This is a long-form post with information about the
$TIG |
@tigfoundation (i, i) vision (1) and team (2).
This post gives you a clear understanding about the project and how it differentiates from
$TAO |
@opentensor (t, t) and other projects.
All the information provided here is directly copied from the
@tigfoundation X and website in order to offer easy access to X-users that encounter the project for the first time. Continue this enjoyable read by starting off with reading about the vision. The team members are presented in the latter part of this post.
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1. [ TIG FOUNDATION - VISION ]
The Innovation Game (TIG) is the first and only protocol designed to accelerate algorithmic innovation, by coordinating global intelligence.
TIG is underpinned by a novel proof-of-work mechanism that allows for optimisation of the proof-of-work algorithms.
TIG combines its proof-of-work with a crypto-economic incentive structure to not only incentivise miners (benchmarkers) to identify the best performing algorithms, but to also motivate and reward anyone worldwide who can contribute to algorithmic innovation.
TIG creates an open, collaborative and competitive ecosystem harnessing computational efforts over a wide spectrum of real-world challenges, spanning artificial intelligence, cryptography, biomedical research and climate science.
TIG is driven by the desire to direct the power of decentralised systems towards truly valuable purposes. By transcending limitations inherent in centralised entities, be they corporations or nation states, TIG can facilitate collaboration and achieve output on a scale previously impossible. Crucially, the protocol offers an alternative to the privatisation and monopolisation of algorithmic research, and accelerates open innovation in respect of science's most significant challenges.
Our Vision Is Clear
We envision a meritocratic world in which anyone who is capable of algorithmic innovation has the opportunity to do so, and is recognised and rewarded.
We envision an open world in which anyone who can utilise an algorithm to solve a real world problem can do so, with innovation not merely the province of tech giants.
We envision a collaborative world, in which humanity works together to address its most pressing issues.
With TIG We Will
- Incentivise anyone in the world who is capable of making a material contribution to algorithmic innovation to do so
- Concentrate the efforts of innovation on challenges that map on to real world problems
- Create a means of investing in algorithms as an asset class
- Foster an open, collaborative, and competitive ecosystem that will hyper-accelerate computational progress and prevent the rise of monopolies around it
This kind of coordination protocol in relation to science and technology has never before existed, or even been possible. The consequence of success will be algorithmic innovation and value capture at a scale previously inconceivable, along with the potential to build the most secure and advanced decentralised network state we have seen to date.
Why: The Background
Algorithmic innovation is hugely impactful.
Two of the main drivers of technological progress are a) improvements in hardware infrastructure, and b) algorithmic innovation. Improvements in hardware performance allow the execution of more complex and sophisticated algorithms, while new algorithms can optimise the use of hardware resources and greatly amplify their impact.
Recent advancements in deep learning and artificial intelligence, for example, have been fuelled by a combination of powerful GPUs and, crucially, new algorithmic approaches, such as convolutional neural networks and the Transformer architecture (arguably the most significant contributor to the recent AI boom).
Algorithmic innovations are the building blocks of science and technology, with singular algorithms having a multiplicity of applications.
Notable examples of algorithmic innovation and a sample set of use cases include:
- The backpropagation algorithm: foundational to the field of deep learning and modern AI
- Fast Fourier Transform: essential for Medical Imaging, Radar and Sonar Systems, Audio and Speech processing and Spectral Analysis and more
- Dijkstra's Shortest Path Algorithm: essential for Network Routing, Transportation and Navigation (GPS, airlines, public transportation), Robotics and Path Planning and more
- Hashing Algorithms: essential for Blockchain/Cryptocurrency, Password Storage and Verification, Data integrity, Digital signatures and more
Moreover, algorithmic innovation is not just impactful but necessary as we move into the future, particularly in light of concerns around Moore's law plateauing. Some of the key areas where innovations must happen include quantum-resistant cryptography and security, climate change and sustainability, and healthcare and personalised medicine. With hardware advancements looking increasingly unlikely to be able to solve all of our problems, algorithmic innovation will be critical.
The Mechanism
Optimisable Proof of Work (OPoW)
At the heart of TIG lies a novel proof of work mechanism called Optimisable Proof of Work (OPoW). OPoW is a significant departure from both standard Proof of Work (PoW) mechanisms and Proof of Useful Work (PoUW) systems.
In standard PoW, miners compete to solve arbitrary mathematical puzzles to create new blocks and receive token rewards, incentivising them to contribute computational power to secure the network. PoUW systems aim to direct this computational power towards solving real-world challenges instead of arbitrary puzzles. In both, miners look to optimise their hardware in order to achieve rewards at a lower cost.
OPoW incentivises two categories of actors: a) innovators who create and optimise algorithms, and b) benchmarkers (analogous to miners within the system) who identify the most efficient algorithms. Within this system, computational work establishes a synthetic market for algorithms, with miners being incentivised to identify and adopt the most efficient ones.
The key innovation of Optimisable proof of work is allowing proof of work algorithms themselves to be optimised, while avoiding centralisation risk- retaining a decentralised and secure network.
Key Features
The following are key features of TIG that keep it secure:
- Multiple independent challenges representing real-world computational problems. This mitigates the risk of centralisation, as the system's overall security is not compromised if a single challenge is dominated by any one entity.
- Balanced reward distribution to encourage uniform participation across challenges. OPoW's reward distribution mechanism is designed to incentivise benchmarkers (miners) to distribute their efforts uniformly across all challenges. Imbalances in the allocation of computational resource are penalised, ensuring that there is an economic incentive to work on all challenges equally and maintain the decentralisation of the network.
- A token economy that aligns participant interests with the overall health and security of the ecosystem. We believe that these features, together with the underlying mechanism, allow for the incentivised creation of a type of value that neither uPoW or PoW are capable of โ namely the innovation and sharing of improved algorithms within a fully decentralised system.
The Roadblock
Algorithmic Innovation is Slow, Uncoordinated, and Underfunded
Despite its importance, algorithmic innovation faces significant challenges. It is difficult and relies on critical insights, making progress less certain and therefore more risky and less attractive for investment compared to hardware innovation.
While hardware innovation has attracted significant investment from both industry and governments, much algorithmic innovation has occurred within disparate university departments, relying on far more limited resources.
In recent years, algorithmic innovation in AI has been an exception, benefiting from well-funded research labs. However, this has highlighted its own problems. Making the results of research open and accessible has proved financially unsustainable, meaning that companies which originally invested in algorithmic innovation and made the results open, have been forced to change policy. This sees innovation trending towards being proprietary and monopolistic- this is particularly dangerous in the context of a field such as AI, where control over innovation could be tantamount to control over the global future.
Moreover, due to the general lack of funding, individuals with the skills and knowledge to make material contributions to algorithmic innovation may not have the financial incentives or necessary support to pursue this work.
The Solution: A New Funding Model
TIG is the first protocol to create a coordination mechanism for algorithmic innovation, despite many efforts to create protocols for hardware. Utilising MfPoW, TIG aims to create a dedicated ecosystem and funding model for algorithmic development.
In this ecosystem, innovators will develop new algorithms and optimise existing ones; Benchmarkers will identify the most efficient algorithms. Algorithms will be available for licensing, with all license payments flowing into the system to reward contributors.
This model will provide anyone capable of materially contributing to algorithmic innovation with a platform to do so, along with recognition and reward. Additionally, it makes algorithms an investable asset class and captures the value of algorithmic innovation across various domains, from AI/ML and quantum computing to computational biology and climate science. TIG enables private investment while ensuring open results- in this way, it can be considered complementary to the current open source movement.
The Future
By leveraging the principles of Optimisable Proof of Work and the incentive structures of The Innovation Game, a successful implementation would give rise to a network state that places algorithmic innovation at its core, fostering a global community of innovators and benchmarkers who collaboratively push the boundaries of computational problem-solving.
The resulting ecosystem would be inherently secure, as the multi-factor nature of the proof of work consensus mechanism ensures a high degree of decentralisation and resilience against attacks. The continuous competition among miners to identify and adopt the most efficient algorithms would not only drive innovation but also contribute to the overall security and stability of the network.
Moreover, the meritocratic reward system and transparent governance model enabled by the network state could create a self-reinforcing cycle of innovation and growth, attracting top talent and resources from around the world to contribute to its development and success.
As the network state evolves and expands, it has the potential to become a global hub for algorithmic innovation, driving breakthroughs in fields such as artificial intelligence, cryptography, biomedical research, and beyond. The value captured within this ecosystem, both in terms of intellectual property and the dynamics of its native token, could reach unprecedented levels, creating a new paradigm for funding and sustaining scientific and technological progress.
How You Can Help
We invite you to be a part of this transformative journey. If you can develop algorithms, do so. If you can optimise, try. If you can run a Benchmarker, set one up.
Above all, join the community. Question, learn, teach, and contribute. Together, we can shape the future of algorithmic innovation and fully unlock its potential.
With your support and participation, we can create a world of open science where algorithmic breakthroughs are not just possible but inevitable. Let's build a future defined by the power of algorithms and the collective intelligence of a global community united in the pursuit of innovation.
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2. [ TIG FOUNDATION - TEAM ]
I. Dr. John Fletcher | CEO & Co-Founder
Dr. John Fletcher holds a PhD in applied mathematics and theoretical physics from the University of Cambridge. He has been researching frameworks for distributed incentives full-time since 2016, and is the inventor of more than 30 patents in the areas of cryptography and distributed systems.
II. Ying Chan | CTO & Co-Founder
Ying, an Imperial College London alumnus, is a pioneering inventor with over 15 patents in blockchain and machine learning, known for his OP_PUSHTX technique that proved Bitcoin's smart contract capabilities. His expertise is highlighted by his work on prototyping a nation-state cryptocurrency for the Bank of England and leading a team at FiveAI to advance self-driving technology.
III. Philip David | I.P. & General Counsel
Phil, former ARM Limited Director and General Counsel (2003-2018), spearheaded the company's IP strategy as Senior VP of IP during SoftBank's acquisition. With 30+ years in technology IP law, he shaped open source licensing frameworks through Linaro's establishment and now advises tech startups including New Motion Labs and CHS Group.
IV. Lee Hughes | COO
After a successful career in derivatives trading, Lee co-founded Cube Financial, an institutional sales business with offices in London and Chicago. Cube Financial was acquired by Societe Generale in 2010. Following the acquisition, Lee became a founding partner at Coex Partners, where he remained until the firm was sold to TP ICAP in 2018. Throughout his career, Lee has held multiple Financial Conduct Authority (FCA) control functions, ranging from significant management to compliance.
V. Steven Robinson | Strategy
Steven holds a Master's degree from the University of Oxford in Mathematics and Philosophy. He is a founding partner at ARKN ventures, a London-based investment firm focused on decentralised social co-ordination solutions and incentive design. Previously a trader in charge of crypto strategy at derivatives market maker Mako, he made his way up from graduate trainee to being the youngest partner in the firm's history.
VI. Thibault Vidal | Technical Advisor
With a background at MIT, Professor at Polytechnique Montrรฉal, and the SCALE AI Research Chair, Dr. Vidal is a global leader in Operations Research. His work has set the global standards for vehicle-routing benchmarks, and he was an organizer for the 2022 DIMACS global vehicle routing competition.
VII. Ben Atueyi | Community
Ben graduated from the University of Oxford with a Degree in Law (Jurisprudence). Convinced of the potential of DLT in underpinning solutions to coordination challenges, he became a founding partner at ARKN Ventures. His focus is on finding and working with protocols that can remove barriers to global collaboration and transform the ways in which we approach desired outcomes.
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Sources:
tig.foundation/network
tig.foundation/team