Generative AI: eight questions that developers and users need to ask
The Future of Life Institute’s open letter to pause AI development led to multiple major news outlets publishing features on the existential threat that AI could pose to humanity. ChatGPT’s instant internet virality is possibly its biggest benefit to the tech industry so far. Appropriate governance is central to responsible AI use and procurement, and is an area of focus for lawmakers and regulators globally. Meta and Qualcomm’s on-device artificial intelligence (AI) partnership aims to take generative AI mainstream – to smartphones and laptops with an AI large language model (LLM).
Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. Many of the laws and regulatory principles referenced above (see section 2 above) include requirements regarding governance, oversight and documentation. 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.
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A generalized AI is one that is theoretically capable of carrying out many different types of tasks, even ones it wasn’t originally created to carry out, much the same way as a naturally intelligent entity (such as a human) can. Current AI applications are typically designed to carry out one task, becoming increasingly good at it as they are fed more data. Some examples include analyzing images, translating languages, or navigating self-driving vehicles. Because of this, they are sometimes referred to as “specialized AI,” “narrow AI,” or “weak AI.” Insiders within organisations, such as employees or contractors, can pose a risk, either with malicious intent to steal or sabotage AI datasets, or by unintentionally causing data corruption.
It all starts with how you discover and map the information you already have, to get a clear view of your entire data picture. Because while the release of public LLMs has reignited the conversation about the use of AI in business, many organisations still need to get these essential foundations in place before they can get any valuable output. And once your enterprise data is in good shape, there’s also a wealth of simpler, risk-free things you can do with AI too. It’s unclear if AI-generated content itself can be copyrighted since US law protects only “original works of authorship” created by humans.
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This fact is significant for contact centres because Azure adds the kinds of security, reliability, compliance, and data privacy factors that contact centres require. While ChatGPT is not available outside of the chat interface on OpenAI’s website, many of these GPT models are available from OpenAI via paid APIs. If you’ve played with ChatGPT, which uses GPT-3 under the hood, you know that the text completions are very good. In particular, it was the latest iteration of GPT models, referred to as GPT 3.5 DaVinci, that crossed the chasm from interesting to amazing. Each generation of these models has an increasing number of “parameters,” which you can think of like neurons in the brain.
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- Now picture AI that’s built on customer service interactions and, as a result, fully optimised for customer service.
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- Someone could easily modify the code in the autonomous agent model to give them backdoor access to Auto-GPT and take over your life.
This allows stakeholders to gain a more comprehensive understanding of their data, identify correlations, and make more informed decisions. The model is trained to predict the next best course of action by analyzing the context of data inputs that came before it. The method, called autoregressive language modelling, helps the model learn the ins and outs of syntax, semantics, and context. Creating Large Language Models (LLMs) that can generate natural-sounding outputs like text by leveraging high-volume data sets, grammar, semantics, and context is a clear example of the power of generative AI. There is no limit to how Generative AI could transform existing industries or spark innovative new business models as the technology evolves. We’re at the beginning of a massive shift in how brands will deliver customer service, and the LLMs behind ChatGPT and other generative AI systems are going to drastically impact contact centre operations.
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This type of setup allows more security when dealing with protected client details, legal documents, or other sensitive matters. Generative AI models like ChatGPT and DALL-E are trained on vast amounts of genrative ai data scraped from the internet, including copyrighted material. Recent lawsuits have accused companies like OpenAI and Meta of illegally copying authors’ work without permission to train their AI models.
The simplest way of looking at it is that Auto-GPT is able to carry out more complex, multi-step procedures than existing LLM-powered applications by creating its own prompts and feeding them back to itself, creating a loop. Enter Auto-GPT – a technology that attempts to overcome this hurdle with a simple solution. Some believe it may even be the next step towards the “holy grail” of AI – the creation of general, or strong, AI. Impressive as they are, until now, LLMs have been limited in one significant way. They tend to only be able to complete one task, such as answering a question or generating a piece of text, before requiring more human interaction (known as “prompts”). Ask any student who had to take online exams at home during the COVID lockdowns.
The core benefit offered by generative AI, like any good technology, is the ability to speed up jobs and processes that currently consume a lot of time and resources. The advent of transformers and large language models (LLMs) in 2017 was a major turning point in the accuracy, quality and capability of generative AI programs. Once the content has been created, users can customise the results and add additional information to assist the AI in refining its output. This prompt could be text, an image, a video, a design, a music sample, or any input that an AI system can process. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length.
Rather than intensive manual analysis, generative algorithms can rapidly uncover buried patterns and insights from customer data. Startup companies are pioneering this smart data analysis to optimise content strategy based on consumer behaviour and preferences. This data-driven approach revolutionises how organizations create content that truly resonates. Rather than rely on expensive manual video production, generative AI enables marketers to efficiently scale high-quality video content. As the technology advances, auto-generated video may reach Hollywood-caliber production values and be indistinguishable from human-made films and commercials. The core argument is that while the capabilities of generative AI for content are powerful and transformative, blind reliance without human creativity, strategy, ethics and oversight poses risks.
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Other risks recently called out by the authors of the social dilemma include the genuine risk of reality collapse, mass fakes, collapse of trust, exponential blackmail, biology automation, and exponential scams. This question of ownership is something that more brands are taking seriously in recent weeks. Some content platforms, such as Getty Images, have banned AI content due to potential copyright issues. AI systems can only be as unbiased as the data they are trained on, and if that data is skewed or biassed in some way, the AI will reflect those biases. The tools, which all launched within the past year, open up new opportunities for brands to bring down the cost of the creative process by speeding up the way we conceive, design, produce and refine creative ideas.
To understand what LLMs could be used for, it is helpful to understand their limitations. Notoriously, LLMs have a tendency to make up facts (to “hallucinate”) or miss key bits of information. Experts have concerns around bias and IP infringement and some LLMs, like ChatGPT, have a knowledge ‘cut-off’ meaning that they do not have access to recent factual information (in ChatGPT’s case, nothing more recent than September 2021). In general, it is therefore best to use an LLM as an assistant or collaborator, something that produces work for you to review and develop to create the best possible output. Hallucination still presents an obstacle in this use case, but the effect can be reduced using additional rules and logic as a post-processing step applied to the LLM output. For example, if multiple documents cite the same person as the CEO of a company, we can be confident that the model has made the proper connection.
Remember, the resubmission of the complete conversation has implications, and there is a limit. A critical feature of any chatbot is its ability to keep the conversation’s context as it progresses. And as stated genrative ai earlier, this is possible by re-submitting the conversation history on each interaction. The screenshot below shows that the chatbot has picked up the user’s question and added it to the conversation history.