Summary

While Generative AI is still in its early stages, its potential is immense. Businesses that can harness this technology effectively will be well-positioned for success in future years. In this white paper, Mosaic discusses how firms can tap into this transformative force and achieve their business goals by leveraging Generative AI for cost savings, revenue generation, competitive differentiation, employee empowerment, and enhanced customer engagement.

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Introduction

The evolution of technology is marked by transformative moments. From the internet revolution to the rise of social media, businesses that recognized and embraced change reaped significant rewards. Today, large language models (LLMs) like ChatGPT are indicative of a market ready to embrace modern AI. Artificial Intelligence (AI) and Machine Learning (ML) are not new concepts, but Generative AI, represented by models like ChatGPT, signifies a shift in how we approach AI.

By now, almost everyone is aware of artificial intelligence (AI) and machine learning (ML), even if they don’t exactly understand what they are. While the past generations of AI development have unlocked new potentials in almost every sector imaginable, it has essentially amounted to advanced pattern recognition – given X and Y, predict Z. Generative AI, though, seems to mark a line in the sand, not only in new use cases and avenues for business but in how we approach AI in general. In a recent McKinsey survey, 60% of organizations with reported AI adoption are using Gen AI. What’s more, 40% of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to Generative AI, and 28% say generative AI use is already on their board’s agenda.

State of the Union in Generative AI

Generative AI, especially language and image models, is at the forefront of machine learning discussions. OpenAI’s ChatGPT and models like DALL-E 2 have showcased remarkable capabilities, raising questions about productivity impacts and job market changes. These models create content that mimics human output, achieved through massive training datasets.

Generative AI models seek to produce content such as natural language or images that mimic human output. In general, they do this by training on a massive number of examples. For LLMs, this may take the form of text pairs, where one piece of text follows another. For image models that create pictures from users’ text prompts, training examples comprise text and image pairs, where the text is a caption for that particular image. Fundamentally, this is like other machine learning models in that, at the core, they are still attempting to predict the most likely output given a certain input (even if some or all of that input is random noise). However, it is much easier for a user to think of the process as a “creative” one.

Milestones: Large Language & Image Models

When looking at the various flavors of AI and Machine Learning that exist today, Generative AI is helping organizations unlock the power of predictive analytics and automation.

Natural Language Processing (NLP) Innovation

Significant advancements in natural language generation (NLG) have been made in recent years, particularly with the development of Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformers (GPT).

  • BERT and GPT models represent major advancements in natural language generation.
  • BERT’s latent vector space encoding and decoding approach allows for more nuanced textual understanding.
  • GPT models enable the generation of longer and more coherent text sequences.
  • Increased model training costs for GPT-3 and GPT-4 pose a challenge
  • Advancements in NLG have wide-ranging applications in various fields.

Mosaic Data Science is helping firms realize the power of GenAI and LLMs with our custom-built Neural Search Engine framework, which automates text, image, video, and audio information retrieval from any set of documents.

Computer Vision Innovation

The field of computer vision has made significant strides in recent years, particularly in the area of image generation.

  • Generative Adversarial Networks (GANs) have played a crucial role in the advancement of image generation.
  • StyleGAN and its successors have addressed the limitations of GANs by providing greater user control over generated images.
  • CLIP, Stable Diffusion, and DALL-E models have further enhanced image generation capabilities.
  • Open-source models like Stable Diffusion promote transparency and accessibility.
  • DALL-E and DALL-E 2 models demonstrate impressive image generation capabilities while addressing ethical considerations.

Evaluating GenAI Models

Evaluating the effectiveness of Generative AI models is more complex than traditional machine learning models due to the subjective nature of the output. However, metrics like the Frechet Inception Distance (FID) score can be used to assess the quality of generated images.

The FID score compares the distribution of real images to the distribution of AI-generated images. A lower FID score indicates that the AI-generated images are more like the real images. For example, Researchers developing image generation models often use a standard benchmark, such as the MS-COCO dataset, to report the FID score of their model. Reducing FID scores on the COCO dataset is one measure of progress and a progression of FID scores, along with model sizes, can be seen in Figure 1.

generative ai for business image of FID score.

Figure 1: Evolution of Model Size and FID Score. Horizontal axis shows time of release and dot size shows model size

An interesting note from above is that model size has appeared to plateau on the scale of a few hundred billion parameters. While models such as the 170T parameter BaGuaLua have been “trained”, they exist more as POCs for exascale computing versus unlocking new capabilities. The rise in model accuracy coupled with a leveling off in requisite compute resources suggests that Generative AI may be ready for real application and business value.

In other words, GGenerative AI is no longer just a theoretical concept; it is now a mature technology that can be used to solve real-world problems. Businesses that are able to harness the power of Generative AI will be well-positioned for success in the years to come.

Using Generative AI to Deliver Business Value  

While direct-to-consumer applications exist, the real impact of Generative AI lies in business applications. While Generative AI is making its way to consumers, businesses are expected to reap the most benefits from this technology.

Unlike previous technological advancements, AI and ML have primarily been directed toward businesses. This trend is expected to continue with Generative AI, with businesses that effectively leverage this technology gaining a significant advantage.

Data availability is often a roadblock for model training, especially in fields like healthcare and computer vision. While the open-source dataset landscape is more mature than ever, there are still many cases where real data is unavailable or extremely restricted due to environmental, budgetary, or privacy reasons. Generative AI can be used to create new datasets for downstream model training, even when real data is unavailable or restricted. With human supervision and good prompting, generative techniques can build up new datasets for downstream model training that would otherwise be impossible.

Traditionally, accessing insights from large corporate data lakes has required technical expertise. However, Generative AI can make this data accessible through natural language interfaces, enabling more people to benefit from these insights.

Overall, Generative AI is poised to significantly impact businesses, offering benefits in areas such as data availability, model development, and analytics.

generative ai for business image of stable diffusion for a prompt

Figure 2: Stable Diffusion image resulting from the prompt: “An overhead image of a land-based oil spill in Alaska.

How GenAI Can Empower Businesses

Generative AI is rapidly transforming the business landscape, offering a wealth of opportunities for both cost savings and growth. Automation powered by generative techniques is already being implemented in sectors like customer service and education, offering significant efficiencies and cost reductions. According to Salesforce, 54% of organizations are seeing cost savings and efficiencies from using GenAI, while 71% expect GenAI will help eliminate busy work and allow them to focus more on strategic work.

Beyond cost savings, Generative AI provides a range of tactical advantages:

  • Unwavering Consistency: Unlike human employees, Generative AI models never experience fatigue, frustration, or emotional fluctuations. They consistently deliver high-quality performance, ensuring continuous service availability.
  • Personalized Interactions: Generative AI can analyze vast amounts of data to understand individual customer preferences and needs. This enables businesses to provide personalized interactions, enhancing customer satisfaction and loyalty.

While concerns about job displacement and potential social disruptions due to automation are understandable, Generative AI also presents a compelling path to growth and innovation:

  • Productivity Enhancement: Generative AI can significantly boost productivity by automating routine tasks, freeing up valuable time for employees to focus on more strategic and creative endeavors.
  • Research Acceleration: Data scientists utilize Generative AI to expedite research and discovery, streamlining the process of extracting insights from vast datasets.
  • Code Generation: Generative AI can assist in code generation, reducing the time spent on syntax and library exploration, allowing developers to focus on architectural design and problem-solving.
  • Scaling and Growth: Generative AI empowers businesses to tackle larger projects and scale their operations more effectively, particularly for small and midsize companies.

The influx of new funding, increased mobility, and the emergence of innovative players will further accelerate the impact of Generative AI. Businesses that strategically leverage Generative AI while understanding its limitations will be well-positioned to capture the greatest opportunities in this transformative era.

A New Frontier with Cautions

While Generative AI holds immense potential for innovation and growth, it’s crucial to approach its adoption with forethought and caution.

Emerging Challenges:

  • Misinformation and Bias: The vast amounts of data used to train Generative AI models can inadvertently introduce biases and inaccuracies. This is particularly concerning in sensitive fields like education, where distinguishing between AI-generated and human-written content is becoming increasingly difficult.
  • Data Integrity and Traceability: As AI-generated content proliferates online, ensuring data integrity and traceability becomes increasingly challenging. This can hinder accurate analysis and decision-making.
  • Technological Dependence: Over-reliance on Generative AI can lead to a loss of institutional knowledge and expertise, particularly as senior employees retire. Businesses should carefully consider the balance between automation and human engagement.
  • Data Rights and Ownership: Commercial Generative AI models often utilize user data for continuous improvement, raising concerns about data privacy and ownership. Businesses should explore open-source alternatives to maintain control over their data.

How to Navigate the Generative AI Landscape:

  1. Stay Informed: Continuously monitor advancements in Generative AI to identify potential opportunities and challenges.
  2. Empower Employees: Reframe automation as a tool for efficiency, growth, and new opportunities, not a threat to jobs.
  3. Problem-Driven Approach: Start with specific business problems and identify how Generative AI can provide solutions.
  4. Make Informed Choices: Weigh the trade-offs between commercial and open-source Generative AI models in terms of compute resources, application needs, and data rights.

Generative AI is a powerful tool, but its successful utilization requires careful consideration of its implications and limitations. By adopting a thoughtful and strategic approach, businesses can harness the transformative power of Generative AI while mitigating potential risks.

A Rising Tide for Businesses

The field of Generative AI is rapidly evolving, offering businesses a plethora of opportunities for growth and innovation. As the technology matures, its capabilities are becoming increasingly impressive, blurring the lines between human-generated and AI-generated content. This shift towards seamless AI-generated content gives businesses a unique opportunity to leverage this technology.

Key Benefits of Generative AI for Businesses:

  • Cost Savings: Automate routine tasks and enhance productivity, reducing labor costs and improving efficiency.
  • Revenue Generation: Create new products and services, personalized experiences, and targeted marketing campaigns.
  • Competitive Advantage: Gain a strategic edge by adopting cutting-edge technology and differentiating your offerings.
  • Empowerment of Employees: Upskill employees with AI-powered tools, enhancing their capabilities and knowledge base.
  • Streamlined Research and Development: Accelerate research and discovery through AI-powered data analysis and insights.
  • Enhanced Customer Engagement: Provide personalized experiences, tailored recommendations, and frictionless interactions.

Integrating Generative AI into Your Business:

  • Start by Identifying Problems: Identify specific business challenges and how Generative AI can provide solutions.
  • Choose the Right Tools: Evaluate open-source and commercial Generative AI models based on your needs and resources.
  • Invest in Employee Training: Provide employees with the skills and knowledge to effectively utilize Generative AI tools.
  • Embrace Ethical Considerations: Ensure responsible use of Generative AI, addressing potential biases and ethical implications.
  • Stay Ahead of the Curve: Continuously monitor advancements in Generative AI to stay ahead of the competition.

A Transformative Force Requires Targeted Expertise

While Generative AI is still in its early stages, its potential is immense. Businesses that can harness this technology effectively will be well-positioned for success in future years. Businesses can tap into this transformative force and achieve their business goals by leveraging Generative AI for cost savings, revenue generation, competitive differentiation, employee empowerment, and enhanced customer engagement.

Are you prepared to harness the transformative power of GenAI and Large Language Models (LLMs) to revolutionize your business?

Mosaic Data Science is your strategic partner in evaluating and optimizing your organization’s readiness for this groundbreaking era. Our tailored GenAI Readiness Assessment is designed to guide your journey into the future of data-driven decision-making, ensuring you’re equipped to leverage the full potential of GenAI and LLMs. Reach out to us to initiate a strategic consultation and unlock the potential of Generative AI for your business.