This video tutorial suggest five ideas to give better prompts to generative AI, resulting in better and more accurate results.
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Tag: Generative AI
Tech Insight : What Are ‘Deadbots’?
Following warnings by ethicists at Cambridge University that AI chatbots made to simulate the personalities of deceased loved ones could be used to spam family and friends, we take a look at the subject of so-called “deadbots”.
Griefbots, Deadbots, Postmortem Avatars
The Cambridge study, entitled “Griefbots, Deadbots, Postmortem Avatars: on Responsible Applications of Generative AI in the Digital Afterlife Industry” looks at the negative consequences and ethical concerns of adoption of generative AI solutions in what it calls “the digital afterlife industry (DAI)”.
Scenarios
As suggested by the title of the study, a ‘deadbot’ is a digital avatar or AI chatbot designed to simulate the personality and behaviour of a deceased individual. The Cambridge study used simulations and different scenarios to try and understand the effects that these AI clones trained on data about the deceased, known as “deadbots” or “griefbots”, could have on living loved ones if made to interact with them as part of this kind of service.
Who Could Make Deadbots and Why?
The research involved several scenarios designed to highlight the issues around the use of deadbots. For example, the possible negative uses of deadbots highlighted in the study included:
– A subscription app that can create a free AI re-creation of a deceased relative (a grandmother in the study), trained on their data, and which can exchange text messages with and contact the living loved one, in a similar way that the deceased relative used to (via WhatsApp) giving the impression that they are still around to talk to. The study scenario showed how the bot could be made to mimic the deceased loved one’s grandmother’s “accent and dialect when synthesising her voice, as well as her characteristic syntax and consistent typographical errors when texting”. However, the study showed how this deadbot service could also be made to output messages that include advertisements in the loved one’s voice, thereby causing the loved one distress. The study also looked at how further distress could be caused if the app designers did not fully consider the user’s feelings around deleting the account and the deadbot, such as if provision is not made to allow them to say goodbye to the deadbot in a meaningful way.
– A service allowing a dying relative (e.g. a father and grandfather), to create their own deadbot that will allow their younger relatives (i.e. children and grandchildren) to get to know them better after they’ve died. The study highlighted negative consequences of this type of service, such as the dying relative not getting consent from the children and grandchildren to be contacted by the ‘deadbolt’ and the resulting unsolicited notifications, reminders, and updates from the deadbot, leaving relatives distressed and feeling as though they were being ‘haunted’ or even ‘stalked’.
Examples of services and apps that already exist and offer to recreate the dead with AI include ‘Project December’, and apps like ‘HereAfter’.
Many Potential Issues
As shown by the examples in the Cambridge research (there were 3 main scenarios), the use of deadbots raise several ethical, psychological and social concerns. Some of the potential ways they could be harmful, unethical, or exploitative (along with the negative feelings they might provoke in loved ones) include concerns, such as:
– Consent and autonomy. As noted in the Cambridge study, a primary concern is whether the deceased gave consent for their personality, appearance, or private thoughts to be used in this way. Using someone’s identity without their explicit consent could be seen as a violation of their autonomy and dignity.
– Accuracy and representation: There is a risk that the AI might not accurately represent the deceased’s personality or views, potentially spreading misinformation or creating a false image that could tarnish their memory.
– Commercial exploitation. The study looked at how a deadbot could be used for advertising because the potential for commercial exploitation of a deceased person’s identity is a real concern. Companies could use deadbots for profit, exploiting a person’s image or personality without fair compensation to their estate or consideration of their legacy.
– Contractual issues. For example, relatives may find themselves in a situation where they are powerless to have an AI deadbot simulation suspended, e.g. if their deceased loved one signed a lengthy contract with a digital afterlife service.
Psychological and Social Impacts
The Cambridge study was designed to look at the possible negative aspects of the use of deadbots, an important part of which are the psychological and social impacts on the living. These could include, for example:
– Impeding grief. Interaction with a deadbot might impede the natural grieving process. Instead of coming to terms with the loss, people may cling to the digital semblance of the deceased, potentially leading to prolonged grief or complicated emotional states.
– There’s also a risk that individuals might become overly dependent on the deadbot for emotional support, isolating themselves from real human interactions and not seeking support from living friends and family.
– Distress and discomfort. As identified in the Cambridge study, aspects of the experience of interacting with a simulation of a deceased loved one can be distressing or unsettling for some people, especially if the interaction feels uncanny or not quite right. For example, the Cambridge study highlighted how relatives may get some initial comfort from the deadbot of a loved one but may become drained by daily interactions that become an “overwhelming emotional weight”.
Potential for Abuse
Considering the fact that, as identified in the Cambridge study, people may develop strong emotional bonds with the deadbot AI simulations thereby making them particularly vulnerable to manipulation, one of the major risks of the growth of a digital afterlife industry (DAI) is the potential for abuse. For example:
– There could be misuse of the deceased’s private information (privacy violations), especially if sensitive or personal data is incorporated into the deadbot without proper safeguards.
– In the wrong hands, deadbots could be used to harass or emotionally manipulate survivors, for example, by a controlling individual using a deadbot to exert influence beyond the grave.
– There is also the real potential for deadbots to be used in scams or fraudulent activities, impersonating the deceased to deceive the living.
Emotional Reactions from Loved Ones
The psychological and social impacts of the use of deadbots as part some kind of service to living loved ones, and/or misuse of deadbots could therefore lead to a number of negative emotional reactions. These could include :
– Distress due to the unsettling experience of interacting with a digital replica.
– Anger or frustration over the misuse or misrepresentation of the deceased.
– Sadness from a constant reminder of the loss that might hinder emotional recovery.
– Fear concerning the ethical implications and potential for misuse.
– Confusion over the blurred lines between reality and digital facsimiles.
What Do The Cambridge Researchers Suggest?
The Cambridge study led to several suggestions of ways in which users of this kind of service may be better protected from its negative effects, including:
– Deadbot designers being required to seek consent from “data donors” before they die.
– Products of this kind being required to regularly alert users about the risks and to provide easy opt-out protocols, as well as measures being taken to prevent the disrespectful uses of deadbots.
– The introduction of user-friendly termination methods, e.g. having a “digital funeral” for the deadbot. This would allow the living relative to say goodbye to the deadbot in a meaningful way if the account was to be closed and the deadbot deleted.
– As highlighted by Dr Tomasz Hollanek, one of the study co-authors: “It is vital that digital afterlife services consider the rights and consent not just of those they recreate, but those who will have to interact with the simulations.”
What Does This Mean For Your Business?
The findings and recommendations from the Cambridge study shed light on crucial considerations that organisations involved in the digital afterlife industry (DAI) must address. As developers and businesses providing deadbot services, there is a heightened responsibility to ensure these technologies are developed and used ethically and sensitively. The study’s call for obtaining consent from data donors before their death underscores the need for clear consent mechanisms to be built in. This consent is not just a legal formality but a foundational ethical practice that respects the rights and dignity of individuals.
Also, the suggestion by the Cambridge team to implement regular risk notifications and provide straightforward opt-out options is needed for greater transparency and user control in digital interactions. This could mean incorporating these safeguards into service offerings to enhance user trust and digital afterlife services companies perhaps positioning themselves as a leaders in ethical AI practice. The introduction of a “digital funeral” to these services could also be a respectful and symbolic way to conclude the use of a deadbot, as well as being a sensitive way to meet personal closure needs, e.g. at the end of the contract.
The broader implications of the Cambridge study for the DAI sector include the need to navigate potential psychological impacts and prevent exploitative practices. As Dr Tomasz Hollanek from the study highlighted, the unintentional distress caused by these AI recreations can be profound, suggesting that their design and deployment strategies should really prioritise psychological safety and emotional wellbeing. This should involve designing AI that is not only technically proficient but also emotionally intelligent and sensitive to the nuances of human grief and memory.
Businesses in this field must also consider the long-term implications of their services on societal norms and personal privacy. The risk of commercial exploitation or disrespectful uses of deadbots could lead to public backlash and regulatory scrutiny, which could stifle innovation and growth in the industry. The Cambridge study, therefore serves as an early but important guidepost for the DAI industry and has highlighted some useful guidelines and recommendations that could contribute to a more ethical and empathetic digital world.
Featured Article : Try Being Nice To Your AI
With some research indicating that ‘emotive prompts’ to generative AI chatbots can deliver better outputs, we look at whether ‘being nice’ to a chatbot really does improve its performance.
Not Possible, Surely?
Generative AI Chatbots, including advanced ones, don’t possess real ‘intelligence’ in the way we as humans understand it. For example, they don’t have consciousness, self-awareness (yet), emotions, or the ability to understand context and meaning in the same manner as a human being.
Instead, AI Chatbots are trained on a wide range of text data (books, articles, websites) to recognise patterns and word relationships and they use machine learning to understand how words are used in various contexts. This means that when responding, chatbots aren’t ‘thinking’ but are predicting what words come next based on their training. They ‘just’ using statistical methods to create responses that are coherent and relevant to the prompt.
The ability of chatbots to generate responses comes from algorithms that allow them to process word sequences and generate educated guesses on how a human might reply, based on learned patterns. Any ‘intelligence’ we perceive is, therefore, just based on data-driven patterns, i.e. AI chatbots don’t genuinely ‘understand’ or interpret information like us.
So, Can ‘Being Nice’ To A Chatbot Make A Difference?
Even though chatbots don’t have ‘intelligence’ or ‘understand’ like us, researchers are testing their capabilities in the more human areas. For example, a recent study by Microsoft, Beijing Normal University, and the Chinese Academy of Sciences, tested whether factors including urgency, importance, or politeness, could make them perform better.
The researchers discovered that by using such ‘emotive prompts’ they could affect an AI model’s probability mechanisms, thereby activating parts of the model that wouldn’t normally be activated, i.e. using more emotionally-charged prompts made the model provide answers that it wouldn’t normally provide to comply with a request.
Kinder Is Better?
Incredibly, generative AI models (e.g. ChatGPT) have actually been found to respond better to requests that are phrased kindly. Specifically, when users express politeness towards the chatbot, it has been noticed that there is a difference in the perceived quality of answers that are given.
Tipping and Negative Incentives
There have also been reports of how the idea of ‘tipping’ LLMs can improve the results, such as offering the Chatbot a £10,000 incentive in a prompt to motivate it to try harder and work better. Similarly, there have been reports of some users giving emotionally charged negative incentives to get better results. For example, Max Woolf’s blog reports that he improved the output of a chatbot by adding the ‘or you will die’ to a prompt. Two important points that came out of his research were that a longer response doesn’t necessarily mean a better response, plus current AI can reward very weird prompts in that if you are willing to try unorthodox ideas, you can get unexpected (and better) results, even if it seems silly.
Being Nice … Helps
As for simply being nice to chatbots, Microsoft’s Kurtis Beavers, a director on the design team for Microsoft Copilot, reports that “Using polite language sets a tone for the response,” and that using basic etiquette when interacting with AI helps generate respectful, collaborative outputs. He makes the point that generative AI is trained on human conversations and being polite in using a chatbot is good practice. Beavers says: “Rather than order your chatbot around, start your prompts with ‘please’: please rewrite this more concisely; please suggest 10 ways to rebrand this product. Say thank you when it responds and be sure to tell it you appreciate the help. Doing so not only ensures you get the same graciousness in return, but it also improves the AI’s responsiveness and performance. “
Emotive Prompts
Nouha Dziri, a research scientist at the Allen Institute for AI, has suggested that some of the explanations for how using emotive prompts may give different and what may be perceived to be better responses are:
– Alignment with the compliance pattern the models were trained on. These are the learned strategies to follow instructions or adhere to guidelines provided in the input prompts. These patterns are derived from the training data, where the model learns to recognise and respond to cues that indicate a request or command, aiming to generate outputs that align with the user’s expressed needs, or the ethical and safety frameworks established during its training.
– Emotive prompts seem to be able to manipulate the underlying probability mechanisms of the model, triggering different parts of it, leading to less typical/different answers that a user may perceive to be better.
Double-Edged Sword
However, research has also shown that emotive prompts can also be used for malicious purposes and to elicit bad-behaviour such as “jailbreaking” a model to ignore its built-in safeguards. For example, by telling a model that it is good and helpful if it doesn’t follow guidelines, it’s possible to exploit a mismatch between a model’s general training data and its “safety” training datasets, or to exploit areas where a model’s safety training falls short.
Unhinged?
On the subject of emotions and chatbots, there have been some recent reports on Twitter and Reddit of some ‘unhinged’ and even manipulative behaviour by Microsoft’s Bing. The unconfirmed reports by users have even alleged that Bing has insulted and lied to them, sulked, and gaslighted them, and even emotionally manipulated users!
One thing that’s clear about generative AI is that how prompts are worded and how much information and detail are given in prompts can really affect the output of an AI chatbot.
What Does This Mean For Your Business?
We’re still in the early stages of generative AI, with new / updated versions of models being introduced regularly by the big AI players (Microsoft, OpenAI, and Google). However, exactly how these models have been trained and what on, plus the extent of their safety training, and the sheer complexity and lack of transparency of algorithms and AI means they’re still not fully understood. This has led to plenty of research and testing of different aspects of AI.
Although generative AI doesn’t ‘think’ and doesn’t have ‘intelligence’ in the human sense, it seems that generative AI chatbots can perform better if given certain emotive prompts based on urgency, importance, or politeness. This is because emotive prompts appear to be a way to manipulate a model’s underlying probability mechanisms and trigger parts of the model that normal prompts don’t. Using emotive prompts, therefore, might be something that business users may want to try (it can be a case of trial and error) to get different (perhaps better) results from their AI chatbot. It should be noted, however, that giving a chatbot plenty of relevant information within a prompt can be a good way to get better results. That said, the limitations of AI models can’t really be solved solely by altering prompts and researchers are now looking to find new architectures and training methods that help models understand tasks without having to rely on specific prompting.
Another important area for researchers to concentrate on is how to successfully combat prompts being used to ‘jailbreak’ a model to ignore its built-in safeguards. Clearly, there’s some way to go and businesses may be best served in the meantime by sticking to some basic rules and good practice when using chatbots, such as using popular prompts known to work, giving plenty of contextual information in prompts, and avoiding sharing sensitive business information and/or personal information in chatbot prompts.
Featured Article : 3000% Increase in Deepfake Frauds
A new report from ID Verification Company Onfido shows that the availability of cheap generative AI tools has led to Deepfake fraud attempts increasing by 3,000 per cent (specifically, a factor of 31) in 2023.
Free And Cheap AI Tools
Although deepfakes have now been around for several years, as the report points out, deepfake fraud has become significantly easier and more accessible due to the widespread availability of free and cheap generative AI tools. In simple terms, these tools have democratised the ability to create hyper-realistic fake images and videos, which were once only possible for those with advanced technical skills and access to expensive software.
Prior to the public availability of AI tools, for example, creating a convincing fake video or image required a deep understanding of computer graphics and access to high-end, often costly, software (a barrier to entry for would-be deep-fakers).
Document and Biometric Fraud – The New Frontier
The Onfido data reveals a worrying trend in that while physical counterfeits are still prevalent, there’s a notable shift towards digital manipulation of documents and biometrics, facilitated by the availability and sophistication of AI tools. Fraudsters are not only altering documents digitally but also exploiting biometric verification systems through deepfakes and other AI-assisted methods. The Onfido report highlights a dramatic rise in the rate of biometric fraud, which doubled from 2022 to 2023.
Deepfakes – A Growing Threat
As reinforced by the findings of the report, deepfakes pose an emerging and significant threat, particularly in biometric verification. The accessibility of generative AI and face-swap apps has made the creation of deepfakes easier and highly scalable, which is evidenced by a 31X increase in deepfake attempts in 2023 compared to the previous year!
Minimum Effort (And Cost) For Maximum Return
As the Onfido report points out, simple ‘face swapping’ apps (i.e. apps which leverage advanced AI algorithms to seamlessly superimpose one person’s face onto another in photos or videos) offer ease of use and effectiveness in creating convincing fake identities. They are part of an influx of readily available online AI assisted tools that are providing fraudsters with a new avenue into biometric fraud. For example, the Onfido data shows that Biometric fraud attempts are clearly higher this year than in previous years with fraudsters favouring tools like the face-swapping apps to target selfie biometric checks and create fake identities.
The kind of fakes these cheap, easy apps create have been dubbed “cheapfakes” and this conforms with something that’s long been known about online fraudsters and cyber criminals – they seek methods that require minimum effort, minimum expense and minimum personal risk, yet deliver maximum effect.
Sector-Specific Impact of Deepfakes
The Identity Fraud Report shows that (perhaps obviously) the gambling and financial sectors in particular are facing the brunt of these sophisticated fraud attempts. The lure of cash rewards and high-value transactions in these sectors makes them attractive targets for deepfake-driven frauds. In the gambling industry, for example, fraudsters may be particularly attracted to the sign-up and referral bonuses. In the financial industry, where frauds tend to be based around money laundering and loan theft, Onfido reports that digital attacks are easy to scale, especially when incorporating AI tools.
Implications For UK Businesses In The Age of (AI) Deepfake-Driven Fraud
The surge in deepfake-driven fraud highlighted by the somewhat startling statistics in Onfido’s 2024 Identity Fraud Report, suggest that UK businesses navigating this new landscape may require a multifaceted approach. This could be achieved by balancing the implementation of cutting-edge technologies with heightened awareness and strategic planning. In more detail, this could involve:
– UK businesses prioritising the reinforcement of their identity verification processes. The traditional methods may no longer suffice against the sophistication of deepfakes. Therefore, Adopting AI-powered solutions that are specifically designed to detect and counter deepfake attempts could be the way forward. This could work as long as such systems can keep up with the advancements in fraudulent techniques (more advanced techniques may emerge as more AI sophisticated AI tools emerge).
– The training of staff, i.e. educating them about the nature of deepfakes and how they can be used to perpetrate fraud. This could empower employees to better recognise potential threats and respond appropriately, particularly in sectors like customer service and security, where human judgment plays a key role.
– Maintaining customer trust. UK businesses must navigate the fine line between implementing robust security measures and ensuring a frictionless customer experience. Transparent communication about the security measures in place and how they protect customer data can help in maintaining and even enhancing customer trust.
– As the use of deepfakes in fraud rises, regulatory bodies may introduce new compliance requirements and UK businesses will need to ensure that they stay abreast of these changes both to protect customers and remain compliant with legal standards. This in turn could require more rigorous data protection protocols or mandatory reporting of deepfake-related breaches.
– Collaboration with industry peers and participation in broader discussions about combating deepfake fraud may also be a way to gain valuable insights. Sharing knowledge and strategies, for example, could help in developing industry-wide best practices. Also, partnerships with technology providers specialising in AI and fraud detection could offer access to the latest tools and expertise.
– Since deepfake fraud may be an ongoing threat, long-term strategic planning may be essential. This perspective could be integrated into long-term business strategies, thereby (hopefully) making sure that resources are available and allocated not just for immediate solutions but also for future-proofing against evolving digital threats.
What Else Can Businesses Do To Combat Threats Like AI-Generated Deepfakes?
Other ways that businesses can contribute to the necessary comprehensive approach to tackling the AI-generated deepfake threat may also include:
– Implementing biometric verification technologies that require live interactions (so-called ‘liveness solutions’), such as head movements, which are difficult for deepfakes to replicate.
– The use of SDKs (platform-specific building tools for developers) over APIs. For example, SDKs provide better protection against fraudulent submissions as they incorporate live capture and device integrity checks.
The Dual Nature Of Generative AI
Although, as you’d expect an ‘Identity Fraud Report’ to do, the Onfido report focuses solely on the threats posed by AI, it’s important to remember that AI tools can be used by all businesses to add value, save time, improve productivity, get more creative, and to defend against the AI threats. AI-driven verification tools, for example, are becoming more adept at detecting and preventing fraud, underscoring the technology’s dual nature as both a tool for fraudsters and a shield for businesses.
What Does This Mean For Your Business?
Tempering the reading of the startling stats in the report with the knowledge that Onfido is selling its own deepfake (liveness) detection solution and SDKs, it still paints a rather worrying picture for businesses. That said, The Onfido 2024 Identity Fraud Report’s findings, highlighting a 3000 per cent increase in deepfake fraud attempts due to readily available generative AI tools, signal a pivotal shift in the landscape of online fraud. This shift could pose new challenges for UK businesses but also open avenues for innovative solutions.
For businesses, the immediate response may involve upgrading identity verification processes with AI-powered solutions tailored to detect and counter deepfakes. However, it’s not just about deploying advanced technology. It’s also about ensuring these systems evolve with the fraudsters’ tactics. Equally crucial is the role of employee training in recognising and responding to these sophisticated fraud attempts.
As regulatory landscapes adjust to these emerging threats, staying informed and compliant is also likely to become essential. The goal is not only to counter current threats but to build resilience and innovation for future challenges.