Category Archives: Machine Learning

Machine learning was shown to identify patients with rheumatoid arthritis (RA) who present an increased chance of achieving clinical response with sarilumab, with those selected also showing an inferior response to adalimumab, according to an abstract presented at ACR Convergence, the annual meeting of the American College of Rheumatology (ACR).

In prior phase 3 trials comparing the interleukin 6 receptor (IL-6R) inhibitor sarilumab with placebo and the tumor necrosis factor (TNF-) inhibitor adalimumab, sarilumab appeared to provide superior efficacy for patients with moderate to severe RA. Although promising, the researchers of the abstract highlight that treatment of RA requires a more individualized approach to maximize efficacy and minimize risk of adverse events.

The characteristics of patients who are most likely to benefit from sarilumab treatment remain poorly understood, noted researchers.

Seeking to better identify the patients with RA who may best benefit from sarilumab treatment, the researchers applied machine learning to select from a predefined set of patient characteristics, which they hypothesized may help delineate the patients who could benefit most from either antiIL-6R or antiTNF- treatment.

Following their extraction of data from the sarilumab clinical development program, the researchers utilized a decision tree classification approach to build predictive models on ACR response criteria at week 24 in patients from the phase 3 MOBILITY trial, focusing on the 200-mg dose of sarilumab. They incorporated the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm, including 17 categorical and 25 continuous baseline variables as candidate predictors. These included protein biomarkers, disease activity scoring, and demographic data, added the researchers.

Endpoints used were ACR20, ACR50, and ACR70 at week 24, with the resulting rule validated through application on independent data sets from the following trials:

Assessing the end points used, it was found that the most successful GUIDE model was trained against the ACR20 response. From the 42 candidate predictor variables, the combined presence of anticitrullinated protein antibodies (ACPA) and C-reactive protein >12.3 mg/L was identified as a predictor of better treatment outcomes with sarilumab, with those patients identified as rule-positive.

These rule-positive patients, which ranged from 34% to 51% in the sarilumab groups across the 4 trials, were shown to have more severe disease and poorer prognostic factors at baseline. They also exhibited better outcomes than rule-negative patients for most end points assessed, except for patients with inadequate response to TNF inhibitors.

Notably, rule-positive patients had a better response to sarilumab but an inferior response to adalimumab, except for patients of the HAQ-Disability Index minimal clinically important difference end point.

If verified in prospective studies, this rule could facilitate treatment decision-making for patients with RA, concluded the researchers.

Reference

Rehberg M, Giegerich C, Praestgaard A, et al. Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data. Presented at: ACR Convergence 2020; November 5-9, 2020. Accessed January 15, 2021. 021. Abstract 2006. https://acrabstracts.org/abstract/identification-of-a-rule-to-predict-response-to-sarilumab-in-patients-with-rheumatoid-arthritis-using-machine-learning-and-clinical-trial-data/

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Machine Learning Shown to Identify Patient Response to Sarilumab in Rheumatoid Arthritis - AJMC.com Managed Markets Network

As financial services firms increasingly turn to artificial intelligence (AI), banking regulators warn that despite their astonishing capabilities, these tools must be relied upon with caution.

Last week, the Board of Governors of the Federal Reserve (the Fed) held a virtual AI Academic Symposium to explore the application of AI in the financial services industry. Governor Lael Brainard explained that particularly as financial services become more digitized and shift to web-based platforms, a steadily growing number of financial institutions have relied on machine learning to detect fraud, evaluate credit, and aid in operational risk management, among many other functions.[i]

In the AI world, machine learning refers to a model that processes complex data sets and automatically recognizes patterns and relationships, which are in turn used to make predictions and draw conclusions.[ii] Alternative data is information that is not traditionally used in a particular decision-making process but that populates machine learning algorithms in AI-based systems and thus fuels their outputs.[iii]

Machine learning and alternative data have special utility in the consumer lending context, where these AI applications allow financial firms to determine the creditworthiness of prospective borrowers who lack credit history.[iv] Using alternative data such as the consumers education, job function, property ownership, address stability, rent payment history, and even internet browser history and behavioral informationamong many other datafinancial institutions aim to expand the availability of affordable credit to so-called credit invisibles or unscorables.[v]

Yet, as Brainard cautioned last week, machine-learning AI models can be so complex that even their developers lack visibility into how the models actually classify and process what could amount to thousands of nonlinear data elements.[vi] This obscuring of AI models internal logic, known as the black box problem, raises questions about the reliability and ethics of AI decision-making.[vii]

When using AI machine learning to evaluate access to credit, the opaque and complex data interactions relied upon by AI could result in discrimination by race, or even lead to digital redlining, if not intentionally designed to address this risk.[viii] This can happen, for example, when intricate data interactions containing historical information such as educational background and internet browsing habits become proxies for race, gender, and other protected characteristicsleading to biased algorithms that discriminate.[ix]

Consumer protection laws, among other aspects of the existing regulatory framework, cover AI-related credit decision-making activities to some extent. Still, in light of the rising complexity of AI systems and their potentially inequitable consequences, AI-focused legal reforms may be needed. At this time, to help ensure that financial services are prepared to manage these risks, the Fed has called on stakeholdersfrom financial services firms to consumer advocates and civil rights organizations as well as other businesses and the general publicto provide input on responsible AI use.[x]

[i] Lael Brainard, Governor, Bd. of Governors of the Fed. Reserve Sys., AI Academic Symposium: Supporting Responsible Use of AI and Equitable Outcomes in Financial Services (Jan. 12, 2021), available at https://www.federalreserve.gov/newsevents/speech/brainard20210112a.htm.

[ii] Pratin Vallabhaneni and Margaux Curie, Leveraging AI and Alternative Data in Credit Underwriting: Fair Lending Considerations for Fintechs, 23 No. 4 Fintech L. Rep. NL 1 (2020).

[iii] Id.

[iv] Id.; Brainard, supra n. 1.

[v] Vallabhaneni and Margaux Curie, supra n.2; Kathleen Ryan, The Big Brain in the Black Box, Am. Bar Assoc. (May 2020), https://bankingjournal.aba.com/2020/05/the-big-brain-in-the-black-box/.

[vi] Brainard, supra n.1; Ryan, supra n.5.

[vii] Brainard, supra n.1; Ryan, supra n.5.

[viii] Brainard, supra n.1.

[ix] Id. (citing Carol A. Evans and Westra Miller, From Catalogs to Clicks: The Fair Lending Implications of Targeted, Internet Marketing, Consumer Compliance Outlook (2019)).

[x] Id.

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AI in Credit Decision-Making Is Promising, but Beware of Hidden Biases, Fed Warns - JD Supra

Its stunning what innovation can do nowadaysnow and again, taking on jobs and decisions that once required human thought. Think about the capability of artificial intelligence, machine learning and predictive analytics, and the effect that these advances could have on humans.

Theoretically, you would already be able to do a lot of things and much more utilizing technology. Yet, are the decisions that algorithms can make dependent on predictive analytics and big data fundamentally any better than decisions seasoned managers may make, taking into considerations their years of experience?

Not every person fears our machine overlords. Truth be told, as indicated by Penn State scientists, with regards to private data and access to financial data, individuals will trust machines more than humans, which could prompt both positive and negative online practices.

The study showed that individuals who trusted machines were essentially bound to surrender their Mastercard numbers to a computerized travel planner than a human travel planner. Experts in both innovation and business are united in accepting that AI isnt yet prepared to overtake the human components of decision-making identified with different business choicesif it actually will be. It is, they state, a balance.

Technology, and the data it very well may be programmed to capture, is a massively important tool for quick decision-making or to carry business activities to a set of conclusions. However, these should be placed into context by a human, indeed, more than one human. Human decision-making is vulnerable to predisposition thus, in light of a legitimate concern for fairness, more than one individuals instinct should be thought of.

In a car accident, individuals judge the action of a self-driving vehicle as more destructive and corrupt, despite the fact that the action performed by the human was actually the equivalent. In another situation, we consider an emergency response system responding to a tidal wave. A few people were informed that the town was effectively evacuated. Others were informed that the evacuation effort failed.

Studies demonstrate that for this situation machines additionally got the worst part of the deal. Truth be told, if the rescue effort failed, individuals assessed the action of the machine adversely and that of the human positively. The data demonstrated that individuals appraised the action of the machine as essentially more hurtful and less good, and furthermore revealed needing to hire the human, yet not the machine.

That confidence in machines might be set off in light of the fact that individuals accept that machines dont talk, or have unlawful plans on their private data. In any case, while machines probably wont have ulterior intentions in their data, individuals creating and running those computers could prey on this gullibility to harness personal data from clueless users, for instance, through phishing tricks, which are endeavors by criminals to get client names, passwords, credit card numbers and different bits of private data by acting like trustworthy sources.

Another study supported by Oracle and Future Workplace sullen that individuals have more trust in robots than their managers. The study of 8,370 employees, directors and managers across 10 nations found that AI has changed the relationship among individuals and technology at work, and is reshaping the job HR teams and leaders need to play in pulling in, holding and creating talent.

The most recent headways in AI and machine learning are quickly arriving at standard, bringing about a huge shift in the way individuals across the world interface with technology and their teams, said Emily He, senior VP of the Human Capital Management Cloud Business Group at Oracle. As this study shows, the connection between humans and machines is being reimagined at work, and there is no one-size-fits-all approach to deal with effectively dealing with this change. All things considered, companies need to band together with their HR companies to customize the way to implement AI at work to meet the changing expectations for their teams the world over.

Individuals surely dont care for one-sided humans or machines, yet when we test their repudiation experimentally, individuals rate human bias as marginally more destructive and less good than those of machines.

We are moving from a time of imposing standards on machine behavior to one of finding laws which dont reveal to us how machines should act, however, how we judge them. Furthermore, the primary principle is incredible and straightforward: individuals judge people by their intentions and machines by their results.

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Has the Time Come to Trust Machines more than Humans? - Analytics Insight

The apex power sector regulator, the Central Electricity Regulatory Commission (CERC), is planning to set up an artificial intelligence (AI)-based regulatory expert system tool (REST) for improving access to information and assist the commission in discharge of its duties. So far, only the Supreme Court (SC) has an electronic filing (e-filing) system and is in the process of building an AI-based back-end service.

The CERC will be the first such quasi-judicial regulatory body to embrace AI and machine learning (ML). The decision comes at a time when the CERC has been shut for four ...

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First Published: Fri, January 15 2021. 06:10 IST

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CERC plans to embrace AI, machine learning to improve functioning - Business Standard

Ian Brooks, AM Technical Fellow, AMRC North West with Renishaws RenAM 500Q metal Additive Manufacturing machine (Courtesy Renishaw/ AMRC North West)

UK-based Intellegens, a University of Cambridge spin-out specialising in artificial intelligence; the University of Sheffield Advanced Manufacturing Research Centre (AMRC) North West, Preston, Lancashire, UK; and Boeing will collaborate on Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design.

The project aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise Additive Manufacturing processing parameters for new metal alloys at a lower cost and faster rate. The research will focus on metal Laser Beam Powder Bed Fusion (PBF-LB), specifically on key parameter variables required to manufacture high density, high strength parts.

Project MEDAL is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10 million initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK. Intellegens was a startup in the first group of companies to complete the ATI Boeing Accelerator last year.

We are very excited to be launching this project in conjunction with the AMRC, stated Ben Pellegrini, CEO of Intellegens. The intersection of machine learning, design of experiments and Additive Manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.

James Hughes, Research Director for University of Sheffield AMRC North West, explained that the project will build the AMRCs knowledge and expertise in alloy development so it can help other UK manufacturers.

Hughes commented, At the AMRC we have experienced first-hand, and through our partner network, how onerous it is to develop a robust set of process parameters for AM. It relies on a multi-disciplinary team of engineers and scientists and comes at great expense in both time and capital equipment.

It is our intention to develop a robust, end-to-end methodology for process parameter development that encompasses how we operate our machinery right through to how we generate response variables quickly and efficiently. Intellegens AI-embedded platform Alchemite will be at the heart of all of this.

There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, Hughes continued. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.

Sir Martin Donnelly, president of Boeing Europe and managing director of Boeing in the UK and Ireland, reported that the project shows how industry can successfully partner with government and academia to spur UK innovation.

Donnelly noted, We are proud to see this project move forward because of what it promises aviation and manufacturing, and because of what it represents for the UKs innovation ecosystem. We helped found the AMRC two decades ago, Intellegens was one of the companies we invested in as part of the ATI Boeing Accelerator and we have longstanding research partnerships with Cambridge University and the University of Sheffield.

He added, We are excited to see what comes from this continued collaboration and how we might replicate this formula in other ways within the UK and beyond.

Aerospace components have to withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace material mix.

One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1 million to undertake.

Pellegrini explained that experimental design techniques are extremely important to develop new products and processes in a cost-effective and confident manner. The most common approach is Design of Experiments (DOE), a statistical method that builds a mathematical model of a system by simultaneously investigating the effects of various factors.

Pellegrini added, DOE is a more efficient, systematic way of choosing and carrying out experiments compared to the Change One Separate variable at a Time (COST) approach. However, the high number of experiments required to obtain a reliable covering of the search space means that DOE can still be a lengthy and costly process, which can be improved.

The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80%. The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircrafts and improved environmental impact, concluded Pellegrini.

Intellegens will produce a software platform with an underlying machine learning algorithm based on its Alchemite platform. It has reportedly already been used successfully to overcome material design problems in a University of Cambridge research project with a leading OEM where a new alloy was designed, developed and verified in eighteen months rather than the expected twenty-year timeline, saving approximately $10 million.

http://www.intellegens.ai

http://www.amrc.co.uk

http://www.boeing.com

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New research project will use machine learning to advance metal alloys for aerospace - Metal Additive Manufacturing magazine

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers particularly in but not limited to artificial intelligence and explain why they matter.

This week has a bit more basic research than consumer applications. Machine learning can be applied to advantage in many ways users benefit from, but its also transformative in areas like seismology and biology, where enormous backlogs of data can be leveraged to train AI models or as raw material to be mined for insights.

Were surrounded by natural phenomena that we dont really understand obviously we know where earthquakes and storms come from, but how exactly do they propagate? What secondary effects are there if you cross-reference different measurements? How far ahead can these things be predicted?

A number of recently published research projects have used machine learning to attempt to better understand or predict these phenomena. With decades of data available to draw from, there are insights to be gained across the board this way if the seismologists, meteorologists and geologists interested in doing so can obtain the funding and expertise to do so.

The most recent discovery, made by researchers at Los Alamos National Labs, uses a new source of data as well as ML to document previously unobserved behavior along faults during slow quakes. Using synthetic aperture radar captured from orbit, which can see through cloud cover and at night to give accurate, regular imaging of the shape of the ground, the team was able to directly observe rupture propagation for the first time, along the North Anatolian Fault in Turkey.

The deep-learning approach we developed makes it possible to automatically detect the small and transient deformation that occurs on faults with unprecedented resolution, paving the way for a systematic study of the interplay between slow and regular earthquakes, at a global scale, said Los Alamos geophysicist Bertrand Rouet-Leduc.

Another effort, which has been ongoing for a few years now at Stanford, helps Earth science researcher Mostafa Mousavi deal with the signal-to-noise problem with seismic data. Poring over data being analyzed by old software for the billionth time one day, he felt there had to be better way and has spent years working on various methods. The most recent is a way of teasing out evidence of tiny earthquakes that went unnoticed but still left a record in the data.

The Earthquake Transformer (named after a machine-learning technique, not the robots) was trained on years of hand-labeled seismographic data. When tested on readings collected during Japans magnitude 6.6 Tottori earthquake, it isolated 21,092 separate events, more than twice what people had found in their original inspection and using data from less than half of the stations that recorded the quake.

Image Credits: Stanford University

The tool wont predict earthquakes on its own, but better understanding the true and full nature of the phenomena means we might be able to by other means. By improving our ability to detect and locate these very small earthquakes, we can get a clearer view of how earthquakes interact or spread out along the fault, how they get started, even how they stop, said co-author Gregory Beroza.

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Deep Science: Using machine learning to study anatomy, weather and earthquakes - TechCrunch