How Generative AI Is Changing Creative Work
Pharma companies typically spend approximately 20 percent of revenues on R&D,1Research and development in the pharmaceutical industry, Congressional Budget Office, April 2021. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques. Foundation models and generative AI can enable organizations to complete this step in a matter of weeks. Generative AI is a type of artificial intelligence that involves training MLL (machine learning models) to generate new, original content based on a delivered prompt. A prompt can be anything from text and images to music and video, and even new chemical compounds for use in drug development.
Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. The speed at which generative AI Yakov Livshits technology is developing isn’t making this task any easier. Generative AI enables users to quickly generate new content based on a variety of inputs.
Work and productivity implications
Over the past few months, there has been a huge amount of hype and speculation about the implications of large language models (LLMs) such as OpenAI’s ChatGPT, Google’s Bard, Anthropic’s Claude, Meta’s LLaMA, and, most recently, GPT4. ChatGPT, in particular, reached 100 million users in two months, making it the fastest growing consumer application of all time. This powerful technology has the potential to disrupt nearly every industry, promising both competitive advantage and creative destruction. Although generative AI technology is promising, some near-term caution is warranted. There are several inherent risks that providers must address before broad adoption in health care can occur.
Moreover, generative AI applications and tools are empowering both organizations and individuals to automate tedious tasks, make better decisions, and streamline operations for maximum efficiency. Here’s a deeper look into generative AI, its benefits, models, known risks, and popular examples. Generative AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media). They are commonly used for text-to-image generation and neural style transfer. Datasets include LAION-5B and others (See Datasets in computer vision).
Video and speech Generation
Until recently, most AI applications used predictive engines to correlate data or make decisions. Although various forms of generative AI have existed for decades, interest within enterprises was mild due to limited capabilities. All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities. Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great. Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies.
- The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth.
- AI developers build different AI models embodying a variety of techniques, including neural networks, genetic algorithms, deep or machine learning and reinforcement learning.
- In our fast-paced, technologically advancing world, the realm of artificial intelligence (AI)…
- Interested users can join the API waitlist for GPT-4, but even before they gain access to the API, they can reap the technology’s benefits with public access to ChatGPT Plus.
What is even more promising is that existing GAI users anticipate their consumption will increase over the next 12 months, and to a significant degree for 37% of respondents. Omdia’s 2023 Consumer AI survey explores attitudes towards and usage of GAI among 3,000 plus people in the US, UK and China. The findings reveal that regular usage of GAI applications is still low, 10% overall across the three markets. These results may seem surprising given the intense supply-side activity, service/product launches and extensive media coverage.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
Predictive AI offers great value across different business applications, including fraud detection, preventive maintenance, recommendation systems, churn prediction, capacity management and logistics optimization. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12). Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process. We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs.
Generative AI systems trained on words or word tokens include GPT-3, LaMDA, LLaMA, BLOOM, GPT-4, and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. Data sets include BookCorpus, Wikipedia, and others (see List of text corpora). The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers.
Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents.
What we do know now is that generative AI has captured the imagination of the wider public and that it is able to produce first drafts and generate ideas virtually instantaneously. The technology has a range of uses in medtech, and the main challenge is knowing how and where to start. Public-health agencies, other health organizations, and government ministries could leverage generative AI to improve resource planning and allocation, anticipate public-health needs and interventions, and execute programs more effectively. Activ Surgical, a digital-surgery pioneer, recently announced completion of its first AI-enabled case, which provides enhanced visualization and real-time, on-demand surgical insights inside the operating room. DigitalOwl is automating much of the underwriting and claims management process, reducing operating expenses and turnaround times to boost affordability. Payers are starting to leverage generative AI to reduce costs and improve risk management and member engagement, with the overall goal of offering higher-quality coverage at less cost to consumers.
We focused on real-world applications with examples but given how novel this technology is, some of these are potential use cases. For other applications of AI for requests where there is a single correct answer (e.g. prediction or classification), read our list of AI applications. It operates on AI models and algorithms that are trained on large unlabeled data sets, which require complex Yakov Livshits math and lots of computing power to create. These data sets train the AI to predict outcomes in the same ways humans might act or create on their own. 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.
Successful generative AI models are only possible with massive amounts of relevant, clean, ethical, and unbiased training data. You’ve probably seen that generative AI tools (toys?) like ChatGPT can generate endless hours of entertainment. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy.
Generative AI allowed Insilico Medicine to go from novel-target discovery to preclinical candidate in just 18 months, spending only $2.6 million. The company’s idiopathic pulmonary fibrosis drug recently received the agency’s Orphan Drug Designation after completing the preclinical phase in 30 months, much faster than average for a new treatment. Generative AI is accelerating drug discovery, improving clinical-trial planning and execution, and leading to more precision medicine therapies. If you want to benefit from the AI, you can check our data-driven lists for AI platforms, consultants and companies. In this article, we explore what generative AI is, how it works, pros, cons, applications and the steps to take to leverage it to its full potential. To use generative AI effectively, you still need human involvement at both the beginning and the end of the process.