Generative data augmentation (GDA) has emerged as a promising technique to alleviate data scarcity in machine learning applications. This thesis presents a comprehensive survey and unified framework of the GDA landscape. We first provide an overview of GDA, discussing its motivation, taxonomy, and key distinctions from synthetic data generation. We then systematically analyze the critical aspects of GDA - selection of generative models, techniques to utilize them, data selection methodologies, validation approaches, and diverse applications. Our proposed unified framework categorizes the extensive GDA literature, revealing gaps such as the lack of universal benchmarks. The thesis summarises promising research directions, including , effective data selection, theoretical development for large-scale models' application in GDA and establishing a benchmark for GDA. By laying a structured foundation, this thesis aims to nurture more cohesive development and accelerate progress in the vital arena of generative data augmentation.

We present a method for zero-shot recommendation of multimodal non-stationary content that leverages recent advancements in the field of generative AI. We propose rendering inputs of different modalities as textual descriptions and to utilize pre-trained LLMs to obtain their numerical representations by computing semantic embeddings. Once unified representations of all content items are obtained, the recommendation can be performed by computing an appropriate similarity metric between them without any additional learning. We demonstrate our approach on a synthetic multimodal nudging environment, where the inputs consist of tabular, textual, and visual data.

ChatGPT is preparing to take on Apple in a ground-breaking move to craft an'iPhone of artificial intelligence', a report has claimed. Ex-iPhone designer, Sir Jony Ive, is in'advanced talks' with OpenAI's CEO, Sam Altman, as the pair seek to unleash an AI-centred device to the mass market. The device, which is still in its brainstorming phases, is goaled towards a seamless integration of AI that is'more natural' for users to navigate, according to The Financial Times. It's a leap that's been compared to the revolution of Apple's first touchscreen device in 2007, but comes as many believe Tim Cook's innovation has plateaued. Billionaire Masayoshi Son, who founded the Japanese telecom giant SoftBank, is said to be in on the talks too, and has even proposed $1billion in funds.

ChatGPT has proven it can help students with their homework, but now it is helping teachers create those very courses, a computer science professor told Fox News. The popular artifical intelligence platform ChatGPT will now be able to respond to spoken words and images, causing concern among some experts who believe the application could lead to unwanted invasions of privacy. OpenAI, the company behind ChatGPT, released the new version of the chatbot on Monday, allowing it for the first time to interact with users with the spoken word, according to a report from the New York Times. "We're looking to make ChatGPT easier to use – and more helpful," Peter Deng, OpenAI's vice president of consumer and enterprise product, told the New York Times. GOOGLE'S AI IS TRYING TO ONE-UP CHATGPT AND BING WITH NEW EVERYDAY AI FEATURES Microsoft Bing Chat and ChatGPT AI chat applications are seen on a mobile device.

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe.

Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging. A critical problem lies in effectively predicting the various potential outcomes that may emerge in response to the vehicle's actions as the world evolves. To address this challenge, we introduce GAIA-1 ('Generative AI for Autonomy'), a generative world model that leverages video, text, and action inputs to generate realistic driving scenarios while offering fine-grained control over ego-vehicle behavior and scene features. Our approach casts world modeling as an unsupervised sequence modeling problem by mapping the inputs to discrete tokens, and predicting the next token in the sequence. Emerging properties from our model include learning high-level structures and scene dynamics, contextual awareness, generalization, and understanding of geometry. The power of GAIA-1's learned representation that captures expectations of future events, combined with its ability to generate realistic samples, provides new possibilities for innovation in the field of autonomy, enabling enhanced and accelerated training of autonomous driving technology.

Google has announced a new control in its robots.txt The control is a crawler called Google-Extended, and publishers can add it to the file in their site's documentation to tell Google not to use it for those two APIs. In its announcement, the company's vice president of "Trust" Danielle Romain said it's "heard from web publishers that they want greater choice and control over how their content is used for emerging generative AI use cases." Romain added that Google-Extended "is an important step in providing transparency and control that we believe all providers of AI models should make available." As generative AI chatbots grow in prevalence and become more deeply integrated into search results, the way content is digested by things like Bard and Bing AI has been of concern to publishers.

Six months ago this week, many prominent AI researchers, engineers, and entrepreneurs signed an open letter calling for a six-month pause on development of AI systems more capable than OpenAI's latest GPT-4 language generator. It argued that AI is advancing so quickly and unpredictably that it could eliminate countless jobs, flood us with disinformation, and--as a wave of panicky headlines reported--destroy humanity. As you may have noticed, the letter did not result in a pause in AI development, or even a slow down to a more measured pace. Companies have instead accelerated their efforts to build more advanced AI. Elon Musk, one of the most prominent signatories, didn't wait long to ignore his own call for a slowdown. In July he announced xAI, a new company he said would seek to go beyond existing AI and compete with OpenAI, Google, and Microsoft.

ChatGPT can now browse the internet to provide users with current information, its parent company OpenAI has announced. The chatbot was previously trained to use data up to September 2021 and was unable to provide real-time information. On Wednesday, Microsoft-backed OpenAI announced on X, formerly Twitter, that the new update allows it to move past the September 2021 cutoff and access current information on the internet. ChatGPT can now browse the internet to provide you with current and authoritative information, complete with direct links to sources. It is no longer limited to data before September 2021.

The artificial intelligence revolution is fully underway, but soaring demand for its most crucial component has startups scratching their heads on how they can deliver on AI's promise. Generative AI's lifeblood is a book-sized semiconductor known as the graphics processing unit (GPU) -- built by one company, Nvidia. Nvidia's CEO and founder Jensen Huang made a wild bet years ago that the world would soon clamor for a powerful chip usually used for making video games, but that could build AI as well.