Generative AI has witnessed a remarkable ascent in recent years, reshaping industries and revolutionizing how our everyday lives interact with technology. This cutting-edge technology, driven by deep learning models, has transformed how we generate content, innovate in various domains, and even understand the human mind. Businesses across sectors have been rushing to fully comprehend the implications of this revolutionary technology on their core concerns and the ways of swiftly adapting to this brave new world.
The foundations of Generative AI are the first intellectual frameworks laid by leading scientific minds such as Alan Turing in the 1960s. The origins of what we recognize as Generative AI can be traced back to the emergence of neural networks along with the development of deep learning techniques in the 1990s. Neural networks, inspired by the human brain, comprise the layers of interconnected nodes or neurons, each performing simple calculations. While these networks were initially applied to tasks like image and speech recognition, researchers soon realized their potential for generating novel content.
The 2010s were a transformative period for Generative AI. Deep learning techniques, enabled by advances in hardware and large datasets, became the driving force behind generative models. One of the most significant breakthroughs was the development of Generative Adversarial Networks (GANs) in 2014, which introduced a novel approach to generative modeling, where two neural networks, a generator and a discriminator, compete against each other in a game-like framework to produce high-quality, realistic data.
In 2023, with the public release of ChatGPT, a technology already on the ascent in the technology world, now burst upon the wider public and created a fascination with a tech product that had not been witnessed since the launch of the internet itself.
Businesses and developers have long been aware of the potential impact of AI on their functioning and efficiency. Firms specializing in digital transformations have been on the AI bandwagon for some years now, focusing on harnessing the power of this once-in-a-generation development in tandem with the unique demands of their customers and partners in varied sectors and fields. Oracle, one of the leading technology firms in the digital transformation sector, remains a trailblazer and a market leader in the adoption of AI and the offerings of its many capabilities to practical, everyday business challenges.
Oracle’s AI Journey
Oracle invested in the AI revolution before the 2023 Generative AI buzz that captivated the entire world, including policymakers and the general public.
In 2021, Oracle launched Oracle Cloud Infrastructure (OCI) AI services, a collection of services that made it easier for developers to add AI capabilities to their applications without requiring data science expertise. These services were out-of-the-box models that could be pre-trained on business-oriented data or be custom-trained on the customer’s own data.
The OCI AI services were the core of Oracle and AI, a collection of AI, machine learning, and data science offerings that included Oracle Digital Assistant, OCI Data Science, and Oracle Database Machine Learning. The six OCI AI services helped developers with complex tasks ranging from language to computer vision and time-series forecasts.
OCI Language
This service could carry out scaled text analysis of unstructured text in documents, customer feedback interactions, support tickets, and social media interactions. The built-in pre-trained models of OCI Language eliminated the need for machine learning expertise. Businesses and developers could leverage its capabilities to conduct sentiment analysis, key-phrase extraction, text classification, named entity recognition, and more.
OCI Speech
This service was based on prebuilt models trained on more than thousands of native and non-native language speakers for real-time speech recognition and could provide automatic speech recognition. The tool enables developers to convert file-based audio data containing speech into highly accurate text transcriptions and is seen as a powerful tool for developing in-workflow closed captions, indexing content, and enhancing audio and video content analytics.
OCI Vision
OCI Vision included pre-trained computer vision models for tasks like image recognition and document analysis. This service could identify visual irregularities in manufacturing, automate workflow tasks by extracting text from forms, and count products or shipments by tagging items in images. Additionally, users could customize and adapt these models to various industry-specific applications, including scene monitoring, defect detection, and document processing, using their own data.
OCI Anomaly Detection
This service delivered business-specific anomaly detection models that could flag critical irregularities early, enableing faster resolution and minimal disruption. OCI Anomaly Detection also provided APIs and SDKs for several programming languages, which developers could further leverage to integrate its anomaly detection models into custom business applications.
OCI Forecasting
This service was designed to deliver time-series forecasts through Oracle AI and machine learning and statistical algorithms that eliminated the need for data science expertise for the customer. OCI Forecasting empowered developers to generate precise forecasts for essential business metrics such as product demand, revenue, and resource needs. These forecasts came with exact confidence intervals and substantial clarity, enabling businesses to make well-informed and profitable decisions.
OCI Data Labelling
The sixth service provided unprecedented flexibility and customizable options to the customer by letting the users build labeled datasets to train custom AI models. Users could gather data, establish and explore datasets, and affix labels to data records through user interfaces and public APIs. These labeled datasets were exportable and could be utilized for model development across various Oracle AI and data science services, such as OCI Vision and OCI Data Science, ensuring a uniform model-building process.
OCI Language
This service could carry out scaled text analysis of unstructured text in documents, customer feedback interactions, support tickets, and social media interactions. The built-in pre-trained models of OCI Language eliminated the need for machine learning expertise. Businesses and developers could leverage its capabilities to conduct sentiment analysis, key-phrase extraction, text classification, named entity recognition, and more.
OCI Speech
This service was based on prebuilt models trained on more than thousands of native and non-native language speakers for real-time speech recognition and could provide automatic speech recognition. The tool enables developers to convert file-based audio data containing speech into highly accurate text transcriptions and is seen as a powerful tool for developing in-workflow closed captions, indexing content, and enhancing audio and video content analytics.
OCI Vision
OCI Vision included pre-trained computer vision models for tasks like image recognition and document analysis. This service could identify visual irregularities in manufacturing, automate workflow tasks by extracting text from forms, and count products or shipments by tagging items in images. Additionally, users could customize and adapt these models to various industry-specific applications, including scene monitoring, defect detection, and document processing, using their own data.
OCI Anomaly Detection
This service delivered business-specific anomaly detection models that could flag critical irregularities early, enableing faster resolution and minimal disruption. OCI Anomaly Detection also provided APIs and SDKs for several programming languages, which developers could further leverage to integrate its anomaly detection models into custom business applications.
OCI Forecasting
This service was designed to deliver time-series forecasts through Oracle AI and machine learning and statistical algorithms that eliminated the need for data science expertise for the customer. OCI Forecasting empowered developers to generate precise forecasts for essential business metrics such as product demand, revenue, and resource needs. These forecasts came with exact confidence intervals and substantial clarity, enabling businesses to make well-informed and profitable decisions.
OCI Data Labelling
The sixth service provided unprecedented flexibility and customizable options to the customer by letting the users build labeled datasets to train custom AI models. Users could gather data, establish and explore datasets, and affix labels to data records through user interfaces and public APIs. These labeled datasets were exportable and could be utilized for model development across various Oracle AI and data science services, such as OCI Vision and OCI Data Science, ensuring a uniform model-building process.
The Generative AI Revolution and Oracle’s Strategy
While the adoption of OCI AI Services across businesses and sectors increased, the buzz surrounding new technology in the AI world took everyone by storm. In 2023, Generative AI and LLMs became the most discussed technological development globally. Oracle was not just swift in integrating generative capabilities in its existing AI bouquet; it also emerged as a critical part and key industry leader in developing these capabilities.
Oracle’s established history of effectively managing some of the world’s most business-critical, valuable data while constantly providing modern data platforms and an existing track record of creating low-cost, high-performance AI infrastructure made it a natural choice for the implementation of Generative AI capabilities in practical business applications and the development of new and exciting AI capabilities.
Oracles Generative AI capabilities were aimed primarily at the enterprise and built around the reality that enterprises would interact with AI capabilities in three different and distinct modalities: Infrastructure, models and services, and within applications. This clarity in its strategy enabled Oracle to focus on the following key deliverables for its partners and customers:-
1. Generative AI Training Infrastructure: Realizing the intrinsic need for Generative AI to be flexible and responsive to the unique requirements of users, Oracle focused on creating a robust infrastructure for training and serving AI models at scale. Its partnership with NVIDIA, which is the undisputed leader in the hardware sector that powers all Generative AI capabilities, Oracle was able to offer its customers critical hardware superclusters – a mesh of the latest NVIDIA GPUs in the market connected with an ultra-low-latency RDMA Over Converged Ethernet (RoCE) network. This solution provided a highly performant, cost-effective method for training generative AI models at scale.
2. Cloud Based, API Enabled Generative AI Services: The ease of use of Generative AI, which encourages users with limited technical knowledge of its functioning, is a crucial reason for the world’s fascination with the technology and its swift implementation in many parts of our lives. Oracle leveraged the existing OCI AI Services and the Oracle Cloud capabilities to roll out Generative AI offerings with minimal disruptions or implementation challenges to its customers. Oracle partnered with Cohere, a leading Generative AI company for enterprise-grade large language models (LLMs), to enable new Generative AI services and business functions. This upcoming service, OCI Generative AI, is embedded in the existing OCI AI framework and aims to allow OCI customers to add Generative AI capabilities to their applications and workflows through simple APIs. AI in Oracle also embeds these capabilities in its existing easy-to-use cloud services for developers and users to utilize in fully managed implementations of Generative AI capabilities.
3. Seamless Incorporation of Generative AI Services: Oracle’s customer-focused approach also emphasized the easy and seamless incorporation of these capabilities with minimal disruption. Oracle, while developing new and exciting Oracle AI services, also focused on the methods to embed generative models into the applications and workflows that business users currently use. It has planned to embed Generative AI from Cohere into its existing portfolio of solutions, including Fusion, NetSuite, and vertical software-as-a-service (SaaS) offerings to immediately provide organizations with the full power of Generative AI.
The Oracle Advantage
A legacy of developing and incorporating AI services, robust cloud presence, and crucial partnerships with industry leaders in Generative AI enables Oracle to provide native Generative AI-based features to help organizations automate key business functions, improve decision-making, and enhance customer experiences.
Its depth of experience and varied offerings have allowed Oracle to be included in every aspect of Generative AI incorporation, including development and end-use. AI developers like Adept and MosaicML are now building their products directly on the OCI platform, and even vendors such as Cohere, NVIDIA, and X.AI are using it to train their large language models (LLMs). As an illustrative example at the end user level, Oracle Cerner now manages billions of electronic health records (EHR) using anonymized data. Oracle’s generative models, adapted specifically to the healthcare domain, automate routine tasks such as generating a patient discharge summary or a medical insurance authorization letter.
Oracle confidently asserts its Generative AI offerings to be truly built for enterprises with an approach that spans varied considerations and unique requirements, such as cloud-to-on-premises data, deployment to business apps, security, data privacy, and now the best LLMs for enterprise success.
High-performance AI models: Oracle’s intrinsic capabilities and fusion with AI development processes and key acquisitions allow it to leverage its unique data and industry knowledge and create the optimal AI models for users. Through the legacy and deep experience of its popular suite of business apps, Oracle can train specialized models unique to its verticals and SaaS solutions. It also enables organizations to refine these prebuilt models using their own data to achieve an optimal degree of customization and familiarity of the AI models with the organization’s business.
Data Security and Governance: Oracle fully understands the requirement of protecting user data even as more and more customers look to refine and train prebuilt generative AI models with their data. Unlike many other Generative AI offerings, Oracle’s Generative AI doesn’t mix customer data and ensures that the models trained by customers are unique to them, are fully secure, and are empowered through instant availability of tools for accessing data provenance and lineage.
Embedded Generative AI Services: Oracle has leveraged its existing suite of business solutions and its portfolio of cloud applications, including CRM, ERP, HCM, CX, and EMR applications, and is swiftly integrating and embedding AI across the entire spectrum. Just as it took the lead in introducing machine learning (ML) features in Oracle Database service and MySQL HeatWave earlier, Oracle is now making generative AI capabilities available in its database portfolio similarly.
Flexible Generative AI Deployment Options: Oracle is focused on providing Generative AI services wherever customers need them. Customers can use Oracle’s upcoming OCI Generative AI service on Oracle Cloud Infrastructure (OCI) and reap all the benefits of the public cloud, such as paying for what you use, scaling on demand, customizing models, and creating private model endpoints.
The Future of Generative AI with Oracle
Oracle has firmly invested itself in developing Generative AI capabilities and visualizes it as a critical component of its product offerings in the future. Even the internal functioning of Oracle itself is impacted by the capabilities of the technology. While Oracle plans to continue supporting older applications written in Java, they have now embarked on developing new Oracle AI apps using code generated automatically in Oracle APEX by GenAI tools based on developer prompts. This is a fundamental change in how Oracle builds and runs applications powered by the capabilities of Generative AI.
For developers and businesses, Oracle is best positioned among cloud vendors to help develop Generative AI models. This is primarily due to the next-generation OCI services that leverage ultrafast Remote Data Memory Access (RDMA) networking. This enables each computer in the network to access the memory of another computer in the network without interrupting its functioning and can thereby, move immense quantities of data at speeds that outpace conventional networks by several degrees. This RDMA networking is the backbone of the superclusters discussed earlier that bring together cutting-edge NVIDIA GPUs and can efficiently train Generative AI models at twice the speed of other clouds and at less than half the cost. Oracle’s Chairman and CTO Larry Ellison explained this approach as the key differentiator for Oracle. “In the cloud, time is money,” he said at the recent Oracle CloudWorld conclave. “We are much faster and many times less expensive than the other clouds for training AI models.”
Oracle CloudWorld 2023: Must-See Innovations for Businesses To Watch Out
Oracle CloudWorld 2023 was also the platform for the company to showcase how it is embedding Generative AI into its vast portfolio of cloud services, to help customers and society at large tackle their most vexing problems. During his keynote at Oracle CloudWorld, Ellison announced a slew of new AI-enabled services.
Oracle AI Database Capabilities
Oracle is developing a new, integrated vector database that will store the semantic content of documents, images, and other unstructured data as vectors and uses these to run fast similarity queries.
The new capability will allow applications built on Oracle Database and Autonomous Database to add LLM-based natural language interface, letting end users ask for the data they need by posing questions using natural language.
This capability is required to adapt generalized pre-trained LLMs to more specialized contexts such as medicine and law.
Oracle and Healthcare
Oracle is creating an Internet of Things platform that can be leveraged by healthcare providers for inventory management of stores and medical supplies. The IoT capabilities can also be harnessed to operate sensors that track patient symptoms and other data.
Oracle also plans to help healthcare providers better store and access electronic patient records, medical imaging, and diagnostics at a lower price point while developing Generative AI capabilities to help with diagnoses.
AI and Oracle Cloud
• Oracle Analytics Cloud service now offers Generative AI data interactions, which allow users to ask questions and get answers from data using natural language and AI-generated avatars. The avatars, powered by a partnership with Synthesia, can act as news readers and deliver data stories to business decision-makers.
• The cloud service is now fully integrated with OCI AI Services, which can read documents such as JPEG and PDF files and extract key values and their context. This can help users get information from documents and generate additional insights for their analytics.
• OCI AI Services now offers cloud-based contextual insights, which use machine learning to recommend insights based on the type and state of the data being viewed. These insights can help users understand the meaning of complex data without additional interpretation.
Oracle has been a pathfinder and an early adopter of the AI revolution and continues to push itself to create newer ways to act as the bridge between business challenges and AI solutions. The plans of launching the next-gen OCI services, the symbiosis between AI and Oracle’s excellent cloud services profile, and being a partner in the development cycle of the next iteration of AI capabilities make Oracle one of the most exciting participants in the way our world is being reshaped by technology.
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Frequently Asked Questions (FAQs)
Oracle invested in AI with the 2021 launch of Oracle Cloud Infrastructure (OCI) AI services, making AI more accessible to developers.
OCI Language helps analyze unstructured text for sentiment analysis and key-phrase extraction, while OCI Speech converts audio data into text for analytics.
OCI Vision can be used to detect visual anomalies, automate workflows, and tag items in images for counting products or shipments.
OCI Anomaly Detection flags irregularities early, and OCI Forecasting provides accurate time-series forecasts for business metrics.
Oracle focuses on flexible infrastructure, cloud-based services, and seamless incorporation to empower businesses with Generative AI.
Deep learning, inspired by the human brain, plays a crucial role in AI development, enabling tasks like image and speech recognition.
In 2023, Generative AI and LLMs became globally significant, reshaping technology and business landscapes.
Oracle is known for effectively managing valuable data, providing modern data platforms, and creating high-performance Oracle AI infrastructure.
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