In the domain of artificial intelligence, human ingenuity has birthed entities capable of feats once relegated to science fiction. Yet within this triumph of creation resides a profound paradox: we have designed systems whose inner workings often elude our understanding. Like medieval alchemists who could transform substances without grasping the underlying chemistry, we stand before our algorithmic progeny with a similar mixture of wonder and bewilderment. This is the essence of the “black box” problem in AI — a philosophical and technical conundrum that cuts to the heart of our relationship with the machines we’ve created.

The term “black box” originates from systems theory, where it describes a device or system analyzed solely in terms of its inputs and outputs, with no knowledge of its internal workings. When applied to artificial intelligence, particularly to modern deep learning systems, the metaphor becomes startlingly apt. We feed these systems data, they produce results, but the transformative processes occurring between remain largely opaque. As Pedro Domingos (2015) eloquently states in his seminal work The Master Algorithm: “Machine learning is like farming. The machine learning expert is like a farmer who plants the seeds (the algorithm and the data), harvests the crop (the classifier), and sells it to consumers, without necessarily understanding the biological mechanisms of growth” (p. 78).

This agricultural metaphor points to a radical reconceptualization in how we create computational systems. Traditionally, software engineering has followed a constructivist approach — architects design systems by explicitly coding rules and behaviors. Yet modern AI systems, particularly neural networks, operate differently. Rather than being built piece by piece with predetermined functions, they develop their capabilities through exposure to data and feedback mechanisms. This observation led AI researcher Andrej Karpathy (2017) to assert that “neural networks are not ‘programmed’ in the traditional sense, but grown, trained, and evolved.”

Tuochao Chen, a University of Washington doctoral student, recently toured a museum in Mexico. Chen doesn’t speak Spanish, so he ran a translation app on his phone and pointed the microphone at the tour guide. But even in a museum’s relative quiet, the surrounding noise was too much. The resulting text was useless.

Various technologies have emerged lately promising fluent translation, but none of these solved Chen’s problem of public spaces. Meta’s new glasses, for instance, function only with an isolated speaker; they play an automated voice translation after the speaker finishes.

Now, Chen and a team of UW researchers have designed a headphone system that translates several speakers at once, while preserving the direction and qualities of people’s voices. The team built the system, called Spatial Speech Translation, with off-the-shelf noise-canceling headphones fitted with microphones. The team’s algorithms separate out the different speakers in a space and follow them as they move, translate their speech and play it back with a 2–4 second delay.

Computer simulations help materials scientists and biochemists study the motion of macromolecules, advancing the development of new drugs and sustainable materials. However, these simulations pose a challenge for even the most powerful supercomputers.

A University of Oregon graduate student has developed a new mathematical equation that significantly improves the accuracy of the simplified computer models used to study the motion and behavior of large molecules such as proteins, nucleic acids and synthetic materials such as plastics.

The breakthrough, published last month in Physical Review Letters, enhances researchers’ ability to investigate the motion of large molecules in complex biological processes, such as DNA replication. It could aid in understanding diseases linked to errors in such replication, potentially leading to new diagnostic and therapeutic strategies.

Scientists have developed an AI algorithm that can identify different types of neurons from brain activity recordings with 95% accuracy—without needing genetic tools.

Eyes may be the window to the soul, but a person’s biological age could be reflected in their facial characteristics. Investigators from Mass General Brigham developed a deep learning algorithm called “FaceAge” that uses a photo of a person’s face to predict biological age and survival outcomes for patients with cancer.

They found that patients with cancer, on average, had a higher FaceAge than those without and appeared about five years older than their chronological age.

Older FaceAge predictions were associated with worse overall survival outcomes across multiple cancer types. They also found that FaceAge outperformed clinicians in predicting short-term life expectancies of patients receiving palliative radiotherapy.

What happens when AI starts improving itself without human input? Self-improving AI agents are evolving faster than anyone predicted—rewriting their own code, learning from mistakes, and inching closer to surpassing giants like OpenAI. This isn’t science fiction; it’s the AI singularity’s opening act, and the stakes couldn’t be higher.

How do self-improving agents work? Unlike static models such as GPT-4, these systems use recursive self-improvement—analyzing their flaws, generating smarter algorithms, and iterating endlessly. Projects like AutoGPT and BabyAGI already demonstrate eerie autonomy, from debugging code to launching micro-businesses. We’ll dissect their architecture and compare them to OpenAI’s human-dependent models. Spoiler: The gap is narrowing fast.

Why is OpenAI sweating? While OpenAI focuses on safety and scalability, self-improving agents prioritize raw, exponential growth. Imagine an AI that optimizes itself 24/7, mastering quantum computing over a weekend or cracking protein folding in hours. But there’s a dark side: no “off switch,” biased self-modifications, and the risk of uncontrolled superintelligence.

Who will dominate the AI race? We’ll explore leaked research, ethical debates, and the critical question: Can OpenAI’s cautious approach outpace agents that learn to outthink their creators? Like, subscribe, and hit the bell—the future of AI is rewriting itself.

Can self-improving AI surpass OpenAI? What are autonomous AI agents? How dangerous is recursive AI? Will AI become uncontrollable? Can we stop self-improving AI? This video exposes the truth. Watch now—before the machines outpace us.

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It’s easy to take joint mobility for granted. Without thinking, it’s simple enough to turn the pages of a book or bend to stretch out a sore muscle. Designers don’t have the same luxury. When building a joint, be it for a robot or wrist brace, designers seek customizability across all degrees of freedom but are often restricted by their versatility to adapt to different use contexts.

Researchers at Carnegie Mellon University’s College of Engineering have developed an algorithm to design metastructures that are reconfigurable across six degrees of freedom and allow for stiffness tunability. The algorithm can interpret the kinematic motions that are needed for multiple configurations of a device and assist designers in creating such reconfigurability. This advancement gives designers more precise control over the functionality of joints for various applications.

The team demonstrated the structure’s versatile capabilities via multiple wearable devices tailored for unique movement functions, body areas, and uses.

Such questions quickly run into the limits of knowledge for both biology and computer science. To answer them, we need to figure out what exactly we mean by “information” and how that’s related to what’s happening inside cells. In attempting that, I will lead you through a frantic tour of information theory and molecular biology. We’ll meet some strange characters, including genomic compression algorithms based on deep learning, retrotransposons, and Kolmogorov complexity.

Ultimately, I’ll argue that the intuitive idea of information in a genome is best captured by a new definition of a “bit” — one that’s unknowable with our current level of scientific knowledge.

Digital transformation is blurring the lines between the physical, digital and biological spheres. From cloud computing, to Artificial Intelligence (AI) and Big Data, technologies of the Fourth Industrial Revolution (4IR) are shaping every aspect of our lives.

In the oil and gas industry, digital transformation is revolutionizing how we supply energy to the world. By deploying a range of 4IR technologies across our business, we aim to meet the world’s energy needs while enhancing productivity, reducing CO₂ emissions, and creating next-generation products and materials.

Exactly 100 years ago, famed Austrian physicist Erwin Schrödinger (yes, the cat guy) postulated his eponymous equation that explains how particles in quantum physics behave. A key component of quantum mechanics, Schrödinger’s Equation provides a way to calculate the wave function of a system and how it changes dynamically in time.

“Quantum mechanics, along with Albert Einstein’s theory of general relativity are the two pillars of modern physics,” says Utah State University physicist Abhay Katyal. “The challenge is, for more than half a century, scientists have struggled to reconcile these two theories.”

Quantum mechanics, says Katyal, a doctoral student and Howard L. Blood Graduate Fellow in the Department of Physics, describes the behavior of matter and forces at the subatomic level, while general relativity explains gravity on a large scale.