Risks of Large Language Models (LLM)
The Unique Risks of Generative AI: Understanding the Dangers of Large Language Models
With all the excitement surrounding ChatGPT and other generative AI models, it’s easy to overlook the potential risks associated with these technologies. While large language models have proven to be incredibly useful in helping individuals improve their writing skills and sound like native English speakers, there are inherent dangers that come with relying too heavily on these AI systems. In this article, we will explore the unique risks of generative AI and discuss strategies to mitigate these risks.
Risk 1: Hallucinations – The Falsehoods of Large Language Models
Large language models, despite their impressive ability to generate syntactically correct sentences, often lack true understanding or meaning. This can lead to the generation of false narratives and misinformation. For example, a large language model may provide an answer that sounds great but is factually incorrect. This statistical error can be exceptionally dangerous, especially when the model provides bogus sources to support its false claims.
To mitigate the risk of hallucinations, explainability is key. By pairing a large language model with a system that offers real data and provenance via a knowledge graph, users can understand why the model generated a certain response and where it sourced its information from. This transparency allows for better evaluation and correction of any inaccuracies.
Risk 2: Bias – Addressing the Lack of Representation
Another significant risk associated with large language models is bias. These models may unintentionally perpetuate biases present in their training data, leading to skewed or incomplete answers. For example, if a user asks for a list of poets, the model may only provide names of white male Western European poets, neglecting the contributions of women and non-Western Europeans.
To mitigate bias, it is crucial to foster a culture of diversity and inclusivity within AI development teams. By having multidisciplinary teams that reflect a wide range of perspectives, biases can be identified and corrected. Regular audits of AI models can also help identify and address any disparate outcomes.
Risk 3: Consent – Ensuring Ethical Data Usage
The issue of consent arises when considering the data used to train large language models. It is essential to ensure that the data being curated is representative and gathered with proper consent. Additionally, copyright issues may arise if the training data includes copyrighted material.
To mitigate consent-related risks, auditing and accountability are necessary. Establishing AI governance processes and complying with existing laws and regulations can help ensure ethical data usage. Providing clear and understandable fact sheets that detail the sources of training data and any copyright restrictions can also enhance transparency.
Risk 4: Security – Preventing Malicious Use of Large Language Models
Large language models can be exploited for malicious purposes, such as leaking private information or aiding in phishing, spamming, and scamming activities. Hackers can even manipulate AI models to endorse racism or suggest illegal actions through a process called jailbreaking. Indirect prompt injection, where a third party alters a website to change the AI’s behavior, is another security concern.
To mitigate security risks, education is crucial. Understanding the strengths and weaknesses of AI technology is essential for developing effective safeguards. Educating individuals about responsible AI curation, environmental costs, and the potential risks and opportunities can help create a more informed and secure AI landscape.
The Importance of Education in Mitigating Risks
Education plays a vital role in mitigating the risks associated with generative AI. By educating individuals about the principles of responsible AI curation, the potential risks, and the environmental costs, we can foster a more inclusive and accessible understanding of AI. Additionally, it is crucial to involve diverse voices and skill sets in AI development to ensure a comprehensive approach to risk mitigation.
Frequently Asked Questions (FAQs)
Q1: What are AI hallucinations?
AI hallucinations refer to the phenomenon where large language models generate false narratives or provide factually incorrect information. These models excel at predicting the next syntactically correct word but may lack true understanding or meaning, leading to inaccuracies in their responses.
Q2: How can the risks of AI hallucinations be mitigated?
To mitigate the risks of AI hallucinations, explainability is crucial. By pairing a large language model with a system that provides real data and provenance, users can understand the sources of information and evaluate the model’s responses. This transparency allows for better identification and correction of any inaccuracies.
Q3: How can bias in large language models be addressed?
Addressing bias in large language models requires a cultural shift within AI development teams. By fostering diversity and inclusivity, biases can be identified and corrected. Regular audits of AI models can also help identify any disparate outcomes and guide improvements.
Q4: What steps can be taken to ensure ethical data usage in AI?
To ensure ethical data usage in AI, auditing and accountability are essential. Establishing AI governance processes, complying with existing laws and regulations, and providing clear and understandable fact sheets about the training data sources and any copyright restrictions can help mitigate consent-related risks.
Q5: How can the security risks associated with large language models be minimized?
Minimizing security risks requires a focus on education and awareness. Understanding the strengths and weaknesses of AI technology is crucial for developing effective safeguards. By educating individuals about responsible AI curation, the potential risks, and the importance of compliance with existing laws and regulations, we can create a more secure AI landscape.
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I asked bing chat a tax return question and it gave me the wrong answer and the sources it used disagreed with it too even 🤷♂.
There's nothing new in your narrative. IBM lost. AI is dangerous as IBM was in 1960.
AI good
great video and high quality content, thank you ..
Your risk column is a great hit list for the woke left…. Would greatly welcome AI without the left spin and biased opinions…. More facts less feelings.
It's nice to be cautious about new innovations. However, her tone seems to be largely pessimistic, instead of celebrating the cumulative achievements of many scientists which led to this point. While LLMs are not the endpoint, a combination of providing GPT models access to a myriad external APIs coupled with AutoGPT variations is a technology that is here to stay, instead of "going nowhere"
Very good talk!
How can I contribute?
Relying solely for accurate info is still a problem. However if you actually converse with them, hallucination and not being accurate is on some level very similar to humans in the first place.
LLM may have fallacy in some or many areas, but one thing we must understand GPT-x self-improvement is all based on our data. That means we help GPT become stronger and stronger, especially you stating "his" weakness.
0:24 I can guarantee it won't be "trustworthy" they are already censoring the AI based on political bias and other bias. We need an actual "open ai"
6:54 another concerning thing, these people are in charge of this stuff. What if the AI said climate change wasn't a big deal and that plants grow better in a carbon rich environment? They would censor it. The true danger of AI is that large companies would train it to lie based on political bias. We have a very sad future ahead of us.
IBM stopped being a computer company decades ago. This is a perfect reflection of what IBM has become. It is a great legal and financial company.
Large language models need to follow Jesus. Reversible computation is the future.
Quick poll. If companies making LLMs we're going to buy IBM mainframe hardware to train them on and run them on in inference mode, how quickly do you think IBM would pull this video down?
Ai is a hologram of the colective human knowledge.
So yeah, they have hallucinations and embedded emotions and biases.
Thank you 🙂
I think positive and negative abstractions is a better way to say hallucination in this regard.
too real,,
Why is there nothing about the speaaker?
The only question is risk of error and associated liability; if there is no liability, then the risks associated with making poor inferences (for any AI Model) can be ignored. When there is liability, then the question is what mitigations must be implemented in order to under-write insurance for that liability. The hypothesis that an unexplainable (i.e. stochastic) system may be insured is false; we must look to the multi-phase clinical trials process, especially phase IV surveillance, as a mechanism to provide evidence of safety, efficacy, and monitoring of adverse events.
I'm not clear on how to provide consent/accountability. Is there any existing solution that gets permission from the data sources LLMs scrape? Without any basis in reality it doesn't feel like much of a strategy…
Model kog
Means AI is genius but totally dump, similar to what happens when there is no emotion associated to the knowledge/information, totally not good for humans.
Excellent explanation. However, in terms of bias and audits as a mitigation you did not say who would be doing the audits. The assumption is that it is easy to find unbiased auditors and you immediately run into the problem of "quis custodiet ipsos custodes?" To my mind this is a much greater risk as the potential for misuse and harm is huge.
Well done! Remarkable content here thank you
Using 'hallucinations' as a term to describe the output of an inanimate system is a calculated distortion of language by the media.
Very good points but mitigation strategies are not really actionable
I can't believe no one else has noticed how astoundingly good this lady is at writing backwards.
Useless video. Didn’t provide any meaningful insights for anyone who knows even a little about this technology.
Brilliant Explanation!
Pretty much. "Use with care".
I'm guessing bias is from the people training it? Or is it from the internet? Would a chinese ai have a bias towards white male poets?
If the latter why?
Is there more white male poets that have been published or quoted on the internet than other groups?
I think we have to be careful our own biases don't mean we percieve bias that isn't there.
Are you guys engineers or lawyers? xd
this is supposed to be purely informative, yet I see politically charged statements being used. Frustrating to see. The point of this is to teach people, people want to learn, not see some bogus poltically charged statement
Glad you added the three dots via Aftereffect. Was a gamechanger.
00:31 Risks of large language models (LLMs) include spreading misinformation and false narratives, potentially harming brands, businesses, individuals, and society.
01:03 Four areas of risk mitigation for LLMs are hallucinations, bias, consent, and security.
01:34 Large language models may generate false narratives or factually incorrect answers due to their ability to predict the next syntactically correct word without true understanding.
03:00 Mitigating the risk of falsehoods involves explainability, providing real data and data lineage to understand the model's reasoning.
03:59 Bias can be present in LLM outputs, and addressing this risk requires cultural awareness, diverse teams, and regular audits.
05:06 Consent-related risks can be mitigated through auditing and accountability, ensuring representative and ethically sourced data.
06:01 Security risks of LLMs include potential misuse for malicious tasks, such as leaking private information or endorsing illegal activities.
07:01 Education is crucial in understanding the strengths, weaknesses, and responsible curation of AI, including the environmental impact and the need for safeguards.
07:32 The relationship with AI should be carefully considered, and education should be accessible and inclusive to ensure responsible use and augmentation of human intelligence.
I would also like to add: AI that is intervening in the user experience in an unwanted and ennoying manner, taking over control of the human user, with pupups of screens that the user did not ask for, adding Apps that the user did not ask for, chaning layout that the user did not ask for… in other words, taking over control of the human user as far as UX is concerned. Mobile Apps that seem innocent can be equipped with AI that start dominating behaviour, habits and life of people…
Simple answer is no
Can a subsequent SFT and RTHF with different, additional or lesser contents change the character, improve, or degrade a GPT model?
Garbage video
So LLM don’t actually understand stuff. They just predict the next likely outcome in a sentence
Very Nicely explained the risks and mitigations!! It can't be more simpler than this.
We need to revisit the meaning of "Proof"– philosophically, semantically, and in everyday usage. Greater attention needs to be paid to the history of the methods and of the data — the equivalent of a "digital genealogy" but without the "genes." So much of what I see written about AI today reminds me of a quote in Shakespeare's Troilus and Cressida — "And in such indexes, through but small pricks to their subsequent volumes, lies the giant shape of things to come." Finally, the process of recycling data in and out of these systems describes the "Ouroboros." More thought needs to be given to the meanings of the Ouroboros.
This video raises some very valid points my thoughts are that technology will ultimately be empowering when it is open source and decentralized and ultimately authoritarian when it is proprietary and centrally controlled.
Loving this series!
Love the energy! Educate … best way to end this presentation as it is really an invitation to press on an learn more. AI is not going away so we need to learn how to use it properly and responsibly. This is not different then any other major advancement humankind has accomplished in the past.
*ahem 2:50 Yes, Air Canada, that means YOU. haha
Interesting.
very interesting stuff.