Daily Archives: September 1, 2021

As the use cases for AI in financial services continue to grow and deliver value for organizations and customers alike, Id like to provide some insight on where I think the technology is delivering most value at the moment, and also where I think we are headed. Firstly, though, a little on how far weve come.

Many people dont realize that AI has been around since the 1950s. Models like linear regression, support vector machines, and many more have been used for decades. The application of traditional and novel algorithmic design choices continues to unlock real value in financial services.

Deep learning has also been around for a long time, but use cases only gained traction in the mid-2000s as datasets and computational power expanded enough to showcase its true potential. As financial services use cases evolved, deep learning became a key tool to solving problems we otherwise could not accomplish with more rudimentary machine learning models.

Today, we are seeing a lot of investment in neural network-based models, totaling billions of parameters, and being trained on multi-billion point datasets. Compared to first-generation models, these models are computationally expensive and highly complex. The ability to train models of this size, with increasing ease, shows how far technology has come.

Competition, collaboration and customer experience

The computational hardware and the advent of increasingly powerful GPUs is expanding the boundary for larger neural networks being trained on massive datasets. All of this advancement is paying off for banks, consumers and the fintech ecosystem at large. Creative solutions from third parties, fintechs, and challenger banks are solving tough problems in the financial services sector, which is pushing incumbent banks to challenge the challengers and harness the power of AI in the products and services they offer to their customers. One thing incumbents have over their agile rivals, however, is troves of data, which is the lifeblood of AI. Knowing how to harness this data is perhaps the key challenge incumbents face, which is naturally leading to increased collaboration with fintechs and third-party data specialists.

Customers are greatly benefitting from the current competitive environment on both the corporate and retail banking sides. Personal finance is a great example of the latter, as AI now allows consumers to have a personal assistant for their own finances, democratizing access to advisory services.

While chatbots have existed for a number of years, it is only recent advancements that have enabled more compelling use cases. From traditional rule-based bots to research in deep learning based generative model based bots, we have seen tremendous advancement in chat-bot quality. Neural network-based chatbots, for example, can provide an easy interface for users to get spending advice, understand their balance and spending, and get insight into transaction details.

With the advent of multi-billion parameter knowledge models, finetuned on personal finance data, the performance and usability of chatbots is better than ever, with most capable of delivering detailed account insights. The increase in the move towards digital channels brought about by the pandemic has also created a wealth of data on which models can be trained, further improving personal finance products and services.

These use cases have tangible and immediate benefits for both banks and consumers. Customers no longer have to spend time waiting in line to speak to customer services representatives when they know exactly the information they need and how to access it via automated channels. Meanwhile, banks can improve customer experience and reduce overheads tied to their customer support cost-centers.

Looking to the future

Over the next few years, I believe there will be an increase in data-sharing between banks and fintechs. The data banks hold is sensitive and highly safeguarded, but improvements in federated learning and synthetic data generation methods will allow partnerships between those developing models, and those holding the data, to flourish.

The next area is natural language processing (NLP). As mentioned above, there have been huge advances in massive billion parameter neural network-based architectures trained on multi-billion point datasets. From these, there are countless possibilities for transfer learning and knowledge distillation on more specific tasks. One only needs to look at the incredible use cases enabled by GPT-3 to understand the potential of such models.

Another area where I believe data and AI will create opportunities is in providing access to credit through leveraging alternative data. Using such data, banks can begin to provide services to the credit invisible, unlocking financial support for those without traditional credit histories, providing a fairer environment for consumers to gain access to capital.

In a similar vein, an area that I believe will see significant investment is algorithmic fairness and the push for elimination of algorithmic bias in predictive and decision-making models. Increasingly, banks will be required to understand and explain how and why decision-making models arrive at certain outcomes, particularly if they negatively affect certain groups or individuals.

AI can often be maligned by those who believe every advancement will move us closer to the decimation of the job market or even a more outlandish science fiction scenario. But AI is increasingly being used for good across industries, from healthcare and environmental modeling to financial services. Data is the most effective asset we have in the fight against inefficiency, inequality and injustice and AI is the means by which we will unlock its true potential.

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The past, present, and future of AI in financial services - Finextra

Before Patrick Crusius killed 23 people in a Walmart in El Paso, Texas, in 2019, he posted a manifesto of white nationalist and anti-immigrant rhetoric on 8chan, an Internet message board. Before John Earnest shot up a synagogue outside San Diego, he posted an anti-Semitic open letter on 8chan and tried to livestream the attack on Facebook. Before Brenton Tarrant killed 51 people in Christchurch, New Zealand, he shared a 74-page manifesto about the white genocide conspiracy theory on Twitter and 8chan before livestreaming one of his crimes on Facebook.

Typical of domestic terrorists and violent extremists today, each of these culprits was active on social media, leaving online records of words and thoughts related to their crimes. Now building upon military tactics to locate terror threats online, researchers at the Illinois Institute of Technology think machine learning and artificial intelligence could turn these social media posts into breadcrumb trails for governments and investigators to identify anonymous accounts.

In a paper co-authored by assistant professors from Illinois Tech and the University of Nebraska, graduate students Andreas Vassilakos and Jose Luis Castanon Remy combined Maltego software, an application in the digital forensics platform Kali Linux, with a process used by the military called open source intelligence (OSINT). With Maltego, they compiled various social media posts on Twitter, 4chan and Reddit and did a link analysis to find the same entity appearing in more than one place for instance connecting a Twitter feed to a name in online court documents.

One problem with the manual process: Its highly time-consuming, and there are already too few people doing those jobs. There are 464,420 job openings nationwide in the cybersecurity field, public and private sector combined, according to cyberseek.org. Dawson said his research team is in the process of coding the AI and machine learning to automate some of the work of scraping and link analysis, and he mentioned domestic terrorism and gang activity as possible use cases.

What were trying to do is find a way to make this fully open source and available to anyone who wants to do it, namely state and federal government, and have it automated. If we have a domestic terrorism event, lets create an intelligence profile of this event. This profile can be created from tweets and stuff like that, so weve created the process, and now were continuing to use AI and machine learning to further automate this, he said. You could take this technology and identify who these people are and go to these entities even before something happens.

In order to mitigate the problem, you have to automate a lot of these tasks, he said.

Andrew Westrope is managing editor of the Center for Digital Education. Before that, he was a staff writer for Government Technology, and previously was a reporter and editor at community newspapers. He has a bachelors degree in physiology from Michigan State University and lives in Northern California.

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Illinois Tech Research Wants AI to ID Online Extremists - Government Technology

Researchers from the University of Birmingham have used a series of Artificial Intelligence (AI) techniques to identify the heart failure patients most likely to benefit from treatment with beta-blockers.

The findings from the study have been published in The Lancet.

Beta-blockers work predominantly by slowing down the heart, which they do by blocking the action of hormones like adrenaline. Although they are commonly used to treat conditions such as angina, heart failure, and atrial fibrillation (AF), beta-blockers are not suitable for everyone. For example, beta-blockers are not recommended for patients with low blood pressure, metabolic acidosis, or lung disease.

Aiming to integrate AI techniques to improve the care of cardiovascular patients, researchers looked at data involving 15,669 patients with heart failure and reduced left ventricular ejection fraction (low function of the hearts main pumping chamber), 12,823 of which were in normal heart rhythm and 2,837 of which had atrial fibrillation- a heart rhythm condition commonly associated with heart failure that leads to worse outcomes.The research was led by the cardAIc group, a multi-disciplinary team of clinical and data scientists at the University of Birmingham and the University Hospitals Birmingham NHS Foundation Trust.

Using AI techniques to deeply investigate the clinical trial data, the team found that this approach could determine different underlying health conditions for each patient, as well as the interactions of these conditions, to isolate response to beta-blocker therapy.This worked in patients with normal heart rhythm, where doctors would normally expect beta-blockers to reduce the risk of death, as well as in patients with AF where previous work has found a lack of effectiveness. In normal heart rhythm, a cluster of patients (who had a combination of older age, less severe symptoms, and lower heart rate than average) was identified with reduced benefit from beta-blockers.Conversely, in patients with AF, the research found a cluster of younger patients with lower rates of prior heart attack but similar heart function to the average AF patient who had a substantial reduction in death with beta-blockers (from 15% to 9%).

The study used data collated and harmonised by the Beta-blockers in Heart Failure Collaborative Group, a global consortium dedicated to enhancing treatment for patients with heart failure. The research used individual patient data from nine landmark trials in heart failure that randomly assigned patients to either beta-blockers or a placebo. The average age of study participants was 65 years, and 24% were women. The AI-based approach combined neural network-based variational autoencoders and hierarchical clustering within an objective framework, and with detailed assessment of robustness and validation across all the trials.

The researchers say that these AI approaches could go further than this research into a specific treatment, with the potential to be applied to a range of other cardiovascular conditions and more.

Corresponding author Georgios Gkoutos, Professor of Clinical Bioinformatics at the University of Birmingham, Associate Director of Health Data Research Midlands, and co-lead for the cardAIc group, said: Although tested in our research in trials of beta-blockers, these novel AI approaches have clear potential across the spectrum of therapies in heart failure, and across other cardiovascular and non-cardiovascular conditions.

Corresponding author Dipak Kotecha, Professor andConsultant in Cardiology at the University of Birmingham, international lead for the Beta-blockers in Heart Failure Collaborative Group, and co-lead for the cardAIc group, added: Development of these new AI approaches is vital to improving the care we can give to our patients; in the future this could lead to personalised treatment for each individual patient, taking account of their particular health circumstances to improve their well-being.

First Author Dr Andreas Karwath, Rutherford Research Fellow at the University of Birmingham and member of the cardAIc group, added: We hope these important research findings will be used to shape healthcare policy and improve treatment and outcomes for patients with heart failure.

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AI identifies heart failure patients best suited to beta-blocker treatment - Health Europa

The modern warfighter needs to rely on various technologies and increasingly advanced systems to help provide advantages over capable adversaries and competitors. The US Department of Defense (DoD) understands this all too well and must therefore integrate Artificial Intelligence and Machine Learning more effectively across their operations to maintain advantages.

Kristin Saling, Chief Analytics Officer & Acting Dir., Army People Analytics

To remain competitive, the US Army has created the Army Talent Management Task Force to address the current and future needs of the war fighter. In particular, the Data and Artificial Intelligence (AI) Team shapes the creation and implementation of a holistic Officer/NCO/Civilian Talent Management System. This system has transformed the Army's efforts to acquire, develop, employ, and retain human capital through a hyper-enabled data-rich environment and enables the Army to dominate across the spectrum of conflict as a part of the Joint Force. LTC Kristin Saling is an integral part of getting the Army AI ready and shared her insights with us for this article. She will also be presenting at an upcoming AI in Government event where she will discuss where the US Army currently stands on its data collection and AI efforts, some of the challenges they face, and a roadmap for where the DoD and Army is headed.

What are some innovative ways youre leveraging data and AI to benefit the Army Talent Management Task Force?

LTC Kristin Saling: We are leveraging AI in a number of different ways. But one of the things were doing that most people dont think about is leveraging AI in order to leverage AI and by that I mean were using optical character recognition and natural language processing to read tons and tons of paper documents and process their contents into data we can use to fuel our algorithms. Were also reading in and batching tons of occupational survey information to develop robust job competency models we can use to make recommendations in our marketplace.

On the other end, were leveraging machine learning models to predict attrition and performance for targeted retention incentives. We have partnered with the Institute for Defense Analysis to field the Retention Prediction Model Army (RPM-A) which generates an individual prediction vector for retention for every single Active Army member. Were developing the Performance Prediction Model Army (PPM-A) as a companion model to use a number of different factors, from performance to skills crosswalked with market demand, to identify the individuals the Army most wants to keep. These models used in tandem and informed by a number of retention incentive randomized controlled trials will provide a powerful toolkit for Army leaders to provide the most likely to succeed incentive menus to the personnel likely to attrition that the Army most wants to keep.

How are you leveraging automation at all to help on your journey to AI?

LTC Kristin Saling: We are looking at ways to employ Robotic Process Automation throughout the people enterprise. RPA is an unsung hero when it comes to personnel processes and talent management, especially in a distributed environment. We can automate a huge portion of task tracking, onboarding, leave scheduling, and so forth, but Im particularly looking at it in terms of data management. Were migrating a huge portion of our personnel data from 187 different disparate systems into a smaller number of data warehouses and enterprise systems, and this is the perfect opportunity to use RPA to ensure that we have data compatibility and model ready datasets.

How do you identify which problem area(s) to start with for your automation and cognitive technology projects?

LTC Kristin Saling: We do a lot of process mapping and data mapping before we start digging into a project. We need to understand all the different parts of the system that our changes are going to effect. And we revisit this frequently as we develop an automation solution. Sometimes the way were developing the solution renders different parts of the system obsolete and we need to make sure were bypassing them appropriately with the right data architecture. Sometimes there are some additional things we need to build because of where the information generated by the new automation needs to be fed. Its just important for us to remember that nothing we build truly stands alone, that its all part of a larger system.

What are some of the unique opportunities the public sector has when it comes to data and AI?

LTC Kristin Saling: The biggest opportunities I think we have (in the Army at least) are that we have extremely unique and interesting problem sets and applications, and we also have an extremely large and innovative workforce. While we have a number of challenges, we also have a lot of really talented people joining our workforce who were drawn here by the variety of applications we have to solve and some of the unique data sets we have to work with.

What are some use cases you can share where you successfully have applied AI?

LTC Kristin Saling: Successfully applying AI is a tricky question. Weve created successful AI models, but applying them becomes extremely difficult when you consider the ethics of taking actions on the information were generating. The first I can cite is the STARRS program Studies to Assess Readiness and Resilience in Service members. Its an AI model in development that identifies personnel at the highest risk for harmful behaviors, particularly suicide. Taking that information and applying it in an ethical way that enables commanders and experts to enact successful interventions is extremely difficult. We have a team of scientific experts working on this problem.

Can you share some of the challenges when it comes to AI and ML in the public sector?

LTC Kristin Saling: The availability of good data is a challenge. We have a lot of data, but not all of it is good data. We also have a lot of restrictions on our ability to use data, from the Privacy Act of 1974, the Paperwork Reduction Act, and all of the policies and directives derived from those. Without an appropriate System of Record Notice (SORN) that states how the data was collected and how it is to be used, we cant collect data, and that SORN significantly limits how that data can be used. The best AI models cant make better decisions on bad data than we can they can just make bad decisions faster. We really have to get at our data problem.

How do analytics, automation, and AI work together at your agency?

LTC Kristin Saling: We see all of these things as solutions in our data and analytics toolkit to improve processes. Everything starts with good data first and foremost, and automation, when inserted in the right places in the process, helps us get to good data. We treat AI as the top end of the progression of analytics descriptive analytics help us see ourselves, diagnostic analytics help us see what has happened over time and potentially why, predictive analytics help us see what is likely to happen, prescriptive analytics recommend a course of action using the prediction, and if you add one more step in decision autonomy, enabling the machine to make the decision instead of just recommending a course of action, you have narrow artificial intelligence. Weve been most successful when weve looked at our data, our analytics, our people, our decision processes, and the environment these operate in as a total system than when weve tried to develop solutions piecemeal.

How are you navigating privacy, trust, and security concerns around the use of AI?

LTC Kristin Saling: Our privacy office, human research protection program, and cyber protection programs do a lot to mitigate some concerns about the use of AI. However, there are still a lot of concerns about the ethical use of AI. To a large portion of the population, its a black box entity or black magic at best, Skynet in the making at worst. The best way for us to combat this is education. Were sending many of our leaders to executive courses on analytics and artificial intelligence, and developing a holistic training program for the Army on data and analytics literacy. I firmly believe when our leaders better understand how artificial intelligence works and walk through appropriate use cases, they will be able to make better decisions about how to ethically employ AI, better trust how we employ it, and ensure that we are preserving privacy and data/cyber security.

What are you doing to develop an AI ready workforce?

LTC Kristin Saling: Our Army AI Integration Center (AI2C - formerly the Army AI Task Force) has established an education program called AI Scholars, where about 40 students a year, both military and civilian, will take part in graduate degree programs at Carnegie Mellon and eventually at other institutions in advanced data science and data engineering, followed by a tour at the AI2C applying their skills to developing AI solutions. Our HQDA G-8 has sponsored over 50 Army leaders through executive courses in AI at Carnegie Mellon, and ASA(ALT) has sponsored still more through executive courses at the University of Virginia. Our FA49 Operations Research and Systems Analysis career specialty and FA26 Network Science and Data Engineering career specialty have sponsored officers through graduate level AI programs. Through all of this education and its application to a host of innovative problem sets, the Army has created a significant AI workforce and is continually working to improve how we employ this workforce.

What AI technologies are you most looking forward to in the coming years?

LTC Kristin Saling: Im a complexity scientist by background, and Im fascinated about the applications of this field in autonomous systems and particularly swarm, and the host of things well be able to do with these applications. Thats my futurist side speaking. My practical side is just looking forward to simple automation being widely adopted. If we can just modernize our Army leave system from its current antiquated process, I will count that as a success.

LTC Kristin Saling has a lot to say on this subject. If youd like to engage with her directly she will be presenting at an upcoming AI in Government event where she will discuss where the US Army currently stands on its data collection and AI efforts, some of the challenges they face, and a roadmap for where the DoD and Army is headed.

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How The United States Army Is Leveraging AI: Interview With Kristin Saling, Chief Analytics Officer & Acting Dir., Army People Analytics - Forbes

Our Artificial Intelligence Consulting Services Can Help Businesses Breakthrough The Pandemics Barriers.

AI Exosphere, an AI Startup, begins offering artificial intelligence consulting services to help companies hurt by the pandemic recover.

Sal Peer, CEO of AI Exosphere.

ORLANDO, FLORIDA, USA, August 31, 2021 /EINPresswire.com/ AI Exosphere, an AI Startup, begins offering artificial intelligence consulting services to help companies hurt by the pandemic recover. Formed by Sal Peer and Alex Athey, a pair of dedicated professionals with a vision to free the entrepreneur, resolve enterprise-level problems, and empower the everyday Joe.

Our team is dedicated to increasing inclusion, accessibility, and scalability through AI innovations. Experienced in helping small business owners automate time-consuming tasks through AI and machine learning practices. Therefore, WE FEEL CONFIDENT WE CAN HELP SAVE OUR SMALL BUSINESSES.

From NLP solutions to full server deployment of cloud-based applications, our team is ready to help our customers build the future.

Our team's expertise lies in Python full stack server development. Applying our expertise in supervised, unsupervised, and reinforcement machine learning, neural networks, and deep learning, we build intelligent systems that make the best decisions with little to no human help.

We build real-time speech recognition and conversational AI applications that drive user experience and increase engagement. Some of our current projects feature bleeding-edge tech AI operations by GPT-3 and GPT-J.

We also offer server development. Cloud and server development is essential for artificial intelligence operations. Our trained IT professionals can help design and deploy customizable server environments for any AI task your business could need.

"My goal with our consulting service division is to increase access to bleeding-edge tools for our small business community and find AI automation that can address the current situation," said Sal Peer, CEO of AI Exosphere.

We know for sure that artificial intelligence has many possibilities to transform your business. The use cases below are just a few examples of how our AI consulting and development services drive business efficiency and improve the bottom line.

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About AI ExosphereAt AI Exosphere, our focus is on Project Hail (HailyAI), an AI voice business assistant who can take complex digital actions and act in a sales and customer support role.

Sal PeerAI Exosphere LLC+1 888-578-2485email us hereVisit us on social media:TwitterLinkedIn

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AI Startup Begins Offering Artificial Intelligence Consulting Services To Help Companies Hurt By The Pandemic Recover - The Free Press Tampa