Your Friend Asked You a Question. Your friend came to you because they respect your knowledge and opinion, and outsourcing the answer to a machine is lazy and rude. Back in the 2010s, a website called Let Me Google That For You gained a notable amount of popularity for serving a single purpose: snark. The site lets you generate a custom link that you can send somebody who asks you a question. When they click the link, it plays an animation of the process of typing a question into Google.

In this paper, we study how generative AI and specifically large language models (LLMs) impact learning in coding classes. We show across three studies that LLM usage can have positive and negative effects on learning outcomes. Using observational data from university-level programming courses, we establish such effects in the field. We replicate these findings in subsequent experimental studies, which closely resemble typical learning scenarios, to show causality. We find evidence for two contrasting mechanisms that determine the overall effect of LLM usage on learning. Students who use LLMs as personal tutors by conversing about the topic and asking for explanations benefit from usage. However, learning is impaired for students who excessively rely on LLMs to solve practice exercises for them and thus do not invest sufficient own mental effort. Those who never used LLMs before are particularly prone to such adverse behavior. Students without prior domain knowledge gain more from having access to LLMs. Finally, we show that the self-perceived benefits of using LLMs for learning exceed the actual benefits, potentially resulting in an overestimation of one's own abilities. Overall, our findings show promising potential of LLMs as learning support, however also that students have to be very cautious of possible pitfalls.

Analogical reasoning is a unique ability of humans to address unfamiliar challenges by transferring strategies from relevant past experiences. One key finding in psychology is that compared with irrelevant past experiences, recalling relevant ones can help humans better handle new tasks. Coincidentally, the NLP community has also recently found that self-generating relevant examples in the context can help large language models (LLMs) better solve a given problem than hand-crafted prompts. However, it is yet not clear whether relevance is the key factor eliciting such capability, i.e., can LLMs benefit more from self-generated relevant examples than irrelevant ones? In this work, we systematically explore whether LLMs can truly perform analogical reasoning on a diverse set of reasoning tasks. With extensive experiments and analysis, we show that self-generated random examples can surprisingly achieve comparable or even better performance, e.g., 4% performance boost on GSM8K with random biological examples. We find that the accuracy of self-generated examples is the key factor and subsequently design two improved methods with significantly reduced inference costs. Overall, we aim to advance a deeper understanding of LLM analogical reasoning and hope this work stimulates further research in the design of self-generated contexts.

Republican activist Ying Tan joined'Fox & Friends First' to discuss her reaction and why she believes the Democrat is taking advantage of artificial intelligence. New York City Democratic Councilwoman-elect Susan Zhuang used modern technology to her advantage by answering a news outlet's questionnaire with the help of artificial intelligence, and it left her former opponent outraged. "If she can't answer a single question on her own, how can she represent a district?" Republican activist Ying Tan, who ran against Zhuang, said Tuesday on "Fox & Friends First." "And as a first-generation immigrant myself, I don't feel shy to speak with the accent I have, and during the campaign I insisted on going out on the street, to reach out to the voters…" she continued. Microsoft Bing Chat and ChatGPT AI chat applications are seen on a mobile device in this photo illustration in Warsaw, Poland, on July 21, 2023.

When ChatGPT came out at the end of 2022, It caused a huge surprise for millions of people around the world due to its capabilities. I experienced the rise of it firsthand: I have personally seen the reactions of people using it and have had numerous interactions with GPT. For some people, It may even sound surreal to build a game with ChatGPT without writing code, but it is possible now. In case you have never heard it before, ChatGPT is a super powerful AI tool created by OpenAI, a company founded in San Francisco in late 2015 by Sam Altman, Elon Musk, and others. You can take a look here to learn its capabilities briefly.

Artificial intelligence caught the world's attention with the rise of ChatGPT. However, this generative AI tool can do more than your homework. It can explain complicated ideas, create social media posts, and so much more! This article will discuss actions you didn't know ChatGPT could do. That way, you could significantly enhance your career and other aspects of daily life.

Students may want to think twice before using a chatbot to complete their next assignment. Language models that generate text in response to user prompts plagiarize content in more ways than one, according to a Penn State-led research team that conducted the first study to directly examine the phenomenon. "Plagiarism comes in different flavors," said Dongwon Lee, professor of information sciences and technology at Penn State. "We wanted to see if language models not only copy and paste but resort to more sophisticated forms of plagiarism without realizing it." The researchers focused on identifying three forms of plagiarism: verbatim, or directly copying and pasting content; paraphrase, or rewording and restructuring content without citing the original source; and idea, or using the main idea from a text without proper attribution.

Image-generating AI models like DALL-E 2 and Stable Diffusion can -- and do -- replicate aspects of images from their training data, researchers show in a new study, raising concerns as these services enter wide commercial use. The study hasn't been peer reviewed yet, and the co-authors submitted it to a conference whose rules forbid media interviews until the research has been accepted for publication. But one of the researchers, who asked not to be identified by name, shared high-level thoughts with TechCrunch via email. "Even though diffusion models such as Stable Diffusion produce beautiful images, and often ones that appear highly original and custom tailored to a particular text prompt, we show that these images may actually be copied from their training data, either wholesale or by copying only parts of training images," the researcher said. "Companies generating data with diffusion models may need to reconsider wherever intellectual property laws are concerned. It is virtually impossible to verify that any particular image generated by Stable Diffusion is novel and not stolen from the training set."

Artificial intelligence comes to fruition by teaching a machine to think and react like a human being. Google refers to content created with the help of artificial intelligence as automatically generated content, better known as spam. Maybe creative thinking will be something machines can eventually master, but we are not there yet. Machines can't grasp things like style, voice, and perspective, no matter how intelligent they might be. Though a machine can't do creative writing, artificial intelligence can be helpful in your content marketing processes.

Samsung thinks it has a better way to develop brain-like chips: borrow existing brain structures. The tech firm has proposed a method that would "copy and paste" a brain's neuron wiring map to 3D neuromorphic chips. The approach would rely on a nanoelectrode array that enters a large volumes of neurons to record both where the neurons connect and the strength of those connections. You could copy that data and'paste' it to a 3D network of solid-state memory, whether it's off-the-shelf flash storage or cutting-edge memory like resistive RAM. Each memory unit would have a conductance that reflects the strength of each neuron connection in the map.