Monthly Archives: September 2021

"LXT is committed to providing the customized solutions that have made our clients successful." - Mohammad Omar, CEO

According to IDC, the global AI market is expected to reach more than $550 billion with a five-year compound annual growth rate (CAGR) of 17.5% by 2024. Demand has never been greater, and to scale globally and take advantage of this opportunity, organizations need to train AI technology on data that captures the unique cultural and linguistic nuances of every region.

"We are pleased to welcome Phil, Asser and Jodie to our executive team as we pursue our vision to provide the data that powers a much more intelligent and automated world," said Mohammad Omar, LXT founder and CEO. "All three have enterprise leadership experience at companies such as Appen and Shaw Communications that will help to fuel our next phase of growth. While many providers are focused on one-size-fits all solutions, LXT is committed to providing the customized solutions that have made our clients successful and these will remain core to our business."

Phil Hall brings more than 20 years' experience in working with the world's top technology companies and has extensive background in speech and linguistics, AI and machine learning data. Asser ElShanawany has a wealth of experience in leading public organizations, transforming and scaling start-ups to large technology companies, particularly in the telecommunications sector. Jodie Ruby comes to LXT with over 20 years of B2B technology marketing experience, including building and leading marketing teams from the ground up for high-growth organizations.

"LXT is a trusted partner to some of the world's largest technology companies - enabling them to deliver cutting-edge AI applications - and has achieved impressive growth based on its ongoing relationships with these organizations," said Phil Hall, LXT's new Chief Growth Officer. "I am thrilled to join a team with such a strong reputation for agile, reliable and cost-effective delivery of high-quality AI data, and look forward to helping LXT capitalize on its huge growth potential."

For enterprises developing AI-based solutions at scale, the ability to collect and annotate the data needed to train these solutions is limited and slows the pace of innovation. Data annotation requires countless hours of human intelligence and translation while data hungry businesses need reliable data collection and annotation partners with global coverage.

"I am excited to join LXT to help establish the company as a leading player in AI training data in partnership with global top 10 technology organizations," commented LXT's new Chief Financial Officer Asser ElShanawany. "Since we are already profitable, debt free and cash flow positive, our immediate focus is on scale, recapitalization, and many other promising financial milestones on the horizon."

LXT provides data generation, collection and annotation services in more than 200 languages through a secure technology platform that facilitates human insight to improve accuracy and streamlines workflow to reduce costs and optimize turnaround times.

"With the acceleration of AI investment across a wide range of use cases and industries, the demand for high-quality, human-annotated data is stronger than ever," said Jodie Ruby, LXT's new Vice President of Marketing. "LXT has proven itself as a trusted partner to leading global enterprises, and I am excited to join this talented team to help build on the momentum it has already created."

LXT was chosen as the exclusive data collection and annotation partner for the SUPERB program, working alongside leading researchers from National Taiwan University, MIT, Carnegie Mellon University, Johns Hopkins University, and Facebook AI. Its stated goal is tofuel research in representation learning and general speech processing. Learn more here.

"SUPERB is a unique effort to create a benchmark for models across a wide variety of tasks and will benefit the broader speech industry by enabling the detection of emotion, intent, content and other semantic information," commented Hung-yi Lee, an associate professor of the Department of Computer Science & Information Engineering at National Taiwan University. "High-quality data is key to the success of this effort, and LXT was chosen as the exclusive partner based on its flexibility, reliability, and collaborative culture."

LXT data services are delivered through its crowdsourced workforce in more than 80 countries around the world, its own secure facilities or onsite client deployment. To meet the most stringent security requirements, LXT facilities are ISO 27001 certified and PCI DSS compliant, and offer supervised annotation to safeguard customer data.

About LXTLXT is an emerging leader in AI training data to power intelligent technology for global organizations, including the largest technology companies in the world. In partnership with an international network of contributors, LXT collects and annotates data across multiple modalities with the speed, scale and agility required by the enterprise. Our global expertise spans 80countries and over 200 languages. Founded in 2014, LXT is headquartered in Toronto, Canada with presence in the United States, Australia, India, Turkey and Egypt. The company serves customers in North America, Europe, Asia Pacific and the Middle East. Learn more at lxt.ai.

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LXT to Capitalize on Massive Opportunity in AI Training Data with Agile and Customized Solutions, Expanded Management Team and Exclusive Partnership -...

The EUs proposed AI legislation published in April, sparked debate on the true impact that new AI rules would have on businesses. Overall, it seems to be that the legislation has the potential to benefit society as a whole, but this could ultimately hinder companies and how they use AI in the long term.

However, OReillys recent AI in the Enterprise research discovered that, while more businesses are continuing to use AI or are considering implementing it in the near future, only 52 percent of these companies are checking for issues of fairness or bias within their AI systems.

One of the major roadblocks to AIs advancement has been a lack of trust in the technology. This is especially true in the public sector, where AI-assisted choices may have a significant influence on peoples lives. The EUs AI legislation aims to correct this by assisting organizations in navigating ethical AI usage. This will help to establish trust over time, allowing businesses to ultimately realize AIs full potential. Businesses must now change how they use and implement AI to ensure that they always fall on the right side of the line.

The AI-train has rapidly been gaining momentum in recent years, both in terms of business usage and results. Were now seeing the technology being used for cancer detection, climate change analysis, the control of traffic and marketing for businesses. Globally, a quarter (26 percent) of businesses have reached the mature stage of AI usage. This means that they have revenue-yielding AI products in production. In the UK, this figure is even higher, with 36 percent classifying their AI usage as mature.

Looking at the industry breakdown, retail came out on top, with 40 percent claiming that their usage of AI was mature. This was closely followed by financial services (38 percent) and telecommunications (37 percent). Comparatively, education (10 percent) and government (16 percent) were the least mature in their usage of AI.

The stats suggest that, while AI adoption in the private sector is snowballing, the public sector is struggling to keep up. The question is: why?

There is likely more than one factor as to why the public sector is struggling in its uptake of AI. Budgetary concerns could certainly be a key issue, but perhaps not enough to account for such a large difference between the public and private sector. The other glaring issue is public trust.

The general public already had their guard up against the use of AI in the public sector. Their worst fears were then proven correct in 2020 when A-Level and GCSE grades were predicted using an AI algorithm that faced accusations of bias. This led to the results being scrapped and replaced by predicted grades given by teachers. Its examples like these which damage public trust in AI.

In terms of checking AI models for bias, the UK is ahead of the global standard. Across the globe, just 52 percent of companies are checking their algorithms for bias. Meanwhile, in the UK, this figure rises to 56 percent. However, when it comes to decisions that impact peoples lives and their futures, a little better than half isnt enough. This counts for both the public and the private sector. Private sector companies, such as banks, also have the power to make decisions that can impact peoples lives.

The EUs AI legislation, which focuses heavily on AI ethics, should force companies to confront these shortcomings and be the starting point for organizations to build public trust and, in time, release the handbrake which is holding AI back. A more educated approach to AI will be key to achieving this.

Its clear that not enough businesses are checking for bias in their AI models. However, research suggests that this isnt necessarily negligence but, instead, a lack of training and skills. Globally, the biggest bottlenecks to AI adoption are a lack of skilled people (19 percent) and data quality (18 percent). In the UK, a quarter (25 percent) labeled a lack of data/data quality as a major hindrance and 14 percent said the same about skills within the organization.

This skills gap is already having a huge impact on the adoption of AI and, with the introduction of the EUs AI legislation, will have an even greater impact if businesses do not act soon. Half of UK businesses admitted that only about 50 percent of their AI projects are actually completed. Meanwhile, as weve seen, those that are completed run a risk of being biased. Moving forward, neither of these options will be profitable for companies.

To close this skills gap, businesses must ensure that they are providing adequate training for their AI-handling employees. This means equipping them with the necessary knowledge to develop and train an algorithm that is highly functional and ethical. Feeding the algorithm with high-quality and unbiased data is the first step, but employees must also be trained to consistently check the algorithm for bias or inconsistencies and make the necessary changes.

With the introduction of the new AI laws, some employees may be nervous to make a mistake. Businesses can take this fear away by empowering their employees to learn in the flow of work. This means allowing them to ask questions and receive quick answers, based on the most up-to-date guidance, which they can apply to their work. The learning platforms to enable this exist, and its now time for employers to start leaning on them. Or they could be one of the first organizations to feel the sting of the new AI legislation.

Businesses and organizations may be tempted to interpret the new AI regulation as a restriction on their technological ambitions. Instead, it should be viewed as advice that will assist them in making the most out of AI. Companies can roll out AI initiatives without fear of public backlash if they stay inside the confines of the new legislation. This will then enable them to test new AI technologies with greater confidence in the long term. However, to build this trust, businesses need to continually keep getting it right when it comes to AI. This means no more instances of AI bias or technologies which push the boundaries of privacy. Regular education and training is the only way to achieve this level of continued excellence.

Rachel Roumeliotis, Vice President of Data and AI, OReilly

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A spotlight on the EUs AI legislation Realizing the full potential of AI - ITProPortal

Humans are active listeners; we create meaning where there is none, or none intended. It is not that the octopuss utterances make sense, but rather that the islander can make sense of them, Bender says.

For all their sophistication, todays AIs are intelligent in the same way a calculator might be said to be intelligent: they are both machines designed to convert input into output in ways that humanswho have mindschoose to interpret as meaningful. While neural networks may be loosely modeled on brains, the very best of them are vastly less complex than a mouses brain.

And yet, we know that brains can produce what we understand to be consciousness. If we can eventually figure out how brains do it, and reproduce that mechanism in an artificial device, then surely a conscious machine might be possible?

When I was trying to imagine Roberts world in the opening to this essay, I found myself drawn to the question of what consciousness means to me. My conception of a conscious machine was undeniablyperhaps unavoidablyhuman-like. It is the only form of consciousness I can imagine, as it is the only one I have experienced. But is that really what it would be like to be a conscious AI?

Its probably hubristic to think so. The project of building intelligent machines is biased toward human intelligence. But the animal world is filled with a vast range of possible alternatives, from birds to bees to cephalopods.

A few hundred years ago the accepted view, pushed by Ren Descartes, was that only humans were conscious. Animals, lacking souls, were seen as mindless robots. Few think that today: if we are conscious, then there is little reason not to believe that mammals, with their similar brains, are conscious too. And why draw the line around mammals? Birds appear to reflect when they solve puzzles. Most animals, even invertebrates like shrimp and lobsters, show signs of feeling pain, which would suggest they have some degree of subjective consciousness.

But how can we truly picture what that must feel like? As the philosopher Thomas Nagel noted, it must be like something to be a bat, but what that is we cannot even imaginebecause we cannot imagine what it would be like to observe the world through a kind of sonar. We can imagine what it might be like for us to do this (perhaps by closing our eyes and picturing a sort of echolocation point cloud of our surroundings), but thats still not what it must be like for a bat, with its bat mind.

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What would it be like to be a conscious AI? We might never know. - MIT Technology Review

SUNNYVALE, Calif.--(BUSINESS WIRE)--FogHorn, a leading developer of Edge AI software for industrial and commercial Internet of Things (IoT) solutions, today announced its ranking as the Overall Leader, Top Innovator and Top Implementer by ABI Researchs competitive vendor assessment on IoT Edge Analytics: Hardware-Agnostic SaaS and PaaS.

ABI Research assessed vendors based on their deployment of advanced edge analytics and artificial intelligence (AI) technologies to customers in various industries, go-to-market strategies, scalability and efficiency. The competitive ranking offers an unbiased assessment of edge-cloud software-as-a-service (SaaS) and platform-as-a-service (PaaS) technologies enabling the Internet of Things (IoT) for enterprises, covering vendors that are providing hardware agnostic machine learning (ML) and AI.

Leveraging edge intelligence enables enterprises to achieve operational efficiency, reduce costs and enhance workplace and asset monitoring, said Chris Penrose, Chief Operating Officer at FogHorn. Were honored to be recognized by ABI Research for enabling our customers to reach these goals with our Lightning Edge AI Platform and Solutions. This competitive assessment showcases the value were driving for our customers and highlighting a variety of edge AI use cases that ultimately enhance their decision-making and ROI with data-driven insights.

FogHorn established itself as the leader due to its performance in the industrial vertical and wide range of clients and strategic partnerships. Additionally, ABI Research noted FogHorns ability to serve multiple IoT use cases and sophisticated capabilities for predictive analytics and ML as a key consideration of its evaluation. As highlighted by ABI Research, FogHorn received higher implementation scores because of its influence and adoption rate of video analytics for the IoT domain.

FogHorn was also ranked as a vendor successfully monetizing market opportunities resulting from the COVID-19 pandemic. In June 2020, FogHorn announced its Health and Safety monitoring solution, which enables enterprises to address employee wellbeing and help prevent exposure to COVID-19 and monitor workplace safety through personal protective equipment detection and hazard monitoring. This solution, delivered as a ready-to-use package that utilized ML combined with video analytics, diversified FogHorns product portfolio compared to other edge AI vendors by ABI Research.

FogHorns Lightning Edge AI Platform was the first edge-native AI solution built for secure, on-site intelligence. Its edge processing capabilities are ideal for low-latency use cases enabling real-time data processing harnessing ML and AI capabilities within a minimal compute footprint. In addition to its recognition as an Overall Leader, FogHorn earned a top mark for predictive and ML modeling, as well as measurement against ABI Researchs unique innovation criteria.

Download a copy of ABI Researchs competitive assessment ranking on IoT Edge Analytics: Hardware-Agnostic SaaS/PaaS from the FogHorn website here.

About FogHorn

FogHorn is a leading developer of edge AI software for industrial and commercial IoT application solutions. FogHorns software platform brings the power of advanced analytics and machine learning to the on-premises edge environment enabling a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance, and operational intelligence use cases. FogHorns technology is ideally suited for OEMs, systems integrators and end customers in manufacturing, power and water, oil and gas, renewable energy, mining, transportation, healthcare, retail, as well as smart grid, smart city, smart building, and connected vehicle applications.

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FogHorn and Lightning Edge AI Platform Recognized as Overall Leader by ABI Research - Business Wire

31 August 2021

Sheppard Mullin Richter & Hampton

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In recent years, AI patent activity has exponentially increased.The figure below shows the volume of public AI patent applicationscategorized by AI component in the U.S. from 1990-2018. The eightAI components in FIG. 1 are defined inan article published in 2020by theUSPTO. Most of the AI components have experienced explosive growthin the past decade, especially in the areas of planning/control andknowledge processing (e.g., using big data in automatedsystems).

Figure 1. AI patent activities byyear

AI technology is complex and includes different parts acrossdifferent fields. Inventors and patent attorneys often face thechallenge of effectively protecting new AI technology development.The rule of thumb is to focus the patent protection on what theinventors improve over the conventional technology. However,inventors often need to improve various aspects of an existing AIsystem to make it fit and work for their applications. In thefollowing sections, we will discuss an illustrative list of subjectareas that may offer patentable AI inventions.

The training phase of an AI system includes most of the excitingtechnical aspects of machine learning algorithms exploring thelatent patterns embedded in the training data. A typical trainingprocess includes preparing training data, transforming the trainingdata to facilitate the training process, feeding the training datato a machine learning model, fitting (training) the machinelearning model based on the training data, testing the trainedmachine learning model, and so on. Different AI models or machinelearning models may have different training processes, such assupervised training based on labeled training data, unsupervisedtraining that infers a function to describe a hidden structure fromunlabeled training data, semi-supervised training based onpartially-labeled training data, reinforcement learning (RL), etc.Common areas in the training phase that may yieldpatent-protectable ideas include:

The application phase of an AI system includes applying thetrained models to make predictions, inferences, classifications,etc. This phase generally covers the real application of the AIsystem. It can provide easier infringement detectability and thusvaluable patent protection for the AI system. In this digital era,AI systems can be applied to almost every aspect of our life. Forexample, an AI patent can claim or describe how the AI system helpsthe user to make better decisions or perform previously impossibletasks. These applications may be deemed as practical applicationsthat are powerful in overcoming potential "abstract idea"rejections during the prosecution of the AI patent.

On the other hand, simply claiming an AI system as a magicalblack box that generates accurate predictions based on input datawill likely trigger rejections during prosecution, such aspatentable subject matter rejections (e.g., a simple application ofthe black box may be categorized as human activities). There arevarious ways to reduce the chances of getting such rejections. Forexample, adding a brief description of the training process or themachine learning model structure helps overcome U.S.C. 101rejections.

Another flavor of AI patents is related to accelerators,hardware pieces with built-in software logic accelerating trainingand/or inferencing process. These AI patents may be claimed fromeither a software perspective or hardware perspective. Someexamples include specially designed hardware to improve trainingefficiency by working with GPU/TPU/NPU/xPU (e.g., by reducing datamigrations among different components/units), memory layout changesto improve the computational efficiency of computing-intensivesteps, arrangement of processing units for easy data sharing, andefficient parallel training (e.g., segmenting tensors to evenlydistribute workloads to processors), an architecture that fullyexploits the sparsity of tensors to improve computationefficiency.

The state-of-art AI systems are far from perfection. Robustness,safety, reliability, data privacy, are just some of the mostnoticeable pain points in training and deploying AI systems. Forexample, an AI model trained from a first domain may havenear-perfect accuracy for inferencing in the first domain, butgenerate disastrous inferences when being deployed in a seconddomain, even though the domains share some similarities. Therefore,how to train an AI model efficiently and adaptively so that it isrobust when being deployed in all domains of interest is bothchallenging and intriguing.

As another example, AI systems trained based on training datamay be easily fooled by adversarial attacks. For instance, asecond deep neural network may be designed to compete against thefirst one to identify its weaknesses. The safety and reliability ofsuch AI systems will be critical in the coming years and may beimportant patentable subject matters.

As yet another example, training data in many cases may includesensitive data (e.g., customer data), directly using such trainingdata may result in serious data privacy breaches. This problembecomes more alarming when a plurality of entities collectivelytrain a model using their own training data. Accordingly,researchers and engineers have been exploring differential privacyprotection and federated learning to address these issues.

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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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

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