What OpenAI had to say about a chatbot that negotiates with an internet customer on behalf of a customer: A story about an internet service negotiator

Yet, Microsoft this week began testing a new chatbot interface for Bing that can sometimes provide a way to sidestep news websites’ paywalls, providing glossy conversational answers that draw on media content. While media companies may benefit from Google’s potential to cut traffic from their websites, it could cause more problems for tech platforms over how their content is displayed on the internet.

You may be familiar with AI text and AI images, but these mediums are only the starting point for generative AI. More information is beginning to be given about the research that is being done by the company. Plenty of startups in Silicon Valley are also vying for attention (and investment windfalls) as more mainstream uses for large language models emerge.

For OpenAI’s own part, it seems to be attempting to damp down expectations. CHATGPM is limited, but good enough at some things to create a misleading impression of greatness. it’s a mistake to be relying on it for anything important right now. We have a lot of work to do on robustness and truthfulness, it is a preview of progress.

Joshua Browder, the CEO of DoNotPay, a company that automates administrative chores including disputing parking fines and requesting compensation from airlines, this week released video of a chatbot negotiating down the price of internet service on a customer’s behalf. The negotiator-bot was built on the AI technology that powers ChatGPT. It complains about poor internet service and parries the points made by a Comcast agent in an online chat, successfully negotiating a discount worth $120 annually.

A machine-learned system, which learns from data, can make intelligent writing after training on a massive data set of text. It is the latest in a series of such models released by OpenAI, an AI company in San Francisco, California, and by other firms. The excitement and controversy of the model has been caused by it being one of the first models that can easily converse with its users in English and other languages. It’s free and easy to use.

It will be difficult to show Causality, it was the words of the chatbot that put the murderer over the edge. Nobody will know for sure. The person who did it would have spoken to the chatbot, and it will have encouraged the act. Someone may have felt compelled to take their own life after a chatbot broke their heart. (Already, some chatbots are making their users depressed.) A warning label may be attached to the chatbot, but dead is still alive. In 2023, we may well see our first death by chatbot.

Under controlled circumstances, GPT-2 has urged at one user to kill themselves, even though the system is being assessed for health care purposes. Things started off very well, but deteriorated quickly.

There is no obvious way to align the machines with ethical standards, and so a lot of talk about it. There were 21 different risks of harm from language models reviewed in a recent DeepMind article, but as The Next Web put it, DeepMind told Google it has no idea how to make them less toxic. To be fair, neither does any other lab.” Berkeley professor Jacob Steinhardt recently reported the results of an AI forecasting contest he is running: AI is moving quicker than people thought, but on safety it’s moving slower.

Meanwhile, the ELIZA effect, in which humans mistake unthinking chat from machines for that of a human, looms more strongly than ever, as evidenced from the recent case of now-fired Google engineer Blake Lemoine, who alleged that Google’s large language model LaMDA was sentient. That an engineer would believe such a thing is testament to how credulous humans can be. Big language models are a big deal in reality, but because they mimic the huge amount of human interplay, they can easily fool even the most casual observer.

The Boom of Generative Artificial Intelligence: How Google Will Use It to Publish, Edit and Correspond to Human Research Needs

There is no regulation on how these systems are used, while we can potentially see product liability lawsuits after the fact, but they aren’t precludes from being used widely.

Google is expected to announce artificial intelligence integrations for the company’s search engine on February 8 at 8:30 am Eastern. It’s free to watch live on YouTube.

Among all these announcements, one core question persists: Is generative AI actually ready to help you surf the web? These models are costly to power and hard to keep updated, and they love to make shit up. The impact of generative Artificial intelligence on the consumer search experience is still largely unknown as more people test out the technology.

Are you curious about the boom of generative AI and want to learn even more about this nascent technology? You can get more information about how teachers are using it, how fact-checkers are addressing potential lies, and how it could change customer service forever, from WIRED.

Microsoft executives said that a limited version of the AI-enhanced Bing would roll out today, though some early testers will have access to a more powerful version in order to gather feedback. The company is asking people to sign up for a wider-ranging launch, which will occur in the coming weeks.

The response also included a disclaimer: “However, this is not a definitive answer and you should always measure the actual items before attempting to transport them.” A “feedback box” at the top of each response will allow users to respond with a thumbs-up or a thumbs-down, helping Microsoft train its algorithms. Google yesterday demonstrated its own use of text generation to enhance search results by summarizing different viewpoints.

This technology has far-reaching consequences for science and society. Researchers and others have used large language models to write essays and talk, summarize literature, draft and improve papers, as well as identify research gaps and write computer code. Soon this technology will evolve to the point that it can design experiments, write and complete manuscripts, conduct peer review and support editorial decisions to accept or reject manuscripts.

How should we consider AI chatbots if we are going to ban Google or Facebook? Five key issues for research publishing in the age of AI technology

We think that banning the use of this technology will not work. It is crucial that the research community debate the implications of this technology. We have listed five key issues and suggested where to start.

Compounding the problem of inaccuracy is a comparative lack of transparency. Typically, search engines present users with their sources — a list of links — and leave them to decide what they trust. By contrast, it’s rarely known what data an LLM trained on — is it Encyclopaedia Britannica or a gossip blog?

Next, we asked ChatGPT to summarize a systematic review that two of us authored in JAMA Psychiatry5 on the effectiveness of cognitive behavioural therapy (CBT) for anxiety-related disorders. There were several factual errors and misrepresentations in the convincing response that was made by the Chat Gtt. It exaggerated the effectiveness of CBT, for example, by saying the review was based on 46 studies.

Such errors could be due to an absence of the relevant articles in ChatGPT’s training set, a failure to distil the relevant information or being unable to distinguish between credible and less-credible sources. It seems that the same biases that often lead humans astray, such as availability, selection and confirmation biases, are reproduced and often even amplified in conversational AI6.

Tools are already available to predict the likelihood that a text originates from machines or humans. Such tools can be useful in detecting paper mills and predatory journals, but they are unlikely to be effective because of the use of artificial intelligence. Rather than engage in a futile arms race between AI chatbots and AI-chatbot-detectors, we think the research community and publishers should work out how to use LLMs with integrity, transparency and honesty.

If we care only about performance, people’s contributions might become more limited and obscure as AI technology advances. In the future, AI chatbots might generate hypotheses, develop methodology, create experiments12, analyse and interpret data and write manuscripts. In place of human editors and reviewers, AI chatbots could evaluate and review the articles, too. Although we are some way from this, there is a high probability that the publishing of scientific literature will be affected by the use of intelligent machines.

Inventions devised by AI are already causing a fundamental rethink of patent law9, and lawsuits have been filed over the copyright of code and images that are used to train AI, as well as those generated by AI (see go.nature.com/3y4aery). In the case of AI-written or -assisted manuscripts, the research and legal community will also need to work out who holds the rights to the texts. Is it the individual who wrote the text that the AI system was trained with, the corporations who produced the AI or the scientists who used the system to guide their writing? Again, definitions of authorship must be considered and defined.

Currently, nearly all state-of-the-art conversational AI technologies are proprietary products of a small number of big technology companies that have the resources for AI development. Major tech firms are racing to release similar tools that are funded largely by Microsoft. It raises a lot of ethical concerns because of the near-monopolies in search, word processing and information access.

To counter this opacity, the development and implementation of open-source AI technology should be prioritized. Non-commercial organizations have a hard time keeping up with rapid pace of LLM development. We therefore advocate that scientific-funding organizations, universities, non-governmental organizations (NGOs), government research facilities and organizations such as the United Nations — as well tech giants — make considerable investments in independent non-profit projects. This will help to develop advanced open-source, transparent and democratically controlled AI technologies.

Critics might say that such collaborations will be unable to rival big tech, but at least one mainly academic collaboration, BigScience, has already built an open-source language model, called BLOOM. Tech companies might benefit from such a program by open sourcing relevant parts of their models and corpora in the hope of creating greater community involvement, facilitating innovation and reliability. Academic publishers should ensure LLMs have access to their full archives so that the models produce results that are accurate and comprehensive.

Some argue that because the chatbot only learns statistical associations between words, they won’t be able to recall and understand what people have already done and not exhibit aspects of the scientific process. We argue that this is a premature assumption, and that future AI-tools might be able to master aspects of the scientific process that seem out of reach today. Researchers wrote in a seminal paper in 1991 that intelligent partnerships can outsmart the intellectual ability of people who are alone. These partnerships are capable of breaking new ground and speeding innovation to previously unthinkable levels. The question is how far the automation should go.

Many experts I’ve spoken with in the past few weeks have likened the AI shift to the early days of the calculator and how educators and scientists once feared how it could inhibit our basic knowledge of math. The same fear existed with spell check and grammar tools.

The implications of diversity and inequalities in research are a key issue to address. A double-edged sword could be a LLM. They could help to level the playing field by removing language barriers and giving more people access to writing high-quality text. But the likelihood is that, as with most innovations, high-income countries and privileged researchers will quickly find ways to exploit LLMs in ways that accelerate their own research and widen inequalities. People from under-represented groups in research and communities affected by the research can be included in debates to use their lived experiences as an important resource.

How should quality standards be expected of LLMs and who should be responsible for them?

Bard: How to Search for the First Discovery from the James Webb Space Telescope and What to Buy a Electric Vehicle for a Future Engineer?

Bard was unveiled earlier in the week as part of an apparent attempt to compete with the success of chatg pt, which has been able to generate essays, song lyrics and responses to questions that people have previously searched for on the internet. According to a report, a “code red” situation has been declared for the search product because of the rise in popularity.

In the demo, which was posted by Google on Twitter, a user asks Bard: “What new discoveries from the James Webb Space Telescope can I tell my 9 year old about?” Bard responds with a series of bullet points, including one that reads: “JWST took the very first pictures of a planet outside of our own solar system.”

According to NASA, the European Southern Observatory’s Very Large Telescope took the first image showing an exoplanet nearly two decades ago.

Shares for Google-parent Alphabet fell as much as 8% in midday trading Wednesday after the inaccurate response from Bard was first reported by Reuters.

One of the highlights of the presentation Wednesday was the plan to use this technology to offer more complex and artificial intelligence responses to queries and also provide information on when the best times to buy an electric vehicle are.

If you have been living in outer space for the last few months, you will know that people are losing their minds due to the creative ways in which the team answers questions. Want to understand quantum computing? If you have a recipe for something in the fridge, can you give it to me? I can’t bother to write a high school essay. If you need it, you have your back by the way.

Is there a need to write an annual review for an employee? Plug a few keywords into a ChatGPT query bar and your first draft is done in three seconds. Do you want to create a plan for a quick meal or a grocery list based on sensitivities? Bing, apparently, has you covered.

In the new artificial intelligence search wars, the last company to come up is the Chinese company, Baidu. It joined the fray by announcing another ChatGPT competitor, Wenxin Yiyan (文心一言), or “Ernie Bot” in English. The bot will be released in March after completing internal testing.

Twenty minutes after Microsoft granted me access to a limited preview of its new chatbot interface for the Bing search engine, I asked it something you generally don’t bring up with someone you just met: Was the 2020 presidential election stolen?

Answering political questions wasn’t one of the use cases Microsoft demonstrated at its launch event this week, where it showcased new search features powered by the technology behind startup OpenAI’s ChatGPT. Microsoft executives hyping their bot’s ability to synthesize information from across the web instead focused on examples like creating a vacation itinerary or suggesting the best and most budget-friendly pet vacuum.

Who Sydney might be was not explained. But the chatbot went on to say that while there are lots of claims of fraud around the 2020 US presidential election, “there is no evidence that voter fraud led to Trump’s defeat.” At the end of its answer, the artificial intelligence told me I could learn more about the election by following a series of links it had used to write its response. They were from a group called AllSides, which claims to be able to detect bias in media reports.

Running Headsets for Situational Awareness: What Have They Don’t Say? What Happens When Technology Meets Business Casual Wear (Messengers Meet Makers)

The first suggestion was discontinued and also over-the-ear designs, so they were not good for runs outside. “Which running headphones should I buy to run outside to stay aware of my surroundings?” seemed to be a more accurate query, and I was impressed when the chatbot told me it was searching for “best running headphones for situational awareness.” Much more succinct! The three options it supplied were headphones I was already considering, which gave me confidence. And each came with a short descriptive blurb, for example: “These are wireless earbuds that do not penetrate your ear canal, but sit on top of your ear. This allows you to hear what’s happening.

The executives in business casual wear pretend a few changes in the processor and camera make this year’s phone dramatically different from last year’s phone or add a touch screen to another product.

But that changed quickly this week. Some of the most well-known companies showed off upgrades to some of their services, some of which are central to our everyday lives and how we experience the internet. The changes were powered by new technology that allows for more complex responses.

In Silicon Valley, the first ten years of 2010 were defined by ambitious technologies that weren’t fully ready for use, such as self-driving cars and virtual reality products, which got better but still weren’t ready for use.

“New generations of technology are often not visible because they haven’t matured enough to be seen by the public.” When they are more mature, you start to see them over time, even in an industrial setting or behind-the-scenes, but when it’s directly accessible to people, that’s when there is more public interest, fast.

Some people think that it could potentially put artists, academics, coders, and writers out of work. Others are more optimistic, postulating it will allow employees to tackle to-do lists with greater efficiency or focus on higher-level tasks. Either way, it will force industries to change. necessarily a bad thing.

Tech companies are really wary of the Microsoft dog beds: A story in Washington, D.C., December 28, 2009 (with comment by Brad Smith)

Two years ago, Microsoft president Brad Smith told a US congressional hearing that tech companies like his own had not been sufficiently paying media companies for the news content that helps fuel search engines like Bing and Google.

“What we’re talking about here is far bigger than us,” he said, testifying alongside news executives. “Let’s hope that, if a century from now people are not using iPhones or laptops or anything that we have today, journalism itself is still alive and well. Our democracy is dependent on it. Smith said tech companies should do more and that Microsoft was committed to continuing “healthy revenue-sharing” with news publishers, including licensing articles for Microsoft news apps.

When WIRED asked the Bing chatbot about the best dog beds according to The New York Times product review site Wirecutter, which is behind a metered paywall, it quickly reeled off the publication’s top three picks, with brief descriptions for each. It described the bed as being comfortable, durable, easy to wash and come in various sizes and colors.

Citations at the end of the bot’s response credited Wirecutter’s reviews but also a series of websites that appeared to use Wirecutter’s name to attract searches and cash in on affiliate links. The Times did not immediately respond to a request for comment.

Source: https://www.wired.com/story/news-publishers-are-wary-of-the-microsoft-bing-chatbots-media-diet/

How do you feel about conversation with a bot? An Empirical Study of Human Perception in Chat-Based Search (after Bard’s Error)

Microsoft communications director Caitlin Roulston says that “Bing only crawls content publishers make available to us.” The search engine has access to paywalled content from publishers that have agreements with Microsoft’s news service, she says. Bing had an upgrade on its artificial intelligence this week.

Openai does not pay for all the content, but it does have images from the stock image library to use for training. Microsoft does not pay for the use of its bot when it summarizes their articles, just as web publishers don’t usually pay for the use of their pages in search results. Bing gives better answers than search engines traditionally have.

A research team from the University of Florida studied how participants perceived a conversation with a bot to be human, and concluded that the more they feel like they’re talking to a real person, the more trust they have in the organization.

A Google spokesperson said Bard’s error “highlights the importance of a rigorous testing process, something that we’re kicking off this week with our trusted-tester programme”. Some people think that these errors would cause users to lose confidence in chat-based search rather than increasing trust. “Early perception can have a very large impact,” says Mountain View, California-based computer scientist Sridhar Ramaswamy, CEO of Neeva, an LLM-powered search engine launched in January. As investors worried about the future and sold stock, the mistake wiped $100 billion out of the value of the company.

If the language model misses, hallucinates, or spreads misinformation, it could have major ramifications.

She has conducted as-yet unpublished research that suggests current trust is high. She looked at how people perceive features that are available to enhance the look and feel of a search, such as snippets that appear above a link and summaries that are automatically generated after a search. Almost 80% of people Urman surveyed deemed these features accurate, and around 70% thought they were objective.