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Converged infrastructure is needed to support the growth and sustainability of AI. This is particularly important in the need for a single solution designed to integrate compute, storage, networking, and virtualization. Data volumes are trending to grow beyond hyperscale data, and with such massive data processing requirements the ability to execute is critical. The demands on existing infrastructure are already heavy., so bringing everything together to work in concert will be key, to maintain and grow support the growth in demand for resources. In order to do that the components will need to work together efficiently and the network will play an important role in linking specialized hardware accelerators like GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) to accelerate AI workloads beyond current capability. Converged infrastructure solutions will lead to the ability to deploy AI models faster, iterate them more efficiently, and extract insights faster. This can will pave the way for the next generation of AI.
Converged Service and integrated solutions that combine AI with traditional services has have the potential to deliver enhanced services to end customers, but more importantly these services need to leverage AI-driven insights, automation, and personalization to optimize user experience, improve efficiency, and drive innovation across industries. There are many existing industry use cases for this already, which include healthcare, legal, retail, telecommunications, networking, and incidence incident tracking. The analytics delivered by a converged service provide automated insights and tools that can provide real-time analysis, tracking, response and remediation/response–either with or without human intervention.
Business Innovation - New Revenue Streams: Data monetization encompasses various strategies, including selling the raw data, offering data analytics services, and developing data-driven products or solutions to customers. Organizations can monetize their data by identifying valuable insights, patterns, or trends hidden within their datasets that no group of human resources can possibly identify quickly. These insights can then be used to create new products and services that will better serve customers and organizations. This is a new strategic business opportunity for organizations looking to monetize anonymous data and leverage it for increased business efficiency and to determine product direction and determine new go to market strategies.
Data Privacy and Security: The ability to monetize the data comes with a big caveat, which is that the use of customer data must be handled with care to ensure data privacy, security and regulatory compliance. This requires clear policies and security procedures to ensure anonymity, safety and privacy at all times. The good news is that AI can be used to address the growing threat of network vulnerabilities, zero day exploits and other security related issues with predictive analytics and threat analysis.
Data Model Simplification: Large language models can be used to understand large amounts of unstructured operation and maintenance data (for example, system logs, operation and maintenance work orders, operation guides, company documents, etc., which are traditionally used in human-computer interaction or human-to-human collaboration scenarios), from which effective knowledge is extracted to provide guidance for guide further automatic/intelligent operation and maintenance, thereby effectively expanding the scope of the application of autonomous mechanism.
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This breakthrough in AI research is characterized by vast amounts of easily accessible data, extensive training models, and the ability to quickly generate human-like text, in almost an instant. These models are trained on enormous datasets from sources particular to a given area of research. LLMs have changed the way natural language processing tasks are interpreted. Some of the areas that have been particularly fruitful includeincluding: text generation, language translation, summarization, and automated chatbots, image and video generation, and routine query responses/search results.
Generative AI (Gen AI)
Gen AI is a much broader category of artificial intelligence systems capable of generating new content, ideas, or solutions autonomously based on a human text, video, image or sound-based input. This includes LLMs as resource data for content generation. As such, Gen AI seems to be able to produce human-like creative content in a fraction of the time. Content creation for web siteswebsites, images, videos, and music are a few of the capabilities of Gen AI. The rise of Gen AI has inspired numerous opportunities for business cases, from creating corporate logos , to corporate videos , to saleable products to end-consumers and businesses , to creating visual network maps based on the basis of the datasets being accessed. Further, even being able to provide optimized maps for implementation to improve networking either autonomous or with human intervention are useful areas for further exploration.
The two combined open the question as to what should Gen AI be used for, and more importantly how is it distinguishable from human work. There are many Many regulatory bodies are looking at solutions around the identification of decisions and what content has been generated to solve a particular problem and solution. The foundation of this combination is to ensure security , and safety, mitigate biases, and identify which behaviors were illustrated and acted upon by Gen AI, and which were not.
The advent of transformer models and attention mechanisms [1][2] and the sudden popularity of ChatGPT and other LLMs, transfer learning, and foundation models in the NLP domain have all sparked vivid discussions and efforts to apply generative models in many other domains. Interestingly, let us not forget that word embeddings [3][4][5], sequence models such as LSTMs [6] and GRUs [7], attention mechanisms [8], transformer models [1] and pretrained pre trained LLMs [2] have been around long before the launch of the ChatGPT tool in late 2022. Pretrained transformers like BERT [2] in particular (especially transformer-encoder models) were widely used in NLP for tasks like sentiment analysis, text classification [9], extractive question answering [10] etc. long before ChatGPT made chatbots and decoder-based generative models go viral. That said, there has clearly been a spectacular explosion of academic research, commercial activity, and ecosystems that have emerged since ChatGPT was launched to the public, in the area of both open [11][12] and closed source [13][14][15] LLM foundation models, related software, services and training datasets.
Beyond typical chatbot-style applications, LLMs have been extended to generate code [16][17][18][19], solve math problems (stated either formally or informally) [20], pass science exams [21], or act as incipient "AGI"-style agents for different tasks, including advising on investment strategies, or setting up a small business [22]. Recent advancements to the basic LLM text generation model include instruction finetuning [23], retrieval augmented generation using external vector stores [24][25], using external tools such as web search [26], external knowledge databases or other APIs for grounding models, code interpreters [27], calculators and formal reasoning tools [28]. Beyond LLMs and NLP, transformers have also been used to handle non-textual data, such as images, sound, and arbitrary sequence data.
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