Generative AI: Practical suggestions for legal teams Slaughter and May Insights
But ignoring its potential for supercharging your operations would be a significant oversight. Instead of seeing ChatGPT as a potential threat to employment, it must be seen as a way to boost potential and efficiency. Organizations will foster more productive, creative, and competitive working conditions by adopting and implementing AI technologies like Generative AI and LLM in the workplace.
As this is just a generic chatbot, it’ll take on the persona of ‘Max, the Mule’ – a helpful personal assistant. However, the documentation is extensive, and several Python libraries (e.g. Streamlit) already exist that can be used to build chatbot interfaces if preferred. For Automatic Speech Recognition (ASR), OpenAI’s Whisper Speech-to-Text (STT) service will interact with a microphone. OpenAI doesn’t have a Text-to-Speech (TTS) service, so Google Translate’s free TTS API will be used (sufficient for this PoC). The white paper discussed a possible use case of an AI ecommerce chatbot, where MuleSoft was used as the integration layer to access the relevant LLM.
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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. As the data protection regulator, genrative ai we will be asking these questions of organisations that are developing or using generative AI. We will act where organisations are not following the law and considering the impact on individuals. Stephen Almond, Executive Director, Regulatory Risk, leads the ICO’s team responsible for anticipating, understanding and shaping the impacts of emerging technology and innovation on people and society.
They can come up with laws and theories that can explain yet-to-be-seen phenomena (i.e., science). If you’re interested in learning more about our exploration of AI for development, see our previous articles in the ‘New Beings’ series. Discover how customers across a range of industries have realised value and growth with Aiimi.
We may analyse your personal information to create a profile of your interests and preferences so that we can contact you with information relevant to you. This Data Protection Policy explains when and why we collect personal information about people who visit our website, how we use it, the conditions under which we may disclose it to others and how genrative ai we keep it secure. Over the next two years, besides a growing market, we can expect AI to become gradually more reliable and easier to integrate and use on your own devices, coupled with the rise of open-source availability. As we’ve discussed, the AI landscape is evolving at a rapid pace, with over 1000 new AI tools released just last month alone.
Testing and understanding the outputs for a given input is critical when deciding how this type of technology can be applied in real-world applications. Anyone who has studied chaos theory or who has heard of the butterfly effect will know that naturally complex systems are often highly sensitive to initial conditions. The Large Language Models (LLMs) that we are building have highly complex inputs in the form of vast amounts of data that are used to train these intelligences.
Hallucination, where an LLM makes up answers to questions, can be a significant challenge when working with this technology. Techniques for controllingLLMs are still in early development, and Filament Syfter recommends caution when working with generative outputs for some use cases. In this use case, we flip hallucination on its head and make it an asset rather than a challenge. LLMs can be prompted to generate fake information that can be used for training downstream models. Continuing with the company sector classification example, an LLM can be given some example company descriptions from a given operating sector and asked to generate more.
I know it is confusing as ChatGPT has GPT in the name so suggests that ChatGPT can harness the full power of GPT. We need to be careful that we don’t use ChatGPT and GenerativeAI (GPT) interchangeably, or see the potential of Generative AI through our experiences of ChatGPT. And, at the moment I am seeing lots of examples where this is happening around the Salesforce ecosystem in blog posts, LinkedIn posts and training that is being offered.  Risto Uuk, ‘General Purpose AI and the AI Act’ (Future of Life Institute 2022) accessed 26 March 2023.
GenAI doesn’t always identify its sources
IBM Research has unveiled a groundbreaking analog AI chip that demonstrates remarkable efficiency and accuracy in performing complex computations for deep neural networks (DNNs). AMD has unveiled insights from a comprehensive survey of IT leaders, indicating that nearly 50 percent of enterprises are facing the risk of lagging behind in the adoption of AI. The potential problems don’t stop there – eminent AI expert and philosopher Nick Bostrom has recently said he believes the newest generation of AI chatbots (such as GPT-4) are even beginning to show signs of sentience. Which could create a whole new moral and ethical quandary if as a society we are planning to start creating and operationalizing them on a large scale. Are Auto-GPT and other agents that follow the same principles the next step of that journey? At the very least, we can expect AI tools that allow us to carry out far more complex tasks than the relatively simple things that ChatGPT can do, to begin to become commonplace.
- These are examples of work created by ChatGPT, an artificial intelligence (AI) platform developed by Silicon Valley-based start-up Open AI and released in November 2022.
- However, BDÜ estimates that the relative fluidity and readability of GPT output will actually make post-editing of AI-generated translations much harder compared to NMT output even for seasoned post-editing experts.
- On some Sites, Exporta Publishing & Events Ltd collects personal data such as your name, job title, department, company, e-mail, phone, work and/or home address, in order to register you for access to certain content, subscriptions and events.
- Regulation also needs to be outcome-seeking, with a focus on human-rights protections, because technology develops and evolves relatively quickly, resulting in loopholes companies can exploit.
- Organisations will need to consider the level of disclosure they are required to make regarding their use of generative AI, both internally to personnel and more publicly, depending on the AI use cases.
- These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter.
AGI is perhaps what we traditionally thought of when we pictured what AI would look like back in the days before machine learning and deep learning made weak/narrow AI an everyday reality around the start of the previous decade. Think of the science fiction AI demonstrated by robots like Data in Star Trek that can do just about anything a human being can do. The current rage in the ChatGPT space are so-called chained GPT-4 models like Auto-GPT or BabyAGI.
But as far as an enterprise business is concerned, there are certain characteristics that make Llama 2 a safe, near-term option to explore and exploit the technology with virtually no risk to information security. Because you can run it inside your infrastructure in a completely disconnected way, it’s not subject to the same risks as cloud services, so it overcomes those genrative ai barriers to corporate adoption. For us, the benefits of being able to run a model securely outweigh the additional power you get with a cloud-based service. And arguably, would an enterprise business get additional value from the extra power anyway? From what we’ve seen, Llama 2 is more than powerful enough for users to start exploring the technology in a safe way.
But perhaps the largest factor is the relationship people have built with their screens. Someone could easily modify the code in the autonomous agent model to give them backdoor access to Auto-GPT and take over your life. The trouble is that the AutoGPT agent will need access to the internet, the permission to impersonate you on the site, access to your PC to get your personal information, and permission to make payments. Many expect some kind of flourishing of original and creative content thanks to generative AI, whether text-based like ChatGPT or image-based like Midjourney. “Recent advances in generative AI and LLMs are an important addition to the growing arsenal of AI techniques that will transform cyber security. But they are not one-size-fits-all and must be applied with guardrails to the right use cases and challenges,” said Jack Stockdale, CTO, Darktrace.