Is it Possible to Predict the Growth of Technological Progress?

Lately, I found myself thinking more and more about the future. Not in the existential dread, time is passing type of manner, but more out of curiosity regarding humanity’s role in technological progress and promises of future revolutions. AI, nanotechnology, computation, biotechnology, warfare, and medicine are in constant flux.

Futurists (individuals who think and predict the future) work on forecasting the trends and technologies that will shape our world in the years to come, especially as those technologies gain more relevance with the rapid pace of technological progress and the improvements we face as a society. As I was going through resources on emerging technologies, I got a hold of a thought-provoking, albeit extremely optimistic book on the future of AI, neuroscience, and nanotechnology by Ray Kurzweil named – “The Singularity Is Near: When Humans Transcend Biology“.

I’m quite skeptical about Kurzweil’s case for the future of humanity. To me personally, it looks like it is too cheerful, and not cautious enough (not even taking computers into account). But I did gain a newfound appreciation of humanity and the struggles, challenges, and opportunities that lie ahead for us. Of course, I want to thank Mr. Kurzweil for the wonderful book too. I am still reading through it, and I’m impressed by the information provided in it. Although the book is quite dated for the topics it presents the ideas are still relevant as they were 15 years ago when it was first released.

Linear vs. exponential progress

In the first chapter, Kurzweil has an interesting theory.

Human progress is exponential (that is, it expands by repeatedly multiplying it by a constant) rather than linear (expanding by repeatedly adding a constant).

It is true that humans have made incredible progress over the past few centuries, with advancements in technology, medicine, and science transforming our world in ways that were once unimaginable. But the idea that progress increases at an accelerating rate by an exponential factor was wild to me. And the implications that it brings – the fact that the future is hardly predictable based on our viewpoint of the present, it is fundamentally different. As we continue to accelerate our rate of progress, we will see increasingly rapid changes in the world around us, and the pace of change will become increasingly difficult to predict.

The red line represents linear growth, while the green one represents exponential growth

So what is the answer to why hasn’t humanity been aware of its growth earlier? Well, a theory says that in the past, the stage of growth was flat and slow, and the trend for improvement was non-existent. Thus, the future was easily predictable. Someone from ten thousand years ago could with almost 100% confidence predict the next hundred years. On the other hand, someone born in the 1900s would be amazed and stunned in regard to the technological progress in the same time period. We tend to view history in a linear way, rather than recognizing the exponential nature of certain trends and phenomena. In a linear view of history, we assume that things will continue to progress at a constant rate, without considering the possibility of sudden and rapid change.

Paradigm shift

A pretty optimistic scenario Kurzweil presents is the speed at which we are doubling the paradigm shift (a major change in the way that people understand and approach a particular concept or idea) is that we will experience 20 years of relative progress taking the year 2000 as the basis in only seven years in our current year of 2023. One bold prediction that Kurzweil made is the invention of the magic weight-loss pill by the years between 2010 and 2015, which still hasn’t been produced. Back to the treadmill it is! And hands off the pizza!

On the other hand, scientists in 1990 were skeptical of the goal of completing the human genome project in the next 15 years, as they managed to transcribe only a ten-thousand of a genome in a year, yet they managed to do just that by 2003! And let’s not forget about lab-grown meat. Taking these examples into account, we can see that we can correctly predict some future events, and others – although they might seem obvious are still out of our reach. A universal cure for cancer is one of those paradigm shifts, for example.

In order to show the impact of technology today, we can place both biological evolution and technological progress on a single graph plotting the x-axis and y-axis as the numbers of years in the past and the paradigm-shift time we can see a straight line, in which the biological evolution is directly related to human-directed development.

Key events

Now, the key events here are handpicked by Mr. Kurzweil himself. Key events refer to a significant and noteworthy occurrence that plays a significant role in shaping the course of history or a particular area of study. Key events are often seen as pivotal moments that mark a turning point or a critical stage in a particular process, movement, or development. Although the list of key events here is highly subjective, even according to different sources and observations the logarithmic list will appear in the same structure, even if the milestones in question are different. Paradigm changes in the past were radically slow to appear or develop, but now we might only need a decade for a paradigm shift (take the example of the World Wide Web for example).

Some predictions of technological progress

The beginning of the book was amazing, but what I was even more amazed about were the principles that will be the ‘harbingers’ of Singularity and technological progress. I will mention some of them below, with my accompanying comment that went through small research.

Half-way there

• One prediction says that we will have detailed brain scanning and that we’ll reverse engineer the brain in two decades. Although we made new breakthroughs, such as new imaging techniques and computational modeling, the brain is so complex that it will likely take many more years of research and study to fully understand how it works. And, of course, we are still far from understanding how memories are stored and retrieved in the brain or how consciousness arises from neural activity. We have made progress in this field; unfortunately, it is a far cry from this prediction.

• How about emulating human intelligence with supercomputers, and having effective models of human intelligence by the mid-2020s? Well, although we developed AI models that can perform many tasks at a level comparable to or even better than humans, we have not yet been able to fully emulate human intelligence. Some experts believe that it may be possible to develop AI models that can fully emulate human intelligence in the future, using approaches such as neural emulation, which involves creating an artificial neural network that mimics the structure and function of the human brain. However, this is a complex and challenging goal that will likely require significant advances in neuroscience, computer science, and other fields.

• Passing the Turing test is another forecast that still didn’t happen (fully). One question is whether the test is a reliable measure of intelligence and/or consciousness. It focuses only on a machine’s ability to mimic human responses in a specific context. Still, although we did have some explosive breakthroughs in the field of AI, passing the Turing test is still a challenge for the field of artificial intelligence.

  
  

Somewhat there

• Another one says that machines will be able to download skills and knowledge from other machines and humans, which is actually somewhat true nowadays. Using the internet, computers can access sources such as databases, online libraries, educational resources, research papers, and social media sites. With the growth of AI, machine learning algorithms can be trained on large datasets of information to develop knowledge and expertise in specific domains. Interacting with humans poses some challenges though, but even then machines can use natural language processing for direct communication or external devices (smartphones, smartwatches, medical devices). The challenge though is that computers do not have the same level of understanding or intuition as humans. They cannot always interpret or apply the information in the same way as a human would and may require additional programming or training to fully utilize the knowledge they have acquired.

• Machines will have access via the Internet to all the knowledge of our civilization? This one is a no-brainer, nowadays mostly everything is online (physical books and undigitized media are actually in minority now), but as mentioned above the lingering question is the fact of understanding and intuition still is open. How much of this data is understandable by our computers? Will it be dangerous if they manage to understand it on their own?

  
  

Somewhat impossible (for now)

• Now, some of the other predictions delve into science fiction too much. For example, one such example is that machine intelligence will improve on its own in a feedback cycle that human intelligence won’t be able to follow, the speed of those iterations will be faster and faster, making computers intellectually superior to us. Another one is that nanotechnology will enable the manipulation of physical reality at the atomic and molecular levels and that using nanobots we will be able to cure most if not all ailments, that will be able to reverse aging and just leave our work to the machines while the humans tend to other matters. How about visual realities? You might say, we already have them with VR headsets and metaverses. But, the prediction says that the future visual realities will come from the nervous system itself using nanotechnology to simulate our environment using sensory manipulation.

Conclusion

Some of the points above seem really difficult to imagine, and others feel so far-fetched and sci-fi that they almost feel impossible. But if the rule of exponential growth is really true, then maybe all of those points might happen, and maybe earlier than predicted (or assumed). It is our limitations in predicting the future using current linear models that stop us from imagining the future in an exponential way. And we will see if this utopian future comes to fruition. For other tech-related articles please check my personal blog.

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