As advancements in artificial intelligence continue, the confluence of biology and technology presents an unprecedented field of exploration: biological intelligence, encompassing a spectrum from acellular chemical systems to human cognitive capabilities. Of particular interest are wetware AI systems, a subcategory of biological intelligence, characterized by the cultivation of organic material, including but not limited to neurons, in conjunction with technology to facilitate learning and task execution. These systems pose a unique array of ethical, technical, and legal considerations, contrasting from traditional digital AI systems.
Our project aims to traverse these complexities by formulating a comprehensive policy framework, cognizant of these considerations, to ensure the responsible, ethical, and beneficial utilization of biological intelligence and, more specifically, wetware AI.
This proposal elucidates our exhaustive methodology, beginning with an in-depth stakeholder analysis. Our goal is to grasp the diverse perspectives and concerns of a broad array of stakeholders, such as researchers, ethicists, legal experts, industry representatives, and the general public. The insights derived will serve as the compass for our policy formation. Subsequently, we will undertake a thorough review of literature and the current state of the field, culminating in a white paper detailing our findings and providing the bedrock for our subsequent policy work.
As we progress through our roadmap, we will construct research protocols, delineate standards and best practices, and formulate internal policy documents, alongside a code of ethics tailor-made for our organization. We will then engage external stakeholders through a prospectus, detailing the benefits, risks, and ethical considerations associated with wetware AI.
In the later stages of the project, our focus will pivot to public policy advocacy. Depending on the degree of biological intelligence in question, this could include the draft of a Bill of Rights for sentient wetware AI and proposing legislation to regulate the use of this technology.
Through this initiative, we aspire to pave the way for the safe and efficacious deployment of wetware AI technology. By establishing a policy framework that balances the potential benefits of biological intelligence and wetware AI with ethical and societal considerations, we aim to make a pivotal contribution to the evolution of AI, steering it towards a responsible, beneficial, and socially conscious direction.
In the rapidly evolving field of artificial intelligence, the emergence of biological intelligence systems presents a pivotal juncture where life sciences and information technology intersect. These organic systems, ranging from acellular chemical reactions to the complex neuronal networks within the human brain, possess an inherent capacity to process information, learn, adapt and interact with their environments in purposeful ways, albeit to varying degrees. This proposal introduces a project designed to investigate the need for and requirements of a policy framework addressing the use of Wetware AI systems.
Wetware AI, a subset of biological intelligence, involves the cultivation of organic matter, including neurons and other cell types, interfaced with computational technology for learning, sensation, and task execution. This novel approach to AI marks a stark departure from conventional digital AI systems, bringing forth a unique spectrum of ethical, technical, and legal considerations. This project's primary objective is to address these challenges by crafting a comprehensive policy framework that safeguards the ethical utilization of biological intelligence systems, particularly wetware AI, while promoting their beneficial deployment.
Our project's guiding principle is the belief that the extraordinary potential of wetware AI can be harnessed responsibly only if we understand and address the broad array of ethical, technical, and legal aspects associated with it. By embarking on an exploratory journey through an intensive stakeholder analysis, literature review, roadmap creation, policy development, and advocacy, we aim to build an all-encompassing policy framework that balances the manifold benefits of biological intelligence and wetware AI with societal, ethical, and legal considerations.
Through this journey, we aim to shape the future of AI, guiding it towards a more responsible, beneficial, and socially conscious direction. This proposal offers a comprehensive overview of our project, outlining its purpose, goals, and methodology, and sets the stage for a detailed exploration of the road ahead.
Artificial Intelligence
Artificial Intelligence (AI) is a subfield of computer science that aims to create systems capable of performing tasks that would normally require human intelligence. These tasks include learning from experience (machine learning), recognizing patterns (pattern recognition), understanding natural language (natural language processing), perceiving the environment (computer vision), and making decisions. AI systems can range from rule-based systems that follow pre-programmed algorithms to deep learning systems that can learn from large amounts of data. Unlike biological intelligence, AI systems are typically constructed of digital, silicon-based components and do not possess consciousness or self-awareness.
Artificial Neural Networks
Artificial Neural Networks (ANNs) are computing systems inspired by biological neural networks that make up animal brains. An ANN is based on a collection of connected units or nodes, known as artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal artificial neurons connected to it. Basic neural network models are composed of layers of neurons and the connections between them, stored as numerical values representing the values of the neurons, as well as parameters (weights, biases) which modulate the impact of and connections held by a neuron.
Bio-computing
Bio-computing, also known as biological computing, refers to the use of biological materials or biological architectures for computational purposes. This could include anything from using DNA for data storage, utilizing biological neurons in a wetware AI system, or creating computational models based on the principles of biological systems. Bio-computing represents a convergence of biology and information technology and offers the potential for highly efficient, resilient, and adaptable computational systems.
Biological Intelligence
Biological Intelligence refers to the inherent ability of biological systems, from acellular to multicellular organisms, to process information, adapt to changes, and interact with their environment in a purposeful manner. It represents the system's capacity to learn from experience, adjust to new inputs, solve problems, and predict future scenarios based on historical data. The complexity and capabilities of biological intelligence can vary significantly, giving rise to different classes or degrees of intelligence.
This definition can serve as a broad umbrella that encompasses a diverse range of systems and degrees of intelligence. Here is a rudimentary classification of these degrees, however our final classification schema will likely diverge greatly from these:
Please note that these classes are simplifications and there's a spectrum of capabilities within each of them. They also don't strictly follow evolutionary lines, as some organisms might exhibit traits commonly associated with "higher" classes.
Deep Learning
Deep Learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain's neural networks. It is particularly effective at processing large and complex data sets, and it has led to significant advances in computer vision, natural language processing, and other AI subfields. Deep Learning algorithms involve the use of artificial neural networks with several "hidden" layers between the input and output layer, hence the term "deep."
In Vitro
"In vitro" is a Latin term meaning "in the glass." In the context of biological research, it refers to experiments that are conducted outside of a living organism, typically in a controlled laboratory environment like a petri dish or a test tube. This could involve studying cells in culture, observing the behavior of proteins in a solution, or other similar types of experimentation.
In the context of wetware AI, in vitro could refer to neurons or other types of cells that are cultured and maintained outside of a living organism, interfaced with technology to form a wetware AI system.
In Vivo
"In vivo," Latin for "within the living," describes experiments that are conducted within a living organism. This could include anything from animal models and human clinical trials to ecological field studies. In vivo studies allow for a greater understanding of the biological context of certain phenomena but can also be more complex and difficult to control compared to in vitro studies.
For wetware AI, in vivo experiments could potentially involve the integration of bio-technological systems within a living organism, although such procedures would raise significant ethical questions and are not currently common.
In Silico
"In silico" is a term used to describe experiments that are conducted using computer simulation or computation. This can include a wide range of activities, from simulating the behavior of a single protein to modeling the spread of a disease through a population. In silico experiments can often supplement in vitro and in vivo experiments by enabling researchers to explore a wide range of conditions quickly and without the need for physical resources.
In the context of artificial intelligence, in silico refers to traditional, digital AI systems that are completely based on silicon chips and do not involve any biological components.
Wetware Artificial Intelligence
Wetware AI, a term used to signify departure from traditional hardware and software systems, represents a form of AI that relies on biological materials, often cells or tissues, interfaced with technology for sensation and/or information processing. This blend of biology and technology allows Wetware AI to exhibit a form of biological intelligence where the system can learn, adapt, and interact with its environment. Unlike conventional AI systems that are completely silicon-based, Wetware AI is characterized by the integration of biological matter, typically living neurons that may be human or nonhuman derived, and computational interface such as micro-electrode arrays, which convert analogue biological signals into digital ones which can be processed by traditional hardware.
The organic component of these systems provides unique capabilities and attributes, such as adaptability, resilience, and energy efficiency, which are difficult to replicate in silicon-based digital systems. However, these advantages come with a unique array of challenges and considerations. The use of living tissue raises significant ethical questions, the biological nature of these systems introduces technical challenges related to maintenance and longevity, and the novel capabilities of Wetware AI present legal and regulatory issues.
Machine Learning
Machine Learning (ML) is a subset of artificial intelligence. It refers to the method of data analysis that automates the construction of analytical models. It's based on the concept that systems can learn from data, identify patterns, and make decisions with minimal human intervention. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Micro-Electrode Arrays
Micro-Electrode Arrays (MEAs) are devices that contain a grid of tightly packed microelectrodes, through which electrical activity can be measured or induced. They are used as a platform for neuronal culture in wetware AI to facilitate bi-directional communication with the cultured neurons. This allows the in vitro neurons to interact with an artificial system, enabling both the reading of neuronal signals and the delivery of output signals back to the neurons.
The main objective of our project is to build a comprehensive policy framework for the ethical and responsible use of biological intelligence, with a particular emphasis on wetware AI systems. Our aim is to create a roadmap that encourages the exploration of this technology while providing robust safeguards against misuse and unforeseen consequences.
To achieve this objective, we have identified the following key goals:
Through these goals, we hope to not only shape the evolution of wetware AI but also to ensure its deployment is done in a manner that is beneficial and ethically sound. Our ultimate aspiration is to pave the way for a future where wetware AI can fulfill its potential as a powerful tool for human advancement, while being guided by strong principles of ethical conduct and social responsibility.
Our approach to this project employs a methodical and structured framework designed to cater to the intricate dynamics, potential risks, and ethical connotations surrounding wetware Artificial Intelligence and the use of systems of biological intelligence. The methodology can be seen as a roadmap, consisting of distinct stages and documentation development that have been tailored to address these multifaceted considerations.
This segment of the project proposal outlines the procedure for the stakeholder analysis as part of our larger project for wetware AI development. The following steps detail the systematic approach we plan to undertake to identify, understand, map, and engage our stakeholders.
1. Identification of Stakeholders
Our initial endeavor is to identify all relevant stakeholders in the project. These are entities that either have a vested interest in the project or whose actions can significantly impact the project's outcome. Our list of potential stakeholders include:
To ensure a comprehensive and structured approach in the identification and analysis of these stakeholders, we've devised a Stakeholder KOL Research Template. This will be used to guide us in the identification process, focusing on the identification of Key Opinion Leaders (KOLs) within each stakeholder group.
The Stakeholder KOL Research Template (provided in the appendix) is divided into several categories:
Each category has specific points for consideration to ensure we consider all relevant aspects. For each stakeholder group, a copy of the template will be filled out, outlining the specific processes, tools, and considerations pertinent to that group. This will help us to remain organized and efficient in our stakeholder analysis, as well as provide a transparent and reproducible process for identifying KOLs.
Using this systematic approach, we aim to identify the most influential and relevant individuals and entities across these stakeholder groups, enabling us to effectively engage and collaborate with them throughout the project.
We have included an example of the filled out template for one stakeholder group in the Stakeholder KOL Research Template document in the appendix.
We have also drafted sample guides for implementing these solutions (KOL ID guides) which we have included in the appendix.
2. Research Stakeholder Interests and Concerns
Understanding our stakeholders' interests, concerns, and perspectives is paramount. We plan to gather this information through the following means:
3. Stakeholder Mapping
Following data collection, we intend to synthesize the information and map our stakeholders based on the following factors:
A stakeholder matrix will be utilized for visualization, plotting each stakeholder based on their interest level and their power or influence.
4. Stakeholder Engagement Plan
Our final step will be to create a detailed engagement plan that dictates our interactions with each stakeholder group throughout the project. The plan will include:
We anticipate that our stakeholder analysis will be a continuous process, adapting as the project progresses to accommodate the emergence of new stakeholders and changing interests or influence levels of existing stakeholders.
We believe that this comprehensive stakeholder analysis will ensure the success of our wetware AI development project, facilitating effective communication and engagement with all relevant parties.
We will carry out comprehensive research on wetware AI and related ethical, technical, and legal issues. This will involve:
Informed by our research findings and stakeholder analysis, this phase will involve developing two crucial documents.
We will develop the following documents based on our research findings and stakeholder input:
This stage aims to facilitate active engagement with stakeholders and the public at large.