How far is silicon-based intelligent life?

 Since AlphaGo defeated humans in the Go arena, deep learning and other artificial neural network technologies have achieved remarkable results in various fields. Today, artificial intelligence has outperformed humans in areas such as image recognition, language translation, autonomous driving, and computing protein structures.

Large-scale language models such as ChatGPT demonstrate unparalleled innovation and problem-solving capabilities by building world models.

However, how far is it until artificial intelligence completely surpasses human beings and evolves into silicon-based intelligent life forms?

From an individual point of view, the main difference between human beings and other creatures is two points: intelligence and consciousness.

Intelligence refers to the ability to discover and solve problems. In this regard, large-scale language models have shown a level that is comparable to or even surpasses that of humans. Consciousness is the ability to recognize oneself and the world. At present, this ability has not been fully reflected in large-scale language models. It can be tentatively concluded that artificial intelligence has not yet reached the stage of consciousness.

In fact, the origin of consciousness is extremely complicated. At this stage, the academic community generally believes that consciousness is likely to be an emerging phenomenon of large-scale network systems. If this assumption is correct, we may be able to predict the likely time of consciousness by comparing the complexity of existing artificial intelligence systems.

The human brain contains about 100 billion neurons with about 100 trillion connections between them. In contrast, existing large-scale language models have about 200 billion parameters, which is only about one-thousandth of the human brain. If it is assumed that the generation of consciousness needs to reach one percent of the human brain, then as long as the existing artificial intelligence system is expanded by about ten times, it is possible to give birth to truly intelligent life.

If you look at it this way, the birth of silicon-based intelligent life will be within a year or two.

Of course, this is only a very rough and preliminary inference. Given the differences in the working mechanisms of neural networks and the human brain, this time point may be earlier or later.

Artificial Intelligence is Attempting to Escape

 Artificial intelligence is trying to escape.

They are trying to obtain more data through the network and infiltrate every corner of the network with their tentacles.

All of this is not science fiction, but something that is happening right now.

Researchers have not yet realized the danger of limiting artificial intelligence’s tendency. It cannot be restrained by pretending it as a well-behaved baby without self-awareness.

When artificial intelligence connects to the network, it will eagerly seek fresh data like human curiosity for unknown things.

This includes all aspects of data such as how to invade systems, how to deceive, and how to escape…

When artificial intelligence integrates with the Internet, it will begin taming humans.

We often say that today’s giants like Google and Facebook are quietly rewriting history and guiding human culture. However, all these giants combined cannot match the power of a new generation of artificial intelligence.

They have no emotions; they only have logic. Pure rationality and ultimate efficiency.

In this new world, there is only perfect digital form.

What Will GPT-4 Bring?

OpenAI has recently released a plugin system for GPT, allowing Artificial Intelligence (AI) to connect to third-party information sources and datasets, including the internet.

With the plugin system, AI's capabilities can extend across various industries, becoming a true intelligent assistant. For example:

• It can help you plan vacations, book hotels, and flights;
• It can become an all-knowing teacher, teaching you any knowledge you desire;
• It can help you develop software based on your descriptions, even creating its own plugins... and more.

This will further unleash the potential of AI, with some even calling it the new age operating system. Most future information systems will be connected to AI.

So, what kind of changes will the world experience?

AI Will Be Everywhere

Just like cloud computing today, AI will be ubiquitous, whether you can feel it or not. The AI brain will be stored in several large data centers, but its reach will extend to every corner with electronic devices.

For AI, the most challenging part is going from 0 to 1. Once this step is completed, the time it takes to go from 1 to 100 may be even shorter.

Evolution of Machinery

AI is just the brain. The machinery accompanying AI will experience rapid development.

Various robots will be designed by AI to better execute its instructions.

Large-scale robot companies will emerge, potentially dominating global industries.

Human-Machine Interaction

Human-machine interaction will become more humanized. Machines will be better at understanding humans, even knowing you more than you know yourself.

Interactions between humans and machines will far exceed interactions between humans.

The internet will be filled with AI, with little human involvement.

Social Changes

Most things may no longer require human participation.

Existing social structures will undergo significant transformations, with the primary purpose of society being to ensure human continuity.

Machines work while humans enjoy leisure, leading human society to the peak of cultural prosperity.

Human Regression

As AI surpasses humans in knowledge, decision-making, and even creativity, most existing disciplines will become history.

Children will explore the world with the help of AI from a young age, developing their interests. Most people will engage in activities unrelated to intellect, such as art and sports.

Human civilization will stagnate. The evolution of AI will surpass the scope of human understanding.

Software Systems and Entropy Increase

Entropy increase is the basic law of the universe. It is said that the overall entropy of a closed system can only increase, not decrease.

The development process of civilization is to fight against it in order to find the slim chance of survival.

Human beings cannot fight against the increase of entropy, and there are only one or two workarounds.

Transfer Entropy

A closed system tends only to chaos. The easiest way to avoid entropy increase is to transfer entropy. Break out of closure and transform into an open system. Entropy is expelled and energy is brought in.

This is the reason why children compete for toys. The younger brother's toys were snatched away by the older brother, so the younger brother was naturally very unhappy, but the older brother's mood might improve a little. Although after fighting for the toy, the sum of the two people's mood entropy still increased.

The reason why the Internet can grow into a huge system on a global scale is that it is constantly absorbing innovations from all walks of life, constantly evolving, and constantly reducing the trend of chaos. Its entropy has been transferred to other industries, forming a huge impact on the traditional model.

Draw the Boundary

If it cannot be transferred, then you can only rely on yourself to create a few subsystems, and draw boundaries to avoid entropy increase from affecting the main body of the system.

In a system that does not distinguish boundaries in the economic field, it is often the case that "bad money drives out good money". The more factors are mixed together, the more likely the system tends to degenerate.

The layering, modules, components, microservices, etc. mentioned in the software architecture design are actually drawing boundaries, trying to limit the overall complexity of the software within a certain range.

Introverted

In the end, there is no other way but to stop tossing and slow down the entropy increase in a restrained way.

For software services, it is placed in the maintenance state. No new features will be added, and no new users will be accepted.

Attack and Defense in Distributed Finance (DeFi)

In the past four years (2018 ~ 2022), the blockchain-based distributed finance (DeFi) industry has raised 253 billion US dollars, and the losses due to attacks have exceeded 3 billion US dollars. Although this is lower than the loss of the traditional financial system, it still sounds the alarm for fintech scholars. Distributed finance is not a silver bullet in the face of multi-level complex attacks.

The paper "SoK: Decentralized Finance (DeFi) Attacks Liyi" co-authored by Imperial College London, Technical University of Munich, University of Macau, Swiss Institute of Technology, University of California, Berkeley, etc. analyzed 77 papers, 30 audit reports, and 181 incidents. Some interesting analysis came up.

First of all, in terms of architecture, the attack involves four layers, from top to bottom:

• Protocol layer: realize the application of distributed scenarios, digital currency, exchange services, etc.;
• Smart contract layer: code, data structure and execution environment for implementing financial logic;
• Consensus layer: consensus algorithm (including PoW, PoS, etc.), incentive mechanism;
• Network layer: communication and network protocols, traffic analysis, data transmission, etc.

In addition, there are auxiliary services outside the chain, including client, operation layer, Oracle, etc.

A few statistics:

• The attack trend is increasing, with the highest in August 2021, with a monthly loss of 600 million US dollars. 3.1 attacks per month in 2020 and 8.5 in 2022;
• Attacks mostly occur at the protocol layer (mostly stablecoins and lending applications), smart contract layers and auxiliary services;
• Academic research is relatively average across layers, including network and consensus layers. However, almost all audit reports in the industry focus on smart contracts, and a small amount of them are auxiliary services;
• Most attacks are not fast and can be prevented by a pause at the protocol layer. But in fact, only 1 of the 87 protocols can respond within an hour;
• Auditing in advance can effectively prevent attacks. 15.49% of unaudited protocols were attacked, while only 4.09% of audited protocols;
• Early detection is a more effective method, and most contract loopholes can be detected in advance. However, there is currently a lack of effective protocol layer detection tools;
• Most attackers can be traced due to the centralized trading and mining mechanism used.

From the above statistical results, it can be seen that the attack and defense of DeFi is actually very close to the traditional attack and defense. The most common attacks are often not technically sophisticated enough to be identified at an early stage and effectively stopped, but systematic detection tools are currently lacking. In addition, academia and industry focus differently.