CFOs are experimenting with AI use cases to free up capacity for business-critical work. Generative AI has the potential to transform the finance function. By taking on some of the more mundane tasks that can occupy a lot of time, generative AI tools can help free up capacity for more high-value strategic work. For chief financial officers, this could mean spending more time and energy on proactively advising the business on financial strategy as organizations around the world continue to weather ongoing geopolitical and financial uncertainty. CFOs can use large language models (LLMs) and generative AI tools to support everyday tasks like generating quarterly reports, communicating with investors, and formulating strategic summaries, says Andrew W. Lo, Charles E. and Susan T. Harris professor and director of the Laboratory for Financial Engineering at the MIT Sloan School of Management. "LLMs can't replace the CFO by any means, but they can take a lot of the drudgery out of the role by providing first drafts of documents that summarize key issues and outline strategic priorities."

President Donald Trump's administration has dramatically expanded its work with Palantir, elevating the company cofounded by Trump ally Peter Thiel as the government's go-to software developer. Following massive contract terminations for consulting giants and government contractors like Accenture, Booz Allen, and Deloitte, Palantir has emerged ahead. Now the data analytics firm is partnering with those companies--offering them a lifeline while consolidating its own power. Palantir has become one of the few winners in the Trump administration's cost-cutting efforts, receiving more than 113 million in federal spending since the beginning of the year, according to The New York Times. Palantir's US government revenue has grown by more than 370 million compared to this time last year, according to the company's most recent quarterly earnings report.

To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have demonstrated ability to predict various properties of materials with the level of accuracy similar to explicit calculation of quantum mechanical theories, but with significantly reduced run time and computational resources. Within ML, graph neural networks have emerged as an important algorithm within the field of machine learning, since they are capable of predicting accurately a wide range of important physical, chemical and electronic properties due to their higher learning ability based on the graph representation of material and molecular descriptors through the aggregation of information embedded within the graph. In parallel with the development of state of the art classical machine learning applications, the fusion of quantum computing and machine learning have created a new paradigm where classical machine learning model can be augmented with quantum layers which are able to encode high dimensional data more efficiently. Leveraging the structure of existing algorithms, we developed a unique and novel gradient free hybrid quantum classical convoluted graph neural network (HyQCGNN) to predict formation energies of perovskite materials. The performance of our hybrid statistical model is competitive with the results obtained purely from a classical convoluted graph neural network, and other classical machine learning algorithms, such as XGBoost. Consequently, our study suggests a new pathway to explore how quantum feature encoding and parametric quantum circuits can yield drastic improvements of complex ML algorithm like graph neural network.

Rolling bearing fault diagnosis has garnered increased attention in recent years owing to its presence in rotating machinery across various industries, and an ever increasing demand for efficient operations. Prompt detection and accurate prediction of bearing failures can help reduce the likelihood of unexpected machine downtime and enhance maintenance schedules, averting lost productivity. Recent technological advances have enabled monitoring the health of these assets at scale using a variety of sensors, and predicting the failures using modern Machine Learning (ML) approaches including deep learning architectures. Vibration data has been collected using accelerated run-to-failure of overloaded bearings, or by introducing known failure in bearings, under a variety of operating conditions such as rotating speed, load on the bearing, type of bearing fault, and data acquisition frequency. However, in the development of bearing failure classification models using vibration data there is a lack of consensus in the metrics used to evaluate the models, data partitions used to evaluate models, and methods used to generate failure labels in run-to-failure experiments. An understanding of the impact of these choices is important to reliably develop models, and deploy them in practical settings. In this work, we demonstrate the significance of these choices on the performance of the models using publicly-available vibration datasets, and suggest model development considerations for real world scenarios. Our experimental findings demonstrate that assigning vibration data from a given bearing across training and evaluation splits leads to over-optimistic performance estimates, PCA-based approach is able to robustly generate labels for failure classification in run-to-failure experiments, and $F$ scores are more insightful to evaluate the models with unbalanced real-world failure data.

Large Language Models (LLMs) have shown impressive performance across a variety of Artificial Intelligence (AI) and natural language processing tasks, such as content creation, report generation, etc. However, unregulated malign application of these models can create undesirable consequences such as generation of fake news, plagiarism, etc. As a result, accurate detection of AI-generated language can be crucial in responsible usage of LLMs. In this work, we explore 1) whether a certain body of text is AI generated or written by human, and 2) attribution of a specific language model in generating a body of text. Texts in both English and Spanish are considered. The datasets used in this study are provided as part of the Automated Text Identification (AuTexTification) shared task. For each of the research objectives stated above, we propose an ensemble neural model that generates probabilities from different pre-trained LLMs which are used as features to a Traditional Machine Learning (TML) classifier following it. For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place (with macro $F1$ scores of 0.733 and 0.649) for English and Spanish texts, respectively. For the second task on model attribution, our model ranked in first place with macro $F1$ scores of 0.625 and 0.653 for English and Spanish texts, respectively.

In Meta's quarterly earnings call in April, chief executive Mark Zuckerberg was on the defensive. The metaverse, the vision of a globe-spanning virtual reality that he had literally bet his multibillion-dollar empire on creating, had been usurped as the new hot thing by the growing hype around artificial intelligence (AI). Critics had even noticed Meta itself changing its tune, highlighting the difference between a November statement from Zuckerberg, in which he described the project as a "high-priority growth area" and a March note that instead focused on how "advancing AI" was the company's "single largest investment". Not so, said the world's richest millennial. "A narrative has developed that we're somehow moving away from focusing on the metaverse vision, so I just want to say upfront that that's not accurate. "We've been focusing on AI and the metaverse for years now, and we will continue to focus on both … Building the metaverse is a long-term project, but the rationale for it remains the same and we remain committed to it." But more than 18 months after Facebook changed its name to Meta – demonstrating Zuckerberg's firm belief that "the metaverse will be the successor of the mobile internet" – the future he promised seems no closer to existence than it did backthen. Reams of concept art, tech demos and prototype devices have given way to little meaningful progress. The company has even struggled to actually define what it is hoping to build: in a lengthy blogpost published last May, Nick Clegg, the former UK deputy prime minister who is now Meta's president of global affairs, described the ambition only in vague terms, despite elaborating across 8,000 words how it would nonetheless change the world. "The metaverse is a logical evolution.

There's been a lot of discussion on the use of artificial intelligence and the future of work. Will human creativity be usurped by bots? How will A.I. be incorporated into the finance function? These are just some of the questions organizations will face. I asked Sayan Chakraborty, copresident at Workday (sponsor of CFO Daily), who also leads the product and technology organization, for his perspective on a balance between tech and human capabilities.

Building machine learning models and applying them to business processes requires collaboration between data scientists, data engineers, designers, business professionals, and IT professionals. Efficient collaboration and orchestration is especially critical for businesses that want to adopt AI and ML at scale, which leads to a three-fold increase in ROI over companies in the AI proof-of-concept stage. Inspired by DevOps practices for software development, MLOps brings diverse teams in an organization together to speed up the development and deployment of machine learning models. In this article, we'll provide an in-depth guide to MLOps, how it helps streamline end-to-end ML processes, and some case studies from companies who have adopted it. MLOps (Machine Learning Operations) is a set of practices to standardize and streamline the process of construction and deployment of machine learning systems.

In the financial services industry, banks, insurers, asset managers and fintech companies are increasing the speed at which they deploy artificial intelligence (AI)-enabled applications, confident that AI will help them assess risk more accurately, enable operational efficiencies, and reduce costs, results from a new study by American tech firm Nvidia show. The 2023 State of AI in Financial Services report, released on February 02, 2023, draws on a survey of nearly 500 global financial services professionals that sought to understand AI trends in the sector, as well as the opportunities perceived and challenges faced by the industry. Results from the study show that the adoption of AI in the finance sector is accelerating at a fast pace, with over half of the respondents indicating having deployed three or more of the 21 different AI-enabled use cases analyzed by the survey. A fifth of respondents said they had six or more use cases in market. Accelerated adoption of AI in the sector comes on the back of increased awareness of the imperative among executive leadership teams.

Maybe it stems from my childhood fascination with crystal balls and the Magic 8 Ball, but I have always been interested in predictions of the future. Machine learning has done a great job with predictions based on past data about events and behaviors, but it hasn't generally been applied to making sense of the broader world. But that is just what Signal AI is doing with machine learning. They produce "external intelligence" intended as an aid to decision-making. It could also be called "environmental sensing."