A lot of deep learning (DL) research these days is mainly focused on improving quantitative metrics regardless of other factors. In human-centered applications, like skin lesion classification in dermatology, DL-driven clinical decision support systems are still in their infancy due to the limited transparency of their decision-making process. Moreover, the lack of procedures that can explain the behavior of trained DL algorithms leads to almost no trust from clinical physicians. To diagnose skin lesions, dermatologists rely on visual assessment of the disease and the data gathered from the patient's anamnesis. Data-driven algorithms dealing with multi-modal data are limited by the separation of feature-level and decision-level fusion procedures required by convolutional architectures. To address this issue, we enable single-stage multi-modal data fusion via the attention mechanism of transformer-based architectures to aid in diagnosing skin diseases. Our method beats other state-of-the-art single- and multi-modal DL architectures in image-rich and patient-data-rich environments. Additionally, the choice of the architecture enables native interpretability support for the classification task both in the image and metadata domain with no additional modifications necessary.

Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database. Large language models (LLMs) work well in natural language generation tasks, but they are not specifically pre-trained to understand the syntax and semantics of SQL commands. In this paper, we propose an LLM-based framework for Text-to-SQL which retrieves helpful demonstration examples to prompt LLMs. However, questions with different database schemes can vary widely, even if the intentions behind them are similar and the corresponding SQL queries exhibit similarities. Consequently, it becomes crucial to identify the appropriate SQL demonstrations that align with our requirements. We design a de-semanticization mechanism that extracts question skeletons, allowing us to retrieve similar examples based on their structural similarity. We also model the relationships between question tokens and database schema items (i.e., tables and columns) to filter out scheme-related information. Our framework adapts the range of the database schema in prompts to balance length and valuable information. A fallback mechanism allows for a more detailed schema to be provided if the generated SQL query fails. Ours outperforms state-of-the-art models and demonstrates strong generalization ability on three cross-domain Text-to-SQL benchmarks.

Recently, the ChatGPT LLM has received great attention: it can be used as a bot for discussing source code, prompting it to suggest changes, provide descriptions or even generate code. Typical demonstrations generally focus on existing benchmarks, which may have been used in model training (i.e., data leakage). To assess the feasibility of using an LLM as a useful assistant bot for programmers, we must assess its realistic capabilities on unseen problems as well as its capabilities on various tasks. In this paper, we present an empirical study of ChatGPT's potential as a fully automated programming assistant, focusing on the tasks of code generation, program repair, and code summariziation. The study investigates ChatGPT's performance on common programming problems and compares it with state-of-the-art approaches on two benchmarks. Among several findings, our study shows that ChatGPT is effective in dealing with common programming problems. However, our experiments also reveal limitations in terms of its attention span: detailed descriptions will constrain the focus of ChatGPT and prevent it from leveraging its vast knowledge to solve the actual problem. Surprisingly, we have identified the ability of ChatGPT to reason the original intention of the code. We expect future work to build on this insight for dealing with the open question of the oracle problem. Our findings contribute interesting insights to the development of LLMs for programming assistance, notably by demonstrating the importance of prompt engineering, and providing a better understanding of ChatGPT's practical applications for software engineering.

Last December, Moby-Dick made one of my students gasp. It wasn't the first time this had happened (weird book), but nothing about the text itself produced the response. For the final project in my English class for high-school seniors, where we spend a semester reading Moby-Dick, I assigned a pretty standard eight-to-10-page research paper. One student, interested in finance, saw a connection between the plot and the 2008 financial crisis. He spent weeks thinking about the parallels, trying to find a way to make all of the pieces fit together into a cohesive argument about whaling and the exploitations of global capitalism.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Tech giant Baidu on Wednesday received approval by Chinese authorities to launch its artificial intelligence Ernie Bot to the general public starting Aug. 31, a spokesperson told Reuters. Baidu became the first company to receive such approval after regulatory setbacks and is also set to launch a suite of new AI-native apps. The company has been embedding Ernie, which resembles OpenAI's ChatGPT, into its search engine and other products, allowing many of them to gain market share while waiting for Chinese regulators' approval.

When Ernie Bot was released on March 16, the response was a mix of excitement and disappointment. Many people deemed its performance mediocre relative to the previously released ChatGPT. But most people simply weren't able to see it for themselves. The launch event didn't feature a live demonstration, and later, to actually try out the bot, Chinese users need to have a Baidu account and apply for a use license that could take as long as three months to come through. Because of this, some people who got access early were selling secondhand Baidu accounts on e-commerce sites, charging anywhere from a few bucks to over $100.

I teach in the computer science department at Vanderbilt University. In my Algorithms class this past spring, I decided to regularly expose my students to ChatGPT so they could see firsthand that it can't replace their critical thinking skills. I did this primarily for selfish reasons--like most instructors, I wanted my students to rely on their own creative problem-solving abilities rather than ChatGPT to answer their homework questions. I hypothesized that demonstrating the fallibility of ChatGPT would be a more effective deterrent than a syllabus policy statement. I just needed to find its weaknesses when it came to our course materials.

In the past few years, multiple researchers claim to have shown that large language models can pass cognitive tests designed for humans, from working through problems step by step, to guessing what other people are thinking. These kinds of results are feeding a hype machine predicting that these machines will soon come for white-collar jobs; that they could replace teachers, doctors, journalists, and lawyers. Geoffrey Hinton has called out GPT-4's apparent ability to string together thoughts as one reason he is now scared of the technology he helped create. There's little agreement on what those results really mean. Some people are dazzled by what they see as glimmers of human-like intelligence, while others aren't convinced one bit.

No, is the conclusion of the most thorough and rigorous investigation of the question, despite the impressive abilities of the latest AI models like ChatGPT. But the team of philosophy, computing and neuroscience experts behind the study say there's no theoretical barrier for AI to reach self-awareness. Debate over whether AI is, or even can be, sentient has raged for decades and only ramped up in recent years with the advent of large language models that can hold convincing conversation and generate text on a variety of topics. Microsoft recently tested OpenAI's GPT-4 and claimed the model was already displaying "sparks" of general intelligence. While Blake Lemoine, a former Google engineer, infamously went a step further to claim that the firm's LaMDA artificial intelligence had actually become sentient – having hired a lawyer to protect its rights before parting ways with the company.

Last month Webb and his colleagues published an article in Nature, in which they describe GPT-3's ability to pass a variety of tests devised to assess the use of analogy to solve problems (known as analogical reasoning). On some of those tests GPT-3 scored better than a group of undergrads. "Analogy is central to human reasoning," says Webb. "We think of it as being one of the major things that any kind of machine intelligence would need to demonstrate." What Webb's research highlights is only the latest in a long string of remarkable tricks pulled off by large language models. For example, when OpenAI unveiled GPT-3's successor, GPT-4, in March, the company published an eye-popping list of professional and academic assessments that it claimed its new large language model had aced, including a couple of dozen high school tests and the bar exam.