Generative AI - The next wave of innovation in the Enterprise AI Landscape

What is generative AI and why is it getting all the attention?

Here's everything you need to know

The new wave of generative AI systems have the potential to transform entire industries. To be an industry leader in five years, you need a clear and compelling generative AI strategy today.

What is generative AI?

Generative AI is the process of AI algorithms generating or creating an output, such as text, photo, video, code, data, and 3D renderings, from data they are trained on.?

The purpose of generative AI is to create content, as opposed to other forms of AI, which might be used for other purposes, such as analyzing data or helping to control a self-driving car.

Why is generative AI a hot topic right now?

The term generative AI is causing a buzz because of the increasing popularity of generative AI programs, such as OpenAI's ChatGPT and DALL-E. The conversational chatbot and AI image generator both use generative AI to produce new content, including Developer codes, Essays, emails, social media captions, images, poems, Excel formulas, and more within seconds, drawing in people's attention.??

While public generative AI models such as ChatGPT, Google Bard AI, DALL-E, and other and more specialized offerings are intriguing, there are valid concerns about their use in the enterprise. These concerns include ownership of output, which encompasses issues of accuracy, truthfulness, and source attribution. Therefore, there is a compelling need for enterprises to develop their own Large Language Models (LLMs) that are trained on proprietary datasets or developed and finetuned from known pretrained models

How does generative AI work?

Generative AI uses machine learning to process a huge amount of visual or textual data, much of which is scraped from the internet, and then determine what things are most likely to appear near other things. Much of the programming work of generative AI goes into creating algorithms that can distinguish the "things" of interest to the AI's creators—words and sentences in the case of chatbots like ChatGPT, or visual elements for DALL-E. But fundamentally, generative AI creates its output by assessing an enormous corpus of data on which it’s been trained, then responding to prompts with something that falls within the realm of probability as determined by that corpus.

Autocomplete—when your cell phone or Gmail suggests what the remainder of the word or sentence you're typing might be—is a low-level form of generative AI. Models like ChatGPT and DALL-E just take the idea to significantly more advanced heights.

What are the implications of generative AI art?

To create AI art, these models have to be trained on billions of images from the internet. These images are often artworks that were produced by a specific artist, which are then reimagined and repurposed by AI to generate your image.?

Although it's not the same image, the new image has elements of an artist's original work, which is not credited to them. A specific style that is unique to the artist can, therefore, end up being replicated by AI and used for a new image, without the original artist knowing or approving. The debate about whether AI-generated art is really 'new' or even 'art' is likely to continue for many years.?

Potential negative impacts of generative AI

These examples show you one of the major limitations of generative AI: what those in the industry call?hallucinations, which is a perhaps misleading term for output that is, by the standards of humans who use it, false or incorrect. All computer systems occasionally produce mistakes, of course, but these errors are particularly problematic because end users are unlikely to spot them easily: If you are asking a production AI chatbot a question, you generally won't know the answer yourself. You are also more likely to accept an answer delivered in the confident, fully idiomatic prose that ChatGPT and other models like it produce, even if the information is incorrect.

Even if a generative AI could produce output that's hallucination-free, there are various potential negative impacts:

• Cheap and easy content creation: Hopefully it's clear by now that ChatGPT and other generative AIs are not real minds capable of creative output or insight. But the truth is that not everything that's written or drawn needs to be particularly creative. Many research papers at the high school or college undergraduate level only aim to synthesize publicly available data, which makes them a perfect target for generative AI. Spam artists are already using ChatGPT to write phishing Email for instance.
• Intellectual property: Who owns an AI-generated image or text? If a copyrighted work forms part of an AI's training set, is the AI "plagiarizing" that work when it generates synthetic data, even if it doesn't copy it word for word? These are thorny, untested legal questions.
• Bias: The content produced by generative AI is entirely determined by the underlying data on which it's trained. Because that data is produced by humans with all their flaws and biases, the generated results can also be flawed and biased, especially if they operate without human guardrails.
• Power consumption: In addition to heady philosophical questions, generative AI raises some very practical issues: for one thing, training a generative AI model is hugely compute intensive.

Use cases for generative AI

Despite these potential problems, the promise of generative AI is hard to miss. Generative AI models have the potential to address a wide range of use cases and solve numerous business challenges across different industries. Generative AI models can be used for:

? Customer service─To improve chatbot intent identification, summarize conversations, answer customer questions, and direct customers to appropriate resources.

? Content creation─To create content such as product descriptions, social media posts, news articles, and even books. This ability can help businesses save time and money by automating the content creation process.

? Sales and marketing─To create personalized experiences for customers, such as customized product recommendations or personalized marketing messages.

? Product design─To design new products or improve existing products. For example, a generative AI model can be trained on images of existing products to generate new designs that meet specific criteria.

? Education─To create personal learning experiences, similar to tutors, and generate learning plans and custom learning material.

? Fraud detection─To detect and prevent fraud in financial transactions or other contexts. For example, a generative AI model can be trained to recognize patterns of fraudulent behavior and flag suspicious transactions.

? Healthcare─To analyze medical images or patient data to aid in diagnosis or treatment. For example, a generative AI model can be trained to analyze medical images to identify cancerous cells or analyze protein structures for new drug discovery.

? Gaming─To create more realistic and engaging gaming experiences. For example, a generative AI model can be trained to create more realistic animations or to generate new game levels.

? Software development─To write code from human language, convert code from one programming language to another, correct erroneous code, or explain code.

These examples show the many business challenges that generative AI models can help solve. The key is to identify the specific challenges that are most pressing for a specific business or industry, and then to determine how generative AI models can be used to address those challenges.

Looking at the scope and environment, various Tech Giants in the Industry are collaborating to make the promise of generative AI real for the enterprise. To be able to deliver a full-stack solution, infrastructure and software, and accelerate technology to enable organizations to automate complex processes, improve customer interactions and unlock new possibilities with better machine intelligence.

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