Generative AI: A Tale of Two Faces: Google, Facebook, Twitter, YouTube, and the Memmer Paradigm: A Brief History of Silicon Valley
“We’re starting with AI-powered features in Search that distill complex info into easy-to-digest formats, so you can see the big picture, then explore more,” Google CEO Sundar Pichai wrote on Twitter in the lead-up to the event. Despite recent layoffs, the company remains an assertive force in Silicon Valley. Pressure on the company to give the public more access to experimental research was put on by the success of other generativeAI models.
Something weird is happening in the world of AI. The field burst out of boredom in the early part of this century with the invention of deep learning led by three academics. Many of our applications have been made more useful by this approach to artificial intelligence, as well as everything that is “smart”, such as language translation, search, and the like. We have been in this area for a dozen years. But in the past year or so there has been a dramatic aftershock to that earthquake as a sudden profusion of mind-bending generative models have appeared.
Daily life is being impacted by the text created from tools. As part of classroom lessons, teachers are testing it out. Marketers are champing at the bit to replace their interns. Memers are going buck wild. Is that me? It would be a lie to say I’m not a little anxious about the robots coming for my writing gig. (ChatGPT, luckily, can’t hop on Zoom calls and conduct interviews just yet.)
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 shared about the possibilities of audio and video with the internet giant. A lot of startups in Silicon Valley are vying for attention and investment windfalls as more mainstream uses for large language models are emerging.
Stealing is not stealing: An educator’s pedagogical critique of the invention of the Internet and the challenges of plagiarism
In December of his sophomore year, a Rutgers University student came to believe that Artificial intelligence may be dumber than humans.
“The quality of writing was appalling. The wording was awkward and lacked complexity. I don’t think a student would ever use writing generated through ChatGPL for a paper, even if it was just bad content.
The emergence of concerns about improper use of the internet in academia is not a part of the reason for the birth of ChatGPT. When Wikipedia launched in 2001, universities nationwide were scrambling to decipher their own research philosophies and understandings of honest academic work, expanding policy boundaries to match pace with technological innovation. Now, the stakes are a little more complex, as schools figure out how to treat bot-produced work rather than weird attributional logistics. As other professions adjust, the world of higher education is playing a familiar game of catch-up. The only difference now is that the internet can think for itself.
Stealing is any act of using someone else’s work without giving proper credit to the original author. This definition is hard to apply when the work is generated by something other than someone. Emily Hipchen is a board member of the Academic Code Committee at Brown University and says that generative artificial intelligence leads to a critical point of contention. “If [plagiarism] is stealing from a person,” she says, “then I don’t know that we have a person who is being stolen from.”
Hipchen is not alone in her speculation. Alison Daily, chair of the Academic Integrity Program at Villanova University, is also grappling with the idea of classifying an algorithm as a person, specifically if the algorithm involves text generation.
Daily believes that eventually professors and students are going to need to understand that digital tools that generate text, rather than just collect facts, are going to need to fall under the umbrella of things that can be plagiarized from.
How Artificial Intelligence Could Detect Human-Human Synthesis and its Effect on Text Detection and Mimicry of Natural Language
On February 8, the company is expected to announce integrations for its search engine. It is free to watch on the internet.
During the event, there is a chance that Google will release information about one of its responses to the chat Gppt. The feature is not open to the public but the company says that it will be available to more people in the future.
More synthetic content will likely be encountered when surfing the web with generative machine learning tools now publicly accessible. An auto-generated BuzzFeed quiz about which dessert matches your political beliefs could be benign. (Are you Democratic beignet or a Republican zeppole?) There could be a sophisticated propaganda campaign from a foreign government.
Academic researchers are looking into ways to detect whether a string of words was generated by a program like ChatGPT. What is the most likely indicator that something was spun up with the help of Artificial Intelligence?
Algorithms with the ability to mimic the patterns of natural writing have been around for a few more years than you might realize. An experimental tool was released in the year 2019: it scans text and highlights words that are random.
Why would this be helpful? An Artificial intelligence text generator is good at mimicry and weak at throwing curve balls. Sure, when you type an email to your boss or send a group text to some friends, your tone and cadence may feel predictable, but there’s an underlying capricious quality to our human style of communication.
While these detection tools are helpful for now, Tom Goldstein, a computer science professor at the University of Maryland, sees a future where they become less effective, as natural language processing grows more sophisticated. “These kinds of detectors rely on the fact that there are systematic differences between human text and machine text,” says Goldstein. The goal of these companies is to make machine text as close to human text as possible. Does this mean all hope of synthetic media detection is lost? Absolutely not.
There are possible watermark methods that can be built into large language models that are used for text generators. It’s not foolproof, but it’s a fascinating idea. Remember, ChatGPT tries to predict the next likely word in a sentence and compares multiple options during the process. A watermark might be able to designate certain word patterns to be off-limits for the AI text generator. When a watermark rule is broken multiple times, it shows that a human was likely behind that masterpiece.
The New Chatty Search War: Ernie Bot, or How to Search in China’s Biggest Search Company (Baidu)
The all-new Bing is similarly chatty. Aarian Marshall of WIRED was among the people who saw demos at the company’s headquarters in Redmond, and had a short test drive of the product. It’s a long way from Microsoft’s hapless and hopeless Office assistant Clippy, which some readers may recall bothering them every time they created a new document.
In case you’ve been living in outer space for the past few months, you’ll know that people are losing their minds over ChatGPT’s ability to answer questions in strikingly coherent and seemingly insightful and creative ways. Want to know more about quantum computing? Need a recipe for whatever’s in the fridge? Can’t be bothered to write that high school essay? ChatGPT has your back.
China’s biggest search company is the last thing in the new search wars. It announced another competitor called “Ernie Bot” in English. Baidu says it will release the bot after completing internal testing this March.
AI as a tool to help with work: A survey from a Biologist at the Central Leather Research Institute of Chennai, India, on the use of ChatGPT and similar tools
A considerable proportion of respondents — 57% — said they use ChatGPT or similar tools for “creative fun not related to research”. Among the uses related to science, brainstorming research ideas was the most common, with 27% of respondents indicating they had tried it. Almost 24% of respondents said they use artificial intelligence tools for writing computer code, and a similar amount say they use the tools to help write research manuscripts, produce presentations or conduct literature reviews. Some people use them to help with grant applications and others to create graphics and pictures. A technical error in the poll prevented many people from selecting more than one option, so these numbers are based on a subset of around 500 responses.
Some hoped that AI could speed up and streamline writing tasks, by providing a quick initial framework that could be edited into a more detailed final version.
rative language models are helpful for people like me who don’t use English as their first language. It helps me more with my writing than before. It’s like having a professional language editor by my side while writing a paper,” says Dhiliphan Madhav, a biologist at the Central Leather Research Institute in Chennai, India.
This is for obvious reasons: The current AI tools are prone to both errors and bias, and often produce dull, unoriginal writing. In addition, we think someone who writes for a living needs to constantly be thinking about the best way to express complex ideas in their own words. An artificial intelligence tool may plagiarize another person’s words. If a writer uses it to create text for publication without a disclosure, we’ll treat that as tantamount to plagiarism.
The key, many agreed, is to see AI as tool to help with work, rather than to replace work altogether. “AI can be a useful tool. However, it has to remain one of the tools. As a retired Biologist from Milan, Italy, Maria Lucia Lampugnani says that its limitations and defects have always been clearly kept in mind.
Using Artificial Intelligence for Editorial Text Generation in Social Media and in News Articles: A Closer Look at the Case of Snippets
When the whole point of the story is generated by artificial intelligence, we don’t publish stories with text generated by it. (In such cases we’ll disclose the use and flag any errors.) This applies not just to whole stories but also to snippets—for example, ordering up a few sentences of boilerplate on how Crispr works or what quantum computing is. It also applies to editorial text on other platforms, such as email newsletters. It will be made public if it’s used for non editorial purposes like marketing emails which are already automated.
We may try using AI to suggest headlines or text for short social media posts. An editor must approve the final choices in order for them to be accurate. Using an AI tool to speed up idea generation won’t change this process substantively.