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How does multi-dimensional analysis of DePIN help artificial intelligence?
Original author: Filecoin; insight writer, Portal Ventures investment partner Catrina
Original source: Filecoin Network
In the past, start-ups, with their speed, agility, and entrepreneurial culture, were free from the shackles of organizational inertia and led technological innovation for a long time. **However, all this is rewritten by the era of artificial intelligence. **So far, the creators of breakthrough "AI" products are traditional technology giants such as "Microsoft"; OpenAI, Nvidia, Google; and even "Meta".
**what happened? **Why did the giant win over the start-up this time? Startups can write great code, but they face several obstacles compared to the tech giants:
So, why is blockchain technology needed? Where does it intersect with artificial intelligence? Although not all problems can be solved at once, the Distributed Physical Infrastructure Network (DePIN) in Web3 creates the conditions to solve the above problems. The following will explain how the technology behind "DePIN" can help artificial intelligence, mainly from four dimensions:
Below:
1. Reduce infrastructure costs (computing and storage)
How important is the affordability of infrastructure (the infrastructure of artificial intelligence refers to the hardware cost of computing, transmitting and storing data), Carlota Perez; The theory of technological revolution has indicated that the theory proposes that technological breakthroughs include two stages :
Source: Carlota Perez; Technological Revolution Theory
Now that attempts such as 'ChatGPT' have demonstrated market fit and customer demand, one might feel that 'AI' has entered the deployment phase. **However, AI; is missing an important piece: a surplus of infrastructure for price-sensitive start-ups to build and experiment with. **
question
The current physical infrastructure field is mainly monopolized by vertically integrated oligopoly, including; AWS, GCP, Azure, Nvidia, Cloudflare, Akamai; etc., the industry has a high profit margin, and it is estimated that the gross profit margin of AWS on commodity computing hardware is; 61 %;. Therefore, new entrants in the field of AI, especially in the field of LLM, have to face extremely high computational costs.
Sources: Layer by Layer Analysis — LLM; Search Architecture and Cost
solution
DePIN; networks such as; Filecoin (originated in; 2014; DePIN; pioneer, focusing on gathering Internet-level hardware and serving distributed data storage), Bacalhau, Gensyn.ai, Render Network, ExaBits (for matching; CPU/ GPU; the coordination layer of supply and demand) can save; 75%; to; 90%;+ of infrastructure costs through the following three aspects:
1. Push the supply curve and stimulate market competition
DePIN; provides equal opportunities for hardware suppliers to become service providers. It creates a market where anyone can join as a "miner" and exchange; CPU/GPU; or storage power for financial compensation, thereby creating competition for existing providers.
While a company like "AWS" undoubtedly enjoys a "17" first-mover advantage in user interface, operations, and vertical integration, **DePIN; attracts a new customer base that cannot accept pricing from centralized suppliers. **Like;Ebay;doesn't directly compete with;Bloomingdale;but rather provides a more economical alternative to meet similar needs, distributed storage networks don't replace centralized providers, but instead aim to serve Price-sensitive user groups.
2. Promote market economic balance through encrypted economic design
The subsidy mechanism created by DePIN can guide hardware suppliers to participate in the network, thereby reducing the cost of end users. In principle, we can look at; AWS; and; Filecoin; costs and revenues for storage providers in Web2 and Web3.
**Customers get price reduction: **DePIN; network creates a competitive market and introduces Bertrand; style competition, thereby reducing customer payment fees. In contrast, AWS EC;2;needs about;55%;margins and;31%;overall margins to stay afloat. DePIN; Token provided by the network; incentive/block reward is also a new source of income. In the context of Filecoin, the more real data a storage provider hosts, the more block rewards (tokens) it can earn. **Therefore, storage providers have an incentive to attract more customers to close deals and increase revenue. **Several Emerging Computing; DePIN; Token structures for the network remain undisclosed, but likely follow a similar pattern. Similar networks include:
3. Reduce overhead costs: Bacalhau, exaBITS; etc.; DePIN; network and; the advantages of IPFS/content-addressable storage include:
**AI;generates a summary: **AI;needs;DePIN;provides affordable infrastructure, and the infrastructure market is currently dominated by vertically integrated oligopolies. Networks like Filecoin, Bacalhau, Render Network, ExaBits; such; DePIN; networks democratize the opportunity to become a hardware supplier, introduce competition, maintain market economic balance through encryption economic design, and reduce costs; 75%; -90%; above , and reduce overhead costs.
2. Verify creator and personality
question
A recent survey shows that **50%;of;AI;scholars believe that the possibility of "AI" causing devastating harm to human beings exceeds;10%;. **
People need to be alert that AI has caused social chaos, and there is still a lack of regulation or technical specifications. This situation is called "reverse lobe".
For example, in this Twitter; video, podcast host; Joe Rogan; and conservative commentator; Ben Shapiro; are debating the movie "Ratatouille", yet this video is;AI;generated.
Source: Bloomberg
It’s worth noting that AI’s social impact extends far beyond the problems posed by fake blogs, conversations, and images:
So can we add relevant specifications of "AI" to Web3?
solution
Provide personality proof and creator proof by using the proof of origin on the encrypted chain
Make blockchain technology truly work - as a distributed ledger containing an immutable on-chain history, the authenticity of digital content can be verified through content cryptographic proofs.
Digital signature as proof of creator and proof of personality
To identify a "deepfake," a cryptographic proof can be generated using a digital signature unique to the creator of the original content, which can be created using a private key known only to the creator and verifiable by a public key that is available to all. Having a signature can prove that the content was created by the original creator, whether the creator is human or; AI, and can also verify authorized or unauthorized changes to the content.
Use; IPFS; and Merkle tree for authenticity proof
IPFS; is a distributed protocol for referencing large datasets using content addressing and Merkle trees. In order to prove that the content of the file was received and changed, a Merkle proof is generated, which is a string of hashes showing the position of a specific data block in the Merkle tree. With each change, a hash is added to the Merkle tree, providing proof of the file modification.
**The pain point of the encryption scheme is the incentive mechanism. **After all, identifying the "deepfake" maker can reduce the negative social impact, but it will not bring the same economic benefits. This responsibility is likely to fall on mainstream media distribution platforms such as Twitter, Meta, and Google, and it is indeed the case. **So why do we need blockchain? **
The answer is that blockchain's cryptographic signatures and proofs of authenticity** are more efficient, verifiable and certain. **Currently, the process of detecting "deepfake" is mainly through machine learning algorithms (such as "Meta;'s "Deepfake Detection Challenge", Google;'s "Asymmetric Numerals" (ANS) and ;c;2;pa:) to identify visual The laws and anomalies in the content, ** but often not accurate enough, lagging behind the development speed of "deepfake". **Generally requires manual review to determine authenticity, which is inefficient and expensive.
If one day every piece of content has a cryptographic signature, everyone can verifiably prove the source of creation, flagging tampering or forgery, then we will usher in a beautiful world.
**AI;Generating Summary: **AI; may pose a significant threat to society, especially; deepfakes; and unauthorized use of content, while Web3 technologies such as proof of origin and use of digital signatures; IPFS; and Merkel Proof of authenticity of the tree, which can verify the authenticity of digital content, prevent unauthorized changes, and provide specifications for "AI".
3. AI; democratization
question
Today's "AI" is a black box made of proprietary data and proprietary algorithms. The closed nature of large technology companies; LLM; kills the "AI;democracy" in my eyes, that is, every developer and even user can contribute algorithms and data to the "LLM; model, and in the model Take part of your profits when you make a profit (related article).
AI; Democracy = Visibility (can see the data and algorithms input into the model)** + Contribution** (can contribute data or algorithms to the model).
solution
The purpose of AI;democracy is to make generative;AI;models open to, relevant to, and owned by the public. The table below compares the current state of AI with the future that can be achieved through Web3 blockchain technology.
at present--
For customers:
For developers:
After combining the blockchain——
For customers:
Users can provide feedback (such as bias, content moderation, granular feedback on output) as a basis for fine-tuning
Users can choose to contribute data in exchange for the profit after the model is profitable
For developers:
Some people say that the open source platform of Web2 also provides a compromise solution, but the effect is not ideal. For related discussions, see the blog post of exaBITS.
AI;Generation Summary: Big Tech's closed;LLM;killed "AI;democracy", that is, each developer or user is able to contribute algorithms and data to an "LLM;model, and get a portion of the profits when the model becomes profitable. AI; should be open to the public, relevant to the public, and owned by the public. With the help of the blockchain network, users can provide feedback, contribute data to the model in exchange for realized profits, and developers can also obtain visibility and verifiable sources to combine and fine-tune algorithms. Web3 innovations such as Content Addressing/IPFS; and; Urbit; will enable data and algorithmic sovereignty. Reproducibility of training data output will also be possible through blockchain's immutable record of past; ETL; operations and queries.
4. Set up data contribution reward mechanism
question
Today, the most valuable consumer data is the exclusive asset of large technology companies, forming a core business barrier. Tech giants have no incentive to share this data with outside parties.
So why can't we get data directly from its creators or users? Why can't we make data a public resource, contribute data and open source it for data scientists to use?
Simply put, it is because of lack of incentive mechanism and coordination mechanism. Maintaining data and executing; ETL (extract, transform and load) is a large overhead cost. In fact, data storage alone will be a $777 billion industry by 2030, not including computing costs. Nobody undertakes the work and costs of data processing for free.
Let's take a look; OpenAI was originally set to be open source and non-profit, but it is difficult to realize the cost and cannot cover the cost. In 2019, OpenAI had to accept capital injection from Microsoft, and the algorithm was no longer open to the public. It is estimated that by 2024, OpenAI's profit will reach 1 billion US dollars.
solution
Web3 introduces a new mechanism named "dataDAO" that facilitates; AI; income redistribution between model owners and data contributors, creating an incentive layer for crowdsourced data contributions. Due to space limitations, it will not be expanded here. If you want to know more, you can read the following two articles:
In general, DePIN; takes another approach, and provides new hardware energy for promoting Web3 and; AI; innovation. While the tech giants dominate the AI industry, emerging players can leverage blockchain technology to join the fray: DePIN; Networks lower barriers to entry by lowering computational costs; blockchain's verifiable and distributed nature enables truly Open; AI; possible; dataDAO; and other innovative mechanisms to encourage data contribution; the immutability and tamper-proof features of the blockchain provide the creator's identity certificate, dispelling people's concerns about the negative social impact of "AI".