Table of Contents:
If you are a chess enthusiast, you would probably recollect.
Garry Kasparov vs Deep Blue - The 1997 Match created a new chapter in the history of mankind as it was the first time a machine, matching the capabilities and prowess of a human mind, defeated the undisputed World Champion and an all-time great at the game.
Cut back to the present.
Generative AI has sneaked into our daily lives and is gradually shaping our habits and behavior. If you want to know about popular summer destinations, just ask Alexa. Tired of writing a long essay; the speech-to-text feature is at your service. Looking for a new summer dress on the shopping site- strike up a conversation with the bot to know more about that outfit. These are but a few examples of how this technology has created an unfathomable presence in everyone's lives. The mundane tasks, which once required multiple brainstorming sessions, are now being taken up and solved by generative AI, making lives quite relaxing.
But it's not just human interactions that generative AI is impacting. This sophisticated invention is making steady headway in the business landscape as well. By 2026, more than 80% of organizations are going to adopt generative AI. While this is indeed a good move, it also leaves us with some pertinent questions: Is generative AI going to be a driving force or a disruptive force? How are they going to make efficient use of it? Before this blog delves deep into this conundrum, let's discuss the origins of GenAI in brief detail.
Tracing the History of GenAI: The Past, Present, and Future
People may think that generative AI is a modern-day invention but what if we say that the premise of this technology dates back to 1906. A Russian mathematician by the name of Andrey Markov, formulated a statistical calculation to simulate the behavior of random processes. This mathematical technique came to be known as the Markov chain. The Markov chain then formed the basis of many upcoming generative AI models. In fact, readers will be surprised to know, that most ML algorithms use Markov models to perform next-word prediction tasks such as the autocomplete feature in an email. To some extent, OpenAI models such as ChatGPT work on the principles of the Markov Model. However, given that the Markov model fails to accurately capture complex patterns and dependencies in the data, there was a need for more advanced generative AI models.
In 2014, researchers at the University of Montreal introduced an ML architecture called generative adversarial network (GAN). GAN primarily consists of two neural networks - a generator network that produces new data samples, and a discriminator network to separate generated samples from the real samples. Since GANs are used for generating videos, samples, and audio, they can be applied to art generation, synthetic data generation, and data augmentation.
After one year, a collaboration between Stanford University and the University of California, Berkely, launched diffusion models. These models can generate new data sets that replicate samples in training datasets. Commonly known for generating hyper-realistic images, this model became the crux of text-to-image generation systems.
In 2017, Google gave inception to transformer architecture, that has been known for developing large-scale language models to drive ChatGPT. This architectural framework works on encoder-decoder principles. The encoder generates an attention map of input sequences by assessing the relationship between different components through a mathematical formula called attention to do so. The decoder, on the other hand, receives this sequence and produces an output sequence. This attention map is specifically designed to understand the context of the text.
1906
Andrey Markov, a Russian mathematician, invented the Markov chain, which became the basis of future generative AI models.
2014
University of Montreal introduced an ML architecture called generative adversarial network (GAN) that is used for art generation, synthetic data generation, and data augmentation.
2015
Stanford University and the University of California, Berkely, launched diffusion models which became the basis of text-to-image generation systems.
2017
Google introduced transformer architecture, known for developing large-scale language models to drive ChatGPT.
Generative AI and Cloud: The Collaborative Approach for a Transformative Future
While Generative AI has been a strong driving force towards reshaping innovation and creativity, Cloud acts as a catalyst to expand the transformative abilities of GenAI. What we mean by this is that Cloud offers a scalable infra with in-built tools to unearth the possibilities of GenAI. The synergistic relationship between GenAI and Cloud democratizes this technology, allowing users across the globe to harness its full potential and capabilities. Take a look at how Cloud is building a GenAI-driven future where human imagination can seamlessly collaborate with technical ingenuity.
Infra: Did you know that building a GPU server can cost approximately $27,000? Developers can rent virtual GPU servers from leading cloud providers. When they are required to run the next training iterations, the IT teams can just rent the server and pay only for the time they need to use. However, for producing new data, developers must depend on the processing abilities of GPU for high inference speed. This is where renting a cloud infra comes across as a suitable option.
Accessibility: By democratizing access to generative AI features, the cloud makes this technology accessible to businesses of different sizes and scopes. Rather than building and managing their own GenAIs or LLMs on costly infra, the cloud helps enterprises harness cloud-based AI tools and technologies on-demand. This is beneficial for small and medium-sized companies that do not have to spend a dime on building AI teams. In addition, they can leverage cloud services for small-based generative AI projects to check if they meet the objectives.
Knowledge Sharing: Creating a massive AI project is not a one-person job. It is a collaborative process among engineers, data scientists, and researchers. Cloud platforms come with in-built collaboration tools, shared development environments, and version control systems to encourage seamless cooperation and communication among teams, breaking down the age-old problem of silos. Along with this, cloud-native solutions like debugging, project management, and easy code-sharing streamline the development, deployment, and management of generative AI models.
Data Management: Generative AI models need to be injected with huge volumes of training data. With cloud-powered data management and storage solutions, organizations are equipped with a robust infrastructure to store, process, and manage this data for training the AI models. For learning new patterns, the Gen AI models rely on deep learning and machine learning to form neural networks. Neural networks replicate an actual human brain as it forms a system of interconnected nodes to process information faster and better. Once this AI model has finished training, it produces new data. This process is called inference. When a user prompts for an image or text, the Gen AI can generate data based on the prompt. This is why its important to train AI models on contextual and relevant data.
To further process this data quickly and more cost-effectively, cloud gives options of either building a data lake, data warehouse, or data pipeline depending on the business requirements, maintaining the consistency and quality of the data.
Security: Data is at the heart of leading LLMs. If these models were trained on bad data, they would generate poor output. Some common instances of poor data include bias, plagiarism, and manipulation. Cloud comes with security controls and tools to detect potential risks and bias in the data deployed for training foundational models. Additionally, these tools can be used for assessing, \b]g and monitoring training data.
Read blog on how Cloud4C helps in managing your data on hybrid cloud and multi-cloud environments
Real-time Inference: Real-time inference banks on quick response and low latency. Cloud-powered edge computing deploys trained generative AI models near the data source, minimizing latency and empowering real-time decision-making. This is useful in situations involving speech generation and real-time images where rapid responses play a critical role.
Unraveling 3 Key Players in Cloud-native AI Technologies
Want to know how cloud-native tools are advancing the growth of Generative AI? Look no further than the major 3 cloud companies dominating this space.
AWS: Making Impressive Strides in Generative AI.
In the realm of generative AI, AWS offers three key solutions- Amazon Bedrock, Amazon SageMaker, and Amazon Titan.
Amazon SageMaker Jumpstart offers more than 600 pre-trained, open-source models for developers to integrate ML into their production workflows. They offer templates to create an environment for accessing, customizing, and deploying new-age ML models. This enables developers to train and fine-tune these models before final deployment. By collaborating with Hugging Face, AWS made it more convenient to enhance these pre-existing models and execute an inference from a host of curated open-source models, adding advanced generative AI capabilities to SageMaker.
Amazon Bedrock is a platform that enables creating generative AI-powered apps through pre-trained models outsourced from leading startups such as AI21Labs, Anthropic, and Stability AI. It offers access to a catalog of Titan foundation models that are mostly trained by AWS in-house teams. Coupled with the serverless feature of Amazon Bedrock, developers can pick the right model suited to their needs, personalize foundation models, embed them into applications, and deploy them via AWS tools and technologies. The best part is that one can test and operate these models without having to deal with the hassle of managing the overall infrastructure.
It should be noted that the tech giant is planning to roll out a series of foundation models for different functionalities such as word completion, chat completion, code completion, and image generation. These models would be integrated with Amazon Bedrock for further fine-tuning.
Amazon Titan comprises a series of foundation models developed by AWS internal researchers. Some of these models are integrated into widely known services such as CodeWhisperer, Alexa, Rekognition, and Polly.
Google Cloud: Transforming Cloud-native Foundations with GenAI
Generative AI has played a significant role in Google' search and cloud businesses. They have introduced four key foundation models: PaLM, Codey, Imagen, and Chirp. Developers can access these models through Vertex AI. Vertex AI also offers a model garden that acts as a gateway to third-party and open-source foundation models. This enables customers to consume and fine-tune these models. In addition, Google also came up with GenAI Studio and no-code tools such as GenApp Builders for creating generative AI apps.
To power generative AI in DevOps, Google has embedded the PaLM 2 API in Google Cloud Shell, Google Cloud Workstations, and Google Cloud Console. This, in turn, helps in adding an assistant to speed up processes and workflows.
Microsoft Azure: Making the Most out of OpenAI
Microsoft has set a benchmark in the generative AI landscape. Partnering with Open AI, Microsoft has launched its own foundation model called Azure Open AI. Azure Open AI derives most foundation models from Open AI and integrates them into the cloud. Models such as text-davinci-003 and gpt-3.5 and GPT 4-turbo are integrated into Azure. What's more, the data remains highly secured as the customers can access these models via a private virtual network.
Integrating foundation models with Azure ML helps the customers access libraries and tools to fine-tune and consume these models. In their future pipeline, Microsoft is planning to introduce Semantic Kernel that will enable LLM orchestration services such as augmentation and prompt engineering.
Industries Impacted by Generative AI
Below are the 5 major industries that will be quick to generative AI adoption:
Finance: Generative AI has gained huge traction among financial companies and firms. This is because leveraging language models can equip their agents with more personalized to deliver enriching customer experiences and boost operational efficiency. It can also be applied to revamping conversational interfaces. This means that generative AI will add more conversational, impersonal capabilities to its agents to humanize customer interactions. For instance, Sedgwick, a global insurance claim provider company, embedded Sidekick, an open API version of ChatGPT, into its claim processing systems to enhance customer experiences and claim handling processes.
Read the blog on 16 Use Cases of AI in BFSI Transformations
Manufacturing: In the manufacturing sector, generative AI can enhance processes such as product lifecycle management, content management, data analysis quality control, code generation for programmable logic controllers, and production planning.
Healthcare: Generative AI can help streamline the knowledge management process to facilitate seamless information flow among medical professionals. Additionally, generative AI can assist in integrating patient histories, assessing disease patterns, improving diagnosis and designing treatment plans.
Retail: To reduce redundancies, generative AI can help in optimizing time and resource-intensive tasks such as writing marketing copies, product page descriptions, and images/video descriptions. For instance, a Chinese eCommerce company giant, JD.com integrated ChatGPT solution into its product listing so that the product descriptions are tailored to the customer's requirements and preferences.
Government: Generative AI can be a great tool to enhance the competencies of government officials who usually put a lot of time and effort into managing huge volumes of paperwork and documentation. For instance, the Portugal Government integrated Chat GPT into its business operations to offer succinct and comprehensive legal information to its citizens, especially immigrants and out-of-station students.
Lead the Generative AI Revolution with Cloud4C
The inception of OpenAI has reignited the age-old debate of Man vs Machine? Will AI cause job displacement? Will it quash away emotional intelligence and creativity? As humans, we have shared a paradoxical relationship with AI, where the genuine fear of machines surpassing human intelligence and capability has always been a pressing concern. While the moral and ethical considerations of AI come from the right place, one cannot negate the unbridled impact of Gen AI in the next 10 years. Research says that generative AI market will reach $1.3 trillion by 2032. An overwhelming number of businesses should master how to leverage technology with care and caution, without wasting any of their human potential.
While integrating AI solutions into applications or systems can be a massive challenge for most organizations, Cloud4C, a leading cloud managed services company, helps to create AI infrastructure with a strategic roadmap around AI data, storage, and networking. Our experts help to embed AI, ML, and deep learning tools to revamp processes around customer management, supply chain management, and service delivery among others. Along with this, we can integrate Artificial Intelligence (AI) capabilities to optimize data collection, processing, and analysis from varied sources and produce precise insights for accurate decision-making.
Want to know how you can keep your business ahead of the GenAI curve? Get in touch with our representative or visit our website.
