Table of Contents
Generative AI: eight questions that developers and users need to ask
But it’s a similar concept, providing a public-facing chatbot to assist in search results. Whether this latest iteration of AI applications will be the end of us as a species is a topic for another time. But with the sudden rush to adopt this new technology into our lives and businesses, many have been caught unaware of its history, uses, benefits and risks. Innovators identifying novel data protection questions can get advice from us through our Regulatory Sandbox and new Innovation Advice service. Building on this offer, we are in the process of piloting a Multi-Agency Advice Service for digital innovators needing joined up advice from multiple regulators with our partners in the Digital Regulation Cooperation Forum. LLMs (such as ChatGPT) and their use cases – from writing essays to powering chatbots or creating websites without human coding involved – have captured the world’s imagination.
In the telecoms industry, which Ofcom regulates, Generative AI is being used to manage power distribution, spot network outages, and both detect and defend against security anomalies and fraudulent behaviour. In financial services, Generative AI could be used to create synthetic training datasets to enhance the accuracy of models genrative ai that identify financial crime. Each of the four digital regulators has reason to be concerned about the misuse of this technology. As the incoming online safety regulator, Ofcom is closely monitoring the potential for these tools to be used to generate illegal and harmful content, such as synthetic CSEA and terror material.
Generative AI Won’t Revolutionize Game Development Just Yet
When identifying and exploring opportunities for the use of generative AI, having multidisciplinary teams involved to ask the right questions to support responsible, informed decision making is crucial. Organisations will also need to identify appropriate decision-makers, look at their governance structures and processes, and consider their AI-related communications. Although the legal landscape for AI is evolving, now is the time to develop AI legal and ethical strategies and risk-management frameworks. Processes that exist in other contexts regarding procurement, genrative ai development, implementation, testing and ongoing monitoring of IT systems should be reviewed, adapted and applied as necessary across the roll-out and use lifecycle of a generative AI system. This adaptive governance would need to be sensitive to differences between types of AI systems in order to apply effectively to the changing technology landscape. Organisations should also review how their related processes, including for training, record keeping and audit, would be applied in this context to support any policies, principles and guidelines.
On the other, it was written by a machine, and there’s no way to easily identify where that information was sourced or if it’s even accurate. 2023 could well be remembered as the year artificial intelligence (AI) truly took off. A development journey spanning decades has suddenly accelerated to deliver the likes of ChatGPT, Dall-E, and Google Bard into the mainstream. There really can be no excuse for getting the privacy implications of generative AI wrong. The information you provide when registering for an event will be retained by the RSA and will be used to record attendance at the event.
UK at risk of falling behind in AI regulation, MPs warn
As technology continues to advance, we can expect generative AI to play an increasingly significant role in shaping the future of various industries, including insurance. But OpenAI’s ChatGPT large language model, the model that’s powering ChatGPT, was the breakout success because it delivered more humanlike responses than ever before. A large language model is a type of neural network, or it’s a flavour of AI model, that’s been trained on large quantities of unlabelled text.
As the technology behind generative artificial intelligence (AI) continues to advance, so too does the potential for its misuse. One particularly concerning application of this technology is the creation of deepfakes, which are increasingly being used to spread disinformation online. It’s important to note that the field of generative AI is continuously evolving, and there may be newer and more popular models beyond my last update. Researchers and developers are constantly exploring new architectures and techniques to improve generative AI capabilities.
As the field continues to develop, we can expect to see even more disruption and transformation in the years to come. It is clear that generative AI is a powerful tool that has the potential to revolutionize many industries, and businesses that embrace this technology will be well-positioned to reap the benefits of this transformative technology. Before using generative AI in business processes, organisations should consider whether generative AI is the appropriate tool for the relevant task.
Traditional cyber professionals can no longer effectively defend against the most sophisticated threats as the speed and complexity of attack and defense exceed human capabilities. Artificial intelligence can be trained to detect such threats by scanning for suspicious behavior or traffic patterns that conflict with known signatures. We are still in the early stages of this revolution and many practices still need to be perfected, we are only at the starting line. There’s still a lot of work to do as we figure out how to apply this new technology to cybersecurity, and there’s a huge opportunity for companies that move quickly into this new space. JLL research shows that in 2022, the total capital raised to fund AI-powered PropTech reached US$4 billion globally, almost double the total amount raised in 2021. Venture capital (VC) is the main driving force backing the development of AI products.