We argue that human language learning proceeds in a manner that is different in nature from current approaches to training LLMs, predicting a difference in learning biases. We then present evidence from German plural formation by LLMs that confirm our hypothesis that even very powerful implementations produce results that miss aspects of the logic inherent to language that humans have no problem with. We conclude that attention to the different structures of human language and artificial neural networks is likely to be an avenue to improve LLM performance.

Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at-tacks can exploit. Traditional hardware vulnerability detection methods, such as random regression and formal verification, have limitations. Random regression, while scalable, is slow in exploring hardware, and formal verification techniques are often concerned with manual effort and state explosions. Hardware fuzzing has emerged as an effective approach to exploring and detecting security vulnerabilities in large-scale designs like modern processors. They outperform traditional methods regarding coverage, scalability, and efficiency. However, state-of-the-art fuzzers struggle to achieve comprehensive coverage of intricate hardware designs within a practical timeframe, often falling short of a 70% coverage threshold. We propose a novel ML-based hardware fuzzer, ChatFuzz, to address this challenge. Ourapproach leverages LLMs like ChatGPT to understand processor language, focusing on machine codes and generating assembly code sequences. RL is integrated to guide the input generation process by rewarding the inputs using code coverage metrics. We use the open-source RISCV-based RocketCore processor as our testbed. ChatFuzz achieves condition coverage rate of 75% in just 52 minutes compared to a state-of-the-art fuzzer, which requires a lengthy 30-hour window to reach a similar condition coverage. Furthermore, our fuzzer can attain 80% coverage when provided with a limited pool of 10 simulation instances/licenses within a 130-hour window. During this time, it conducted a total of 199K test cases, of which 6K produced discrepancies with the processor's golden model. Our analysis identified more than 10 unique mismatches, including two new bugs in the RocketCore and discrepancies from the RISC-V ISA Simulator.

Learning to search is the task of building artificial agents that learn to autonomously use a search box to find information. So far, it has been shown that current language models can learn symbolic query reformulation policies, in combination with traditional term-based retrieval, but fall short of outperforming neural retrievers. We extend the previous learning to search setup to a hybrid environment, which accepts discrete query refinement operations, after a first-pass retrieval step via a dual encoder. Experiments on the BEIR task show that search agents, trained via behavioral cloning, outperform the underlying search system based on a combined dual encoder retriever and cross encoder reranker. Furthermore, we find that simple heuristic Hybrid Retrieval Environments (HRE) can improve baseline performance by several nDCG points. The search agent based on HRE (HARE) matches state-of-the-art performance, balanced in both zero-shot and in-domain evaluations, via interpretable actions, and at twice the speed.

We all know that the machine is able to understand our language. But have we ever tried to understand how is that possible and which codes or software or programs are made to run through it to make it happen? This blog, therefore, is devoted to Natural Language Processing (NLP) which is behind this hyper-intelligent technology. Natural Language Processing is simply defined as the automation developed with the algorithms that make the natural language understandable by the machine. NLP algorithms convert the human subfield of linguistics into the computer language to make the interaction between the human and the machine possible.

Are you an IT leader feeling stuck in your digital transformation goals? One of the most challenging questions in digital transformation is how to go from vision to execution. You may not be as far behind as you think. You simply need to adopt a better approach. One approach that will make the whole process easier for you to achieve your digital transformation goals is called hyperautomation.

Rapid technological developments are the most distinguishing feature of the twenty-first century, wherever we look we find that everything we use has to do with technology, from the alarm clock on our mobile phones, to the coffee machine that helps to prepare coffee with the push of a button, smart phones, and the Internet that make our lives easier, behind Everything we see on the internet has someone programmed it, but even so, why should you learn to code? Here is a list of reasons why you should learn programming. Any computer program consists of code that is executed on a computer to perform certain tasks. This code is written by programmers. So programming is the process of giving machines a set of instructions that explain to them how to implement the program.

It's not new that the emerging artificial intelligence technology aims to take over the writing space. High-end and intermediate writers have expressed cynical views and even fears over the AI writing software introduction. According to them, the concept behind the creation is to help lessen the workload of writers. In the meantime, the number of AIs has surpassed expectations. From small companies to big names in tech, AIs are attempting to become the next big thing for content marketing.

Representation is a core issue in artificial intelligence. Humans use discrete language to communicate and learn from each other, while machines use continuous features (like vector, matrix, or tensor in deep neural networks) to represent cognitive patterns. Discrete symbols are low-dimensional, decoupled, and have strong reasoning ability, while continuous features are high-dimensional, coupled, and have incredible abstracting capabilities. In recent years, deep learning has developed the idea of continuous representation to the extreme, using millions of parameters to achieve high accuracies. Although this is reasonable from the statistical perspective, it has other major problems like lacking interpretability, poor generalization, and is easy to be attacked. Since both paradigms have strengths and weaknesses, a better choice is to seek reconciliation. In this paper, we make an initial attempt towards this direction. Specifically, we propose to combine symbolism and connectionism principles by using neural networks to derive a discrete representation. This process is highly similar to human language, which is a natural combination of discrete symbols and neural systems, where the brain processes continuous signals and represents intelligence via discrete language. To mimic this functionality, we denote our approach as machine language. By designing an interactive environment and task, we demonstrated that machines could generate a spontaneous, flexible, and semantic language through cooperation. Moreover, through experiments we show that discrete language representation has several advantages compared with continuous feature representation, from the aspects of interpretability, generalization, and robustness.

The Shell is an interpreter that presents the command-line interface to users and allows them to interact with the kernel. It lets them control the system using commands entered from a keyboard. The Interpreter is a program that reads through instructions that are written in human readable programming languages and executes the instructions from top to bottom. It translates each instruction to a machine language the hardware can understand, executes it, and proceeds to the next instruction. The Command-Line Interface (CLI) is a program that accepts text input from the user to run commands on the operating system. It lets them configure the system, install software, and access features that aren't available in the graphical user interface.

As enterprises consider how they might take advantage of adopting a hybrid cloud approach, AI may be poised to accelerate and shake up the possibilities, says Ruchir Puri, chief scientist with IBM Research. Known for his work with IBM Watson, Puri discussed with InformationWeek how IBM is approaching the digital era, hybrid cloud, and AI. This includes evolving AI technology born out of Watson and teaching AI to essentially speak code. Puri's team works on AI technology to assist with data migration from legacy systems and languages such as COBOL to the cloud, and he says AI augmentation could boost productivity through automation of IT. How did AI become part of IBM's plans for the cloud?