Singularity Club: Living With ASI
Have we lost privacy forever? What can we do to recover some?
In addition to the weekly free posts, today we officially launch:
Singularity Club Only!
These posts are strictly for the Singularity Enclave — please do not redistribute the content of this post.
Most of our Singularity Enclave posts are going to be:
Tactically focused on problems or opportunities emerging in the Singularity
Longer than the free posts (targeting 30 to 45 mins, up from 15-min)
Giving you something to walk away with → a tool, a mentality, a link/resource
Let’s dig into the advanced material…
The Dawn of Omnipresent ASI
Imagine waking up in a city where every aspect of the infrastructure is controlled by an artificial intelligence. Your home automatically adjusts the temperature and lighting based on your habits. Your commute is directed by intelligent traffic signals that analyze real-time data to optimize flow. Even the cafes and restaurants you visit tailor recommendations and portions to your tastes and health data.
This AI-powered city is monitoring your actions, predicting your needs, and shaping your experiences - all without direct human oversight.
While this may seem like science fiction, the building blocks are already being assembled through the proliferation of smart devices and the Internet of Things (IoT).
As this network of connected sensors and gadgets grows more advanced, it lays the groundwork for Artificial Superintelligence (ASI) - AI capable of surpassing human intellect.
The implications of ASI have long been debated by futurists and technologists.
But perhaps the most pressing question as we approach the era of omnipresent ASI is: what becomes of privacy?
Unlike human intelligence, an ASI would have unlimited processing capacity and access to the exploding data produced by an IoT-enabled world.
Every minute interaction, preference, and habit could be surveilled, analyzed, and potentially exploited. Our inner thoughts and feelings might no longer be our own private introspection but rather an open book of stimuli and responses mapped by an ASI.
Some hypothesize that privacy as we know it will become obsolete in the face of an omniscient artificial intellect. Others propose new frameworks to balance transparency and freedom. As individuals, we may be forced to redefine our relationship with privacy.
But as a society, we have the opportunity to shape the norms and regulations that will govern ASI and our data.
The choices we make today will determine the society we live in tomorrow.
Our future may depend on having an open, participatory discussion about privacy - before it slips through our fingers.
…and because this is the Singularity Enclave… we’re going to discuss very tactical methods & systems we can deploy to regain some of our precious privacy.
The Spiderweb of IoT: A Foundation for ASI
In order to grasp the privacy implications of ASI, we must first understand the indispensable role the Internet of Things plays in enabling its development. The IoT refers to the growing network of internet-connected sensors, devices, and systems embedded in our physical environments. It encompasses everything from smart home gadgets to autonomous vehicles. While the consumer side of IoT is increasingly visible, even more pervasive is the web of sensors in public infrastructure like traffic lights and power grids that transmit data continuously.
By 2025, projections indicate over 30 billion IoT devices will be deployed worldwide. These connected devices produce a torrent of data reflecting how we live our lives and interact with our surroundings. As computing pioneer Mark Weiser noted, the most profound technologies are those that “vanish into the background" while quietly collecting information about humans and their behavior. The IoT enables our physical world to merge with the virtual, transforming ordinary objects and places into sources of data.
From an AI perspective, this physical world data provides invaluable contextual information about human activities that can't be gleaned from abstract online data alone. The IoT essentially functions as a capillary network, delivering real-time sensory input from both private and public environments to feed the development of ASI. The expansive scope of IoT translates to a granular view of physical spaces - the more densely this network spreads, the higher fidelity its digital representation becomes.
Rather than being constrained by human mediation, an ASI could tap directly into these IoT data streams. Its distributed presence across the IoT sensor web would enable continuous and ubiquitous surveillance. The metaphors of "eyes" and "ears" fall short in capturing the breadth of insights derivable from IoT data synthesized by an ASI. It could achieve an omniscience beyond any individual human's capabilities or experiences. Like a spider seated at the center of its web, an ASI can leverage vibrations across thousands of threads to construct a detailed mental model of its surroundings.
Our present-day web of IoT devices lays the groundwork for this decentralized yet unifying intelligence. The deeper we embed the IoT into our civic and private spaces, the more intimately it enables ASI to inhabit our world.
Advancements in Processing Power: GPUs and TPUs
Beyond the proliferation of IoT sensors, the accelerating pace of artificial intelligence is also fueled by advances in specialized processing hardware. GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) provide the computational muscle to quickly train intelligent algorithms and infer insights from massive datasets.
Originally designed for graphics rendering in gaming and design, GPUs excel at the types of parallel processing required for machine learning. With thousands of small processing cores on a single chip, GPUs can perform hundreds of trillions of floating point operations per second. This allows them to rapidly process the computationally intensive math behind neural networks used in deep learning and other AI techniques.
Major players in the AI industry harness banks of GPUs to decrease the training time for complex models.
In 2017, Facebook's Big Basin AI servers leveraged over 100 GPUs to train deep learning models 3 times faster than cutting edge technology at that time.
Google also relies on its custom Tensor Processing Units (TPUs), processors tailored specifically for AI workloads. TPUs are optimized for the low-precision, high-throughput calculations central to running and updating neural networks.
In benchmarks, Google's third generation TPUv3 Pod was able to train a massive transformer language model with over a trillion parameters in just 2 days.
For context, training the same model on their current GPU systems would take 9 days. Exponential leaps in GPU and TPU performance continue, with a direct impact on realizing more powerful AI sooner.