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Policymakers around the world are grappling with the new opportunities and dangers that artificial intelligence presents. Of all the effects that AI can have on the world, among the most consequential would be integrating it into the command and control for nuclear weapons. Improperly used, AI in nuclear operations could have world-ending effects. If properly implemented, it could reduce nuclear risk by improving early warning and detection and enhancing the resilience of second-strike capabilities, both of which would strengthen deterrence. To take full advantage of these benefits, systems must take into account the strengths and limitations of humans and machines. Successful human-machine joint cognitive systems will harness the precision and speed of automation with the flexibility of human judgment and do so in a way that avoids automation bias and surrendering human judgment to machines. Because of the early state of AI implementation, the United States has the potential to make the world safer by more clearly outlining its policies, pushing for broad international agreement, and acting as a normative trendsetter.

The United States has been extremely transparent and forward-leaning in establishing and communicating its policies on military AI and autonomous systems, publishing its policy on autonomy in weapons in 2012, adopting ethical principles for military AI in 2020, and updating its policy on autonomy in weapons in 2023. The department stated formally and unequivocally in the 2022 Nuclear Posture Review that it will always maintain a human in the loop for nuclear weapons employment. In November 2023, over 40 nations joined the United States in endorsing a political declaration on responsible military use of AI. Endorsing states included not just U.S. allies but also nations in Africa, Southeast Asia, and Latin America.

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Building on this success, the United States should push for international agreements with other nuclear powers to mitigate the risks of integrating AI into nuclear systems or placing nuclear weapons onboard uncrewed vehicles. The United Kingdom and France released a joint statement with the United States in 2022 agreeing on the need to maintain human control of nuclear launches. Ideally, this could represent the beginning of a commitment by the permanent members of the United Nations Security Council if Russia and China could be convinced to join this principle. Even if they are not willing to agree, the United States should further mature its own policies to address critical gaps and work with other nuclear-armed states to strengthen their commitments as an interim measure and as a way to build international consensus on the issue.

The Dangers of Automation

As militaries increasingly adopt AI and automation, there is an urgent need to clarify how these technologies should be used in nuclear operations. Absent formal agreements, states risk an incremental trend of creeping automation that could undermine nuclear stability. While policymakers are understandably reluctant to adopt restrictions on emerging technologies lest they give up a valuable future capability, U.S. officials should not be complacent in assuming other states will approach AI and automation in nuclear operations responsibly. Examples such as Russias Perimeter dead hand system and Poseidon autonomous nuclear-armed underwater drone demonstrate that other nations might see these risks differently than the United States and might be willing to take risks that U.S. policymakers would find unacceptable.

Existing systems, such as Russias Perimeter, highlight the risks of states integrating automation into nuclear systems. Perimeter is reportedly a system created by the Soviet Union in the 1980s to act as a failsafe in case Soviet leadership was destroyed in a decapitation strike. Perimeter reportedly has a network of sensors to determine if a nuclear attack has occurred. If these sensors are triggered while Perimeter is activated, the system would wait a predetermined period of time for a signal from senior military commanders. If there is no signal from headquarters, presumably because Soviet/Russian leadership had been wiped out, then Perimeter would bypass the normal chain of command and pass nuclear launch authority to a relatively junior officer on duty. Senior Russian officials have stated the system is still functioning, noting in 2011 that the system was combat ready and in 2018 that it had been improved.

The system was designed to reduce the burden on Soviet leaders of hastily making a nuclear decision under time pressure and with incomplete information. In theory, Soviet/Russian leaders could take more time to deliberate knowing that there is a failsafe guaranteeing retaliation if the United States succeeded in a decapitation strike. The cost, however, is a system that risks easing pathways to nuclear annihilation in the event of an accident.

Allowing autonomous systems to participate in nuclear launch decisions risks degrading stability and increasing the dangers of nuclear accidents. The Stanislav Petrov incident is an illustrative example of the dangers of automation in nuclear decision-making. In 1983, a Soviet early warning system indicated that the United States had launched several intercontinental ballistic missiles. Lieutenant Colonel Stanislav Petrov, the duty officer at the time, suspected that the system was malfunctioning because the number of missiles launched was suspiciously low and the missiles were not picked up by early warning radars. Petrov reported it (correctly) as a malfunction instead of an attack. AI and autonomous systems often lack the contextual understanding that humans have and that Petrov used to recognize that the reported missile launch was a false alarm. Without human judgment at critical stages of nuclear operations, automated systems could make mistakes or elevate false alarms, heightening nuclear risk.

Moreover, merely having humans in the loop will not be enough to ensure effective human decision-making. Human operators frequently fall victim to automation bias, a condition in which humans overtrust automation and surrender their judgment to machines. Accidents with self-driving cars demonstrate the dangers of humans overtrusting automation, and military personnel are not immune to this phenomenon. To ensure humans remain cognitively engaged in their decision-making, militaries will need to take into account not only the automation itself but also human psychology and human-machine interfaces.

More broadly, when designing human-machine systems, it is essential to consciously determine the appropriate roles for humans and machines. Machines are often better at precision and speed, while humans are often better at understanding the broader context and applying judgment. Too often, human operators are left to fill in the gaps for what automation cant do, acting as backups or failsafes for the edge cases that autonomous systems cant handle. But this model often fails to take into account the realities of human psychology. Even if human operators dont fall victim to automation bias, to assume that a person can sit passively watching a machine perform a task for hours on end, whether a self-driving car or a military weapon system, and then suddenly and correctly identify a problem when the automation is not performing and leap into action to take control is not realistic. Human psychology doesnt work that way. And tragic accidents with complex highly automated systems, such as the Air France 447 crash in 2009 and the 737 MAX crashes in 2018 and 2019, demonstrate the importance of taking into account the dynamic interplay between automation and human operators.

The U.S. military has also suffered tragic accidents with automated systems, even when humans are in the loop. In 2003, U.S. Army Patriot air and missile defense systems shot down two friendly aircraft during the opening phases of the Iraq war. Humans were in the loop for both incidents. Yet a complex mix of human and technical failures meant that human operators did not fully understand the complex, highly automated systems they were in charge of and were not effectively in control.

The military will need to establish guidance to inform system design, operator training, doctrine, and operational procedures to ensure that humans in the loop arent merely unthinking cogs in a machine but actually exercise human judgment. Issuing this concrete guidance for weapons developers and operators is most critical in the nuclear domain, where the consequences of an accident could be grave.

Clarifying Department of Defense Guidance

Recent policies and statements on the role of autonomy and AI in nuclear operations are an important first step in establishing this much-needed guidance, but additional clarification is needed. The 2022 Nuclear Posture Review states: In all cases, the United States will maintain a human in the loop for all actions critical to informing and executing decisions by the President to initiate and terminate nuclear weapon employment. The United Kingdom adopted a similar policy in 2022, stating in their Defence Artificial Intelligence Strategy: We will ensure that regardless of any use of AI in our strategic systems human political control of our nuclear weapons is maintained at all times.

As the first official policies on AI in nuclear command and control, these are landmark statements. Senior U.S. military officers had previously emphasized the importance of human control over nuclear weapons, including statements by Lt. Gen. Jack Shanahan, then-director of the Joint Artificial Intelligence Center in 2019. Official policy statements are more significant, however, in signaling to audiences both internal and external to the military the importance of keeping humans firmly in charge of all nuclear use decisions. These high-level statements nevertheless leave many open questions about implementation.

The next step for Department of Defense is to translate what the high-level principle of human in the loop means for nuclear systems, doctrine, and training. Key questions include: Which actions are critical to informing and executing decisions by the president? Do those only consist of actions immediately surrounding the president, or do they also include actions further down the chain of command before and after a presidential decision? For example, would it be acceptable for a human to deliver an algorithm-based recommendation to the president to carry out a nuclear attack? Or does a human need to be involved in understanding the data and rendering their own human judgment?

The U.S. military already uses AI to process information, such as satellite images and drone video feeds. Presumably, AI would also be used to support intelligence analysis that could support decisions about nuclear use. Under what circumstances is AI appropriate and beneficial to nuclear stability? Are some applications and ways of using AI more valuable than others?

When AI is used, what safeguards should be put in place to guard against mistakes, malfunctions, or spoofing of AI systems? For example, the United States currently employs a dual phenomenology mechanism to ensure that a potential missile attack is confirmed by two independent sensing methods, such as satellites and ground-based radars. Should the United States adopt a dual algorithm approach to any use of AI in nuclear operations, ensuring that there are two independent AI systems trained on different data sets with different algorithms as a safeguard against spoofing attacks or unreliable AI systems?

When AI systems are used to process information, how should that information be presented to human operators? For example, if the military used an algorithm trained to detect signs of a missile being fueled, that information could be interpreted differently by humans if the AI system reported fueling versus preparing to launch. Fueling is a more precise and accurate description of what the AI system is actually detecting and might lead a human analyst to seek more information, whereas preparing to launch is a conclusion that might or might not be appropriate depending on the broader context.

When algorithmic recommendation systems are used, how much of the underlying data should humans have to directly review? Is it sufficient for human operators to only see the algorithms conclusion, or should they also have access to the raw data that supports the algorithms recommendation?

Finally, what degree of engagement is expected from a human in the loop? Is the human merely there as a failsafe in case the AI malfunctions? Or must the human be engaged in the process of analyzing information, generating courses of actions, and making recommendations? Are some of these steps more important than others for human involvement?

These are critical questions that the United States will need to address as it seeks to harness the benefits of AI in nuclear operations while meeting the human in the loop policy. The sooner the Department of Defense can clarify answers to these questions, the more that it can accelerate AI adoption in ways that are trustworthy and meet the necessary reliability standards for nuclear operations. Nor does clarifying these questions overly constrain how the United States approaches AI. Guidance can always be changed over time as the technology evolves. But a lack of clear guidance risks forgoing valuable opportunities to use AI or, even worse, adopting AI in ways that might undermine nuclear surety and deterrence.

Dead Hand Systems

In clarifying its human-in-the-loop policy, the United States should make a firm commitment to reject dead hand nuclear launch systems or a system with a standing order to launch that incorporates algorithmic components. Dead hand systems akin to Russias Perimeter would appear to be prohibited by current Department of Defense policy. However, the United States should explicitly state that it will not build such systems given their risk.

Despite their danger, some U.S. analysts have suggested that the United States should adopt a dead hand system to respond to emerging technologies such as AI, hypersonics, and advanced cruise missiles. There are safer methods for responding to these threats, however. Rather than gambling humanitys future on an algorithm, the United States should strengthen its second-strike deterrent in response to new threats.

Some members of the U.S. Congress have even expressed a desire for writing this requirement into law. In April 2023, a bipartisan group of representatives introduced the Block Nuclear Launch by Autonomous Artificial Intelligence Act, which would prohibit funding for any system that launches nuclear weapons without meaningful human control. There is precedent for a legal requirement to maintain a human in the loop for strategic systems. In the 1980s, during development of the Strategic Defense Initiative (also known as Star Wars), Congress passed a law requiring affirmative human decision at an appropriate level of authority for strategic missile defense systems. This legislation could serve as a blueprint for a similar legislative requirement for nuclear use. One benefit of a legal requirement is that it ensures that such an important policy could not be overturned by a future administration or Pentagon leadership that is more risk-accepting without Congressional authorization.

Nuclear Weapons and Uncrewed Vehicles

The United States should similarly clarify its policy for nuclear weapons on uncrewed vehicles. The United States is producing a new nuclear-capable strategic bomber, the B-21, that will be able to perform uncrewed missions in the future, and is developing large undersea uncrewed vehicles that could carry weapons payloads. U.S. military officers have stated a strong reticence for placing nuclear weapons aboard uncrewed platforms. In 2016, then-Commander of Air Force Global Strike Command Gen. Robin Rand noted that the B-21 would always be crewed when carrying nuclear weapons: If you had to pin me down, I like the man in the loop; the pilot, the woman in the loop, very much, particularly as we do the dual-capable mission with nuclear weapons. General Rands sentiment may be shared among senior military officers, but it is not official policy. The United States should adopt an official policy that nuclear weapons will not be placed aboard recoverable uncrewed platforms. Establishing this policy could help provide guidance to weapons developers and the services about the appropriate role for uncrewed platforms in nuclear operations as the Department of Defense fields larger uncrewed and optionally crewed platforms.

Nuclear weapons have long been placed on uncrewed delivery vehicles, such as ballistic and cruise missiles, but placing nuclear weapons on a recoverable uncrewed platform such as a bomber is fundamentally different. A human decision to launch a nuclear missile is a decision to carry out a nuclear strike. Humans could send a recoverable, two-way uncrewed platform, such as a drone bomber or undersea autonomous vehicle, out on patrol. In that case, the human decision to launch the nuclear-armed drone would not yet be a decision to carry out a nuclear strike. Instead, the drone could be sent on patrol as an escalation signal or to preposition in case of a later decision to launch a nuclear attack. Doing so would put enormous faith in the drones communications links and on-board automation, both of which may be unreliable.

The U.S. military has lost control of drones before. In 2017, a small tactical Army drone flew over 600 miles from southern Arizona to Denver after Army operators lost communications. In 2011, a highly sensitive U.S. RQ-170 stealth drone ended up in Iranian hands after U.S. operators lost contact with it over Afghanistan. Losing control of a nuclear-armed drone could cause nuclear weapons to fall into the wrong hands or, in the worst case, escalate a nuclear crisis. The only way to maintain nuclear surety is direct, physical human control over nuclear weapons up until the point of a decision to carry out a nuclear strike.

While the U.S. military would likely be extremely reluctant to place nuclear weapons onboard a drone aircraft or undersea vehicle, Russia is already developing such a system. The Poseidon, or Status-6, undersea autonomous uncrewed vehicle is reportedly intended as a second- or third-strike weapon to deliver a nuclear attack against the United States. How Russia intends to use the weapon is unclear and could evolve over time but an uncrewed platform like the Poseidon in principle could be sent on patrol, risking dangerous accidents. Other nuclear powers could see value in nuclear-armed drone aircraft or undersea vehicles as these technologies mature.

The United States should build on its current momentum in shaping global norms on military AI use and work with other nations to clarify the dangers of nuclear-armed drones. As a first step, the U.S. Defense Department should clearly state as a matter of official policy that it will not place nuclear weapons on two-way, recoverable uncrewed platforms, such as bombers or undersea vehicles. The United States has at times foresworn dangerous weapons in other areas, such as debris-causing antisatellite weapons, and publicly articulated their dangers. Similarly explaining the dangers of nuclear-armed drones could help shape the behavior of other nuclear powers, potentially forestalling their adoption.

Conclusion

It is imperative that nuclear powers approach the integration of AI and autonomy in their nuclear operations thoughtfully and deliberately. Some applications, such as using AI to help reduce the risk of a surprise attack, could improve stability. Other applications, such as dead hand systems, could be dangerous and destabilizing. Russias Perimeter and Poseidon systems demonstrate that other nations might be willing to take risks with automation and autonomy that U.S. leaders would see as irresponsible. It is essential for the United States to build on its current momentum to clarify its own policies and work with other nuclear-armed states to seek international agreement on responsible guardrails for AI in nuclear operations. Rumors of a U.S.-Chinese agreement on AI in nuclear command and control at the meeting between President Joseph Biden and General Secretary Xi Jinping offer a tantalizing hint of the possibilities for nuclear powers to come together to guard against the risks of AI integrated into humanitys most dangerous weapons. The United States should seize this moment and not let this opportunity pass to build a safer, more stable future.

Michael Depp is a research associate with the AI safety and stability project at the Center for a New American Security (CNAS).

Paul Scharre is the executive vice president and director of studies at CNAS and the author of Four Battlegrounds: Power in the Age of Artificial Intelligence.

Image: U.S. Air Force photo by Senior Airman Jason Wiese

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Artificial Intelligence and Nuclear Stability - War On The Rocks

Tools powered by artificial intelligence can create lifelike images of people who do not exist.

See if you can identify which of these images are real people and which are A.I.-generated.

Were you surprised by your results? You guessed 0 times and got 0 correct.

Ever since the public release of tools like Dall-E and Midjourney in the past couple of years, the A.I.-generated images theyve produced have stoked confusion about breaking news, fashion trends and Taylor Swift.

Distinguishing between a real versus an A.I.-generated face has proved especially confounding.

Research published across multiple studies found that faces of white people created by A.I. systems were perceived as more realistic than genuine photographs of white people, a phenomenon called hyper-realism.

Researchers believe A.I. tools excel at producing hyper-realistic faces because they were trained on tens of thousands of images of real people. Those training datasets contained images of mostly white people, resulting in hyper-realistic white faces. (The over-reliance on images of white people to train A.I. is a known problem in the tech industry.)

The confusion among participants was less apparent among nonwhite faces, researchers found.

Participants were also asked to indicate how sure they were in their selections, and researchers found that higher confidence correlated with a higher chance of being wrong.

We were very surprised to see the level of over-confidence that was coming through, said Dr. Amy Dawel, an associate professor at Australian National University, who was an author on two of the studies.

It points to the thinking styles that make us more vulnerable on the internet and more vulnerable to misinformation, she added.

The idea that A.I.-generated faces could be deemed more authentic than actual people startled experts like Dr. Dawel, who fear that digital fakes could help the spread of false and misleading messages online.

A.I. systems had been capable of producing photorealistic faces for years, though there were typically telltale signs that the images were not real. A.I. systems struggled to create ears that looked like mirror images of each other, for example, or eyes that looked in the same direction.

But as the systems have advanced, the tools have become better at creating faces.

The hyper-realistic faces used in the studies tended to be less distinctive, researchers said, and hewed so closely to average proportions that they failed to arouse suspicion among the participants. And when participants looked at real pictures of people, they seemed to fixate on features that drifted from average proportions such as a misshapen ear or larger-than-average nose considering them a sign of A.I. involvement.

The images in the study came from StyleGAN2, an image model trained on a public repository of photographs containing 69 percent white faces.

Study participants said they relied on a few features to make their decisions, including how proportional the faces were, the appearance of skin, wrinkles, and facial features like eyes.

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Test Yourself: Which Faces Were Made by A.I.? - The New York Times

Data acquisition

This study was carried out in accordance with the tenets of the Declaration of Helsinki and with the approval of the Brest, France hospitals (CHRUs) Ethics Committee. Patients had also signed an informed consent. Our work was conducted between 2021 and 2022. Data collected between June 2006 and June 2021 from 734 patients (115 adults and 619 children) who had undergone clinical 3D gait analysis were used. Their identities were preserved by respecting medical secret and protecting patient confidentiality. All data were recorded using the same motion analysis system (Vicon MX, Oxford Metrics, UK) and four force platforms (Advanced Mechanical Technology, Inc., Watertown, MA, USA) in the same motion laboratory (CHU Brest) between 2006 and 2022. The data collected by the 15 infrared cameras (sampling rate of 100 or 120Hz) were synchronized with the ground reaction forces recorded by the force platforms (1000Hz or 1200Hz). The 16 markers were placed according to the protocol by Kadaba et al.11. Marker trajectories and ground reaction forces were dual-pass filtered with a low-pass Butterworth filter at a cut-off frequency of 6 Hz. After an initial calibration in the standing position, all patients were asked to walk at a self-selected speed along a 10m walkway.

Gait kinematics were processed using the Vicon Plug-in Gait model. Kinematics were time-normalized to stride duration, from 0 to 100% from initial contact (IC) to the next IC of the ipsilateral foot. Nine gait joint angles (kinematic gait variables) were used: anteversion/retroversion of the pelvis, rotation of the pelvis, pelvic tilt, flexion/extension of the hip, abduction/adduction of the hip, internal/external rotation of the hip, flexion/extension of the knee, plantar/dorsiflexion of the ankle, and the foots angle of progression. As a result, a gait cycle yielded 101 (times) 9 measurements. Let (E_{p,d}) denote the gait session of patient p at datetime d. It can be written as follows:

$$begin{aligned} E_{p,d} = left{ {C_{ E_{p,d}}}^{1}, {C_{ E_{p,d}}}^{2}, ldots , {C_{ E_{p,d}}}^{K} right} end{aligned}$$

(1)

where ({C_{ E_{p,d}}}^{k}) is the k-th gait cycle of a gait session (E_{p,d}) and K the total number of gait cycles. Let (c_{t,n}^{E_{p,d}^{k}}) denote the gait cycle ({C_{E_{p,d}}}^{k}) value at time step t and joint angle n. To keep notations simple, (c_{t,n}^{E_{p,d}^{k}}) is referred to as (c_{t,n}) in what follows. ({C_{E_{p,d}}}^{k}) can simply be represented with a matrix of 101 lines and 9 columns, as follows:

$$begin{aligned} {C_{ E_{p,d}}}^{k} = begin{bmatrix} c_{1,1} &{} c_{1,2} &{}cdots &{} c_{1,9} \ c_{2,1} &{} c_{2,2} &{}cdots &{} c_{2,9}\ vdots &{} &{} &{} \ c_{101,1} &{} c_{101,2} &{}cdots &{} c_{101,9}\ end{bmatrix} end{aligned}$$

(2)

The Gait Profile Score (GPS), a walking behavior score, was computed for each gait cycle from the previously described joint angles12,13,14. The GPS is a single index measure that summarizes the overall deviation of kinematic gait data relative to normative data. It can be decomposed to provide Gait Variable Scores (GVS) for nine key component kinematic gait variables, which are presented as a Movement Analysis Profile (MAP). The GVS corresponding to the n-th kinematic variable, GVS(_{textrm{n}}), is given by15,16,17:

$$begin{aligned} GVS_n = sqrt{frac{1}{T}sum _{t=1}^{T}(c_{t,n} - c_{t,n} ^{ref})^{2}} end{aligned}$$

(3)

where t is a specific point in the gait cycle, T its total number of points (typically equal to 10118,19), (c_{t,n}) the value of the kinematic variable n at point t, and (c_{t,n}^{textrm{ref}}) is its mean on the reference population (physiological normative). The GPS is obtained from the GVS scores15,17 as follows:

$$begin{aligned} GPS = sqrt{frac{1}{N}sum _{n=1}^{N}GVS_n^{2}} end{aligned}$$

(4)

where N is the total number of kinematic variables (equal to 9 by definition).

We had a total of 1459 gait sessions from 734 patients (115 adults and 619 children). Each patient had an average of 1.988 gait sessions with a standard deviation of 1.515. 53,693 gait cycles were collected. Their average number per gait session is equal to 18 with a standard deviation of 6. Neurological conditions, notably cerebral palsy, are the most frequent etiologies, as we can see in Fig.1.

The average patient age within the first gait session is equal to 14years, with a standard deviation of 16years. The time delay between the first and last gait session (for patients with more than one gait session, i.e., 319) is equal to 3.92years on average with a standard deviation of 3.24years. Directly consecutive gait sessions are, on average, separated by approximately 740days, with a standard deviation of 577days. The shortest (resp. longest) time delay was equal to 4 (resp. 4438) days. We had 1384 pairs of directly consecutive gait sessions belonging to 319 patients (the remaining patients were removed since they had only one gait session). Involved gait conditions are various: without any equipment, with a cane, with a rollator, with an orthosis, with a prosthesis.. Only pairs of gait sessions without equipment were selected in order to be in the same condition (79% of all available pairs, i.e. 1152). The first gait sessions in these pairs were used for training. Models were fed the gait cycles of these first gait sessions (i.e., 21,167 gait cycles in total).

GPS variation prediction is similar enough to a Time Series Classification (TSC) issue that its proposed popular architectures should be adopted. Consecutive gait session pairs ((E_{p,d}, E_{p,d+Delta d})) were considered. For each gait cycle ({C_{ E_{p,d}}}^{k}) of the current gait session (E_{p,d}), a GPS variation (Delta {}GPS) was computed using:

$$begin{aligned} Delta {}GPS({C_{ E_{p,d}}}^{k}) = GPS_{avg}( E_{p,d+Delta d}) - GPS({C_{ E_{p,d}}}^{k}) end{aligned}$$

(5)

where (GPS_{avg}(E_{p,d+Delta d})) is the average GPS per cycle of (E_{p,d+Delta d}) and (GPS({C_{ E_{p,d}}}^{k})) the GPS of the current gait cycle ({C_{E_{p,d}}}^{k}). The average GPS per cycle (GPS_{average}(E_{p,d})) of a gait session (E_{p,d}) is simply equal to:

$$begin{aligned} GPS_{avg}(E_{p,d}) = frac{sum _{k=1}^{K} GPS({C_{ E_{p,d}}}^{k}) }{K} end{aligned}$$

(6)

(Delta {})

GPS was ranked in a binary fashion. Either it is negative, in which case the patients gait improves (class 1), or it is positive, in which case the patients gait worsens (class 0). The metric used is the Area Under the Curve (AUC).

The distribution of patients between training, validation, and test groups is provided in Table1. Such a split put 73%, 12%, and 14% of total gait cycles within the training, validation, and test groups, respectively.

To be exhaustive, one MLP, one recurrent neural network (LSTM), one hybrid architecture (Encoder), several CNN architectures (FCN, ResNet, t-LeNet), and a one-dimensional Transformer20 were included. The MLP and LSTM were designed and developed from scratch. Their hyper-parameters were optimized manually. FCN, ResNet, Encoder, and t-LeNet are among the most effective end-to-end discriminative architectures regarding the TSC state-of-the-art10. These methods were also compared to the Transformer, a more recent and popular architecture. The Transformer does not suffer from long-range context dependency issues compared to LSTM21. In addition, it is notable for requiring less training. The Adam optimizer22 and binary cross-entropy loss were employed23.

For MLP, gait cycles were flattened so that the input length was equal to 909 time steps. The number of neurons was the same across all the fully connected layers. Many values of this number were tested to find the best structure for our task. In the same way, the number of layers was optimized. The corresponding architecture is shown in Fig.2.

MLP architecture for prediction.

LSTM layers were stacked, and a dropout was added before the last layer to avoid overfitting. The corresponding architecture is shown in Fig.3.

LSTM architecture for prediction.

For FCN, ResNet, Encoder and t-LeNet, the architectures proposed in Ref.10 were considered. They are shown in Figs. 4, 5, 6 and 7, respectively. We followed an existing implementation24 to set up the Transformer.

FCN architecture for prediction.

ResNet architecture for prediction.

Encoder architecture for prediction.

t-LeNet architecture for prediction.

Different techniques of data augmentation were tested as a pre-processing step to avoid overfitting: jittering, scaling, window warping, permutation, and window slicing. Their hyperparameters were empirically optimized for each model. These are among the TSC literatures most frequently utilized techniques, particularly when it comes from sensor data10.

Image-based time series representation initiated a new branch of deep learning approaches that consider image transformation as an innovative pre-processing of feature engineering25. In an attempt to reveal features and patterns less visible in the one-dimensional sequence of the original time series, many transformation methods were developed to encode time series as input images.

In our study, sensor modalities are transformed to the visual domain using 2D FFT in order to utilize a set of pre-trained CNN models for transfer learning on the converted imagery data. The full workflow of our framework is represented in Fig.8.

Proposed (Delta GPS) prediction workflow for the image-based approach.

2D FFT is used to work in the frequency domain or Fourier domain because it efficiently extracts features based on the frequency of each time step in the time series. It can be defined as:

$$F(u,v) = frac{1}{{T.N}}sumlimits_{{t = 0}}^{T} {sumlimits_{{n = 0}}^{N} {c_{{t,n}} } } exp left( { - j2pi left( {frac{{ut}}{T} + frac{{vn}}{N}} right)} right)$$

(7)

where F(u,v) is the direct Fourier transform of the gait cycle. It is a complex function that shows the phase and magnitude of the signal in the frequency domain. u and v are the frequency space coordinates. The magnitude of the 2D FFT |F(u,v)|, also known as the spectrum, is a two-dimensional signal that represents frequency information. Because the 2D FFT has translation and rotation attributes, the zero-frequency component can be moved to the center of |F(u,v)| without losing any information, making the spectrum image more visible. The centralized FFT spectrums were computed and fed to the proposed deep learning models. A centralized FFT spectrum for a given gait cycle is represented in Fig.9.

2D FFT for a given gait cycle. (a) The gait cycle; (b) FFT spectrum of the gait cycle; (c) Centralized FFT spectrum of the gait cycle.

The Timm librarys26 pre-trained VGG16, ResNet34, EfficientNet_b0, and the Vision Transformer vit_base_patch16_224 were investigated. They were pre-trained on a large collection of images, in a supervised fashion. For the Transformer, the pre-training was at a resolution of (224 times 224) pixels. Its input images were considered as a sequence of fixed-size patches (resolution (16 times 16)), which were linearly embedded.

Converting our grayscale images to RGB images was not necessary because Timms implementations support any number of input channels. The models minimum input size for VGG16 is (32 times 32). The images width dimension (N) equals 9, which is less than 32. In order to fit the minimum needed size, 2D FFT images were repeated 4 times in this width dimension. Transfer learning with fine-tuning methods was employed. One neurons final fully connected layer was used. In the same way that the top layers were trainable, all convolutional blocks were.

The pre-trained Timm models are deep and sophisticated, with many layers. As a result, a CNN model with fewer parameters, designed from scratch, was conceived. The number of used two-dimensional convolutional layers was a hyper-parameter to optimize in a finite range of values {1, 2, 3, 4, 5}. After the convolutional block, a dropout function was applied. Following that, two-dimensional max-pooling (MaxPooling2D) and batch normalization were used. The flattened output of the batch normalization was then fed to a dense layer of a certain number of neurons to tune. In order to predict the (Delta GPS), our model had a dense output layer with a single neuron. The corresponding architecture is shown in Fig.10.

Tailored 2D CNN for prediction.

The following are all of the architecture hyper-parameters to tune: the number of convolutional layers (num_layers), the number of filters for each convolution layer (num_filters), the kernel size of each convolution layer (kernel_size), the dropout rate (dropout), the pooling size of the MaxPooling2D (pool_size), the number of neurons in the dense layer (units), and the learning rate (lr). Five models with a varying number of convolutional layers (from 1 to 5) were tested. For each of them, the rest of the hyper-parameters were tuned using KerasTuner9 to maximize the validation AUC.

Originally posted here:
Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders | Scientific Reports - Nature.com

Artificial Intelligence (AI) has swiftly emerged as a game-changer, transforming various aspects of our lives. With recent advancements in generative AI tools like ChatGPT, its evident that we are standing on the brink of an AI revolution that will reshape our world.

AI is not a futuristic concept anymore; it is already ingrained in our day-to-day lives. Although we might not always realize it, AI is all around us, seamlessly integrated into the technology we use.

From online shopping and internet searches to food deliveries and ride-hailing services, AI has become an integral part of our digital experiences. While it might not resemble the AI portrayed in science fiction movies, todays AI possesses the ability to learn and improve, simulating cognitive functions similar to our own.

One common concern associated with AI is the fear of job displacement. Its true that AI has the potential to automate certain tasks, but it is not yet capable of fully replicating the diverse skill sets required for most jobs. While manual labor and routine tasks like cashiering have already seen automation, knowledge-intensive roles and those involving human interaction are less susceptible to immediate replacement.

Moreover, the rise of AI also brings forth new job opportunities, particularly in areas related to technology and AI itself.

Ignoring the emergence of AI and its potential impact on businesses is a grave mistake. Embracing AI and understanding how it can benefit your industry or business is crucial for staying competitive in the rapidly evolving landscape.

Failing to adapt to AI-driven changes may result in being overtaken by competitors who have capitalized on the opportunities presented by this transformative technology. Just as Blockbuster Video and Kodak failed to acknowledge the threats to their core business models, businesses today must start planning for AI integration to ensure their long-term success.

Generative AI tools have opened up new possibilities for enhancing our own work and productivity. With tools like ChatGPT, professionals can leverage AI to generate drafts, outlines, and important points for reports and presentations. Creative fields, such as music and design, can benefit from generative AI tools that assist in creating videos, music, and images. While the output of these tools may not be perfect for finished work, they significantly speed up tasks like ideation and drafting, offering instant answers and advice on a wide range of topics.

As AI becomes increasingly intertwined with our lives, ensuring its ethical use and transparency is of paramount importance. Trust is the bedrock of AIs potential to address pressing global challenges, such as climate change and healthcare.

To establish trust, AI must be explainable, enabling users to understand the basis of its decisions. Moreover, ethical considerations are crucial to prevent biases and discrimination that may arise from biased or incomplete data. Addressing these challenges will pave the way for AIs positive impact on society.

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The field of education has been significantly influenced by AI advancements. AI-powered tools can revolutionize the way students learn and interact with educational content. Personalized learning experiences, adaptive assessments, and intelligent tutoring systems hold the potential to enhance student outcomes and engagement.

AI can also streamline administrative tasks, freeing up educators time to focus on individualized instruction and student support. However, it is essential to strike a balance between AI integration and human interaction to ensure a holistic and effective learning environment.

AI tools are poised to revolutionize the recruitment and admissions processes in the education sector. With AI-powered search engines and chatbots, educational institutions can enhance their outreach efforts and provide personalized support to prospective students. Rich search prompts based on student profiles, reduced response times for queries and applications, and personalized communication can significantly improve the recruitment experience. Leveraging AI in these areas enables institutions to better understand student needs, optimize their marketing strategies, and improve conversion rates.

While AI holds immense potential, it is not without limitations and challenges. Large language models like ChatGPT are prone to generating incorrect or nonsensical answers, highlighting the need for cautious interpretation of AI-generated content.

Concerns regarding the automation of propaganda and the spread of disinformation have also arisen. It is crucial to strike a balance between the benefits and potential risks associated with AI, ensuring that its development and deployment prioritize ethical considerations and address societal concerns.

Looking ahead, the future of AI is brimming with possibilities. As AI models continue to evolve and improve, we can expect even more powerful and sophisticated applications. OpenAIs GPT-4, with its potential for hundreds of billions of parameters, represents the ongoing advancements in AI capabilities.

While challenges and disruptions may arise on the path to artificial general intelligence, the potential benefits far outweigh the obstacles. It is through overcoming these challenges that we can unlock the full potential of AI and usher in an era of unprecedented innovation and progress.

The AI revolution is here, and it is transforming the way we live, work, and interact with technology. Rather than fearing AI or underestimating its impact, we must embrace this transformative technology and harness its potential for positive change.

By understanding the nuances of AI, exploring its applications, and prioritizing ethical considerations, we can navigate the AI era with confidence. Let us seize the opportunities presented by AI, shaping a future where human intelligence and AI coexist harmoniously to create a better world.

The AI revolution is not a distant dream; it is unfolding before our eyes. AIs ability to simulate human-like cognitive functions and augment our capabilities holds immense promise.

As AI becomes an integral part of various industries and sectors, understanding its potential, limitations, and ethical implications becomes imperative.

By embracing AI, we can unlock a world of possibilities and pave the way for a future where human ingenuity and AI-driven advancements coexist harmoniously. Let us embark on this journey together, shaping a future that harnesses the true potential of AI to create a better world for all.

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AI Revolution: Unleashing the Power of Artificial Intelligence in Our Lives - Medium

December 26, 2023 -In what seems like the blink of an eye, mentions of artificial intelligence (AI) have become ubiquitous in the healthcare industry.

From deep learning algorithms that can read computed tomography (CT) scans faster than humans tonatural language processing(NLP) that can comb through unstructured data in electronic health records (EHRs), the applications for AI in healthcare seem endless.

But like any technology at the peak of its hype curve, artificial intelligence faces criticism from its skeptics alongside enthusiasm from die-hard evangelists.

Despite its potential to unlock new insights and streamline the way providers and patients interact with healthcare data, AI may bring considerable threats ofprivacy problems, ethical concerns, and medical errors.

Balancing the risks and rewards of AI in healthcarewill require a collaborative effort from technology developers, regulators, end-users, and consumers.

READ MORE: Providers, Payers Sign Pledge for Ethical, Responsible AI in Healthcare

The first step will be addressing the highly divisive discussion points commonly raised when considering the adoption of some of the most complex technologies the healthcare world has to offer.

AI in healthcare will challenge the status quo as the industry adapts to new technologies. As a result, patient-provider relationships will be forever changed, and the idea that AI will change the role of human workers to some extent is worth considering.

Seventy-one percent of Americanssurveyed by Gallupin 2018 believed that AI will eliminate more healthcare jobs than it creates, with just under a quarter indicating that they believe the healthcare industry will be among the first to see widespread handouts of pink slips due to the rise of machine learning tools.

However, more recent data around occupational shifts and projected job growth dont necessarily bear this out.

A report published earlier this year by McKinsey & Co. indicates that AI could automate up to 30 percent of the hours worked by US employees by 2030, but healthcare jobs are projected to remain relatively stable, if not grow.

READ MORE: The Clinical Promise and Ethical Pitfalls of Electronic Phenotyping

The report notes that health aides and wellness workers will have anywhere from 4 to 20 percent more of their work automated, and health professionals overall can expect up to 18 percent of their work to be automated by 2030.

But healthcare employment demand is expected to grow 30 percent by then, negating the potential harmful impacts of AI on the healthcare workforce.

Despite these promising projections, fears around AI and the workforce may not beentirelyunfounded.

AI tools that consistently exceed human performance thresholds are constantly in the headlines, and the pace of innovation is only accelerating.

Radiologists and pathologists may be especially vulnerable, as many of themost impressive breakthroughsare happening aroundimaging analytics and diagnostics.

READ MORE: Ethical Artificial Intelligence Standards To Improve Patient Outcomes

In a 2021 report, Stanford University researchersassessedadvancements in AI over the last five years to see how perceptions and technologies have changed. Researchers found evidence of growing AI use in robotics, gaming, and finance.

The technologies supporting these breakthrough capabilities are also finding a home in healthcare, and physicians are starting to be concerned that AI is about to evict them from their offices and clinics. However, providers perceptions of AI vary, with some cautiously optimistic about its potential.

Recent years have seen AI-based imaging technologies move from an academic pursuit to commercial projects.Tools now exist for identifying a variety of eye and skin disorders,detecting cancers,and supporting measurements needed for clinical diagnosis, the report stated.

Some of these systems rival the diagnostic abilities of expert pathologists and radiologists, and can help alleviate tedious tasks (for example, counting the number of cells dividing in cancer tissue). In other domains, however, the use of automated systems raises significant ethical concerns.

At the same time, however, one could argue that there simply arent enough radiologists and pathologists or surgeons, or primary care providers, or intensivists to begin with. The US is facing a dangerousphysician shortage, especially in rural regions, and the drought is even worse in developing countries around the world.

AI may also help alleviatethe stresses of burnout that drive healthcare workers to resign. The epidemic affectsthe majority of physicians, not to mention nurses and other care providers, who are likely to cut their hours or take early retirements rather than continue powering through paperwork that leaves them unfulfilled.

Automating some of the routine tasks that take up a physicians time, such asEHR documentation, administrative reporting, or even triaging CT scans, can free up humans to focus on the complicated challenges of patients with rare or serious conditions.

Most AI experts believe that this blend of human experience and digital augmentation will be the natural settling point for AI in healthcare. Each type of intelligence will bring something to the table, andboth will work togetherto improve the delivery of care.

Some have raised concerns that clinicians may become over-reliant on these technologies as they become more common in healthcare settings, but experts emphasize that this is unlikely to occur, as automation bias isnt a new topic in healthcare, and there are existing strategies to prevent it.

Patients also appear to believe that AI will improve healthcare in the long run, despite some concerns about the technologys use.

A research letter published in JAMA Network Open last year that surveyed just under 1,000 respondents found that over half believed that AI would make healthcare either somewhat or much better. However, two-thirds of respondents indicated that being informed if AI played a big role in their diagnosis or treatment was very important to them.

Concerns about the use of AI in healthcare appear to vary somewhat by age, but research conducted by SurveyMonkey and Outbreaks Near Me a collaboration between epidemiologists from Boston Children's Hospital and Harvard Medical School shows that generally, patients prefer that important healthcare tasks, such as prescribing pain medication or diagnosing a rash, be led by a medical professional rather than an AI tool.

But whether patients and providers are comfortable with the technology or not, AI is advancing in healthcare. Many health systems are already deploying the tools across a plethora of use cases.

Michigan Medicine leveraged ambient computing a type of AI designed to create an environment that is responsive to human behaviors to further its clinical documentation improvement efforts in the midst of the COVID-19 pandemic.

Researchers from Mayo Clinic are taking a different AI approach: they aim to use the tech to improve organ transplant outcomes. Currently, these efforts are focused on developing AI tools that can prevent the need for a transplant, improve donor matching, increase the number of usable organs, prevent organ rejection, and bolster post-transplant care.

AI and other data analytics tools can also play a key role in population health management. A comprehensive strategy to manage population health requires that health systems utilize a combination of data integration, risk stratification, and predictive analytics tools. Care teams at Parkland Center for Clinical Innovation (PCCI) and Parkland Hospital in Dallas, Texas are leveraging some of these tools as part of their program to address preterm birth disparities.

Despite the potential for AI in healthcare, though, implementing the technology while protecting privacy and security is not easy.

AI in healthcare presents a whole new set of challenges around data privacy and security challenges that are compounded by the fact that most algorithms need access to massive datasets for training and validation.

Shuffling gigabytes of data between disparate systems is uncharted territory for most healthcare organizations, and stakeholders are no longer underestimating the financial and reputational perils of a high-profile data breach.

Most organizations are advised to keep their data assets closely guarded in highly secure, HIPAA-compliant systems. In light of anepidemic of ransomwareand knock-out punches from cyberattacks of all kinds, chief information security officers have every right to bereluctantto lower their drawbridges and allow data to move freely into and out of their organizations.

Storing large datasets in a single location makes that repository a very attractive target for hackers. In addition to AIs position as an enticing target to threat actors, there is a severe need for regulations surrounding AI and how to protect patient data using these technologies.

Experts caution that ensuring healthcare data privacy will require that existing data privacy laws and regulations be updated to include information used in AI and ML systems, as these technologies can re-identify patients if data is not properly de-identified.

However, AI falls into a regulatory gray area, making it difficult to ensure that every user is bound to protect patient privacy and will face consequences for not doing so.

In addition to more traditional cyberattacks and patient privacy concerns, a 2021 study by University of Pittsburgh researchers found thatcyberattacks using falsified medical images could fool AI models.

The study shed light on the concept of adversarial attacks, in which bad actors aim to alter images or other data points to make AI models draw incorrect conclusions. The researchers began by training a deep learning algorithm to identify cancerous and benign cases with more than 80 percent accuracy.

Then, the researchers developed a generative adversarial network (GAN), a computer program that generates false images by misplacing cancerous regions from negative or positive images to confuse the model.

The AI model was fooled by 69.1 percent of the falsified images. Of the 44 positive images made to look negative, the model identified 42 as negative. Of the 319 negative images doctored to look positive, the AI model classified 209 as positive.

These findings show not only how these types of adversarial attacks are possible, but also how they can cause AI models to make a wrong diagnosis, opening up the potential for major patient safety issues.

The researchers emphasized that by understanding how healthcare AI behaves under an adversarial attack, health systems can better understand how to make models safer and more robust.

Patient privacy can also be at risk in health systems that engage in electronic phenotyping via algorithms integrated into EHRs. The process is designed to flag patients with certain clinical characteristics to gain better insights into their health and provide clinical decision support. However, electronic phenotyping can lead to a series of ethical pitfalls around patient privacy, including unintentionally revealing non-disclosed information about a patient.

However, there are ways to protect patient privacy and provide an additional layer of protection to clinical data, like privacy-enhancing technologies (PETs). Algorithmic, architectural, and augmentation PETs can all be leveraged to secure healthcare data.

Security and privacy will always be paramount, but this ongoing shift in perspective as stakeholders get more familiar with the challenges and opportunities of data sharing is vital for allowing AI to flourish in ahealth IT ecosystem where data is siloed and access to quality information is one of the industrys biggest obstacles.

The thorniest issues in the debate about AI are the philosophical ones. In addition to the theoretical quandaries about who gets the ultimate blame for a life-threatening mistake, there are tangible legal and financial consequences when the word malpractice enters the equation.

Artificial intelligence algorithms are complex by their very nature. The more advanced the technology gets, the harder it will be for the average human to dissect the decision-making processes of these tools.

Organizations are already struggling with the issue of trust when it comes to heeding recommendations flashing on a computer screen, and providers are caught in the difficult situation of having access to large volumes of data but not feeling confident in the tools that are available to help them parse through it.

While some may assume that AI is completely free of human biases, these algorithms will learn patterns and generate outputs based on the data they were trained on. If these data are biased, then the model will be, too.

There are currently few reliable mechanisms to flag such biases.Black box artificial intelligence toolsthat give little rationale for their decisions only complicate the problem and make it more difficult to assign responsibility to an individual when something goes awry.

When providers arelegally responsiblefor any negative consequences that could have been identified from data they have in their possession, they need to be certain that the algorithms they use are presenting all of the relevant information in a way that enables optimal decision-making.

However, stakeholders are working to establish guidelines to address algorithmic bias.

In a 2021 report, the Cloud Security Alliance (CSA)suggested that the rule of thumb should be to assume that AI algorithms contain bias and work to identify and mitigate those biases.

The proliferation of modeling and predictive approaches based on data-driventechniques has helped to expose various social biases baked into real-world systems, and there is increasing evidence that the general public has concerns about the societal risks of AI, the report stated.

Identifying and addressing biases early in the problem formulation process is an important step to improving the process.

The White House Blueprint for an AI Bill of Rights and the Coalition for Health AI (CHAI)s Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare have also recently provided some guidance for the development and deployment of trustworthy AI, but these can only go so far.

Developers may unknowingly introduce biases to AI algorithms or train the algorithms using incomplete datasets. Regardless of how it happens, users must be aware of the potential biases and work to manage them.

In 2021, the World Health Organization (WHO) released thefirst global report on the ethics and governance of AI in healthcare. WHO emphasized the potential health disparities that could emerge as a result of AI, particularly because many AI systems are trained on data collected from patients in high-income care settings.

WHO suggested that ethical considerations should be taken into account during the design, development, and deployment of AI technology.

Specifically, WHO recommended that individuals working with AI operate under the following ethical principles:

Bias in AI is a significant negative, but one that developers, clinicians, and regulators are actively trying to change.

Ensuring that AI develops ethically, safely, and meaningfully in healthcarewill be the responsibility of all stakeholders: providers, patients, payers, developers, and everyone in between.

There are more questions to answer than anyone can even fathom. But unanswered questions are the reason to keep exploring not to hang back.

The healthcare ecosystem has to start somewhere, and from scratch is as good a place as any.

Defining the industrys approaches to AI is a significant responsibility and a golden opportunity to avoid some of the past mistakes and chart a better path for the future.

Its an exciting, confusing, frustrating, optimistic time to be in healthcare, and the continuing maturity of artificial intelligence will only add to the mixed emotions of these ongoing debates. There may not be any clear answers to these fundamental challenges at the moment, but humans still have the opportunity to take the reins, make the hard choices, and shape the future of patient care.

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Arguing the Pros and Cons of Artificial Intelligence in Healthcare - HealthITAnalytics.com