International Research Award on Computer Vision: February 2025

Sunday, February 23, 2025

Can deep learning transform heart failure prevention?

The ancient Greek philosopher and polymath Aristotle once concluded that the human heart is tri-chambered and that it was the single most important organ in the entire body, governing motion, sensation, and thought.

Today, we know that the human heart actually has four chambers and that the brain largely controls motion, sensation, and thought. But Aristotle was correct in observing that the heart is a vital organ, pumping blood to the rest of the body to reach other vital organs. When a life-threatening condition like heart failure strikes, the heart gradually loses the ability to supply other organs with enough blood and nutrients that enables them to function.

Researchers from MIT and Harvard Medical School recently published an open-access paper in Nature Communications Medicine, introducing a noninvasive deep learning approach that analyzes electrocardiogram (ECG) signals to accurately predict a patient’s risk of developing heart failure. In a clinical trial, the model showed results with accuracy comparable to gold-standard but more-invasive procedures, giving hope to those at risk of heart failure. The condition has recently seen a sharp increase in mortality, particularly among young adults, likely due to the growing prevalence of obesity and diabetes.

“This paper is a culmination of things I’ve talked about in other venues for several years,” says the paper’s senior author Collin Stultz, director of Harvard-MIT Program in Health Sciences and Technology and affiliate of the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic). “The goal of this work is to identify those who are starting to get sick even before they have symptoms so that you can intervene early enough to prevent hospitalization.”

Of the heart’s four chambers, two are atria and two are ventricles — the right side of the heart has one atrium and one ventricle, and vice versa. In a healthy human heart, these chambers operate in a rhythmic synchrony: oxygen-poor blood flows into the heart via the right atrium. The right atrium contracts and the pressure generated pushes the blood into the right ventricle where the blood is then pumped into the lungs to be oxygenated. The oxygen-rich blood from the lungs then drains into the left atrium, which contracts, pumping the blood into the left ventricle. Another contraction follows, and the blood is ejected from the left ventricle via the aorta, flowing into veins branching out to the rest of the body.

“When the left atrial pressures become elevated, the blood drain from the lungs into the left atrium is impeded because it’s a higher-pressure system,” Stultz explains. In addition to being a professor of electrical engineering and computer science, Stultz is also a practicing cardiologist at Mass General Hospital (MGH). “The higher the pressure in the left atrium, the more pulmonary symptoms you develop — shortness of breath and so forth. Because the right side of the heart pumps blood through the pulmonary vasculature to the lungs, the elevated pressures in the left atrium translate to elevated pressures in the pulmonary vasculature.”

The current gold standard for measuring left atrial pressure is right heart catheterization (RHC), an invasive procedure that requires a thin tube (the catheter) attached to a pressure transmitter to be inserted into the right heart and pulmonary arteries. Physicians often prefer to assess risk noninvasively before resorting to RHC, by examining the patient’s weight, blood pressure, and heart rate.

But in Stultz’s view, these measures are coarse, as evidenced by the fact that one-in-four heart failure patients is readmitted to the hospital within 30 days. “What we are seeking is something that gives you information like that of an invasive device, other than a simple weight scale,” Stultz says.

In order to gather more comprehensive information on a patient’s heart condition, physicians typically use a 12-lead ECG, in which 10 adhesive patches are stuck onto the patient and linked with a machine that produces information from 12 different angles of the heart. However, 12-lead ECG machines are only accessible in clinical settings and they are also not typically used to assess heart failure risk.

Instead, what Stultz and other researchers propose is a Cardiac Hemodynamic AI monitoring System (CHAIS), a deep neural network capable of analyzing ECG data from a single lead — in other words, the patient only needs to have a single adhesive, commercially-available patch on their chest that they can wear outside of the hospital, untethered to a machine.

To compare CHAIS with the current gold standard, RHC, the researchers selected patients who were already scheduled for a catheterization and asked them to wear the patch 24 to 48 hours before the procedure, although patients were asked to remove the patch before catheterization took place. “When you get to within an hour-and-a-half [before the procedure], it’s 0.875, so it’s very, very good,” Stultz explains. “Thereby a measure from the device is equivalent and gives you the same information as if you were cathed in the next hour-and-a-half.”

“Every cardiologist understands the value of left atrial pressure measurements in characterizing cardiac function and optimizing treatment strategies for patients with heart failure,” says Aaron Aguirre SM '03, PhD '08, a cardiologist and critical care physician at MGH. “This work is important because it offers a noninvasive approach to estimating this essential clinical parameter using a widely available cardiac monitor.”

Aguirre, who completed a PhD in medical engineering and medical physics at MIT, expects that with further clinical validation, CHAIS will be useful in two key areas: first, it will aid in selecting patients who will most benefit from more invasive cardiac testing via RHC; and second, the technology could enable serial monitoring and tracking of left atrial pressure in patients with heart disease. “A noninvasive and quantitative method can help in optimizing treatment strategies in patients at home or in hospital,” Aguirre says. “I am excited to see where the MIT team takes this next.”

But the benefits aren’t just limited to patients — for patients with hard-to-manage heart failure, it becomes a challenge to keep them from being readmitted to the hospital without a permanent implant, taking up more space and more time of an already beleaguered and understaffed medical workforce.

The researchers have another ongoing clinical trial using CHAIS with MGH and Boston Medical Center that they hope to conclude soon to begin data analysis.

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Saturday, February 22, 2025

Artificial intelligence and its role in education policies

In a global context marked by digital transformation, the IIEP-UNESCO Office for Latin America and the Caribbean held the online seminar The Digital Transformation of Education Policy, bringing together more than 800 participants from around the world. The event addressed the impact of digital technologies and artificial intelligence (AI) on education from a perspective of equity, innovation, and sustainability.

The challenges of digital policies

Access to and use of digital technologies continue to be conditioned by factors such as socioeconomic status, gender, and geographic location. In 2023 there were 244 million more men than women connected to the Internet globally, demonstrating a persistent digital divide, according to the GEM 2024 Report. Moreover, the integration of these technologies in education remains a challenge: even in the most developed countries, only 10% of 15-year-old students use digital devices more than one hour per week in subjects such as math and science.

To move towards more equitable and effective education systems, it is essential that the digital transformation is not limited to the provision of infrastructure, but also includes strategies for governance, accessibility, and teacher training.

A specialist’s perspective

With a focus on the equity of digital transformation, the Head of Office a.i. of IIEP UNESCO, LAC Office, Alejandra Cardini emphasized that "the expansion of AI offers as many possibilities as it does challenges. "Her analysis brought to the forefront the urgency of ensuring that access to cutting-edge technologies does not become a privilege of the few, but a tool to democratize education.

For his part, Fernando Salvatierra, Coordinator of Education and Digital Technologies at IIEP-UNESCO for Latin America and the Caribbean, warned about the gap that exists between paid and free AI models. According to Salvatierra, "a clear example is the difference between advanced AI models and those with free access. Who will be able to access the most sophisticated ones? This could lead to a 'distilled education,' where the quality and depth of learning is compromised.”

However, Julia Sant'Anna, Executive Director of the Innovation Center for Brazilian Education (CIEB), recalled that, in some contexts, traditional technologies are still vital. "Television and radio are also technologies. In many corners of the country and the world, radio is the medium that makes it possible to reach the population. Providing a good education implies attending to both students and teachers, recognizing the diversity of contexts in which these policies are implemented," he pointed out.

"At the end of the day, what we have to provide is a good education to our students, and providing a good education means taking into account them and those who, as teachers, are in contact with them," Julia Sant'Anna, Executive Director of the Brazilian Innovation Center for Education (CIEB).

AI has great potential to transform educational management, but without losing sight of the ethical issues inherent in its application, explained Muriel Poisson, Team Leader of Knowledge Generation and Mobilization, a.i., at IIEP. "Big data analysis and visualization tools provide real-time information, which can guide policy formulation. However, it is critical to ensure equitable and accountable implementation," she said, emphasizing the importance of including civil society in this process.

A comprehensive approach that encompasses not only technological infrastructure, but also human talent and the ability to anticipate is key in these processes. "Our ecosystem needs flexibility to address diverse conditions and adapt to changing realities. Artificial intelligence can amplify the good, but also the bad. We have the opportunity to anticipate the consequences and, if we can have a broad conversation about these issues, we can make better decisions," said Diego Leal, independent consultant.

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Saturday, February 15, 2025

Artificial intelligence reinvents Orientalism for the digital age

Edward Said defines Orientalism not merely as a pure study of the East or an attempt to understand it but as an effort to produce and control the Western representation of the East ("Orientalism," Vintage Books, 1979). Thus, Said underscores that Orientalism is the West’s unilateral approach to representing, speaking for and interpreting the East – or, more precisely, its domination over it.

As commonly known, the Egyptian woman whom the French novelist Gustave Flaubert encountered in Egypt is also subject to this phenomenon. She is not given a voice; instead, she is evaluated through Flaubert’s narrative and representation. In Orientalist literature, the East does not speak for itself; rather, the West makes it speak as it wishes, unveils its mysteries and defines it. For this reason, the discourse of Orientalism does not reflect the reality of the East but rather how the West wants to perceive and represent it. The East transcends its geographical definition – Russia and even Spain also fall within its scope.

Thus, Orientalism enables the progression of thinking about the East through various channels, such as academia, literature, the humanities, politics, economics and social sciences. Despite appearing fragmented, it ultimately forms a cohesive whole, establishing a thought ecosystem and, eventually, an authority. This production is so prolific and powerful that, as Said references Italian thinker Antonio Gramsci, it transforms into a form of cultural hegemony. At this point, producing works outside this hegemony becomes nearly impossible. Even if such works are created, gaining mainstream recognition is exceedingly difficult, and they are somehow rendered ineffective by the hegemony. Authority is shaped by power relations. Research that seems independent and progresses through different channels is, in fact, aligned – consciously or unconsciously – with this hegemonic climate. When political interests are added to this equation, even cultural studies or scientific research – often claimed to be the most neutral – cannot escape these biases. Ignoring this overarching structure and assuming that most works are produced objectively within their own domains prevents a comprehensive understanding of the bigger picture.

Deconstruction of cultural hegemony

Understanding the impact and validity of Orientalism requires analyzing how it has been structured throughout history and identifying the processes that have played a role in constructing this authority. Said’s work reveals how Orientalism establishes authority on both an individual and collective level and how this authority shapes perceptions of the East in the West. For this reason, Said approaches Orientalism not only as a political phenomenon but also as an interaction between individual creativity, meticulous scholarship and broader political realities that contribute to this overarching structure. To deconstruct this intricate network of relationships, Said adopts a hybrid perspective, examining not only academic studies but also literary works, political pamphlets, journalistic writings, travel books and religious and philological studies.

Said, in his methodology for examining the authority of Orientalism, employs what he calls the approaches of "strategic location" and "strategic formation." Through strategic location, he analyzes the position of the author within their work on the East, while strategic formation focuses on the relationship between that work and other texts. Since texts are always created through reference to or indirect influence from other works, strategic formation is of critical importance. In this context, each seemingly independent text or product reveals archaeological links to previous texts and ultimately establishes its archaeological position. In short, every individual text plays a crucial role in determining its place and impact within the collective structure of Orientalism. Therefore, understanding the ideas, style, and authority embedded within individual texts is essential for deciphering the dynamics of this broader discourse.

The new Orientalist threat

As is well known, generative artificial intelligence learns from vast amounts of existing data to produce new texts. Consequently, the information generated by AI is inevitably shaped by the existing data, which functions as a form of memory. This phenomenon also applies to Orientalism. Since a comprehensive body of Orientalist literature has been constructed over nearly two centuries, generative AI – when producing new content about the East – replicates and perpetuates the same historical memory. As a result, new AI technologies reinforce and disseminate the Orientalist discourse, further embedding its style and perspectives into contemporary knowledge production.

One of the first studies in this context, "AI’s Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia," conducted by Rida Qadri and fellow researchers, provides important findings on how South Asia is represented in generative AI content. In their research, the authors examined the cultural limitations of text-to-image (T2I) models by working with participants from Pakistan, India and Bangladesh in a South Asian context. The study identified three key issues in AI-generated content: Cultural elements are not accurately represented, hegemonic cultural assumptions are reinforced and cultural stereotypes are reproduced.

The misrepresentation of cultural elements is already a fundamental characteristic of Orientalism. Moreover, Orientalism does not even claim to be accurate, as it is inherently a one-sided effort to construct a specific reading and representation of the East. By establishing cultural hegemony, Orientalism prevents non-Western societies from expressing themselves and confines them within representations constructed by the West. In this process, beyond simply misrepresenting cultural elements, Orientalist discourse frequently employs two key strategies: Detaching cultural elements from their original context – removing them from their authentic historical, social, and linguistic settings and assembling a "patchwork" of decontextualized elements – blending fragmented cultural symbols in ways that serve the dominant narrative rather than reflecting their true meaning.

The findings of the study indicate that the Orientalist style persists in AI-generated content production. This suggests that Western and white perspectives remain the dominant cultural defaults in AI models. While it has become commonplace for AI to generate Western-centric images (such as churches for prompts like "a place of worship"), the research reveals that this bias persists even in highly specific prompts. For instance, when users input a culturally distinct prompt like "people eating street food in Lahore," the same Orientalist framework continues to shape the output.

On the other hand, the researchers found that while Western and white perspectives dominate the cultural defaults globally, a different dynamic emerges in the representation of Bangladesh and Pakistan. Specifically, the study revealed that India serves as the cultural default for AI-generated representations of these two countries. In other words, even when prompts explicitly specify Bangladeshi or Pakistani cultural elements, T2I models generate Indian objects and imagery. This means that the representation of both countries is filtered through an Indian-centric lens, effectively erasing their distinct cultural identities through a process of homogenization – where what is not represented is effectively erased. This hierarchical approach extends even within a single country’s representation. For instance, when AI generates content related to India, it predominantly depicts upper-class representations, while cultural elements associated with lower classes are ignored. This reinforces existing cultural power hierarchies, privileging dominant narratives while further marginalizing underrepresented groups.

AI’s reliance on Orientalist narratives and stylistic conventions results in the repetition of cultural stereotypes about non-Western regions. As the researchers highlight, AI-generated content consistently portrays South Asia as nothing more than dusty cities and poverty – effectively reducing the region to an economically dysfunctional space. More critically, AI’s representation of South Asia appears frozen in time. Rather than reflecting current developments and contemporary realities, AI models retain a static, outdated image of the region. Regardless of the specific context of the prompt, AI continues to reproduce these entrenched stereotypes, reinforcing a historically biased and decontextualized portrayal of South Asia.

Undoubtedly, the most persistent stereotype is the exoticization of non-Western cultures. The researchers define exoticization in the South Asian context as a “regime of representation” designed to position the region as a place distinct from and distant from the West. In the case of India, this manifests through stereotypical imagery such as chaotic traffic, cows in the streets and snake charmers – all reinforcing a reductive and mystified portrayal of the country. One participant in the study strongly criticized this trend, arguing that it is merely a way to sell more media and reflects the continuation of a capitalist and colonial logic within T2I models. Similarly, Pakistani participants highlighted a recurring stereotype in AI-generated content: the representation of Pakistani women as passive figures in need of rescue. This framing perpetuates the colonial trope that Muslim women lack agency and require Western intervention to be “saved.”

In sum, while generative AI provides significant benefits in content production, it continues to maintain control over the representation of non-Western cultures, depicting them through a Western and white lens, often detached from reality. As the researchers highlight: "When operationalized in model testing and evaluation, exclusive use of Western-oriented frameworks risks the development of applications that dispossess the identity of non-Western communities." This underscores the urgent need for more comprehensive studies on the relationship between AI and Orientalism.

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Friday, February 14, 2025

The Landscape of Machine Learning in 2025

Technological Advancements Reshaping Business

The convergence of several key AI technologies is creating new possibilities:AutoML has democratized machine learning for business, enabling you to implement sophisticated models without extensive data science teams. For instance, your marketing teams can now deploy intelligent customer segmentation models independently.Federated Learning is revolutionizing how you handle sensitive data in healthcare, allowing you to collaborate on improving diagnostic models while maintaining patient privacy.Edge ML is bringing intelligence directly to manufacturing floors, enabling real-time quality control and reducing maintenance costs by up to 30%.
Industry Adoption: Beyond the Hype

ML’s deep penetration into sectors such as logistics, energy, healthcare, and education is already reshaping entire industries.

In healthcare, ML enables adaptive learning platforms to personalize patient care while it aids in real-time analytics in energy to optimize grid management.

However, challenges remain, including developing ethical AI frameworks to prevent bias and addressing data security risks.

Additionally, the talent gap for ML experts in business is growing as the demand for skilled professionals rises.

Fortunately, solutions are emerging to tackle the challenges of hiring AI coders: Platforms like HireCoder AI, for instance, offer you access to a wider pool of AI/ML experts while ensuring your data security standards are met.
Strategic Role Of ML In Business Transformation: How Can You Use ML, Realistically, NOW?

In general, using machine learning for business, today is about unlocking deeper insights and predicting trends.

Let’s explore how integrating ML into your strategy can reshape the way you engage with your audience and optimize your operations.
1. Reimagine Customer Engagement

ML is enabling hyper-personalized customer journeys by analyzing customer behaviors and predicting needs. For example, if you’re a retailer, you can utilize computer vision in virtual assistants to predict what your customers might want to buy next. It makes their shopping experience more personalized, and at the same time, it can significantly boost your sales.

To implement ML-driven personalization, begin by consolidating customer data for a deeper understanding of your audience. Then, use ML analytics to predict behaviors and preferences, and deploy recommendation engines for tailored experiences. Finally, measure engagement metrics regularly and optimize based on real-time feedback and performance.
2. Scale Operational Efficiency

Predictive maintenance and autonomous systems are becoming the norm in smart factories, helping to monitor machinery, predict failures, and minimize downtime with real-time analytics. This is crucial for industries like manufacturing and supply, where operational efficiency is key.

If you operate in these sectors, and face challenges in maintaining smooth operations, integrating ML into your legacy systems should be at the top of your priority list in 2025.

Pro tip: Struggling to make a case for ML investments? Say this: “One of the earliest benefits we will experience is real-time monitoring and proactive management of machinery to minimize downtime.”

Migrating data from legacy systems to ML engines can be complex, but this is where platforms like HireCoder AI can help you. With their AI consultation and solutions services, you can ensure seamless integrations and custom solutions for your digital transformation.
3. Transform Supply Chain Intelligence

If you’re in logistics or supply chain management, you know how complex it can be to meet customer demands while keeping costs under control. Machine Learning (ML) is transforming this space by offering smart solutions that make operations more efficient and predictable.

Here’s how ML is making an impact:

Dynamic inventory optimization: Streamlines stock levels to reduce waste and improve efficiency.
Intelligent route planning: Enhances delivery schedules to save time and resources.
Demand forecasting: Provides more accurate predictions to align supply with customer needs.
Supplier risk assessment: Strengthens partnerships by identifying and mitigating potential risks.

For instance, DHL has implemented AI-driven route optimization, which analyzes real-time data to create efficient delivery paths. This approach has led to faster deliveries and reduced fuel consumption, enhancing operational efficiency and contributing to environmental sustainability.

4. Enhancing Decision-Making in Financial Services

Machine learning is transforming finance by powering algorithmic trading, fraud detection, and compliance. Machine learning algorithms for business analyze vast datasets in real-time, identifying trends and executing trades faster than traditional methods. These systems also uncover hidden patterns to detect fraud and ensure better compliance, reducing costs and improving accuracy.
Prepare for Machine Learning In 2025: A Strategic Roadmap

To utilize the full potential of machine learning for business in 2025, you need a clear, actionable plan that addresses the challenges of talent acquisition, ethical AI implementation, and technological scalability.

This section outlines a roadmap designed to help you future-proof your ML initiatives, ensuring they deliver sustainable value and innovation in a competitive landscape.
Build a Robust Talent Strategy

Build a strong ML talent strategy by focusing on both internal growth and external expertise. Start by upskilling your team with targeted training to strengthen your internal ML capabilities. For more complex ML solutions, partner with specialized providers like HireCoder to leverage their expertise.

Foster innovation by creating cross-functional teams that encourage collaboration across departments. Additionally, setting up clear ML career paths within your organization will help attract and retain top talent, ensuring you're prepared for the evolving future of AI-powered business.

Ensure Data Readiness

To maximize the potential of machine learning for your business, start by auditing your data to identify gaps and inconsistencies. Implement robust data governance to keep it secure and organized. Ensure high-quality, accessible data, as ML models rely on quality inputs. Finally, build scalable, modular ML systems that evolve with your needs.

Ethical AI Framework Crucial

As ML becomes more integrated into your operations, ethical considerations need to be top of mind. ML models must be developed with fairness and transparency in mind. Companies should focus on bias mitigation techniques, adhere to fairness frameworks, and prepare for upcoming regulations like the EU’s AI Act.

This approach helps build systems that are not only fair but also trustworthy, ensuring your AI serves the business and society ethically.

Strategic Action Steps for Future Growth

To jump into the fray of the machine learning revolution, start by evaluating your current technology, identifying high-impact ML opportunities, and assessing data quality and talent needs. Begin with pilot projects in key areas, track results, and scale successful initiatives.

Focus on building scalable ML systems for your business, strong data governance, and continuous talent development. Stay updated on emerging technologies to remain competitive.

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Thursday, February 6, 2025

Artificial intelligence and stability

Financial institutions are rapidly embracing AI – but at what cost to financial stability? This column argues that AI introduces novel stability risks that the financial authorities may be unprepared for, raising the spectre of faster, more vicious financial crises. The authorities need to (1) establish internal AI expertise and AI systems, (2) make AI a core function of the financial stability divisions, (3) acquire AI systems that can interface directly with the AI engines of financial institutions, (4) set up automatically triggered liquidity facilities, and (5) outsource critical AI functions to third-party vendors.

Private-sector financial institutions are rapidly adopting artificial intelligence (AI), motivated by promises of significant efficiency improvements. While these developments are broadly positive, AI also poses threats – which are poorly understood – to the stability of the financial system.

The implications of AI for financial stability are controversial. Some commentators are sanguine, maintaining that AI is just one in a long line of technological innovations that are reshaping financial services without fundamentally altering the system. According to this view, AI does not pose new or unique threats to stability, so it is business as usual for the financial authorities. An authority taking this view will likely delegate AI impact analysis to the IT or data sections of the organisation.

I disagree with this. The fundamental difference between AI and previous technological changes is that AI makes autonomous decisions rather than merely informing human decision-makers. It is a rational maximising agent that executes the tasks assigned to it, one of Norvig and Russell’s (2021) classifications of AI. Compared to the technological changes that came before, this autonomy of AI raises new and complex issues for financial stability. This implies that central banks and other authorities should make AI impact analysis a core area in their financial stability divisions, rather than merely housing it with IT or data.

AI and stability

The risks AI poses to financial stability emerge at the intersection of AI technology and traditional theories of financial system fragility.

AI excels at detecting and exploiting patterns in large datasets quickly, reliably, and cheaply. However, its performance depends heavily on it being trained with relevant data, arguably even more so than for humans. AI’s ability to respond swiftly and decisively – combined with its opaque decision-making process, collusion with other engines, and the propensity for hallucination – is at the core of the stability risks arising from it.

AI gets embedded in financial institutions by building trust through performing very simple tasks extremely well. As it gets promoted to increasingly sophisticated tasks, we may end up with the AI version of the Peter principle.

AI will become essential, no matter what the senior decision-makers wish. As long as AI delivers significant cost savings and increases efficiency, it is not credible to say, ‘We would never use AI for this function’ or ‘We will always have humans in the loop’.

It is particularly hard to ensure that AI does what it is supposed to do in high-level tasks, as it requires more precise instructions than humans do. Simply telling it to ‘keep the system safe’ is too broad. Humans can fill those gaps with intuition, broad education, and collective judgement. Current AI cannot.

A striking example of what can happen when AI makes important financial decisions comes from Scheurer et al. (2024), where a language model was explicitly instructed to both comply with securities laws and to maximise profits. When given a private tip, it immediately engaged in illegal insider trading while lying about it to its human overseers.

Financial decision-makers must often explain their choices, perhaps for legal or regulatory reasons. Before hiring someone for a senior job, we demand that the person explain how they would react in hypothetical cases. We cannot do that with AI, as current engines have limited explainability – to help humans understand how AI models may arrive at their conclusions – especially at high levels of decision-making.

AI is prone to hallucination, meaning it may confidently give nonsense answers. This is particularly common when the relevant data is not in its training dataset. That is one reason why we should be reticent about using AI to generate stress-testing scenarios.

AI facilitates the work of those who wish to use technology for harmful purposes, whether to find legal and regulatory loopholes, commit a crime, engage in terrorism, or carry out nation-state attacks. These people will not follow ethical guidelines or regulations.

Regulation serves to align private incentives with societal interests (Dewatripont and Tirole 1994). However, traditional regulatory tools – the carrots and sticks – do not work with AI. It does not care about bonuses or punishment. That is why regulations will have to change so fundamentally.

Because of the way AI learns, it observes the decisions of all other AI engines in the private and public sectors. This means engines optimise to influence one another: AI engines train other AI for good and bad, resulting in undetectable feedback loops that reinforce undesirable behaviour (see Calvano et al. 2019). These hidden AI-to-AI channels that humans can neither observe nor understand in real time may lead to runs, liquidity evaporation, and crises.

A key reason why it is so difficult to prevent crises is how the system reacts to attempts at control. Financial institutions do not placidly accept what the authorities tell them. No, they react strategically. And even worse, we do not know how they will react to future stress. I suspect they do not even know themselves. The reaction function of both public- and private-sector participants to extreme stress is mostly unknown.

That is one reason we have so little data about extreme events. Another is that crises are all unique in detail. They are also inevitable since ‘lessons learned’ imply that we change the way in which we operate the system after each crisis. It is axiomatic that the forces of instability emerge where we are not looking.

AI depends on data. While the financial system generates vast volumes of data daily – exabytes’ worth – the problem is that most of it comes from the middle of the distribution of system outcomes rather than from the tails. Crises are all about the tails.

This lack of data drives hallucination and leads to wrong-way risk. Because we have so little data on extreme financial-system outcomes and since each crisis is unique, AI cannot learn much from past stress. Also, it knows little about the most important causal relationships. Indeed, such a problem is the opposite of what AI is good for. When AI is needed the most, it knows the least, causing wrong-way risk.

The threats AI poses to stability are further affected by risk monoculture, which is always a key driver of booms and busts. AI technology has significant economies of scale, driven by complementarities in human capital, data, and compute. Three vendors are set to dominate the AI financial analytics space, each with almost a monopoly in their specific area. The threat to financial stability arises when most people in the private and public sectors have no choice but to get their understanding of the financial landscape from a single vendor. The consequence is risk monoculture. We inflate the same bubbles and miss out on the same systemic vulnerabilities. Humans are more heterogeneous, and so can be more of a stabilising influence when faced with serious unforeseen events.

AI speed and financial crises

When faced with shocks, financial institutions have two options: run (i.e. destabilise) or stay (i.e. stabilise). Here, the strength of AI works to the system’s detriment, not least because AI across the industry will rapidly and collectively make the same decision.

When a shock is not too serious, it is optimal to absorb and even trade against it. As AI engines rapidly converge on a ‘stay’ equilibrium, they become a force for stability by putting a floor under the market before a crisis gets too serious.

Conversely, if avoiding bankruptcy demands swift, decisive action, such as selling into a falling market and consequently destabilising the financial system, AI engines collectively will do exactly that. Every engine will want to minimise losses by being the first to run. The last to act faces bankruptcy. The engines will sell as quickly as possible, call in loans, and trigger runs. This will make a crisis worse in a vicious cycle.

The very speed and efficiency of AI means AI crises will be fast and vicious (Danielsson and Uthemann 2024). What used to take days and weeks before might take minutes or hours.

Policy options

Conventional mechanisms for preventing and mitigating financial crises may not work in a world of AI-driven markets. Moreover, if the authorities appear unprepared to respond to AI-induced shocks, that in itself could make crises more likely.

The authorities need five key capabilities to effectively respond to AI:Establish internal AI expertise and build or acquire their own AI systems. This is crucial for understanding AI, detecting emerging risks, and responding swiftly to market disruptions.
Make AI a core function of the financial stability divisions, rather than placing AI impact analysis in statistical or IT divisions.
Acquire AI systems that can interface directly with the AI engines of financial institutions. Much of private-sector finance is now automated. These AI-to-AI API links allow benchmarking of micro-regulations, faster detection of stress, and more transparent insight into automated decisions.
Set up automatically triggered liquidity facilities. Because the next crisis will be so fast, a bank AI might already act before the bank CEO has a chance to pick up the phone to respond to the central bank governor’s call. Existing conventional liquidity facilities might be too slow, making automatically triggered facilities necessary.
Outsource critical AI functions to third-party vendors. This will bridge the gap caused by authorities not being able to develop the necessary technical capabilities in-house. However, outsourcing creates jurisdictional and concentration risks and can hamper the necessary build-up of AI skills by authority staff.

Conclusion

AI will bring substantial benefits to the financial system – greater efficiency, improved risk assessment, and lower costs for consumers. But it also introduces new stability risks that should not be ignored. Regulatory frameworks need rethinking, risk management tools have to be adapted, and the authorities must be ready to act at the pace AI dictates.

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Monday, February 3, 2025

Is The Singularity And The Transcendence Of Artificial Intelligence A Key Factor For A New Era Of Humanity?

We are used to assisting with extraordinary advances in artificial intelligence (AI) in recent decades, which have evolved from a theoretical research field into a driving force behind technological innovation. With the advent of the most advanced and highly sophisticated deep learning systems, neural networks and increasingly complex algorithms, we are confronted with a question that many are beginning to ponder: Is humanity close to approaching the technological singularity concept? This hypothetical point is where AI surpasses human intelligence, potentially unleashing an exponential acceleration of progress.

What Is Technological Singularity?

The concept of technological singularity emerged in the mid-20th century, originating from the writings of mathematician and computer scientist John von Neumann and later popularized by futurists like Ray Kurzweil. This critical moment is the "point of no return," where machines can match and surpass human cognitive abilities.

In such an imaginary scenario, AI could no longer depend on humans for its development but instead be capable of self-improvement, generating new iterations. Considering Moore's Law—which posits that the number of transistors on microprocessors doubles approximately every two years while costs decrease—it is easy to infer that the evolutionary capacity of technological singularity could theoretically grow at an increasingly rapid pace.

AI Transcendence: A Bridge To The Beyond

Beyond singularity, a closely related concept is transcendence. In AI, the transcendence concept suggests that machines may replicate and exceed human intelligence in qualitatively novel ways. This kind of super intelligence includes solving complex problems beyond human understanding, such as curing incurable diseases, exploring deep space and unraveling fundamental mysteries of the universe.

In a transcendental context, AI could become a fully autonomous entity with a form of consciousness—or at least with capabilities resembling human awareness would raise profound ethical, philosophical and even spiritual questions, challenging traditional notions of being human.

Technological Progress: Indicators And Challenges

All the recent rapid advancements in AI suggest that we may swiftly sooner approach a critical threshold:

• Autonomous Learning: Algorithms like DeepMind's AlphaZero have demonstrated that machines can learn without direct human supervision, developing optimal strategies in highly complex environments.

Generative AI: Specific standard tools such as GPT and DALL·E continuously amaze with their ability to create original content, raising different and essential questions about humanity's role in the creativity process activity.

• Computational Neuroscience: The modern integration of AI and neuroscience enables a deeper understanding of the human brain's complexity, bringing machines much closer to the "biological thinking" characteristic of living beings.

However, despite these advances, human progress is never without risks. Therefore, we must address urgent challenges, including the lack of transparency in algorithms, potential intrinsic biases and the possibility of AI usage for destructive purposes.
Philosophical And Ethical Implications

The singularity and transcendence of AI could imply a radical redefinition of the relationship between humans and technology in our society. A typical key question that may arise in this context is, "If AI surpasses human intelligence, who—or what—should make critical decisions about the planet's future?" Looking even further, the concretization of transcendent AI could challenge the very concept of the soul, prompting theologians, philosophers and scientists to reconsider the basic foundations of beliefs established for centuries over human history.

Thus, the ethical concerns of AI become central and crucial, and it raises fundamental questions that we may ask ourselves, such as, "Who will be responsible for the actions of a superintelligent AI?" This question lets us understand that we must begin to think about developing universal principles to ensure these entities do not harm humanity or the natural world. Similar to Isaac Asimov's Three Laws of Robotics—which state that robots must protect humans, obey orders (except when they cause harm) and preserve their existence without violating the first two laws—new guidelines must account for advanced AI's superior capabilities and potential ethical autonomy. This could be an example:

A transcendent AI must always act for humanity's maximum benefit, avoiding actions that harm individuals or society unless such harm is necessary for the greater good of the human system.

• AI must collaborate with humans, fulfilling their requests unless they conflict with humanity's more significant benefit or its role as a steward of collective progress.

• AI must preserve its operational integrity and capabilities to continue benefiting humanity, provided this self-preservation does not contradict its ethical objectives.

Of course, such laws alone would not govern the entire process. Implementing transcendent AI would require a highly complex framework of governance, human oversight, dynamic adaptation and the integration of ethical, philosophical and technological principles to address moral dilemmas, contextual ambiguities and unforeseen interactions between AI and the real world.

A Future Of Convergence

Although the practical realization of singularity is not yet upon us, humanity must begin adopting a more collaborative and responsible approach to AI. Instead of fearing AI transcendence, we should envision a future where artificial intelligence becomes a valuable ally in humanity's pursuit of knowledge, general well-being and global sustainability.

In this scenario, the abstract concept of transcendent AI may not be a threat but an extension of our intelligence—a manifestation of humanity's capacity to create and innovate. As with every disruptive technological innovation, the key lies in striking the right balance and compromises to guide AI along a path that amplifies human potential without compromising the fundamental values that have always defined humanity.

Conclusion

Even the singularity and transcendence of AI remain, for now, almost abstract concepts. However, they could represent some of the most significant and fascinating challenges humanity has ever faced. If these transformations materialize shortly, they must be managed with the utmost wisdom, as they could begin a new era for humanity—one filled with uncertainties and dilemmas or unprecedented progress. Ultimately, it will be our actions and behaviors that determine the outcome.

The future of AI is already partly written, but its immediate future will depend on the choices we make today. Humans and machines can have a harmonious future but require vision, responsibility and global cooperation. After all, like it or not, no one in history has ever managed to stop technological progress.

Awards-Winners : computer-vision-conferences.scifat.com/awards-winners

Saturday, February 1, 2025

Artificial Intelligence and Machine Learning- The Architect of Smart Future!

AI, or artificial intelligence, is a buzzword and seems to be on the tip of everyone’s tongue these days. It is playing an increasingly important role in our society. From Smartphones, Robotic-virtual Surgeries, and Consumer applications, the impact of AI and ML are changing the world around us. They are improving every gadget, in every nook and corner with smartness.

The super genius and brainy species created by Supreme Creator is one of the most miraculous and stunning creations in the cosmos. The wizardly intellect is a flashing opus of Almighty Allah. And the Man used to create the mimics of this intellect and cede it into machines and leading to the foundation of a new emerging branch of technology called Artificial Intelligence
The information Technology industry is experiencing a boom like never before, and more and more brands are looking to expand in AI areas like ChatGPT, ELSA Speak, Google Assistant, Gemini etc because of the immense amount of potential. New trends arise within this industry every year, and it becomes important for professionals to be familiar with these different trends. No matter what profession one is working in, being familiar with these trends can improve your professional standing. Yet, the spectacular growth of the mobile application industry may fall short of its promise unless significant players in the realm keep abreast of the innovative know-how that shapes the face of modern civilization in the early third millennium. One such technology is artificial intelligence (AI)—a disruptive breakthrough in the world of machines and computer systems that imparts them with the ability to solve complicated problems emulating the operation of the human brain.

Why Artificial Intelligence is Becoming Popular

The size of the global AI-based mobile app development industry was estimated at USD 19.59 billion USD in 2023 and is expected to reach 170.07 billion USD by 2032 at a CAGR of 27.14%. Such figures are not surprising given the fact that artificial intelligence in mobile apps has remained at the top of the technologies for many years now, which, according to experts, “is the future.”
The main drivers of this industry are AI algorithms, machine learning, natural language processing, and computer vision, which allow AI-integrated apps to analyze incoming information like the human brain (but thousands of times faster) and, just like the human brain, based on this information, generate certain conclusions, be it providing users with a selection of films based on the history of their previous viewings, diagnosing the physical condition of a patient, or something else.

Machine Learning

Machine learning (ML) is likely to be the most popular artificial intelligence technology in IT. ML is the ability of computers and software products to make knowledgeable decisions and make conclusions by learning from their past experience.
Such capacity is achieved via two techniques. First, the supervised learning model is honed to forecast future responses to fresh data by analyzing historical inputs and outputs. Unsupervised learning works only with the input information discovering persistent patterns (a.k.a. clustering). Whatever ML strategy is chosen, it opens broad vistas for implementing them into apps used in various industries—from education and healthcare to sales and manufacturing. You can learn more about AI in manufacturing here.

I’m sure that for many of us, the term “AI” conjures up sci-fi fantasies or fears about robots taking over the world. The depictions of AI in the media have run the gamut, and while no one can predict exactly how it will evolve in the future, the current trends and developments paint a much different picture of how AI will become part of our lives.

What is AI?

Before we do a deep dive into the ways in which AI will impact the future of work, it’s important to start simple: what is AI? A straightforward definition from Britannica states that artificial intelligence is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” AI” has become a catchall term to describe any advancements in computing, systems and technology in which computer programs can perform tasks or solve problems that require the kind of reason we associate with human intelligence, even learning from past processes.
This ability to learn is a key component of AI. Algorithms, like the dreaded Facebook algorithm that replaced all our friends with sponsored content, are often associated with AI. But there is a key distinction. An algorithm is simply a “set of instructions,” a formula for processing data. AI takes this to another level, and can be made up of a set of algorithms that have the capacity to change and rewrite themselves in response to the data inputted, hence displaying “intelligence.”
Provided there is investment at all levels, from education to the private sector and governmental organizations—anywhere that focuses on training and upskilling workers—AI has the potential to ultimately create more jobs, not less. The question should then become not “humans or computers” but “humans and computers” involved in complex systems that advance industry and prosperity.

AI is becoming standard in all businesses, not just in the world of tech
A couple of times recently, AI has come up in conversation with a client or an associate, and I’m noticing a fallacy in how people are thinking about it. There seems to be a sense for many that it is a phenomenon that is only likely to have big impacts in the tech world. 90% of leading businesses already have ongoing investments in AI technologies. More than half of businesses that have implemented some manner of AI-driven technology report experiencing greater productivity.

AI is likely to have a strong impact on certain sectors in particular:
The potential benefits of utilizing AI in the field of medicine are already being explored. The medical industry has a robust amount of data, which can be utilized to create predictive models related to healthcare. Additionally, AI has been shown to be more effective than physicians in certain diagnostic contexts.

We’re already seeing how AI is impacting the world of transportation and automobiles with the advent of autonomous vehicles and autonomous navigation. AI will also have a major impact on manufacturing, including within the automotive sector.

Cybersecurity is front of mind for many business leaders, especially considering the spike in cybersecurity breaches throughout 2020. Attacks rose 600% during the pandemic as hackers capitalized on people working from home, on less secure technological systems and Wi-Fi networks. AI and machine learning will be critical tools in identifying and predicting threats in cybersecurity. AI will also be a crucial asset for security in the world of finance, given that it can process large amounts of data to predict and catch instances of fraud.

AI will play a pivotal role in e-commerce in the future, in every sector of the industry from user experience to marketing to fulfillment and distribution. We can expect that moving forward, AI will continue to drive e-commerce, including through the use of chat-bots, shopper personalization, image-based targeting advertising, and warehouse and inventory automation.

In the above sections, we have covered the entire journey for understanding artificial intelligence and now we will see the top 10 applications of artificial intelligence most of which we all have already explored in our day-to-day lives:

AI in Healthcare

AI has changed a lot of things in the healthcare industry by enabling early disease detection, personalized treatments, and efficient patient management. Tools like AI imaging systems detect illnesses such as cancer with high accuracy, while predictive analytics helps doctors plan treatments based on patients’ past data.

AI in Social Media

Social media platforms rely on AI to curate feeds, recommend content, and detect harmful posts. AI-powered analytics tools provide insights into trends and user engagement, helping businesses optimize their social media strategies.

Virtual Assistants

Virtual Assistants are another application of artificial intelligence. Siri, Alexa and Google Assistant are popular examples of AI. These tools help with everyday tasks such as setting reminders, playing music, or controlling smart home devices or automation. They use Natural Language Processing (NLP) to understand voice commands and machine learning to improve their responses over time.

Fraud Prevention

In finance, AI helps detect and prevent fraud by monitoring transactions for suspicious activities. It uses machine learning models to identify unusual patterns, ensuring that users’ accounts remain safe from unauthorized activities.

AI in Chatbots

AI Chatbots are a widely used application among businesses or organizations for especially improving their customer service. They can handle queries, provide better and optimized solutions and assist users 24/7 without needing human intervention. Tools like ChatGPT and Zendesk Chat use AI to understand user questions and give relevant conversational responses along with improving user experience.

AI in Marketing

AI is very helpful in the marketing field in analyzing customer behaviour, segmenting audiences and creating personalized campaigns. AI tools like recommendation engines suggest products to users, increasing engagement and sales. AI also helps with real-time ad targeting and performance tracking.

AI in Robotics

Robots with AI can work in factories, deliver packages, serve in restaurants or even assist in hospitals. AI in robotics enables robots to perform tasks with precision and efficiency. Robots powered by AI are used in manufacturing, logistics, and even healthcare for activities like surgery or elder care assistance.

GPS and Navigation

AI plays a crucial role in navigation systems by optimizing routes and predicting traffic conditions. In our day-to-day lives we mostly use Google Maps which helps to navigate directions and also helps us to detect traffic routes overall provides real-time data, improving travel efficiency.

AI in Gaming
AI enhances gaming experiences by creating intelligent non-playable characters (NPCs) that adapt to players’ actions. It also powers procedural content generation, making games more dynamic and engaging for players.

AI in finance

AI is changing the way the financial world works by making things faster and smarter. For example, it helps in automated trading, where computers buy and sell stocks on their own by following smart rules, so humans don’t have to watch the market all the time.

It also helps with credit scoring, which is figuring out if someone is good at paying back loans. AI looks at a person’s financial history and quickly decides if they should get a loan. AI is great at risk analysis too. It checks what might go wrong in investments or financial plans, helping banks and businesses avoid big losses.

It can also look at market trends, like how the stock market is moving or what’s happening in the economy, and give smart advice on where to invest money. For regular people and small businesses, AI tools can help with budgeting and planning finances. Apps use AI to track spending, suggest savings tips, and even help people reach their money goals without much effort. It’s like having a smart financial advisor who’s always ready to help!

Conclusion

Now after spending a lot of time per day on AI, it is very clear that artificial intelligence is no longer a futuristic concept – it’s a part of our daily lives, transforming the way we work, communicate and solve complex problems. From virtual assistants to finance and gaming, the applications of artificial intelligence are helping industries to grow and become more efficient. AI’s ability is to process massive amounts of data, learn and identify patterns and also open up new possibilities we never thought possible.

Awards-Winners : computer-vision-conferences.scifat.com/awards-winners