How do you overcome the data and computational challenges of generative AI?

In my opinion, generative AI is a transformative force in technology, reshaping industries by enhancing efficiency and innovation. This advanced technology, powered by models like GANs and transformers, streamlines complex problem-solving and accelerates the development of solutions across fields, from healthcare to automotive. By using generative AI, businesses of all sizes can unlock unprecedented levels of creativity and productivity, propelling them into a future where the possibilities are limitless.

💡 Generative AI's core lies in advanced algorithms that create novel outcomes through data interaction. 🎨 Beyond GANs, VAEs, and Transformers, autoregressive and diffusion models advance generative AI. 🌐 Integration with AI domains like reinforcement and supervised learning enhances generative capabilities. 🔄 Generative AI's key aspect is continuous learning, improving output quality and diversity over time. 📊 Data efficiency innovations enable high-quality outputs with less data, a significant stride. 📌 Understanding the expansive and evolving landscape of generative AI techniques and their interplay with other AI domains is vital. Continuous learning and data efficiency are key to its advancement.

Generative AI is a subset of Artificial Intelligence distinguished by its capacity to produce new and lifelike content, spanning text, images, audio, video, and software code, etc. This branch of AI employs neural networks to recognize patterns within existing data, leveraging this understanding to generate novel content. Examples of Generative AI models include GPT-4 by OpenAI, PaLM2 by Google, and Llama2 by Meta.

Having worked in the field of natural language processing (NLP) for over 4 years, I have witnessed the significant shift from predictive to generative models. This transition has been driven by increasing compute capabilities. My definition of Generative AI - Today, GenAI is the science and art of solving human problems without using a lot of resources and energy of that person, saving time, and helping that person generate money. - In 5 years, Generative AI will help assist people in regular financially beneficial tasks as a Co-Pilot and Autonomous systems. - In 10 years, Generative AI will be used to build autonomous robotics to elevate society and change dreams into reality for every human being.

We have leveraged Generative AI for multiple use cases for enterprise. Sharing a few of them below: a) Automated Contract compliance to make few thousand contracts with customers and suppliers are complaint to diverse rules and regulations which differ across countries. Fortune 500 firms in B2B space, have thousands of contracts, and this has been a chronic problem for them. b) Build scripts for leaders at the CXO level when addressing security analysts. Using Generative AI, content is tailored keeping in my market sentiment about the specific firm in consideration, company's goals, macro economic trends, TAM of the industry in consideration etc.

As a CEO deeply embedded in the Gen AI space, I've seen the solution redefine the landscape of innovation and strategy. This technology helps empower businesses to solve complex, high-value problems with unprecedented efficiency. One use case that I can think of is its role in optimizing supply chains by predicting and adapting to disruptions in real-time, using advanced models that process vast datasets far more quickly than conventional methods. All in all, Gen AI isn't just an improvement but a leap forward that enables businesses to tackle challenges that were once considered insurmountable, fostering a new era of growth and innovation.

- 🔄 Implement data augmentation techniques to effectively increase the diversity and volume of your training datasets, which can help overcome data scarcity challenges. -🚀 Leveraging distributed computing frameworks and cloud-based solutions can address the computational demands of training generative AI models by providing scalable resources. - 🧠 Optimize model architectures, such as pruning and quantization, which reduce the computational load without significantly impacting the performance of the AI. - Utilizing transfer learning 🌐 can be effective in generative AI, where a model trained on one task is fine-tuned for another. This method can significantly reduce the data and computational resources required.

Generative AI can create content in various forms, including text, images, videos, and audio. Powered by advanced language capabilities inherent in Large Language Models, it excels in tasks such as processing and summarizing extensive textual data. Furthermore, its inferential capabilities allow it to decipher intricate patterns and derive meaningful insights from different pieces of information , making it an invaluable tool for data analysis and informed decision-making. Beyond these capabilities, Generative AI finds widespread use in crafting human-like chatbots for natural conversational interactions and providing highly accurate translation services, effectively breaking down language barriers on a global scale.

As GenAI tools become more widely available, risks will also increase. For example, employees could accidentally leak confidential business data into the real world when experimenting with certain foundation models (not Google's - we do not train our models with customer's data, unless they give us permission). There’s also the risk of inaccurate responses and hallucinations, which could damage your brand and reputation. To mitigate these risks — and to build trust into your enterprise genAI apps — it’s critical to ensure that you ground the outputs in accurate data. To do this, leverage capabilities like Retrieval Augmented Generation (RAG) together with vector embeddings.

Many firms opt for pre-trained models due to the exorbitant cost and computational resources required to train from scratch. However, this adoption brings two significant data challenges. 1. IP and Bias Risks from training data Pre-trained models draw from diverse data sources, including potentially proprietary or confidential data. Pre-trained models can inherit biases from their training data, perpetuating harmful stereotypes in generated content. Addressing bias is both an ethical and legal imperative. 2. Securing Cloud LLMs for Internal Data: Applying cloud-based large models to internal data raises data privacy, security, and compliance concerns. Adherence to data protection regulations (e.g., GDPR, HIPAA) is paramount.

Since 2018 we have seen the rise of (and a focus on) generative models that were increasingly "large". Bigger models are not always better, though, if what you need is specialized knowledge. In the legal profession, for example, a lot of key information sits behind paywalls or on websites in formats that are not innately machine-readable. That means a large, multi-purpose model that has been trained on publicly available data won't be able to provide reliable, accurate responses to complex legal questions. This is one of the core data challenges of generative AI, but it can be overcome by shifting focus to developing targeted, specialized models for specific purposes that have been trained on the right libraries of structured, sound data.

In my opinion, the data challenges in generative AI highlight a critical bottleneck in the field. The need for vast, diverse, and high-quality datasets underscores a broader issue: the ethical and practical complexities of data acquisition and use. We must prioritize innovative solutions for data sourcing and processing, while rigorously upholding ethical standards to ensure the responsible development of AI technologies.

When you are deploying generative AI at scale, it pays off to have an experimentation platform set up. Minor modifications in prompts can have immense payoffs with generative AI. For example, adding the word "succinctly" to a prompt will cause the responses to be shorter. If you have an experimentation framework, you can test whether they are still the same quality or better now that they are shorter. If they are, you have saved substantial computational costs just by adding one word.

I believe the strategic integration of synthetic data and transfer learning represents a pivotal advancement in overcoming the data hurdles in generative AI. Synthetic data not only curtails the substantial costs tied to data acquisition and annotation but also provides a safeguard for data privacy. On the other hand, transfer learning redefines model efficiency by harnessing previously acquired knowledge to boost performance across varied tasks. These methodologies are not just solutions; they are transformative strategies that will shape the future trajectory of AI development.

To tackle generative AI's data challenges, synthetic data emerges as a potent solution. It not only supplements real data but also curtails costs and mitigates privacy concerns. This approach enables the creation of diverse, high-quality datasets, essential for training robust AI models. Additionally, transfer learning is a strategic asset, allowing models to apply learnings from one domain to another, thereby reducing the need for extensive data and computational resources. This technique enhances model efficiency and broadens their applicability, making it a key strategy in overcoming data limitations and advancing AI capabilities in various fields.

Firms with data constraints and challenges can focus on using retrieval automated generation (RAG) along with foundation models and fine tuning the foundation models to build generative AI powered applications. The need for data in RAG and fine tuning foundation models is limited. This is because foundation models have already done a significant amount of learning using large volumes of data. Single source of truth in a data lake should be present. Established data access processes should be there to provide easy access to data post validating the need for the data being requested.

IP Management: Understand the models vendors use and training data of those models, lean in solutions that provide legal protection for buyers, seek legal advice. Bias Mitigation: Audit training data, diversify sources, employ bias mitigation techniques. Secured Cloud LLMs: opt for enterprise cloud LLMs that have required infosec certs, check vendors ‘ terms of use, especially how they store and use prompt data, consider prompting and Retrieval Augmented Generation techniques before fine-tuning, start with public data use cases first to learn. Provide AI literacy training, establish incident response plans. Continuous Monitoring with frequent audits and feedback loops. Responsible AI Governance: Develop policies, ethics committees.

I believe one of the most pressing issues is the disparity in access to computational resources. For instance, startups and academic researchers often struggle to compete with tech giants due to limited hardware capabilities. This imbalance stifles innovation and diversity in AI development. We need to advocate for more democratized access to advanced computing infrastructure to level the playing field and ensure that the benefits of generative AI are widely distributed.

Access to high-performance computing (HPC) clusters or cloud services like AWS, Google Cloud, and Azure provides the necessary computational power for AI workloads. Utilizing optimized hardware such as NVIDIA DGX systems or Google TPUs can further enhance performance and efficiency, ensuring that computational resources are effectively utilized.

💡🔴Generative AI Challenges Let's talk about the tough technical stuff in Generative AI using simple examples. Imagine a huge AI model called Meta Llama 2, with a whopping 70 billion parameters. Training this beast takes 6000 powerful GPUs working non-stop for 12 days, costing almost $2 million! Now, if we compare it to models like ChatGPT or BERT LLM, the computation and cost are even crazier, easily surpassing $100 million. Key Issues: 1/ Costly Hardware (Huge GPUs and TPUs) 2/ Lengthy Training hours 3/ Fine-tuning challenges 4/ Carbon emission Solutions: 1/ Improved algorithms 2/ Distributed computing 3/ Leveraging cloud services 4/ Advances in GPUs and TPUs Companies like Mea and Qualcomm are working on eco-friendly AI chips.

To address computational challenges in generative AI, researchers & developers have been exploring various strategies to optimize model architectures, training procedures, & deployment methods. Techniques such as model distillation, pruning & quantization aim to reduce the computational complexity of generative AI models without compromising performance. Additionally, advancements in hardware acceleration technologies, such as GPUs, TPUs, & specialized AI chips, have enabled faster training & inference times for large-scale generative models. Furthermore, the adoption of cloud computing and distributed computing frameworks has facilitated the efficient scaling & deployment of generative AI models across different platforms and environments.

🖥️ - Training and Inference computing is the major challenge in Generative AI. Training models require GPU, TPU, or custom ASIC to achieve speed and performance. The latest model code-llama from Meta, is trained on A100-80GB hardware, and 400K GPU hours were consumed (Sept 2023). 😶🌫️ Carbon footprint of these models is a concern as enterprises are targeting for sustainability. 💲Due to the high compute demand cost of training, maintaining and providing GenAI as a service is costly. 🔔 Companies like Mea and Qualcomm are investing in custom ASIC for sustainable AI chips. The next wave of innovation in this space should focus on low-resource training and inference.

As for any data problem, it’s important to consider the right techniques for the right challenge (“horses for courses” if you will). No one suggestion will fit all scenarios. Having said that, it can be helpful to find some good frameworks that can be applied across your workflow. One that I really like is Ray. It’s libraries and API support distributed workloads across data processing, hyperparameter tuning, training and serving. It’s particularly handy because it abstracts out all the cluster management through a simple YAML and CLI workflow, that you can use on your cloud platform of choice (Azure is a community supported, AWS and Google Cloud are natively supported by the project). Great GenAI examples are available in the docs.

I think overcoming the computational challenges of generative AI requires innovative approaches. Leveraging cloud computing offers the scalability and flexibility needed to handle complex AI workloads efficiently. Distributed computing enhances speed and reliability by spreading tasks across multiple nodes, making the process more robust. Additionally, model compression plays a crucial role in improving efficiency and portability by reducing model size and complexity. Embracing these solutions can significantly advance our ability to manage and optimize generative AI applications.

- 🌐 Leverage cloud computing platforms like AWS, Google Cloud, or Microsoft Azure, which provide robust infrastructure and scalable resources. These platforms can handle the heavy computational loads required by generative AI, allowing for cost-effective scalability. - 🚀 Adopt distributed computing frameworks such as Apache Spark or Dask is crucial for managing the massive data sets and complex computations involved in generative AI. - 🛠 Application of model compression techniques like quantization, pruning, and knowledge distillation. - Optimizing your algorithms and data structures can also greatly improve computational efficiency.

While generative AI faces formidable computational challenges, recent breakthroughs provide promising solutions. Apple's innovative approach enables the efficient use of limited device memory, reducing memory consumption and accelerating inference. Meanwhile, Mistral's AI 8x7B model employs a sparse architecture to maintain speed and cost efficiency while diminishing computational load. These advancements not only mitigate the challenges but also democratize access to powerful AI technologies, benefiting a broader spectrum of users and organizations.

In the case of Retrieval Augmented Generation (RAG), one way to overcome (some) of the computational challenges is massively limiting the amount of information collected in the retrieval stage (search) of the process. But that does increase the chance of an important information not being included in the prompts context which could lead to higher inaccuracy. It is important to experiment with these limits and automate some of the testing to find the optimal balance.