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Sam Altman, chief executive officer and co-founder of OpenAI, speaks during a Senate Judiciary Subcommittee hearing in Washington, DC, US, on Tuesday, May 16, 2023. Congress is debating the potential and pitfalls of artificial intelligence as products like ChatGPT raise questions about the future of creative industries and the ability to tell fact from fiction.

Eric Lee | Bloomberg | Getty Images

This past week, OpenAI CEO Sam Altman charmed a room full of politicians in Washington, D.C., over dinner, then testified for about nearly three hours about potential risks of artificial intelligence at a Senate hearing.

After the hearing, he summed up his stance on AI regulation, using terms that are not widely known among the general public.

"AGI safety is really important, and frontier models should be regulated," Altman tweeted. "Regulatory capture is bad, and we shouldn't mess with models below the threshold."

In this case, "AGI" refers to "artificial general intelligence." As a concept, it's used to mean a significantly more advanced AI than is currently possible, one that can do most things as well or better than most humans, including improving itself.

"Frontier models" is a way to talk about the AI systems that are the most expensive to produce and which analyze the most data. Large language models, like OpenAI's GPT-4, are frontier models, as compared to smaller AI models that perform specific tasks like identifying cats in photos.

Most people agree that there need to be laws governing AI as the pace of development accelerates.

"Machine learning, deep learning, for the past 10 years or so, it developed very rapidly. When ChatGPT came out, it developed in a way we never imagined, that it could go this fast," said My Thai, a computer science professor at the University of Florida. "We're afraid that we're racing into a more powerful system that we don't fully comprehend and anticipate what what it is it can do."

But the language around this debate reveals two major camps among academics, politicians, and the technology industry. Some are more concerned about what they call "AI safety." The other camp is worried about what they call "AI ethics."

When Altman spoke to Congress, he mostly avoided jargon, but his tweet suggested he's mostly concerned about AI safety a stance shared by many industry leaders at companies like Altman-run OpenAI, Google DeepMind and well-capitalized startups. They worry about the possibility of building an unfriendly AGI with unimaginable powers. This camp believes we need urgent attention from governments to regulate development an prevent an untimely end to humanity an effort similar to nuclear nonproliferation.

"It's good to hear so many people starting to get serious about AGI safety," DeepMind founder and current Inflection AI CEO Mustafa Suleyman tweeted on Friday. "We need to be very ambitious. The Manhattan Project cost 0.4% of U.S. GDP. Imagine what an equivalent programme for safety could achieve today."

But much of the discussion in Congress and at the White House about regulation is through an AI ethics lens, which focuses on current harms.

From this perspective, governments should enforce transparency around how AI systems collect and use data, restrict its use in areas that are subject to anti-discrimination law like housing or employment, and explain how current AI technology falls short. The White House's AI Bill of Rights proposal from late last year included many of these concerns.

This camp was represented at the congressional hearing by IBM Chief Privacy Officer Christina Montgomery, who told lawmakers believes each company working on these technologies should have an "AI ethics" point of contact.

"There must be clear guidance on AI end uses or categories of AI-supported activity that are inherently high-risk," Montgomery told Congress.

See also: How to talk about AI like an insider

It's not surprising the debate around AI has developed its own lingo. It started as a technical academic field.

Much of the software being discussed today is based on so-called large language models (LLMs), which use graphic processing units (GPUs) to predict statistically likely sentences, images, or music, a process called "inference." Of course, AI models need to be built first, in a data analysis process called "training."

But other terms, especially from AI safety proponents, are more cultural in nature, and often refer to shared references and in-jokes.

For example, AI safety people might say that they're worried about turning into a paper clip. That refers to a thought experiment popularized by philosopher Nick Bostrom that posits that a super-powerful AI a "superintelligence" could be given a mission to make as many paper clips as possible, and logically decide to kill humans make paper clips out of their remains.

OpenAI's logo is inspired by this tale, and the company has even made paper clips in the shape of its logo.

Another concept in AI safety is the "hard takeoff" or "fast takeoff," which is a phrase that suggests if someone succeeds at building an AGI that it will already be too late to save humanity.

Sometimes, this idea is described in terms of an onomatopeia "foom" especially among critics of the concept.

"It's like you believe in the ridiculous hard take-off 'foom' scenario, which makes it sound like you have zero understanding of how everything works," tweeted Meta AI chief Yann LeCun, who is skeptical of AGI claims, in a recent debate on social media.

AI ethics has its own lingo, too.

When describing the limitations of the current LLM systems, which cannot understand meaning but merely produce human-seeming language, AI ethics people often compare them to "Stochastic Parrots."

The analogy, coined by Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell in a paper written while some of the authors were at Google, emphasizes that while sophisticated AI models can produce realistic seeming text, the software doesn't understand the concepts behind the language like a parrot.

When these LLMs invent incorrect facts in responses, they're "hallucinating."

One topic IBM's Montgomery pressed during the hearing was "explainability" in AI results. That means that when researchers and practitioners cannot point to the exact numbers and path of operations that larger AI models use to derive their output, this could hide some inherent biases in the LLMs.

"You have to have explainability around the algorithm," said Adnan Masood, AI architect at UST-Global. "Previously, if you look at the classical algorithms, it tells you, 'Why am I making that decision?' Now with a larger model, they're becoming this huge model, they're a black box."

Another important term is "guardrails," which encompasses software and policies that Big Tech companies are currently building around AI models to ensure that they don't leak data or produce disturbing content, which is often called "going off the rails."

It can also refer to specific applications that protect AI software from going off topic, like Nvidia's "NeMo Guardrails" product.

"Our AI ethics board plays a critical role in overseeing internal AI governance processes, creating reasonable guardrails to ensure we introduce technology into the world in a responsible and safe manner," Montgomery said this week.

Sometimes these terms can have multiple meanings, as in the case of "emergent behavior."

A recent paper from Microsoft Research called "sparks of artificial general intelligence" claimed to identify several "emergent behaviors" in OpenAI's GPT-4, such as the ability to draw animals using a programming language for graphs.

But it can also describe what happens when simple changes are made at a very big scale like the patterns birds make when flying in packs, or, in AI's case, what happens when ChatGPT and similar products are being used by millions of people, such as widespread spam or disinformation.

Read more from the original source:

Parrots, paper clips and safety vs. ethics: Why the artificial intelligence debate sounds like a foreign language - CNBC

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Tribune Web Desk

Vibha Sharma

Chandigarh, May 22

Amid warnings regarding its disastrous effect on the job market and catastrophic effects on the human race, the buzz is that more companies are joining the ongoing Artificial Intelligence race in the world.

They include tech major Apple Inc which is said to be launching its own version of popular chatbot ChatGPT. According to reports, Apple, which recently banned employees from using OpenAIs ChatGPT, is hiring for positions across machine learning and AI in the company.

Though AI takeover (a hypothetical scenario in with AI becomes the dominant form of intelligence, controlling the planet as also the human species) is still a hypothetical scenario, experts are warning about data privacy, centralisation of power in a handful of companies and AGI (artificial general intelligence) surpassing human cognitive ability.

After all, AI is also a popular theme in science fiction movies, highlighting benefits and dangers, including the possibility of machines taking over the world and the human race.

According to the Twitter Bio of OpenAIthe AI research and deployment company which developed ChatGPTtheir mission is to ensure that artificial general intelligence benefits all of humanity.

While those in favour of AI endorse the feeling, an equal number also advise caution, saying that those in power are not prepared for what may be coming.

Is 'humanity sleepwalking into a catastrophe'

While there has been an increase in AI products for general consumer use, including from tech giants like Google and Microsoft, the job scenario in tech companies is not so encouraging.

According to Layoffs.fyi, 696 tech companies laid off as many as 1,97,985 employees as on date, this year. The data published by the website tracking layoffs in the tech industry around March stated that 454 tech companies had laid off 1,23,882 employees since the beginning of 2023.

The emergence and subsequent popularity of ChatGPT type AI show that the day is not far when thousands of jobs related to research, coding, writing, human resources, etc, may become redundant but there are other fears as well, including from job-seekers.

According to job advice platform, Resumebuilder.com, Many employers now use applicant tracking system (ATS) software to automate the initial stage of the hiring process. If the formatting of your resume isnt optimised for such software, it might get filtered out before it even reaches the person who decides whether or not you get an interview.

ChatGPT is not the only AI in market

ChatGPT, which crossed the one million user mark in just five days after it was made public in November 2022, currently has over 100 million users. Its website currently generates 1.8 billion visitors per month, and it is not the only one in the market.

Currently, there are several alternatives with different features and benefits to rival the tool owned and developed by OpenAI founded in December 2015 by a group which included multi-billionaire Elon Musk who is now warning against evils of AI.

Recently, an open letter signed by whos who of the tech world, including Musk, had called for a six-month pause in the out of control race for AI development, warning of its profound risks to society and humanity.

The letter came after the public release of GPT-4.

Though Musk is said to be involved in several tech/AI companies, he warned that AI could lead to civilisation destruction.

AI is more dangerous than, say, mismanaged aircraft design or production maintenance or bad car production, in the sense that it is, it has the potential however small one may regard that probability, but it is non-trivial it has the potential of civilisation destruction, Musk was quoted as saying in the interview with Tucker Carlson, an American political commentator.

#Apple Inc #Artificial Intelligence #ChatGPT

Continued here:

Amid job losses and fears of AI take-over, more tech majors are joining Artificial Intelligence race - The Tribune India

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AI developers must move quickly to develop and deploy systems that address algorithmic bias, said Kathy Baxter, principal Architect of Ethical AI Practice at Salesforce. In an interview with ZDNET, Baxter emphasized the need for diverse representation in data sets and user research to ensure fair and unbiased AI systems. She also highlighted the significance of making AI systems transparent, understandable, and accountable while protecting individual privacy. Baxter stresses the need for cross-sector collaboration, like the model used by the National Institute of Standards and Technology (NIST), so that we can develop robust and safe AI systems that benefit everyone.

One of the fundamental questions in AI ethics is ensuring that AI systems are developed and deployed without reinforcing existing social biases or creating new ones. To achieve this, Baxter stressed the importance of asking who benefits and who pays for AI technology. It's crucial to consider the data sets being used and ensure they represent everyone's voices. Inclusivity in the development process and identifying potential harms through user research is also essential.

Also:ChatGPT's intelligence is zero, but it's a revolution in usefulness, says AI expert

"This is one of the fundamental questions we have to discuss," Baxter said. "Women of color, in particular, have been asking this question and doing research in this area for years now. I'm thrilled to see many people talking about this, particularly with the use of generative AI. But the things that we need to do, fundamentally, are ask who benefits and who pays for this technology. Whose voices are included?"

Social bias can be infused into AI systems through the data sets used to train them. Unrepresentative data sets containing biases, such as image data sets with predominantly one race or lacking cultural differentiation, can result in biased AI systems. Furthermore, applying AI systems unevenly in society can perpetuate existing stereotypes.

To make AI systems transparent and understandable to the average person, prioritizing explainability during the development process is key. Techniques such as "chain of thought prompts" can help AI systems show their work and make their decision-making process more understandable. User research is also vital to ensure that explanations are clear and users can identify uncertainties in AI-generated content.

Also:AI could automate 25% of all jobs. Here's which are most (and least) at risk

Protecting individuals' privacy and ensuring responsible AI use requires transparency and consent. Salesforce follows guidelines for responsible generative AI, which include respecting data provenance and only using customer data with consent. Allowing users to opt in, opt-out, or have control over their data use is critical for privacy.

"We only use customer data when we have their consent," Baxter said. "Being transparent when you are using someone's data, allowing them to opt-in, and allowing them to go back and say when they no longer want their data to be included is really important."

As the competition for innovation in generative AI intensifies, maintaining human control and autonomy over increasingly autonomous AI systems is more important than ever. Empowering users to make informed decisions about the use of AI-generated content and keeping a human in the loop can help maintain control.

Ensuring AI systems are safe, reliable, and usable is crucial; industry-wide collaboration is vital to achieving this. Baxter praised the AI risk management framework created by NIST, which involved more than 240 experts from various sectors. This collaborative approach provides a common language and framework for identifying risks and sharing solutions.

Failing to address these ethical AI issues can have severe consequences, as seen in cases of wrongful arrests due to facial recognition errors or the generation of harmful images. Investing in safeguards and focusing on the here and now, rather than solely on potential future harms, can help mitigate these issues and ensure the responsible development and use of AI systems.

Also:How ChatGPT works

While the future of AI and the possibility of artificial general intelligence are intriguing topics, Baxter emphasizes the importance of focusing on the present. Ensuring responsible AI use and addressing social biases today will better prepare society for future AI advancements. By investing in ethical AI practices and collaborating across industries, we can help create a safer, more inclusive future for AI technology.

"I think the timeline matters a lot," Baxter said. "We really have to invest in the here and now and create this muscle memory, create these resources, create regulations that allow us to continue advancing but doing it safely."

Read the original post:

Today's AI boom will amplify social problems if we don't act now, says AI ethicist - ZDNet

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whether we like it or not.getty

In todays column, lets consider for a moment turning the world upside down.

Heres what I mean.

Generative AI such as the wildly and widely successful ChatGPT and GPT-4 by OpenAI is based on scanning data across the Internet and leveraging that examined data to pattern-match on how humans write and communicate in natural language. The AI development process also includes a lot of clean-up and filtering, via a technique known as RLHF (reinforcement learning via human feedback) that seeks to either excise or at least curtail unsavory language from being emitted by the AI. For my coverage of why some people nonetheless ardently push generative AI and relish stoking hate speech and other untoward AI-generated foulness, see the link here.

When the initial scanning of the Internet takes place for data training of generative AI, the websites chosen to be scanned are generally aboveboard. Think of Wikipedia or similar kinds of websites. By and large, the text found there will be relatively safe and sane. The pattern-matching is getting a relatively sound basis for identifying the mathematical and computational patterns found within everyday human conversations and essays.

Id like to bring to your attention that we can turn that crucial precept upside down.

Suppose that we purposely sought to use the worst of the worst that is posted on the Internet to do the data training for generative AI.

Imagine seeking out all those seedy websites that you would conventionally be embarrassed to even accidentally land on. The generative AI would be entirely focused exclusively on this bad stuff. Indeed, we wouldnt try to somehow counterbalance the generative AI by using some of the everyday Internet and some of the atrocious Internet. Full on we would mire the generative AI in the muck and mire of wickedness on the Internet.

What would we get?

And why would we devise this kind of twisted or distorted variant of generative AI?

Those are great questions and I am going to answer them straightforwardly. As you will soon realize, some pundits believe data training generative AI on the ugly underbelly of the Internet is a tremendous idea and an altogether brilliant strategy. Others retort that this is not only a bad idea, it could be a slippery slope that leads to AI systems that are of an evil nature and we will regret the day that we allowed this to ever get underway.

Allow me a quick set of foundational remarks before we jump into the meat of this topic.

Please know that generative AI and indeed all manner of todays AI is not sentient. Despite all those blaring headlines that claim or imply that we already have sentient AI, we dont. Period, full stop. I will later on herein provide some speculation about what might happen if someday we attain sentient AI, but thats conjecture, and no one can say for sure when or if that will occur.

Modern generative AI is based on a complex computational algorithm that has been data trained on text from the Internet. Generative AI such as ChatGPT, GPT-4, Bard, and other similar AI apps entail impressive pattern-matching that can perform a convincing mathematical mimicry of human wording and natural language. For my explanation of how generative AI works, see the link here. For my analysis of the existent doomster fearmongering regarding AI as an existential risk, see the link here.

Into all of this comes a plethora of AI Ethics and AI Law considerations.

There are ongoing efforts to imbue Ethical AI principles into the development and fielding of AI apps. A growing contingent of concerned and erstwhile AI ethicists are trying to ensure that efforts to devise and adopt AI takes into account a view of doing AI For Good and averting AI For Bad. Likewise, there are proposed new AI laws that are being bandied around as potential solutions to keep AI endeavors from going amok on human rights and the like. For my ongoing and extensive coverage of AI Ethics and AI Law, see the link here and the link here, just to name a few.

The development and promulgation of Ethical AI precepts are being pursued to hopefully prevent society from falling into a myriad of AI-inducing traps. For my coverage of the UN AI Ethics principles as devised and supported by nearly 200 countries via the efforts of UNESCO, see the link here. In a similar vein, new AI laws are being explored to try and keep AI on an even keel. One of the latest takes consists of a set of proposed AI Bill of Rights that the U.S. White House recently released to identify human rights in an age of AI, see the link here. It takes a village to keep AI and AI developers on a rightful path and deter the purposeful or accidental underhanded efforts that might undercut society.

Now that weve covered those essentials about generative AI, lets look at the seemingly oddish or scary proposition of data training generative AI on the most stinky and malicious content available on the web.

The Dark Web Is The Perfect Foundation For Bad Stuff

There is a part of the Internet that you might not have visited that is known as the Dark Web.

The browsers that you normally use to access the Internet are primed to only explore a small fraction of the web known as the visible or surface-level web. There is a lot more content out there. Within that other content is a segment generally coined as the Dark Web and tends to contain all manner of villainous or disturbing content. Standard search engines do not usually look at Dark Web pages. All in all, you would need to go out of your way to see what is posted on the Dark Web, doing so by using specialized browsers and other online tools to get there.

What type of content might be found on the Dark Web you might be wondering?

The content varies quite a bit. Some of it entails evildoers that are plotting takeovers or possibly contemplating carrying out terrorist attacks. Drug dealers find the Dark Web very useful. You can find criminal cyber hackers that are sharing tips about how to overcome cybersecurity precautions. Conspiracy theorists tend to like the Dark Web since it is a more secretive arena to discuss conspiratorial theories. And so on.

Im not saying that the Dark Web is all bad, but at least be forewarned that it is truly the Wild West of the Internet, and just about anything goes.

In a research paper entitled Dark Web: A Web of Crimes, there is a succinct depiction of the components of the Internet and the role of the Dark Web, as indicated by these two excerpts:

I realize it is perhaps chilling to suddenly realize that there is an entire segment of the Internet that you perhaps didnt know existed and that it is filled with abysmal content. Sorry to be the bearer of such gloomy news.

Maybe this will cheer you up.

The Dark Web is seemingly the ideal source of content to train generative AI if you are of the mind that data training on the worst of the worst is a presumably worthwhile and productive endeavor. Rather than having to try and bend over backward to find atrocious content on the conventional side of the Internet (admittedly, there is some of that there too), instead make use of a specialized web crawler aimed at the Dark Web and you can find a treasure trove of vile content.

Easy-peasy.

I know that I havent yet explained why data training generative AI on the Internets ugly underbelly is presumably useful, so lets get to that next. At least we now know that plentiful content exists for such a purpose.

What Does Dark Web Trained Generative AI Provide

Ill give you a moment to try and brainstorm some bona fide reasons for crafting generative AI that is based on foulness.

Any ideas?

Well, heres what some already proclaim are useful reasons:

Any discussion about the Dark Web should be careful to avoid pegging the Dark Web as exclusively a home of evildoers. There are various justifications for having a Dark Web.

For example, consider this listing by the researchers mentioned earlier:

Given those positive facets of the Dark Web, you could argue that having generative AI trained on the Dark Web would potentially further aid those benefits. For example, enabling more people to find scarce products or discover content that has been entered anonymously out of fear of governmental reprisals.

In that same breath, you could also decry that the generative AI could severely and lamentably undercut those advantages by providing a means for say government crackdowns on those that are dissenting from government oppression. Generative AI based on the Dark Web might have a whole slew of unanticipated adverse consequences including putting innocent people at risk that were otherwise using the Dark Web for ethically or legally sound purposes.

Ponder seriously and soberly whether we do or do not want generative AI that is based on the Dark Web.

The good news or bad news is that we already have that kind of generative AI. You see, the horse is already out of the barn.

Lets look at that quandary next.

The DarkGPT Bandwagon Is Already Underway

A hip thing to do involves training generative AI on the Dark Web.

Some that do so have no clue as to why they are doing so. It just seems fun and exciting. They get a kick out of training generative AI on something other than what everyone else has been using. Others intentionally train generative AI on the Dark Web. Those with a particular purpose are usually within one or more camps associated with the reasons I gave in the prior subsection about reasons to do so.

All of this has given rise to a bunch of generative AI apps that are generically referred to as DarkGPT. I say generically because there are lots of these DarkGPT monikers floating around. Unlike the bona fide trademarked name such as ChatGPT that has spawned all kinds of GPT naming variations (I discuss the legal underpinnings of the trademark at the link here), the catchphrase or naming of DarkGPT is much more loosey-goosey.

Watch out for scams and fakes.

Heres what I mean. You are curious to play with a generative AI that was trained on the Dark Web. You do a cursory search for anything named DarkGPT or DarkWebGPT or any variation thereof. You find one. You decide to try it out.

Yikes, turns out that the app is malware. You have fallen into a miserable trap. Your curiosity got the better of you. Please be careful.

Legitimate Dark Web Generative AI

Ill highlight next a generative AI that was trained on the Dark Web and serves as a quite useful research-oriented exemplar and can be a helpful role model for other akin pursuits.

The generative AI app is called DarkBERT and is described in a research paper entitled DarkBERT: A Language Model For The Dark Side Of The Internet by researchers Youngjin Jin, Eugene Jang, Jian Cui, Jin-Woo Chung, Yongjae Lee, and Seungwon Shin (posted online on May 18, 2023). Here are some excerpted key points from their study:

Lets briefly examine each of those tenants.

First, the researchers indicated that they were able to craft a Dark Web-based instance of generative AI that was comparable in natural language fluency as could be found in a generative AI trained on the conventionally visible web. This is certainly encouraging. If they had reported that their generative AI was less capable, the implication would be that we might not readily be able to apply generative AI to the Dark Web. This would have meant that efforts to do so would be fruitless or that some other as-yet-unknown new AI-tech innovation would have been required to sufficiently do so.

Bottom-line is that we can proceed to apply generative AI to the Dark Web and expect to get responsive results.

Secondly, it would seem that a generative AI solely trained on the Dark Web is likely to do a better job at pattern-matching of the Dark Web than would a generative AI that was partially data-trained on the conventional web. Remember that earlier I mentioned that we might consider data training of generative AI that mixes both the conventional web and the Dark Web. We can certainly do so, but the result here seems to suggest that making queries and using the natural language facility of the Dark Web specific generative AI is better suited than would be a mixed model (there are various caveats and exceptions, thus this is an open-research avenue).

Third, the research examined closely the cybersecurity merits of having a generative AI that is based on the Dark Web, namely being able to detect or uncover potential cyber hacks that are on the Dark Web. The aspect that the generative AI seemed especially capable in this realm is a plus for those fighting cybercriminals. You can consider using Dark Web data-trained generative AI to pursue the wrongdoers that are aiming to commit cybercrimes.

You might be somewhat puzzled as to why the name of their generative AI is DarkBERT rather than referring to the now-classic acronym of GPT (generative pre-trained transformer). The BERT acronym is particularly well-known amongst AI insiders as the name of a set of generative AI apps devised by Google that they coined BERT (bi-directional encoder representations from transformers). I thought you might like a smidgeon of AI insider terminology and ergo able to clear up that possibly vexing mystery.

A quick comment overall before we move on. Research about generative AI and the Dark Web is still in its infancy. You are highly encouraged to jump into this evolving focus. There are numerous technological questions to be addressed. In addition, there are a plethora of deeply intriguing and vital AI Ethics and AI Law questions to be considered.

Of course, youll need to be willing to stomach the stench or dreadful aroma that generally emanates from the Dark Web. Good luck with that.

When Generative AI Is Bad To The Bone

Ive got several additional gotchas and thought-provoking considerations for you on this topic.

Lets jump in.

We know that conventional generative AI is subject to producing errors, along with emitting falsehoods, producing biased content, and even making up stuff (so-called AI hallucinations, a catchphrase I deplore, for the reasons explained at the link here). These maladies are a bone of contention when it comes to using generative AI in any real-world setting. You have to be careful of interpreting the results. The generated essays and interactive dialogues could be replete with misleading and misguided content produced by the generative AI. Efforts are hurriedly underway to try and bound these problematic concerns, see my coverage at the link here.

Put on your thinking cap and get ready for a twist.

What happens if generative AI that is based on the Dark Web encounters errors, falsehoods, biases, or AI hallucinations?

In a sense, we are in the same boat as the issues confronting conventional generative AI. The Dark Web generative AI might showcase an indication that seems to be true but is an error or falsehood. For example, you decide to use Dark Web data-trained generative AI to spot a cyber crook. The generative AI tells you that it found a juicy case on the Dark Web. Upon further investigation with other special browsing tools, you discover that the generative AI falsely made that accusation.

Oops, not cool.

We need to always keep our guard up when it comes to both conventional generative AI and Dark Web-based generative AI.

Heres another intriguing circumstance.

People have been trying to use conventional generative AI for mental health advice. Ive emphasized that this is troublesome for a host of disconcerting reasons, see my analysis at the link here and the link here, just to name a few. Envision that a person is using conventional or clean generative AI for personal advice about something, and the generative AI emits an AI hallucination telling the person to take actions in a dangerous or unsuitable manner. Im sure you can see the qualms underlying this use case.

A curious and serious parallel would be if someone opted to use a Dark Web-based generative AI for mental health advice. We might assume that this baddie generative AI is likely to generate foul advice from the get-go.

Is it bad advice that would confuse and confound evildoers? I suppose we might welcome that possibility. Maybe it is bad advice in the sense that it is actually good advice from the perspective of a wrongdoer. Generative AI might instruct the evildoer on how to better achieve evil deeds. Yikes!

Or, in a surprising and uplifting consideration, might there be some other mathematical or computational pattern-matching contrivance that manages to rise above the flotsam used during the data training? Could there be lurking within the muck a ray of sunshine?

A bit dreamy, for sure.

More research needs to be done.

Speaking of doing research and whatnot, before you run out to start putting together a generative AI instance based on the Dark Web, you might want to check out the licensing stipulations of the AI app. Most of the popular generative AI apps have a variety of keystone restrictions. People using ChatGPT for example are typically unaware that there are a bunch of prohibited uses.

For example, as Ive covered at the link here, you cannot do this with ChatGPT:

If you were to develop a generative AI based on the Dark Web, you presumably might violate those kinds of licensing stipulations as per whichever generative AI app you decide to use. On the other hand, one supposes that as long as you use the generative AI for the purposes of good, such as trying to ferret out evildoers, you would potentially be working within the stated constraints of the licensing. This is all a legal head-scratcher.

One final puzzling question for now.

Will we have bad-doers that purposely devise or seek out generative AI that is based on the Dark Web, hoping to use the generative AI to further their nefarious pursuits?

I sadly note that the answer is assuredly yes, this is going to happen and is undoubtedly already happening. AI tools tend to have a dual-use capability, meaning that you can turn them toward goodness and yet also turn them toward badness, see my discussion on AI-based Dr. Evil Projects at the link here.

Conclusion

To end this discussion on the Dark Web-based generative AI, I figured we might take a spirited wooded hike into the imaginary realm of the postulated sentient AI. Sentient AI is also nowadays referred to as Artificial General Intelligence (AGI). For a similar merry romp into a future of sentient AI, see my discussion at the link here.

Sit down for what I am about to say next.

If the AI of today is eventually heading toward sentient AI or AGI, are we making ourselves a devil of time by right now proceeding to create instances of generative AI that are based on the Dark Web?

Heres the unnerving logic. We introduce generative AI to the worst of the worst of humankind. The AI pattern matches it. A sentient AI would presumably have this within its reach. The crux is that this could become the keystone for how the sentient AI or AGI decides to act. By our own hand, we are creating a foundation showcasing the range and depth of evildoing of humanity and displaying it to the AGI for all its glory to examine or use.

Some say it is the perfect storm for making a sentient AI that will be armed to wipe out humankind. Another related angle is that the sentient AI will be so disgusted by this glimpse into humankind, the AGI will decide it is best to enslave us. Or maybe wipe us out, doing so with plenty of evidence as to why we ought to go.

I dont want to conclude on a doom and gloom proposition, so give me a chance to liven things up.

Turn this unsettling proposition on its head.

By the sentient AI being able to readily see the worst of the worst about humanity, the AGI can use this to identify how to avoid becoming the worst of the worst. Hooray! You see, by noting what should not be done, the AGI will be able to identify what ought to be done. We are essentially doing ourselves a great service. The crafting of Dark Web-based generative AI will enable AGI to fully discern what is evil versus what is good.

We are cleverly saving ourselves by making sure that sentient AI is up to par on good versus evil.

Marcus Tullius Cicero, the famed Roman statesman, said this: The function of wisdom is to discriminate between good and evil. Perhaps by introducing AI to both the good and evil of humankind, we are setting ourselves up for a wisdom-based AGI that will be happy to keep us around. Maybe even help us to steer toward being good more than we are evil.

Thats your happy ending for the saga of the emergent sentient AI. I trust that you will now be able to get a good night's sleep on these weighty matters. Hint: Try to stay off the Dark Web to get a full nights slumber.

Dr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 6.8+ million amassed views of his AI columns. As a seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research. Previously a professor at USC and UCLA, and head of a pioneering AI Lab, he frequently speaks at major AI industry events. Author of over 50 books, 750 articles, and 400 podcasts, he has made appearances on media outlets such as CNN and co-hosted the popular radio show Technotrends. He's been an adviser to Congress and other legislative bodies and has received numerous awards/honors. He serves on several boards, has worked as a Venture Capitalist, an angel investor, and a mentor to founder entrepreneurs and startups.

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Generative AI Thats Based On The Murky Devious Dark Web Might Ironically Be The Best Thing Ever, Says AI Ethics And AI Law - Forbes

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