Category Archives: Ai

The USLibrary of Congress has released an AI tool that lets you search through 16 million historical newspaper pages for images that help explain the stories of the past.

TheNewspaper Navigatorshows how seminal events and characters, such as wars and presidents, have been depicted in the press. Jim Casey, an assistant professor of African American Studies at Penn State University whos tested the tool, said it would add a visual component to his historical research:

As I am writing a history of editors in the early United States, Newspaper Navigator will be an invaluable tool for charting the visual culture of the press. It provides us with a wealth of clues about the work of editors (behind the scenes) to forge the look and feel of the first drafts of history.

[Read: Are EVs too expensive? Here are 5 common myths, debunked]

The tool is the brainchild of Ben Lee, a Washington University researcher and the Library of Congress Innovator in Residence.

Lee first identified the visual content using an object detection model trained on annotations of World War 1-era pages from the Librays digitized collection of newspapers published between 1900 and 1963. This enabled the AI to detect photographs, illustrations, maps, cartoons, comics, headlines, and advertisements.It also uses Optical Character Recognition to extract a headline and caption from the corresponding article.

To use the system, simply enter a keyword in the Newspaper Navigatorand the AI will surface matches from a dataset of 1.56 million newspaper photos.You can alsospecify a date range and a state in which the newspaper was published.

You can then click on any image to download it, read the article it accompanied, view the full issue, or learn more about the newspaper.

The tool should be particularly useful for archivists, but it can also help all of us learn more about the stories of our past.

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Published September 16, 2020 18:08 UTC

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US Library of Congress launches AI tool that lets you search 16 million old newspaper pages for historical images - The Next Web

LONDON--(BUSINESS WIRE)--Bigger technology budgets and greater use of artificial intelligence and automation are the main predictions for change post-coronavirus, according to a survey of over 1,000 project practitioners* by Association for Project Management (APM).

The poll, undertaken for APM by research company Censuswide, found that 36 per cent** of respondents anticipate technology budgets will increase within their business or team in the wake of the COVID-19 pandemic, while 30 per cent expect their business or team will make greater use of artificial intelligence and automation.

The survey revealed a split in opinions when it comes to how new solutions will be implemented, however. Some expect to grow relationships with existing suppliers while others anticipate new providers will be brought in.

When it comes to choosing a technology supplier, integration emerged as the most important factor overall for project management professionals, although this varied by industry sector.

Most important criteria for choosing a technology supplier (survey of 1,000 project professionals*)

Will Webster, head of technology at APM, said: Since the onset of lockdown, we have seen increased emphasis on the value of technology in facilitating new ways of working. As these new working methods become the norm, technology will be essential to support increased flexibility and productivity. Nowhere will this be more important than in the project profession, where successful outcomes are vital for delivering economic and social benefits for businesses and communities.

The findings of this survey reveal an opportunity for providers of technology solutions to strengthen existing partnerships and forge new ones through differentiation. While cost is clearly a factor, integration, reputation and scalability could be the difference to an organisation in selecting a particular hardware or software solution.

-ENDS-

Notes to editors

* 1,003 project professionals based in the UK responded to the questions What changes do you anticipate in the way your team or business uses technology post-coronavirus? and What is your most important criteria for choosing a technology supplier?

** All figures rounded to the nearest 1 per cent

About APM

About Association for Project Management (APM)

Association for Project Management (APM) is the chartered body for the project profession and is committed to creating and upholding leading standards for the profession through chartership, qualifications, knowledge and insight.

Promoting the power of the profession for good, APM has over 30,000 individual members and more than 500 organisations participating in the Corporate Partnership Programme. As an educational charity, APM advocates for and represents the entire project profession - all sectors, all locations and at every stage of the career path. Please see https://www.apm.org.uk/ for further details.

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Project Managers Anticipate Increased Tech Budgets and Enhanced Focus on AI in the Wake of Coronavirus, New Survey Reveals - Business Wire

MONTREAL andTORONTO and NEW YORK, Sept. 17, 2020 /PRNewswire/ --Overbond Ltd., a fixed income analytics solution provider, today announced the launch of COBI-Pricing LIVE, the company's powerful new real-time AI bond pricing product. COBI-Pricing LIVE has the capacity to process real-time historical pricing data on coverage of over 30,000 securities with a refresh rate under three seconds, making it the fastest in the industry and the first-to-market offering such technology.

COBI-Pricing LIVE is the newest part of Overbond's innovative suite of AI algorithms, and fills a gap in the current market by aggregating multiple data sources on the client side across trading venues, data aggregators themselves and fundamental and settlement layer data. Bond markets suffer from illiquidity and non-uniform data sources used to price bonds and build credit curves, and fixed income traders require accurate bond pricing that can measure the liquidity of individual securities and enable automatic execution.

COBI-Pricing LIVE provides this requirement by leveraging the power of AI models that optimize prices for bonds with various liquidity profiles and performing deep historical benchmarking and curve fitting.With the entire market expected to see approximately 40% of trading performed by AI algorithms by 2024, COBI-Pricing LIVE is well-positioned given its intuitive, customizable nature and the fact that it is designed to help traders make more informed buying and selling decisions.

"Both sell side dealers and buy side asset managers are increasingly relying on AI applications to price fixed income securities in live trading and automate part of their daily workflows," says Vuk Magdelinic, CEO of Overbond. "However, most of the existing fixed income capital market data sources do not have enough coverage to provide traders with a view of true liquidity and price precision that can be executed automatically."

The bond market, notes Magdelinic, heavily relies on segregated data disseminated between counterparties, which creates fragmented data sets that do not cover the bulk of over-the-counter traded flows. COBI-Pricing LIVE collates and organizes large volumes of disparate data, including non-traditional data sets such as fundamental and settlement layer data. Using novel AI liquidity scoring, COBI-Pricing LIVE tiers all trades and determines if these qualify for full-automation, trader supervision, or should not be traded at the current time.

How COBI-Pricing LIVE works

The Overbond platform first sources raw live trading and fundamental data from a range of suppliers. These include Refinitiv, S&P Global Market Intelligence, ICE, EDI, Euroclear and FINRA TRACE, among others, as well as major credit agencies. It also collates company-level fundamental data, dealer quotations, internal client executed trade records and investor preferences through feedback.

COBI-Pricing LIVE then utilizes an advanced three phase AI algorithm:

Traders can train and customize models on their internal data to find a competitive edge in the market. The COBI-Pricing LIVE output can also be integrated into a data feed via an API, a desktop side-by-side integration with order-management-system (OMS), or viewed on the Overbond Platform as a downloadable table.

COBI-Pricing LIVE's Key Benefits for Traders

Built by traders, for traders, COBI-Pricing LIVE is designed to eliminate pain points, streamline and automate the trading process. The product does so through the following:

"Counterparties are increasingly adopting quantitative investing and liquidity risk monitoring techniques to automate their trading workflows. This includes the consumption of increasing amounts of alternative data and using new methods of fixed income pricing analysis such as AI analytics like Overbond's COBI-Pricing LIVE algorithm," continued Mr. Magdelinic.

About Overbond

Founded in 2015, Overbond is transforming how global investment banks, institutional investors, corporations and governments connect and access fixed income markets through advanced AI analytics. Overbond's fully-digital platform and suite of AI algorithms (COBI) eliminates inefficiencies, provides systematic price discovery and trade execution as well as predictive analytics to all counterparties in the fixed income market. The company's growing client base includes buy-side institutions with over $2 trillion of assets under management globally across both passive and active strategies, regulatory reporting regimes and global corporate and government issuers with more than $20 billion in outstanding bonds.

For more information, contact http://www.overbond.com.

SOURCE Overbond

http://www.overbond.com

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Overbond launches real-time AI bond pricing trading product - PRNewswire

Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread.

Image: Sompong Rattanakunchon / Getty Images

Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and IBM's director of the Watson AI Lab said during a webinar this week.

Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said.

The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning.

"The striking success right now in particular, in machine learning, is in problems that require interpretation of signalsimages, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.

For years, people have tried to solve problems like detecting faces and images and directly engineering solutions that didn't work, she said.

We have become good at engineering algorithms that take data and use that to derive a solution, she said. "That's been an amazing success." But it takes a lot of data and a lot of computation so for some problems formulations aren't available yet that would let us learn from the amount of data available, Kaelbling said.

SEE:9 super-smart problem solvers take on bias in AI, microplastics, and language lessons for chatbots(TechRepublic)

One of her areas of focus is in robotics, and it's harder to get training examples there because robots are expensive and parts break, "so we really have to be able to learn from smaller amounts of data," Kaelbling said.

Neural networks and deep learning are the "latest and greatest way to frame those sorts of problems and the successes are many," added Josh Tenenbaum, a professor of cognitive science and computation at MIT.

But when talking about general intelligence and how to get machines to understand the world there is still a huge gap, he said.

"But on the research side really exciting things are starting to happen to try to capture some steps to more general forms of intelligence [in] machines," he said. In his work, "we're seeing ways in which we can draw insights from how humans understand the world and taking small steps to put them in machines."

Although people think of AI as being synonymous with automation, it is incredibly labor intensive in a way that doesn't work for most of the problems we want to solve, noted David Cox, IBM director of the MIT-IBM Watson AI Lab.

Echoing Kaelbling, Cox said that leveraging tools today like deep learning requires huge amounts of "carefully curated, bias-balanced data," to be able to use them well. Additionally, for most problems we are trying to solve, we don't have those "giant rivers of data" to build a dam in front of to extract some value from that river, Cox said.

Today, companies are more focused on solving some type of one-off problem and even when they have big data, it's rarely curated, he said. "So most of the problems we love to solve with AIwe don't have the right tools for that."

That's because we have problems with bias and interpretability with humans using these tools and they have to understand why they are making these decisions, Cox said. "They're all barriers."

However, he said, there's enormous opportunity looking at all these different fields to chart a path forward.

That includes using deep learning, which is good for pattern recognition, to help solve difficult search problems, Tenenbaum said.To develop intelligent agents, scientists need to use all the available tools, said Kaelbling. For example, neural networks are needed for perception as well as higher level and more abstract types of reasoning to decide, for example, what to make for dinner or to decide how to disperse supplies.

"The critical thing technologically is to realize the sweet spot for each piece and figure out what it is good at and not good at. Scientists need to understand the role each piece plays," she said.

The MIT and IBM AI experts also discussed a new foundational method known as neurosymbolic AI, which is the ability to combine statistical, data-driven learning of neural networks with the powerful knowledge representation and reasoning of symbolic approaches.

Moderator Schubmehl commented that having a combination of neurosymbolic AI and deep learning "might really be the holy grail" for advancing real-world AI.

Kaelbling agreed, adding that it may be not just those two techniques but include others as well.

One of the themes that emerged from the webinar is that there is a very helpful confluence of all types of AI that are now being used, said Cox. The next evolution of very practical AI is going to be understanding the science of finding things and building a system we can reason with and grow and learn from, and determine what is going to happen. "That will be when AI hits its stride," he said.

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The path to real-world artificial intelligence - TechRepublic

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Researchers from Googles DeepMind and the University of Oxford recommend that AI practitioners draw on decolonial theory to reform the industry, put ethical principles into practice, and avoid further algorithmic exploitation or oppression.

The researchers detailed how to build AI systems while critically examining colonialism and colonial forms of AI already in use in a preprint paper released Thursday. The paper was coauthored by DeepMind research scientists William Isaac and Shakir Mohammed and Marie-Therese Png, an Oxford doctoral student and DeepMind Ethics and Society intern who previously provided tech advice to the United Nations.

The researchers posit that power is at the heart of ethics debates and that conversations about power are incomplete if they do not include historical context and recognize the structural legacy of colonialism that continues to inform power dynamics today. They further argue that inequities like racial capitalism, class inequality, and heteronormative patriarchy have roots in colonialism and that we need to recognize these power dynamics when designing AI systems to avoid perpetuating such harms.

Any commitment to building the responsible and beneficial AI of the future ties us to the hierarchies, philosophy, and technology inherited from the past, and a renewed responsibility to the technology of the present, the paper reads. This is needed in order to better align our research and technology development with established and emerging ethical principles and regulation, and to empower vulnerable peoples who, so often, bear the brunt of negative impacts of innovation and scientific progress.

The paper incorporates a range of suggestions, such as analyzing data colonialism and decolonization of data relationshipsand employing the critical technical approach to AI development Philip Agre proposed in 1997.

The notion of anticolonial AI builds on a growing body of AI research that stresses the importance of including feedback from people most impacted by AI systems. An article released in Nature earlier this week argues that the AI community must ask how systems shift power and asserts that an indifferent field serves the powerful. VentureBeat explored how power shapes AI ethics in a special issue last fall. Power dynamics were also a main topic of discussion at the ACM FAccT conference held in early 2020 as more businesses and national governments consider how to put AI ethics principles into practice.

The DeepMind paper interrogates how colonial features are found in algorithmic decision-making systems and what the authors call sites of coloniality, or practices that can perpetuate colonial AI. These include beta testing on disadvantaged communities like Cambridge Analytica conducting tests in Kenya and Nigeria or Palantir using predictive policing to target Black residents of New Orleans. Theres also ghost work, the practice of relying on low-wage workers for data labeling and AI system development. Some argue ghost work can lead to the creation of a new global underclass.

The authors define algorithmic exploitation as the ways institutions or businesses use algorithms to take advantage of already marginalized people and algorithmic oppression as the subordination of a group of people and privileging of another through the use of automation or data-driven predictive systems.

Ethics principles from groups like G20 and OECD feature in the paper, as well as issues like AI nationalism and the rise of the U.S. and China as AI superpowers.

Power imbalances within the global AI governance discourse encompasses issues of data inequality and data infrastructure sovereignty, but also extends beyond this. We must contend with questions of who any AI regulatory norms and standards are protecting, who is empowered to project these norms, and the risks posed by a minority continuing to benefit from the centralization of power and capital through mechanisms of dispossession, the paper reads. Tactics the authors recommend include political community action, critical technical practice, and drawing on past examples of resistance and recovery from colonialist systems.

A number of members of the AI ethics community, from relational ethics researcher Abeba Birhane to Partnership on AI, have called on machine learning practitioners to place people who are most impacted by algorithmic systems at the center of development processes. The paper explores concepts similar to those in a recent paper about how to combat anti-Blackness in the AI community, Ruha Benjamins concept of abolitionist tools, and ideas of emancipatory AI.

The authors also incorporate a sentiment expressed in an open letter Black members of the AI and computing community released last month during Black Lives Matter protests, which asks AI practitioners to recognize the ways their creations may support racism and systemic oppression in areas like housing, education, health care, and employment.

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DeepMind researchers propose rebuilding the AI industry on a base of anticolonialism - VentureBeat

Every department in a company has its own challenges.

In the case of Human Resources, recruitment and onboarding processes, employee orientations, process paperwork, and background checks is a handful and many a time painstaking mostly because of the repetitive and manual nature of the work. The most challenging of all is engaging with employees on human grounds to understand their needs.

As leaders today are observing the AI revolution across every process, Human resources is no exception: there has been a visible wave of AI disruption across HR functions. According to an IBMs survey from 2017, among 6000 executives, 66% of CEOs believe that cognitive computing can drive compelling value in HR while half of the HR personnel believe this may affect roles in the HR organization. The study clearly exhibits the apprehension of HR executives caused by the AI disruption in their field.

While one aspect of AI is creating uneasiness: the other is promising convenience. AI aims to empower the HR department with the right knowledge to optimize processes with less manual power and guarantees to mitigate errors.

TheCOVID-19 pandemic has highlighted thepower of AIin real-time< Backlink-https://us.sganalytics.com/blog/ai-can-detect-infections-with-96-percent-accuracy-can-ai-predict-the-next-pandemic/>, including its shortcomings. At the crux of the AI evolution is the minimization of human labored processes. Sophisticated AI algorithms can analyze large amounts of data in no time and self-educate themselves to recognize and map patterns, which can come in handy for HR staffs to plan and operate strategically.

While a human can be biased, get bored and make unintended mistakes provoking inadequacy in productivity and efficiency, AI programs are unbiased and diligent, enabling more productivity and efficiency.

HR executives who perform tasks like applicant tracking, payroll, training, and job postings manually without automation, state that they spend 14 hours a week on an average on these tasks. Leveraging AI to automate these HR processes can be extremely pertinent for meeting the following key business requirements: First, save time and increase efficiency; Second, provide real-time responses and solutions that meet employee expectations.

As per a Mckinseys study AI will drastically change business regardless of the industry. AI could potentially deliver an additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year.

Lets dive deeper to understand how AI can help sophisticate HR processes while not necessarily replacing human resource personnel.

1. Improved Employee Experience

Employees are the first customers for any organization. Hence employee experience is as important as customer experience.

As employee experience is becoming the next competitive edge for businesses, the coming days will be focused on providing personalized engagement and improving employee experience for human resources.

According to a Deloitte survey, 80% HR executives rate employee experience as important, while only 22% believe their organization excels at providing a differentiated employee experience.

Additionally, the advent of smart workplace has raised the bars of employees expectations for work-space experience and engagement factors.

Jennifer Stroud, HR Evangelist & Transformation Leader atServiceNow, says,We have seen the need for chatbots, AI and machine learning in the workplace to drive more productivity as well as modern, consumerized employee experiences. These consumer technology solutions are exactly what employees want in the workplace.

Engaging AI can help the HR department provide personalized employee engagement experiences across the entire employee lifecycle, right from recruitment and onboarding to career pathing.

2. Empowering HRs to make Data-Driven decisions

In common, the data-to-decision workflow looks like the below figure for many people.

Source: jobsoffice

Many HR technologies still follow the above workflow and depend on manual methods to glean insights from data. This task grows tedious and creates a bottleneck for end-users (data analysts) to draw insights within the stipulated time leading to decision making on outdated data.

While frontier technologies like data analytics are advancing to provide real-time data to make fast and fact-based decisions, AI can assist Human Resource professionals in harnessing this real-time data and making quick, consistent, and data-driven decisions. After all, the bottom line of HR agility is decision making.

3. Intelligent Automation

Intelligent automation fuses automation with AI. This will enable machines to make human-like decisions by self-educating themselves. Apart from augmenting productivity and efficiency for repetitive manual processes, this can help remove human interventions deployed for automated process completely.

1.More work in less time!

Crafting job descriptions for a particular role, filtering resumes and analyzing skillsets to find the apt talent is not only tiring and tedious, but also tricky for human resource professionals as a simple overlooked aspect can lead to a significant mistake, which may cost the company dearly in the long run. Well, AI can help HR staff overcome such scenarios by crafting bespoken job descriptions automatically and assist them in reading through thousands of resumes within a short time, thus effectively reducing the time and manual hard work put in by recruiters.

2. Identify the right talent without bias

HR personnel are humans and are likely to exhibit bias subconsciously. AI, on the other hand, is immune to human emotions which makes it the perfect fit to process candidate profiles based on required skillset without any disregard for candidates age, race, gender, geographic areas or organizational relationship. An unbiased recruitment is a win-win for both HR staff and organization. Furthermore, AI can be instrumental in increasing retention rates and establishing cultural diversity.

Consider programs like Texito, they help recognize gender bias in ads enabling recruiters to embrace a neutral language.

3. Streamline employee onboarding

The first day of an employee in an organization is like the first day of a transferred student in a new school. Although employees are grown-ups and possess the cognitive intelligence to adapt easily to an environment, deep down they look for a guidance to help them settle down in a new environment. Fortunately, organizations have HR staff to do this job. Employees generally have numerous queries on their first day regarding company policies, leaves, compensations, notice period, insurance claims, etc. As intriguing as the questions may be for an employee, these queries may turn repetitive and exhausting for an HR personnel over the time. Engaging AI chatbots makes it simple to answer such repetitive questions and make more time for the HR staff to concentrate on other essential tasks.

4. Optimize employee engagement to build better relationships

Apart from recruitment and onboarding, AI can be used to streamline processes like scheduling meetings, training employees and other such business processes. AIs capabilities to recognize personas will help Human resources professionals understand the human aspect of every single employee in-depth and enable them to shape a friendly and exciting company culture to provide unique and personalized employee engagement experiences.

5. Manage employee churn

Understanding factors that cause and arrest employee churn is the toughest part of an HRs job. People change jobs for various reasons like financial growth, career growth, shift in profiles, unsatisfied work environment, etc. Leveraging AI capabilities can help the HR department in continuously monitoring and evaluating employees thoughts about the organization, work culture, the degree of satisfaction with their job, etc. Knowing what offends or drives an employee can help in underlining the employee churn factors precisely. AI can help HR executives in performing this task more precisely.

All said and done, even though AIs capabilities would help reduce manual work and boost efficiency and productivity, artificial intelligence doesnt possess the emotional intelligence of humans. AI also cannot compensate for the humane connection that HR personnel form with employees and leverage to drive engagement and responsiveness.

Therefore, to answer the critical question that haunts HR executives Will AI be the reason why I might lose my job? No. Not really. The whole idea of AI in HR is the integration of technology to automate the more monotonous HR related tasks and optimize processes to add value to human work in less time. In the AI era, new jobs will evolve that will have new skills requirements unleashing the evolution of the HR function in an AI-first world.

Author Detail:

Jency is a technology content writer with SG Analytics. She contributes to the companys advancements by writing creative and engaging for their website and blogs. Her hobbies include music, reading, and trekking.

Company designation: Content Writer, SG Analytics

Location: Pune

Links to my blogs: https://us.sganalytics.com/blog/75-percent-consumers-anticipate-financial-impact-effects-of-covid-on-consumer-behaviour/, https://us.sganalytics.com/blog/social-media-analytics-is-truly-a-game-changer-heres-why/

Social media profile: LinkedIn https://www.linkedin.com/in/jency-durairaj-21225aa9

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AI In Human Resources To AI or not to AI? - Analytics Insight

Artificial intelligence (AI) presents an opportunity to transform how we allocate credit and risk, and to create fairer, more inclusive systems. AIs ability to avoid the traditional credit reporting and scoring system that helps perpetuate existing bias makes it a rare, if not unique, opportunity to alter the status quo. However, AI can easily go in the other direction to exacerbate existing bias, creating cycles that reinforce biased credit allocation while making discrimination in lending even harder to find. Will we unlock the positive, worsen the negative, or maintain the status quo by embracing new technology?

This paper proposes a framework to evaluate the impact of AI in consumer lending. The goal is to incorporate new data and harness AI to expand credit to consumers who need it on better terms than are currently provided. It builds on our existing systems dual goals of pricing financial services based on the true risk the individual consumer poses while aiming to prevent discrimination (e.g., race, gender, DNA, marital status, etc.). This paper also provides a set of potential trade-offs for policymakers, industry and consumer advocates, technologists, and regulators to debate the tensions inherent in protecting against discrimination in a risk-based pricing system layered on top of a society with centuries of institutional discrimination.

AI is frequently discussed and ill defined. Within the world of finance, AI represents three distinct concepts: big data, machine learning, and artificial intelligence itself. Each of these has recently become feasible with advances in data generation, collection, usage, computing power, and programing. Advances in data generation are staggering: 90% of the worlds data today were generated in the past two years, IBM boldly stated. To set parameters of this discussion, below I briefly define each key term with respect to lending.

Big data fosters the inclusion of new and large-scale information not generally present in existing financial models. In consumer credit, for example, new information beyond the typical credit-reporting/credit-scoring model is often referred to by the most common credit-scoring system, FICO. This can include data points, such as payment of rent and utility bills, and personal habits, such as whether you shop at Target or Whole Foods and own a Mac or a PC, and social media data.

Machine learning (ML) occurs when computers optimize data (standard and/or big data) based on relationships they find without the traditional, more prescriptive algorithm. ML can determine new relationships that a person would never think to test: Does the type of yogurt you eat correlate with your likelihood of paying back a loan? Whether these relationships have casual properties or are only proxies for other correlated factors are critical questions in determining the legality and ethics of using ML. However, they are not relevant to the machine in solving the equation.

What constitutes true AI is still being debated, but for purposes of understanding its impact on the allocation of credit and risk, lets use the term AI to mean the inclusion of big data, machine learning, and the next step when ML becomes AI. One bank executive helpfully defined AI by contrasting it with the status quo: Theres a significant difference between AI, which to me denotes machine learning and machines moving forward on their own, versus auto-decisioning, which is using data within the context of a managed decision algorithm.

Americas current legal and regulatory structure to protect against discrimination and enforce fair lending is not well equipped to handle AI. The foundation is a set of laws from the 1960s and 1970s (Equal Credit Opportunity Act of 1974, Truth in Lending Act of 1968, Fair Housing Act of 1968, etc.) that were based on a time with almost the exact opposite problems we face today: not enough sources of standardized information to base decisions and too little credit being made available. Those conditions allowed rampant discrimination by loan officers who could simply deny people because they didnt look credit worthy.

Today, we face an overabundance of poor-quality credit (high interest rates, fees, abusive debt traps) and concerns over the usage of too many sources of data that can hide as proxies for illegal discrimination. The law makes it illegal to use gender to determine credit eligibility or pricing, but countless proxies for gender exist from the type of deodorant you buy to the movies you watch.

Americas current legal and regulatory structure to protect against discrimination and enforce fair lending is not well equipped to handle AI.

The key concept used to police discrimination is that of disparate impact. For a deep dive into how disparate impact works with AI, you can read my previous work on this topic. For this article, it is important to know that disparate impact is defined by the Consumer Financial Protection Bureau as when: A creditor employs facially neutral policies or practices that have an adverse effect or impact on a member of a protected class unless it meets a legitimate business need that cannot reasonably be achieved by means that are less disparate in their impact.

The second half of the definition provides lenders the ability to use metrics that may have correlations with protected class elements so long as it meets a legitimate business need,andthere are no other ways to meet that interest that have less disparate impact. A set of existing metrics, including income, credit scores (FICO), and data used by the credit reporting bureaus, has been deemed acceptable despite having substantial correlation with race, gender, and other protected classes.

For example, consider how deeply correlated existing FICO credit scores are with race. To start, it is telling how little data is made publicly available on how these scores vary by race. The credit bureau Experian is eager to publicize one of its versions of FICO scores by peoples age, income, and even what state or city they live in, but not by race. However, federal law requires lenders to collect data on race for home mortgage applications, so we do have access to some data. As shown in the figure below, the differences are stark.

Among people trying to buy a home, generally a wealthier and older subset of Americans, white homebuyers have an average credit score 57 points higher than Black homebuyers and 33 points higher than Hispanic homebuyers. The distribution of credit scores is also sharply unequal: More than 1 in 5 Black individuals have FICOs below 620, as do 1 in 9 among the Hispanic community, while the same is true for only 1 out of every 19 white people. Higher credit scores allow borrowers to access different types of loans and at lower interest rates. One suspects the gaps are even broader beyond those trying to buy a home.

If FICO were invented today, would it satisfy a disparate impact test? The conclusion of Rice and Swesnik in their law review article was clear: Our current credit-scoring systems have a disparate impact on people and communities of color. The question is mute because not only is FICO grandfathered, but it has also become one of the most important factors used by the financial ecosystem. I have described FICO as the out of tune oboe to which the rest of the financial orchestra tunes.

New data and algorithms are not grandfathered and are subject to the disparate impact test. The result is a double standard whereby new technology is often held to a higher standard to prevent bias than existing methods. This has the effect of tilting the field against new data and methodologies, reinforcing the existing system.

Explainability is another core tenant of our existing fair lending system that may work against AI adoption. Lenders are required to tell consumers why they were denied. Explaining the rationale provides a paper trail to hold lenders accountable should they be engaging in discrimination. It also provides the consumer with information to allow them to correct their behavior and improve their chances for credit. However, an AIs method to make decisions may lack explainability. As Federal Reserve Governor Lael Brainard described the problem: Depending on what algorithms are used, it is possible that no one, including the algorithms creators, can easily explain why the model generated the results that it did. To move forward and unlock AIs potential, we need a new conceptual framework.

To start, imagine a trade-off between accuracy (represented on the y-axis) and bias (represented on the x-axis). The first key insight is that the current system exists at the intersection of the axes we are trading off: the graphs origin. Any potential change needs to be considered against the status-quonot an ideal world of no bias nor complete accuracy. This forces policymakers to consider whether the adoption of a new system that contains bias, but less than that in the current system, is an advance. It may be difficult to embrace an inherently biased framework, but it is important to acknowledge that the status quo is already highly biased. Thus, rejecting new technology because it contains some level of bias does not mean we are protecting the system against bias. To the contrary, it may mean that we are allowing a more biased system to perpetuate.

As shown in the figure above, the bottom left corner (quadrant III) is one where AI results in a system that is more discriminatory and less predictive. Regulation and commercial incentives should work together against this outcome. It may be difficult to imagine incorporating new technology that reduces accuracy, but it is not inconceivable, particularly given the incentives in industry to prioritize decision-making and loan generation speed over actual loan performance (as in the subprime mortgage crisis). Another potential occurrence of policy moving in this direction is the introduction of inaccurate data that may confuse an AI into thinking it has increased accuracy when it has not. The existing credit reporting system is rife with errors: 1 out of every 5 people may have material error on their credit report. New errors occur frequentlyconsider the recent mistake by one student loan servicer that incorrectly reported 4.8 million Americans as being late on paying their student loans when in fact in the government had suspended payments as part of COVID-19 relief.

The data used in the real world are not as pure as those model testing. Market incentives alone are not enough to produce perfect accuracy; they can even promote inaccuracy given the cost of correcting data and demand for speed and quantity. As one study from the Federal Reserve Bank of St. Louis found, Credit score has not acted as a predictor of either true risk of default of subprime mortgage loans or of the subprime mortgage crisis. Whatever the cause, regulators, industry, and consumer advocates ought to be aligned against the adoption of AI that moves in this direction.

The top right (quadrant I) represents incorporation of AI that increases accuracy and reduces bias. At first glance, this should be a win-win. Industry allocates credit in a more accurate manner, increasing efficiency. Consumers enjoy increased credit availability on more accurate terms and with less bias than the existing status quo. This optimistic scenario is quite possible given that a significant source of existing bias in lending stems from the information used. As the Bank Policy Institute pointed out in its in discussion draft of the promises of AI: This increased accuracy will benefit borrowers who currently face obstacles obtaining low-cost bank credit under conventional underwriting approaches.

One prominent example of a win-win system is the use of cash-flow underwriting. This new form of underwriting uses an applicants actual bank balance over some time frame (often one year) as opposed to current FICO based model which relies heavily on seeing whether a person had credit in the past and if so, whether they were ever in delinquency or default. Preliminary analysis by FinReg Labs shows this underwriting system outperforms traditional FICO on its own, and when combined with FICO is even more predictive.

Cash-flow analysis does have some level of bias as income and wealth are correlated with race, gender, and other protected classes. However, because income and wealth are acceptable existing factors, the current fair-lending system should have little problem allowing a smarter use of that information. Ironically, this new technology meets the test because it uses data that is already grandfathered.

That is not the case for other AI advancements. New AI may increase credit access on more affordable terms than what the current system provides and still not be allowable. Just because AI has produced a system that is less discriminatory does not mean it passes fair lending rules. There is no legal standard that allows for illegal discrimination in lending because it is less biased than prior discriminatory practices. As a 2016 Treasury Department study concluded, Data-driven algorithms may expedite credit assessments and reduce costs, they also carry the risk of disparate impact in credit outcomes and the potential for fair lending violations.

For example, consider an AI that is able, with a good degree of accuracy, to detect a decline in a persons health, say through spending patterns (doctors co-pays), internet searches (cancer treatment), and joining new Facebook groups (living with cancer). Medical problems are a strong indicator of future financial distress. Do we want a society where if you get sick, or if a computer algorithm thinks you are ill, that your terms of credit decrease? That may be a less biased system than we currently have, and not one that policymakers and the public would support. Of all sudden what seems like a win-win may not actually be one that is so desirable.

AI that increases accuracy but introduces more bias gets a lot of attention, deservedly so. This scenario represented in the top left (quadrant II) of this framework can range from the introduction of data that are clear proxies for protected classes (watch Lifetime or BET on TV) to information or techniques that, on a first glance, do not seem biased but actually are. There are strong reasons to believe that AI will naturally find proxies for race, given that there are large income and wealth gaps between races. As Daniel Schwartz put it in his article on AI and proxy discrimination: Unintentional proxy discrimination by AIs is virtually inevitable whenever the law seeks to prohibit discrimination on the basis of traits containing predictive information that cannot be captured more directly within the model by non-suspect data.

Proxy discrimination by AI is even more concerning because the machines are likely to uncover proxies that people had not previously considered.

Proxy discrimination by AI is even more concerning because the machines are likely to uncover proxies that people had not previously considered. Think about the potential to use whether or not a person uses a Mac or PC, a factor that is both correlated to race and whether people pay back loans, even controlling for race.

Duke Professor Manju Puri and co-authors were able to build a model using non-standard data that found substantial predictive power in whether a loan was repaid through whether that persons email address contained their name. Initially, that may seem like a non-discriminatory variable within a persons control. However, economists Marianne Bertrand and Sendhil Mullainathan have shown African Americans with names heavily associated with their race face substantial discrimination compared to using race-blind identification. Hence, it is quite possible that there is a disparate impact in using what seems like an innocuous variable such as whether your name is part of your email address.

The question for policymakers is how much to prioritize accuracy at a cost of bias against protected classes. As a matter of principle, I would argue that our starting point is a heavily biased system, and we should not tolerate the introduction of increased bias. There is a slippery slope argument of whether an AI produced substantial increases in accuracy with the introduction of only slightly more bias. Afterall, our current system does a surprisingly poor job of allocating many basic credits and tolerates a substantially large amount of bias.

Industry is likely to advocate for inclusion of this type of AI while consumer advocates are likely to oppose its introduction. Current law is inconsistent in its application. Certain groups of people are afforded strong anti-discrimination protection against certain financial products. But again, this varies across financial product. Take gender for example. It is blatantly illegal under fair lending laws to use gender or any proxy for gender in allocating credit. However, gender is a permitted use for price difference for auto insurance in most states. In fact, for brand new drivers, gender may be the single biggest factor used in determining price absent any driving record. America lacks a uniform set of rules on what constitutes discrimination and what types of attributes cannot be discriminated against. Lack of uniformity is compounded by the division of responsibility between federal and state governments and, within government, between the regulatory and judicial system for detecting and punishing crime.

The final set of trade-offs involve increases in fairness but reductions in accuracy (quadrant IV in the bottom right). An example includes an AI with the ability to use information about a persons human genome to determine their risk of cancer. This type of genetic profiling would improve accuracy in pricing types of insurance but violates norms of fairness. In this instance, policymakers decided that the use of that information is not acceptable and have made it illegal. Returning to the role of gender, some states have restricted the use of gender in car insurance. California most recently joined the list of states no longer allowing gender, which means that pricing will be more fair but possibly less accurate.

Industry pressures would tend to fight against these types of restrictions and press for greater accuracy. Societal norms of fairness may demand trade-offs that diminish accuracy to protect against bias. These trade-offs are best handled by policymakers before the widespread introduction of this information such as the case with genetic data. Restricting the use of this information, however, does not make the problem go away. To the contrary, AIs ability to uncover hidden proxies for that data may exacerbate problems where society attempts to restrict data usage on the grounds of equity concerns. Problems that appear solved by prohibitions then simply migrate into the algorithmic world where they reappear.

The underlying takeaway for this quadrant is one in which social movements that expand protection and reduce discrimination are likely to become more difficult as AIs find workarounds. As long as there are substantial differences in observed outcomes, machines will uncover differing outcomes using new sets of variables that may contain new information or may simply be statistically effective proxies for protected classes.

The status quo is not something society should uphold as nirvana. Our current financial system suffers not only from centuries of bias, but also from systems that are themselves not nearly as predictive as often claimed. The data explosion coupled with the significant growth in ML and AI offers tremendous opportunity to rectify substantial problems in the current system. Existing anti-discrimination frameworks are ill-suited to this opportunity. Refusing to hold new technology to a higher standard than the status quo results in an unstated deference to the already-biased current system. However, simply opening the flood gates under the rules of can you do better than today opens up a Pandoras box of new problems.

The status quo is not something society should uphold as nirvana. Our current financial system suffers not only from centuries of bias, but also from systems that are themselves not nearly as predictive as often claimed.

Americas fractured regulatory system, with differing roles and responsibilities across financial products and levels of government, only serves to make difficult problems even harder. With lacking uniform rules and coherent frameworks, technological adoption will likely be slower among existing entities setting up even greater opportunities for new entrants. A broader conversation regarding how much bias we are willing to tolerate for the sake of improvement over the status quo would benefit all parties. That requires the creation of more political space for sides to engage in a difficult and honest conversation. The current political moment in time is ill-suited for that conversation, but I suspect that AI advancements will not be willing to wait until America is more ready to confront these problems.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Microsoft provides support to The Brookings InstitutionsArtificial Intelligence and Emerging Technology (AIET) Initiative, and Apple, Facebook, and IBM provide general, unrestricted support to the Institution. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

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Reducing bias in AI-based financial services - Brookings Institution

Cognitive deficiency tests, AI, and self-driving cars.

Seems like the news recently has been filled with revelations about the taking of cognitive deficiency tests.

This is especially being widely noted by some prominent politicians that appear to be attempting to vouch for having mental clarity upon reaching an age in life whereby cognitive decline often surfaces.

Such tests are more aptly referred to as cognitive assessment tests rather than deficiency oriented tests, though the notion generally being that if a score earned is less than what might be expected, the potential conclusion is that the person has had a decline in their mental prowess.

Oftentimes also referred to as cognitive impairment detection exams, the person seeking to find out how they are mentally doing is administered a test consisting of various questions and asked to answer the questions. The administrator of the test then grades the answers as to correctness and fluidity, producing a score to indicate how the person overall performed.

The score is then compared to the scores of others that have taken the test, trying to gauge how the cognitive capacity of the person is rated or ranked in light of some larger population of test-takers.

Also, if a person takes the test over time, perhaps say once per year, their prior scores are compared to their most recent score, attempting to measure whether there is a difference emerging as they age.

There are some crucial rules-of-thumb about all of this cognitive test-taking.

For example, if the person takes the same test word-for-word, repeatedly over time, this raises questions about the nature of the test versus the nature of the cognitive abilities of the person taking the test. In essence, you can potentially do better on the test simply because youve seen the same questions before and likely also had been previously told what the considered correct answers are.

One argument to be made is that this is somewhat assessing your ability to remember having previously taken the test, but thats not usually the spirit of what such cognitive tests are supposed to be about. The idea is to assess overall cognition, and not merely be focused on whether you perchance can recall the specific questions of a specific test previously taken.

Another facet of this kind of cognitive test-taking consists of being formally administered the test, rather than taking the test entirely on your own.

Though there are plenty of available cognitive tests that you can download and take in private, some would say that this is not at all the same as taking a test under the guiding hands and watch of someone certified or otherwise authorized to administer such tests.

A key basis for claiming that the test needs to be formally administered is to ensure that the person taking the test is not undermining the test or flouting the testing process. If the test taker were to ask a friend for help, this obviously defeats the purpose of the test, which is supposed to focus on your solitary cognition and not be a collective semblance of cognition. Likewise, these tests are usually timed, and a person on their own might be tempted to exceed the normally allotted time, plus the person might be tempted to look-up answers, use a calculator, etc.

Perhaps the most important reason to have a duly authorized and trained administrator involves attempting to holistically evaluate the results of the cognition test.

Experts in cognitive test-taking are quick to emphasize that a robust approach to the matter consists of not just the numeric score that a test taker achieves, but also how they are overall able to interact with a fully qualified and trained cognitive-test administrator.

Unlike taking a secured SAT or ACT test that you might have had to painstakingly sit through for college entrance purposes, a cognitive assessment test is typically intended to assess in both a written way and in a broader manner how the person interacts and cognitively presents themselves.

Imagine for example that someone aces the written test, yet meanwhile, they are unable to carry on a lucid conversation with the administrator, and similarly, they mentally stumble on why they are taking the test or otherwise have apparent cognitive difficulties surrounding the test-taking process. Those facets outside of the test itself should be counted, some would vehemently assert, and thus would be unlikely to be valued if a person merely took the test on their own.

Despite all of the foregoing and the holistic nuances that Ive mentioned, admittedly, most of the time all that people want to know is what was their darned score on that vexing cognitive test.

You might be wondering whether there is one standardized and universal cognitive test that is used for these purposes.

No, there is not just one per se.

Instead, there are a bewildering and veritable plethora of such cognition tests.

It seems like each day there is some new version that gets announced to the world. In some cases, the cognitive test being proffered has been carefully prepared and analyzed for its validity. Unfortunately, in other cases, the cognitive test is a gimmick and being fronted as a moneymaker, whereby those pushing the test are aiming to get people to believe in it and hoping to generate gobs of revenue by how many take the test and charge them fees accordingly.

Please do not fall for the fly-by-night cognitive tests.

Sadly, sometimes a known celebrity or other highly visible person gets associated with a cognitive test promotion and adds a veneer of authenticity to something that does not deserve any bona fide reputational stamp-of-approval.

Some cognitive tests have lasted the test of time and are considered the dominant or at least well-regarded for their cognitive assessing capacity and validity.

On a related note, if a cognitive test takes a long time to complete, lets say hours of completion time, the odds are that it is not going to be overall well-received and considered onerous for testing purposes. As such, the popular cognitive tests tend to be the ones that take a relatively short period to undertake, such as an hour or less, and in many cases even just 15 minutes or less (these are usually depicted as screening tests rather than full-blown cognitive assessment tests).

Some decry that only requiring a few minutes to take a cognitive test is rife with problems and seems like a fast-food kind of approach to tackling a very complex topic of measuring someones cognition. Those in this camp shudder when these quickie tests are used by people that then go around touting how well they scored.

The counter-argument is that these short-version cognitive tests are reasonable and amount to using a dipstick to gauge how much gasoline there is in the tank of your car. The viewpoint is that it only takes a little bit of measurement to generally know how someone is mentally faring. Once an overall gauge is taken, you can always do a follow-up with a more in-depth cognitive test.

Given all of the preceding discussion, it might be handy to briefly take a look at a well-known cognitive test that has been around since the mid-1990s and continues to actively be in use today, including having been the test that reportedly President Trump took in 2018 (according to news reports).

The Famous MoCA Cognitive Test

That test is the Montreal Cognitive Assessment (MoCA) test.

Some mistakenly get confused by the name of the test and think that it is maybe just a test for Canadians since it refers to Montreal in the naming, but the test is globally utilized and was named for being initially developed by researchers in Montreal, Quebec.

Generally, the MoCA is one-page in size (see example here), which is handily succinct for doing this kind of testing, and the person taking the test is given 10 minutes to answer the questions. There is some leeway often allowed in the testing time allotted, and also some latitude related to having the person first become oriented to the test and its instructions.

Nonetheless, the person taking the test should not be provided say double the time or anything of that magnitude. The reason why the test should be taken in a prescribed amount of time is that the aspect of time is considered related to cognitive acuity.

In other words, if the person is given more time than others have previously gotten, presumably they can cognitively devote more mental cycles or effort and might do better on the test accordingly.

A timed test is not just about your cognition per se, but also about how fast you think and whether your thinking processes are as fluid as others that have taken the test.

If it took someone an hour and they got a top score, while someone else got a top score in ten minutes, we would be hard-pressed to compare their results. You might liken this to playing timed chess, whereby the longer you have, the more chess moves you can potentially mentally foresee, which is fine in some circumstances, but when trying to make for a balanced playing field, you put a timer on how long each player has to make their move.

That being said, the time allotted for a given test should not be so short as to shortchange the cognitive opportunities, which would once again presumably hamper the measurement of cognition. A chess player that has to say just two seconds to make a move will likely randomly take a shot rather than try to devote mental energy to the task.

In theory, the amount of time provided should be the classic Goldilocks amount, just enough time to allow for a sufficient dollop of mental effort, and not so much time that it inadvertently extends the cognition and perhaps enables a lesser cognitive capacity to use time as a crutch to imbue itself (assuming thats not what the test is attempting to measure).

I am about to explain specific details of the MoCA cognitive test, so if you want to someday take the test, please know that I am about to spoil your freshness (this is a spoiler alert).

The test attempts to cover a lot of cognitive ground, doing so by providing a variety of cognition tasks, including the use of numbers, the use of words, the use of sentences, the use of the alphabet, the use of visual cognitive capabilities such as interpreting images and composing writing, and so on.

Thats worth mentioning because a cognitive test that only covered say counting and involved the addition of numbers would be solely focused on your arithmetic cognition. We know that humans have a fuller range of cognitive abilities. As such, a well-balanced cognitive test tries to hit upon a slew of what are considered cognitive dimensions.

Notably, this can be hard to pack into one short test, and raises some criticisms by those that argue it is dubious to have someone undertake a single question on numbers and a single question on words, and so on, and then attempt to generalize overall about their cognition within each respective entire dimension of cognitive facets.

Lets try out a numbers and arithmetic related question.

Are you ready?

You are to start counting from 100 down to 0 and do so by subtracting 7 each time rather than by one.

Okay, your first answer should be 93, and then your next would be 86, and then 79, and so on.

You cannot use a pencil and paper, nor can you use a calculator. This is supposed to be off the top of your head. Using your fingers or toes is also considered taboo.

How did you do?

Try this next one.

Remember these words: Face, Velvet, Church, Daisy, Red.

I want you to look away from these words and say them aloud, without reading them from the page.

In about five minutes, without looking at the page to refresh your memory, try to once again speak aloud what the words were.

What do those cognitive tests signify?

The counting backward is usually a tough one for most people as they do not normally count in that direction. This forces your mind to slow down and think directly about the numbers and the doing of arithmetics in your head (this is also partially why the same kind of quiz is used for DUI roadway sobriety assessment). If I had asked you to count by sevens starting at zero and counting upward, you would likely do so with much greater ease, and the effort would be less cognitively taxing on you.

For the word memorization, this is an assessment of your short-term memory capacity. It is only five words versus if I had asked you to remember ten words or fifty words. Some people will try to memorize the five words by imagining an image in their minds of each word, while others might string together the words into making a short story that will allow them to recall the words.

Either way, this is an attempt to exercise your cognition around several facets, involving short-term memory, the ability to follow and abide by instructions, a semblance of encoding words in your mind, and has other mental leveraging cerebral components.

Some of the questions on these cognitive tests are considered controversial.

In the case of MoCA, there is typically a clock drawing task that some cognitive test experts have heartburn about.

You are asked to draw a clock and indicate the time on the clock as being a stated time such as perhaps 10 minutes past 7. In theory, you would draw a circle or something similar, you would write the numbers of 1 to 12 around the oval to represent each hour, and you would then sketch a short line pointing from the center toward the 7, and a longer mark pointing from the center to the 2 position (since the marks for minutes are normally representative of five minutes each).

Why is this controversial as a cognitive test question?

One concern is that in todays world, we tend to use digital clocks that display numerically the time and are less likely to use the conventional circular-shaped clock to represent time anymore.

If a person taking the cognitive test is unfamiliar with oval clocks, does it seem appropriate that they would lose several cognition points for poorly accomplishing this task?

This brings up a larger scope qualm about cognitive tests, namely, how can we separate knowledge versus the act of cognition.

I might not know what a conventional clock is and yet have superb cognitive skills. The test is unfairly ascribing knowledge of something in particular to the act of cognition, and so it is falsely measuring one thing that is not necessarily the facet that is being presumably assessed.

Suppose I asked you a question about baseball, such as please go ahead and name the bases or what the various player positions are called. If perchance you know about baseball, you can answer the question, while otherwise, you are going to fail that question.

Do the baseball question and your corresponding answer offer any reasonable semblance of your cognitive capabilities?

In any case, the MoCa cognitive test is usually scored based on a top score of 30, for which the scale typically used is this:

Score 26-30: No cognitive impairment detected

Score 18-25: Mild cognitive impairment

Score 10-17: Moderate cognitive impairment

Score00-09: Severe cognitive impairment

Research studies tend to indicate that people with demonstrative Alzheimers tend to score around 16, ending up in the moderate cognitive impairment category. Presumably, a person with no noticeable cognitive impairment, at least per this specific cognitive test, would score at 26 or higher.

Is it possible to achieve a score in the top tier, the score of 26 or above (suggesting that one does not possess any cognitive impairment), and yet still nonetheless have some form of cognitive deficiency?

Yes, certainly so, since this kind of cognitive test is merely a tiny snapshot or sliver and does not cover an entire battery or gamut of cognition, plus as mentioned earlier there is the possibility of being a priori familiar with the test and/or actively prepare beforehand for the test which can substantively boost performance.

Is it possible to score in the mild, moderate, or severe categories of cognitive impairment and somehow not truly be suffering from cognitive impairment?

Yes, certainly so, since a person might be overly stressed and anxious in taking the test, thus perform poorly due to the situation at hand, or could find the given set of tasks unrelated to their cognition prowess such as perhaps someone that is otherwise ingeniously inventive and cognitively sharp, but find themselves mentally cowed when doing simple arithmetic or memorizing seemingly nonsense words.

All told, it is best to be cautious in interpreting the results of such cognitive tests (and, once again, reinforces the need for a more holistic approach to cognitive assessments).

AI And Cognitive Tests

Another popular topic in the news and one that is seemingly unrelated to this cognitive testing matter is the emergence of AI (hold that thought, for a moment, well get back to it).

You are likely numbed by the multitude of AI systems that seem to keep being developed and released into and affecting our everyday lives, including the rise of facial recognition, the advent of Natural Language Processing (NLP) in the case of AI systems such as Alexa and Siri, etc.

On top of that drumbeat, there are the touted wonders of AI, entailing a lot of (rather wild) speculation about where AI is headed and whether AI will eclipse human intelligence, possibly even deciding to take over our planet and choosing to enslave or wipe out humanity (for such theories, see my analysis at this link here).

Why bring up AI, especially if it presumably has nothing to do with cognitive tests and cognitive testing?

Well, for the simple fact that AI does have to do with cognitive testing, very much so.

The presumed goal for AI is to achieve the equivalent of human intelligence, as might somehow be embodied in a machine. We do not yet know what the machine will be, though likely to consist of computers, but the specification does not dictate what it must be, and thus if you could construct a machine via Legos and duct tape that exhibited human intelligence, more power to you.

In brief, we want to craft artificial cognitive capabilities, which are the presumed crux of human intelligence.

Logically, since thats what we are attempting to accomplish, it stands to reason that we would expect AI to be able to readily pass a human-focused cognitive test since doing so would illustrate that the AI has arrived at similar cognitive capacities.

I dont want to burst anyones bubble, but there is no AI today that can do any proper semblance of common-sense reasoning, and we are a long way away from having sentient AI.

Bottom-line: AI today would essentially flunk the MoCA cognitive test and any others of similar complexity too.

Some might try to argue and claim that AI and computers can countdown from 100, and can memorize words, and do the other stated tasks, but this is a misleading assertion. Those are tasks undertaken by an AI system that has been constructed for and contrived to perform those specific tasks, and inarguably is a far cry from understanding or comprehending the test in a manner akin to human capacities and misleadingly anthropomorphize the matter (for more details, see my analysis at this link here).

There is not yet any kind of truly generalizable AI, which some are now calling Artificial General Intelligence (AGI).

As added clarification, there is a famous test in the AI field known as the Turing Test (see my explanation at this link here). No AI of today and nor in the foreseeable near future could pass a fully ranging Turing Test, and in some respects, being able to pass a cognitive test like those of MoCA is a variant of a Turing Test (in an extremely narrow way).

AI Cognition And Self-Driving Cars

Another related topic entails the advent of AI-based true self-driving cars.

We are heading toward the use of self-driving cars that involve AI autonomously driving the vehicle, doing so without any human driver at the wheel.

Some wonder whether the AI of today, lacking any kind of common-sense reasoning and nor any inkling of sentience, will be sufficient for driving cars on our public roadways. Critics argue that we are going to have AI substituting for human drivers and yet the AI is insufficiently robust to do so (see more on this contention at my analysis here).

Others insist that the driving task does not require the full range of human cognitive capabilities and thus the AI will do just fine in commanding self-driving cars.

Do you believe that the AI driving you to the grocery store needs to be able to first pass a cognitive test and showcase that it can adequately draw a clock and indicate the time of day?

For now, all we can say is that time will tell.

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How To Flunk Those Cognitive Deficiency Tests And What This Means Too For AI Self-Driving Cars - Forbes

Company recognized among rising stars of artificial intelligence for its AI-guided robots transforming the recycling industry

Forbes has named AMP Robotics Corp. ("AMP"), a pioneer and leader in artificial intelligence (AI) and robotics for the recycling industry, one of Americas most promising AI companies. The publications annual "AI 50" list distinguishes private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. To be included on the list, companies needed to show that techniques like machine learning, natural language processing, or computer vision are a core part of their business model and future success.

"Earlier this year, we notched a milestone of one billion picks over 12 months that demonstrates the productivity, precision, and reliability of our AI application for the recycling industry. Its an honor to be deemed one of the countrys most promising AI companies, and were just getting started," said Matanya Horowitz, AMP founder and chief executive officer. "Theres growing appreciation for the role of recycling in the domestic supply chain, in terms of keeping resources flowing and products on shelves, and resultant momentum around supportive policy initiatives that are putting some real wind in the sail for the industry. Were pleased to play a role in enabling better efficiency, safety, and transparency to help transform recycling."

AMPs technology recovers plastics, cardboard, paper, metals, cartons, cups, and many other recyclables that are reclaimed for raw material processing. AMPs AI platform uses computer vision to visually identify different types of materials with high accuracy, then guides high-speed robots to pick out and recover recyclables at superhuman speeds for extended periods of time. The AI platform transforms images into data to recognize patterns, using machine learning to train itself by processing millions of material images within an ever-expanding neural network of robotic installations.

"We consider AMP a category-defining business and believe its artificial intelligence and robotics technology are poised to solve many of the central challenges of recycling," said Shaun Maguire, partner at Sequoia Capital and AMP board member. "The opportunity for modernization in the industry is robust as the demand for recycled materials continues to swell, from consumers and the growing circular economy."

AMPs "AI 50" recognition comes on the heels of receiving a 2020 RBR50 Innovation Award from Robotics Business Review for the companys Cortex Dual-Robot System. Earlier this year, Fast Company named AMP to its "Worlds Most Innovative Companies" list for 2020, and the company captured a "Rising Star" Company of the Year Award in the 2020 Global Cleantech 100.

Since its Series A fundraising in November, AMP has been on a major growth trajectory as it scales its business to meet demand. The company announced a 50% increase in revenue in the first quarter of 2020, a rapidly growing project pipeline, a facility expansion in its Colorado headquarters, and a new lease program that makes its AI and robotics technology even more attainable for recycling businesses.

About AMP Robotics Corp.

AMP Robotics is applying AI and robotics to help modernize recycling, enabling a world without waste. The AMP Cortex high-speed robotics system automates the identification and sorting of recyclables from mixed material streams. The AMP Neuron AI platform continuously trains itself by recognizing different colors, textures, shapes, sizes, patterns, and even brand labels to identify materials and their recyclability. Neuron then guides robots to pick and place the material to be recycled. Designed to run 24/7, all of this happens at superhuman speed with extremely high accuracy. With deployments across the United States, Canada, Japan, and now expanding into Europe, AMPs technology recycles municipal waste, e-waste, and construction and demolition debris. Headquartered and with manufacturing operations in Colorado, AMP is backed by Sequoia Capital, Closed Loop Partners, Congruent Ventures, and Sidewalk Infrastructure Partners ("SIP"), an Alphabet Inc. (NASDAQ: GOOGL) company.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200710005481/en/

Contacts

Media Contact Carling Spelhaugcarling@amprobotics.com

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AMP Robotics Named to Forbes AI 50 - Yahoo Finance

A new study from West Virginia University (WVU) Rockefeller Neuroscience Institute (RNI) uses a wearable device and artificial intelligence (AI) to predict COVID-19 up to 3 days before symptoms occur. The study has been an impressive undertaking involving over 1000 health care workers and frontline workers in hospitals across New York, Philadelphia, Nashville, and other critical COVID-19 hotspots.

The implementation of the digital health platform uses a custom smartphone application coupled with an ura smart ring to monitor biometric signals such as respiration and temperature. The platform also assesses psychological, cognitive, and behavioral data through surveys administered through a smartphone application.

We know that wearables tend to suffer from a lack of accuracy, particularly during activity. However, the ura ring appears to take measurements while the user is very still, especially during sleep. This presents an advantage as the accuracy of wearable devices greatly improves when the user isnt moving. RNI noted that the ura ring has been the most accurate device they have tested.

Given some of the early warning signals for COVID-19 are fever and respiratory distress, it would make sense that a device able to measure respiration and temperature could be used as an early detector of COVID-19. In fact, weve seen a few wearable device companies attempt much of what RNI is doingas well as a few DIY attempts. RNIs study has probably been the most thorough work released so far, but were sure that many more are upcoming.

The initial phase of the study was deployed among healthcare and frontline workers but is now open to the general public. Meanwhile the National Basketball Association (NBA) is coordinating its re-opening efforts using uras technology.

We hope to see more results emerge from RNIs very important work. Until then, stay safe Hackaday.

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Detect COVID-19 Symptoms Using Wearable Device And AI - Hackaday