Probably needed to read this at least a decade ago but sorry I'm young
(ok its been a year and i think the above line is still quite funny)
TBA BT notes
The Singularity, if you’ve never heard it, is a term given to a theoretical point in the future when our technology will have become so advanced (compared to today) that it becomes impossible to see beyond it or understand its ramifications.
Let’s be realistic, the explosion of interest in Generative AI (or as some call it – ‘plagiarism machines’ or ‘computer models that can lie’) has brought us no closer to ‘the singularity.’ However, it has made AI more conversational and has the potential to bring more existing information to more people faster. In addition, clearly technology adoption rates have accelerated (at least in some cases). It took Facebook four and a half years to reach 100 million users, ChatGPT reached that milestone in two months.
Notes:
- In 2005, Kurzweil wrote the seminal book The Singularity Is Near. In the years since, the amount of computing power that one can buy per dollar has increased by 11,200 times.
- These aren’t flukes, but rather manifestations of what Kurzweil calls the “law of accelerating returns.” This law states that information technologies become exponentially cheaper over time, as each advance makes it easier to design the next iteration. The operation of this law is seen in the fact that, between 1959 and 2023, the amount of computational power one could buy for a dollar multiplied by 1.6 trillion.
- The pace of change is accelerating, and we’re entering what Kurzweil calls the “sharply steepening part of the exponential curve.”
- the path to intellienge machines or the path of smart intelligence
The Singularity Is Nearer
In 2005, Kurzweil wrote the seminal book The Singularity Is Near. In the years since, the amount of computing power that one can buy per dollar has increased by 11,200 times. In 2005, smartphones and social media were in their infancy. Today, they’re ubiquitous, connecting billions of people worldwide. In the realm of biology, the cost of sequencing a human genome has plummeted by 99.997 percent.
These aren’t flukes, but rather manifestations of what Kurzweil calls the “law of accelerating returns.” This law states that information technologies become exponentially cheaper over time, as each advance makes it easier to design the next iteration. The operation of this law is seen in the fact that, between 1959 and 2023, the amount of computational power one could buy for a dollar multiplied by 1.6 trillion.
The pace of change is accelerating, and we’re entering what Kurzweil calls the “sharply steepening part of the exponential curve.”
-From the rudimentary computers of the 1950s to today’s AI chatbots, the quest for machine intelligence has been a story of breakthroughs, setbacks, and paradigm shifts. It’s a tale of two competing philosophies, technological breakthroughs, and looming questions about the nature of cognition.
- two schools of thought emerged on how to create machine intelligence. The symbolic approach, championed by researchers like John McCarthy, sought to replicate human reasoning through explicit rules and logic. Picture a massive flowchart dictating how to respond to every possible situation. This method showed early promise in narrow domains but quickly hit a wall when faced with the nuances of the real world. Meanwhile, the connectionist approach drew inspiration from the human brain itself. Instead of hard-coded rules, it used networks of simple nodes to learn patterns from data. Frank Rosenblatt's Perceptron, an early neural network from the 1960s, could recognize basic shapes. Yet it struggled with more complex tasks, leading many to dismiss the approach as a dead end.
- For decades, AI research oscillated between these two paradigms, making incremental progress but failing to achieve human-like flexibility. The game began to change in the 2010s with the rise of deep learning. By leveraging vast amounts of data and exponentially increasing computational power, researchers created neural networks with many layers, capable of discovering subtle patterns humans might never notice.
- In 2015, Google’s DeepMind shocked the world when AlphaGo defeated the world champion in Go, a game long considered too complex for machines to master. But this was just the beginning. By 2023, AI systems were writing coherent essays, generating photorealistic images from text descriptions, and engaging in open-ended conversations that could fool human judges.
- Yet for all their impressive capabilities, today’s AI systems still lack two crucial elements of human cognition: contextual memory and common sense reasoning. Contextual memory allows us to maintain coherence in long conversations or while writing extended documents. Current AI models often lose track of context after a few paragraphs, leading to inconsistencies or non sequiturs in longer outputs.
- Kurzweil predicts the singularity will arrive around 2045. This will be a world in which biological and artificial intelligence converges. The distinction between the two will become meaningless as brain-computer interfaces enable us to augment our brains with those of AI, expanding our cognitive capabilities by orders of magnitude.
- The implications of such an event are as profound as they are hard to predict. Would superintelligent AI be benevolent toward humanity, or might it pursue goals misaligned with our well-being? Could we merge with these intelligences, augmenting our own cognitive capabilities?
- Kurzweil sees this transformation unfolding in three phases. The first, already in progress, involves applying our current pharmaceutical and nutritional knowledge more effectively. The second phase, which has just begun, combines biotechnology with AI to accelerate treatment discovery. Imagine designing and testing breakthrough therapies within days using digital simulations, rather than spending years on clinical trials. The third phase, which Kurzweil expects in the 2030s, promises to radically overcome our biological limitations.
- Kurzweil believes that, by the 2050s, we may reach a point where $1,000 worth of computing power exceeds the capacity of the human brain by millions of times. This raises profound questions about the nature of consciousness and identity. As we rebuild our bodies and brains, leaving our natural biological limits in the dust, what will it mean to be human – or post-human? As we gain the power to reshape our bodies and minds at will, what will we choose to become?
TBA SEPERATE
- What is the future of work in the coming decade – and how do we prepare for it? Without understanding the transformative power of artificial intelligence and automation, we could be wasting valuable time and resources trying to prepare for a world that won’t exist.
- For over two centuries, technology has reshaped the landscape of production and employment. In the early nineteenth century, over 80 percent of Americans worked in agriculture. Today, that figure is less than 1.5 percent. Manufacturing employment peaked at 27 percent in 1920, and has since declined to about eight percent. Yet despite these massive shifts, overall employment and living standards have consistently risen, with new industries emerging to replace the old.
- Is the current wave of technological disruption different? Artificial intelligence and robotics are now capable of automating a wide range of cognitive tasks once thought to be the exclusive domain of humans. Self-driving vehicles, for instance, threaten to displace millions of professional drivers in the coming years. A landmark 2013 Oxford University study estimated that almost half of US jobs were at high risk of automation by the early 2030s.
As we look to the future, the potential for disruption becomes even more profound. By the 2030s, Kurzweil expects AI to surpass human capabilities in most cognitive tasks. This doesn’t mean humans will become obsolete, but rather that our roles in the workforce will change fundamentally. We’re likely to adapt by augmenting our own capabilities through direct interfaces with AI and other advanced technologies. Imagine having instant access to the sum of human knowledge or being able to perform complex calculations as easily as you breathe. This human-AI symbiosis will redefine what it means to be “skilled” in the workplace.
- Education systems will need to undergo a radical transformation. Instead of preparing students for specific careers that may not exist in a decade, the focus will shift to developing adaptability, creativity, and the ability to collaborate effectively with AI systems. Lifelong learning will become not just a buzz phrase, but a necessity as the pace of technological change accelerates.
- As we stand on the brink of this technological revolution, our challenge is not just to develop these transformative technologies, but to harness them in ways that benefit all of humanity.
Quotes:
- “But the big feature of human-level intelligence is not what it does when it works but what it does when it’s stuck. —MARVIN MINSKY”
- add others