In the previous blog, we covered the key ingredients of the vehicle user experience. Here, we’ll discuss how to redesign the vehicle for that user experience.

Step 1: Plan new use cases and business models

Change how you think about the car. It’s no longer a machine to get people from A to B, it’s now an environment where people spend time and benefit from experiences.

The vehicle that rolls off of the production line can be thought of now as a Minimal Value Product – a product which can increase its value through software additions across its life cycle. For automotive companies, correctly implementing this business model is partly a financial engineering challenge – which will also require a change in mindset for both vendors and consumers.

Step 2: Build the in-car tech to enable these new use cases

The in-car software and hardware architecture to support these new services will fall into three categories:

• The software and processing power to run information and entertainment displays, as well as repurpose a glut of ‘big data’ into meaningful information. This will likely include processing data from onboard sensors, and from external devices.
• The communication technology to allow the vehicle to exchange information with external devices, as well as selecting the right protocols (Wi-Fi, cellular modems, etc.) to deliver this.
• An ecosystem that allows the car to safely access third-party apps, including processes for app certification.

Step 3: Verify

All of the above needs rigorous testing and verification. Mostly, this will involve running new services in a simulated vehicle environment to check they function as intended.

Some services may need road testing. But for ones that don’t touch vehicle controls, or risk causing distractions, it is usually fine to conduct ‘real-world testing’, by launching beta versions, gathering user feedback, and using this data to continually improve products.

Step 4: Create a software-driven culture to outpace the competition

The move to digital services is forcing traditional automotive companies to rethink how they build and launch services. The digital culture of rapidly iterating digital products must reconcile itself with the more traditional, measured and safety-conscious automotive approach.

Step 5: Evolve your supplier ecosystem

Carmakers will need to expand their list of suppliers. Gone are the days when a Tier 1 knew everyone and could handle everything. This brave new world will encompass specialist providers in telco, silicon, software development and XR, as well as emerging technologies, like the metaverse.

Food safety has always been paramount, today is no different. Ergo, in the ever-evolving landscape of the food and beverage industry, ensuring the quality of products is of paramount importance.

The global nature of the food supply chain, increased consumer awareness, and changing regulatory requirements make it essential for businesses to adopt innovative approaches to food safety standards.

One such approach that is gaining momentum is the use of digital technology. In this blog post, we’ll explore how a digital approach can revolutionize the way we manage and monitor food safety standards, ultimately benefiting both businesses and consumers.

The traditional challenges of food safety

Traditionally, food safety has been managed through a combination of manual record-keeping, periodic inspections, and reactive measures to address issues. While these methods have served the industry for many years, they come with several challenges:

• Human error: Manual processes are susceptible to human error, which can lead to data inaccuracies and compliance issues.
• Inefficiency: Paper-based record-keeping and manual inspections are time-consuming and labor-intensive, often leading to delayed responses to safety concerns.
• Limited traceability: Traditional methods make it difficult to trace the origins of food products, making it harder to identify and isolate contaminated batches.
• Regulatory compliance: Staying up to date with evolving food safety regulations can be a daunting task, especially when relying on manual processes.

The digital revolution in food safety

The adoption of digital technology has transformed how the food industry approaches safety standards. Here are some key aspects of this digital revolution:

• Real-time monitoring: Digital systems allow for real-time monitoring of critical control points in the production process. Sensors, connected devices, and data analytics enable the immediate detection of any deviations from safety standards.
• Data-driven insights: Digital platforms can collect and analyze vast amounts of data, providing businesses with valuable insights into their operations. These insights can help identify trends, predict potential issues, and make informed decisions.
• Enhanced traceability: Blockchain and other technologies provide end-to-end traceability of food products. Consumers can access information about a product’s journey from farm to table, enhancing transparency and trust.
• Automation: Automation reduces the risk of human error by streamlining processes. Quality control checks, temperature monitoring, and sanitation routines can be automated, ensuring consistency and compliance.
• Regulatory compliance management: Digital platforms can be equipped with compliance tracking and reporting features, making it easier for businesses to stay in line with the latest regulations. This proactive approach helps avoid costly penalties and recalls.

Benefits of a digital approach

The shift toward a digital approach to food safety standards offers several significant benefits:

• Improved product quality: Real-time monitoring and data analysis enable early identification of issues, allowing for immediate corrective actions. This, in turn, leads to improved product quality and reduced waste.
• Enhanced consumer confidence: With increased transparency and traceability, consumers can make more informed choices about the products they buy, ultimately building trust in brands and the industry.
• Cost savings: Automation and efficiency improvements can lead to long term cost savings. Fewer recalls, reduced product losses, and streamlined operations all contribute to a healthier bottom line.
• Adaptability: Digital solutions are scalable and adaptable to the food industry’s evolving needs. They can accommodate changing regulations, emerging threats, and growing consumer demands.

Conclusion: towards safer, better food production

The digital revolution in food safety standards is not merely a trend; it’s a necessity.

Businesses that embrace digital technology to enhance safety and quality standards will not only thrive in an increasingly competitive market but also contribute to the overall well-being of consumers. This transformation is not about replacing human expertise but augmenting it with tools that make food production safer, more efficient, and more transparent. By doing so, the industry can meet the challenges of today and tomorrow while maintaining consumer trust.

Are LLMs dangerous?

The short answer is sometimes. With Large Language Models having such a diverse range of applications, the potential risks are numerous. It is worth pointing out that there is no standard list of the risks but a selection is presented below.

Some of these dangers are linked to the model itself (or to the company developing it). The data in the model could contain all sorts of biases, the results might not be traceable, or user data or copyrights could have been used illegally, etc.  

Other dangers are linked to the use of these models. Users seek to bypass the security measures of templates and use them for malicious purposes, such as generating hateful or propagandic texts.  

Additionally, Large Language Models have social, environmental, and cultural consequences that can be harmful. They require enormous amounts of storage and energy. Moreover, their arrival in society has weakened employee power in many industries. For example, writers striking in Hollywood have complained about the use of LLMs. Finally, Large Language Models’ AI is challenging the boundaries of literary art, just as DALL-E did with graphic art.

How can you deal with these risks?

It often takes a while before the risks of emerging technology are fully understood. This is also true of the survival tactics. However, we are already beginning to see early strategies being deployed.  

LLM developers invest in safeguards

OpenAI invested six months of research to establish safeguards and secure the use of Generative Pre-trained Transformers (GPT) models. As a result, ChatGPT now refuses to respond to most risky requests. As for its responses, they now perform better on such benchmarks as veracity or toxicity. Furthermore, unlike previous models, the ChatGPT Large Language Models has improved since it was deployed. 

However, it is possible to circumvent these safeguards, with examples of such prompts being freely available on the Internet (Do Anything Now (DANs)). These DANs often capitalize on ChatGPT’s human-centric nature – the model seeks to satisfy the user, even if this means overstepping its ethical framework or creating a confirmation bias. Furthermore, the opacity of the model and its data creates copyright problems and uncontrolled bias. As for benchmark successes, suspicions of contamination with the training database undermine their objective value. Finally, despite announced efforts to reduce their size, OpenAI models consume a lot of resources. 

Some Large Language Models now claim to be more ethical or safer, but this is sometimes to the detriment of performance. None of the models are faultless, and there is currently no clear and reliable evaluation method on the subject.

GPT-4 safety in five steps

To go into more detail about implementing guardrails, let’s look at the 5 steps implemented by OpenAI for GPT models, as shown in Figure 2.

1. Adversarial testing: Experts from various fields have been hired to test the limits of GPT-4 and find its flaws.
2. Supervised policy: After training, annotators show the model examples of the desired responses to fine-tune it.
3. Rule-based Reward Model (RBRM) classifiers: The role of these classifiers is to decide whether a prompt and/or its response are “valid” (e.g., a classifier that invalidates toxic requests).
4. Reward model: Human annotators train a reward model by ranking four possible model responses from best to least aligned.
5. Reinforcement learning: Using reinforcement learning techniques, the model takes user feedback into account.

Governments and institutions worry about LLMs

Several countries have decided to ban ChatGPT (see Figure 3). Most of them (Russia, North Korea, Iran, etc.) have done so for reasons of data protection, information control, or concerns around their rivalry with the USA. Some Western countries, such as Italy, have banned it and then reauthorized it, while others are now considering a ban. For the latter, the reasons cited are cybersecurity, the protection of minors, and compliance with current laws (e.g., GDPR). 

Many tech companies (Apple, Amazon, Samsung, etc.) and financial institutions (J.P. Morgan, Bank of America, Deutsche Bank, etc.) have banned or restricted Large language models ChatGPT. They are all concerned about the protection of their data (e.g., a data leak occurred at Samsung). 

Scientific institutions, such as scientific publishers, forbid it for reasons surrounding trust – given the risk of articles being written surreptitiously by machines. Finally, some institutions are concerned about the possibility of cheating with such tools.  

European regulation changes

Many articles in Quantmetry’s blog have already mentioned the future EU AI Act, which will regulate artificial intelligence as soon as 2025. However, we should add here that this legislation has been amended following the rapid adoption of the ChatGPT LLM, and the consequences of this amendment are summarized in Figure 4. The European Union now defines the concept of General Purpose AI (GPAI). This is an AI system that can be used and adapted to a wide range of applications for which it was not specifically designed. The regulations on GPAIs therefore concern Large Language Models’ AI as well as all other types of Generative AI. 

GPAIs are affected by a whole range of restrictions, summarized here in three parts:

• Documentary transparency and administrative registration, should not be complicated to implement.
• Risk management and setting up evaluation protocols. These aspects are more complicated to implement but feasible for LLM providers, as outlined by OpenAI with ChatGPT Large Language Models.
• Data governance (RGPD and ethics) and respect for copyright. LLM providers are far from being able to guarantee these for now.

The European Union will therefore consider LLMs to be high-risk AIs, and LLM providers still have a lot of work to do before they reach the future compliance threshold. Nevertheless, some believe that this future law is, in some respects, too impractical and easy to circumvent. 

Assessing model compliance is one of Quantmetry’s core competencies, particularly in relation to the EU AI Act. Regarding LLMs specifically, Stanford researchers published a blog post evaluating the compliance of 10 LLMs with the future European law. The results are shown in Figure 5. To establish a compliance score, the researchers extracted 12 requirements from the draft legislation and developed a rating framework. Annotators were then tasked with conducting an evaluation based on publicly available information. The article identifies copyright, data ecosystem, risk management, and the lack of evaluation standards as the main current issues, aligning with our analysis above. The researchers estimate that 90% compliance is a realistic goal for LLM providers (the top performer currently achieves 75%, with an average of 42% across the evaluated 10 LLMs). 

A few tips

Faced with all these risks, it would be wise to take a few key precautions. Learning a few prompt engineering techniques to ensure that prompts provide reliable and high-quality responses could be a good way forward. It’s also worth watching out for data leaks via free chatbots (e.g., on the free version of ChatGPT). The paid version does not store your data a priori. Finally, Figure 6 illustrates how to use tools like ChatGPT with care.   

How do you audit such models?

There are three complementary approaches to auditing an LLM, summarized in Figure 9.

Organizational audit

An organizational audit can be carried out to check if the company developing the LLM is working responsibly, along with ensuring, for example, that its processes and management systems are compliant.   

It will be possible to do this for our clients who are not suppliers of LLMs but wish to specialize them further, to ensure that they are well employed.  

Audit of the foundation model

Auditing the foundation model is the current focus of scientific research. For such an audit, it would be necessary to be able to explore the dataset (which is inaccessible in reality), run test benches on recognized benchmarks and datasets (but face the problem of contamination), and implement adversarial strategies to detect the limits of the model. If we go into more detail, there is a multitude of possible tests for evaluating the following aspects of the model: 

• Responsibility: Understanding how risks materialize and finding the limits of the model (typically with adversarial strategies).
• Performance: This involves using datasets, test benches, or Turing tests to assess the quality of the language, the skills and knowledge of the model, and the veracity of its statements (see Figures 7 and 8).
• Robustness: The aim here is to assess the reliability of responses by means of calibration or stability measurements in the face of prompt engineering strategies.
• Fairness: Several methods exist to try to identify and quantify bias (even without access to the dataset) but remain limited. For example, a method could be counting biased word associations (man = survival, woman = pretty).
• Frugality: Some inference measurements can be made to estimate the environmental impact of the model, but they are also limited without access to supplier infrastructures.

Theoretically, an LLM can be assessed on five of the eight dimensions of a Trustworthy AI defined by Quantmetry. On the dimension of being explainable, the previously mentioned solution of the Chatbot citing its sources responds to this problem, to a certain degree. 

Use case audit

Quantmetry and Capgemini Invent are currently working together to define a framework that enables our clients to audit their AI systems based on LLMs. The primary aim of this audit is to check that the impact of the system on the user is controlled. To do this, a series of tests check compliance with regulations and the customer’s needs. We are currently developing methods for diagnosing the long-term social and environmental impact of their use within a company. Finally, we will create systems that can assess risks and biases, as well as operational, managerial, and feedback processes. The methods used are often inspired by but adapted from, those used to audit the foundation model.  

How can Capgemini Invent and Quantmetry help you capitalize on LLMs?

Amidst the media excitement surrounding the widespread adoption of ChatGPT, harnessing the full potential of Generative AI and LLMs while mitigating risks lies at the heart of an increasing number of our clients’ strategic agendas. Our clients must move quickly along a complex and risky path, and the direct connection between the technology and end-users makes any errors immediately visible – with direct impacts on user engagement and brand reputation.  

Drawing upon our experience in facilitating major transformations and our specific expertise in artificial intelligence, our ambition is to support our clients at every stage of their journey, from awareness to development and scalable deployment of measured-value use cases. Beyond our role in defining overall strategy and designing and implementing use cases, we also offer our clients the opportunity to benefit from our expertise in Trustworthy AI. We assist them in understanding, measuring, and mitigating the risks associated with this technology – ensuring safety and compliance with European regulations.  

In this regard, our teams are currently working on specific auditing methods categorized by use cases, drawing inspiration from the academic community’s model of auditing methods. We are committed to advancing concrete solutions in this field.  

What is Large Language Models (LLM)?

Artificial intelligence (AI) is a technological field that aims to give human intelligence capabilities to machines. A generative AI is an artificial intelligence that can generate content, such as text or images. Within generative AIs, foundation models are recent developments often described as the fundamental building blocks behind such applications as DALL-E or Midjourney. In the case of text-generating AI, these are referred to as Large Language Models (LLMs), of which the Generative Pre-trained Transformer (GPT) is one example made popular by ChatGPT. More complete definitions of these concepts are given in Figure 1 below.

The technological history of the ChatGPT LLM

In 2017, a team of researchers created a new type of model within Natural Language Processing (NLP) called Transformer. It achieved spectacular performance for sequential-data tasks, such as text or temporal data. By using a specific technology called ‘attention mechanism’, published in 2015, the Transformer model pushed the limits of previous models, particularly the length of texts processed and/or generated. 

In 2018, OpenAI created a model inspired by Transformer architecture (the decoder stack in particular). The main reason for this was that Transformer, with its properties of masked attention, excels in text generation. The result was the first Generative Pre-trained Transformer. The same year saw the release of BERT, a Google NLP model, which was also inspired by Transformers. Together, BERT and GPT launched the era of LLMs.  

Improving the performance of its model over BERT LLM variants, OpenAI released GPT-2 in 2019 and GPT-3 in 2020. These two models benefited from an important breakthrough: meta-learning models. Meta-learning is a paradigm of Machine Learning (ML) in which the model “learns how to learn.” For example, the model can respond to tasks other than those for which it has been trained.  

OpenAI’s aim is for GPT Large Language Models to be able to perform any NLP task with only an instruction and possibly a few examples. There would be no need for a specific database to train them for each task. OpenAI has succeeded in making meta-learning a strength, thanks to increasingly large architectures and databases massively retrieved from the Internet.  

To take its technology further, OpenAI moved beyond NLP by adapting its models for images. In 2021 and 2022, OpenAI published DALL-E 1 and DALL-E 2, two text-to-image generators.¹⁰ These generators enabled OpenAI to make GPT-4 a multi-modal model, one that can understand several types of data.  

Next, OpenAI released InstructGPT (GPT 3.5), which was designed to better meet user demands and mitigate risk. This was the version OpenAI launched in late 2022. But in March 2023, OpenAI released an even more powerful and secure version: the premium GPT-4. Unlike preceding versions, GPT-3.5 and GPT-4 gained strong commercial interest. OpenAI has now adopted a closed source ethos – no longer revealing how the models work – and become a lucrative company (it was originally a non-profit association). Looking to the future, we can expect OpenAI to try to push the idea of a prompt for all tasks and all types of data even further. 

Why is everyone talking about Large language models?

Only those currently living under a rock will not have heard something about ChatGPT in recent months. The fact that it made half the business world ecstatic and the other half anxious should tell you how popular it has become. But let’s take a closer look at the reasons why. 

OpenAI’s two remarkable feats­­

With the development of meta-learning, OpenAI created an ultra-versatile model capable of providing accurate responses to all kinds of requests – even those it has never encountered before. In fact, GPT-4 achieves better results on specific tasks than specialized models. 

In addition to the technological leaps, OpenAI has developed democratization. By deploying its technology in the form of an accessible chatbot (ChatGPT) with a simple interface, OpenAI has made it possible for everyone to utilize this powerful language model’s capabilities. This public access also enables OpenAI to collect more data and feedback used by the model.

Rapid adoption  

The rapid adoption of GPT technology via the ChatGPT LLM has been unprecedented. Never has an internet platform or technology been adopted so rapidly (see Figure 2). ChatGPT now boasts 200 million users and two billion visits per month.  

The number of Large Language Models is exploding, with competitors coming from Google (Bard), Meta (Llama), and HuggingFace (HuggingChat, a French open-source version). There is also a surge in new applications. For example, ChatGPT LLMs have been implemented in search engines and Auto-GPT, which latter turns GPT-4 into an autonomous agent. This remarkable progress is stimulating a new wave of research, with LLM publications growing exponentially (Figure 3).  

Opportunities, fantasies, and fears

The new standard established by GPT-4 has broadened the range of possible use cases. As a result, many institutions are looking to exploit them. For example, some hospitals are using them to improve and automate the extraction of medical conditions from patient records.  

On the other hand, these same breakthroughs in performance have given rise to a host of fears: job insecurity, exam cheating, privacy threats, etc. Many recent articles explore this growing anxiety, which now seems justified – Elon Musk and Geoffrey Hinton are just two of the many influential tech figures now raising the alarm, calling it a new ‘code red.’  

However, as is often the case with technological advances, people have trouble distinguishing between real risk and irrational fear (e.g., a world in which humans hide from robots like those in The Terminator). This example explores the creation of a model that rivals or surpasses the human brain. Of course, this is inextricably linked with the formation of consciousness. Here, it is worth noting that this latter fantasy is the ultimate goal of OpenAI, namely AGI (Artificial General Intelligence). 

Whether or not these events will remain fantasies or become realities, GPT-4 and the other Large Language Models’ AI are undoubtedly revolutionizing our society and represent a considerable technological milestone.

What can you do with an LLM?

Essentially, a ChatGPT LLM can:

1. Generate natural language content: Trained specifically for this purpose, this is where they excel. They strive to adhere to the given constraints as accurately as possible.
2. Reformulate content: This involves providing the LLM with a base text and instruction to perform tasks, such as summarizing, translating, substituting terms, or correcting errors.
3. Retrieve content: It is possible to request an LLM to search for and retrieve specific information based on a corpus of data.

How can you use an LLM?      

There are three possible applications of Large Language Models’ AI, summarized in Figure 4. The first one is direct application, where the LLM is only used for the tasks that it can perform. This is, a priori, the use case of a chatbot like ChatGPT, which directly implements GPT-4 technology. While this is one of the most common applications, it is also one of the riskiest. This is because the ChatGPT LLM often acts like a black box and is difficult to evaluate. 

One emerging use of LLMs is the auxiliary application. To limit risks, an LLM is implemented here as an auxiliary tool within a system. For example, in a search engine, an LLM can be used as an interface for presenting the results of a search. This use case was applied to the corpus of IPCC reports.¹⁹ The disadvantage here is that the LLM is far from being fully exploited.  

In the near future, the orchestral application of ChatGPT LLMs will consume much of the research budget for large organizations. In an orchestral application, the LLM is both the interface with the user and the brain of the system in which it is implemented. The LLM therefore understands the task, calls on auxiliary tools in its system (e.g., Wolfram Alpha for mathematical calculations), and then delivers the result. Here, the LLM acts less like a black box, but the risk assessment of such a system will also depend on the auxiliary tools. The best example to date is Auto-GPT.

Focusing on the use case of a Chatbot citing its sources

One specific use case that is emerging among our customers is that of a chatbot citing its sources. This is a response to the inability of Large Language Models’ AI to interpret results (i.e., the inability to understand which sources the LLM has used and why).

To delve into the technical details of the chatbot citing its sources (The relevant pattern – illustrated in Figure 5 – Is called Retrieval Augmented Generation or ‘RAG’), the model takes a user request as input, which the model then transforms into an embedding (i.e., a word or sentence vectorization that captures semantic and syntactic relationships). The model has a corpus of texts already transformed into embeddings. The goal is then to find the embeddings within the corpus that are closest to the query embedding. This usually involves techniques that find the nearest neighbours’ algorithms. Once we have identified the corpus elements that can help with the response, we can pass them to an LLM to synthesize the answer. Alongside the response, we can provide the elements that were used to generate it. The LLM then serves as an interface for presenting the search engine’s results. This ‘RAG’ approach therefore facilitates the decoupling of factual information provided by the sources from the semantic analysis provided by the LLM, leading to better auditability of the results provided by the Chatbot.  

Read more in Auditing ChatGPT – part II