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Many people understand the concept of bias at some intuitive level. In society, and in artificial intelligence systems, racial and gender biases are well documented.

If society could somehow remove bias, would all problems go away? The late Nobel laureate Daniel Kahneman, who was a key figure in the field of behavioral economics, argued in his last book that bias is just one side of the coin. Errors in judgments can be attributed to two sources: bias and noise.

Bias and noise both play important roles in fields such as law, medicine and financial forecasting, where human judgments are central. In our work as computer and information scientists, my colleagues and I have found that noise also plays a role in AI.

Statistical noise

Noise in this context means variation in how people make judgments of the same problem or situation. The problem of noise is more pervasive than initially meets the eye. A seminal work, dating back all the way to the Great Depression, has found that different judges gave different sentences for similar cases.

Worryingly, sentencing in court cases can depend on things such as the temperature and whether the local football team won. Such factors, at least in part, contribute to the perception that the justice system is not just biased but also arbitrary at times.

Other examples: Insurance adjusters might give different estimates for similar claims, reflecting noise in their judgments. Noise is likely present in all manner of contests, ranging from wine tastings to local beauty pageants to college admissions.

Noise in the data

On the surface, it doesnt seem likely that noise could affect the performance of AI systems. After all, machines arent affected by weather or football teams, so why would they make judgments that vary with circumstance? On the other hand, researchers know that bias affects AI, because it is reflected in the data that the AI is trained on.

For the new spate of AI models like ChatGPT, the gold standard is human performance on general intelligence problems such as common sense. ChatGPT and its peers are measured against human-labeled commonsense datasets.

Put simply, researchers and developers can ask the machine a commonsense question and compare it with human answers: If I place a heavy rock on a paper table, will it collapse? Yes or No. If there is high agreement between the two in the best case, perfect agreement the machine is approaching human-level common sense, according to the test.

So where would noise come in? The commonsense question above seems simple, and most humans would likely agree on its answer, but there are many questions where there is more disagreement or uncertainty: Is the following sentence plausible or implausible? My dog plays volleyball. In other words, there is potential for noise. It is not surprising that interesting commonsense questions would have some noise.

But the issue is that most AI tests dont account for this noise in experiments. Intuitively, questions generating human answers that tend to agree with one another should be weighted higher than if the answers diverge in other words, where there is noise. Researchers still dont know whether or how to weigh AIs answers in that situation, but a first step is acknowledging that the problem exists.

Tracking down noise in the machine

Theory aside, the question still remains whether all of the above is hypothetical or if in real tests of common sense there is noise. The best way to prove or disprove the presence of noise is to take an existing test, remove the answers and get multiple people to independently label them, meaning provide answers. By measuring disagreement among humans, researchers can know just how much noise is in the test.

The details behind measuring this disagreement are complex, involving significant statistics and math. Besides, who is to say how common sense should be defined? How do you know the human judges are motivated enough to think through the question? These issues lie at the intersection of good experimental design and statistics. Robustness is key: One result, test or set of human labelers is unlikely to convince anyone. As a pragmatic matter, human labor is expensive. Perhaps for this reason, there havent been any studies of possible noise in AI tests.

To address this gap, my colleagues and I designed such a study and published our findings in Nature Scientific Reports, showing that even in the domain of common sense, noise is inevitable. Because the setting in which judgments are elicited can matter, we did two kinds of studies. One type of study involved paid workers from Amazon Mechanical Turk, while the other study involved a smaller-scale labeling exercise in two labs at the University of Southern California and the Rensselaer Polytechnic Institute.

You can think of the former as a more realistic online setting, mirroring how many AI tests are actually labeled before being released for training and evaluation. The latter is more of an extreme, guaranteeing high quality but at much smaller scales. The question we set out to answer was how inevitable is noise, and is it just a matter of quality control?

The results were sobering. In both settings, even on commonsense questions that might have been expected to elicit high even universal agreement, we found a nontrivial degree of noise. The noise was high enough that we inferred that between 4% and 10% of a systems performance could be attributed to noise.

To emphasize what this means, suppose I built an AI system that achieved 85% on a test, and you built an AI system that achieved 91%. Your system would seem to be a lot better than mine. But if there is noise in the human labels that were used to score the answers, then were not sure anymore that the 6% improvement means much. For all we know, there may be no real improvement.

On AI leaderboards, where large language models like the one that powers ChatGPT are compared, performance differences between rival systems are far narrower, typically less than 1%. As we show in the paper, ordinary statistics do not really come to the rescue for disentangling the effects of noise from those of true performance improvements.

Noise audits

What is the way forward? Returning to Kahnemans book, he proposed the concept of a noise audit for quantifying and ultimately mitigating noise as much as possible. At the very least, AI researchers need to estimate what influence noise might be having.

Auditing AI systems for bias is somewhat commonplace, so we believe that the concept of a noise audit should naturally follow. We hope that this study, as well as others like it, leads to their adoption.

Mayank Kejriwal, Research Assistant Professor of Industrial & Systems Engineering, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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BEYOND LOCAL: 'Noise' in the machine: Human differences in judgment lead to problems for AI - The Longmont Leader

OpenAI on Friday said that it has disbanded a team devoted to mitigating the long-term dangers of super-smart artificial intelligence (AI).

It began dissolving the so-called superalignment group weeks ago, integrating members into other projects and research, the San Francisco-based firm said.

OpenAI co-founder Ilya Sutskever and team coleader Jan Leike announced their departures from the company during the week.

The dismantling of a team focused on keeping sophisticated AI under control comes as such technology faces increased scrutiny from regulators and fears mount regarding its dangers.

OpenAI must become a safety-first AGI [artificial general intelligence] company, Leike wrote on X on Friday.

Leike called on all OpenAI employees to act with the gravitas warranted by what they are building.

OpenAI CEO Sam Altman responded to Leikes post with one of his own.

Altman thanked Leike for his work at the company and said he was sad to see him leave.

Hes right, we have a lot more to do, Altman said. We are committed to doing it.

Altman promised more on the topic in the coming days.

Sutskever said on X that he was leaving after almost a decade at OpenAI, the trajectory of which has been nothing short of miraculous.

Im confident that OpenAI will build AGI that is both safe and beneficial, he added, referring to computer technology that seeks to perform as well as or better than human cognition.

Sutskever, who is also OpenAIs chief scientist, sat on the board that voted to remove Altman in November last year.

The ousting threw the company into a tumult, as staff and investors rebelled.

The OpenAI board ended up hiring Altman back a few days later.

OpenAI earlier last week released a higher-performing and even more human-like version of the AI technology that underpins ChatGPT, which was made free to all users.

It feels like AI from the movies, Altman said in a blog post.

Altman has previously pointed to Scarlett Johanssons character in the movie Her, where she voices an AI-based virtual assistant dating a man, as an inspiration for where he would like AI interactions to go.

The day would come when digital brains will become as good and even better than our own, Sutskever said at a talk during a TED AI summit in San Francisco late last year.

AGI will have a dramatic impact on every area of life, Sutskever added.

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A machine learning researcher is claiming to have knowledge of kinky drug-fueled orgies in Silicon Valley's storied hacker houses and appears to be linking those parties, and the culture surrounding them, to OpenAI.

"The thing about being active in the hacker house scene is you are accidentally signing up for a career as a shadow politician in the Silicon Valley startup scene," begins the lengthy X-formerly-Twitter post by Sonia Joseph, a former Princeton ML researcher who's now affiliated with the deep learning institute Mila Quebec.

What follows is a vague and anecdotal diatribe about the "dark side" of startup culture made particularly explosive by Joseph's reference to so-called "consensual non-consent" sex parties that she says took place within the artificial general intelligence (AGI) enthusiast community in the valley.

The jumping off point, as far as we can tell, stems from a thread announcing that OpenAI superalignment chief Jan Leike was leaving the company as it dissolved his team that was meant to prevent advanced AI from going rogue.

At the end of his X thread, Leike encouraged remaining employees to "feel the AGI," a phrase that was also ascribed to newly-exited OpenAI cofounder Ilya Sutskever during seemingly cultish rituals revealed in an Atlantic expos last year but nothing in that piece, nor the superalignment chief's tweets, suggests anything having to do with sex, drugs, or kink.

Still, Joseph addressed her second viral memo-length tweet "to the journalists contacting me about the AGI consensual non-consensual (cnc) sex parties." And in the post, said she'd witnessed "some troubling things" in Silicon Valley's "community house scene" when she was in her early 20s and new to the tech industry.

"It is not my place to speak as to why Jan Leike and the superalignment team resigned. I have no idea why and cannot make any claims," wrote the researcher, who is not affiliated with OpenAI. "However, I do believe my cultural observations of the SF AI scene are more broadly relevant to the AI industry."

"I don't think events like the consensual non-consensual (cnc) sex parties and heavy LSD use of some elite AI researchers have been good for women," Joseph continued. "They create a climate that can be very bad for female AI researchers... I believe they are somewhat emblematic of broader problems: a coercive climate that normalizes recklessness and crossing boundaries, which we are seeing playing out more broadly in the industry today. Move fast and break things, applied to people."

While she said she doesn't think there's anything generally wrong with "sex parties and heavy LSD use," she also charged that the culture surrounding these alleged parties "leads to some of the most coercive and fucked up social dynamics that I have ever seen."

"I have seen people repeatedly get shut down for pointing out these problems," Joseph wrote. "Once, when trying to point out these problems, I had three OpenAI and Anthropic researchers debate whether I was mentally ill on a Google document. I have no history of mental illness; and this incident stuck with me as an example of blindspots/groupthink."

"Its likely these problems are not really on OpenAI but symptomatic of a much deeper rot in the Valley," she added. "I wish I could say more, but probably shouldnt."

Overall, it's hard to make heads or tails of these claims.We've reached out to Joseph and OpenAI for more info.

"I'm not under an NDA. I never worked for OpenAI," Joseph wrote. "I just observed the surrounding AI culture through the community house scene in SF, as a fly-on-the-wall, hearing insider information and backroom deals, befriending dozens of women and allies and well-meaning parties, and watching many them get burned."

More on OpenAI: Sam Altman Clearly Freaked Out by Reaction to News of OpenAI Silencing Former Employees

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Machine Learning Researcher Links OpenAI to Drug-Fueled Sex Parties - Futurism

Your favorite streaming service, your email spam filter, and your smart thermostat have one thing in common: Theyre all powered by artificial intelligence(AI). AI was once the stuff of science fiction, but its now part of our daily lives. AI technology can simulate human intelligence, letting machines conquer tasks that were once the sole province of the human brain.

AI systems arent just for consumer use. If you own a business, you can probably use AI tools to simplify your workflow, tackle gnawing problems, and perform tasks youd rather not do yourself. Heres an overview of artificial intelligence.

The term artificial intelligence, or AI, refers to the simulation of human intelligence by machines, mainly computer systems. It includes areas of computer science research such as machine learning (ML), natural language processing (NLP), computer vision, and robotics. Through algorithms and data, an AI system can analyze vast amounts of information and derive insights or make predictions. Advanced AI systems even learn from their mistakes and reprogram themselves, much as a human might do.

Sophisticated AI systems function as artificial neural networks that replicate the human brain. Deep neural networks operate without human intervention, meaning that an AI program teaches itself to perform specific tasks, much in the same way a human can.

Artificial intelligence encompasses the various sub-disciplines of computer science that focus on enabling machines to mimic human intelligence and perform tasks typically requiring human cognition. Much of todays AI capabilities revolve around four key concepts: machine learning, deep learning, reinforcement learning, and natural language processing (NLP). Heres a breakdown of each of these AI techniques:

Machine learning (ML) hinges on AI algorithmscomplex mathematical formulas that let systems learn from and make predictions or decisions based on data. These machine learning algorithms let computers identify patterns in large datasets without being explicitly programmed to do so.

An array of AI training processes makes machine learning possible. These include supervised learning (where AI models learn from labeled data) and unsupervised learning (where AI models discover patterns in unlabeled training data).

Deep learning is a subset of machine learning inspired by the structure and function of the human brains neural networks. Deep learning models are built with more than three layers of artificial neural networks (ANNs).

A neural network can perform different functions depending on its architecture. Convolutional neural networks (CNNs) are particularly effective for recognizing images, while recurrent neural networks (RNNs) excel in sequence data processing, such as language translation and speech recognition. Deep learning algorithms have been instrumental in the development of AI capabilities like speech recognition, image recognition, computer vision, and autonomous driving to name just a few examples.

Reinforcement learning is an area of machine intelligence where computer systems are trained to make sequential decisions. These systems learn through interaction with the environment, receiving feedback based on their actions. Computer scientists leverage mathematical optimization and neural networks to achieve deep reinforcement AI techniques that play a major role in AI projects such as robotics, game playing, recommendation systems, and self-driving cars.

Natural language processing (NLP) is a branch of AI concerned with enabling computers to understand, interpret, and generate human language. NLP techniques include text analysis, sentiment analysis, entity recognition, and machine translation. NLP algorithms use statistical methods, rule-based approaches, machine learning, and deep learning techniques to process and analyze text.

All of this helps generative AI tools build and use large language models (LLM) that communicate with human beings. Data scientists have used NLP to build virtual assistants like Siri, chatbots, language translation services, and text summarization tools.

AI systems are categorized based on their capabilities and functionalities. Here are four core types of AI, with real-life artificial intelligence examples for each:

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Strong AI and weak AI are terms used to differentiate artificial intelligence based on its capabilities and similarities to human intelligence. Heres a breakdown of each:

Weak AI, also known as narrow AI, refers to artificial intelligence systems that operate based on predefined rules, algorithms, or machine learning models trained on specific datasets. These can feature both structured and unstructured datain other words, data that is labeled and organized by programmers and random data that requires more deductive reasoning.

Examples of weak AI include virtual assistants like Siri and Alexa, product recommendation systems, image recognition algorithms, and language translation services. Although these systems can appear intelligent within their limited domains, they do not possess consciousness, self-awareness, or the ability to apply their knowledge to new situations.

Strong AI, also known as artificial general intelligence (AGI) or human-level AI, refers to artificial intelligence systems with the ability to understand, learn, and apply knowledge across a wide range of tasks and domains at a level comparable to human intelligence. Although strong AI is still largely theoretical, it aims to replicate the full spectrum of human cognitive abilities, including reasoning, problem-solving, creativity, and emotional intelligence.

Strong AI systems would possess consciousness, self-awareness, and the capacity to adapt to novel situations, learn from experiences, and absorb knowledge beyond their initial training data. This could theoretically make it quite difficult to distinguish between the output of a generative AI model and a human.

Artificial intelligence offers a multitude of benefits. Here are three benefits of AI:

A significant advantage of AI is its ability to automate repetitive tasks, leading to increased efficiency and productivity. AI-powered systems can perform tasks faster and more accurately than humans, reducing errors and freeing up valuable time for employees to focus on higher-value activities.

Machine learning algorithms can identify patterns, trends, and correlations within data, helping businesses make more informed decisions. From personalized recommendations in ecommerce to predictive maintenance in manufacturing, AI-powered analytics enhance decision-making processes, leading to better outcomes and competitive advantages.

Advanced AI technologies such as natural language processing, computer vision, and autonomous systems drive groundbreaking innovations in various fields such as health care, finance, and transportation. This potential will help make artificial intelligence important to the global economy in the years and decades to come.

To be sure, there are some potential downsides to AI, including:

AI programs can perform an increasing number of tasks performed by humans. Downstream, this could result in unemployment or underemployment in certain industries, such as accounting and software coding, potentially leading to socio-economic upheaval. Additionally, the unequal distribution of the benefits of AI technology could exacerbate income inequality, widening the gap between skilled and unskilled workers.

AI raises ethical and social concerns related to privacy, bias, transparency, and accountability. For instance, AI algorithms may perpetuate or amplify biases present in training data, leading to unfair or discriminatory outcomes. AI used for surveillance and facial recognition could raise questions about privacy and civil liberties.

Excessive reliance on AI systems can pose significant business risks, including the potential for misusing the vast amounts of sensitive data they contain, such as medical records or personal financial information. Moreover, the complexity of AI systems makes them challenging to understand and control fully, increasing the potential for unintended consequences and data breaches.

Applications of AI include automation, data analysis, decision-making support, personalization, natural language processing, image recognition, robotics, and health care diagnostics, among others.

The main purpose of AI is to develop systems and technologies that can mimic human intelligence to perform tasks, make decisions, and solve problems efficiently.

AI is a tool thats neither inherently good nor bad. Its impact depends on how its developed, deployed, and regulated.

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What Is AI? How Artificial Intelligence Works (2024) - Shopify

OpenAIs GPT-4, a generative artificial intelligence (AI) model, has passed the Turing test, according to Ethereum co-founder Vitalik Buterin.

The Turing test is a nebulous benchmark for AI systems purported to determine how human-like a conversational model is. The term was coined on account of famed mathematician Alan Turing who proposed the test in 1950.

According to Turing, at the time, an AI system capable of generating text that fools humans into thinking theyre having a conversation with another human would demonstrate the capacity for thought.

Nearly 75 years later, the person largely credited with conceiving the worlds second most popular cryptocurrency has interpreted recent preprint research out of theUniversity of California San Diego as indicating that a production model has finally passed the Turing test.

Researchers at the University of California San Diego recently published a preprint paper titled People cannot distinguish GPT-4 from a human in a Turing test. In it, they had approximately 500 human test subjects interact with humans and AI models in a blind test to determine whether they could figure out which was which.

According to the research, humans mistakenly determined that GPT-4 was a human 56% of the time. This means that a machine fooled humans into thinking it was one of them more often than not.

According to Buterin, an AI system capable of fooling more than half of the humans it interacts with qualifies as passing the Turing test.

Buterin added:

Buterin qualified his statement by saying, Ok not quite, because humans get guessed as humans 66% of the time vs 54% for bots, but a 12% difference is tiny; in any real-world setting that basically counts as passing.

He also later added, in response to commentary on his original cast, that the Turing test is by far the single most famous socially accepted milestone for AI is serious shit now. So its good to remind ourselves that the milestone has now been crossed.

Artificial general intelligence (AGI) and the Turing test are not necessarily related, despite the two terminologies often being conflated. Turing formulated his test based on his mathematical acumen and predicted a scenario where AI could fool humans into thinking it was one of them through conversation.

It bears mention that the Turing test is an ephemeral construct with no true benchmark or technical basis. There is no scientific consensus as to whether machines are capable of thought as living organisms are or as to how such a feat would be measured. Simply put, AGI or an AIs ability to think isnt currently measurable or defined by the scientific or engineering communities.

Turing made his conceptual predictions long before the advent of token-based artificial intelligence systems and the onset of generative adversarial networks, the precursor to todays generative AI systems.

Complicating matters further is the idea of AGI, which is often associated with the Turing test. In scientific parlance, a general intelligence is one that should be capable of any intelligence-based feat. This precludes humans, as no person has shown general capabilities across the spectrum of human intellectual endeavor. Thus, it follows that an artificial general intelligence would feature thought capabilities far beyond that of any known human.

That being said, its clear that GPT-4 doesnt fit the bill of true general intelligence in the strictly scientific sense. However, that hasnt stopped denizens of the AI community from using the term AGI to indicate any AI system capable of fooling a significant number of humans.

In the current culture, its typical to see terms and phrases such as AGI, humanlike, and passes the Turing test to refer to any AI system that outputs content comparable to the content produced by humans.

Related: Were just scratching the surface of crypto and AI Microsoft exec

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Vitalik Buterin says OpenAI's GPT-4 has passed the Turing test - Cointelegraph