Generative AI is a type of artificial intelligence (AI) that uses machine learning (ML) and deep learning algorithms to produce new content such as text, images, video, music, and computer code in response to users’ prompts and questions. It has the potential to dramatically transform the way humans create original content for personal, business, and artistic purposes.
Generative AI is different from retrieval augmented generation (RAG) AI in that it is a subset of RAG. RAG combines the strengths of both generative AI and retrieval AI. Generative AI is also different from cognitive AI, which mimics the way the human brain works to get its results.
Before it can create new content, a generative AI system must first be trained. Vast quantities of information—in the form of words, images, music, or other content—are fed into a deep learning system.
From there, an AI neural network, which is a subset of ML that teaches computers to process data by mimicking the human brain, sifts through the data, helping the system “learn.” For example, the popular ChatGPT chatbot was trained on massive datasets and more than 300 billion words gathered from the internet, books, magazines, songs, plays, movies, and other publicly available data sources. ChatGPT studies and learns from all this data to discern patterns and structures.
Once the generative AI model has assimilated enough knowledge, it can begin creating. Based on the patterns it extrapolates from the data it was trained on, it can now generate new content based on directives or questions from users. For example, if it was trained on the novels of a particular author, it could write text in the style and use the subject matter found in that author’s novels if prompted to do so by a user. If someone wants a new image, they can enter some characteristics and generative AI can produce a drawing.
Because it is trained to be continuously learning, generative AI produces multiple versions of content and chooses the best one to proceed with at the time. This process of refinement is what makes generative AI systems capable of constantly improving the quality of their output.
There are many potential benefits of generative AI, including the following:
Although generative AI has numerous benefits, it also comes with certain risks and limitations if not managed appropriately and used responsibly. The following are some of them:
For all these reasons, businesses will need to be careful to use generative AI responsibly and ethically.
Generative AI is already being used across many professions and industries:
Cohesity is committed to using artificial intelligence (AI)—particularly generative AI—to stay ahead of security threats by harnessing the power of an organizations’ data.
Cohesity is collaborating with Microsoft Azure OpenAI to give businesses more power when managing, securing, and protecting their data. With growing opportunities for AI to mitigate future threats based on risk profiles and user behavior, Cohesity is at the forefront of understanding generative, cognitive, and retrieval augmented generation AI to stop bad actors.
Generative AI is important to Cohesity’s core business of securing and managing data. Cohesity helps customers back up their entire data estate and improves cyber resilience with data isolation, threat detection, and data classification.
Cohesity provides the world’s largest organizations with the deep insights and analytics they need to improve their security postures. The Cohesity Data Cloud is a modern data security and management platform and is unique in that it is “AI-ready.” It is architected in such a way that is massively scalable, and easily searchable, and enforces granular access controls and security features to ensure the highest levels of data availability and integrity.
For example, with Cohesity’s global search capabilities, people can easily and quickly search globally across multiple workloads and data copies from a centrally managed interface. The Cohesity Data Cloud will allow AI and large language models (LLMs) to quickly answer critical business questions, while ensuring that only the right people see responses regarding the data they have access to.
Data protected in Cohesity Data Cloud is indexed and contains the specific metadata that will make utilizing that data in LLMs possible. In the same way that data is stored and able to be searched and analyzed for threats, this metadata is also AI-ready so that when a person asks questions about the data through the LLM or other power-language AI models (such as Azure OpenAI), the LLM will provide immediate and human-readable responses. By using authoritative data sources backed up on Cohesity, organizations will get more accurate and actionable responses to both user and machine queries.
Enterprise businesses, government agencies, and other organizations can safely and securely introduce AI into their cybersecurity strategies using the power of Cohesity’s Data Cloud platform to deliver comprehensive, clean, and contextual data for AI, security, compliance, and analytics initiatives.