As generative AI systems are being created and utilized, a fresh value chain is arising to facilitate the training and utilization of this potent technology. At first glance, it might appear quite akin to the conventional AI value chain. In essence, out of the six main categories—computer hardware, cloud platforms, foundation models, model hubs and machine learning operations (MLOps), applications, and services—only the inclusion of foundation models is novel.

Generative AI (GenAI) has great potential to streamline and automate many mundane and repetitive tasks that clutter our daily lives. It can assist in summarizing extensive meeting notes, condensing lengthy documents that one might need more time to read thoroughly, and even aiding in creating routine business documents. Generative AI is particularly adept at handling language, reasoning, and amalgamating different components of a task, which makes it a compelling solution for a wide array of use cases that were previously challenging to address.

The remarkable versatility of generative AI is undeniably one of its most thrilling aspects. These models can proficiently generate code, craft documents, compose music, and even produce video content, although the latter is still a work in progress.

Neurealm has been conducted an ‘Everything Products’ series of webinars and fireside chats. A recent session from the series was a ‘Fireside Chat on Generative AI with AWS’. The session had three speakers – Mr. Guiseppe Zappia, GenAI Expert and Senior Solutions Architect, AWS; Mr. Randy DeFauw, GenAI Expert and Senior Principal Solutions Architect, AWS; and Mr. Sameer Mahajan, Principal Architect at Neurealm. Mr. Mandar Gadre, a Technology Evangelist from Neurealm, moderated the session.

An interesting use case of GenAI is in Regulatory Compliance of email correspondence. The ability to finetune a model to adhere to specific SEC compliance guidelines was demonstrated. This enabled the model to scrutinize email content, flagging elements as compliant or non-compliant with the specified regulations. Furthermore, it provided recommendations for rewriting sections to ensure compliance. Such a solution would streamline review processes and mitigate risk for businesses. The company could have been penalized had the original versions of the emails been sent. This showcases the multifaceted value that generative AI can bring, reducing manual effort while safeguarding against potential liabilities.

Before focusing on generative AI for developing applications, it is essential to understand the usage funnel. At the top level, many model consumers build applications or services using existing models. Going further down the funnel, users need more model tuners to finetune existing models according to their domain requirements. The bottom of the funnel has very few model producers who build entirely new models from scratch.

Things to Consider before Development

In developing a general application, and even in machine learning projects, the initial step involves evaluating whether the problem at hand is a suitable candidate for machine learning. The next step is a vetting process to determine if the issue can be addressed using traditional programming techniques. It is essential to weigh the cost and complexity of machine learning solutions against more straightforward programming solutions. Often, it’s more cost-effective to build a specific program for a task rather than investing in creating, training, and deploying machine learning models.

If it is established that machine learning is the way to go, the next step is to do a cost-benefit analysis – the potential business benefits and an estimation of the return on investment, including the Total Cost of Ownership (TCO). This phase also involves decisions about real-time versus batch processing and model selection. Questions like model size, the need for training or finetuning, and techniques like retrieval augmented generation are explored to understand the project’s potential costs and expected outcomes.

These steps eliminate many ideas that might not bring the desired value or could be addressed through alternative means. What remains are the ideas that truly shine and are strong candidates for utilizing generative AI or machine learning.

When choosing the generative AI path, it is essential to avoid tightly coupling your application to a specific model. Instead, build abstraction layers between your application and the underlying model. The reason for this is the rapid evolution of these models. Keeping your application flexible and decoupled allows quicker adaptations and changes, potentially giving you a competitive edge. While others struggle with complete application refactors when switching underlying models, your agility in making these changes can become a strategic advantage.

Focusing on ROI and evaluating the real benefits is crucial before diving into generative AI projects. It’s about making a well-informed choice and not spreading resources thin.

Choosing the correct training data, capturing the context accurately, and ensuring data quality are equally significant factors.

In practice, finding the most miniature model that yields acceptable results often proves beneficial and then additional techniques to enhance its performance can be applied. Users can focus on finetuning, where a small model, with the proper adjustments, can outperform a significant model in specific scenarios. Additionally, leveraging techniques like retrieval augmented generation on a smaller model can provide better outcomes than using a large model without these enhancements.

The critical takeaway is starting with simpler, smaller models and expanding as needed. Rather than beginning with the largest model and paring it down, growing your model size is more effective based on practical reasons and requirements, ensuring a more efficient and cost-effective approach.

In terms of these trade-offs, model size is just one aspect, and there’s also the matter of the training data size. These choices involve striking a balance between the level of intelligence desired in the model and the available computational resources. It’s a complex interplay, and generative AI certainly adds another layer of complexity to these decisions.

While this blog is a gist of the insightful discussion, you can watch it entirely here. To watch our other webinars, please visit https://www.Neurealmtech.com/videos/ and https://www.gslab.com/webinars/.

Neurealm offers Generative AI Services by taking a methodical approach to tackle a wide range of challenges and explore exciting possibilities through the power of GenAI. We offer customized solutions tailored to your unique needs, delivering significant advantages to your business, customers, and employees. Simply stated, we make GenAI work for you responsibly and securely.

Unleashing the power of artificial intelligence, Generative AI has emerged as a ground-breaking technology that pushes the boundaries of automation further than ever before. By training on vast datasets, Generative AI models possess the remarkable ability to create diverse content, including text, visuals, audio, and even code, in response to natural language prompts, as depicted in the diagram below. In this blog, we delve into the exciting world of Generative AI, exploring its potential use cases across various industries, such as healthcare, banking, financial services, and insurance (BFSI), and IT services.

Unleashing the power of artificial intelligence, Generative AI has emerged as a ground-breaking technology that pushes the boundaries of automation further than ever before. By training on vast datasets, Generative AI models possess the remarkable ability to create diverse content, including text, visuals, audio, and even code, in response to natural language prompts, as depicted in the diagram below. In this blog, we delve into the exciting world of Generative AI, exploring its potential use cases across various industries, such as healthcare, banking, financial services, and insurance (BFSI), and IT services.

Generative Artificial Intelligence holds immense potential as a digital assistant for physicians and healthcare providers, revolutionizing the accuracy of patient diagnoses. By analyzing a comprehensive range of data from past health records, including medical notes, medical images, prescription, wearable devices data, lifestyle information etc., it can offer them a personalized treatment plan/wellness plan, recommendation on whether to increase or decrease cost of insurance, value of insurance coverage and so on. The application of Generative AI in healthcare has the potential to significantly enhance patient care and outcomes.

Generative AI offers numerous possibilities for the BFSI industry across the globe in achieving operational excellence. By harnessing its capabilities, the BFSI industry can optimize processes such as fraud detection, risk management, and real-time decision making. Generative AI also facilitates tasks such as writing assistance, data analysis, report summarization, and speech-to-text conversion, streamlining documentation processes. Moreover, virtual assistants powered by Generative AI, such as chatbots, prove instrumental in efficiently handling customer complaints and queries, allowing for more personalized and tailored services that enhance customer satisfaction. By empowering employees with advanced capabilities and driving productivity, Generative AI adds substantial business value to the BFSI industry.

Generative AI can be leveraged to read the logs of events, errors, warnings, and sessions of various devices (router, network switches, firewall, wireless devices, servers etc.,) and recommend early warnings, preventive maintenance plan, customized best practices and recommendation plan on which processes needs attention.

Knowledge Transition between vendor and customer is a challenge. In many cases, documented information about the IT Landscape / Infrastructure, Software application(s), Architecture, Design, known errors / issues/known risk(s), Knowledge Base Articles, SOP – Standard Operating Procedure etc., for Key / critical business system may not be available. By leveraging Generative AI, IT staff members who possess knowledge about the systems and applications can effectively document this knowledge, mitigating the business risks associated with reliance on key individuals or contractors who hold such information. Furthermore, this approach helps new team members get the right information, learn the right processes, and decreases the Mean Time to Repair, resulting in improved productivity through the consistent capture and maintenance of this knowledge.

In Agile projects, grooming plays a pivotal role in ensuring that user stories are thoroughly discussed, enabling the team to gain a comprehensive understanding of the required functionality. This includes considerations for design, integrations, and expected user interactions. Generative AI can be instrumental in documenting these discussions, capturing, and maintaining such knowledge. As a result, productivity is enhanced, and there is reduced dependence on key team members, leading to more efficient project execution.

The advent of Generative AI has opened up a world of possibilities across various industries, revolutionizing the way we approach automation and content creation. From healthcare to banking, financial services, and even IT services, Generative AI has showcased its potential to enhance operations, improve outcomes, and drive productivity. By leveraging its capabilities, healthcare providers can achieve more accurate diagnoses and personalized treatment plans, while the BFSI industry can optimize processes, detect fraud, and deliver tailored services to customers. In the realm of IT, Generative AI can streamline documentation, facilitate knowledge transfer, and improve productivity by capturing and maintaining intellectual knowledge.

With each use case, Generative AI demonstrates its power to transform industries, advance innovation, and unlock new frontiers of possibility. However, there must be a governance/review mechanism should be put in place to review the outcome of Generative AI to ensure the technology is rightly utilized without compromising privacy and compliances.

To develop this ML-based remote urine analysis solution, a systematic data science process was adopted that involved choosing the right ML model after experimentation, extensive sample data collection to train the model, development of applications to capture the test strip images, extraction of relevant features from the images, handling of noise and variability, real-time transmission of results, and user-friendly, diagnostic reporting. Anonymized data was stored and made available for further research. Effective data engineering ensured seamless flow of data throughout the system encased within strict regulatory compliance, data protection, and documentation.

Machine learning and advanced technologies helped provide instant diagnostic reports, facilitated communication with doctors, and streamlined payment processes. Additionally, the data collected across the patient population was used for analytics while ensuring patient confidentiality.

You can find more details on the solution here
https://www.gslab.com/downloads/Remote-urine-analysis-for-cancer-patients-at-home-using-machine-learning.pdf

To find out how the solution can be customized for your specific needs, please reach out to marketing@gslab.com

The health industry has undergone various digital disruptions throughout the years. Healthcare organizations closely monitor emerging digital technologies that may alter the industry. Healthcare organizations were quick to react when AI first entered the scene. They applied AI to many different tasks, including better patient care, report generation, research, and many others. Over the years, new AI-led technologies evolved inside the healthcare sector. AIOps in healthcare and generative AI are the hottest topics in terms of cutting-edge medical technology. For better diagnosis and treatment strategies, generative AI is already widely used in the healthcare industry worldwide. Read on to explore how generative AI and AIOps are revolutionizing the healthcare business.

A subset of artificial intelligence called “generative AI” aims to create new content. The input data is used to generate the new content. The input data could be unstructured or normal text. The input is transformed into various sorts of content using generative AI, depending on the needs. Images, text, audio, and other kinds of artificial data can be created by generative AI. Generative AI could only produce text data when it first appeared in the 1960s. The first generative AI-based content was created by chatbots.

Over the years, advancements in generative AI opened new doors. Generative adversarial networks came into the scene that could produce graphics, videos, and images. The best part of generative AI is that it can produce new content within seconds. Healthcare entities use generative AI to create medical imagery, MRI reports, and X-rays quickly. Since timing is critical in the healthcare sector, generative AI helps increase the speed of generating new content. Many healthcare entities also use generative AI to create custom treatment plans for different patients based on their treatment history. There are other use cases of generative AI in the healthcare sector, from improved drug discovery to health management.

Healthcare sector experiences benefits from every new-age technology. Healthcare entities may not invest in new-age technologies if they don’t see the gain from it. Let’s take AIOps in healthcare for example, it is used to increase service availability of software systems. The following are few of the benefits of using generative AI in the healthcare sector:

1. Modern and Advanced Drug development

Healthcare entities invest heavily in drug discovery and development. The discovery of a new drug for an existing medical condition can help healthcare entities boost their ROIs. Newly synthesised drugs must be tested on animals or humans before launching into the market. However, there is always a risk involved with drug testing on animals or humans. Generative AI can help identify the right candidates for drug testing based on their bodily functions. It can also run simulated tests on different candidates in synthetic environments. When the chances of success are high, a real test is performed on the selected candidates.

2. Personalised care plans

A custom treatment plan is prepared according to the medical history of a patient. The bodily functions of a patient are also considered for creating a custom medical plan. However, healthcare entities fail to create custom treatment plans for each patient. Since the healthcare sector deals with many patients daily, it is a challenge to create a personalised treatment plan for each patient. Generative AI can take the medical history, symptoms, and bodily functions of the patient as input to generate custom treatment plans. Healthcare entities do not have to worry about the service availability of a software system that generates treatment plans. Since generative AI can produce custom treatment plans within seconds, there is no need for an external software system.

3. Modern and advanced medical imagery

Traditional medical imaging solutions produce reports after a long period. At the same time, healthcare entities also must worry about the service availability of the medical imaging solution. If the software solution loses its service reliability, the healthcare entity might fail to produce medical images, x-rays, and MRI reports. With generative AI, the time taken to produce medical images is reduced drastically. Also, generative AI can identify abnormalities in medical images. Doctors and diagnostic experts will be informed of the existing problem by the generative AI system.

4. Better healthcare initiatives

Generative AI can analyse large amounts of data within seconds. Healthcare entities have to focus on population health management to launch specific services. For example, healthcare entities might deploy new treatment techniques for people in an area known to possess hereditary diseases. Generative AI can analyse demographic information on a granular level and generate rich insights. If healthcare entities are looking to launch healthcare schemes for some areas or unrecognised communities, generative AI is the right choice. Even government healthcare organisations can use generative AI for better population health management. Governments are expected to invest heavily in generative AI for launching effective public healthcare schemes.

There is a need to regulate generative AI in the healthcare industry. The healthcare sector is all about precision and accuracy. A second delay in service availability can cause a patient his/her life. For the same rationale, healthcare entities must use technologies that always make the right decisions. Many healthcare organisations are still researching the possibilities of generative AI. It is found that generative AI can be biased or opinionated at times.

When generative AI is biased, a healthcare organisation might face ethical concerns. Healthcare entities cannot depend on biased software solutions that might pose a risk to patients. However, the issue can be solved by training the generative AI model in the right way. Misuse of generative AI is another ethical concern for a healthcare entity. Employees might think that generative AI can replace their jobs. A healthcare entity must focus on navigating through the ethical concerns of generative AI.

The implementation must be done correctly, whether it is generative AI or AIOps in healthcare. You cannot expect generative AI systems that have not been trained to produce better judgements. An organisation must strive to address ethical issues in addition to training generative AI models.