Technology Foundations of Generative AI: Architectures, Algorithms, and Innovations

To realize strategic convergence, augmentation, or automation with human-led generative AI, the following high level solution building blocks should be considered to address the aspects of the computation, capability, control, and connectedness for human-led generative AI driving new or already existing digital programs.

The language models, applications, and interfaces are multimodal - bringing language in combination with other media, especially images, graphics, video, and audio:

Each modality demonstrates the versatility and potential of Generative AI in transforming various industries and creative domains. However, to unlock the full potential of Gen AI the combination of multiple modalities is key.

Throughout this journey, each technological advancement in AI has built upon the last, leading to exponential growth in capabilities. The AI field continues to evolve, driven by ongoing research, innovation, and a keen focus on ethical and responsible development.

As a CTO, making informed choices about technology is crucial, especially when it comes to emerging fields like Generative AI. The potential of Generative AI to create realistic and creative content is immense, but it requires careful consideration.

Factors such as scalability, ethical implications, information security and data privacy must be weighed against the benefits of using Gen AI. By understanding the technology’s capabilities and limitations, CTOs can make strategic decisions that align with their organization’s goals and values.

Open Source, Foundation Models and the use of Hyperscalers

The following points can be used as a practical guide to choosing options for your business:

Overview and Comparison of Cloud based or self hosted
Comparing Generative AI in the context of Open Source, Foundation Models, Hyperscalers, and contrasting Cloud-based vs. Self-Hosted solutions requires a comprehensive understanding of each field:

Cloud-based solutions offer ease and scalability, while self-hosted options provide control and potential security benefits. Open Source is cost-effective but technically demanding, whereas foundation models offer out-of-the-box advanced capabilities. Hyperscalers provide integrated, scalable solutions.

In summary, choosing the best option depends on elements like the number of use cases planned and the governance (data protection etc.) required by the organization to indicate the demand. This must then be matched by the costs for supply, i.e. technical expertise in-house (or external), hardware/infrastructure in-house (or external) and ecosystems. 

Businesses must weigh these factors against their needs, capabilities, and resources to make the best choice.

Popular cloud AI vendors

CTO’s guide to Addressing Scalability and Computational Efficiency

Organizational adaptability and challenges

Generative AI is a powerful software solution that can create new content and insights from data, such as text, images, audio, and video. It has immense potential for growth and productivity, as it can augment human capabilities, automate complex tasks, and converge different functions and domains. However, generative AI also poses significant challenges for scalability and computational efficiency, as it requires massive amounts of data, advanced computational power, and sophisticated algorithms to train and run large multimodal models. Therefore, organizations need to adapt their strategies, architectures, and processes to leverage generative AI effectively and responsibly.

One of the key aspects of organizational adaptability is to align the generative AI solutions with the existing digital platform ecosystems and industry clouds, which provide the customer and employee experience, the business model, and the software-as-a-service capabilities. This can help to integrate generative AI into the products and services, expose the intended behavior to other applications and devices, and orchestrate the complex dataflows to the underlying language models. Moreover, this can help to leverage the existing cloud computing infrastructure, which can provide the needed GPU power and availability for the generative AI models.

Another aspect of organizational adaptability is to address the control and governance of generative AI, which can ensure the quality, reliability, and ethics of the generative AI outputs and interactions. This can involve creating software and organizational controls for the user input and model output, such as filters, plausibility checks, summaries, and overruling cases. It can also involve establishing a generative AI architecture board and a generative AI monitoring office, which can structure, monitor, and innovate the generative AI solutions from a business and technical perspective.

By adapting to the impact of generative AI, organizations can overcome the scalability and computational efficiency challenges and unlock the opportunities for convergence, augmentation, and automation. However, this also requires a human-led, scenario-based strategy, which can identify the relevant use cases, stakeholders, and outcomes for generative AI, and balance the trade-offs between the costs, benefits, and risks.

Data Flows and Data Pipelines

In the context of Generative AI (Gen AI), data pipelines and dataflows are critical components.

Data Pipelines in Gen AI are sequences of processing steps through which data is transformed and transported for AI model training and deployment. This is essential for handling large volumes of data efficiently and effectively. However, this also includes data collection, cleaning, transformation, and loading processes.

In turn, dataflows refer to the movement and transformation of data through the pipeline. This is crucial for ensuring that the right data reaches the AI models in the correct format.

LLM selection and computational efficiency

In an ideal scenario, companies seeking the optimal Large Language Model (LLM) for specific applications would implement a comprehensive evaluation process, contrasting various facets of different models. This comparison might encompass a range of models, including commercial, open-source LLMs, or even the development of proprietary ones. Examples of such models for benchmarking include ChatGPT, LlaMA, Claude, PaLM2, Vicuna, MPT, among others.

Data Protection and Regulations

Generative AI is subject to ethical, legal, and regulatory requirements in Europe, especially under the GDPR and the proposed AI Act. Depending on the use case and the processing steps, generative AI may involve the processing of personal data in different scenarios, such as when training the AI model, when users input personal data, or when the AI model uses the input data for further training. If personal data is involved, the obligations of the controllers and processors the GDPR sets out are such as lawfulness, fairness, transparency, purpose limitation, data minimisation, accuracy, storage limitation, integrity, confidentiality, accountability, and data subject rights.

However, some of these requirements may be difficult to fulfill in practice, due to the complexity and opacity of generative AI systems. For example, it may be hard to ensure the accuracy and fairness of the data and the outputs, to prevent algorithmic bias and discrimination, to explain the logic and the impact of the automated decisions or profiling, or to apply the data minimisation and storage limitation principles.

The draft AI Act of which a said final text has been made available to the public just recently aims to establish a harmonized framework for artificial intelligence in Europe and to classify AI systems according to their risk. The regulation will in some areas impose even stricter transparency obligations for generative AI systems, such as the obligation to inform users when they are interacting with or exposed to AI-generated content.

It is worth noting that existing privacy laws around the globe such as the CCPA have comparable requirements and also that many countries aim for comparable AI legislation as the European Union does. The most relevant topics regulators aim to cover are:

• Ensure Ethical and Responsible AI Development (e.g. in U.S., Canada, India, UK, Brazil, South Korea, UAE, Japan)
  
• Establish High-Risk AI System Oversight (e.g. in U.S., Canada, UK)
• Promote International Collaboration and Standards (e.g. in U.S., Canada, UK)
• Invest in AI Research and Development (e.g. in U.S., Canada, India, UK, Brazil, South Korea, UAE, Japan)
• Ensure Public Trust and Accountability (e.g. in U.S., Canada, UK, India)

PwC, as a community of solvers, builds trust for human-led generative AI leading the impact on human interfaces, software apps, and creative teams with a strategy that focuses on software, investment, and risk. That approach enables organizations to drive the considerably sizable potential of generative AI to succeed with their specific convergence, augmentation, and automation opportunities for their digital programs.

GenAI Readiness Assessment

Assessing the organizational readiness for leveraging GenAI at scale to achieve sustainable business values.

Trustworthy GenAI Strategy

Establishing a holistic corporate GenAI Strategy to transform motivation into real business value while ensuring a responsible and trustworthy usage.

GenAI Use Case Discovery

Identification of value-adding use cases as well as transferring those into a standardized and prioritized portfolio followed by strategic implementation.

GenAI Operating Model & Roles

Defining the target operating model and required roles for ensuring a successful integration, ownership and maintenance of GenAI solutions into enterprise organizations while planning.

GenAI Infrastructure & Data

Enhancing infrastructural depth, maximizing data value with regular monitoring, maintenance, and optimization of technical systems.

Awareness & Enablement

Aligning a holistic change management and education concept with the GenAI integration in order to upskill employees and to shape awareness across the organization as prerequisite for risk mitigation.

Alliance Ecosystem & Living experience

We at PwC are working closely with our established alliance partners like Microsoft and Google to access the latest environments and to bring AI to our clients.