With the ever-growing power of generative artificial intelligence, deepfake and artificially generated (synthetic) media have continued to spread online, which creates various ethical and moral concerns regarding their usage. To tackle this, we thus present a novel exploration of the trends and patterns observed in real, deepfake and synthetic facial images. The proposed analysis is done in two parts: firstly, we incorporate eight deep learning models and analyze their performances in distinguishing between the three classes of images. Next, we look to further delve into the similarities and differences between these three sets of images by investigating their image properties both in the context of the entire image as well as in the context of specific regions within the image. ANOVA test was also performed and provided further clarity amongst the patterns associated between the images of the three classes. From our findings, we observe that the investigated deeplearning models found it easier to detect synthetic facial images, with the ViT Patch-16 model performing best on this task with a class-averaged sensitivity, specificity, precision, and accuracy of 97.37%, 98.69%, 97.48%, and 98.25%, respectively. This observation was supported by further analysis of various image properties. We saw noticeable differences across the three category of images. This analysis can help us build better algorithms for facial image generation, and also shows that synthetic, deepfake and real face images are indeed three different classes.

A text generation model is a machine learning model that uses neural networks, especially transformers architecture to generate contextually relevant text based on linguistic patterns learned from extensive corpora. The models are trained on a huge amount of textual data so that they can model and learn complex concepts of any language like its grammar, vocabulary, phrases, and styles. Text generation models can increase the productivity of humans in their current business processes. These models are already automating the process of content creation across industries for the generation of reports, summaries, and emails among others. These models are also allowing for a greater level of personalization in communications between businesses and their customers.

It now is possible to use technology to raise the dead. We haven't cracked the code on how to live forever, or discovered how to bring someone back to biological life. Instead, it has become much easier and more common to "resurrect" the dead by creating lifelike artificial intelligence (AI) avatars of them. Thanks to advancements in generative AI, artificial intelligence that can generate language, imagery, and audio (among other media), users are now able to speak with "ghostbots" that mimic people who have passed away. Think of it as ChatGPT for the dearly departed.

On Monday, OpenAI began opening up ChatGPT to users without an account. It described the move as part of its mission to "make tools like ChatGPT broadly available so that people can experience the benefits of AI." It also gives the company more training data (for those who don't opt out) and perhaps nudges more users into creating accounts and subscribing for superior GPT-4 access instead of the older GPT-3.5 model free users get. I tested the instant access, which -- as advertised -- allowed me to start a new GPT-3.5 thread without any login info. The chatbot's standard "How can I help you today?" screen appears, with optional buttons to sign up or log in.

Here are a few areas where companies say that the latest A.I. technology is being used in ways that could influence productivity, pulled from interviews, earnings calls and financial filings. Employees spend a lot of time trying to figure out human resources-related questions. Companies have been investing in generative A.I. to help answer those queries more quickly. At Walmart, the largest retailer in the United States with 1.6 million workers, the company's employee app has a section called "My Assistant," which is backed by generative A.I. The feature uses the technology to quickly answer questions like, "Do I have dental coverage?",

Apple and Google are reportedly in cahoots to integrate features from Google's Gemini generative AI service into iOS. Bloomberg broke the news, which was later corroborated by The New York Times. If the deal pans out, it will be a huge collaboration between two tech giants who have long duked it out in the hardware and software space. It also raises lots of questions about how Gemini would function on Apple's devices--and which company would remain in control. Neither Apple nor Google have publicly addressed the news, and neither company responded to requests for comment before this article was published.

OpenAI plans to open an office in Tokyo in April, according to a person familiar with the matter, as the artificial intelligence pioneer begins to build out its international operations. The Japan office will be its first in Asia, the person said, asking not to be identified discussing confidential information. It will be the third international location after opening offices in London and Dublin last year. OpenAI set off a frenzy of interest in artificial intelligence after unveiling ChatGPT in November 2022. The San Francisco startup has been in talks to raise funding at a valuation of at least 100 billion, Bloomberg reported in December.

OpenAI has unveiled a tool for cloning people's voices but is holding back on its public release due to concerns about possible misuse in a key election year. Voice Engine can replicate a person's voice based on a 15-second audio sample, according to an OpenAI blog post demonstrating the tool. But the ChatGPT creator is "taking a cautious and informed approach" to the technology and hopes to start a dialogue on "the responsible deployment of synthetic voices", the company said in the blog post published on Friday. "We recognize that generating speech that resembles people's voices has serious risks, which are especially top of mind in an election year," the San Francisco-based start-up said. "We are engaging with U.S. and international partners from across government, media, entertainment, education, civil society and beyond to ensure we are incorporating their feedback as we build."

The higher education (HE) sector benefits every nation's economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE.

The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs? Recent investigations into model-data feedback loops discovered that such loops can lead to model collapse, a phenomenon where performance progressively degrades with each model-fitting iteration until the latest model becomes useless. However, several recent papers studying model collapse assumed that new data replace old data over time rather than assuming data accumulate over time. In this paper, we compare these two settings and show that accumulating data prevents model collapse. We begin by studying an analytically tractable setup in which a sequence of linear models are fit to the previous models' predictions. Previous work showed if data are replaced, the test error increases linearly with the number of model-fitting iterations; we extend this result by proving that if data instead accumulate, the test error has a finite upper bound independent of the number of iterations. We next empirically test whether accumulating data similarly prevents model collapse by pretraining sequences of language models on text corpora. We confirm that replacing data does indeed cause model collapse, then demonstrate that accumulating data prevents model collapse; these results hold across a range of model sizes, architectures and hyperparameters. We further show that similar results hold for other deep generative models on real data: diffusion models for molecule generation and variational autoencoders for image generation. Our work provides consistent theoretical and empirical evidence that data accumulation mitigates model collapse.