Auto-regressive LLM-based software engineering (SWE) agents, henceforth SWE agents, have made tremendous progress (>60% on SWE-Bench Verified) on real-world coding challenges including GitHub issue resolution. SWE agents use a combination of reasoning, environment interaction and self-reflection to resolve issues thereby generating "trajectories". Analysis of SWE agent trajectories is difficult, not only as they exceed LLM sequence length (sometimes, greater than 128k) but also because it involves a relatively prolonged interaction between an LLM and the environment managed by the agent. In case of an agent error, it can be hard to decipher, locate and understand its scope. Similarly, it can be hard to track improvements or regression over multiple runs or experiments. While a lot of research has gone into making these SWE agents reach state-of-the-art, much less focus has been put into creating tools to help analyze and visualize agent output. We propose a novel tool called SeaView: Software Engineering Agent Visual Interface for Enhanced Workflow, with a vision to assist SWE-agent researchers to visualize and inspect their experiments. SeaView's novel mechanisms help compare experimental runs with varying hyper-parameters or LLMs, and quickly get an understanding of LLM or environment related problems. Based on our user study, experienced researchers spend between 10 and 30 minutes to gather the information provided by SeaView, while researchers with little experience can spend between 30 minutes to 1 hour to diagnose their experiment.

Technology does not exist in a vacuum; technological development, media representation, public perception, and governmental regulation cyclically influence each other to produce the collective understanding of a technology's capabilities, utilities, and risks. When these capabilities are overestimated, there is an enhanced risk of subjecting the public to dangerous or harmful technology, artificially restricting research and development directions, and enabling misguided or detrimental policy. The dangers of technological hype are particularly relevant in the rapidly evolving space of AI. Centering the research community as a key player in the development and proliferation of hype, we examine the origins and risks of AI hype to the research community and society more broadly and propose a set of measures that researchers, regulators, and the public can take to mitigate these risks and reduce the prevalence of unfounded claims about the technology.

Artificial Intelligence (AI) is a field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing objects and patterns, and making decisions. In recent years, AI has experienced its greatest expansion, with advances in machine learning, deep learning, natural language processing, and computer vision leading to unprecedented capabilities in AI-powered systems. In this blog post, we'll take a look at the greatest expansion of AI and the key developments that have made it possible. Machine learning is a subfield of AI that enables computers to learn from data without being explicitly programmed. Instead, machine learning algorithms can analyze large amounts of data and identify patterns and relationships that can be used to make predictions or decisions.

In Part 1 of this article, I wrote about how Artificial intelligence (AI) can advance open-source software. But is the converse true as well? Can the open-source world advance the progress of AI? Let's explore this reverse angle. The role of open-source software in AI has become increasingly important over the past few years. One of the primary benefits of open-source software is the ability for developers to collaborate and share knowledge.

The development of machine learning and deep learning solutions typically follows a workflow that starts from the problem definition and goes through the crucial steps of collecting and exploring useful data, training and evaluating candidate models, deploying a solution, and finally documenting and maintaining the system once it is running in the wild (Figure 1). Despite its predictable structure, some steps of this process are iterative by nature and usually require multiple rounds of adjustments, fine-tuning, and optimizations. In this blog post we look at the process of running multiple machine learning experiments while searching for the best solution for a given problem and discuss the need to document the process in a structured way. We will discuss the Why, What and How of managing experiments and then we'll walk through an example of what this looks like in a real-world example. Machine learning (ML) and deep learning (DL) involve a fair amount of "trial and error" regardless of the task (regression, classification, prediction, segmentation), choice of model architecture, and size or complexity of the associated data set.

Training a neural network has been posing problems for researchers and developers for a long time. There are basically two major problems that arise during the development of DL based solution which are the astronomical costs of training, and the time required to train the network. Since training a neural network includes numerous matrix operations and demands a high computational capability, the cost of operation will escalate if one needs to perform a similar process again for another model. Also, the time to train them escalates at an exponential rate as the networks get deeper and complicated. Using GPUs is one effective way to speed up the process.

When we first released the Unsplash API in 2016, we never dreamed that it would become as popular and useful as it has. What started as a low-key late night slack message exchange-- 'Wouldn't it be cool if we made an API?'--turned into one of the world's most used APIs, bringing 2 million open images from the Unsplash community directly into the workflows of creators, enhancing over 1 billion creations. Earlier this year we had a similar moment on our team Slack: would't it be cool if we made the data we use to run Unsplash open for anyone to use? Today we do just that. We're releasing the most complete high-quality open image dataset ever, free for anyone to use to further research in machine learning, image quality, search engines, and more.

Recent years have seen breakthroughs in neural network technology: computers can now beat any living person at the most complex game invented by humankind, as well as imitate human voices and faces (both real and non-existent) in a deceptively realistic manner. Is this a victory for artificial intelligence over human intelligence? And if not, what else do researchers and developers need to achieve to make the winners in the AI race the "kings of the world?" Over the last 60 years, artificial intelligence (AI) has been the subject of much discussion among researchers representing different approaches and schools of thought. One of the crucial reasons for this is that there is no unified definition of what constitutes AI, with differences persisting even now.

How we act, including how we speak, is more often than not determined by the situation we find ourselves in. We tailor dialogue to appropriately fit the scenario. If trained conversational agents are to continue evolving into dependable resources people can turn to for assistance, they'll need to be trained to do the same. Today, we're excited to make available the Intelligent Conversation Engine: Code and Pre-trained Systems, or Microsoft Icecaps, a new open-source toolkit that not only allows researchers and developers to imbue their chatbots with different personas, but also to incorporate other natural language processing features that emphasize conversation modeling. Icecaps provides an array of capabilities from recent conversation modeling literature.

Examples include IBM Watson, Stanford's DeepDive or Google Prediction. Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.