Domain Specific Language Models

December 2, 2022

Today, AI is more popular than ever in the business world. Corporate investment in AI increased by 5x from 2015 to 2020 (Stanford), 79% of companies are exploring or piloting AI projects (Gartner), and 86% will consider AI a “mainstream technology” (PwC).

Most business-focused conversations around the future of artificial intelligence focus on the immense potential of its capabilities which is often described in terms that leap quickly from optimistic to incredibly speculative. Predictions compare artificial intelligence (AI)’s trajectory with that of the steam engine and predict the potential productivity gains from AI to be anywhere from $13.7 trillion to $15.7 trillion by 2030, according to the McKinsey Global Institute and PricewaterhouseCooper, respectively.

Even once you strip away any misconceptions and ratchet back the hype, it’s clear that AI’s potential to deliver value, even in the near term particularly across vertical industries and specific functions will be significant. It will require a highly focused approach for AI to reach that potential, one that leverages comprehensive, industry-specific data and domain expertise, real, human expertise that only comes from experience alongside cutting-edge technology.

In this article, we discussed how generative AI is emerging as a new technology. No doubt AI is widening its field and capturing every open source software. The emergence of GPUs again is boosting the speed of artificial intelligence with 10X more than with CPU cards. The obsolete presence of traditional approaches is highly avoidable in front of machine learning, NLP and computer vision.

The Case for Domain-Specific Hardware

NLP processing using AI/ML techniques is complementing and reducing the effort in creating dynamic rule driven dashboards with automated conversational & dynamically relevant insights. All this is customized to a user’s context and delivered to the point of consumption over this vision that artificial intelligence is optimized and trained specifically for healthcare and life sciences. 

GPUs were born to support advanced graphics interfaces by covering a well-defined domain of computing—matrix algebra—which is applicable to other workloads such as artificial intelligence (AI) and machine learning (ML). The combination of a domain-specific architecture with a domain-specific language (for example, CUDA and its libraries) led to rapid innovation.


Let’s have a look at building domain specific clip models with an in-depth look at actually training and implementing open-air CLIP. 

In the last decade, there have been severe advancements in AI and this is likely just the beginning.  Let’s introduce a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of visual categories to be recognized similar to the zero shot capabilities of GPT-2 & GPT-3. 

CLIP (Contrastive Language Image Pre-Training) is a multimodal model that combines knowledge of english-language concepts with semantic knowledge of images. There have been GPT and good image models, images and models trained on image that can classify a lot of things. The main question that arises here is, Can we use a model that combines language & vision for domain-specific use cases so most of the data we have is actually unstructured? For example: Catalogs in any domain often have annotations, like free text, describing images. 

What CLIP does essentially is to bring images & texts in the same backlogs page in a way that we can compute the similarities between an image & the description of the image clip. 


Diving into the Model

GPT-3 comes in eight sizes, ranging from 125M to 175B parameters. The largest GPT-3 model is an order of magnitude larger than the previous record holder, T5-11B. The smallest GPT-3 model is roughly the size of BERT-Base and RoBERTa-Base.

All GPT-3 models use the same attention-based architecture as their GPT-2 predecessor. The smallest GPT-3 model (125M) has 12 attention layers, each with 12x 64-dimension heads. The largest GPT-3 model (175B) uses 96 attention layers, each with 96x 128-dimension heads.

GPT-3 expanded the capacity of its GPT-2 by three orders of magnitudes without significant modification of the model architecture — just more layers, wider layers, and more data to train it on.

Startups are all about iterating fast. Most companies can get feedback from customers and ship a new feature over a weekend. But when you’re a deep learning company, and your models take weeks to train, your iteration speed as a startup is significantly hindered.

The main way out of this problem is to train your models on more GPUs that come at a high cost usually not affordable to startup companies if you are going with hyperscalers. Rather than if you choose a cloud platform which allows you to train models like GPT-3 with A100 that hardly takes 34 days of training according to a current research. 

Domain Specific Languages

A domain specific language is a computer language specialized to a particular application domain. This is in contrast to a general purpose language, which is broadly applicable across domains and lacks specialized features for a particular domain. There is a wide variety of DSLs ranging from widely used languages from common domains, such as HTML for web pages, down to languages used by only a single piece of software. 

DSLs can be further subdivided by the kind of language, and include domain specific markup languages, domain-specific modeling languages & domain specific programming languages.  The design and use of appropriate DSLs is a key part of domain engineering by using a language suitable to the domain at hand. This may consist of using an existing DSL or GPL, developing a new DSL. Language-Oriented programming considers the creation of special-purpose languages for expressing problems as a standard part of the problem solving process. 

Simple DSLs, particularly one used by a single application are sometimes informally called mini-languages. 

  • It captures the programmer intent at a higher level of abstraction.
  • Obtain many software engineering benefits: clarity, portability, maintainability, testability etc. 
  • Provides the compiler more opportunities for higher performance 
  • Can encode expert knowledge of domain specific transformations.
  • Better view of the computation performed without heroic analysis.
  • Less low level decisions by the programmer that have to be undone. 

According to a computer scientist and thought leader in AI, “Today’s A.I., or what we call A.I., is actually very narrow, domain-specific, but incredibly capable and superhuman within very limited tasks.”

Domain-specific AI can automate tasks that humans do, but the focus isn’t to imitate humans. It won’t be able to switch from one task to another and then dive into a conversation with you about its plans for the weekend.

Domain-Specific Tools

  • School Management System
  • Inventory Management
  • Payroll Software
  • Financial Accounting
  • Restaurant Management
  • Railway Reservation System
  • Weather Forecasting System

While the Turing test may have revealed that AI as a field is still quite immature, domain-specific AI is definitely a viable and mature technology that businesses can and should consider implementing now.

Domain-specific AI fits right into our existing business processes, especially where organizations are already using data to drive decisions and automate tasks. It helps solve a lot of existing pain points such as talent gaps and the need to process and analyze data at scale.

Now, when we say we use a GPU in neural network programming, we can conclude that parallel computing is done using GPUs, if a computation can be done in parallel, we can accelerate our computation using parallel programming approaches and GPUs. E2E Cloud is helping you to build and launch machine learning applications on the most high-value infrastructure cloud in the market.

Check our Cloud GPUs:

Choose according to your use case, What if you can save huge costs than hyperscalers?:

We hope the information provided here could help your understanding of domain specific AI and how it is contributing to the success of artificial intelligence technology. For reading more such blogs, visit:

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The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

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Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

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  • It can also help in completing DNA sequences.

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  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
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