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Neural Computing and Applications •

A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs

While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a variational quantum circuit could be integrated into a classical neural network for the problem of detecting pneumonia from chest radiographs. Our work serves as an experimental demonstration of the potential of quantum computing to significantly improve neural network performance for real-world, non-trivial problems relevant to society and industry.
The Hindu •

GPT-4 — a shift from ‘what it can do’ to ‘what it augurs’

Do you want help to prepare for the bar examination, plan a birthday party, or even translate Ukrainian to Punjabi? A single artificial intelligence (AI) model can do it all. A U.S. company, OpenAI, has once again sent shock waves around the world, this time with GPT-4, its latest AI model. This large language model can understand and produce language that is creative and meaningful, and will power an advanced version of the company’s sensational chatbot, ChatGPT.
The Wire •

We Must Build AI That Can Dance With Us, Not Replace Us

AI researchers’s primary goal was to improve the autonomy of machines. But we don’t really need AI to be autonomous. We need AI to be reliable and trustworthy. Humans excel at some things and computers excel at others. We need systems that bring out the best in both – so that their combination is more effective than either alone. Achieving this requires a paradigm shift in the mindsets of both AI developers and its eventual users, beginning with the way we measure effectiveness.
The Wire •

In Erik Larson’s New Book, a Cogent Case Against the Inevitability of True AI

Over the last 75 years, many scientists, engineers and entrepreneurs have told us again and again that intelligent computers that can really think are just around the corner. Looking at the enormous hype the field enjoys today, one would be hard-pressed to not believe it. This is inevitable and only a matter of time. But, is it? I review Erik Larson's new book 'The Myth of Artificial Intelligence' for The Wire Science.
arXiv •

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

The use of machine learning to develop intelligent software tools for interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice.
The Wire •

The Inconvenient Truth About Quantum Computing

We are in the middle of what the journal Nature has called the “quantum gold rush”. Governments around the world are ramping up their investments in quantum computing. Venture capitalists are pouring billions of dollars into startups sprouting out of university departments. Established technology companies like IBM, Google, Microsoft, Intel, Amazon and Honeywell have recruited highly qualified teams to build quantum computers.
The Quint •

Our Faustian Bargain With Artificial Intelligence

In the popular German legend, the protagonist Faust strikes a deal with the Devil and trades his soul for unlimited knowledge and pleasure. In today’s age of the Internet and artificial intelligence, we have struck an equally dangerous bargain: we have allowed algorithms to invade our privacy, excavate our minds and manipulate our deepest thoughts, even those we may have kept hidden from ourselves, in exchange for free entertainment and social media. We have mindlessly relinquished our freedom of thought to voyeurist profit-seeking corporations and power-hungry governments
The Week •

Difference between artificial and human intelligence may be smaller than you think

Present AI systems suffer some obvious limitations. They are brittle, incapable of solving problems that deviate even slightly from what they were designed for, and they are data-hungry. Critics use variations of these limitations to conclude that there exists a fundamental difference between human intelligence and artificial intelligence. This, however, may be a premature conclusion. If we look closer, it turns out that humans also suffer from these same limitations.
The Wire •

What’s GPT-3, the Language Model Built by OpenAI, and What’s So Exciting About It?

GPT-3 is a significant achievement that pushes the boundaries of AI research in natural-language processing. OpenAI has demonstrated that, when it comes to AI, bigger is in fact better. GPT-3 uses the same architectural framework as GPT-2 but performs markedly better owing only to its size. This leads us to an important question: can the limitations of GPT-3 be overcome simply by throwing more data and computational horsepower at it?
Cantor’s Paradise •

Time Is An Illusion Born Out Of Our Ignorance

Time is not real. There is nothing special about the present moment; in fact, a universal present moment does not even exist. The past and the future are equal in all respects. Our notion that time flows irreversibly from the past into the future is an illusion born out of our ignorance about the world. It exists only in our subjective perceptions and not as part of objective reality. Let me convince you of this using simple mathematics, 15-mins of patience, and an open mind.
Towards Data Science •

Taking AI models from Jupyter notebooks into real hospitals

Most machine learning models lie unused in academic papers and code repositories. The valuation of AI-enabled healthcare startups is soaring, but they have no revenues, and only a handful of these have seen any form of productizing AI and commercial adoption. As the Principal Data Scientist at DeepTek — one of the few companies that have been successful in achieving commercial adoption of their AI solutions — I would like to share the key challenges involved in developing machine learning models that actually work in practice.
Cantor’s Paradise •

Will You Drown If You Swim After Eating Ice-Cream?

Statistics is like any weapon; whether it is good or evil depends on how it is wielded. Some media analysts and journalists looking for a quick story resort to bad statistics to dramatize events. Don't get fooled by these stat-quacks. Although statistics is invaluable as a tool to convert raw data into information, be aware that it is also a minefield of logical fallacies that we fall prey to a bit too often.
arXiv •

Quantum Computing Methods for Supervised Learning

Quantum computers can benefit machine learning research and application across all science and engineering domains. In this paper, we provide a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems. By eschewing results from physics that have little bearing on quantum computation, we hope to make this introduction accessible to data scientists, machine learning practitioners, and researchers from across disciplines.
arXiv •

Survey of Personalization Techniques for Federated Learning

Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.
The Wire •

Why We Must Unshackle AI From the Boundaries of Human Knowledge

Discrimination is as old as humankind; religious preaching, moral education, processes or legislation may mitigate its consequences but can’t eliminate it altogether. But today, as we increasingly cede decision-making to AI algorithms, we have a unique opportunity. For the first time in history, we have a real shot at building a fair society that is free of human prejudices by building machines that are fair by design.
Towards Data Science •

Cross-Entropy for Dummies

Cross-entropy is commonly used as a loss function for classification problems, but due to historical reasons, most explanations of cross-entropy are based on communication theory which data scientists may not be familiar with. You cannot understand cross-entropy without understanding entropy, and you cannot understand entropy without knowing what information is. This article builds the concept of cross-entropy in an easy-to-understand manner without relying on its communication theory background.
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