“End Nuclear Insanity Before Nuclear Insanity Ends Humanity”
LLAW’s COMMENTARY:
As randomly planned in order to provide an expanded term of understanding to what this nightly Post is all about, especially for new readers who may have missed most of the first year-plus of the cometary, this article from October 5th, 2023, is posted here tonight as what will become a usual Sunday review in general, but more importantly because of the daily increasing concerns about AI (Artificial Intelligence) and its rapidly growing ties and technical relationships to dangers related to operational controls and administration of nuclear power plants as well as, perhaps, nuclear weapons up to and even including nuclear war. ~llaw
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LLAW’s “ALL THINGS NUCLEAR” #410 (10/05/2023)
“End nuclear insanity before nuclear insanity ends humanity”
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Tonight, while I’m working on the “Organization” plan for beginning a step-by-step “blue print” about how humanity might prevent “All Things Nuclear” from existing in the future, I am turning the Post over to “The Bulletin’s” evaluation of a growing AI/nuclear relationship.
There are six steps, as described in my Post #409 from last night that I will be working on. I will post each one of them in a draft form as consideration for convincing a majority of humanity to turn against all things nuclear in order to survive. The basic outline (brief and unedited) is described in last night’s Post.
Always keep in mind that the only way this proposal would never work is through the vocal or functional discontent or disconnect of a majority of planet Earth’s already divided human World(s) that I refer to as the “Organization”. If we can’t accomplish that then the rest is a futile dream not worth thinking about. I suspect the “Blue Print”, when completed will read much like or resemble an outline for a novel or a screenplay because it will seem like ‘science fiction’ to most of us. But to guarantee our survival as well as other life on our only home, it has to be more than imaginary and it has to happen. We could also use some unknown help from somewhere.
So, in that light I am going to post a recent “Bulletin” article that deals with the relationship of the advancing romance between AI and nuclear management that human beings may not be able to control. This article places an even more questionable light on the issue of human capability and expertise to deal with and control “All Things Nuclear” than ever before.
And it should be noted that we have not done a very good job of shepherding nuclear arms nor nuclear power and the heated issue of ‘all things nuclear’ is growing more frightening by the day. ~llaw
The following article is from “The Bulletin’s” September edition by Jingjie He, Nikita Degtyarev on September 11, 2023 (Please note that images are not shown, although some descriptions or captions are presented. Also, the references in the footnotes are not shown.
AI and Atoms: How Artificial Intelligence is Revolutionizing Nuclear Material
By Jingjie He and Nikita Degtyarev, September 11, 2023
Over the last decade, there has been an accelerated integration of artificial intelligence (AI)[1] into both the civilian and military fields. As a result, rising attention to the challenges of AI governance has manifested in three ways. The first challenge lies in the dual-use nature of AI in the civilian and military domains, which renders it difficult to monitor and oversee its militarization. The second derives from the policy-influencing power of the private sector, which has traditionally been limited to utilizing lobbying instruments. The final difficulty results from the changing nature of government-industry relations, where industries are leading the development and application of AI, and governments are falling behind industry in understanding its technological potential and regulating military applications.
A review of existing literature demonstrates that AI is well discussed within the military and broader strategic stability domain[2], including discussions surrounding AI use within the nuclear sphere to hack cyber systems, poison AI training data, and manipulate its inputs (Avin and Amadae 2019). The expert community further addresses AI and its applicability to nuclear safeguards.[3] However, current available research largely ignores nuclear material production (NMP), which is an essential phase in the development of nuclear weapons
This article bridges that gap by assessing the potential role of AI in nuclear material production while considering industrial practices.[4] In employing an industrial approach to technology scouting, we argue that AI has significant potential to improve nuclear material production by enhancing system efficiencies with the aim of optimizing output, reducing costs, and boosting safety in production associated with the development and production of nuclear weapons. A comprehensive list of the existing AI applications to nuclear material production-critical equipment and to related non-nuclear industry applications integrable to the nuclear material production is presented in Appendix 1, (immediately below the main text).
The AI-powered nuclear material production process raises concerns of the illicit and covert development of nuclear weapons. Therefore, a three-fold solution with feasible action plans is discussed in the final section. Although nuclear material production is the focus of this article, the findings, concerns, and solutions being addressed are also applicable to the broader debate on the production of material used to build weapons of mass destruction, including radiological, biological, and chemical weapons.
Proliferation-sensitive stages in nuclear material production
Nuclear material production consists of several steps, including mining and milling, conversion, enrichment, fuel fabrication, electricity generation, spent fuel storage, and reprocessing.[5] While each production step is important, the enrichment and reprocessing phases are the most proliferation sensitive phases. These phases provide the basis for enriching uranium and/or the separating the uranium and plutonium isotopes that are pillars[6] integral to the development of a nuclear weapon; thus, improved accessibility to these technologies through AI presents both horizontal and vertical proliferation risks[7] (Gartzke and Kroenig 2014).
Although the application of AI within the enrichment and reprocessing phases is an ongoing effort to further the application of nuclear science and technology for good, AI as a dual-use technology within nuclear material production space has been largely neglected within the academic and practitioner communities. Therefore, a widening opportunity for AI to aid in illicit and covert non-peaceful applications exists.
AI’s potential applications in the nuclear material production
Industrial applications of AI can be broadly divided into three categories: anomaly detection, automated optimization, and automated discovery. Each of these techniques has functioned and been applied in civilian industries, and each can affect proliferation-sensitive stages of nuclear material production. A summary of the key potential application of AI in the nuclear material production is illustrated in Table 1 with potential use cases specified in Appendix 1.
Table 1. Key potential applications of AI in nuclear material production
Anomaly detection. An AI anomaly-detection algorithm is trained to recognize machine or system data featuring “normal behaviors.” When real-time data deviates from the normality pattern, the AI algorithm will identify the anomaly. Early-stage defection alarms make inspection and fixation possible before mechanical or system breakdown.
The use cases of AI anomaly detection fall into two categories. First, AI is used by industries to monitor, detect, and diagnose faults in machines. An example is the anomaly detection of centrifugal pumps (Al Tobi et al. 2022; Nabli and Hassani 2009). Second, AI is also widely applied in cyber defense products to detect anomalies led by cyberattacks or infections, which is a growing threat to sophisticated nuclear programs (Al Tobi et al. 2022; Nabli and Hassani 2009). An example is General Electric’s AI cyber defense solution, Digital Ghost, which serves the US Department of Energy, an agency responsible for managing the nation’s enriched uranium supply, in protecting critical infrastructure (General Electric n.d.a).
The aforementioned applications can be readily integrated into the nuclear material production process, provided that the training, testing, and verification data of the critical machines and computer systems involved in the producing process are accessible. Specific applications include adopting AI anomaly detection solutions to prevent failure of critical nuclear material production equipment (like centrifuges) and computer systems (such as management or cyber defense systems). Similar AI solutions can also advance efficiency and safety in human-centric knowledge production processes that facilitate nuclear material production (like advanced fissile isotope separation methods[8]).
Automated optimization. Automated optimization solutions train AI algorithms to analyze data with predefined parameters in an industrial process. Based on this analysis, the algorithms can predict product quality and correct problematic parameters to improve it. When applied to complex systems, AI algorithms can set up many factors at different levels, simulate their performance, and identify the best combination for achieving optimized solutions.
The use cases of AI automated optimization in civilian industries are three-fold. The first is industrial production. For instance, artificial neural networks, a type of deep learning algorithm, are used to monitor and adjust the performance of centrifuges in the separation processes (Funes et al. 2009; Jiménez et al. 2008; Menesklou et al. 2021). The second is industrial design. Examples include determining the optimum configuration for race cars used in different races (Monolith AI n.d.), the optimum design of computer chips (Mirhoseini et al. 2021), and the optimum shape for the crown of a piston in a diesel engine (Bogaisky 2019). The third is logistic planning. Examples of this include the reduction of the airplane turnaround time and the optimization of delivery fleet routing (General Electric n.d.b; Google n.d.).
These AI optimization solutions can also be integrated into the nuclear material production with the availability of machine or system data. This has already been applied to optimize the dimensions of a rotating baffle in gas centrifuges for uranium enrichment (Migliavacca et al. 2002). Further potential nuclear material production use cases include improvement of machine, such as nuclear centrifuges, configuration; the design of machine (including centrifuge) parts; and the efficiency of the nuclear material production lines, such as the arrangement of centrifuge cascades and the broader management of the nuclear material production process. Other human-centric nuclear research can also benefit from automated optimization solutions, which may in turn revolutionize the nuclear material production.
Automated discovery. AI algorithms are trained to understand the rules of a game by identifying key parameters at an initial stage, then developing their own algorithms to determine the best solution for the game. For example, from playing games like AlphaGo, AlphaZero (Silver et al. 2017; Silver et al. 2018), to protein structure prediction, such as AlphaFold (Jumper et al. 2021), code generation (e.g., AlphaCode) (Li et al. 2022), and faster matrix multiplication discovery (e.g., AlphaTensor) (Fawzi et al. 2022), AI has demonstrated its capability to revolutionize the scientific world at an exponential rate. Nevertheless, in the nuclear sciences, the application of automated discovery remains in early development stages. Consequently, few existing industrial applications are ready to be integrated into the nuclear material production or even feasibility research.[9]
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Automated discovery techniques not only advance computational and data processing power through hardware (examples include AI chips and computers) innovation, but also accelerate the development and upgradation of computer systems through automatic code generation (such as in cyber defense or industrial management systems). More significantly, the technique foresees the realization of machine-centric nuclear material knowledge production, as in the case of protein structure prediction[10], which accelerates the speed and accuracy of human-based research, for instance on new fissile isotope separation methods[11] and more efficient materials[12]. Automated discovery has the most potential among the three AI applications mentioned; as such applications advance, they could fundamentally affect the entirety of the nuclear material production lifecycle process.
The way forward . . .
As demonstrated, AI has already impacted several stages of nuclear material production, and its premise as a dual-use technology must be properly managed. While this endeavor requires an all-out effort from all involved parties, the scope of this discussion may focus on a three-dimensional solution.
Recommendation 1: State actors should be responsible for designing and executing effective nuclear material production-related data and infrastructure governance.
To account for the emergence of new dual-use technologies such as AI, existing legal and non-legal frameworks need to evolve.[13] However, the current political environment has constrained global consensus-building, even in cases where reaching consensus benefits all parties.[14] Nonetheless, states remain decisive actors in monitoring and regulating dual-use applications of AI as it relates to nuclear material production.
The scope of monitoring and regulating dual-use applications of AI should exclude AI algorithms; they are open-sourced and globally accessible, and therefore, essentially impossible to monitor and regulate. Instead, the scope should focus on two AI-supporting elements, the first of which is data. Since the precision of an AI solution depends on the quality and quantity of the training and testing data, the transfer of sensitive data around nuclear material production, including the peaceful production of nuclear material, should be safeguarded through enacting proper regulatory measures on technologies, data transfer, and security standards like cybersecurity.[15] The second focus should be on information infrastructure. As the function of AI-powered systems depend on advanced information infrastructures, including fast-speed broadband, cloud storage, AI chips, and supercomputers, among other things, the acquisition and transfer of these critical AI infrastructures should also be monitored. Therefore, export control of AI systems should focus on the transfer of training and testing data, as well as supporting infrastructure.
Data and infrastructure governance can be achieved via unilateral, bilateral, or multilateral solutions, as well as informal and formal means. A ready-to-implement platform is national export control regimes. Hitherto, the United States, the European Union, Russia, the People’s Republic of China, and other political entities with nuclear capabilities have increasingly fortified national legislation around functional export control mechanisms for technologies and data critical to their national security interests (Pacific Northwest National Laboratory, n.d.; PRC 2017; PRC 2020; PRC 2021; European Union 2021; Federal Service for Technical and Export Control of Russia n.d.; Vladimirova et al. 2014). In addition to unilateral efforts, states should also pursue related multilateral discussions based on a shared interest in improving AI-specific export control regime mechanisms, rather than enabling diverging political positions to hinder such discussions (Fisher 1991). An existing conduit for facilitating discussions and future negotiations in this regard is the Nuclear Suppliers Group (NSG), where member state participants agreed to voluntarily implement “guidelines for nuclear exports and nuclear-related exports” (Nuclear Suppliers Group n.d.a).[16]
Recommendation 2: The non-proliferation sector should develop an AI-proficient workforce supported by external AI industry partnerships.
State actors, nongovernmental organizations, and intergovernmental organizations within the nuclear domain have had limited interaction with AI experts, resulting in a knowledge gap that can be reduced through collective discussions.[17] As such, building awareness and sustainable partnerships, both formal and informal, is vital.
To mobilize industry engagement in the non-proliferation sphere, a three-step approach should be taken by states, nongovernmental organizations, and intergovernmental organizations. First, researchers and scientists must develop and maintain a comprehensive understanding regarding the state-of-the-art AI research as well as most advanced industrial use cases associated with the nuclear material production. A visualized example is illustrated in Appendix 1. This can be self-initiated or under institutional cooperation[18]. Ideally, a fully developed table, as illustrated in Appendix 1, summarizing AI’s applicability to nuclear material production would be shared within the nuclear policy making community to develop a shared understanding on the subject, which in turn could serve as the foundation for future policy discussions.
Second, platforms and initiatives must be created and expanded to integrate the AI-related industry into the nuclear policy debate. For example, several United Nations (UN)-based organizations initiated an “AI for Good” program to identify and promote AI applications that accelerate the furtherment of the United Nations Sustainable Development Goals (UN SDGs). A recent sub-initiative, entitled “AI for Atom,” addresses AI applications, methodologies, and tools that can advance nuclear science and technology (Peeva 2021). However, the impact of AI on nuclear material production and modernization, as well as its potential risks, has yet to be addressed. The broader “AI for Good” program could be expanded to include AI industrial partners to facilitate knowledge exchange around dual-use applications of AI and their potential implications in maintaining the non-proliferation regime.
Third, industrial advisory boards must be established within the relevant policy-making bodies. These advisory boards would serve two purposes: the minimization of the AI knowledge gap and the creation of effective export control guidelines. This effort could rely on intergovernmental organizations— including the International Atomic Energy Agency and the UN Office for Disarmament Affairs, and by extension, the Wassenaar Arrangement and World Customs Organization—to promote discussions around emerging technologies and their potential implications for the maintenance of the non-proliferation regime. Meanwhile, the Nuclear Suppliers Group, as a binding mechanism, presents another means for member states to create effective export control guidelines through the inclusion of an industrial advisory board.[19]
Recommendation 3: Civil society and the international community should promote ethical AI as a means to incentivize government- and self-compliance in the AI industry.
Industries do not always comply with states’ policy goals or collective interests. Therefore, measures should be taken to stimulate industry compliance and engagement in non-proliferation efforts. Building a narrative that encourages compliance with AI ethical guidelines and regulations may involve highlighting the reputational costs of failing to comply and supporting the moral considerations of employees; such efforts could require outreach programs and government action (Stewart et al. 2016). For example, demonstrable industry-based association with the UN sustainable development goals has become increasingly important as companies manage employee and customer expectations surrounding sustainability, integrity, and values in an increasingly global and competitive market (United Nations Global Compact n.d.). Some of the leading suppliers of AI technology, including Amazon (Amazon n.d.), IBM (IBM 2018), and C3.ai (C3.ai n.d.), have expanded their business model to this end. Thus, the UN’s sustainability goals are promising instruments for governments, nongovernmental organizations, and intergovernmental organizations to leverage when negotiating for transparency and accountability within the AI industry.
Civil society organizations must be fully aware of their responsibility as gatekeepers of the non-proliferation regime and utilize their influence to counteract governmental policy preferences and industrial incentives that have the potential to negatively affect the effectiveness of efforts to manage the risk associated with AI’s use in nuclear materials processing. The first step toward achieving this goal is to increase civil society’s efforts to expose the potential of AI-driven industrial activities to increase the proliferation of nuclear material. The second step is to translate the policy preferences of civil society into customer-based reputational costs for the AI industry. For example, civil society groups could foster a grassroots initiative that encourages companies to agree to report end-users when transferring data, AI-powered systems, and supporting infrastructures with a potential to facilitate high-enriched uranium and plutonium production. Such an effort could stimulate market self-regulation, as companies see a way to reduce the possibility of reputational damage by adhering to the precepts of the UN’s AI for Good and Sustainable Development Goals programs.
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A Digest of tonight’s (11/12/23) major nuclear media headlines is listed below by Category. There are no bonus Yellowstone Caldera headlines available. The link to Sky News’ coverage of the Russia/Ukraine war is available at the end of this Post. (Just a reminder: When there is no link to a media story of interest, copy and paste the headline and lead line into your browser to find the article you are seeking.)
ACCESS TO “LLAW’s ALL THINGS NUCLEAR” RELATED MEDIA:
Whenever there is an underlined link to a Category media news story, if you press or click on the link provided, you no longer have to cut and paste to your web browser, since this Post’s link will take you directly to the article in your browser.
A current Digest of major nuclear media headlines with automated inks is listed below by nuclear Category. There are two Yellowstone Caldera bonus stories available in this Post. The latest Sky News coverage of the Russia/Ukraine war is available at the end of the other categorized Posts.
(Just a reminder: When linked, the access to the media story will be underlined. If there is no link to a media story of interest you can still copy and paste the headline and lead line into your browser to find the article you are seeking. Hopefully this will never happen.)
And today’s nuclear world’s News:
All Things Nuclear
NEWS
A Pennsylvania man saw the true power of Atomic Annie – Little Rock Public Radio
Little Rock Public Radio
All Things Considered. Next Up: 6:30 PM Marketplace. 0:00. 0:00. All Things … Russia is trying to change the dynamics by talking nuclear war and …
A Pennsylvania man saw the true power of Atomic Annie | New Hampshire Public Radio
NHPR
All Things Considered · Today’s Schedule · All Radio Programs · Printable … Russia is trying to change the dynamics by talking nuclear war and …
Nuclear reactor deal collapse challenges Portland company’s clean energy plan – KLCC
KLCC
All Things Considered. KLCC. All Things Considered. Next Up: 6:00 PM The World. 0:00. 0:00. All Things Considered. KLCC. 0:00 0:00. Available On Air …
Nuclear Power
NEWS
U.S. Nuclear Power Outages vs. Capacity – CleanTechnica
CleanTechnica
U.S. Nuclear Power Outages vs. Capacity. Every morning, each nuclear electricity generator in the United States reports its operating status to the …
U.S. Bets on Small Nuclear Reactors to Help Fix a Huge Climate Problem
The New York Times
A tangle of pipes and tanks in a large room. Steam feeding into the Unit 3 turbine generator of the Vogtle nuclear power plant in Waynesboro, Ga. When …
Nuclear project canceled in Biden clean energy agenda blow – Lincoln Journal Star
Lincoln Journal Star
A project to build a first-of-a-kind small modular nuclear reactor power plant has been terminated, a blow to the Biden administration’s clean …
Nuclear War
NEW
Putin ally outlines how Russia could kickstart horror nuclear war with NATO – Daily Express
Daily Express
Putin and his cronies have repeatedly threatened Europe and the US they with Russia’s nuclear arsenal since the start of the war in Ukraine.
Russian military official predicts how nuclear war with Nato might start – The Times of India
The Times of India
Europe News: A former Russian military officer has warned that tensions between Russia and NATO countries could escalate into a nuclear …
Putin Ally Threatens to Obliterate NATO Countries With Nuclear Weapons – Newsweek
Newsweek
An attack by Russia against one of these members, therefore, could lead to a much broader international conflict. Newsweek reached out to NATO …
Nuclear War Threats
NEWS
Russian military official predicts how nuclear war with Nato might start – The Times of India
The Times of India
The threat of using nuclear weapons against Western nations has increased amid Russia’s invasion of Ukraine. Russian officials and state media have …
Putin Ally Threatens to Obliterate NATO Countries With Nuclear Weapons – Newsweek
Newsweek
Russian threats of a potential nuclear conflict with the West have … Russian colonel predicts how nuclear war with NATO could begin · Russia’s …
Nuclear-Armed Submarine-Launched Cruise Missile Could Come Back to Life
Warrior Maven
… nuclear threat environment is changing rapidly.” The Arms Control Today essay also cites support for the SLCM also coming from Senators Deb …
Yellowstone Caldera
NEWS
Katmai Volcano Volcanic Ash Advisory: VA NOT IDENTIFIABLE FROM SATELLITE DATA
Volcano Discovery
Katmai volcano. Stratovolcano with central caldera 2047 m / 6716 ft … List and interactive map of current and past earthquakes near Yellowstone …
The latest Sky News coverage of the Russia/Ukraine war:
Key points
