Harnessing generative AI for innovation
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of business opportunities to 2030
Rarely has a technology captured the world’s attention so quickly. Generative AI is a branch of artificial intelligence that generates new content, including words, images, and audio.
Different techniques, including generative adversarial networks (GANs) and diffusion models, have been developed with this is mind.
Technology segments in generative AI include transformer-based models, diffusion models, variational autoencoders, autoregressive models, generative adversarial networks, and energy-based models.
The most well-known of the different models, it was introduced in 2017 by ex-Googler Ashish Vaswami in the paper "Attention Is All You Need". The transformer model is designed to process sequential data, such as natural language text, and can achieve state-of-the-art performance on a variety of natural language processing tasks. Transformer-based models can be deployed for language translation, text classification, question answering, text generation, and image and video processing.
Diffusion models in AI are a class of generative models used to model the spread of information or influence through a network. These models are based on the diffusion process that describes how information or influence spreads through a network over time.
Diffusion models are typically used in social network analysis, to model the spread of ideas, opinions, or behaviours through a network. They can also have applications in epidemiology to model the spread of diseases through a population.
Variational autoencoders (VAEs) are a type of generative model in machine learning used for unsupervised learning of complex data distributions.
VAEs are a versatile and powerful tool in machine learning, with applications in generative modelling, data compression, anomaly detection, and data augmentation.
Autoregressive models are a class of statistical models that are used to model time series data. These models are based on the idea that the value of a variable at a given time point is dependent on its previous values.
Autoregressive models are a powerful tool in time series analysis and forecasting, with applications in a wide range of domains, including finance, climate modelling, and signal processing.
Generative adversarial networks (GANs) are a type of deep learning model that are used for generative modelling. GANs consist of two neural networks: a generator network and a discriminator network. GANs have a wide range of applications, including image and video generation, natural language processing, and data augmentation
Energy-based AI models are a class of machine learning models that are based on the concept of energy function/s. These models are used for a variety of tasks, including classification, regression, and generative modelling.
Energy-based models have been used in a variety of applications, including image and video analysis, natural language processing, and anomaly detection. They are particularly useful for tasks where the data is complex and difficult to model using traditional machine learning models.
Foundation models are a powerful class of generative model, typically based upon a transformer, which serve as a starting point for a wide variety of applications.
These models are trained using unsupervised learning techniques, where they learn from vast amounts of text data without explicit human annotations. This training forms the basis for a “reusable” underlying model – a foundation – with considerable flexibility in how it could be deployed.
The models can then be fine-tuned with further data for specific downstream tasks, ranging from text classification and language translation, to sophisticated predictive and analytical tasks.
Projected market size of generative AI by 2030
Raised by generative AI startups in the first quarter of 2023
The value of OpenAI in 2023
Generative AI may have a role to play in helping firms optimise and manage disruptions to supply chains, including those caused by unusual or extreme weather events, which can be notoriously difficult to predict. Because generative AI can analyse vast amounts of data to generate highly accurate forecasts, it offers the chance to facilitate more informed decision-making. It also offers applications for running autonomous simulations, forecasting demand, and managing inventory, optimising production and continency planning and optimising delivery routes.
The potential for generative AI to offer enhanced predictions via real-time simulations has far-reaching consequences for supply-chain management. Generative AI also offers the possibility of enhancing communication across potentially multinational supply chains. Furthermore, there may be possible applications for accelerating information transfers, assisting in training of new starters, and improving communications.
Microsoft and Amazon have both launched solutions that integrate AI and machine learning for supply chain optimisation. Microsoft launched Copilot in Microsoft Supply Chain Center in March 2023 which harnesses generative AI to facilitate real-time communication with suppliers regarding specific news, lightening the load on supply chain managers.
Microsoft claims leveraging AI-powered supply chain management will help businesses gain better insights, helping them proactively address potential disruptions before they materialise. Managers can access information on weather, finance, and geopolitics, which could impact critical supply chain processes. Predictive insights can produce contextualised email drafts, helping supply chain users collaborate with impacted suppliers in real-time. Initially, Copilot is primarily being used to streamline communication of supplier news. But the firm says it plans to expand its application to other areas of its product in the future.
Generative AI can be prone to security risks because they can be vulnerable to cyberattacks and data breaches. The technology can also be difficult to implement because of data quality and availability, complexity of supply chain operations, and integration with legacy systems. When it comes to generative AI, there may also be difficulties with interpretation. Generative AI models can be difficult to explain so can be hard to gain an insight into how they generate recommendations or decisions.
Given these considerations, organisations may find it costly to implement generative AI solutions (particularly the most ‘ambitious’ use cases). Where significant changes to IT infrastructure are required, this can drive up costs further and entail a delay in the time before a firm sees a return on investment.
