Safe, Productive, Efficient, Accurate, and Responsible (SPEAR) AI Systems

This is the first portion of a two-part series of a working paper (see part 2 here). It's in draft form and hasn't been reviewed, so you may find typos, formatting is quite different on LinkedIn, and I may make changes before the final draft. However, given the rapid evolution in AI, investigations, and regulations, I thought it would be best to share the work in progress.

(MM: I changed the name from original post on 12/28/2023) to reflect broader scope of working paper. Part 2 is now published on LinkedIn here).

Abstract

Since the first large language model (LLM) chatbot was released to the public, leading experts in artificial intelligence (AI), catastrophic risk, economics, and cybersecurity, among others, have warned about the unique and unprecedented risks caused by LLM chatbots trained on large-scale, unstructured data with interactive capabilities [1, 2]. Although theoretical methods have been proposed for LLM chatbots to achieve safety levels similar to those required of other safety-critical industries, such as biological contagions or nuclear power, to date none have been proven [3, 5]. We therefore have an urgent need to adopt safety methods and designs in AI systems based on the limitations within the proven laws of physics and economics without sacrificing the many benefits. This paper is focused on the inefficiencies, risks, and limitations of safety techniques in LLMs, the business and economic incentives influencing decisions, and the prerequisite architecture to provide safe, productive, efficient , accurate, and responsible (SPEAR) AI systems. One such system is described.

KEYWORDS: Artificial Intelligence, Data Governance, Risk Management, Systems Engineering, Safety Engineering, Sustainability, Disaster Management, Catastrophic Risk, Existential Risk, Bioweapons, Large Language Models, Cybersecurity

1.    Introduction

The recent popularity and media exposure about large language model (LLM) chatbots has been so prevalent that LLMs have become synonymous with all of AI, which is false. Many different AI methods and models exist, including several large-scale language models, small language models, symbolic AI, and neurosymbolic AI [4].  LLMs were developed on the shoulders of giants in AI research over a period of seven decades. Most of the early research was funded by government in small academic labs or small corporate labs. In recent years, the super majority of AI research investment has been sourced from a few large tech companies investing hundreds of billions of dollars attempting “dominance in AI”, in part by attracting and retaining the majority of top talent in the industry, and in part by directing the majority of research funding towards models that align with their interests, such as LLMs [5, 6].  

One example of strategic research is the Transformer developed by Google researchers, which has become the building block for LLMs [7].  The Transformer model includes encoders and decoders, which when trained and tuned provides text tokens that are assigned probabilities for generating text strings. Although the efficiency gains are small with incremental increases in scale, at massive scale with data stores that contain trillions of words and hundreds of billions of parameters, it becomes possible to produce the LLM chatbots we see today [8].  

As the dominant search engine with one of the largest data stores and cloud infrastructure investments, Google has a powerful incentive to invest heavily in natural language processing (NLP) research and related talent.  Another example of strategic investment was by Nvidia in the optimization of Bidirectional Encoder Representations from Transformers (BERT) neural network architecture, improving speed in Google’s Tensor Cores without losing accuracy [9]. 

Since LLM chatbots require vast amounts of computing resources, the revenue growth alone in cloud services can justify the approximately $20 billion investment in LLM startups to date by Microsoft, Google, and Amazon [10].  However, the large investments combined with integrating with their market leading products, and strategic agreements for technology infrastructure, such as the exclusive agreement in the OpenAI/Microsoft partnership, also provides the potential to “co-opt” competition, and extend and expand monopolies, duopolies, and the larger oligopoly over the rest of the economy [11, 12].

“It means that industry domination of applied work also gives it power to shape the direction of basic research. Given how broadly AI tools could be applied across society, such a situation would hand a small number of technology firms an enormous amount of power over the direction of society [13]”.

The strategic interests of a few large tech companies do not necessarily align with the interests of the majority of organizations, individuals, or society. The scale necessary for the incremental improvements in efficiency is so vast that it causes massive economic and environmental risks and costs, including catastrophic risk discussed in this paper, and existential risk for vast sectors of our economy, such as those relying on copyright, which contributed more than $1.8 trillion dollars to the U.S. economy in 2021 [14, 15]

Fortunately, safe and responsible AI is currently possible that can deliver critical functions by designing and deploying systems of integrity based on similar principles in executable architecture as other safety-critical industries, which is the focus of this paper.

2.    Risks and Security

The strategic interests of a few large technology companies do not align with the interests of many customers and society when it comes to risk and security. LLMs for example, while aligned very well to the interests of a few large tech companies, they are inherently unsafe. The so-called guardrails are false equivalencies, as guardrails imply a physical barrier, whereas LLM guardrails are text-based and easy to work around due to the nature of self-generating text models and the interactive dynamics of LLM chatbots. 

“We find that jailbreak prompts are introducing more creative attack strategies and disseminating more stealthily over time, which pose significant challenges to their proactive detection. Moreover, we find current LLMs and safeguards cannot effectively defend against jail- break prompts in various scenarios. Particularly, we identify two highly effective jailbreak prompts which achieve 0.99 attack success rates on ChatGPT (GPT-3.5) and GPT-4, and they have persisted online for over 100 days”……“LLM vendors and adversaries have been engaged in a continuous cat-and-mouse game since the first jailbreak prompt emerged. As safeguards evolve, so do the jailbreak prompts to bypass them [16].”

An increase in cybersecurity risks were expected from LLM bots following the launch of ChatGPT, which were then rapidly realized. However, catastrophic risks such as assistance in developing biological weapons are of even greater concern [1].  LLM chatbots are trained on many large data sets that include scientific journals in every discipline. Planning instructions and suggestions for biological terrorism and other catastrophic events that would require terrorist cells decades to research have been returned in seconds, demonstrating the ability to increase catastrophic risks at an exponential rate [17].

“In less than 6 hours after starting on our in-house server, our model generated forty thousand molecules that scored within our desired threshold. In the process, the AI designed not only VX, but many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible. These new molecules were predicted to be more toxic based on the predicted LD in comparison to publicly known chemical warfare agents. This was unexpected as the datasets we used for training the AI did not include these nerve agents [18].” 

The trigger of the LLM arms race was the premature unleashing of LLM bots to the general public without benefit of rigorous safety engineering processes required of other safety-critical systems.  Since one company took the risk to launch an LLM, experienced no government intervention due to safety risks, and then scaled rapidly, competitors felt the need to engage in similar behavior or risk being left behind. The arms race rapidly expanded to include nations where the LLM companies were headquartered, most recently in Europe where new LLM entrants reportedly successfully lobbied to block the proposed regulations for foundation models in EU AI Act, which includes LLMs.

One example of previous AI adoption is autonomous driving, which falls within the jurisdiction of the National Highway Traffic Safety Administration (NHTSA). The NHTSA adopted a six-level safety standard for autonomous vehicles, ranging from level 0 with no autonomous technology to level 5 for full automation. Most new cars today fall between level 1-3, offering augmentation that enhances safety. Automation technology employed by autonomous vehicles isn’t restricted to one model like LLMs, but is rather a hybrid of many different types of technology across dozens of companies, ranging from new independent companies to incumbents and after-market products. Most importantly from a risk perspective, autonomous driving does not represent catastrophic risk, but rather limited individual events.

When inevitable accidents occur in autonomous vehicles, they are obvious, limited to a small number of people, and analyzed by expert third parties like police who file reports. If the autonomous system is found to be the cause, cars can be recalled and tested. In contrast, with the unique risk profile in LLMs, it may be years before catastrophic risk manifests, potentially impacting millions or even billions of people.

Technology that contains potential catastrophic risk face rigorous requirements, including biological and nuclear risk. To transport the Ebola virus, for example, requires a special permit from the Department of Transportation, which has jurisdiction for transport, working “closely with CDC, OSHA, HHS, DOD, EPA, and state and local government to assure that our respective safety missions are adequately addressed in these scenarios”. However, the influenza virus is subject only to CDC recommendations [2].

Nuclear power is regulated by the U.S. Nuclear Regulatory Commission (NRC). Created in 1974 as an independent agency, the NRC “regulates commercial nuclear power plants and other uses of nuclear materials, such as in nuclear medicine, through licensing, inspection and enforcement of its requirements”. The International Atomic Energy Agency (IAEA) was created in 1957 as an autonomous organization within the United Nations to promote peaceful use of nuclear power and inhibit military use.  

The catastrophic risks in LLMs are similarly well understood by objective scientists, but it’s been more than a year now since LLM chatbots have been released to the public and very little has been done to address current risk beyond minor safeguards that have been proven easily breached.

3.    Environmental Costs

LLMs trained on web-scale data cause many different types of risks and costs, including significant environmental costs, particularly in water and energy use [19, 20]. For example, the world’s largest data center market is Northern Virginia, with over 275 data centers, is experiencing a dramatic spike in energy use due to datacenters. CBRE, a leading commercial real-estate services firm, reported the amount of power available in the Northern Virginia market shrank to 38.4 megawatts earlier this year from 46.6 megawatts the previous year, despite an increase in inventory of 19.5% for a total of 2,131 megawatts [21].

Much of the worldwide increase in energy for datacenters is due to compute-intensive AI, particularly in datacenters owned by the leading cloud providers—AWS, Microsoft, and Google, all of which have experienced significant growth in recent years [3]. Although the volume of data continues to expand at a rapid pace, the exponentially expanding size of LLM models is driving the majority of demand for datacenters. LLM models have grown by 1000x in a few short years.  The explosive growth in AI computing has caused serious shortages in GPUs [22], as well as explosive demand for more efficient and less costly alternatives.

McKinsey is forecasting power consumption in datacenters to reach 35 gigawatts (GW) by 2030 in the U.S. alone—more than doubling from 2022, representing about 40 percent of the global market [23]. Compute-intensive datacenters training LLMs also require very large quantities of water to cool servers, so water demand is growing on a similar trajectory to energy. The subject of water use is sensitive and often kept secret. However, researchers have reported that training GPT-3 in Microsoft’s most efficient datacenters can evaporate 700,000 liters of clean freshwater, and that global AI demand for water is forecast to require 5-6 billion cubic meters of water in 2027, which is the equivalent of the annual use for Denmark [24].

Footnotes

[1] I’ve warned about the LLM risks in biological weapons since November of 2022, in private communications, social media posts, and my enterprise AI newsletter. In 2019 I unveiled our synthetic genius machine (SGM) in a talk at the leading technical conference in New Mexico, ‘Metamorphic transformation with enterprise-wide AI’. Shortly thereafter traffic patterns on our web sites convinced me to voluntarily restrict additional information on the SGM in public due in part to catastrophic risks similar in nature to LLMs. Our SGM employs strong security and compression—it’s much more accurate and environmentally friendly than LLMs, but the intention of the system is to accelerate discovery. Our SGM is a dual-purpose system similar to LLMs in catastrophic risk if made widely available to anyone in the public.

[2] Pathogens with various levels of risk ranging from near zero to near certain death is a good model for risk management in AI systems as some systems and applications contain near zero risk while others represent near certain death (e.g. autonomous weapons) or the most deadly viruses. Some models like LLMs trained on web-scale data and made available to the general public with only minor restrictions represent among the greatest risks in AI systems, whereas rules-based AI systems trained on high quality curated data with strong security, provenance, and verification represent low risk.

[3] The pay-for-use elastic computing model known as the ‘cloud’ pioneered by Amazon (AWS) was a brilliant innovation that has proven to be one of the most successful business models in the history of technology industry. However, as the cloud model rapidly expanded, serious perverse incentives became more problematic, including investing heavily in research that would expand dependency on the cloud model, such as LLMs, and systemic risk due to an ever increasing portion of critical functions in our economy on any of the three top cloud providers. For example, while I supported the CIA’s adoption of AWS a few years earlier as a wise decision, I warned the Department of Defense on the systemic risk in awarding a sole-source contract for $10 billion for the initial ‘Jedi’ contract. By that time a good portion of the S&P 500 and critical government functions were all hosted on AWS, representing unprecedented dependence on a single host.

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