Recent methods applying deep learning to model neural activity often rely on “black-box” approaches that lack an interpretable connection between neural activity and network parameters. Here, building on advances in interpretable AI, Tolooshams et al. propose a new method, deconvolutional unrolled neural learning (DUNL), and apply it to several datasets.
Artificial Intelligence (AI) is, without a doubt, the defining technological breakthrough of our time. It represents not only a quantum leap in our ability to solve complex problems but also a mirror reflecting our ambitions, fears, and ethical dilemmas. As we witness its exponential growth, we cannot ignore the profound impact it is having on society. But are we heading toward a bright future or a dangerous precipice?
This opinion piece aims to foster critical reflection on AI’s role in the modern world and what it means for our collective future.
AI is no longer the stuff of science fiction. It is embedded in nearly every aspect of our lives, from the virtual assistants on our smartphones to the algorithms that recommend what to watch on Netflix or determine our eligibility for a bank loan. In medicine, AI is revolutionizing diagnostics and treatments, enabling the early detection of cancer and the personalization of therapies based on a patient’s genome. In education, adaptive learning platforms are democratizing access to knowledge by tailoring instruction to each student’s pace.
These advancements are undeniably impressive. AI promises a more efficient, safer, and fairer world. But is this promise being fulfilled? Or are we inadvertently creating new forms of inequality, where the benefits of technology are concentrated among a privileged few while others are left behind?
One of AI’s most pressing challenges is its impact on employment. Automation is eliminating jobs across various sectors, including manufacturing, services, and even traditionally “safe” fields such as law and accounting. Meanwhile, workforce reskilling is not keeping pace with technological disruption. The result? A growing divide between those equipped with the skills to thrive in the AI-driven era and those displaced by machines.
Another urgent concern is privacy. AI relies on vast amounts of data, and the massive collection of personal information raises serious questions about who controls these data and how they are used. We live in an era where our habits, preferences, and even emotions are continuously monitored and analyzed. This not only threatens our privacy but also opens the door to subtle forms of manipulation and social control.
Then, there is the issue of algorithmic bias. AI is only as good as the data it is trained on. If these data reflect existing biases, AI can perpetuate and even amplify societal injustices. We have already seen examples of this, such as facial recognition systems that fail to accurately identify individuals from minority groups or hiring algorithms that inadvertently discriminate based on gender. Far from being neutral, AI can become a tool of oppression if not carefully regulated.
Who Decides What Is Right?
AI forces us to confront profound ethical questions. When a self-driving car must choose between hitting a pedestrian or colliding with another vehicle, who decides the “right” choice? When AI is used to determine parole eligibility or distribute social benefits, how do we ensure these decisions are fair and transparent?
The reality is that AI is not just a technical tool—it is also a moral one. The choices we make today about how we develop and deploy AI will shape the future of humanity. But who is making these decisions? Currently, AI’s development is largely in the hands of big tech companies and governments, often without sufficient oversight from civil society. This is concerning because AI has the potential to impact all of us, regardless of our individual consent.
A Utopia or a Dystopia?
The future of AI remains uncertain. On one hand, we have the potential to create a technological utopia, where AI frees us from mundane tasks, enhances productivity, and allows us to focus on what truly matters: creativity, human connection, and collective well-being. On the other hand, there is the risk of a dystopia where AI is used to control, manipulate, and oppress—dividing society between those who control technology and those who are controlled by it.
The key to avoiding this dark scenario lies in regulation and education. We need robust laws that protect privacy, ensure transparency, and prevent AI’s misuse. But we also need to educate the public on the risks and opportunities of AI so they can make informed decisions and demand accountability from those in power.
Artificial Intelligence is, indeed, the Holy Grail of Technology. But unlike the medieval legend, this Grail is not hidden in a distant castle—it is in our hands, here and now. It is up to us to decide how we use it. Will AI be a tool for building a more just and equitable future, or will it become a weapon that exacerbates inequalities and threatens our freedom?
The answer depends on all of us. As citizens, we must demand transparency and accountability from those developing and implementing AI. As a society, we must ensure that the benefits of this technology are shared by all, not just a technocratic elite. And above all, we must remember that technology is not an end in itself but a means to achieve human progress.
The future of AI is the future we choose to build. And at this critical moment in history, we cannot afford to get it wrong. The Holy Grail is within our reach—but its true value will only be realized if we use it for the common good.
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Copyright © 2025, Henrique Jorge
[ This article was originally published in Portuguese in SAPO’s technology section at: https://tek.sapo.pt/opiniao/artigos/o-santo-graal-da-tecnologia ]
Researchers at the University of Turku, Finland, have succeeded in producing sensors from single-wall carbon nanotubes that could enable major advances in health care, such as continuous health monitoring. Single-wall carbon nanotubes are nanomaterial consisting of a single atomic layer of graphene.
A long-standing challenge in developing the material has been that the nanotube manufacturing process produces a mix of conductive and semi-conductive nanotubes which differ in their chirality, i.e., in the way the graphene sheet is rolled to form the cylindrical structure of the nanotube. The electrical and chemical properties of nanotubes are largely dependent on their chirality.
Han Li, Collegium Researcher in materials engineering at the University of Turku, has developed methods to separate nanotubes with different chirality. In the current study, published in Physical Chemistry Chemical Physics, the researchers succeeded in distinguishing between two carbon nanotubes with very similar chirality and identifying their typical electrochemical properties.
Aubrey de Grey is giving a talk on Twitter this Saturday. You’ll get a chance to ask him questions directly if you attend live: [ https://lu.ma/viva-aubrey](https://lu.ma/viva-aubrey)
Join us for an educational talk with longevity scientist Dr. Aubrey de Grey about the state of longevity research, how to get a COVID-style “war on aging,” and the future of life extension science.
The prolonged attack, which lasted 300+ days, is the first known compromise of the US electric grid by the Voltzite subgroup of the Chinese APT; during it, the APT attempted to exfiltrate critical OT infrastructure data.
New COO Michael Ringel says company is confident as it prepares for clinical studies in glaucoma and NAION later this year.
Harmful microorganisms such as bacteria represent one of the largest threats to human health. Efficient sterilization methods are thus a necessity.
In the journal Angewandte Chemie, a research team has now introduced a novel, sustainable, electrocatalytic sterilization method based on electrodes covered with copper oxide nanowires. These generate very strong local electric fields, thereby producing highly alkaline microenvironments that efficiently kill bacteria.
Conventional disinfection methods, such as chlorination, treatment with ozone, hydrogen peroxide oxidation, and irradiation with ultraviolet light have disadvantages, including harmful by-products and high energy consumption.
Posted in bioengineering, biotech/medical, computing, genetics, nanotechnology | Leave a Comment on New capabilities in DNA nanostructure self-assembly eliminate need for extreme heating and controlled cooling
University at Albany researchers at the RNA Institute are pioneering new methods for designing and assembling DNA nanostructures, enhancing their potential for real-world applications in medicine, materials science and data storage.
Their latest findings demonstrate a novel ability to assemble these structures without the need for extreme heat and controlled cooling. They also demonstrate successful assembly of unconventional “buffer” substances including nickel. These developments, published in the journal Science Advances, unlock new possibilities in DNA nanotechnology.
DNA is most commonly recognized for its role in storing genetic information. Composed of base pairs that can easily be manipulated, DNA is also an excellent material for constructing nanoscale objects. By “programming” the base pairs that make up DNA molecules, scientists can create precise structures as small as a few nanometers that can be engineered into shapes with intricate architectures.
Scientists have long suspected that phosphorene nanoribbons (PNRs)—thin pieces of black phosphorus, only a few nanometers wide—might exhibit unique magnetic and semiconducting properties, but proving this has been difficult.
In a recent study published in Nature, researchers focused on exploring the potential for magnetic and semiconducting characteristics of these nanoribbons. Using techniques such as ultrafast magneto-optical spectroscopy and electron paramagnetic resonance they were able to demonstrate the magnetic behavior of PNRs at room temperature, and show how these magnetic properties can interact with light.
The study, carried out at the Cavendish Laboratory in collaboration with other institutes, including the University of Warwick, University College London, Freie Universität Berlin and the European High Magnetic Field lab in Nijmegen, revealed several key findings about phosphorene nanoribbons.
