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Originally published on bloomberg.com

NORTHAMPTON, MA / ACCESSWIRE / May 12, 2023 / The Bloomberg Terminal provides access to more than 35 million financial instruments across all asset classes. That's a lot of data, and to make it useful, AI and machine learning (ML) are playing an increasingly central role in the Terminal's ongoing evolution.

Machine learning is about scouring data at speed and scale that is far beyond what human analysts can do. Then, the patterns or anomalies that are discovered can be used to derive powerful insights and guide the automation of all kinds of arduous or tedious tasks that humans used to have to perform manually.

While AI continues to fall short of human intelligence in many applications, there are areas where it vastly outshines the performance of human agents. Machines can identify trends and patterns hidden across millions of documents, and this ability improves over time. Machines also behave consistently, in an unbiased fashion, without committing the kinds of mistakes that humans inevitably make.

"Humans are good at doing things deliberately, but when we make a decision, we start from whole cloth," says Gideon Mann, Head of ML Product & Research in Bloomberg's CTO Office. "Machines execute the same way every time, so even if they make a mistake, they do so with the same error characteristic."

The Bloomberg Terminal currently employs AI and ML techniques in several exciting ways, and we can expect this practice to expand rapidly in the coming years. The story begins some 20 years ago

Keeping Humans in the Loop

When we started in the 80s, data extraction was a manual process. Today, our engineers and data analysts build, train, and use AI to process unstructured data at massive speeds and scale - so our customers are in the know faster.

The rise of the machines

Prior to the 2000s, all tasks related to data collection, analysis, and distribution at Bloomberg were performed manually, because the technology did not yet exist to automate them. The new millennium brought some low-level automation to the company's workflows, with the emergence of primitive models operating by a series of if-then rules coded by humans. As the decade came to a close, true ML took flight within the company. Under this new approach, humans annotate data in order to train a machine to make various associations based on their labels. The machine "learns" how to make decisions, guided by this training data, and produces ever more accurate results over time. This approach can scale dramatically beyond traditional rules-based programming.

In the last decade, there has been an explosive growth in the use of ML applications within Bloomberg. According to James Hook, Head of the company's Data department, there are a number of broad applications for AI/ML and data science within Bloomberg.

One is information extraction, where computer vision and/or natural language processing (NLP) algorithms are used to read unstructured documents - data that's arranged in a format that's typically difficult for machines to read - in order to extract semantic meaning from them. With these techniques, the Terminal can present insights to users that are drawn from video, audio, blog posts, tweets, and more.

Anju Kambadur, Head of Bloomberg's AI Engineering group, explains how this works:

"It typically starts by asking questions of every document. Let's say we have a press release. What are the entities mentioned in the document? Who are the executives involved? Who are the other companies they're doing business with? Are there any supply chain relationships exposed in the document? Then, once you've determined the entities, you need to measure the salience of the relationships between them and associate the content with specific topics. A document might be about electric vehicles, it might be about oil, it might be relevant to the U.S., it might be relevant to the APAC region - all of these are called topic codes' and they're assigned using machine learning."

All of this information, and much more, can be extracted from unstructured documents using natural language processing models.

Another area is quality control, where techniques like anomaly detection are used to spot problems with dataset accuracy, among other areas. Using anomaly detection methods, the Terminal can spot the potential for a hidden investment opportunity, or flag suspicious market activity. For example, if a financial analyst was to change their rating of a particular stock following the company's quarterly earnings announcement, anomaly detection would be able to provide context around whether this is considered a typical behavior, or whether this action is worthy of being presented to Bloomberg clients as a data point worth considering in an investment decision.

And then there's insight generation, where AI/ML is used to analyze large datasets and unlock investment signals that might not otherwise be observed. One example of this is using highly correlated data like credit card transactions to gain visibility into recent company performance and consumer trends. Another is analyzing and summarizing the millions of news stories that are ingested into the Bloomberg Terminal each day to understand the key questions and themes that are driving specific markets or economic sectors or trading volume in a specific company's securities.

Humans in the loop

When we think of machine intelligence, we imagine an unfeeling autonomous machine, cold and impartial. In reality, however, the practice of ML is very much a team effort between humans and machines. Humans, for now at least, still define ontologies and methodologies, and perform annotations and quality assurance tasks. Bloomberg has moved quickly to increase staff capacity to perform these tasks at scale. In this scenario, the machines aren't replacing human workers; they are simply shifting their workflows away from more tedious, repetitive tasks toward higher level strategic oversight.

"It's really a transfer of human skill from manually extracting data points to thinking about defining and creating workflows," says Mann.

Ketevan Tsereteli, a Senior Researcher in Bloomberg Engineering's Artificial Intelligence (AI) group, explains how this transfer works in practice.

"Previously, in the manual workflow, you might have a team of data analysts that would be trained to find mergers and acquisition news in press releases and to extract the relevant information. They would have a lot of domain expertise on how this information is reported across different regions. Today, these same people are instrumental in collecting and labeling this information, and providing feedback on an ML model's performance, pointing out where it made correct and incorrect assumptions. In this way, that domain expertise is gradually transferred from human to machine."

Humans are required at every step to ensure the models are performing optimally and improving over time. It's a collaborative effort involving ML engineers who build the learning systems and underlying infrastructure, AI researchers and data scientists who design and implement workflows, and annotators - journalists and other subject matter experts - who collect and label training data and perform quality assurance.

"We have thousands of analysts in our Data department who have deep subject matter expertise in areas that matter most to our clients, like finance, law, and government," explains ML/AI Data Strategist Tina Tseng. "They not only understand the data in these areas, but also how the data is used by our customers. They work very closely with our engineers and data scientists to develop our automation solutions."

Annotation is critical, not just for training models, but also for evaluating their performance.

"We'll annotate data as a truth set - what they call a "golden" copy of the data," says Tseng. "The model's outputs can be automatically compared to that evaluation set so that we can calculate statistics to quantify how well the model is performing. Evaluation sets are used in both supervised and unsupervised learning."

Check out "Best Practices for Managing Data Annotation Projects," a practical guide published by Bloomberg's CTO Office and Data department about planning and implementing data annotation initiatives.

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SOURCE: Bloomberg

Link:
Humans in the Loop: AI & Machine Learning in the Bloomberg ... - AccessWire

A new survey shows that continued inflation and economic disruptions are the top concerns for more than half of organizations in 2023. Despite this, most organizations expect their revenues to either increase or stay the same this year. As a result, three quarters of organizations plan to resume business travel in 2023 and half of organizations surveyed plan to invest in analytic technologies that can help navigate uncertain economic conditions.

The Enterprise Financial Decision-Makers Outlook April 2023 semi-annual survey was published by OneStream Software, a leader in corporate performance management (CPM) solutions for the worlds leading enterprises. Conducted by Hanover Research, the survey targeted finance leaders across North America to identify trends and investment priorities in response to economic challenges and other forces in the upcoming year.

When asked about current business drivers and plans for 2023, financial leaders are focused on the following factors:

COVID is still prevalent, but the business impact is shrinking

As the world returns to some type of normal following the pandemic, organizations are planning to reintroduce business travel but are still wary of supply chains. More than half of financial leaders expect COVID-related supply chain disruptions to continue into 2024 (54%) or beyond, down 18% from the Spring 2022survey.Business travel is poised for a comeback this year as 75% of organizations plan to resume this practice in 2023. In the Spring 2022 survey, most organizations (80%) had planned to resume business travel, but the study shows very few have actually implemented the plan (10%), citing the costs of flights, hotel, food and the lack of necessity.

Analytic technology is gaining focus to help navigate uncertainty

Trends in the survey foreshadow an increased usage of analytic technology that improves productivity and supports more agile decision-making across the enterprise. Cloud-based planning and reporting solutions remain the most used data analysis tool (91%), however, most organizations also use predictive analytics (85%), business intelligence (84%) and ML/AI (75%) tools at least intermittently. About half of organizations are planning to invest more in each of these tools this year, compared to 2022.

Adoption momentum for these tools started during the pandemic with no sign of slowing down. According to theSpring 2021survey, organizations said that in comparison to pre-pandemic they were increasing investments in artificial intelligence (59%), predictive analytics (58%), cloud-based planning and reporting solutions (57%) and machine learning (54%).

Organizations are realizing the value of AI

According to the survey, two-thirds of organizations (68%) have adopted an automated machine learning (AutoML) solution to supplement some of their workforce needs, a significant uptick when compared toSpring 2022(56%). In theFall 2022survey, 48% of respondents planned to investigate an AutoML solution in the future, which suggests respondents stayed true to their word and dove in on the technology in the last six months.

Finance leaders see opportunities for improvement in many areas with the help of AI/ML technologies, including ChatGPT. The tasks and processes they believe these technologies will be most useful for include financial reporting, financial planning & analysis, sales/revenue forecasting, sales & marketing and customer service.

Along with investing in new technology, almost all organizations (91%) are investing or planning to invest in new solutions that specifically support finance functions. The most common solutions are cloud-based applications (52%), AI/ML (43%), advanced predictive analytics (42%) and budgeting/planning systems (42%).

The current economic headwinds have finance leaders acutely aware of their investment decisions and weighing the benefits vs. the costs, said Bill Koefoed, Chief Financial Officer, OneStream. With revenue growth through economic uncertainty in mind, financial leaders are looking to invest in solutions that can support more agile decision-making, while delivering a fast return on investment. AutoAI and other AI innovations coming to light in the last couple of years have the potential to improve the speed and accuracy of forecasting and support more informed, confident decision making. OneStream is a proud innovator in this space and partners with organizations around the globe to help them navigate these challenging times.

Read more here:
Financial Leaders Investing in Analytics, AI and Machine Learning ... - CPAPracticeAdvisor.com

Decoding the Quant Market: A Guide to Machine Learning in Trading

In the ever-changing world of finance and trading, the search for a competitive edge has been a constant driver of innovation. Over the last few decades, the field of quantitative trading has emerged as a powerful force, pushing the boundaries of what is possible and reshaping the way we approach the market. At the heart of this transformation lies the fusion of cutting-edge technology, data-driven insights, and the unwavering curiosity of the human mind. It is this intersection of disciplines that forms the foundation for Decoding the Quant Market: A Guide to Machine Learning inTrading.

In this book, I aim to share my experiences and insights, offering a comprehensive guide to navigating the world of machine learning in quantitative trading. Furthermore, the journey begins with a foundational understanding of the core principles, theories. Moreover, algorithms that have shaped the field. From there, we delve into the practical applications of these techniques, exploring real-world examples and case studies that illustrate the power of machine learning in trading.

Decoding the Quant Market is designed to be accessible to readers from diverse backgrounds, whether they are seasoned professionals or newcomers to the field of finance and technology. As a result, of combining theoretical knowledge with practical insights and examples. Thus, this book aims to provide a well-rounded understanding of the complex world of machine learning in trading.

Amazon.com: Decoding the Quant Market: A Guide to Machine Learning in Trading eBook : Marti, Gautier: Kindle Store

Continued here:
Decoding the Quant Market: A Guide to Machine Learning in Trading - Rebellion Research

The following is a summary of the Machine Learning Methods for Predicting Patient-Level Emergency Department Workload, published in the January 2023 issue of Emergency Medicine by Joseph et al.

Work Relative Value Units (wRVUs) are incorporated into various salary structures as a measure of the time and effort put into patient care. Therefore, predicting the number of wRVUs a patient will generate at triage with high accuracy would have many operational and clinical benefits, including easing the burden on individual doctors by spreading their workload more evenly. This study used data typically collected during triage to test whether deep-learning methods could accurately predict a patients wRVUs. Participants were adults who visited an urban, academic ER between July 1, 2016, and March 1, 2020.

Structured data (age, sex, vital signs, Emergency Severity Index score, language, race, standardized chief complaint) and unstructured data (free-text chief complaint) were used in the de-identified triage information, with wRVUs serving as the outcome measure. The researchers looked at five models, including the mean wRVUs per the primary complaint, linear regression, neural networks, gradient-boosted trees on structured data, and neural networks on unstructured textual data. A mean absolute error was used as a metric to rank the quality of the models. Between January 1, 2016, and February 28, 2020, they analyzed 204,064 visits. Age, gender, and race significantly affected wRVUs, with the median wRVUs being 3.80 (interquartile range 2.56-4.21).

Model errors decreased with increasing model complexity. Predictions with the use of chief complaints demonstrated a mean error of 2.17 wRVUs per visit (95% CI 2.07-2.27). The linear regression model showed an error of 1.00 wRVUs (95% CI 0.97-1.04), the gradient-boosted tree showed an error of 0.85 wRVUs (95% CI 0.84-0.86), the neural network with structured data showed an error of 0.86 wRVUs (95% CI 0.85-0.87), and the neural network with unstructured data showed an error of 0.78 wRVUs (95% CI 0.76-0.80). However, deep learning techniques show promise in overcoming the limitations of chief complaints as a predictor of the time required to evaluate a patient. These algorithms may have numerous useful applications, such as reducing bias in the triage process, quantifying crowding and mobilizing resources, and balancing emergency physicians and compensation.

Source: sciencedirect.com/science/article/abs/pii/S0736467922005686

Link:
Predicting ED Workload with Machine Learning: Patient-Level ... - Physician's Weekly

I used to fall asleep at night with needles in my face. One needle shallowly planted in the inner corners of each eyebrow, one per temple, one in the middle of each eyebrow above the pupil, a few by my nose and mouth. Id wake up hours later, the hair-thin, stainless steel pins having been surreptitiously removed by a parent. Sometimes theyd forget about the treatment, and in the morning wed search my pillow for needles. My very farsighted left eye gradually became only somewhat farsighted, and my mildly nearsighted right eye eventually achieved a perfect score at the optometrists. By the time I was six, my glasses had disappeared from the picture albums.

The story of my recovered eyesight was the first thing Id think to mention when people found out that my parents are specialists in traditional Chinese medicine (TCM) and asked me what I thought of the practice. It was a concrete and rather miraculous firsthand experience, and I knew what it meantto begin to see the world more clearly while under my mother and fathers care.

Otherwise, I rarely knew what to say. I would recall hearing TCM mentioned in relation to poor evidence or badly designed studies and feel challenged to providesome defense for a line of work seen as illegitimate. I would feel a pull of obligation to defend Chinese medicine as a way to protect my parents, their care and toils, but also an urge to resist shouldering that obligation for the sake of someone elses fleeting curiosity and perhaps entertainment.

Mostly, I wished I had a better understanding of TCM, even just for myself. Now that I work in machine learning (ML), Im often struck by the parallels between this cutting-edge technology and the ancient practice of TCM. For one, I cant quite explain either satisfactorily.

Its not that there arent explanations for how the field of Chinese medicine works. I, and many others, just find the theories dubious. According to both classical and modern theory, blood and qipronounced chi, variously interpreted to mean something like vapormove around and regulate the body, which itself is not considered separate from the mind.

Qi flows through channels called meridians. The anatomical charts hanging on the walls of my parents clinics feature meridians scoring the body in neat, straight linesfrom chest to finger, or from the waist to the inner thighoverlaid on diagrams of the bones and organs. At various points along these meridians, needles can be inserted to remove blockages, improving the flow of qi. All TCM treatments ultimately revolve around qi: Acupuncture banishes unhealthy qi and circulates healthy qi from the outside; herbal medicines do so from the inside.

On my parents charts, the meridians and acupuncture points are depicted like a subway map and seem to float slightly upward, tethered only loosely to the recognizable shapes of intestines and joints underneath. This lack of visual correspondence is reflected in the science; little evidence has been found for the physical existence of meridians, or of qi. Studies have investigated whether meridians are special conduits for electrical signalsbut these experiments werebadly designedor whether they arerelated to fascia, the thin stretchy tissue that surrounds almost all internal body parts. All of this work is recent, and results have been inconclusive.

In contrast, the effectiveness of acupuncture, particularly for ailments likeneck disorders andlow back pain, is well-supported in modern scientific journals. Insurance companies are convinced; most of my mothers patients come to her for acupuncture because its covered by New Zealands national insurance plan.

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The Surprising Synergy Between Acupuncture and AI - WIRED