We can learn from vast amounts of data and generate new content, patterns, and characteristics of the training data. The “Training data” in the passage’s context refers to the large sets of information that are used as input to train generative AI algorithms. Generative AI Algorithms can create seemingly real authentic material from the training data, such as your company data, image, and audio. It is basically a type of AI system capable of generating text, photos, and other media in response to prompts the beginning of generative AI. Here we are going to discuss the fundamentals of generative AI.
Possibilities with generative AI are:
- Creating ideas and content
- Generating fresh, original outputs
- Increasing Effectiveness.
Generative AI Models
Most generative AI algorithms are constructed on foundations that have been self-supervised and trained on enormous amounts of unlabeled data to find underlying patterns for various tasks.
Advantages of Generative AI
- Automating the labor-intensive process of content creation.
- Lowering the effort in writing Emails.
- Increasing responsiveness to technical queries.
- Making a logical story out of difficult facts.
- Streamlining productions.
- Ensure the generation of high-quality outputs by self-learning from every data set.
- Lowering the risk associated with a project.
- Training reinforced machine learning models to be less biased.
- Enabling depth prediction without sensors.
- Enabling localization and regionalization of content via deepfakes.
Generative AI Limitations
- Does not specify the source of the contents.
- Identify false information.
- Understanding circumstances.
Applications of Generative AI
- Data Augmentation
- Art and Design
- Drug Discovery
Factors Making Generative AI Possible Now
- Availability of large and diverse datasets.
- AI models learn patterns, correlations, and characteristics of large datasets.
- Pre-trained model
- Advancements in hardware, GPUs.
- Access to cloud computing.
- Open-source software, Hugging face
Innovative DL Models
- Generative Adversarial Networking (GANs).
- Transformers Architecture
- Reinforcement learning from human feedback (RLHF)
Large Language Model (LLM)
Model Trained on datasets to achieve advanced language processing capabilities based on deep learning neural networks.
Large ML model trained on vast amounts of data & fine-tuned for more specific language understanding and generation tasks.
LLMs Generation Outputs for NLP Tasks
Content Creation and Augmentation: Generating coherent and contextually relevant text. LLMs excel at tasks like text completion, creative writing, story generation, and dialogue generation.
Summarization: Summarization long documents or articles into concise summaries.
Question Answering: Comprehend questions and provide relevant answers by extracting information from their pre-trained knowledge and fine-tuning new data ingested.
Machine Translation: Automatically converting a text from one language to another. LLMs are also capable of explaining language structure, such as grammatical rules.
Classification: Categorizing text into predefined classes or topics. LLMs are useful for task classification, spam detection, and sentiment analysis.
Named Entity Recognition (NER): Identifying and extracting named entities like names of persons, organizations, locations, dates, and more from text.
Tone / Level of Content: Adjusting the text’s tone (Professional, humorous, etc.) or complexity level (e.g., fourth-grade level).
Code Generation: Generating code in a specified programming language or converting code from one language to another.
LLMs Business Use Case
- Personalization and customer segmentation:
- Provide personalized product/content recommendations based on customer behavior and performance.
- Feedback Analysis
- Virtual Assistants
Process Automation and Efficiency
- Customer support augmentation and automated question answering.
- Automated customer response
- Social media, product reviews
- Sentiment Analysis, Prioritization
Choose the right LLM Model Flavor
There is no “Perfect” model; trade-offs are required.
LLM Models decision criteria.
Using Proprietary Models (LLMs -as-a-service)
|Speed of development Quick to get started and work this is another API call, it will fit very easily into existing pipelines.
|CostPay for each token sent/received. Data Privacy/SecurityYou may not know how your data is used.
|CostBy harmonizing and streamlining data, Axys optimizes token usage, ensuring that LLMs receive the most relevant and concise information. This approach reduces the number of tokens needed for generating responses, ultimately lowering operational costs. Data Prophesy/Security
|quality can offer state-of-the-art results
|Vendor lock-inSusceptible to vendor outages, deprecated features, etc.
|Vendor AgnosticContinuous MonitoringCustomization and ControlCost Efficiency
|Tesk-tailoringSelect and/or fine-tune a task-specific model for your use case.
|Upfront time investments need time to select, evaluate, and possibly tune.
|Eliminates ETL/ELT ProcessesNo-Code management Data IntegrationPredictable Pricing
|Inference CostMore tailored models are often smaller, making them
|Data requirements Fine-tuning or larger models require large datasets.
|No-Code ConnectorsEfficient Data ManagementOptimized OutputScalability
|Control all data and model information stays entirely within your locus of control.
|Skill requires in-house expertise.
|No-Code SolutionsPre-Existing ConnectorsExpertise in Data Pipelining
Delivering Business Values from Gen AI is Challenging
- Customize LLM with your private data
- Ensure LLMs deliver high-quality answers with your company data context
- Securely connect your company data to LLMs
- Deploy LLMs without new infrastructure
- Integrate LLMs with data governance
- Maintain flexibility to upgrade or change LLMs.
How AXYS can help companies address challenges related to Generative AI and LLM (Large Language Models) by centralizing data accessibility and streamlining data integration. Here’s a summary of the key points:
Challenges Addressed by AXYS:
- Limited Model Capability: The development of AI models is limited by the availability of direct data sources, leading to constrained model capabilities.
- Data Silos: Data resides in isolated sources, making it challenging to access and use for AI model training and development.
- Limited Data Source Interconnectivity: Without involving IT resources, data sources cannot be effectively interconnected.
- Data Access Controls and Ownership: Data access controls and ownership issues prevent data sharing, hindering AI development.
- Data Harmonization: To differentiate AI solutions, data harmonization is necessary but often missing.