Exploring the Future with Ray Kurzweil (Part 4)
Ray Kurzweil’s Predictions up to 2009
[Note: Look here for Part 1: Introducing Ray Kurzweil and Part 2: The Law of Accelerating Returns (Explained) and Part 3: Ray Kurzweil’s Six Epochs of my series on Ray Kurzweil.]
Since introducing you to Ray Kurzweil in Part 1 of this blog series, I hope parts 2 and 3 exposed you to Ray’s unique perspective on technology, evolution, the universe, and if I was successful, at least piqued your interest in this amazing scientist.
So now let’s delve into the heart of his vision: the predictions that have made him both a celebrated and controversial scientist! I can’t claim this is an exhaustive list. It seems every time I Googled his predictions, I found something new. But I think I have captured enough to make it an interesting read.
For ease of reading I have listed his predictions in table format. The first table below contains his predictions for things that should have come to pass by the year 2000; for example, predictions regarding wireless networking, online education, wearable tech, etc. This is followed by predictions that should have happened by 2009; for example, biotech, 3D printing, cloud computing and more.
The second table, in Part 5, contains his predictions for the 2020’s, 2030’s and beyond, which is where things get more interesting because they are things that are now in our future. For example, nanotech, virtual reality, brain-computer interfaces, fusion power, quantum computing, uploading consciousness to a machine and more. I rate each prediction for accuracy and include notes for commentary.
Just to reiterate, Ray Kurzweil is not some kind of Nostradamus. He’s not a prophet. He’s not a quack of any kind. Does he have some mind-bending ideas about what our future holds for us? Yep, he sure does! But that doesn’t make him crazy or mean he should be looked at with derision. What Ray is attempting to do is to paint a vision of the future of humanity, a future that is far beyond our current imagination. Whether we agree with him or not, we can’t fault him for his effort.
When you read Part 5 and finish this series, perhaps you will join me in pondering whether we are truly on the verge of a transformative era, or whether Ray’s predictions are simply the product of an overly active scientific optimism. Hmm.
I hope you will continue to join me as I further explore the fascinating and unsettling possibilities of AI in the future!
| PREDICTION: By 2000 | ACCURACY | NOTES |
|---|---|---|
| Computer processing speed: A $1,000 personal computer will be able to perform about a billion calculations per second. | Correct | This actually came true by 1999, in large part due to Moore’s Law, which I wrote about in Part 2: The Law of Accelerating Returns. |
| Wearable and embedded tech: Personal computers will be available in a wide range of sizes and shapes, and are commonly embedded in clothing and jewelry such as wristwatches, rings, earrings and other bodily ornaments. | Some say he was partially correct, but I’m going to call this incorrect! | Computers did become smaller and some did become more wearable by 2000, but even today they are not yet ubiquitous in clothing and jewelry. According to a report by Statista, in 2020 global shipments of wearable computing devices were about 444.7 million units, barely a fraction of the world’s population. Plus, most wearable devices still rely on smartphones or other external devices for connectivity and functionality and really aren’t capable yet of performing sophisticated applications. Oh, this is coming, to be sure, but we’re not there yet! |
| Data storage: Rotating memories (such as hard drives) are no longer used in personal computers. | Incorrect | Hard drives were and are still widely used, although solid-state drives are becoming more popular as they continue to drop in price. |
| Wireless computing: Cables are disappearing. Communication between components uses short-distance wireless technology. | Partially Correct | Wireless technology has become more prevalent, but cables are still used for some purposes, such as power supply and high-speed data transfer. Just look at the cable mess behind my desktop computer! |
| Speech recognition: Most text is created using continuous speech recognition. | Incorrect | Speech recognition improved, but even today most text is still created using keyboards or touchscreens. |
| Narrow AI agents: The majority of web transactions are conducted by intelligent agents acting on behalf of human users. | Incorrect | I remember working at HP in the 90’s when intelligent agents were being predicted left and right. But, they never materialized! Intelligent agents are not yet sophisticated enough to handle complex web transactions without human supervision. |
| Document creation and retrieval: Documents are routinely created in multiple versions, including auto-abstracts. | Partially Correct | By 2000, some documents, such as academic papers and news articles, were often accompanied by abstracts or summaries, but this was not a universal practice. However, with today’s LLMs, getting a document abstract merely requires that you request one for any document and you will get it. |
| Online education: Most learning is accomplished through intelligent, adaptive courseware presented by computer-simulated teachers. | Incorrect | This certainly didn’t happen by 2000, and even today computer-based learning, while increasing, still relies on human teachers and traditional methods. You see the trend though, right? |
| Computer chip depth: The number of transistors on a chip has exceeded 50 million. | Correct, or at least close enough by 2000 that I’ll give it to him. | The Intel Pentium III processor, released in 1999, had 9.5 million transistors. The Intel Pentium 4 processor, released in 2000, had 42 million transistors. The Intel Core i9 processor, released in 2017, had over 2 billion transistors. Today, Intel’s newer Core i9 processors contain up to 26 billion transistors! |
| Medical treatment for cancer: Bioengineered treatments for cancer and heart disease have greatly reduced the mortality from these diseases. | Incorrect | Bioengineered treatments, such as immunotherapy and gene therapy, have shown promising results for some types of cancer and heart disease, but have not yet become widely available or effective for all cases. This prediction was too optimistic and sadly, premature. |
| The internet: The term “surfing the Internet” is archaic, as the distinction between the physical and virtual worlds is blurred. | Incorrect | It’s hard to believe, but the phrase, “surfing the internet,” was coined way back in 1992 by Jean Armour Polly for the University of Minnesota Wilson Library Bulletin. I remember surfing the net at that time myself via systems at HP in St. Paul, although the only sites I could reach were some major corporations and colleges and universities. There was a fun application called “Internet Roulette,” which would take you to a random internet site after a spin of the wheel. Fun! The term “surfing the Internet” is still used, although less frequently than before. According to Google Trends, the term peaked in its usage in 2004 and has since dropped off. The distinction between the physical and virtual worlds is still clear, although augmented reality and virtual reality technologies are advancing. |
| Beating a chess grandmaster: Ray predicted that a computer would be advanced enough to beat a world chess grandmaster playing chess. | Correct | This feat was actually accomplished in 1996 by IBM’s Deep Blue computer. Such an event did not happen in a vacuum. Scientists had been developing computers to play chess for some years, and believed beating a chess grandmaster to be a worthy target that would demonstrate artificial intelligence prowess. However, none but Ray believed it would happen as soon as the year 2000, most were years past this in their prognostications. |
| PREDICTION: BY 2009 | ACCURACY | NOTES |
| Rise of Cloud Computing: The widespread adoption of cloud-based platforms, providing ubiquitous access to computing power and information. | Partially correct | By 2009, cloud computing services like Amazon Web Services, Google Cloud Platform, and Microsoft Azure were already established and experiencing significant growth, showing the trend towards centralized, on-demand computing resources. Businesses and individuals also began to increasingly rely on cloud-based software and services for various tasks, demonstrating the shift towards a model where users access computing power and data remotely. Kurzweil's general vision of interconnected web servers providing ubiquitous computing resources aligned with the core principles of cloud computing. However, While cloud computing was gaining traction by 2009, it hadn't yet reached the level of widespread adoption and dominance he may have envisioned. On-premises computing solutions still held a significant share of the market. Ray's predictions might not have fully captured the diverse range of cloud computing services that emerged, such as Platform as a Service (PaaS) and SaaS (Software as a Service), beyond just Infrastructure as a Service (IaaS). |
| Personalized Medicine: Genetic analysis and advanced diagnostics enabling tailored healthcare based on individual genetic profiles. | Partially correct | By 2009, genetic testing capabilities had become more accessible and affordable, leading to a rise in individuals undergoing analysis for a variety of purposes, including healthcare. Some targeted therapies based on genetic variations were already in development or clinical trials for specific diseases, showcasing the potential for personalized medicine. Additionally, the field of pharmacogenomics, researching how genetics influence drug response, was gaining traction, highlighting the potential for tailoring medications based on individual needs. However, while genetic testing was growing, widespread integration of genetic data into clinical practice for personalized treatment plans remained limited in 2009. Concerns about data privacy, ethical implications of genetic discrimination, and lack of standardized guidelines for incorporating genetic data into clinical decision-making posed significant hurdles. And genetic testing and tailored therapies could still be expensive, limiting their accessibility and hindering widespread adoption in personalized medicine. |
| 3D Printing Revolution: 3D printing evolving beyond prototyping into mainstream manufacturing, allowing customized object creation on demand. | Partially correct | Between 2000 and 2009, 3D printing technology made significant strides: Printer speeds increased, resolution improved, and material options expanded, making it more versatile and efficient. The technology began to be explored for functional applications in various industries, including aerospace, automotive, medicine, and prosthetics. However, 3D printing in 2009 remained largely confined to niche applications and hobbyists. The cost of high-quality printers and materials was still high, limiting its accessibility and widespread use in mainstream manufacturing. Yet, while printers were improving, they still faced limitations in speed, quality, and scalability, hindering their competitiveness with traditional manufacturing methods. The ecosystem for 3D printing in 2009 was nascent. Standardized material formats, design software, and supply chains were yet to be fully established, hampering mainstream adoption. |
| Biotech Breakthroughs: Stem cell therapies and genetic engineering leading to significant advancements in regenerative medicine and treatment of currently incurable diseases. | Partially correct | By 2009, stem cell research had made considerable strides, with successful applications in areas like bone marrow transplantation and corneal regeneration. Clinical trials for gene therapy treatments for certain diseases like cystic fibrosis were also underway, showcasing the potential for genetic manipulation to address previously incurable conditions. However, despite progress, most potential stem cell and gene therapy applications remained in experimental stages by 2009, with limited availability for widespread clinical use. Concerns surrounding safety, ethical implications, and regulatory hurdles posed significant roadblocks to the widespread adoption of these technologies. Lastly, bridging the gap between promising research findings and translating them into effective, approved treatments for patients proved to be a longer and more complex process than initially anticipated. |
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Full disclosure: I can’t claim to have come up with the title of this article by myself. I read it somewhere and honestly, the statement was made about the very near …
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