Reframing AI Narratives: Bridging the Gap Between Technophiles and Technophobes

Artificial Intelligence (AI) is the hottest topic in the world of technology today. That’s because it is making a significant impact on the way we live and conduct business. As we look to the future, the implications of AI for the human race are far-reaching for business, industry and society in general. In this article, I attempt to consider the multi-faceted future of AI, based on insights from experts taking account reports and recent expert talks, and putting these into context with the recently released ‘2024 Gartner AI Hype Cycle’ report.

The Evolution of AI: Past, Present, and Future

The history of artificial intelligence (AI) is one of explosive development, as it has moved through several successive “dogmas” and ideological phases, from being just a concept to developing and milestones, some of which would have been considered previously as science fiction. Understanding the past journey of AI from its early inception to its current state could offer fresh and unique perspectives but also allow us to expand our understanding about what lies ahead and how it would affect us.

1. The Early Days (1950s–1980s): The term artificial intelligence was first used in 1956 at a conference in Dartmouth (hence the ‘D’ in AI), but the origins of the field stretch back to research in the mid-20th century. The initial focus was on symbolic AI, or rules-based AI, using explicit symbols and logic to solve problems. A pioneer in this work, Marvin Minsky, predicted that it would take only a few years before we reached human-level intelligence. That didn’t happen, of course. For one thing, computing power had still not caught up, and – as we now understand it – human cognition is just too complex to be explicitly modelled using rules. Still, the work of this period laid the basis for many of the techniques that are still used in AI today – techniques such as search algorithms, expert systems and early versions of neural networks.
2. AI Winter: 1980s-1990s: Expectations in AI were dashed (with what were called ‘AI winters’) in the 1970s and again in the late 1980s/early ’90s. The hope of a ‘nice grand theory’ that appeared to underlie symbolic AI proved illusory, and scepticism set in. Yet progress continued in machine learning and others areas, and the increased power of computers that emerged in the late 1980s would enable further advances.?
3. The Rise of Machine Learning (2000s–2010s): The big data/real-time data movement, improved computing power, and more capable algorithms fueled a rebirth of AI in the early 2000s, with the subset of AI called machine learning leading the way. For instance, the method of deep learning, one of the countless variations of machine learning, is a computer process that draws inspiration from the human brain and enables machines to recognise patterns and make predictions. This has led to dramatic breakthroughs in image and speech recognition, natural language processing, and many other related tasks, making AI a reality instead of a lofty concept.

Ray Kurzweil, one of the pioneers of AI, is predicting that AI as a general intelligence (AGI) will get exponentially better and more capable by 2029. Over the past six decades, Kurzweil’s predictions have divided opinion, but the progress of AI technologies is incontrovertible.

AI Today? ( 2020’s onwards)

Today AI is deeply infused into work and life in a whole host of ways, with large language models (LLMs) like OpenAI’s GPT-3 and GPT-4o, Gemini 1.3 or Claude 2 Opus from Antropic capable of producing text that resembles the work of humans, or that can be trained to understand natural language in ways that humans can comprehend. LLMs are used to power digital assistants, chat bots, content creation, and translation services. Its effects are arguably most consequential in healthcare, where algorithms diagnose disease, devise personalised treatment plans, and even discover new drugs. During the COVID-19 pandemic, AI was used to accelerate vaccine development and partly responsible for the unprecedented speedy development of antivirus vaccine that helped the battle against the virus. This is exactly the kind of breakthrough that could help humanity respond to global health threats.

In law, AI can optimise workflows by automating work such as document review and discovery, enabling lawyers to better focus on higher-level strategic aspects of their work. Similarly, in education, AI can potentially help democratise access to quality education by providing personalised tutoring and learning support. Current applications of AI cover everything from healthcare and law to education, and include diagnostics and treatment plans, precision medicine, drug discovery and development. While it will not replace lawyers, AI will revolutionise the profession by taking care of repetitive tasks that can be codified, thereby letting lawyers focus on strategic, complex activities. AI can improve education by providing tailored tutoring and learning assistance. This would lead to the democratisation of quality and access to education all over the world.?

The Long-Term Vision: Superintelligence and Beyond

Looking further into the future, the potential of AI applies to more radical enhancements. Musk, Vinod Khosla and others have suggested that AI could be applied in ways that help solve many of the world’s biggest problems. Musk’s Neuralink project hopes to eventually connect human brains with AI through brain-computer interfaces, potentially enhancing lifespans and improving cognition .

Khosla speaks of the ‘revolutionary potential of AI to remake entire sectors and the fabric of our lives’. He envisions AI leading to health care and education that are virtually free, becoming accessible and affordable to all, with ‘AI doctors in your smartphone, personalised learning, and AI-powered economic models that could reshape the economic fortunes of nations’.?

It’s only in this way that our discussions of AI development can be more clearly ethical, and focused on how the technologies can work best for society. As AI develops, it is clear we must balance the benefits of the technology with the risks it could bring, says Ethan Mollick, a professor at Wharton. ?

‘We have to be thoughtful about how we deploy AI, so that it doesn’t just expose us to its risks, but actually enhances human flourishing,’ he says. ‘We don’t want it to end up just furthering inequality.’

Furthermore, the geopolitics of AI are enormous. The US and China (among other powers) are engaged in a competition over AI supremacy. The impact of this competition will be felt in both domestic and foreign policy, economics and ideology, and will shape global policies, economic strategies, and arguably the ideological direction of the 21st century. It’s imperative that AI development remain consistent with democratic values and ethical standards, and this is in our hands.??

AI and the Quest for Longevity

AI is not only able to help treat new viruses but also to help us tackle ageing itself. This means using AI to help delineate the biological processes of ageing, and to identify ways of intervening in these processes, slowing them down, or even reversing them. One particularly exciting field of research is the use of AI to analyse large datasets of biological information to identify the biological markers of ageing. For example, an AI model can be trained to predict an individual’s biological age from a variety of biomarkers, giving a measure of their health and the rate at which they are ageing. Once it is known what leads to ageing, one can intervene to slow down the progression and potentially prolong life. Such interventions might take the form of lifestyle changes, dietary supplements, drugs, and, eventually, gene therapies.

1) The Longevity Escape Velocity : An especially exciting idea that has emerged in the longevity literature is the concept of ‘longevity escape velocity’. The basic idea, originally proposed by the futurist Ray Kurzweil and others, is that there will be a point when we are able to extend human lifespan faster than the rate at which we age. That is, there would be a time when, for every year that passes, your remaining life expectancy would increase by more than a year, effectively allowing you to stay ahead of the ageing process. To achieve this, scientific and medical progress would have to be continuous and rapid. The power of AI and other emerging technologies would accelerate healthcare progress, enabling radical breakthroughs in regenerative medicine, nanotechnology and other fields, allowing us to repair and rejuvenate body and brain at the cellular and molecular level. While this sounds like science fiction, the accelerating progress in AI and biotechnology makes this a real possibility today.

2) AI and Brain-Computer Interfaces: Yet another futuristic AI application in the longevity space is brain-computer interfaces (BCIs). Examples of enhanced BCIs include Elon Musk’s Neuralink and similar projects that seek to augment cognitive capabilities and potentially merge human consciousness with AI. Enhanced BCIs could enable us to upload and digitally store our memories, thoughts and personalities. While the prospect of fusing with AI raises a range of ethical and philosophical issues, the potential benefits are profound. BCIs could allow people with neurodegenerative disorders to preserve their cognitive function. They could also increase human cognitive abilities, or provide a form of digital immortality by preserving our consciousness beyond the biological confines of our bodies

3) Preventive Health and Wellness: AI is playing a significant role in preventive health and wellness. Maintaining good health is essential to healthy longevity. AI can provide real-time feedback on health metrics such as heart rate, blood pressure, activity levels, and more. AI-equipped wearables and smart health monitors can detect signs of health problems early and prompt interventions. AI-based apps can provide dietary and exercise recommendations tailored to individual needs and goals, helping people adopt healthier lifestyles. Preventive measures and early interventions have the power to reduce chronic diseases, improve the quality of life, and contribute to extending lives that are not just longer but also healthier.

The Gartner AI Hype Cycle: Navigating the Peaks and Troughs

The 2024 Gartner AI Hype Cycle graph depicts the evolution of AI technology over time. The Hype Cycle graph illustrates how technologies evolve from the stage of a shortlist to the stage of decline. There are five stages in the Hype Cycle: the stage of the shortlist, the stage of inflated expectations, the stage of disillusionment, the stage of the plateau of productivity, and the stage of a long runway to high productivity.The stage of the shortlist is the starting point of the Hype Cycle graph. At this stage, the technology is on the shortlist. Once the technology is on the shortlist, the Hype Cycle graph moves on to the stage of inflated expectations. At this stage, technology is considered a good innovation. As seen in the Hype Cycle graph, the technology at this stage is evaluated as ‘high’. Furthermore, as it is an innovation, it is expected to grow. At this stage, the Hype Cycle graph shows that the technology will increase its value. Following that, the Hype Cycle graph shows the process of disillusionment. At this stage, the technology is evaluated as ‘moderate’. This means that the technology cannot meet market expectations.Finally, as shown in the Hype Cycle graph, the technology realises its value. At this point, the technology is considered a productive innovation. Additionally, the productivity of the technology grows. It means that the technology is on the way to being a massively innovative solution.

While it’s plausible that we’re now entering the Slope of Enlightenment for GenAI (we’re certainly past the Peak of Inflated Expectations), we’re also witnessing a tremendous amount of hype. By 2024, we’ll start to see real value from projects that leverage other AI techniques, either independently or in conjunction with GenAI, using standardised processes for getting those AI techniques into production. Whenever there’s a new technology, leaders in the AI space will benefit from thinking about composite AI techniques that combine innovations from different stages of the Hype Cycle to build future system architectures. But the larger AI projects become in volume and scale, and the more they proliferate, the more secondary effects start to matter: governance, risk management, ownership, safety, and mitigation of technical debt—at national, enterprise, team, and even individual practitioner levels. While we have some advanced regulatory developments, the actual maturity is still a work in progress. As we’ve seen, there are still many moving parts to this story.

Prominent innovations on this year’s Hype Cycle—notably, AI engineering and knowledge graphs—highlight the need for proven methods to manage AI models at scale. AI engineering is essential for delivering enterprise AI at scale, requiring new ways of structuring teams. Knowledge graphs provide reliable logic for reasoning, versus the fallible but effective predictive capability of GenAI’s deep-learning approaches. Composite AI, AI-ready data, causal AI, decision intelligence, AI simulation, and multiagent systems all point to expanding needs for increasingly sophisticated process and decision automation. And responsible AI, AI TRiSM, prompt engineering, and sovereign AI all point to rising issues of governance and safety as AI expands.

The Dual Lens of AI Hype: Technophiles vs. Technophobes

Technophiles and technophobes, who are in fact talking past each other, debate the nature and trajectory of AI in terms of stark dichotomies. This is how the debate about AI has come to shape the broader narrative, influencing public opinion and policy. It’s therefore important to identify and scrutinise these two contrasting views of technology.

Technophiles: Embracing the AI Revolution

The technophiles, who tend to be positive about AI and are often willing to make big claims about its transformative potential,. Technophiles celebrate AI and the associated benefits that these new technologies will bring, be it in industry, healthcare, education or everyday life. They imagine the potential benefits of AI, as well as some of its potential downsides, and tend to be optimistic about future possibilities of AI augmenting human capacities, and driving innovation, as well as perhaps helping to solve some of the world’s problems.

Key Arguments of Technophiles:

• Improved efficiency and productivity: Technophiles contend that AI can dramatically enhance efficiency and productivity in a variety of fields. For instance, AI can aid the diagnosis of diseases, develop personalised treatment plans and speed up drug discovery in medicine. In the field of education, AI-based personalised learning can improve educational outcomes and allow a larger number of students to have access to higher-quality education.
• Economic Growth and Innovation: AI will improve economic growth and general innovation by freeing up workers from doing low-level, repetitive jobs, the so-called low-hanging fruit of automation. Being freed up from these tasks, workers can focus on more creative, high-value tasks in the innovative economy, such as strategy. This process of changing the structure of the economy can lead to new industries, organisations and jobs, driving economic growth and prosperity.?
• Global problems will be overcome: While some technophiles are concerned about the existential risks posed by AI, many believe that it can help with more tangible problems. For instance, a professor at the University of Pennsylvania’s Wharton School wrote that AI could help address climate change, healthcare access and food security: AI-optimised energy use and AI-driven practices in agriculture and healthcare delivery could vastly improve access and outcomes, in turn creating a more sustainable and equitable world.
• Human enhancement: Technophile visions of the future include projects such as Elon Musk’s Neuralink, which aim to merge human intelligence with AI, as a way of enhancing our cognitive abilities and prolonging life. Such developments are often taken to be steps along the way to a future in which humans will overcome their biological limitations and realise unprecedented levels of intelligence and longevity.

Technophobes: Caution and Concern

Conversely, technophobes regard AI with apprehension and worry, highlighting the attendant dangers and ethical quandaries of its accelerated development and its unintended consequences, and they argue for strong regulation and oversight to mitigate foreseeable harms.

Key Arguments of Technophobes:

Job Replacement and Economic Disruption: One of the core fears of technophobes is that AI will replace jobs, causing economic disruption and income inequality. As AI can replace manpower that previously performed certain tasks, this can lead to a scenario where many workers become redundant, thereby increasing unemployment and income inequality.

• Privacy and surveillance: AI technologies are often based on massive collections of data, and privacy advocates fear that pervasive use of AI will lead to increased surveillance and data collection, potentially undermining liberties. What is the AI to be used for, and who will have access to the data? What are the privacy implications?
• Bias and Discrimination: Bias and discrimination are amplified by the fact that AI systems function as ‘mirrors of the world’, reflecting the same biases present in the data they are trained on. For technophobes, the most obvious danger is that AI will act as a new tool for discrimination in areas such as hiring, law enforcement and lending. This is one of the main, perhaps the most important, problem with AI: how to make its decisions fair and transparent.
• Ethical and existential risks: Even more long-term are the so-called ethical and existential risks of AI, those associated with the possible creation of AI systems that are smarter than humans (Artificial General Intelligence, or AGI). Such systems would raise profound questions about the possibility of control and alignment to human values, as well as the possibility of existential catastrophe brought about by machines whose intelligence eclipses that of their human creators.

The Higher Education Perspective

Nowhere is the technophile vs technophobe dynamic more pronounced than in higher education, where universities are centres of both technological innovation and of critical ethical reflection—the twin fronts of AI discourse.

Engineers and computer scientists see opportunity in AI research,and seek to claim it by developing new algorithms, applications and technology. These departments celebrate AI’s potential to transform fields such as robotics, data analysis and computational biology. However, business schools and economic departments seek to concentrate on the strategic and economic imperatives: how AI can build growth, increase competitiveness and open new markets, and how businesses need to embrace AI if they want to stay in business.?

As AI systems become more commonplace in day-to-day life, humanities and social sciences departments frequently highlight ethical and governance issues, including the societal implications of AI systems. They advocate for responsible development of AI through ethical and governance guidelines, transparency, and accountability in AI systems. A second cluster of social implications, which is also one of the more established areas, focuses on the social impact of AI. Scholars in this cluster, primarily working in the fields of sociology and anthropology, look at how AI affects work, identity, and human relationships, press for AI policies that are inclusive and equitable, and take into account the views of diverse stakeholders and vulnerable populations.

The Role of Social Domain and Reframing Perspectives

It is also a result of the fact that many of the voices in the AI debate may be very distant from current AI technologies. These ‘AI experts’ are often filtering the social discourse through the lens of their professional work, translating social narratives into technical jargon to feed the machine. This is why it’s important to reflect on the sources of AI commentary, and what motivates different voices.

But we can also see this at work in the academic and industry worlds, where competitive narratives are at play, as disciplines and experts clamour to be seen as in the vanguard of new AI, cloud computing and metaverse technologies, all fodder for AI-powered machine learning. This kind of hyperbole is nothing new. Just look back at the hype surrounding new fields and technologies in the 1990s and 2000s.

AI's Future: Balancing Hype and Reality

Last but not least, the more we can harness the promise of AI, while minimising its risks, the better the future will be for all. They need to be able to distinguish between hype and reality, and think about applications likely to make a meaningful impact and lead to sustainable development. Responsible adoption should go hand in hand with responsible practices, including discussion and consideration of possible ethical implications of AI technologies. Who should develop and deploy AI technologies? And for what purposes??

Embracing the AI Future

AI’s future will be what we make of it. In this transformative era, we need to create an inclusive and open discourse on the ethical, societal and geopolitical aspects of AI. If we use AI responsibly, we can ensure a future where technology amplifies human potential, tackles global challenges and improves the lives of people everywhere.? It will require staying abreast of the latest developments in AI. Business leaders and policymakers, as well as individuals, will need to move beyond a passive stance and become more proactive in learning about AI and how it will impact us.