The Difference Between ChatGPT, LLMs, and Generative AI

It is crucial that businesses have an accurate and up to date understanding of any generative AI used in their organisation by their staff. This can be done by producing an ‘approved list’ of AI tools that have been vetted for use within the organisation. Such vetting would include analysis of the AI’s terms of use to understand whether use of its output would constitute infringement of any IP rights. This could also include an analysis of the AI’s outputs for accuracy or reliability.

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. Explaining how a generative AI system operates to generate output becomes increasingly challenging as the level of sophistication of these systems increases. The challenge of explicability can be further complicated when the AI technology is supplied by another provider or a chain of providers who themselves lack the visibility of how such system operates or functions.

It uses a large language model to generate human-like responses, completing a variety of language tasks from solving code to writing essays and poems, and impressed many in the tech world, including Microsoft who invested $10 billion into the tool. But ChatGPT can produce much of what we ask undergraduate students to generate in order to assess their abilities. It can match quality in listing facts, detailing procedures, and preparing presentations. There are no reliable countermeasures for catching ChatGPT-generated text.

Can you give an example of ChatGPT in action?

You might be following this line of thinking, but wondering if Microsoft Copilots won’t solve this problem once they arrive – but we don’t believe they will. All told, we feel the decision-making process should start from the ‘why not Azure? ’ position for these reasons, as well as the probability that many more supporting reasons will emerge in the years to come. Without repeating the ideas from my last post on AI risk, I want to focus thinking on the use of public AI services for work by inspecting two examples.

People spend far more time interacting with screens than with real people in real places. It is unsurprising – social media apps from Twitter to TikTok are optimised to grab your attention. There is a clear lack of imagination in the development of ChatGPT use cases. It is a bit like when moving pictures and film were created – all movies looked like stage plays. People could not get their heads around how the technology could change the way you could show art.

micro-trends that will shape the future of marketing technologySPONSORED ARTICLE

If students can or will use these tools in your module, you must tell them how to acknowledge this use. The ways we teach and learn are constantly being shaped and reshaped by new technologies, practices and innovations. The latest disruption everyone is talking about is the generative AI tool,  ChatGPT. No doubt you’ve seen headlines about ChatGPT passing exams (NBC, 23 Jan 2023) and being banned by schools and academic journals (Guardian, 26 Jan 2023). On the other hand, you might also have seen people discussing how they use ChatCPT in their professional practice, for writing computer code or marketing copy (Times Higher Education, 14 Oct 2022).

In this blog, we’ll go back to basics, breaking down some of the concepts behind ChatGPT and the Large Language Models that this kind of AI is built on – and what this means for business adoption of AI. “Approaches to AI vary across the globe, from overarching principles, to broad AI-focused legislation, to laws targeting specific use cases such as generative AI. Transparency, fairness, accountability and reliability are in the spotlight across the board.” Will data entered on the AI system be protected, and will the operation of the system be robust? To what degree will your personnel rely on the use of that AI, and are contingencies needed in the event it becomes unavailable (for a temporary period, or permanently)?

Reaching AI superpower status: the need to upskill the UK public sector

Yakov Livshits

As with other software, cyber-security and operational resilience requirements and considerations will apply to the use and procurement of generative AI systems. This briefing is for QAA Members and other sector providers who are concerned about the challenges that artificial intelligence (AI) software tools bring in relation to the academic standards of awards and the integrity of assessment. It discusses Large Language Models (LLM), such as GPT3, which can be accessed through tools including ChatGPT. Take this short course to learn how ChatGPT and other generative AI tools work and how you can use them in business. You’ll get hands-on with real artificial intelligence tools to start inspiring you with ideas for harnessing their power. Other issues arise concerning the output of AI, and the question of whether text, designs, coding, pictures or even inventions generated by an AI program can be protected by existing intellectual property laws.

• Dive into the world of Web3, where groundbreaking technologies create boundless opportunities.
• They are trained on massive datasets of text which can contain biases and inaccuracies.
• Communicating the importance of academic integrity with students is an important foundation when dealing with topics like ChatGPT.
• The AI’s human-like
  conversation abilities and its capacity to generate novel content
  generated a flurry of excitement, with social media soon filled
  with ChatGPT-produced lyrics, sonnets, stories, and so on.
• Therefore, harnessing the social opportunities of ChatGPT could help us make digital innovation more accessible and break the digital ceiling.

A pretty comprehensive response that sounds clever, and impressive – and it took only seconds to generate. There’s one key theme that comes out from this AI-generated response, and that’s ‘scale’. Trained on a massive data set (the internet), ChatGPT has developed a vast knowledge base and a remarkably human-like ability to understand and generate natural language. Other generative AI tools based on Large Language Models (LLMs) simply haven’t been developed at this scale, and the quality, range, and naturality of the results they deliver are rather more limited and less convincing. Generative AI techniques can be used in NLP to create new language content in various applications such as chatbots, machine translation, summarization, and sentiment analysis.

There may also be the potential for unfair discrimination claims and other employment-related issues if, for example, underlying systems have been trained on particular data sets which could unfairly discriminate against certain groups or ethnicities. This webinar looked at how it’s right to address the threat ChatGPT poses to academic integrity, but that it’s also an amazing new tool. This panel explored genrative ai the opportunities ChatGPT presents the sector, from admin, to teaching, to learning. Then there are environmental issues, as large language models are highly data-intensive. Similarly, it might not be able to perfectly replicate the tone that you or your colleagues would use in communications, or the overarching voice of your brand. And in sensitive contexts, it might not provide an appropriate response.

Tech leaders like Elon Musk recently demanded a pause on AI development to avoid risks to humanity, stating that time was needed to enable governments to play catch-up. The use of AI tools should be continually monitored, and the AI strategy generally kept under review. The capabilities of generative AI are changing rapidly and so too will the contractual terms of use and (eventually) the law in this area, and businesses need to be prepared. Some organisations and sectors of the economy need to move faster than others in determining the extent to which generative AI is appropriate to integrate into business practices and systems.

UK at risk of falling behind in AI regulation, MPs warn

But they may not be aware of how they work, their affordances and limitations, the issues of privacy, ownership, and security they raise, or their ethical and moral implications. Consider including discussions about ChatGPT as part of your approach to develop students’ assessment and AI literacy. You may wish to refer to the Student Guide to Chat GPT (under development) to support your discussions with students. To get an idea of what it is and what its capabilities are, you must take a close look at ChatGPT. It is being touted as the next big thing in the fast-evolving AI arena. It is a powerful generative AI language model capable of creating original content triggered by a user prompt.

AI relies on technology, which can be prone to malfunctions and downtime. If the system experiences technical difficulties, users may be unable to access relevant (learning) resources or get the help they need when time is of the essence. As with any technology, there are ethical considerations to using ChatGPT in higher education. Institutions must consider the potential impact on student learning and the potential for bias in the system’s responses. While Hyperscience could handle the validation of complex KYC submissions without the help of large language models, the advantage of using these models is that they understand rules the way humans understand them—it’s no longer necessary to hardcode them.

Hyperscience allows customers to use data extracted from documents for their KYC processes. This data can be used to validate information or suggest changes during the approval process, making it easier for customer representatives to complete their tasks accurately and efficiently. When Combined with Hyperscience’s Flow Studio (a low-code development environment), customers can use these powerful LLMs in tandem with human-in-the-loop supervision to ensure accuracy, mitigate risks, and manage an endless number of advanced use cases. No other technology has been adopted this quickly, and it will be followed by more large language model (LLM) applications. The speed of change and the potential to impact the workplace with such tools as ChatGPT and AI know no boundaries and we are likely to witness significant developments in this area.

There’s a lot of buzz about AI at the moment, much of it prompted by the launch of ChatGPT at the end of last year. ChatGPT has brought AI into the public domain, making it possible for anyone to genrative ai use AI to generate content. For the first time, it’s not just tech companies, business leaders and politicians talking about AI, it’s lawyers, teachers, writers and the mainstream media, too.