By now, most of us would have tried, tested and possibly marveled at the human-like output and response from the various Generative AI-based tools made available over the last few months.
Generative AI is one of the most exciting and rapidly evolving fields in AI today. From catchy product tag lines to cool marketing ideas, immersive art, photographs, or lyrics written in the same style as the current favorite popstar – the world can’t get enough of this shiny new technology.
The growth of AI applications and use cases is astounding. Generative AI, the branch of AI designed to generate new data, images, code, or other types of content that humans do not explicitly program, is becoming particularly impactful and influential.
According to Gartner, by 2025, Generative AI will account for 10% of all data produced, up from less than one percent in 2021. Reports also indicate extensive use of AI to achieve major R&D breakthroughs and new drug discoveries in the near future.
It won’t be far-fetched to assume that there are but few areas of business or society that Generative AI does not impact. But as this initial excitement simmers down, now would be a good time to look at the practical application of this promising technology.
A look back
From the first phase of AI development in the 1950s, where experts encoded their knowledge into a set of rules for the computer to follow, to Machine Learning algorithms which were at their peak in the 1990s, followed by Deep Learning in the 2010s to present day Generative AI, the transformative technology has gone through several phases of development since its inception in the mid-20th century.
While these phases are not strictly defined or mutually exclusive, they represent significant milestones in the development of AI and demonstrate the increasing complexity and sophistication of AI algorithms and applications over time.
Bringing Generative AI to Today’s Enterprise
One of the significant aspects of generative AI is its ability to create content that is indistinguishable from content created by humans, which has numerous applications in industries such as entertainment, design, and marketing. For example, generative AI can create realistic images of products that do not exist yet, generate music that mimics the style of a particular artist, or even generate text that is indistinguishable from content written by humans. From customer service and fraud detection to healthcare and gaming, today, Generative AI models have the potential to address a wide range of use cases and solve numerous business challenges across different industries.
In the banking and finance sector, the AI models for instance, can be trained to recognize patterns of fraudulent behavior and flag suspicious transactions. In healthcare, these models can be trained to analyze medical images to identify cancerous cells or analyze protein structures for new drug discovery.
An important area of generative AI is natural language generation (NLG), which is a subset of natural language processing (NLP) and involves generating natural language text that is coherent, fluent, and similar in style to existing or human-produced text. NLG has been used for various applications, including chatbots, language translation, and content generation.
For businesses, Generative AI can provide numerous benefits across multiple dimensions. These include:
- Improved productivity—To automate repetitive and time-consuming tasks, allowing employees to focus on more high-level tasks and increasing overall productivity
- Enhanced customer experience—To develop conversational interfaces and chatbots that can improve customer engagement and satisfaction by providing personalized and timely responses
- Better decision-making—To generate insights and recommendations from data that can help inform business decisions and improve overall business performance
- Cost savings—To help reduce operational costs by automating tasks and improving process efficiency, ultimately resulting in cost savings
- Increased innovation—To generate new ideas and solutions that can help drive innovation and create new revenue streams
- Competitive advantage—To help enterprises stay ahead of the competition by enabling faster and more efficient processes, better customer engagement, and improved decision making
However, when using these models to accelerate workplace transformation, there are both business and technical challenges to consider, particularly those models in the public domain that have yet to be developed and controlled from within the enterprise. These challenges include factors such as ownership of intellectual property, to data quality, regulatory compliance and more.
But, these also present a compelling need for enterprises to build their own Large Language Models (LLMs) that are trained on proprietary datasets or developed and finetuned from known pre-trained models. It is, therefore, essential to approach each challenge on a case-by-case basis and work with experts in the field to develop the best possible solutions.
Making these enterprise-wide transformations possible is Project Helix, a unique collaboration between Dell Technologies and NVIDIA that makes the promise of Generative AI real for the enterprise. It simplifies and accelerates Generative AI deployment and enables organizations to automate complex processes, improve customer interactions and unlock new possibilities with better machine intelligence.
The Future is AI
There is no doubt that the world around us has changed with the launch of Generative AI. Just as the PCs, the Internet, and smartphones transformed how we live and work, the opportunities that AI presents can’t be overstated. And organizations not using it to reinvent and transform their work processes stand to lose out on the next wave of innovation in the enterprise AI landscape.
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