Embracing Digital Transformation Through AI

Embracing Digital Transformation Through AI
Akruti Acharya
Stephen Oladele

By Stephen Oladele, ML Developer Advocate and Akruti Acharya, Technical Content Writer

The wave of digital transformation that is becoming the most fervent to revolutionize businesses is artificial intelligence (AI). This has been the culmination of years of impact, from the burst of datasets to the advancements in computational power and platforms (GPUs, TPUs, cloud) to algorithm breakthroughs supported by a vibrant community. Everything has led to this moment of AI breakthroughs and those on the horizon.

In this article, we will take a closer look at the top 3 AI trends shaping the future of digital transformation:

  1. Prioritizing high-quality data for superior AI systems.
  2. The democratizing power of open-source large language models (LLMs).
  3. The emergence and integration of multimodal AI systems.

But before then, let’s understand what AI is, how we got here, and how it’s powering the digital explosion.

Today, terms like ML, AI, and deep learning (DL) are often used interchangeably. For business professionals who want to undergo digital transformation, understanding these technologies and how high-quality data powers them is not just academic—it’s foundational to using them effectively.

Let’s explore these terms to see how they can drive innovation and efficiency in your business.

Artificial Intelligence: The Broad Vision

At its core, our goal with AI is to emulate human intelligence so that machines can learn, reason, and solve problems. From conversational AI like ChatGPT to Google’s Multimodal Gemini, AI’s capabilities are expanding, enabling applications across healthcare and finance sectors.

Machine Learning: AI in Action

A subset of AI, ML focuses on teaching machines to learn from data and make decisions with minimal human input. With this capacity for self-learning, companies can streamline operations and provide better customer experiences by using data for insights, predictions, and automated decision-making.

Deep Learning: Delving Deeper into Data

Delving deeper, DL, a subset of ML, mimics the human brain’s neural networks to process complex data types, such as images and speech. With the help of sophisticated algorithms like neural networks, DL has been crucial in natural language processing (NLP) and computer vision (CV).

They have also laid the groundwork for more recent algorithms like Transformers, the architecture behind most AI applications today, like Midjourney, ChatGPT, and AI-assisted data annotation platforms.

Knowing where each technology excels can help you better align your business objectives with the right tools, driving innovation, efficiency, and growth.

In light of that, let’s see the trends shaping the future of digital transformation with AI in the subsequent sections.

Trend 1: Everyone will Build High-Quality AI Systems with High-Quality Data for Digital Transformation

Going through this article, you may have noticed the importance of lots of data in building effective ML models. Yet, the pivotal aspect that determines the precision of these predictions is not just the quantity but the quality of the data.

With models becoming increasingly accessible through APIs or pre-trained formats, data-centric approaches to building AI applications are increasingly becoming the competitive advantage for most businesses.

Understanding the Need for High-Quality Data

In traditional software development, a ‘bug’ signifies a flaw in the code, which could lead to undesired outcomes. Similarly, in ML, beyond algorithmic bugs, data-related bugs are prevalent and arguably more impactful. These ‘bugs’ or errors in your dataset can skew your ML model’s learning, which could cause inaccurate predictions.

Consider Automotus’ endeavor to use ML for building curbside management automation solutions to help reduce emissions, congestion, and safety hazards in our communities. The project involved collecting data from strategically mounted street cameras and training an ML model on the visual data (videos and images) labeled by experts. Despite their expertise, the subjective nature of human labeling introduced inconsistencies and biases in the data, affecting the model’s accuracy.

Their model’s performance was degraded with uninformative and bad images. This forced them to use strategic data curation practices and build active learning pipelines using Encord Active to develop high-quality datasets that increased the model’s performance by over 20%.

It also reduced the amount of duplicate and corrupt data by over 35% and the cost of annotating images.

Why This Matters for Your Business

High-quality data is the linchpin of effective ML models. You set the stage for more accurate, reliable predictions by prioritizing data coverage, cleanliness, and completeness.

As you venture further into ML applications, remember that the data you feed into your models shapes their view of the world. Quality data improves model performance and ensures your ML initiatives drive tangible business value.

Trend 2: The Rise of Multimodal AI Models

Multimodal AI refers to AI systems that can understand, interpret, and process more than one type of input data modality (text, audio, video, images) to perform tasks. For example, a multimodal AI model could analyze a news article by considering the text, the tone of the embedded video, and the sentiment of the image captions to understand and summarize the content comprehensively.

This approach contrasts with unimodal systems, which are limited to a single type of data—text or images—but not both simultaneously.

The Push Towards Multimodality

The desire to develop more natural, intuitive, and contextually aware AI systems drives the push toward multimodal AI. Humans perceive the world through multiple senses and integrate this information seamlessly; multimodal AI mimics this capability. These models can better understand their inputs by processing diverse data types for more accurate and nuanced outputs.

Why This Matters for Your Business

The rise of multimodal AI models transforms business landscapes by enabling more comprehensive data analysis and interaction. The main points businesses need to consider are:

  • Multimodal AI allows businesses to analyze data from multiple sources for a holistic understanding of customer behavior and market trends.
  • Businesses can use diverse data types to create highly personalized customer experiences for satisfaction and loyalty.
  • Using multimodal AI can automate and optimize complex decision-making processes across various operations, from supply chain management to risk assessment, which reduces costs and improves agility.

Theearly adopters of multimodal AI can differentiate themselves in the market with superior services and insights that competitors may lack, thereby securing a market lead.

This trend towards multimodal AI models represents a leap forward in making AI systems more versatile, efficient, and, crucially, more aligned with human ways of understanding and interacting with the world.

Trend 3: Open Source Large Language Models (LLMs) will Continue Performing

Last week, Google released the Gemma family of models as open models, which has seen one more big tech company adopt an open-source LLM approach. More organizations and businesses are beginning to adopt high-performance pre-trained models for generic tasks and personalization, with fine-tuning over API providers for running applications at scale.

Here are the key reasons why open-source LLMs will be instrumental for businesses:

  • Innovation and Collaboration: The open-source nature of hubs like HuggingFace fosters collaboration and innovation that enables developers and researchers to build upon existing models, share advancements, and accelerate AI development.
  • Cost-Effectiveness: By using open-source LLMs, businesses can significantly reduce the costs of using LLM API solutions. This way, they can efficiently allocate resources towards other innovative projects.
  • Rapid Integration and Customization: Open source models offer the flexibility to be easily integrated and customized to suit specific business needs for organizations to leverage AI for various applications (automated customer service, content creation, etc.).

Why This Matters for Your Business

Organizations harnessing open-source LLMs effectively can improve operations, drive innovation, and create more personalized and engaging customer experiences. They can do all that while optimizing costs and fostering a culture of open innovation.

As this trend unfolds, staying informed and adaptable will be key for businesses aiming to thrive in the digital transformation journey.

The path forward for digital transformation with AI is rich with opportunities and challenges for organizations. Here are key takeaways for businesses:

  • Prioritize Data Quality: The foundation of any effective AI system lies in the quality of data it’s trained on. Businesses must invest in strategic data management practices to ensure their AI models are accurate and reliable.
  • Adopt Multimodal AI for Comprehensive Insights: Multimodal AI models, capable of processing and integrating diverse data types, present a new frontier for creating more natural and intuitive AI systems. This approach enables businesses to gain deeper insights and provide enriched customer experiences.
  • Leverage Open Source for Innovation: The rise of open-source LLMs offers businesses a cost-effective way to access cutting-edge AI technologies for innovation and collaboration across industries.

The AI wave is more than just a technological shift; it’s a transformative force that requires businesses to rethink their strategies, processes, and data-centric approaches. Those who adapt and innovate will survive and thrive, leading the charge in the new digital transformation era.


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