F.A.Q.

What is Generative AI?

Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. AI technologies attempt to mimic human intelligence in nontraditional computing tasks like image recognition, natural language processing (NLP), and translation. Generative AI is the next step in artificial intelligence. You can train it to learn human language, programming languages, art, chemistry, biology, or any complex subject matter. It reuses training data to solve new problems. For example, it can learn English vocabulary and create a poem from the words it processes. Your organization can use generative AI for various purposes, like chatbots, media creation, and product development and design.

Why is generative AI important?

We can help reinvent most customer experiences and applications, create new applications never seen before, and help customers reach new productivity levels.

According to Goldman Sachs, generative AI could drive a 7 percent (or almost $7 trillion) increase in global gross domestic product (GDP). They also anticipate it could lift productivity growth by 1.5 percentage points over 10 years.

How did generative AI technology evolve?

Primitive generative models have been used for decades in statistics to aid in numerical data analysis. Neural networks and deep learning were recent precursors for modern generative AI. Variational autoencoders, developed in 2013, were the first deep generative models that could generate realistic images and speech.

VAEs introduced the capability to create novel variations of multiple data types. This led to the rapid emergence of other generative AI models like generative adversarial networks and diffusion models. These innovations were focused on generating data that increasingly resembled real data despite being artificially created.

In 2017, a further shift in AI research occurred with the introduction of transformers. Transformers seamlessly integrated the encoder-and-decoder architecture with an attention mechanism. They streamlined the training process of language models with exceptional efficiency and versatility. Notable models like GPT emerged as foundational models capable of pretraining on extensive corpora of raw text and fine-tuning for diverse tasks.

Transformers changed what was possible for natural language processing. They empowered generative capabilities for tasks ranging from translation and summarization to answering questions.

Many generative AI models continue to make significant strides and have found cross-industry applications. Recent innovations focus on refining models to work with proprietary data. Researchers also want to create text, images, videos, and speech that are more and more human-like.

What are the limitations of generative AI?

Despite their advancements, generative AI systems can sometimes produce inaccurate or misleading information. They rely on patterns and data they were trained on and can reflect biases or inaccuracies inherent in that data. Other concerns related to training data include

1. Security

Data privacy and security concerns arise if proprietary data is used to customize generative AI models. Efforts must be made to ensure that the generative AI tools generate responses that limit unauthorized access to proprietary data. Security concerns also arise if there is a lack of accountability and transparency in how AI models make decisions.

2. Creativity

While generative AI can produce creative content, it often lacks true originality. The creativity of AI is bounded by the data it has been trained on, leading to outputs that may feel repetitive or derivative. Human creativity, which involves a deeper understanding and emotional resonance, remains challenging for AI to replicate fully.

3. Cost

Training and running generative AI models require substantial computational resources. Cloud-based generative AI models are more accessible and affordable than trying to build new models from scratch.

4. Explainability

Due to their complex and opaque nature, generative AI models are often considered black boxes. Understanding how these models arrive at specific outputs is challenging. Improving interpretability and transparency is essential to increase trust and adoption.

How will different industries be impacted by GenAI?

Defense Industrial Base

The defense industrial base will be a top beneficiary of generative AI due to a new profound ability to analyze Government Request For Proposals at speeds unprecedented. Utilizing company knowledge concerning their capabilities, past performance and historical contract records. A DiB contractor can determine the best possible pwin proposals and write sections at lighting speed.

Government agencies can also

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Financial services

Financial services companies can harness the power of generative AI to serve their customers better while reducing costs:

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Healthcare and life sciences

One of the most promising use cases of generative AI is to accelerate drug discovery and research. Generative AI uses models to create novel protein sequences with specific properties for designing antibodies, enzymes, vaccines, and gene therapy.

Healthcare and life sciences companies can use generative models to design synthetic gene sequences for applications in synthetic biology and metabolic engineering. For example, they can create new biosynthetic pathways or optimize gene expression for biomanufacturing purposes.

Lastly, generative AI can be used to create synthetic patient and healthcare data. This is useful to train AI models, simulate clinical trials, or study rare diseases without access to large real-world datasets.

Automotive and manufacturing

Automotive companies can use generative AI technology for many purposes, from engineering to in-vehicle experiences and customer service. For instance, they can optimize the design of mechanical parts to reduce drag in vehicle designs or adapt the design of personal assistants.

Auto companies are using generative AI to deliver better customer service by providing quick responses to the most common customer questions. New material, chip, and part designs can be created with generative AI to optimize manufacturing processes and reduce costs.

Generative AI can also be used for synthetic data generation to test applications. This is especially helpful for data not often included in testing datasets (such as defects or edge cases).

Media and entertainment

From animations and scripts to full-length movies, generative AI models can produce novel content at a fraction of the cost and time of traditional production.

Here are other ways you can use generative AI in the industry:

Telecommunication

Early use cases of generative AI in telecommunication are focused on reinventing the customer experience. Customer experience is defined by the cumulative interactions of subscribers across all touchpoints of the customer journey.

For instance, telecommunication organizations can apply generative AI to improve customer service with live human-like conversational agents. They can also optimize network performance by analyzing network data to recommend fixes. And they can reinvent customer relationships with personalized one-to-one sales assistants.

Energy

Generative AI is suitable for energy sector tasks that involve complex raw data analysis, pattern recognition, forecasting, and optimization. Energy organizations can improve customer service by analyzing enterprise data to identify usage patterns. With this information, they can develop targeted product offerings, energy efficiency programs, or demand-response initiatives.

Generative AI can help with grid management, increase operational site safety, and optimize energy production through reservoir simulation.