Using Generative Artificial Intelligence Large Language Models Do’s and don’ts
If employees have unrestricted use of ChatGPT, it can lead to businesses no longer being in full control of their own and their customers’ (often sensitive) data. If employees are copying data and sending it to ChatGPT, or other public LLMs, you can no longer control what happens to it. Any AI is only as useful as the data it has been trained on, alongside a few other factors. Whilst this is perhaps less relevant in formal business use cases, where you’re unlikely to be using a public model like ChatGPT, this limitation should not to be overlooked when considering how your employees might be using ChatGPT to enhance their day-to-day work. This could lead to instances of introducing bad code, basing research or content on outdated information and even internalising bias or misinformation. But one important point that ChatGPT has missed from its self-generated autobiography is how
it’s been trained.
Bloor is an independent research and analyst house focused on the idea that Evolution is Essential to business success and ultimately survival. For nearly 30 years we have enabled businesses to understand the potential offered by technology and choose the optimal solutions for their needs. Possibly, I see no reason why not, but that is a huge leap and we are no-where near that stage yet, in my opinion. Generative AI will continue to evolve over the coming months and years, becoming more powerful and enabling new types of products and services that we have yet to encounter. It is important that regulators can respond to these developments, protecting citizens and consumers while also creating the space for responsible innovation. Regarding operational resilience requirements in financial services, banks and other regulated firms are expected to meet them irrespective of the technology they use.
Types of foundation model: more contested terms
Innovation News Network brings you the latest science, research and innovation news from across the fields of digital healthcare, space exploration, e-mobility, biodiversity, aquaculture and much more. The best approach is going to be to embrace it with care and work with providers when it comes to decision making around implementation. The possibilities behind generative AI are exciting – so let’s work to get it right and make it a force for good. Like all advanced technologies, generative AI’s impact is positive – so long as you take the steps necessary to ensure you’re using it the right way. For many people, when they think ‘generative AI,’ they think about written content or even AI art, but the use cases for it relate to the day-to-day operations of most office workers.
Generative AI can create virtual simulations and scenarios that mimic real-life work situations. This provides employees with a safe and immersive environment to practice and apply their skills. AI algorithms can generate realistic scenarios, provide feedback, and assess employee performance, allowing for experiential learning and skill refinement. It’s important to note that while generative AI can provide valuable insights and automation, it should be used in conjunction with human judgment and expertise. Additionally, clear communication and transparency with employees are crucial to ensure that the workforce understands, accepts, and trusts AI-based performance management systems.
Generative AI and Large Language Models (LLMs)
A parallel can be drawn with the emergence more than two decades ago of search engines. They didn’t by themselves make us smarter, but they did give us quicker access to information. Sifting through it, choosing the right sources, rejecting what was irrelevant is where the human skill lies, not merely using them.
- Several natural language processing AI models have come to prominence in recent months, such as generative AIs like ChatGPT.
- In comparison, Generative AI, which is at the cutting edge of AI developments, has the ability to create new and original pieces.
- In this video Talkdesk’s Ben Rigby explains the key differences between ChatGPT, large language models (LLMs) and generative AI.
- Additionally, laws that apply to specific types of technology, such as facial recognition software, online recommender technology or autonomous driving systems, will impact how AI should be deployed and governed in respect of those technologies.
- Generative AI often requires access to large volumes of data to train the models.
Every ChatGPT answer comes with the caveat that “ChatGPT may produce inaccurate information about people, places, or facts”. Like any AI system, ChatGPT is only as good as the data it has been trained on – and the engineering that’s been done to make sure bad data doesn’t sway its outputs. With the internet, there’s an inevitable level of inaccurate data or data containing unconscious or overt bias such as racism and sexism. RLHF and other guardrails have been used to help ChatGPT favour certain ways of responding, or avoid troublesome topics entirely, but users have found ways to bypass these measures and churn out some undesirable results.
Founder of the DevEducation project
As we continue to explore generative ai in the education sector, we know educators would appreciate assistance. We want to ensure that the machine helps the human to the level of sophistication required, no more, no less. There would obviously be no point putting a disclaimer reading “this article was created using AI”, then, if consumers are not reading to the bottom of the article. There is also the potential for the online ad model to come under greater pressure as less reading means less valuable ad inventory opportunities. Malik cited this as the often-quoted 80/20 rule that has become familiar with any “future of AI” speak — the tech will be used to carry out 80% of work previously done by humans, with humans then responsible for refining, curating and checking the AI’s output. “The key point is that AI is an increasingly important element of the types of companies being created in the market today”.
In addition, sector-specific frameworks for governance and oversight can affect what ‘responsible’ AI use and governance means in certain contexts. Additionally, laws that apply to specific types of technology, such as facial recognition software, online recommender technology or autonomous driving systems, will impact how AI should be deployed and governed in respect of those technologies. Before using generative AI in business processes, organisations should consider whether generative AI is the appropriate tool for the relevant task. Factors such as cost will also have a role to play here, with the cost of generative AI system based searches currently far outweighing the cost of using, for instance, internet search engines.
Deepfakes can be created to mimic and manipulate almost anyone or anything, creating the potential for fraud and amped up cybersecurity risks through socially engineered cybercrime. Generative AI can be used to build intelligent AI agents that can perform tasks on behalf of humans. For example, in customer service, AI agents can handle customer queries, provide information, and resolve issues. In sales, AI agents can identify leads, engage with potential customers, and even close sales. These AI agents can work round-the-clock across multiple channels, greatly enhancing your business’s operational efficiency and reach.
Artificial Intelligence (AI) has been a buzzword across sectors for the last decade, leading to significant advancements in technology and operational efficiencies. However, as we delve deeper into the AI landscape, we must acknowledge and understand its distinct forms. Among the emerging trends, generative AI, a subset of AI, has shown immense potential in reshaping genrative ai industries. Let’s unpack this question in the spirit of Bernard Marr’s distinctive, reader-friendly style. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length. These characteristics are typical of AI writing, allowing the detector to make a good guess at when text is AI-generated.
Those that get ahead of this trend are set to gain a significant competitive advantage, with this model showing no signs of disappearing anytime soon. Generative AI also has a tendency to “hallucinate” in certain scenarios, i.e. make up information and make it sound real. A naive US lawyer used ChatGPT to speed up his legal research, but it generated completely false case studies, which cost him the case, and his reputation. As information professionals, the Library team are enthusiastic about the opportunities that new technologies bring. AI brings tremendous possibilities for searching, analysing and finding connections between scholarly literature and we’ll be highlighting useful tools on this guide and other sections of our website.
This ensures that learning initiatives are tailored to individual needs, enhancing engagement and knowledge acquisition. By leveraging AI to analyse employee data, HR teams can uncover valuable insights, identify patterns, and make data-driven decisions that lead to better employee performance and satisfaction. Overall, generative AI empowers HR teams with advanced analytics capabilities, enabling genrative ai them to derive actionable insights from people analytics data and make informed decisions to optimise the workforce and improve overall organisational performance. Generative AI models can generate synthetic data that closely resembles real-world HR data. This synthetic data can be used to augment the existing dataset, allowing HR teams to have a larger and more diverse data set for analysis.