If you've ever published a blog, or posted something to Reddit, or shared content anywhere else on the open web, it's very likely you have played a part in creating the latest generation of artificial intelligence. Google's Bard chatbot, OpenAI's ChatGPT, Microsoft's OpenAI-powered version of Bing, and similar tools from the many startups now incorporating these and other AI language models--none of these clever automated writers could exist without the enormous body of text freely available on the web.

A key factor that drives the development of generative AI is foundation model Bommasani et al. [2021] that at inference can generalize to tasks and data distributions different from training. With the success of ChatGPT Zhang et al. [2023b], GPT-3 [Brown et al., 2020] has been widely recognized as one of the most widely recognized foundation models for NLP. Very recently, Meta AI research team has recent released a segment anything project Kirillov et al. [2023] that introduces a promotable segmentation task for training a vision foundation model. The resulting segment anything model (SAM) has been recognized as the GPT-3 moment for vision. The model was trained on over 1 billion masks on 11 million licensed and privacy-respecting images. It represents a significant step towards achieving cognitive recognition for all objects in the world, aiming to handle interactive segmentation tasks while addressing real-world constraints.

ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities for its powerful capabilities, plethora of possible applications, and the great possibility for abuse. At the time of writing this work, several other language models (e.g., Google Bard and Meta LLaMA) just came out in an attempt to get a foothold in the vast possible market. These models have the ability to revolutionize the way we interact with computers and have potential applications in many fields, including education, software engineering, healthcare, and marketing. In this paper, we will discuss the possible applications, drawbacks, and research directions using advanced language Chatbots (e.g., ChatGPT) in each of these fields. We first start with a brief introduction and the development timeline of artificial intelligence based language models, then we go through possible applications of such models, after that we discuss the limitations and drawbacks of the current technological state of the art, and finally we point out future possible research directions.

There has recently been widespread discussion of whether large language models might be sentient or conscious. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for example, their lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future.

Renal transplantation emerges as the most effective solution for end-stage renal disease. Occurring from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice. However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology, while identifying robust biomarkers from such high-dimensional and complex data for prognosis is challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs), we propose MEDIMP -- Medical Images with clinical Prompts -- a model to learn meaningful multi-modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating structural clinicobiological data after translating them into text prompts. MEDIMP is based on contrastive learning from joint text-image paired embeddings to perform this challenging task. Moreover, we propose a framework that generates medical prompts using automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the limited available multi-modal data most efficiently. Extensive experiments and comparisons with other renal transplant representation learning methods with limited data prove the effectiveness of MEDIMP in a relevant clinical setting, giving new directions toward medical prompts. Our code is available at https://github.com/leomlck/MEDIMP.

This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System. Unlike traditional methods, BlockGPT is designed to offer an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a broader range of anomalies. We demonstrate the effectiveness of BlockGPT through its use as an anomaly detection tool for Ethereum transactions. In our experiments, it effectively identifies abnormal transactions among a dataset of 68M transactions and has a batched throughput of 2284 transactions per second on average. Our results show that, BlockGPT identifies abnormal transactions by ranking 49 out of 124 attacks among the top-3 most abnormal transactions interacting with their victim contracts. This work makes contributions to the field of blockchain transaction analysis by introducing a custom data encoding compatible with the transformer architecture, a domain-specific tokenization technique, and a tree encoding method specifically crafted for the Ethereum Virtual Machine (EVM) trace representation.

Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big models have zero-shot capabilities, in general, labeled data are still required to adapt them to downstream tasks. To overcome this critical limitation, we propose an unsupervised fine-tuning framework to directly fine-tune the model or prompt on the unlabeled target data. We demonstrate how to apply our method to both language-augmented vision and masked-language models by aligning the discrete distributions extracted from the prompts and target data. To verify our approach's applicability, we conduct extensive experiments on image classification, sentiment analysis, and natural language inference tasks. Across 13 image-related tasks and 15 language-related ones, the proposed approach achieves consistent improvements over the baselines.

More media outlets are using AI to write articles, if not as aggressively as others. VentureBeat editorial director Michale Nuñez tells Bloomberg his publication is using Microsoft's Bing Chat to help edit and write stories. Reporters are encouraged to slip AI-written "sentences and fragments" into articles so long as they're accurate and independently verifiable. The OpenAI-powered tech is akin to having "another person on the team," Nuñez says. VentureBeat doesn't disclose the use of AI content provided it's limited and authentic, but also doesn't intend to create whole articles using the technology. Word surfaced in January that CNET had been using AI to produce entire financial explainer articles since November.

OpenAI says ChatGPT is once again available in Italy after it addressed a series of conditions set out by regulators. The Garante data protection authority wanted OpenAI to resolve several issues by the end of this month in order to lift a temporary ban on the chatbot. "ChatGPT is available again to our users in Italy," OpenAI told the Associated Press in a statement. "We are excited to welcome them back, and we remain dedicated to protecting their privacy." Italian regulators blocked ChatGPT in March over concerns that the AI's training methods and chatbot violated the European Union's General Data Protection Regulation (GDPR).

Italy's privacy regulator rescinded its temporary ban on ChatGPT after the chatbot's developer, OpenAI, implemented changes demanded by the regulator, the latest twist in the complex regulatory response to new artificial-intelligence technology. Italy's ban was one of the first nationwide measures restricting the use of ChatGPT since it exploded globally in popularity in recent months. The Italian Data Protection Authority ordered the ban late last month, saying that OpenAI had "no legal basis" for using the data it had amassed about Italian residents to train its algorithms and that it was too easy for children to access.