Abrupt and unexpected terminations of software are termed as software crashes. They can be challenging to analyze. Finding the root cause requires extensive manual effort and expertise to connect information sources like stack traces, source code, and logs. Typical approaches to fault localization require either test failures or source code. Crashes occurring in production environments, such as that of SAP HANA, provide solely crash logs and stack traces. We present a novel approach to localize faults based only on the stack trace information and no additional runtime information, by fine-tuning large language models (LLMs). We address complex cases where the root cause of a crash differs from the technical cause, and is not located in the innermost frame of the stack trace. As the number of historic crashes is insufficient to fine-tune LLMs, we augment our dataset by leveraging code mutators to inject synthetic crashes into the code base. By fine-tuning on 64,369 crashes resulting from 4.1 million mutations of the HANA code base, we can correctly predict the root cause location of a crash with an accuracy of 66.9\% while baselines only achieve 12.6% and 10.6%. We substantiate the generalizability of our approach by evaluating on two additional open-source databases, SQLite and DuckDB, achieving accuracies of 63% and 74%, respectively. Across all our experiments, fine-tuning consistently outperformed prompting non-finetuned LLMs for localizing faults in our datasets.

The hard truth is that many machine learning projects fail to get set into production. It takes time and real effort to move from a machine learning model to a real business application. Of course, we can't save the world with just one Hands-On tutorial, but we can at least try to make the life of a data scientist a little easier. In this blog post we will tackle these challenges by bringing the opensource world and SAP world together. In a nutshell, there will be no movement of training data from SAP HANA Cloud to our Python environment.

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time. It's not a simple process for companies to incorporate machine learning into their existing business intelligence systems, though Skymind CEO and past Emerj podcast guest Chris Nicholson advises that it doesn't have to be daunting. "AI is just a box," he says.

Although the Japanese company Honda is widely known as one of the largest automobile manufacturers in the world and also the largest manufacturer of motorcycles, it is increasingly on the front edge of technological innovation outside the automotive world. The company's investment in research and development landed it on the "Top 20 R&D Spenders" list that includes five other automakers but representatives from other industries as well. Based upon the innovations Honda has shared publicly, it's using some of this R&D budget to get ready for the 4th industrial revolution by using AI and big data to not only design safer and more personalized autos, but also create robots. With the tremendous amount of data that's created from a wide variety of sources including sensors on cars, customer surveys, smartphones and social media, Honda's research and development team uses data analytics tools to comb through data sets in order to gain insights it can incorporate into future auto designs. As the company's big data maturity has increased, its engineers are learning to work with and leverage data, that had previously been to cumbersome to find meaning, thanks to the assistance of big data technology and analytics tools.

"My eye was cut in half," McDermott said of the 2015 accident at his brother's home that has required 13 surgeries. "It required thousands of stitches to put it back together." Two years later, he replays the incident in his mind and mulls if he could take it all back. It has only made me better, more self-empowering," he reflects in a board room at a luxury hotel here. "I appreciate every moment, on a deeper emotional level.

SAP Labs in India is the second largest R&D centre for the company after its centre in Walldorf, Germany and among the three hubs in the SAP Labs network of 19 Labs across 16 countries. Dilipkumar Khandelwal, MD for SAP Labs in India, has a dual role as he is also the EVP and Global Head of Enterprise Cloud Services for SAP. In an exclusive interview with Ayushman Baruah, Khandelwal talks about their India focus, latest technologies, and their emphasis on innovation. Excerpts: What is the SAP Lab's focus here? Over a period of 19 years, SAP Labs India has evolved to become an integral part of SAP's global strategy.

SAP Labs in India is the second largest R&D centre for the company after its centre in Walldorf in Germany and among the 3 hubs in SAP Labs network of 19 Labs across 16 countries. Dilipkumar Khandelwal, Managing Director for SAP Labs in India, has a dual role as he is also the Executive Vice President and Global Head of Enterprise Cloud Services for SAP. Edited excerpts: What is the SAP Lab's focus here? Do you focus more on the local needs to serve SAP India or do you essentially serve global needs? SAP Labs India was founded in 1998.

SAP is at work on a new product called Sentinel that is supposed to be "nothing less than the Amazon for Stocks and the Facebook for Investors," according to a job posting associated with the project. "We take our metaphors seriously," adds the job listing, which was first flagged Thursday in a blog post by SAP consultant Richard Hirsch. That said, the Amazon comparison seems more apt than the Facebook one, based on other passages in the ad. "Sentinel blends Algorithmic Trading with deep News Analytics into a disruptive synergy, powered by SAP's revolutionary in-memory HANA platform and deep text analytics capabilities," it states. "Our sole purpose is simply this: to help millions of investors identify profitable trading opportunities with minimal cognitive overload. The end-goal is to build a system that is simple but not trivial; deeply quantitative but not confounding; advanced but not abstruse."

Thirty-five years ago the TV series Knight Rider envisioned an artificially intelligent car that could develop a friendly rapport with its driver. That 1982 Pontiac Trans Am--also known as the Knight Industries Two Thousand (KITT)--dutifully served as Michael Knight's crime-fighting partner, monitored his health through sensors in the seat and even used voice analysis to respond to the sarcasm in Knight's cornball quips. Your next car won't reach KITT's level of awareness, wit or empathy--but Honda, Toyota and several other companies really are planning to make AI standard in all the vehicles they produce. Honda unveiled one of the more ambitious--and fanciful--visions for AI in the cockpit at last week's U.S. Consumer Electronics Show (CES) in Las Vegas. Its New Electric Urban Vehicle (NeuV) is a self-driving concept car that uses Honda's talking Automated Network Assistant, or HANA, to analyze and respond to data the vehicle collects about driver and passenger preferences and behavior.

IBM (NYSE:IBM) jointly announced with the German conglomerate Siemens (OTCPK:SIEGY) that they are planning to include IBM's Watson in Siemens's industry analytics platform MindSphere. Siemens is Europe's largest manufacturing and electronics company with a worldwide presence. Siemens operates in the industrial sector with lots of on-premises software suites, which the company is willing to send to cloud. As a result, IBM's Watson will get a significant boost. This article investigates how IBM's Watson platform will benefit from the development.