What Is Artificial Intelligence (AI)?
Why Artificial Integrity Is The New AI Frontier
A human-centered illustration composed of colorful puzzle pieces, symbolically representing the ... [+] complexity and multi-faceted nature of integrating ethical principles and considerations into AI systems to ensure they operate with integrity by design.
GettyIn the rapidly evolving world of Artificial Intelligence (AI), computational power isn't enough. What we need is Artificial Integrity—a new paradigm that ensures AI systems operate in alignment with human values, prioritizing Integrity over Intelligence, whether it is in Marginal, AI-First, Human-First or Fusion Modes.
AI is like the engine of a car, providing not the driving force, but the computational power needed to achieve efficiency and speed in executing tasks. However, much like a car needs steering and braking systems to ensure safety and adherence to the rules of the road, AI requires something more than raw intelligence—it needs the capacity to demonstrate a form of integrity.
Artificial Integrity is a built-in capability within AI systems that ensures its functions not just efficiently, but also with integrity, respecting human values from the very start.
The Role Of Integrity In AI
While AI can quickly process data, it doesn't inherently consider whether its actions are safe, legal, or ethical. Artificial Integrity aims to ensure that AI data processing and tasks align with human values, prioritizing fairness, safety, and societal well-being over mere efficiency or profitability.
It goes beyond following externally imposed ethical guidelines. It represents a self-regulating quality embedded within the AI system itself. Artificial Integrity is about incorporating ethical principles into AI design to guide its functioning and outcomes, much like how human integrity guides behavior and impact even without external oversight, to mobilize intelligence for good.
From Compliance To Core Functioning
Traditional AI ethical guidelines often focus on compliance, requiring systems to adhere to a set of rules. However, this approach is reactive and limited, essentially ex-post as opposed to ex-ante. Artificial Integrity, on the other hand, is proactively continuous.
It ensures that its decision-support is made based on human values and ethical principles from the outset, without needing constant monitoring or enforcement to function.
It addresses this by being context-sensitive, allowing AI to apply ethical reasoning dynamically in real-time scenarios, rather than rigidly applying general rules that may not fully address the situation's complexity.
An AI with built-in integrity would avoid actions that could cause harm or violate ethical standards, even if such actions are efficient or legal. This proactive approach reduces the likelihood of harmful outcomes and enhances trust in AI systems.
A Practical Application In Healthcare
Without Artificial Integrity, an AI system in a hospital that recommends treatment plans for patients with chronic pain based on their medical history, risk factors, and overall well-being might suggest a medication that is effective for most patients, even if it has potential side effects, because it is efficient based on the initial assessment and available data.
With Artificial Integrity, if the patient later experiences side effects like nausea, the AI system, based on the patient's feedback, would collaborate with the doctor to adjust the treatment and might recommend switching medications or altering the dosage to prioritize the patient's comfort and safety, ensuring the treatment remains aligned with the patient's well-being.
The Need For Artificial Integrity
As we move toward more autonomous AI systems, standing on the verge of Robotic Intelligence, just as it is not sheer engine power that grants autonomy to a car or a plane, so too it is not merely the increase of artificial intelligence that will guide the progress of AI systems.
Integrity, not intelligence, will ensure that the AI system does not solely optimize for efficiency or effectiveness but also adheres to principles of justice, human rights, and the common good by addressing technical, economic, and societal concerns such as:
The Future Of AI Is Integrity Over Intelligence
Without the capability to exhibit a form of integrity, AI would become a force whose evolution is inversely proportional to its necessary control—not just through human agency, but also with regard to human values.
As businesses and governments continue to invest in AI, developing Artificial Integrity will be crucial for navigating the ethical challenges of this new era.
Artificial integrity over intelligence represents the new AI frontier and a critical path to shaping the course of human history in creating a better future for all.
Harnessing AI For Predictive Maintenance And Self-Healing IT Systems
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In today's fast-paced digital world, maintaining the health and performance of complex IT systems is critical for businesses. Pradeep Sambamurthy, a leading expert, explores how artificial intelligence (AI) transforms systems observability, offering advanced capabilities such as anomaly detection, root cause analysis, predictive maintenance, and automated remediation. AI enhances key areas such as anomaly detection, root cause analysis, predictive maintenance, and automated remediation. This innovation is reshaping the landscape of observability, driving greater resilience, reliability, and efficiency across digital infrastructures.
The Need for Enhanced ObservabilityThe growing complexity of IT infrastructures requires a more sophisticated approach to system management. Traditional monitoring tools often fall short in handling the volume of data generated by modern systems. Enterprises, with their large-scale data centers, can produce over 10 terabytes of operational data daily, far exceeding the capabilities of manual analysis.
AI-driven observability addresses the challenges of modern system management by using machine learning to analyze vast amounts of metrics, logs, and traces. This enables organizations to detect patterns and correlations that are often missed by manual methods. As a result, businesses can benefit from improved reliability, faster issue resolution, and a more proactive approach to managing systems. This approach represents not just an evolution but a revolution in how systems are monitored and maintained, providing a comprehensive view of system health and performance and enabling faster, more informed decision-making.
Anomaly Detection: Identifying Issues Before They EscalateOne of AI's most significant contributions to observability is its ability to detect anomalies in real-time. Unlike traditional systems that rely on pre-defined rules, AI-powered tools continuously learn and adapt, identifying abnormal behavior that may signal potential system failures.
According to IBM Research, enterprises generate over 1.5 petabytes of log data per day, but only 1% of this data is actively analyzed. AI-driven anomaly detection systems can process all of this data, identifying subtle patterns and correlations that human analysts might overlook. Beyond cybersecurity, AI-driven anomaly detection plays a critical role in application performance monitoring (APM).
Root Cause Analysis: Faster Resolutions, Reduced Downtime
AI excels in root cause analysis (RCA), a critical aspect of incident management. Traditional RCA processes, relying on manual analysis, are often slow and prone to errors, especially in complex environments with multiple causes for a single issue. AI, with advanced algorithms, can quickly analyze system metrics, logs, and traces to identify the root cause of problems. A major e-commerce platform reported a 40% reduction in the mean time to resolution (MTTR) after implementing AI-driven RCA. AI's ability to perform continuous, real-time analysis enables it to detect potential issues before they escalate, making it a game changer compared to traditional post-mortem RCA methods.
Predictive Maintenance: Proactive, Not ReactiveAI's predictive capabilities allow organizations to shift from reactive to proactive maintenance strategies. By analyzing historical data and identifying patterns that precede system failures, AI can forecast potential issues before they occur, preventing costly downtime and optimizing resource allocation.
According to the IEEE Reliability Society, unplanned downtime costs organizations an average of $260,000 per hour. AI-driven predictive maintenance offers a powerful solution that significantly reduces downtime.
Automated Remediation: The Path to Self-Healing SystemsOne of the most exciting developments in AI-driven observability is the integration of automated remediation. These systems detect and diagnose issues and can implement corrective actions autonomously, minimizing downtime and reducing the need for human intervention.
Amazon Web Services (AWS) is a prime example of how AI-driven auto-scaling systems can optimize resource allocation. AWS's system adjusts compute resources based on real-time demand, reducing over-provisioning by 45% while maintaining a 99.99% service level agreement (SLA) for customers.
Pradeep Sambamurthy highlights that the future of automated remediation lies in cognitive automation—AI systems that learn from past incidents and improve their remediation strategies over time. As these systems evolve, they will drive the development of fully autonomous, self-healing IT infrastructures.
To wrap up, AI-driven systems observability represents a significant leap forward in managing and optimizing digital infrastructures. By harnessing AI's power for anomaly detection, root cause analysis, predictive maintenance, and automated remediation, organizations can achieve unprecedented levels of system reliability and performance. As this technology continues to evolve, we can expect even more innovative solutions that will transform the way we manage complex IT environments.
What Causes Positive Drug Tests? It's Not Just Drugs… Or Poppy Seeds.
You may have heard that poppy seeds in your salad dressing or on your bagel can mess up a drug test–triggering a positive result for opiates in your system. Though there are some caveats to this (washing and proper seed processing can remove contaminants and cooking tends to significantly decrease poppy seeds' opiate content), it's true that poppy seeds can be a problem. Poppy seeds themselves don't actually contain opiate compounds, but they're often adulterated with opiate-containing flower sap during processing.
Yet poppy seeds aren't the only thing that can skew a rapid drug test. "There's a wide range of things that can cause false positive results," says Gwen McMillin, a toxicologist and professor of clinical pathology at the University of Utah. Poppy seeds don't actually cause false positives, McMillin notes. "[Contaminated] poppy seeds actually do have opiates in them," she says–but lots of medications, even common, over-the-counter drugs can confound tests with entirely different compounds.
The most frequently used type of rapid drug tests are called immunoassays. These tests vary widely and are made by many companies. Different versions of them can screen for amphetamines, benzodiazepines, PCP, opiates, synthetic opioids, marijuana, and other regulated substances. Antibodies built-in to the tests react with compounds selected to represent likely drug use: Metabolites that the body excretes in urine. However, the tests aren't looking for the exact target substances they may be used to screen for. McMillin compares them to a wooden game board with slots for marbles–similarly sized and shaped marbles can fit into the same holes.
Another analogy, she says, is a fish net. "A net is designed to catch fish, but doesn't know exactly what kind of fish it's picking up," she tells Popular Science. "The immunoassay is really brilliant in that it can capture all kinds of things that look like fish. If you're just trying to do a screen, that's really important…but the consequence is false positives."
The antibody is the most critical part of an immunoassay's design, and it can be a tricky balancing act to aim for specificity while also ensuring a whole class of related, controlled substances are included in what's meant to be a first line of detection. False positives are often the result of test-makers opting for breadth. Yet they also happen because of basic chemistry constraints: certain drug groups, particularly amphetamines, are metabolized to small, rather nondescript molecules that resemble many things, says Algren.
Cold medicines, antihistamines and decongestants, antidepressants, blood pressure medications, diabetes drugs, are among the long list of things that can lead to false positive amphetamine screens. Over the counter cold medicines can also show up as PCP. Codeine, some antibiotics commonly used to treat urinary tract infections, hypertension medication, and anti-nausea drugs are some examples of pills that can lead to false-positives for opioids and opiates. Quinine, the antimalarial compound used as a flavoring in tonic water, can also trigger some of these tests. CBD products and even certain types of baby wash can show up as a positive result in screens for cannabis/THC. And researchers are discovering previously unknown examples of cross-reactive compounds all the time.
Sensitivity is also a big consideration. Manufacturers don't want tests to miss drugs that are present, but if they design them with low enough thresholds and rapid tests can pick up even erroneous, incidental or second-hand exposures.
Though these weaknesses are widely understood among toxicologists and immunoassay tests come with package inserts listing potential cross-reactivity with non-target drugs, clinicians administering these tests to patients in hospital settings or labor and delivery wards might not be aware of all of the many legal and common routes to a positive urine drug screen. And that can cause problems when time is tight and confirmatory tests aren't ordered. Multiple lawsuits have been brought against hospitals in recent years after newborns were taken into foster care based on parents' rapid drug test results. Patient care can also be compromised when rapid drug screens alone are used as a basis for approving surgery and other treatments, McMillin says.
False negatives are also not uncommon. Some tests aren't sensitive enough to pick up the concentration of a drug circulating in someone's system. And some newer substances may not actually show up on a test theoretically intended to reveal their presence. For instance, synthetic and semi-synthetic opioids like fentanyl and oxycodone aren't detected on all opiate drug tests, as their metabolites and constituent chemicals are different. This, too, can cause problems for patients, and even lead to people being wrongfully kicked out of chronic pain management programs, where a false negative test might erroneously lead providers to believe patients aren't taking their medications as prescribed, says Adam Algren, a medical toxicologist and emergency physician at University Health Hospital and University of Missouri-Kansas City.
In an ideal situation, any surprising immunoassay drug test result would be confirmed with more specific and accurate follow-up laboratory testing, say both Algren and McMillin. Yet, these slower, more expensive confirmatory tests aren't used in every hospital system or care setting. "I think a lot of doctors are quick to take the immunoassay results as being the final result, and then make decisions off of that without confirmation or considering if there's other things involved," Algren says.
"I personally don't feel like there is enough attention to the confirmation test," agrees McMillin. Both experts say they'd like to see that change.
This story is part of Popular Science's Ask Us Anything series, where we answer your most outlandish, mind-burning questions, from the ordinary to the off-the-wall. Have something you've always wanted to know? Ask us.
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