What is Gen AI? Generative AI explained

Generative AI isn’t just a technology or a business case — it is a key part of a society in which people and machines work together.

What is generative AI?

Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs.  

Generative AI uses a number of techniques that continue to evolve. Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. 

Today, generative AI most commonly creates content in response to natural language requests — it doesn’t require knowledge of or entering code — but the enterprise use cases are numerous and include innovations in drug and chip design and material science development. (Also see “What are some practical uses of generative AI?”)

What are the benefits and applications of generative AI?

Foundation models, including generative pretrained transformers (which drives ChatGPT), are among the AI architecture innovations that can be used to automate, augment humans or machines, and autonomously execute business and IT processes. 

The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case. End users should be realistic about the value they are looking to achieve, especially when using a service as is, which has major limitations. Generative AI creates artifacts that can be inaccurate or biased, making human validation essential and potentially limiting the time it saves workers. It is recommended connecting use cases to KPIs to ensure that any project either improves operational efficiency or creates net new revenue or better experiences.

Some of the key benefits and applications of generative AI includes:

• Accelerated Drug Discovery: Generative AI can significantly speed up the drug discovery process by generating novel molecular structures, predicting protein-ligand interactions, and optimizing lead compounds.
• Personalized Medicine: By analyzing patient data, such as genetic information, medical history, and lifestyle factors, generative AI can help develop personalized treatment plans, leading to improved patient outcomes.
• Medical Image Analysis: Generative AI can be used to analyze medical images, such as X-rays, MRIs, and CT scans, to detect anomalies and assist in diagnosis.
• Drug Repurposing: By identifying new therapeutic applications for existing drugs, generative AI can accelerate drug development and reduce costs.
• Synthetic Data Generation: Generative AI can create synthetic datasets that mimic real-world data, enabling researchers to train AI models without compromising patient privacy.
• Automated Data Analysis: By automating routine data analysis tasks, generative AI can free up researchers to focus on more complex and creative work.

What are the risks of generative AI?

The risks associated with generative AI are significant and rapidly evolving. A wide array of threat actors have already used the technology to create “deep fakes” or copies of products, and generate artifacts to support increasingly complex scams.

ChatGPT and other tools like it are trained on large amounts of publicly available data. They are not designed to be compliant with General Data Protection Regulation (GDPR) and other copyright laws, so it’s imperative to pay close attention to your enterprises’ uses of the platforms. 

Oversight risks to monitor include:

• Lack of transparency: Generative AI and ChatGPT models are unpredictable, and not even the companies behind them always understand everything about how they work.

• Accuracy: Generative AI systems sometimes produce inaccurate and fabricated answers. Assess all outputs for accuracy, appropriateness and actual usefulness before relying on or publicly distributing information. 

• Bias:  You need policies or controls in place to detect biased outputs and deal with them in a manner consistent with company policy and any relevant legal requirements.

• Intellectual property (IP) and copyright: There are currently no verifiable data governance and protection assurances regarding confidential enterprise information. Users should assume that any data or queries they enter into the ChatGPT and its competitors will become public information, and we advise enterprises to put in place controls to avoid inadvertently exposing IP. 

• Cybersecurity and fraud: Enterprises must prepare for malicious actors’ use of generative AI systems for cyber and fraud attacks, such as those that use deep fakes for social engineering of personnel, and ensure mitigating controls are put in place. Confer with your cyber-insurance provider to verify the degree to which your existing policy covers AI-related breaches.

• Sustainability: Generative AI uses significant amounts of electricity. Choose vendors that reduce power consumption and leverage high-quality renewable energy to mitigate the impact on your sustainability goals.

It is also recommended considering the following questions:

• Who defines responsible use of generative AI, especially as cultural norms evolve and social engineering approaches vary across geographies? Who ensures compliance? What are the consequences for irresponsible use?​
• In the event something goes wrong, how can individuals take action?
• How do users give and remove consent (opt in or opt out)? What can be learned from the privacy debate?
• Will using generative AI help or hurt trust in your organization — and institutions overall?
• How can we ensure that content creators and owners keep control of their IP and are compensated fairly? What should new economic models look like? 
• Who will ensure proper functioning throughout the entire life cycle, and how will they do so? Do boards need an AI ethics lead, for example?

Finally, it’s important to continually monitor regulatory developments and litigation regarding generative AI. China and Singapore have already put in place new regulations regarding the use of generative AI, while Italy temporarily. The U.S., Canada, India, the U.K. and the EU are currently shaping their regulatory environments.

What are some practical uses of generative AI today?

The field of generative AI will progress rapidly in both scientific discovery and technology commercialization, but use cases are emerging quickly in creative content, content improvement, synthetic data, generative engineering and generative design. 

In-use, high-level practical applications today include the following.

• Written content augmentation and creation: Producing a “draft” output of text in a desired style and length
• Question answering and discovery: Enabling users to locate answers to input, based on data and prompt information
• Tone: Text manipulation, to soften language or professionalize text
• Summarization: Offering shortened versions of conversations, articles, emails and webpages
• Simplification: Breaking down titles, creating outlines and extracting key content
• Classification of content for specific use cases: Sorting by sentiment, topic, etc.
• Chatbot performance improvement: Bettering “sentity” extraction, whole-conversation sentiment classification and generation of journey flows from general descriptions
• Software coding: Code generation, translation, explanation and verification

Emerging use cases with long-term impacts include:

• Creating medical images that show the future development of a disease​

• Synthetic data helping augment scarce data, mitigate bias, preserve data privacy and simulate future scenarios

• Applications proactively suggesting additional actions to users and providing them with information

• Legacy code modernization

Which industries are most impacted by generative AI?

Generative AI will affect the pharmaceutical, manufacturing, media, architecture, interior design, engineering, automotive, aerospace, defense, medical, electronics and energy industries by augmenting core processes with AI models. It will impact marketing, design, corporate communications, and training and software engineering by augmenting the supporting processes that span many organizations. For example:

We believe that by 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today. Generative AI looks promising for the pharmaceutical industry, given the opportunity to reduce costs and time in drug discovery.

We predict that by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022. Text generators like GPT-3 can already be used to create marketing copy and personalized advertising.

In the manufacturing, automotive, aerospace and defense industries, generative design can create designs optimized to meet specific goals and constraints, such as performance, materials and manufacturing methods. This accelerates the design process by producing an array of potential solutions for engineers to explore.

Should I craft a usage policy for generative AI?

Your workforce is likely already using generative AI, either on an experimental basis or to support their job-related tasks. To avoid “shadow” usage and a false sense of compliance, We recommend crafting a usage policy rather than enacting an outright ban. 

Keep the policy simple — it can be as streamlined as three don’ts and two do’s if using ChatGPT or other off-the-shelf model:

• Don’t input any personally identifiable information.

• Don’t input any sensitive information.

• Don’t input any company IP.

• Do turn off history if using external tools (like ChatGPT) that enable that choice.

• Do closely monitor outputs, which are subject to sometimes subtle but meaningful hallucinations, factual errors and biased or inappropriate statements.

If the company is using its own instance of a large language model, the privacy concerns that inform limiting inputs go away. However, the need to keep a close eye on outputs remains.

Who are the major tech providers in the generative AI market?

The Generative AI marketplace is on fire. Beyond the big platform players, there are many hundreds of specialty providers funded by ample venture capital and a wave of new open-source models and capabilities. Enterprise application providers, such as Salesforce and SAP, are building LLM capabilities into their platforms. Organizations like Microsoft, Google, Amazon Web Services (AWS) and IBM have invested hundreds of millions of dollars and massive compute power to build the foundational models on which services like ChatGPT and others depend. 

The current major players to be as follows:

Google has two large language models, Palm, a multimodal model, and Bard, a pure language model. They are embedding their generative AI technology into their suite of workplace applications, which will immediately get it in the hands of millions of people.

Microsoft and OpenAI are marching in lockstep. Like Google, Microsoft is embedding generative AI technology into its products, but it has the first-mover advantage and buzz of ChatGPT on its side.

Amazon has partnered with Hugging Face, which has a number of LLMs available on an open-source basis, to build solutions. Amazon also has Bedrock, which provides access to generative AI on the cloud via AWS, and has announced plans for Titan, a set of two AI models that create text and improve searches and personalization.

IBM has multiple foundation models and a strong ability to fine-tune both its and third-party models by injecting data and retraining and employing the model.

Is this the start of artificial general intelligence (AGI)?

The rapid advancements in generative AI, particularly with the emergence of large language models like GPT-4, have ignited discussions about the potential for artificial general intelligence (AGI). AGI refers to a hypothetical AI that possesses human-level intelligence and can understand, learn, and apply knowledge across a wide range of tasks.

While the current state of AI is impressive, it’s crucial to distinguish between narrow AI and AGI. Narrow AI, also known as weak AI, is designed to perform specific tasks, such as image recognition or natural language processing. AGI, on the other hand, would have the ability to reason, learn, and adapt to new situations in a way that is similar to human intelligence.

While recent advancements in AI have been remarkable, we are still far from achieving true AGI. However, as AI continues to evolve, it’s important to consider the ethical implications and societal impact of such powerful technology. 

The likely path is the evolution of machine intelligence that mimics human intelligence but is ultimately aimed at helping humans solve complex problems. This will require governance, new regulation and the participation of a wide swath of society.

It’s important to approach the development and deployment of AI with caution and responsibility, ensuring that it is used for the benefit of humanity. By understanding the limitations and potential risks, we can harness the power of AI to address global challenges and improve our lives. 

Conclusion

Generative AI is rapidly transforming various industries, including life sciences. Its potential to revolutionize drug discovery, medical image analysis, and personalized medicine is immense. However, it’s crucial to approach this technology with a balanced perspective, acknowledging its limitations and potential risks.

By understanding the foundations of generative AI, its applications, and the challenges associated with its implementation, we can harness its power to address critical healthcare challenges and improve patient outcomes. As AI continues to evolve, it is essential to prioritize ethical considerations, data privacy, and responsible AI development to ensure a positive impact on society. 

To fully leverage the potential of generative AI, collaboration between researchers, clinicians, industry leaders, and policymakers is essential. By working together, we can shape the future of AI in life sciences and create a better world for all.

By partnering with a trusted IT services provider like 9NEXUS, organizations can effectively implement and leverage AI to achieve these benefits. 9NEXUS offers the expertise, resources, and commitment necessary to guide organizations through the AI adoption process and ensure successful outcomes.

Contact us today to learn how 9NEXUS can be your partner in driving scientific innovation. And be sure to follow us on LinkedIn for the latest news and updates!