Overviewing Google Cloud’s Toolkit for Generative AI
Did you know that 70% of AI unicorns operate on Google Cloud? Startups in the field of generative AI are exploring the extensive potentials of this technology, ranging from visual creation and content editing to research and chatbots.
This staggering statistic is further solidified by the recent EMEA Google Cloud Applied AI Summit held on December 14, 2023. During the summit, Google Cloud showcased its powerful advancement in the generative AI toolkit, ensuring that businesses can easily access advanced models while benefiting from the robust security and deployment support offered by Google Cloud.
In this article, we will provide you with a definition of generative AI, describe how it is reshaping work, explain why Google Cloud’s generative AI is groundbreaking, and give an overview of four significant generative AI tools presented by Google Cloud.
What is generative AI?
Generative AI, a subset of artificial intelligence, stands out for its ability to create new content, ranging from text and images to code. Unlike traditional AI models reliant on rule-based systems, generative AI learns from extensive datasets, allowing it to understand patterns and context. Take GPT-3, a prominent example; it excels in generating coherent and contextually relevant text based on input prompts.
5 benefits of generative AI for businesses
Generative AI systems are revolutionizing various aspects of business, offering a plethora of benefits that enhance operations and drive innovation. Below are the key improvements enabled by generative AI applications.
Automated and innovative content generation
Generative AI proves invaluable in content creation, automating tasks such as marketing copy, blog and social media posts, and graphic design. AI tools can generate ideas based on input prompts, allowing for dynamic back-and-forth collaboration, leading to more refined and usable content.
Enhanced customer experience
AI contributes to personalized customer interactions by learning about a company and utilizing collected customer data. E-commerce businesses, for example, can leverage generative AI to match customer preferences with relevant product recommendations, creating a more tailored and satisfying customer experience.
Generative AI plays a crucial role in cybersecurity by analyzing large amounts of data to detect threats efficiently. As cyberattacks become more sophisticated, AI tools identify patterns of activity outside the norm, enabling rapid threat detection and proactive measures to safeguard internal systems.
Streamlined business processes
AI streamlines business processes by automating tasks and analyzing data. For instance, large language models (LLMs) can analyze text reports, generating summaries and simplifying the interpretation of extensive information.
Generative AI serves as a catalyst for creativity, assisting users in generating new ideas. Whether in product design or art, users can interact with AI tools using natural language to explore and develop unique concepts.
Two key factors propelling Google Cloud’s generative AI dominance
So, what is the secret sauce behind Google Cloud’s generative AI excellence over its competitors?
Two key takeaways solidify Google’s dominant position. Firstly, Google Cloud is establishing itself as the go-to platform for the development, deployment, and refinement of generative AI models. Secondly, the evolution of Google’s toolkit into a tool capable of assisting users in more complex tasks underscores its transformation into a powerful productivity tool.
Below, we will provide an overview of four important solutions that exemplify this statement.
Google Cloud’s toolkit for generative AI: 4 top solutions
Alpha Code 2
This robust AI model, tested on industry benchmarks, comes in three sizes—Gemini Ultra, Gemini Pro, and Gemini Nano.
The Gemini model is equipped with native multimodal features and advanced reasoning capabilities, making it a versatile choice for handling complex tasks.
Researchers from Google DeepMind fine-tune Gemini Pro on a dataset comprising 15,000 problems from CodeForces, and 30 million samples of human-written code. Additional fine-tuning on a higher-quality dataset enhances AlphaCode 2’s problem-solving skills.
Unique approach to code generation
AlphaCode 2 operates differently from human programmers by:
- generating around a million different code samples for a given problem.
- employing a rigorous filtering process to eliminate random and irrelevant scripts.
Computational intensity and efficiency
AlphaCode 2’s approach involves extensive trial and error and computationally intensive filtering. Despite challenges, it is over 10,000 times more sample-efficient than its predecessor.
Future prospects and development
Researchers envision a future where AlphaCode 2 becomes a collaborative tool for programmers by assisting them in reasoning about problems, proposing code designs, and in implementation.
Cloud TPU v5p
Google Cloud’s Cloud TPU v5p and AI Hypercomputer introduce advancements in AI capabilities, offering organizations enhanced tools to explore AI innovation with improved efficiency and cost-effectiveness.
Tailored to address the changing requirements of generative AI models, TPU v5p brings advanced sophistication and capabilities to the forefront.
Performance and scalability
- TPU v5p pod
Composed of 8,960 chips over the highest-bandwidth inter-chip interconnect (ICI) at 4,800 Gbps/chip.
TPU v5p features more than 2X greater FLOPS compared to TPU v4.
3X more high-bandwidth memory (HBM) than TPU v4.
- Training speed
TPU v5p can train large models 2.8X faster than TPU v4.
Second-generation SparseCores enable 1.9X faster training for embedding-dense models.
TPU v5p is 4X more scalable than TPU v4.
AI Hypercomputer architecture
AI Hypercomputer employs a systems-level co-design for efficiency and productivity across AI training, tuning, and serving.
Extensive support for popular ML frameworks, including JAX, TensorFlow, and PyTorch.
Multislice training and multihost inferencing
Open and unique software for scaling, training, and serving workloads seamlessly.
Flexible consumption models
Introduces Flex Start mode and Calendar mode for optimized resource obtainability and economics.
Additionally, integration with Google Kubernetes Engine (GKE) and Google Compute Engine ensures efficient resource management and consistent ops environments.
Google’s commitment to accessibility is evident with the integration of Gemini Pro and Gemini Ultra into Vertex AI. This move provides developers with exceptional reasoning skills to analyze complex information at digital speeds
Furthermore, the announcement of reduced pricing and AI-optimized infrastructure underscores Google’s dedication to making advanced AI models like Gemini accessible to a broader audience.
- Unified AI platform
Vertex AI seamlessly integrates with Gemini, a multimodal model capable of understanding diverse inputs and generating an array of outputs.
- Advanced reasoning
Gemini’s advanced reasoning and generation capabilities are ideal for innovative AI applications.
- Text, images, video, and code
Prompting and testing in Vertex AI are made easier with various data types, including text, images, video, and code, making it a versatile choice for AI development.
Generative AI models and tools
- Model Garden
A rich repository of over 130 generative AI models and tools, including first-party (PaLM API, Imagen, Codey), third-party (Anthropic Claude 2), and open source models (Llama 2).
- Real-time information retrieval
Enhanced capabilities with extensions to fetch real-time information and trigger actions.
- Customization options
Capabilities to tailor models to specific use cases with tuning options for text, image, or code models.
Open and integrated AI platform
- Vertex AI platform
Accelerated ML workflow with purpose-built tools for training, tuning, and deploying ML models.
- Integrated notebooks
Vertex AI notebooks are seamlessly integrated with BigQuery, providing a unified surface for all data and AI workloads.
- Training and prediction
Reduced training time and smoother deployment of models with support for open-source frameworks and optimized AI infrastructure.
MLOps for predictive and generative AI
- Purpose-built MLOps tools
The Vertex AI Platform offers modular tools to automate, standardize, and manage ML projects.
- Collaboration and workflow orchestration
Collaboration across teams and enhancements to models with tools such as Vertex AI Pipelines, Model Registry, Feature Store, and more.
- Monitoring capabilities
Tracking input skew and drift to ensure consistent model performance over time.
Search and conversation
Vertex AI Search and Conversation takes the spotlight, simplifying the development of retrieval-augmented generation (RAG) applications. From capabilities and development journeys to addressing challenges in working with language models, Vertex AI Search can be seamlessly integrated into the NLP application development process.
Duet AI for Developers demonstrates its prowess in code explanation, generation, and deployment. Its application in securing applications against cyber threats emphasizes collaboration through partnerships with code-assist and knowledge-base partners.
Versatile conversational assistance
- Chat interface
Quick conversations with Duet AI to seek answers to cloud-related queries or gain insights into best practices.
- Google Cloud expertise
Specifically trained on Google Cloud content, including documents and sample code, Duet AI provides targeted and context-aware assistance.
Fully managed and ready to use
- No AI expertise required
Duet AI is a fully managed service, eliminating the need for users to have in-depth knowledge of AI concepts.
AI-Powered code assistance
- Code completion
Whether coding apps, calling APIs, or querying data, Duet AI enhances the user’s coding experience by suggesting code completions as they write.
- Multilingual support
With support for 20+ programming languages, Duet AI caters to a diverse range of developers and coders.
- IDE compatibility
It seamlessly integrates into multiple Integrated Development Environments (IDEs), including Visual Studio Code, JetBrains IDEs, Colab for Enterprise, Cloud Workstations, Cloud Shell Editor, Cloud Spanner, and BigQuery.
Respect for intellectual property
- Data security
Duet AI is designed with a commitment to respect intellectual property. User’s code, inputs, and recommendations are kept confidential and are not used for product or model learning and development.
Safe and responsible AI
- ISO/IEC certifications
Duet AI in Google Cloud holds prestigious certifications, including ISO/IEC 27001 (Information Security Management), 27017 (Cloud Security), 27018 (Protection of PII), and 27701 (Privacy Information Management).
In conclusion, Google Cloud’s initiatives in the generative AI domain not only align with the current state of the dynamic AI landscape but also point towards a future where AI seamlessly integrates into our daily lives, transforming how developers operate and industries function. The ongoing journey to democratize AI and establish it as a vital tool for innovation and problem-solving is well underway, and Google Cloud stands at the forefront of this transformative wave.
Unlock the full potential of generative AI with Kanda’s expertise in handling complex AI projects. Our team of top-notch AI experts, along with customized solutions, ensures that your organization remains at the forefront of technological advancements. Don’t miss out—contact us today to start your unique generative AI journey.