011: IBM Watson Wins Jeopardy! – IBM’s Watson defeated two of Jeopardy!’s greatest champions, demonstrating the ability of AI to understand and process natural language, as well as to generate human-like responses.
- 2012: AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, won the ImageNet Large Scale Visual Recognition Challenge by a significant margin. This victory showcased the power of deep convolutional neural networks (CNNs) in image recognition.
- 2014: The Rise of Generative Adversarial Networks (GANs) – Ian Goodfellow and his team introduced GANs, a new class of machine learning frameworks where two neural networks, a generator and a discriminator, compete against each other. This concept has since led to significant advances in image generation and other domains.
- 2015: DeepMind’s AlphaGo defeated world champion Go player Lee Sedol in a five-game match. This victory was a landmark in AI, showcasing the potential of reinforcement learning and deep neural networks to tackle complex, strategic games.
- 2018: AI and Healthcare – DeepMind’s AlphaFold made significant advances in protein folding, a challenge that had puzzled scientists for decades. This breakthrough has the potential to revolutionize drug discovery and biology.
- 2019: The Rise of AI in Everyday Life – AI technologies began to integrate more deeply into daily life, from virtual assistants like Siri and Alexa to AI-driven recommendations on platforms like Netflix and YouTube.
And now, in the 2020s, we have ascended into a fervent interest in what AI can do and what its potential impact as it expands and integrates across domains.
In 2020, OpenAI released GPT-3, a language model with 175 billion parameters, capable of generating coherent and contextually relevant text across a wide range of topics. This model demonstrated the profound capabilities of large-scale transformer networks. Around this time, notable AI researcher Timnit Gebru was controversially fired from Google after raising concerns about the company’s approach to ethical AI and transparency. Her departure sparked a significant debate within the AI community and tech industry about corporate responsibility, diversity, and the treatment of researchers who raise ethical concerns. This debate rages on and refuses to be obscured by the hype.
Today, and what comes next
We now see significant investment and developments in AI and…
- Robotics and Autonomous Systems
- Creativity and Art
- Ethics
- Multimodal AI
- Artificial General Intelligence (AGI)
But, it’s generative AI that has captured business leader’s the public’s imagination with its promise of efficiency, increased throughput and potential to replace humans in the creation of creative content. The sheer amount of discussions and mentions on the web is incredible.
The reality of hype, and our futures
Hype around AI has accelerated due to:
- Breakthrough announcements
- Novelty and innovation
- Promises of future Impact and wild predictions
- Marketing, public relations and strategic announcements
- Investment, new startup ecosystems and Financial Interests
- Public fascination, speculation and fear
- Sci-Fi and pop culture
- Influential figures and thought leaders
- Economic and societal context
To be honest, it’s tiring, and in the speaking world everyone talks about AI and 99% of folks stay on the surface level without getting into the critical thinking needed when making any kind of technology investment.
A $20 a month investment into ChatGPT is not the establishment of an organizational capability, it’s an edge case of a hopefully useful tool in a stack of technological capabilities.
Regardless, the field – beyond the limited impact of generative AI – is interesting. The wider application of Artificial Intelligence will have impact across so many parts of life once it becomes cheaper and more commoditized over time with practical and proven applications. We’re talking 10+ years of investment and developments still needed before we really gain solid and affordable capabilities. This is where the hype dies, replaced with valuable applications that have been proven, and can be trusted.
At that stage many of the over-hyped companies will have their IP acquired, their teams acqui-hired with their tech shelved, or just faded into insignificance. In reality, the story of Artificial Intelligence is in its early stages after all of these years of speculation and development. We’ll be in early stages for many more years.
However, with that idea of change and capability taking time, I do think that we have to lean into the idea of Amara’s Law – ‘We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.’
If you want to improve your understanding and critical thinking in the area of Artificial Intelligence and SHIFT FROM HYPE TO HOPE then reach out to Nikolas Badminton to discuss your event.
More of Nikolas Badminton’s critical thinking on Artificial Intelligence and our futures that informs hopeful executives and government leaders:
About Nikolas Badminton
Nikolas Badminton is a world-renowned futurist speaker, consultant, award-winning author, media producer, and executive advisor that has spoken to, and worked with, over 400 of the world’s most impactful organizations and governments.
He helps shape the visions that shape impactful organizations, trillion-dollar companies, progressive governments, and 850+ billion dollar investment funds.
Nikolas Badminton’s book Facing Our Futures: How Foresight, Futures Design and Strategy Creates Prosperity and Growth was named Top-50 Business Books of 2023 by The Next Big Idea Club, and selected for 2023 J.P. Morgan Summer Reading List, and featured as the ‘Next Gen Pick’ to inform the next generation of thinkers that lead us into our futures. Reach out to Nikolas to discuss having him come and speak at your event or board retreat – click here.
