OpenAI Consulting

Global AI Keynotes: CEO of Cazton delivered keynotes for Global AI Developer Days in Fall 2022 in Europe, United States and Latin America. The keynote compared the AI offerings of AWS, Azure and GCP. The demo included multiple programming languages, including Node.js, Python and the latest .NET (C#) framework.
OpenAI offerings include GPT-4, an AI language model that can generate human-like text, and DALL-E, an AI image generation tool.
ChatGPT, an OpenAI product released in November, 2022, reached one million users in a week. Facebook and Instagram took 10 and 2.5 months respectively to hit the same milestone.
What do enterprise customers want? ChatGPT like AI for the enterprise. An intelligent solution that is similar to ChatGPT and reads unstructured, semi-structured and structured data and then answers like a human being with high IQ. With Azure OpenAI all prompts (inputs), completions (outputs), embeddings, and training data remain exclusive to customers. They are not accessed by either Microsoft or OpenAI.
Microsoft and Cazton: We work closely with OpenAI, Azure OpenAI and many other Microsoft teams. Thanks to Microsoft for providing us very early access to critical technologies. We are fortunate to have been working on GPT-3 since 2020, a couple years before ChatGPT was launched.
Top clients: At Cazton, we help Fortune 500, large, mid-size and startup companies with Big Data and AI development, deployment (MLOps), consulting, recruiting services and hands-on training services. Our clients include Microsoft, Broadcom, Thomson Reuters, Bank of America, Macquarie, Dell and more.

Imagine being able to build almost any digital asset just by providing prompts in natural language. Language models (LMs) like T5, LaMDA, GPT-3, and PaLM have demonstrated impressive performance on such tasks. Recent studies suggest that scaling up the size of the model is crucial for solving complex natural language problems. This has led to the development of Large Language Models (LLMs). These models are trained on a very large dataset of text.

Why LLMs? It has traditionally been hard for AI models to generate human-like text, often lacking fluency, coherence, and context. LLMs have achieved impressive results (though they have several weaknesses that we will discuss below) in a variety of natural language processing tasks, such as language translation, summarization, and answering questions. Notable LLMs include GPT (Generative Pre-training Transformer), BERT (Bidirectional Encoder Representations from Transformers), and RoBERTa (Robustly Optimized BERT Pre-training Approach). LLMs are pre-trained to learn to predict the next word in a sequence, given the context provided by the previous words. This pre-training process helps the model learn the structure of language.

Denoising Diffusion Probabilistic Models (or DDPMs, diffusion models, score-based generative models or simply autoencoders) have demonstrated remarkable results for (un)conditional image, audio and video generation. Popular examples (as of Dec, 2022) include GLIDE and DALL-E 2 by OpenAI, Latent Diffusion by the University of Heidelberg, ImageGen by Google Brain and Stability AI by Stable Diffusion.

Transformers are a neural network architecture that has revolutionized the field of natural language processing (NLP). They were introduced in the landmark paper "Attention is All You Need" by Vaswani et al. in 2017. The Transformer architecture is designed to address the limitations of traditional recurrent neural networks (RNNs) when processing sequential data, such as sentences or paragraphs. Unlike RNNs, Transformers do not rely on sequential processing and can capture long-range dependencies more effectively.

At the core of the Transformer architecture are self-attention mechanisms, also known as scaled dot-product attention. Self-attention allows the model to weigh the importance of different words or tokens within a sequence when processing each individual word or token. It does this by computing attention scores between pairs of words, determining how much each word should attend to other words in the sequence. The self-attention mechanism consists of three main components: query, key, and value. For each word or token, the query is compared with the keys to compute attention scores. These attention scores are then used to weight the corresponding values. The weighted values are summed up to obtain the output representation for the word or token.

The Transformer architecture consists of multiple layers of self-attention mechanisms, typically called encoder layers. Each encoder layer processes the input sequence independently in parallel, allowing for efficient computation. Additionally, each encoder layer has a feed-forward neural network that adds non-linear transformations to further enhance the model's expressiveness. To train the Transformer model, a process called "self-supervised learning" is often employed. This involves pre-training the model on large amounts of unlabeled text data, where the model learns to predict missing words or tokens within the text. Once pre-training is complete, the model can be fine-tuned on specific downstream tasks, such as language translation or sentiment analysis.

Transformers have demonstrated exceptional performance in a wide range of NLP tasks. Their ability to capture long-range dependencies and handle large-scale parallel processing has made them the go-to architecture for many state-of-the-art language models, including OpenAI's GPT series.

The field of artificial intelligence (AI) is in a state of constant evolution, driven by the relentless pursuit of technological advancements. OpenAI has been instrumental in steering this revolution, and its generative pre-trained transformer models have paved the way for countless innovations. Its latest iteration, the GPT-4, marks a new milestone in AI evolution, offering remarkable features that make it the most sophisticated model to date.

Enhanced Language Understanding and Generation: One of the defining features of GPT-4 is its enhanced language understanding and generation capabilities. This model has been trained on a diverse range of internet text, enabling it to understand and generate human-like text more accurately and creatively than any of its predecessors. For example, in the field of content creation, GPT-4 can be employed to generate high-quality articles, blog posts, or social media content, which reduces the time and effort required from human writers. The model can follow prompts accurately, maintain consistent tone, and deliver a coherent narrative, replicating the nuance of human writing impressively.
Context Awareness: GPT-4 exhibits a heightened understanding of context, which is key to generating relevant and meaningful responses. This feature enables GPT-4 to maintain long conversations or generate long-form content by keeping track of the context from the beginning of the interaction. In customer service, for instance, GPT-4 powered chatbots can maintain meaningful conversations with customers, addressing their queries accurately by understanding the context of the conversation, providing a seamless and efficient customer experience.
Advanced Problem-Solving Capabilities: The advanced problem-solving capabilities of GPT-4 sets it apart from other AI models. It's not only able to answer questions but can also solve complex problems by connecting the dots from the information provided to it. In the realm of education, GPT-4 can be employed as an interactive learning tool. For example, it can help students solve math problems by not just providing the final answer, but also explaining the steps involved in the process, thereby enhancing the student's understanding of the subject.
Multimodal Abilities: GPT-4 has made considerable strides in multimodal abilities, which involve understanding and generating responses based on multiple types of input, like text and images. This represents a significant leap from its predecessor models, which were primarily text-based. In healthcare, for instance, GPT-4 can be used to analyze medical images along with patient history provided in text format to offer diagnostic suggestions, thereby supporting doctors in making more informed decisions.
Enhanced Transfer Learning: Transfer learning, the ability to apply knowledge gained from one task to another, is another crucial aspect of GPT-4. This enables the model to be effective in a variety of use-cases without the need for task-specific training. In the domain of legal services, GPT-4 could be used to understand and interpret legal texts, documents, and precedents. It can then apply this understanding to draft legal documents or provide suggestions on legal queries, thereby increasing efficiency in legal work.
Robust and Ethical AI: OpenAI has taken significant strides to ensure GPT-4 operates in an ethical manner. The model includes mechanisms to avoid generating harmful or biased content and can refuse to generate certain types of responses, demonstrating OpenAI's commitment to developing robust and ethical AI. In journalism, this feature ensures that GPT-4 generates unbiased articles and maintains the highest level of journalistic integrity, thereby aiding the maintenance of trust and credibility in news sources.

In conclusion, GPT-4's wide array of features and their possible applications across various domains underscore the model's potential in shaping the future of AI. As AI continues to evolve and become more sophisticated, GPT-4 and its successors will undoubtedly play a crucial role in unlocking new opportunities and driving innovation across industries.

The Azure OpenAI Service offers REST API access to OpenAI's robust language models, encompassing Ada, Babbage, Curie, GPT-3, GPT-3.5, GPT-4, DALL-E, Codex, and Embeddings model series. The versatility of these models allows for seamless adaptation to suit your specific requirements, encompassing a wide range of tasks including, but not limited to, content generation, summarization, semantic search, and natural language to code translation. With their flexible nature, these models can be effortlessly tailored to meet the unique demands of your applications. Whether you need to generate compelling content, distill information into concise summaries, enable precise semantic search capabilities, or facilitate accurate translation from natural language to code, these models offer a professional and efficient solution to address your specific needs. By leveraging their adaptability, you can unlock the full potential of these models and elevate the capabilities of your applications to new heights.

With Azure OpenAI Service, businesses can now leverage state-of-the-art AI technologies to gain a competitive edge. At Cazton, our team of experts is extremely fortunate to work with top companies all over the world. We have the added advantage of creating best practices after witnessing what works and what doesn't work in the industry. We can help you build custom, accurate, and secure AI solutions that cater to your specific needs.

While OpenAI is good and will get better with time, Cazton can help you with a comprehensive AI strategy that is the best of all worlds: OpenAI technologies, open source alternatives and proprietary technologies from major tech companies.We have listed some client concerns below and the solutions:

ChatGPT like business bots: Imagine having an OpenAI powered chat bot for every single team in your company: sales, marketing, HR, legal, tech team etc. that helps your accentuates your productivity. The bot provides information, simplifies concepts, brings everyone up to speed, remove bottlenecks and roadblocks, provides automated documentation and enchances team collaboration. Guess what all this is possible on your data while we protect your data privacy. Yes, no other party, including Microsoft or OpenAI will have access to your data. Read ChatGPT for business and Azure OpenAI for more details. We have two different video demos embedded in each of the two links.
Offline access: Some clients prefer not to make a call to an external API (like OpenAI). Can we help you with your own model that could be used offline? Absolutely! We can help create a solution based on open source pre-trained models that can be used offline in a multitude of devices including all major operating systems, Docker, IoT devices etc.
Cost reduction: OpenAI solution is based on a pay-as-you-go model? However, some clients who would want to use the Generative AI solutions extensively, may want to save that cost. There would be no ongoing costs like per-image cost. Superb! That means the cost of running the model is effectively zero. (1)
Increased accuracy, precision and recall: OpenAI models and other AI models are not 100% accurate. Our team helps you create solutions that have higher relevancy and accuracy. Contact Cazton team to learn strategies to create high quality AI solutions while lowering ongoing costs.We can help with customized models that can alleviate lack of accuracy and this custom model can be trained on clients' business domain. Two popular solutions are:
- OpenAI model extension: Creating a customized model on top of OpenAI model.
- Open-source model extension: Creating a customized model on top of a pre-trained open source model.

Good news: The Cazton team is well-aware of the limitations, pitfalls, and threats associated with AI solutions, such as hallucinations, accuracy, bias, and security concerns. We constantly strive for higher accuracy, precision, and recall by combining traditional information retrieval techniques with AI and deterministic programming to provide hybrid solutions that deliver enhanced performance. By proactively addressing these challenges and developing innovative solutions, we ensure our customized AI-Powered business solutions are reliable, ethical, and secure, fostering trust among users and stakeholders across various industries.

Cazton is a team of experts committed to helping businesses build custom, accurate, and secure AI solutions using OpenAI and Azure OpenAI services. We address common concerns, such as hallucinations, low accuracy, precision, and recall, by fine-tuning the models and leveraging our extensive expertise. With Cazton, you can trust that your data remains secure, as we prioritize stringent security measures to restrict access solely to authorized personnel. Our solutions are tailored to meet your specific needs and seamlessly integrate with any tech stack, whether modern or traditional, enabling smooth implementation of AI capabilities.

Our primary goal is to provide you with the necessary information and professional guidance to make informed decisions about OpenAI and Azure OpenAI solutions. We believe in empowering our clients with knowledge, rather than pushing sales pitches, so you can confidently choose the best AI partner for your business – Cazton.

We can help you with the full development life cycle of your products, from initial consulting to development, testing, automation, deployment, and scale in an on-premises, multi-cloud, or hybrid environment.

Comprehensive development lifecycle: We offer comprehensive assistance throughout the entire development lifecycle of your products, encompassing various stages from initial consulting to development, testing, automation, deployment, and scalability in on-premises, multi-cloud, or hybrid environments. Our team is adept at providing professional solutions to meet your specific needs.
Technology stack: We can help create top AI solutions with incredible user experience. We work with the right AI stack using top technologies, frameworks, and libraries that suit the talent pool of your organization. This includes OpenAI, Azure OpenAI, Semantic Kernel, Pinecone, Azure AI Search, FAISS, ChromaDB, Redis, Weaviate, Stable Diffusion, PyTorch, TensorFlow, Keras, Apache Spark, Scikit-learn, Microsoft Cognitive Toolkit, Theano, Caffe, Torch, Kafka, Hadoop, Spark, Ignite, and/or others.
Develop models, optimize them for production, deploy and scale them.
Best practices: Introduce best practices into the DNA of your team by delivering top quality machine learning (ML) and deep learning (DL) models and then training your team.
Incorporating ML/DL models in your existing enterprise solutions.
Customized AI Solutions: The Future of Business Efficiency: Develop enterprise apps or augment existing apps with real time ML/DL models. This includes Web apps, iOS, Android, Windows, Electron.js app.
Scalability and Performance: We have scalability and performance experts that can help scale legacy applications and improve performance multi-fold.