A survey of current developments in imaginative and prescient and multimodal fashions together with issues for leaders as they place their organizations to capitalize on the approaching wave of AI fueled change.

I. Introduction

The previous 18 months have ushered in great change that’s disrupting the very nature of labor. Generative Synthetic Intelligence (GenAI), Massive Language Fashions (LLMs), and basis fashions have develop into ubiquitous in vernacular. These fashions, containing billions of parameters and educated on large quantities of knowledge utilizing self-supervised strategies, are performing advanced pure language duties and exhibiting extra generalized intelligence in comparison with earlier fashions [i][ii]; fueling unparallel productiveness good points throughout various industries by means of quite a few use circumstances reminiscent of personalised buyer care and self-service [iii], data administration [iv] and content material creation [v], analysis and improvement [vi], fraud detection [vii][viii], language translation [ix], and even forecasting of life expectancy [x].

Intently following on this wake are rising developments in laptop imaginative and prescient strategies and approaches. On the forefront of this shift are developments in imaginative and prescient transformer (ViT) architectures which can be propelling laptop imaginative and prescient capabilities into unprecedented ranges of sophistication. Consciousness of the fast improvement and maturation of those capabilities is essential to navigating the quickly evolving AI panorama. Now, greater than ever, protection leaders want to grasp and harness these capabilities inside Processing, Exploitation, and Dissemination (PED) and mission planning workflows to allow sensemaking at scale.

II. Rise of the Imaginative and prescient Transformer Structure

Convolutional neural networks (CNNs) [xi] have historically held dominance inside laptop imaginative and prescient, demonstrating excessive efficiency on widespread duties reminiscent of picture classification, object detection, and segmentation. Nevertheless, coaching such fashions requires vital quantities of labeled information for supervised studying, a extremely labor-intensive activity that’s difficult to scale and sluggish to adapt to dynamic modifications within the atmosphere or necessities. Moreover, the labeled datasets that do exist within the public area might steadily be unsuitable to the distinctive use circumstances and/or imagery varieties that exist throughout the nationwide safety area.

Current years have seen the inception of the ViT structure as a number one contender within the laptop imaginative and prescient enviornment. The facility of ViTs is of their capability to decompose photos into mounted dimension patches and encode these fragments right into a linear sequence of embeddings that seize semantic representations, just like a sentence that describes the picture. The ViT then sequentially understands every fragment, making use of multi-head self-attention to acknowledge patterns and seize relationships globally throughout all fragments, to construct a coherent understanding of the picture [xii].

This ends in a number of advantages over CNNs. At first, ViTs are proven to display efficiency that matches or exceeds the state-of-the-art in comparison with CNNs on many picture classification datasets when educated on giant portions of knowledge (e.g., 14 million — 300 million photos). This degree of efficiency is achieved whereas requiring 2–4 instances much less compute to coach. As well as, ViTs can natively deal with photos of various dimension as a result of their capability to course of arbitrary sequence lengths (inside reminiscence constraints). Lastly, ViTs can seize long-range dependencies between inputs and supply enhanced scalability over CNNs. ViTs do have some limitations compared to CNNs. ViTs are unable to generalize properly when educated on inadequate information as a result of missing robust inductive biases, reminiscent of translation equivariance and locality. Because of this, CNNs outperform ViTs on smaller datasets. Nevertheless, when contemplating the scaling challenges current inside DoD, ViTs present promise as an structure to guide on this area.

2023 noticed a number of laptop imaginative and prescient advances leveraging ViT architectures. Whereas by no means exhaustive, 4 fashions that spotlight the fast evolution of laptop imaginative and prescient are Distillation of Information with No Labels Model 2 (DINOv2), the Section Something Mannequin (SAM), the Joint-Embedding Predictive Structure (JEPA), and the Prithvi geospatial basis mannequin.

DINOv2 [xiii] leverages two ideas that superior laptop imaginative and prescient. The primary idea is that of self-supervised studying of visible options instantly from photos, eradicating the necessity for giant portions of labels to help mannequin coaching. Central to this method is DINOv2’s information processing pipeline, which clusters photos from a big uncurated dataset with photos from a smaller curated dataset by means of a self-supervised retrieval system. This course of ends in the power to create a big augmented curated dataset and not using a drop in high quality, a key hurdle that should be crossed in scaling picture basis fashions. Moreover, DINOv2 employs a teacher-student distillation technique to switch data from a big mannequin to smaller fashions. At a excessive degree, this method works by freezing the weights of the massive mannequin with the aim of minimizing the variations between the embeddings coming from the smaller fashions with that of the bigger mannequin. This technique is proven to attain higher efficiency than making an attempt to coach smaller fashions instantly on the info. As soon as educated, DINOv2 discovered options display superb transferability throughout domains and the power perceive relations between comparable elements of various objects. This ends in a picture basis mannequin whose outputs can be utilized by a number of downstream fashions for particular duties.

SAM [xiv] is a picture segmentation basis mannequin able to promptable zero-shot segmentation of unfamiliar objects and pictures, with out the necessity for extra coaching. That is completed by means of an structure with three elements: a ViT picture encoder, a immediate encoder capable of help each sparse (e.g., factors, packing containers, textual content) and dense (i.e., masks) prompts, and a quick masks decoder that effectively maps the picture embedding, immediate embeddings, and an output token to an autogenerated picture masks. SAM isn’t with out limitations because it requires large-scale supervised coaching, can miss wonderful constructions, undergo from minor hallucinations, and will not produce boundaries as crisp as different strategies. Nevertheless, preliminary efforts current alternative to handle mission use circumstances that require the power to section objects in imagery.

Initially tailored to picture duties, JEPA [xv] is the primary laptop imaginative and prescient structure designed to handle crucial shortcomings in present ML programs wanted to succeed in human ranges of studying and understanding of the exterior world [xvi]. JEPA makes an attempt to beat limitations with present self-supervised studying strategies (e.g., invariance-based strategies, generative strategies) by means of predicting lacking picture info in an summary illustration area. In observe, that is carried out by predicting the representations (e.g., embeddings) of assorted goal blocks (e.g., tail, legs, ears) in a picture primarily based on being supplied a single context block (e.g., physique and head of a canine). By predicting semantic representations of goal blocks, with out explicitly predicting the picture pixels, JEPA is ready to extra intently replicate how people predict lacking elements of a picture. Extra importantly, JEPA’s efficiency is comparable with invariance-based strategies on semantic duties, performs higher on low-level imaginative and prescient duties (e.g., object counting), and demonstrates excessive scalability and computational effectivity. This mannequin structure is continuous to be superior with the introduction of latent variable energy-based fashions [xvii] to attain multimodal predictions in high-dimensional issues with vital uncertainty (e.g., autonomous system navigation) and has not too long ago been tailored to video [xviii].

Lastly, IBM, by means of a public/personal partnership involving NASA and IBM Analysis, developed the primary open-source geospatial basis mannequin for distant sensing information referred to as Prithvi [xix]. Mannequin improvement leveraged a First-of-a-Form framework to construct a consultant dataset of uncooked multi-temporal and multi-spectral satellite tv for pc photos that prevented biases towards the commonest geospatial options and eliminated noise from cloud cowl or lacking information from sensor malfunctions. This dataset was then used for self-supervised basis mannequin pretraining utilizing an encoder-decoder structure primarily based on the masked autoencoder (MAE) [xx] method. Prithvi was subsequently fined tuned utilizing a small set of labeled photos for particular downstream duties, reminiscent of multi-temporal cloud imputation, flood mapping, fire-scar segmentation, and multi-temporal crop segmentation. Importantly, Prithvi is proven to generalize to completely different resolutions and geographic areas from your entire globe utilizing a number of labeled information throughout fine-tuning and is getting used to transform NASA’s satellite tv for pc observations into custom-made maps of pure disasters and different environmental modifications.

III. Fast Evolution: AI Traits in Flux

2023 additionally launched the convergence of LLMs and ViTs (together with different modes) into Massive Multimodal Fashions (LMMs), additionally known as imaginative and prescient language fashions (VLM) or multimodal giant language fashions (MLLM). The power of those fashions lies of their capability to mix the understanding of textual content with the interpretation of visible information [xxi]. Nevertheless, this isn’t with out challenges as coaching giant multimodal fashions in an end-to-end method could be immensely pricey and threat catastrophic forgetting. In observe, coaching such fashions typically entails a learnable interface between a pre-trained visible encoder and an LLM [xxii].

A number of influential fashions had been launched, to incorporate Google’s PALM-E [xxiii] robotics vision-language mannequin with state-of-the-art efficiency on the Outdoors Information Visible Query Answering (OK-VQA) benchmark with out task-specific wonderful tuning and the not too long ago launched Gemini [xxiv] household of fashions, educated multimodally over movies, textual content, and pictures. As well as, Meta launched ImageBind [xxv], an LMM that learns a joint embedding throughout six completely different modalities (i.e., photos, textual content, audio, depth notion, thermal, and inertial measurement unit (IMU) information). Two fashions, specifically, spotlight the fast evolution on this area.

The primary of those is Apple’s Ferret [xxvi] mannequin, which might deal with the issue of enabling spatial understanding in vision-language studying. It does so by means of unified studying of referring (the power to grasp the semantics of a selected level or area in a picture) and grounding (the method of utilizing LLMs with related, use-case particular exterior info) capabilities inside giant multimodal fashions. This mannequin elevates multimodal imaginative and prescient and language capabilities one step nearer to the way in which people course of the world by means of seamless integration of referring and grounding capabilities with dialogue and reasoning. To attain outcomes, Ferret was educated by way of GRIT, a Floor-and-Refer Instruction-Tuning dataset with 1.1M samples together with grounding (i.e., text-in location-out), referring (location-in text-out), and blended (textual content/location-in textual content/location-out) information masking a number of ranges of spatial data. The mannequin was then evaluated on duties collectively requiring referring/grounding, semantics, data, and reasoning, demonstrating superior efficiency when evaluated on typical referring and grounding duties whereas decreasing object hallucinations.

The second of those is Massive Language and Imaginative and prescient Assistants that Plug and Study to Use Abilities (LLaVA-Plus) [xxvii], a general-purpose multimodal assistant that was launched in late 2023 and constructed upon the preliminary LLaVA [xxviii] mannequin launched earlier within the 12 months. The design of LLaVA-Plus was influenced by the Society of Thoughts idea of pure intelligence [xxix], through which emergent capabilities come up from mixture of particular person activity or ability particular instruments. The modularized system structure presents a novel method that permits an LMM, working as a planner, to be taught a variety of expertise. This allows the growth of capabilities and interfaces at scale by means of leveraging a repository of imaginative and prescient and vision-language specialist fashions as instruments to be used when wanted. This facilitates not solely user-oriented dialogues, the place the mannequin instantly responds to person instruction utilizing innate data, but additionally skill-oriented dialogues the place the LMM can provoke requests to name the suitable specialist mannequin in response to an instruction to perform a activity. Whereas there are limitations as a result of hallucinations and power use conflicts in observe, LLaVA-Plus is an progressive step to new strategies for human-computer teaming by means of multimodal AI brokers.

Lastly, as thrilling as these developments are, one could be remiss with out mentioning experimentation with rising architectures which have the potential to revolutionize the sphere some extra. The primary structure is the Rententive Community [xxx] (RetNet), a novel structure that may be a candidate to supersede the transformer because the dominant structure for laptop imaginative and prescient, language, and multimodal basis fashions. RetNets display advantages seen in transformers and recurrent neural networks, with out a number of the drawbacks of every. These embrace coaching parallelism, low value inference, and transformer-comparable efficiency with environment friendly long-sequence modeling. RetNets substitute typical multi-head consideration, used inside transformers, with a multi-scale retention mechanism that is ready to absolutely make the most of GPUs and allow environment friendly O(1) inference when it comes to reminiscence and compute.

The second is IBM’s not too long ago launched Mixtral 8x7B [xxxi] mannequin, a decoder-only Sparse Combination of Consultants (SMoE) language mannequin the place every layer of the mannequin consists of eight feedforward blocks that act as consultants. This novel structure achieves quicker inference speeds with superior cost-performance, utilizing solely 13B lively parameters for every token at inference. It does so by means of an method the place every token is evaluated by two consultants at a given timestep. Nevertheless, these two consultants can differ at every timestep, enabling every token to entry the total sparse parameter rely at 47B parameters. Of word, the mannequin retains the next reminiscence value that’s proportional to the sparse parameter rely. This mannequin structure confers great advantages. At one tenth of the parameters, Mixtral 8x7B is ready to match or exceed the efficiency of LLAMA 2 70B and GPT-3.5 (175B parameters) on most benchmarks. As well as, the associated fee efficiencies of this mannequin are conducive to deployment and inference on tactical infrastructure, the place compute, dimension, and weight constraints are an element.

Though various and developed to perform completely different duties, the fashions lined right here illustrate the numerous innovation pathways which can be being traversed in advancing AI capabilities. Of word, are the distinction mannequin courses (e.g., encoder solely, encoder-decoder, decoder solely) which can be employed throughout the assorted fashions. A future effort could also be to discover if there are efficiency advantages or tradeoffs as a result of class primarily based on the duty.

As these capabilities proceed to mature, we are going to possible see a combining of options inside fashions as sure options develop into expectations for efficiency. There may even be a shift in direction of creation of a multi-model ecosystem in recognition that one dimension doesn’t match all. As a substitute, AI brokers appearing as planners, orchestrators, and teammates will collaborate to dynamically choose one of the best specialist mannequin or software for the duty primarily based on use case or Persona of Question pushed wants [xxxii].

IV. Challenges and Dangers

Whereas the earlier survey of mannequin developments helps illustrate the growing fee of change inside this subject spurred by developments in generative AI and basis fashions, there are a number of challenges that can not be neglected as Federal organizations take into account make use of these capabilities. For the needs of this part, we reference analysis primarily addressing LLMs. This was a deliberate alternative to focus on dangers inherent to fashions that leverage the autoregressive transformer structure.

First, is the difficulty of useful resource constraints, each for enterprise coaching and inferencing of fashions and for mannequin coaching and inferencing on the edge. The rise of ever bigger AI fashions encompassing a number of billions of parameters is resulting in strained sources as a result of infrastructure prices for compute, specialised AI expertise wanted to implement capabilities, and the challenges related to amassing, curating, and coaching on the colossal information volumes required for such fashions. Such challenges can translate into monetary shocks to organizational budgets that will have been set within the years prior because of the must run excessive efficiency servers outfitted with GPUs or entice and retain prime AI expertise. Moreover, there’s an growing must carry out coaching, retraining, and inferencing of fashions on the edge to help the processing, exploitation, and dissemination of detections of multimodal information. This requires the power to run fashions on smaller {hardware} (e.g., human packable gadgets, onboard autonomous programs or sensors), the place dimension, weight, and energy are vital issues.

The second of those is the difficulty of trustworthiness. To depend on generative AI and basis fashions inside mission crucial workflows, one should have the ability to belief the output of such fashions. As such, the trustworthiness of fashions is of paramount concern. A lot of the discourse on this matter has targeted on hallucinations throughout the output, in addition to makes an attempt to outline a broad set of dimensions towards which to measure trustworthiness [xxxiii][xxxiv]. Whereas these are legitimate issues, trustworthiness extends past these dimensions to additionally embrace guaranteeing that the mannequin arrives at the very best end result primarily based on the most recent corpus of knowledge and coaching. One should have the ability to belief that the end result is a worldwide most when it comes to suitability for the duty, versus a neighborhood most, which might have actual world impacts if embedded right into a mission crucial workflow.

Third, and sure essentially the most daunting, is that of safety and privateness. To have the ability to leverage generative AI inside Federal environments, one should be ready to take action with out compromise to the community and the info that resides on that community. Analysis has proven that LLMs can pose dangers safety and privateness and such vulnerabilities might be grouped into AI mannequin inherent vulnerabilities (e.g., information poisoning backdoor assaults, coaching information extraction) and non-AI mannequin inherent vulnerabilities (e.g., distant code execution, immediate injection, aspect channel assaults). To this point, LLMs have been predominantly utilized in person degree assaults reminiscent of disinformation, misinformation, and social engineering [xxxv], though new assaults proceed to look. For instance, it has been proven that one can practice misleading LLMs capable of change their habits from trusted to malicious in response to exterior occasions or triggers, eluding preliminary threat analysis and making a false sense of belief earlier than attacking [xxxvi]. As well as, 2024 heralded the creation of AI worms [xxxvii] that may steal information and unfold malware and spam. Such an assault makes use of an adversarial self-replicating immediate embedded inside multimodal media recordsdata (e.g., textual content, picture, audio) to successfully jailbreak and activity the goal LLM. Ought to future LLM/LMMs be given entry to working system and hardware-level capabilities, then threats from these vectors might escalate dramatically.

These challenges aren’t with out alternatives. NIST not too long ago launched the inaugural model of its Synthetic Intelligence Threat Administration Framework [xxxviii] to assist with mitigating the dangers associated to AI. Nevertheless, the nascent nature of this subject implies that a lot nonetheless stays unknown. Couple this with the truth that rigidity and paperwork throughout the RMF course of implies that, in some circumstances, by the point expertise is authorised to be used and operationalized, it might be one or two generations behind state-of-the-art capabilities. Organizations face a problem of how do they operationalize expertise utilizing a course of that will take 9–12 months to finish when that very same expertise could also be surpassed inside six months.

V. Human-AI Collaboration: Redefining the Workforce

As AI tendencies proceed to advance, this can have a profound impression on the dynamics of the workforce. Collaboration between people and AI programs will develop into the norm as those that are ready and keen to companion with AI will expertise elevated effectivity, innovation, and effectiveness. Supported by autonomous or semi-autonomous actions by AI brokers [xxxix], human-AI groups will reshape how we make sense of and work together with the world.

AI may even play a pivotal function in reworking job roles and ability necessities. The workforce might want to adapt to this shift by buying new expertise and competencies that complement, not compete with, AI’s capabilities and strengths. There shall be a rising want for professionals who can successfully handle and collaborate with AI programs and different human-AI groups, growing the demand for gentle expertise reminiscent of emotional intelligence, crucial considering, and creativity.

This evolution in ability units would require modifications in organizational expertise packages to make sure coaching of the incoming workforce aligns to near-term and long-term organizational wants in AI. Along with specializing in incoming professionals, organizations should prioritize upskilling and reskilling of the prevailing workforce to maneuver the group as an entire by means of the transformation journey to embrace this new AI period. Whereas not lined in depth on this article, this matter is one which should be rigorously thought-about to advertise AI adoption in ways in which take note of moral issues and make sure that AI programs are designed and carried out responsibly.

VI. Future Outlook and Suggestions

The tempo of technological change will proceed to speed up over the following 18-month horizon. The exact path of this transformation is unpredictable, as every advancing month provides method to new developments that reframe the world’s understanding of the artwork of the doable. As breathtaking as some current capabilities are, these applied sciences are nonetheless in a nascent stage. To have enterprise and mission worth, the maturation and commercialization of generative AI capabilities should proceed, which is able to take a while.

As well as, Generative AI stays experimental and has not but been operationalized for crucial mission utility. As organizations take into account transfer ahead with utilizing the great energy of generative AI and basis fashions, any technique should be primarily based upon a Excessive OPTEMPO Concurrency the place one is concurrently experimenting with the latest expertise, growing and coaching on a steady foundation within the mode of “At all times in a State of Changing into” [xl]. To take action, organizations should be keen to just accept further threat, but additionally make use of rising applied sciences to modernize present strategies. For instance, LLMs have been proven to determine safety vulnerabilities in code with better effectiveness than main business instruments utilizing conventional strategies. Such strategies can be utilized to boost velocity and efficacy in detecting weak and malicious code as a part of the RMF course of [xli].

Posturing oneself to capitalize on AI developments, particularly within the realm of laptop imaginative and prescient, necessitates that leaders throughout the group develop into versed and stay present on quickly progressing developments in AI. As a part of their technique, organizations ought to take into account put money into the infrastructure and information basis that can allow an AI-first future. This contains constructing trendy information architectures and approaches to facilitate the fast alternate of data in addition to machine manipulation of knowledge and companies required to help automated discovery, understanding, and actions on the info. Furthermore, organizations want to start common experimentation now with the intention to construct the organizational capability and studying wanted for the long run.

VII. Conclusion

As we progress by means of the rest of the 12 months, the trajectory of technological development is poised to surge into uncharted realms of what’s doable with AI. The arrival of more and more intricate multimodal fashions will revolutionize human-AI collaboration. Interactive evaluation and interrogation of multimodal information, coupled with autonomous or semi-autonomous actions by AI brokers and heightened reasoning capabilities derived from fashions capable of create inside representations of the exterior world, will redefine operational landscapes.

The crucial to wield these capabilities to grasp and decipher huge swimming pools of visible and multimodal information, crucial to nationwide safety, will outline the latter half of this decade. Navigating this transformative period necessitates a forward-thinking mindset, the braveness to extend one’s threat urge for food, and the resilience to form organizational technique and coverage to capitalize on the approaching wave of change. As such, leaders should undertake a proactive stance in integrating AI, whereas putting an emphasis on its accountable deployment. Doing so will allow organizations to harness the total potential of evolving AI applied sciences.

All views expressed on this article are the private views of the creator.

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[xxxii] M. Zhuge, H. Liu, F. Faccio, D. Ashley, R. Csordás, A. Gopalakrishnan, A. Hamdi, H. Hammoud, V. Herrmann, Ok. Irie, L. Kirsch, B. Li, G. Li, S. Liu, J. Mai, P. Piękos, A. Ramesh, I. Schlag, W. Shi, A. Stanić, W. Wang, Y. Wang, M. Xu, D. Fan, B. Ghanem, J. Schmidhuber, “Mindstorms in Pure Language-Primarily based Societies of Thoughts,” arXiv:2305.17066. 2023, 1, 2, 3, 4

[xxxiii] L. Solar, Y. Huang, H. Wang, S. Wu, Q. Zhang, C. Gao, Y. Huang, W. Lyu, Y. Zhang, X. Li, Z. Liu, Y. Liu, Y. Wang, Z. Zhang, B. Kailkhura, C. Xiong, C. Xiao, C. Li, E. Xing, F. Huang, H. Liu, H. Ji, H. Wang, H. Zhang, H. Yao, M. Kellis, M. Zitnik, M. Jiang, M. Bansal, J. Zou, J. Pei, J. Liu, J. Gao, J. Han, J. Zhao, J. Tang, J. Wang, J. Mitchell, Ok. Shu, Ok. Xu, Ok. Chang, L. He, L. Huang, M. Backes, N. Gong, P. Yu, P. Chen, Q. Gu, R. Xu, R. Ying, S. Ji, S. Jana, T. Chen, T. Liu, T. Zhou, W. Wang, X. Li, X. Zhang, X. Wang, X. Xie, X. Chen, X. Wang, Y. Liu, Y. Ye, Y. Cao, Y. Chen, Y. Zhao, “TrustLLM: Trustworthiness in Massive Language Fashions,” arXiv:2401.05561. 2024, 6, 7

[xxxiv] Y. Liu, Y. Yao, J. Ton, X. Zhang, R. Guo, H. Cheng, Y. Klochkov, M. Taufiq, H. Li, “Reliable LLMs: a Survey and Guideline for Evaluating Massive Language Fashions’ Alignment,” arXiv:2308.05374. 2023, 7, 8, 9

[xxxv] Y. Yao, J. Duan, Ok. Xu, Y. Cai, Z. Solar, Y. Zhang, “A Survey on Massive Language Mannequin (LLM) Safety and Privateness: The Good, the Unhealthy, and the Ugly,” arXiv:2312.02003. 2024 1, 2

[xxxvi] E. Hubinger, C. Denison, J. Mu, M. Lambert, M. Tong, M. MacDiarmid, T. Lanham, D. Ziegler, T. Maxwell, N. Cheng, A. Jermyn, A. Askell, A. Radhakrishnan, C. Anil, D. Duvenaud, D. Ganguli, F. Barez, J. Clark, Ok. Ndousse, Ok. Sachan, M. Sellitto, M. Sharma, N. DasSarma, R. Grosse, S. Kravec, Y. Bai, Z. Witten, M. Favaro, J. Brauner, H. Karnofsky, P. Christiano, S. Bowman, L. Graham, J. Kaplan, S. Mindermann, R. Greenblatt, B. Shlegeris, N. Schiefer, E. Perez, “Sleeper Brokers: Coaching Misleading LLMs that Persist By Security Coaching,” arXiv:2401.05566. 2024, 1, 2, 3, 4, 5, 6

[xxxvii] S. Cohen, R. Bitton, B. Nassi, “ComPromptMized: Unleashing Zero-click Worms that Goal GenAI-Powered Functions,” https://websites.google.com/view/compromptmized. 2024

[xxxviii] Nationwide Institute of Requirements and Know-how, “Synthetic Intelligence Threat Administration Framework (AI RMF 1.0),” https://doi.org/10.6028/NIST.AI.100-1. 2023

[xxxix] J. Park, J. O’Brien, C. Cai, M. Morris, P. Liang, M. Bernstein, “Generative Brokers: Interactive Simulacra of Human Habits,” arXiv:2304.03442. 2023, 1, 2, 3

[xl] Idea referenced from Greg Porpora, IBM Distinguished Engineer on 21 February, 2024.

[xli] Y. Yao, J. Duan, Ok. Xu, Y. Cai, Z. Solar, Y. Zhang, “A Survey on Massive Language Mannequin (LLM) Safety and Privateness: The Good, the Unhealthy, and the Ugly,” arXiv:2312.02003. 2024 1, 2