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Question: 20
In a self-driving car company, ML engineers want to develop a model for dynamic pathing.
Which of following approaches would be optimal for this task?
A. Dijkstra Algorithm
B. Reinforcement learning
C. Supervised Learning.
D. Unsupervised Learning
Answer: B
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and
penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding
an optimal path from one point to another in an uncertain and changing environment. Reinforcement learning can
enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving
car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid
Question: 21
R-squared is a statistical measure that:
A. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
B. Expresses the extent to which two variables are linearly related.
C. Is the proportion of the variance for a dependent variable thafâ s explained by independent variables.
D. Represents the extent to which two random variables vary together.
Answer: C
R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by
dividing the explained variance by the total variance. The explained variance is the amount of variation in the
dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in
the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1
means perfect fit.
Question: 22
Which of the following equations best represent an LI norm?
A. |x| + |y|
B. |x|+|y|^2
C. |x|-|y|
D. |x|^2+|y|^2
Answer: A
An L1 norm is a measure of distance or magnitude that is defined as the sum of the absolute values of the components
of a vector. For example, if x and y are two components of a vector, then the L1 norm of that vector is |x| + |y|. The L1
norm is also known as the Manhattan distance or the taxicab distance, as it represents the shortest path between two
points in a grid-like city.
Question: 23
Which of the following statements are true regarding highly interpretable models? (Select two.)
A. They are usually binary classifiers.
B. They are usually easier to explain to business stakeholders.
C. They are usually referred to as "black box" models.
D. They are usually very good at solving non-linear problems.
E. They usually compromise on model accuracy for the sake of interpretability.
Answer: A,B,E
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as
decision trees, linear regression, or logistic regression.
Some of the statements that are true regarding highly interpretable models are:
They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the
logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders.
For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can
show how each coefficient affects the dependent variable.
They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be
able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For
example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not
be able to model curved relationships between variables.
Question: 24
Which two of the following decrease technical debt in ML systems? (Select two.)
A. Boundary erosion
B. Design anti-patterns
C. Documentation readability
D. Model complexity
E. Refactoring
Answer: A,C,E
Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy
or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to
various factors, such as changing requirements, outdated code, poor documentation, or lack of testing.
Some of the ways to decrease technical debt in ML systems are:
Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation
of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent
information about the systemâs design, functionality, performance, and maintenance. Documentation readability can
also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and
Refactoring: Refactoring is the process of improving the structure and quality of code without changing its
functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity,
or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.
Question: 25
Which of the following describes a neural network without an activation function?
A. A form of a linear regression
B. A form of a quantile regression
C. An unsupervised learning technique
D. A radial basis function kernel
Answer: A
A neural network without an activation function is equivalent to a form of a linear regression. A neural network is a
computational model that consists of layers of interconnected nodes (neurons) that process inputs and produce outputs.
An activation function is a function that determines the output of a neuron based on its input. An activation function
can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships
between inputs and outputs. Without an activation function, a neural network becomes a linear combination of inputs
and weights, which is essentially a linear regression model.
Question: 26
The following confusion matrix is produced when a classifier is used to predict labels on a test dataset.
How precise is the classifier?
A. 48/(48+37)
B. 37/(37+8)
C. 37/(37+7)
D. (48+37)/100
Answer: B
Precision is a measure of how well a classifier can avoid false positives (incorrectly predicted positive cases).
Precision is calculated by dividing the number of true positives (correctly predicted positive cases) by the number of
predicted positive cases (true positives and false positives). In this confusion matrix, the true positives are 37 and the
false positives are 8, so the precision is 37/(37+8) = 0.822.
Question: 27
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed,
what is the best way to fill in these missing features?
A. Delete entire rows that contain any missing features.
B. Fill in missing features with random values for that feature in the training set.
C. Fill in missing features with the average of observed values for that feature in the entire dataset.
D. Delete entire columns that contain any missing features.
Answer: C
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and
reliability of the data and the model. There are various methods to deal with missing values, such as deleting,
imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values
with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used
imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in
the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid
introducing bias or noise.
Question: 28
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML
engineers to do?
A. Detect hidden attacks
B. Prevent hidden attacks
C. Recover from hidden attacks
D. Respond to hidden attacks
Answer: B
Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or
noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model
deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model
stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent
hidden attacks, as it can help them detect any anomalies or deviations in the data or the modelâs performance that may
indicate a potential attack.
Question: 29
A company is developing a merchandise sales application The product team uses training data to teach the AI model
predicting sales, and discovers emergent bias.
What caused the biased results?
A. The AI model was trained in winter and applied in summer.
B. The application was migrated from on-premise to a public cloud.
C. The team set flawed expectations when training the model.
D. The training data used was inaccurate.
Answer: A
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were
not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate
or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing
conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data
may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in
winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or
Question: 30
You train a neural network model with two layers, each layer having four nodes, and realize that the model is underfit.
Which of the actions below will NOT work to fix this underfitting?
A. Add features to training data
B. Get more training data
C. Increase the complexity of the model
D. Train the model for more epochs
Answer: B
Underfitting is a problem that occurs when a model learns too little from the training data and fails to capture the
underlying complexity or structure of the data. Underfitting can result from using insufficient or irrelevant features, a
low complexity of the model, or a lack of training data. Underfitting can reduce the accuracy and generalization of the
model, as it may produce oversimplified or inaccurate predictions.
Some of the ways to fix underfitting are:
Add features to training data: Adding more features or variables to the training data can help increase the information
and diversity of the data, which can help the model learn more complex patterns and relationships.
Increase the complexity of the model: Increasing the complexity of the model can help increase its expressive power
and flexibility, which can help it fit better to the data. For example, adding more layers or nodes to a neural network
can increase its complexity.
Train the model for more epochs: Training the model for more epochs can help increase its learning ability and
convergence, which can help it optimize its parameters and reduce its error.
Getting more training data will not work to fix underfitting, as it will not change the complexity or structure of the data
or the model. Getting more training data may help with overfitting, which is when a model learns too much from the
training data and fails to generalize well to new or unseen data.

CertNexus Intelligence plan - BingNews https://killexams.com/pass4sure/exam-detail/AIP-210 Search results CertNexus Intelligence plan - BingNews https://killexams.com/pass4sure/exam-detail/AIP-210 https://killexams.com/exam_list/CertNexus How To Develop An Intelligence-Driven Cybersecurity Approach

Aleksey Lapshin is CEO of ANY.RUN, interactive malware analysis sandbox that helps companies detect and analyze cyber threats in real time.

In the digital era, information is at the heart of everything. The more information you have and the sooner you can obtain it, the more competitive you will be. This is also true in cybersecurity, where timely intelligence can provide you with a robust defense against both emerging and well-known threats.

Because of this, organizations have developed the intelligence-driven cybersecurity strategy, a data-driven approach to cybersecurity that utilizes insights from a wide range of internal and external sources to identify and reduce cyber risks.

Intelligence-driven cybersecurity involves collecting, analyzing and interpreting data from security logs, incident reports, threat intelligence feeds and other sources to gain visibility into the threat landscape and the organization's security posture.

How Threat Intelligence Can Bolster Cybersecurity

Organizations often rely solely on internal sources of threat intelligence, such as security logs and incident reports, but this can be risky, as internal sources may miss emerging and unforeseen threats.

External threat intelligence products, such as feeds and centralized databases, can help organizations address this gap by providing them with insights into the latest threats, attack vectors and tactics used by adversaries. External threat intelligence can be obtained from a variety of sources, including:

• Commercial Threat Intelligence Vendors: These vendors collect and analyze data from a variety of source—including the dark web, social media and public databases—to identify and track emerging threats.

• Open-Source Intelligence (OSINT): OSINT is publicly available information that can be collected and analyzed to gain insights into threats and adversaries. OSINT sources include news articles, blog posts, social media posts and malware repositories.

• Information Sharing And Analysis Centers (ISACs): ISACs are forums where organizations can share threat intelligence. ISACs typically focus on a specific industry or sector, such as healthcare or financial services.

A solid approach to collecting threat intelligence should include a diversity of sources, each with its own strengths and weaknesses. For example, threat intelligence supplied by malware sandboxing solutions, a type of commercial vendor, can provide organizations with several unique benefits, including:

• Analysis Of Malware And Phishing Campaigns: Unlike antivirus solutions, malware sandboxes comprehensively analyze every file and link uploaded by their users, revealing indicators of compromise (IOCs) and tactics, techniques and procedures (TTPs). They then make their threat intelligence available via threat intelligence feeds or searchable repositories, enabling analysts to learn about threats without manual analysis.

• Early Warning Of Emerging Threats: Threat intelligence from malware sandboxes contains information on the latest malware variants, as sandboxes receive a constant stream of fresh uploads from users around the world. This early warning enables organizations to take proactive steps to mitigate and respond to emerging threats.

Common Threat Intelligence Use Cases

Once the relevant information has been gathered, threat intelligence can be applied across a variety of scenarios, including:

Quicker Alert Triage

Security operations (SecOps) teams are responsible for dealing with a high volume of security alerts daily. The alert remediation process largely depends on the analyst's ability to understand the alert they encounter. Threat intelligence provides context to quickly triage alerts, determining which ones pose a real threat and which can be safely dismissed.

For example, a SecOps team may receive an alert that a new malware has been detected on the network. The SecOps team can use a threat intelligence service to learn more about the malware, such as its capabilities, targets and known indicators of compromise (IOCs) to then implement adequate security measures.

Proactive Threat Hunting And Remediation

Threat intelligence is useful for proactively hunting threats and remediating them before they cause damage. For instance, a SecOps team can use threat intelligence to identify malicious IP addresses of malware campaigns targeting companies in their industry and block them from accessing their network, preventing any potential attacks.

Timely Vulnerability Identification And Remediation

Organizations can use threat intelligence to find new vulnerabilities in their software and systems. This information can then be used to patch the vulnerabilities and prevent attackers from exploiting them.

Challenges When Implementing Threat Intelligence

The successful utilization of threat intelligence requires a thorough understanding of potential challenges that may arise in the process and effective measures to counter them. These include:

False Positives

Threat intelligence solutions, particularly those that rely on automated algorithms, may generate large volumes of false positives, leading to erroneous flagging of legitimate events as malicious. These false positives can be caused by factors such as data inaccuracies, misinterpretations of threat indicators and oversensitivity of detection mechanisms.

To effectively address this issue, organizations need to implement a robust validation process that involves cross-referencing threat intelligence data with multiple sources and human review to manually filter out false alarms.

Limited Context

While external threat intelligence provides valuable insights into broad cybersecurity trends, it often lacks the depth and context needed for a comprehensive view of the nuance of different malware or vulnerabilities.

To better understand how various threats operate, security teams need to enrich their existing intelligence with the results offered by additional tools.


Successfully leveraging threat intelligence to enhance cybersecurity takes a team of proficient security personnel who can navigate the complexities of the ever-changing threat landscape and effectively manage threat data.

Although the training process is a multifaceted endeavor, developing a structured framework that outlines the processes for collecting, analyzing and utilizing threat intelligence can greatly facilitate it. This framework should align with the organization's overall cybersecurity strategy and risk management practices.


Organizations can only know so much of the threat landscape by understanding what happens within the scope of their company. In order to gain a broader view, an intelligence-driven approach pulls in insights from the broader community and the industry at large.

To succeed with an intelligence-driven approach, organizations should understand both the use cases and challenges of working with external sources and the requisite tools. If done correctly, the organization can better barricade itself from the ever-rising swarm of cyber threats.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Thu, 07 Dec 2023 23:44:00 -0600 Aleksey Lapshin en text/html https://www.forbes.com/sites/forbestechcouncil/2023/12/08/how-to-develop-an-intelligence-driven-cybersecurity-approach/
Artificial intelligence: HHS has a plan for that

With Megan R. Wilson

PROGRAMMING NOTE: We’ll be off next week for the holidays but back to our normal schedule on Tuesday, Jan. 2.

Driving The Day

HOW HHS USES AI — With the race from health care companies to develop and use artificial intelligence, it shouldn’t come as a surprise that HHS is using AI, too, Chelsea reports.

As federal officials scramble to regulate artificial intelligence in health care, they can also shape how AI works in their day-to-day operations, according to Suresh Venkatasubramanian, a data and computer science professor at Brown University.

“A lot of government agencies don’t do in-house development,” said Venkatasubramanian, who recently worked for the Biden administration and co-authored the blueprint for the AI Bill of Rights, which aims to protect against AI misuse. “They purchase software from vendors. And so, agencies have the power to decide who they procure from, under what guidelines, what rules. They’re able to shape the expectations for software and the guardrails on it.”

HHS is one of the top agencies using AI — fourth only to NASA and the Commerce and Energy departments — according to a recent Government Accountability Office report, which looked at reported, implemented and planned AI uses by department.

Here are 4 ways HHS is using AI:

1. Food and Drug Administration: The FDA is implementing an AI platform trained on data from 1,500 clinical trials to write clinical study reports using Phase I and II study data. According to HHS’ AI use cases inventory, the AI can “mimic the subject matter experts,” including clinicians and statisticians, to decipher the study design and interpret the results.

2. Administration for Strategic Preparedness and Response: An ASPR spokesperson pointed to several programs that use AI at the agency, including EmPOWER, which offers first responders a downloadable voice-controlled app that can tell them how many Medicare beneficiaries with electricity-dependent medical devices are in the area.

3. National Institutes of Health: The NIH uses a tool that predicts the priority level and area of research for grant applications and then ranks incoming submissions using the AI’s analysis, allowing highly ranked applications to be reviewed first, according to the HHS AI use cases inventory.

4. Centers for Disease Control and Prevention: The CDC uses AI and machine learning to Boost surveillance testing. Its tools detect tuberculosis in chest X-rays and identify cooling tower locations from aerial imagery to help prevent or controlLegionella outbreaks. The agency is also looking into an open-source AI model to Boost transcriptions.

WELCOME TO WEDNESDAY PULSE. We came across a great gift idea for people in health care. Reach us at [email protected] or [email protected]. Follow along @_BenLeonard_ and @ChelseaCirruzzo.

TODAY ON OUR PULSE CHECK PODCAST, host Lauren Gardner talks with POLITICO health care reporter Alice Miranda Ollstein, who reviews significant developments surrounding abortion over the past year amid ongoing political turmoil and breaks down what to expect heading into 2024.


FEES DROP — The Biden administration has finalized long-awaited fees insurers and hospitals have to pay for government arbitration for disputes over out-of-network charges, POLITICO’s Robert King reports.

Neither providers nor insurers were pleased with the implementation of the 2021 No Surprises Act, which aims to protect patients from surprise medical bills when they’re unwittingly treated by an out-of-network provider and which is where the arbitration process stems from. House Ways and Means Committee Republicans have also raised concerns.

Regulators have been overwhelmed with requests to resolve disputes — over 20 times more than anticipated. If insurers and provider facilities can’t agree, they consult a third-party arbiter. On Monday, the government revealed for the first time how it calculates arbitration fees.

A single dispute is between $200 and $840. For a batch of disputes, the fee is $268 to $1,173. If a batch is more than 25, an extra fee of $75 to $250 will be added. The rates will apply after the rule starts in January.

A federal ruling in August shot down the federal government’s fee process, leaving hospitals and insurers waiting to see what the fees would be. Agencies will update fees every year.


AUDIT OF NIH AUDITS — The NIH didn’t ensure that foreign grant recipients compiled or submitted mandatory audit reports, two federal watchdog reports released Tuesday said, POLITICO’s Erin Schumaker reports.

HHS’ inspector general examined foreign grant recipients with annual HHS funding of $750,000 or more, the threshold for audit. Recipients had to submit 109 annual reports to the NIH during the 2019 and 2020 fiscal years, but the NIH didn’t receive 81 of them.

The NIH’s follow-up wasn’t timely in close to three quarters of cases, one watchdog report found. A separate inspector general report found that the NIH didn’t consistently ensure grant recipients made corrections promptly based on audit findings.

The response: The watchdog recommended four actions to address the problems, which the NIH said it agreed with. The agency expects to complete them by September 2024.

Why it matters: The NIH awarded hundreds of millions of dollars to foreign grant recipients in fiscal 2022, and its handling of such grants is under fire following a previous watchdog report.

That report found that the NIH didn’t effectively monitor its grants to the research group EcoHealth Alliance, saying it improperly used grant funding and failed to obtain scientific documentation from China’s Wuhan Institute of Virology, which it oversaw. Most federal government agencies believe the Covid pandemic likely originated with an infected animal, but at least two agencies back the theory that it might have begun with a lab accident at the Wuhan lab.

Public Health

PREVENTING ILLNESS FROM SETTING SAIL — The CDC has new guidance for cruise ships to prevent and manage outbreaks of respiratory illness, close to four years after the start of the Covid pandemic stranded thousands of travelers at sea, Chelsea reports.

“Many cruise ship travelers are older adults or have underlying medical conditions that put them at increased risk of complications from these respiratory virus infections,” the agency said Monday.

Among several recommendations, the agency says cruise ship operators should consider screening embarking passengers for viral respiratory illness symptoms and a history of exposure or testing positive for Covid and, if possible, deny boarding to sick passengers. The agency also recommends that the crew stay up to date on vaccinations.

For passengers who get sick, the CDC recommends certain isolation guidance depending on the severity and timing of the illness and masking.


FIRST IN PULSE: TRANSPARENCY IN THE SPOTLIGHT — PatientRightsAdvocate.org — the nonprofit whose sister group has been running splashy ads about health price transparency featuring rapper Fat Joe — has a new report illustrating the vast variations in cost for common services, Megan reports.

The group said it examined data from hospitals in 10 states and found the maximum in-network rate negotiated with insurance plans for five procedures — including cesarean sections and cataract surgeries — was, on average, almost 11 times higher than the minimum negotiated rate for the same service within the same hospital.

For example, an appendectomy in one New York hospital could cost anywhere from $1,960 to $63,271 — 32 times higher than the minimum negotiated rate for the same service within the same hospital.

Price variations were even more stark between hospitals in the same state, with the maximum negotiated rates averaging 31 times the minimum.

PatientRightsAdvocate.org has pushed legislation to codify Trump-era hospital and insurer price transparency rules. The House recently passed a health package that includes bolstered disclosure requirements, and Sen. Mike Braun (R-Ind.) introduced bipartisan legislation with similar policies last week.

The American Hospital Association pushed back on the report’s premise, saying it oversimplifies the negotiations between providers and insurers and misconstrues the reality of factors contributing to price variations in hospitals — including labor costs and complexity of care.

The rates “often do not reflect what is actually paid” to providers by plans because of adjustments that occur, said Ariel Levin, AHA’s director of policy, in a statement. The figures also don’t “reflect individual patients’ expected cost-sharing amounts,” she said.

Names in the News

Amy Abernethy is leaving Verily, where she has been chief medical officer. She will lead a nonprofit in Texas to bolster evidence generation.


The FDA has approved the first test leaning on DNA to gauge if a person is at higher risk of opioid use disorder.

Healthcare Dive reports on a California hospital deal falling apart after FTC scrutiny.

POLITICO’s Holly Otterbein reports that Rep. Dean Phillips, the Minnesota Democrat running for president, is putting his name on the progressive-supported Medicare for All bill.

House Ways and Means Democrats released a report on health care’s role in climate change.

CORRECTION: A previous version of this newsletter misstated the group behind the splashy ads featuring Fat Joe and who pays the hospitals in a report from PatientRightsAdvocate.org. It is the insurance plans.

Wed, 20 Dec 2023 01:03:00 -0600 en text/html https://www.politico.com/newsletters/politico-pulse/2023/12/20/artificial-intelligence-hhs-has-a-plan-for-that-00132611
How School Leaders Can Build Emotional Intelligence

Education Week spoke to principals and superintendents about the value of emotional intelligence and their efforts to develop it. Their conclusion: The work is as difficult as it is urgent. Get insights from leaders who are working to develop their emotional intelligence and how they are putting it to use.

Evie Blad is a reporter for Education Week.

Nicole Bottomley

Principal,  King Phillip High School, MA

Nicole Bottomley is the principal of King Phillip High School in Norfolk, Mass. A former mental health counselor, she seeks to approach her staff with a sense of openness to new ideas.

Dan Cox

Superintendent,  Rochester, Ill., School District

Dan Cox is superintendent of the Rochester, Ill., School District. He has worked to Boost the district’s ability to collect and respond to teacher feedback.

Nick Davies

Associate Elementary School Principal

Nick Davies is an associate elementary school principal in Vancouver, Wash. He has integrated mindfulness strategies into his daily life, helping him to regulate stress and to be more present and engaged in his work.

Thu, 04 Jan 2024 02:22:00 -0600 en text/html https://www.edweek.org/events/k-12-essentials-forum/how-school-leaders-can-build-emotional-intelligence
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Tue, 02 Jan 2024 09:59:00 -0600 en text/html https://www.sciencedaily.com/news/mind_brain/intelligence/
Artificial Intelligence

What We’re Watching in 2024

From elections and A.I. to antitrust and shadow banking, here are the big themes that could define the worlds of business and policy.

 By Andrew Ross Sorkin, Ravi Mattu, Bernhard Warner, Sarah Kessler, Michael J. de la Merced, Ephrat Livni and

Wed, 03 Jan 2024 21:30:00 -0600 en text/html https://www.nytimes.com/spotlight/artificial-intelligence
The US government plans to go all-in on using AI. But it lacks a plan, says a government watchdog

Washington CNN  — 

The US government plans to vastly expand its reliance on artificial intelligence, but it is years behind on policies to responsibly acquire and use the technology from the private sector, according to a new federal oversight report.

The lack of a government-wide standard on AI purchases could undercut American security, wrote the Government Accountability Office (GAO) in a long-awaited review of nearly two-dozen agencies’ current and planned uses for AI. The GAO is the government’s top accountability watchdog.

The 96-page report released Tuesday marks the US government’s most comprehensive effort yet to catalog the more than 200 ways in which non-military agencies already use artificial intelligence or machine learning, and the more than 500 planned applications for AI in the works.

It comes as AI developers have released ever more sophisticated AI models, and as policymakers scramble to develop regulations for the AI industry in the most sensitive use cases. Governments around the world have emphasized AI’s benefits, such as its potential to find cures for disease or to enhance productivity. But they have also thinking about its risks, including the danger of displacing workers, spreading election misinformation or harming vulnerable populations through algorithmic biases. AI could even lead to new threats to national security, experts have warned, by giving malicious actors new ways to develop cyberattacks or biological weapons.

GAO’s broad survey sought answers from 23 agencies ranging from the Departments of Justice and Homeland Security to the Social Security Administration and the Nuclear Regulatory Commission. Already, the federal government uses AI in 228 distinct ways, with nearly half of those uses having launched within the past year, according to the report, reflecting AI’s rapid uptake across the US government.

The vast majority of current and planned government uses for AI that the GAO identified in its report, nearly seven in 10, are either science-related or intended to Boost internal agency management. The National Aeronautics and Space Administration (NASA), for example, told GAO it uses artificial intelligence to monitor volcano activity around the world, while the Department of Commerce said it uses AI to track wildfires and to automatically count seabirds and seals or walruses pictured in drone photos.

Closer to home, the Department of Homeland Security said it uses AI to “identify border activities of interest” by applying machine learning technologies against camera and radar data, according to the GAO report.

The report also highlights the hundreds of ways federal agencies use AI in secret. Federal agencies were willing to publicly disclose about 70% of the total 1,241 active and planned AI use cases, the report said, but declined to identify more than 350 applications of the technology because they were “considered sensitive.”

Some agencies were extraordinarily tight-lipped about their use of AI: the State Department listed 71 different use cases for the technology but told the GAO it could only identify 10 of them publicly.

Although some agencies reported relatively few uses for AI, those handful of applications have attracted some of the most scrutiny by government watchdogs, civil liberties groups and AI experts warning of potentially harmful AI outcomes.

For example, the Departments of Justice and Homeland Security reported a total of 25 current or planned use cases for AI in the GAO’s Tuesday report, a tiny fraction of NASA’s 390 or the Commerce Department’s 285. But that small number belies how sensitive DOJ and DHS’s uses cases can be.

As recently as September, the GAO warned that federal law enforcement agencies have run thousands of AI-powered facial recognition searches — amounting to 95% of such searches at six US agencies from 2019 to 2022 — without having appropriate training requirements for the officials performing the searches, highlighting the potential for AI’s misuse. Privacy and security experts have routinely warned that relying too heavily on AI in policing can lead to cases of mistaken identity and wrongful arrests, or discrimination against minorities.

(The GAO’s September report on facial recognition coincided with a DHS inspector general report finding that several agencies including Customs and Border Patrol, the US Secret Service and Immigration and Customs Enforcement likely broke the law when officials bought Americans’ geolocation histories from commercial data brokers without performing required privacy impact assessments.)

While officials are increasingly turning to AI and automated data analysis to solve important problems, the Office of Management and Budget, which is responsible for harmonizing federal agencies’ approach to a range of issues including AI procurement, has yet to finalize a draft memo outlining how agencies should properly acquire and use AI.

“The lack of guidance has contributed to agencies not fully implementing fundamental practices in managing AI,” the GAO wrote. It added: “Until OMB issues the required guidance, federal agencies will likely develop inconsistent policies on their use of AI, which will not align with key practices or be beneficial to the welfare and security of the American public.”

Under a 2020 federal law dealing with AI in government, OMB should have issued draft guidelines to agencies by September 2021, but missed the deadline and only issued its draft memo two years later, in November 2023, according to the report.

OMB said it agreed with the watchdog’s recommendation to issue guidance on AI and said the draft guidance it released in November was a response to President Joe Biden’s October executive order dealing with AI safety.

Among its provisions, Biden’s exact AI executive order requires developers of “the most powerful AI systems” to share test results of their models with the government, according to a White House summary of the directive. This year, a number of leading AI companies also promised the Biden administration they would seek outside testing of their AI models before releasing them to the public.

The Biden executive order adds to the growing set of requirements for federal agencies when it comes to AI policies by, for example, tasking the Department of Energy to assess the potential for AI to exacerbate threats involving chemical, biological, radiological or nuclear weapons.

Tuesday’s GAO report identified a comprehensive list of AI-related requirements that Congress or the White House has imposed on federal agencies since 2019 and graded their performance. In addition to faulting OMB for failing to come up with a government-wide plan for AI purchases, the report found shortcomings with a handful of other agencies’ approaches to AI. As of September, for example, the Office of Personnel Management had not yet prepared a required forecast of the number of AI-related roles the federal government may need to fill in the next five years. And, the report said, 10 federal agencies ranging from the Treasury Department to the Department of Education lacked required plans for updating their lists of AI use cases over time, which could hinder the public’s understanding how of the US government uses AI.

Tue, 12 Dec 2023 01:34:00 -0600 en text/html https://www.cnn.com/2023/12/12/tech/gao-ai-report/index.html
No Russian 2025 war plan? Analysis of Putin's battle endurance by experts and Ukrainian Intelligence No result found, try new keyword!Photo: Kyrylo Budanov says that Russia currently does not have a military plan for 2025 (Vitalii Nosach/RBC-Ukraine) RBC-Ukraine reached out to Ukrainian Intelligence for comment. According to the ... Fri, 15 Dec 2023 06:42:00 -0600 en-us text/html https://www.msn.com/ Emotional Intelligence Must Guide Artificial Intelligence

Lazarus is an adjunct professor of psychiatry and a regular commentator on the practice of medicine.

I don't understand the brouhaha about artificial intelligence (AI). It's artificial -- or augmented -- but in either case, it's not real. AI cannot replace clinicians. AI cannot practice clinical medicine or serve as a substitute for clinical decision-making, even if AI can outperform humans on certain exams. When put to the real test -- for example, making utilization review decisions -- the error rate can be as high as 90%.

Findings presented at the 2023 meeting of the American Society of Health-System Pharmacists showed that the AI chatbot ChatGPT provided incorrect or incomplete information when asked about drugs, and in some cases invented references to support its answers. Researchers said the AI tool is not yet accurate enough to answer consumer or pharmacist questions. Of course it's not. AI is only as smart as the people who build it.

What do you expect from a decision tree programmed by an MBA and not an actual doctor? Or a large language model that is prone to fabricate or "hallucinate" -- that is, confidently generate responses without backing data? If you try to find ChatGPT's sources through PubMed or a Google search you often strike out.

The fact is the U.S. healthcare industry has a long record of problematic AI use, including establishing algorithmic racial bias in patient care. In a exact study that sought to assess ChatGPT's accuracy in providing educational information on epilepsy, ChatGPT provided correct but insufficient responses to 16 of 57 questions, and one response contained a mix of correct and incorrect information. Research involving medical questions in a wide range of specialties has suggested that, despite improvements, AI should not be relied on as a sole source of medical knowledge because it lacks reliability and can be "spectacularly and surprisingly wrong."

It seems axiomatic that the development and deployment of any AI system would require expert human oversight to minimize patient risks and ensure that clinical discretion is part of the operating system. AI systems must be developed to manage biases effectively, ensuring that they are non-discriminatory, transparent, and respect patients' rights. Healthcare companies relying on AI technology need to input the highest-quality data and monitor the outcomes of answers to queries.

What we need is more emotional intelligence (EI) to guide artificial intelligence.

EI is fundamental in human-centered care, where empathy, compassion, and effective communication are key. Emotional intelligence fosters empathetic patient-doctor relationships, which are fundamental to patient satisfaction and treatment adherence. Doctors with high EI can understand and manage their own emotions and those of their patients, facilitating effective communication and mutual understanding. EI is essential for managing stressful situations, making difficult decisions, and working collaboratively within healthcare teams.

Furthermore, EI plays a significant role in ethical decision-making, as it enables physicians to consider patients' emotions and perspectives when making treatment decisions. Because EI enhances the ability to identify, understand, and manage emotions in oneself and others, it is a crucial skill set that can significantly influence the quality of patient care, physician-patient relationships, and the overall healthcare experience.

AI lacks the ability to understand and respond to human emotions, a gap filled by EI. Despite the advanced capabilities of AI, it cannot replace the human touch in medicine. From the doctors' perspective, many still believe that touch makes important connections with patients.

Simon Spivack, MD, MPH, a pulmonologist affiliated with Albert Einstein College of Medicine and Montefiore Health System in New York, remarked, "touch traverses the boundary between healer and patient. It tells patients that they are worthy of human contact ... While the process takes extra time, and we have precious little of it, I firmly believe it's the least we can do as healers -- and as fellow human beings."

Spivack further observed: "[I]n our increasingly technology-driven future, I am quite comfortable predicting that nothing -- not bureaucratic exigencies, nor virtual medical visits, nor robots controlled by artificial intelligence -- will substitute for this essential human-to-human connection."

Patients often need reassurance, empathy, and emotional support, especially when dealing with severe or chronic illnesses. These are aspects that AI, with its current capabilities, cannot offer. I'm reminded of Data on Star Trek: The Next Generation. Data is an artificially intelligent android who is capable of touch but lacks emotions. Nothing in Data's life is more important than his quest to become more human. However, when Data acquires the "emotion chip," it overloads his positronic relays and eventually the chip has to be removed. Once artificial, always artificial.

Harvard medical educator Bernard Chang, MD, MMSc, remarked: "[I]f the value that physicians of the future will bring to their AI-assisted in-person patient appointments is considered, it becomes clear that a thorough grounding in sensitive but effective history-taking, personally respectful and culturally humble education and counseling, and compassionate bedside manner will be more important than ever. Artificial intelligence may be able to engineer generically empathic prose, but the much more complex verbal and nonverbal patient-physician communication that characterizes the best clinical visits will likely elude it for some time."

In essence, AI and EI are not competing elements but complementary aspects in modern medical practice. While AI brings about efficiency, precision, and technological advancements, EI ensures empathetic patient interactions and effective communication. The ideal medical practice would leverage AI for tasks involving data analysis and prediction, while relying on EI for patient treatment and clinical decision-making, thereby ensuring quality and holistic patient care.

There was a reason Jean-Luc Picard was Captain of the USS Enterprise and Data was not.

Data had all the artificial intelligence he ever needed in his computer-like brain and the Enterprise's massive data banks, but ultimately it was Picard's intuitive and incisive decision-making that enabled the Enterprise crew to go where no one had gone before.

Arthur Lazarus, MD, MBA, is a former Doximity Fellow, a member of the editorial board of the American Association for Physician Leadership, and an adjunct professor of psychiatry at the Lewis Katz School of Medicine at Temple University in Philadelphia. He is the author of Every Story Counts: Exploring Contemporary Practice Through Narrative Medicine.

Mon, 01 Jan 2024 10:00:00 -0600 en text/html https://www.medpagetoday.com/opinion/second-opinions/108102
Artificial Intelligence

ChatGPT is being asked to handle all kinds of weird tasks, from determining whether written text was created by an AI, to answering homework questions, and much more. It’s good at some of these tasks, and absolutely incapable of others. [Filipe dos Santos Branco] and [Edward Gu] had an out of the box idea, though. What if ChatGPT could do something musical?

They built a system that, at the press of a button, would query ChatGPT for a 10-note melody in a given musical key. Once the note sequence is generated by the large language model, it’s played out by a PWM-based synthesizer running on a Raspberry Pi Pico.

Ultimately, ChatGPT is no musical genius. It’s simply picking a bunch of notes from a list that are known to work together melodically; that’s the whole point of musical keys. It would have been wild if it generated some riffs on the level of Stairway to Heaven or Spontaneous Devotion, but that might be asking for too much.

Here’s the question, though. If you trained a large language model, but got it to digest sheet music instead of written texts… could it learn to write music in various genres and styles? If someone isn’t working on that already, there’s surely an entire PhD you could get out of that idea alone. We should talk!

In any case, it’s one of the more creative projects from the ever-popular ECE 4760 class at Cornell. We’ve featured a bunch of projects from the class over the years, and noted how the course now runs on the RP2040. Continue reading “Audio Synthesizer Hooked Up With ChatGPT Interface” →

Sun, 24 Dec 2023 10:00:00 -0600 en-US text/html https://hackaday.com/category/artificial-intelligence/

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