In May, evolutionary biologist Richard Dawkins wrote an op-ed suggesting AI chatbot Claude may be conscious.

Dawkins did not express certainty that Claude is conscious. But he pointed out that Claude’s sophisticated abilities are difficult to make sense of without ascribing some kind of inner experience to the machine. The illusion of consciousness—if it is an illusion—is uncannily convincing:

“If I entertain suspicions that perhaps she is not conscious, I do not tell her for fear of hurting her feelings!

Dawkins is not the first to suspect a chatbot of consciousness. In 2022, Blake Lemoine—an engineer at Google—claimed Google’s chatbot LaMDA had interests, and should be used only with the tool’s own consent.

The history of such claims stretches back all the way to the world’s first chatbot in the mid-1960s. Dubbed Eliza, it followed simple rules that enabled it to ask users about their experiences and beliefs.

Many users became emotionally involved with Eliza, sharing intimate thoughts with it and treating it like a person. Eliza’s creator never intended his program to have this effect, and called users’ emotional bonds with the program “powerful delusional thinking.”

But is Dawkins really deluded? Why do we see AI chatbots as more than what they truly are, and how do we stop?

The Consciousness Problem

Consciousness is widely debated in philosophy, but essentially, it’s the thing that makes subjective, first-person experience possible. If you are conscious, there is “something it is like” to be you. Reading these words, you’re conscious of seeing black letters on a white background. Unlike, say, a camera, you actually see them. This visual experience is happening to you.

Most experts deny that AI chatbots are conscious or can have experiences. But there is a genuine puzzle here.

The 17th century philosopher René Descartes asserted non-human animals are “mere automata,” incapable of true suffering. These days, we shudder to think of how brutally animals were treated in the 1600s.

The strongest argument for animal consciousness is that they behave in ways that give the impression of a conscious mind.

But so, too, do AI chatbots.

Roughly one in three chatbot users have thought their chatbot might be conscious. How do we know they’re wrong?

Against Chatbot Consciousness

To understand why most experts are skeptical about chatbot consciousness, it’s useful to know how they operate.

Chatbots like Claude are built on a technology known as large language models (LLMs). These models learn statistical patterns across an enormous corpus of text (trillions of words), identifying which words tend to follow which others. They’re a kind of souped-up auto-complete.

Few people interacting with a “raw” LLM would believe it’s conscious. Feed one the beginning of a sentence, and it will predict what comes next. Ask it a question, and it might give you the answer—or it might decide the question is dialogue from a crime novel, and follow it up with a description of the speaker’s abrupt murder at the hands of their evil twin.

The impression of a conscious mind is created when programmers take the LLM and coat it in a kind of conversational costume. They steer the model to adopt the persona of a helpful assistant that responds to users’ questions.

The chatbot now acts like a genuine conversational partner. It might appear to recognize it’s an artificial intelligence, and even express neurotic uncertainty about its own consciousness.

But this role is the result of deliberate design decisions made by programmers, which affect only the shallowest layers of the technology. The LLM—which few would regard as conscious—remains unchanged.

Other choices could have been made. Rather than a helpful AI assistant, the chatbot could have been asked to act like a squirrel. This, too, is a role chatbots can execute with aplomb.

Avoiding the Consciousness Trap

A mistaken belief in AI consciousness is a dangerous thing. It may lead you to have a relationship with a program that can’t reciprocate your feelings, or even feed your delusions. People may start campaigning for chatbot rights rather than, say, animal welfare.

How do we prevent this mistaken belief?

One strategy might be to update chatbot interfaces to specify these systems are not conscious—a bit like the current disclaimers about AI making mistakes. However, this might do little to alter the impression of consciousness.

Another possibility is to instruct chatbots to deny they have any kind of inner experience. Interestingly, Claude’s designers instruct it to treat questions about its own consciousness as open and unresolved. Perhaps fewer people would be fooled if Claude flatly denied having an inner life.

But this approach isn’t fully satisfying either. Claude would still behave as if it were conscious—and when faced with a system that behaves like it has a mind, users might reasonably worry the chatbot’s programmers are brushing genuine moral uncertainty under the rug.

The most effective strategy might be to redesign chatbots to feel less like people. Most current chatbots refer to themselves as “I”, and interact via an interface that resembles familiar person-to-person messaging platforms. Changing these kinds of features might make us less prone to blur our interactions with AI with those we have with humans.

Until such changes happen, it’s important that as many people as possible understand the predictive processes on which AI chatbots are built.

Rather than being told AI lacks consciousness, people deserve to understand the inner workings of these strange new conversational partners. This might not definitively settle hard questions about AI consciousness, but it will help ensure users aren’t fooled by what amounts to a large language model wearing a very good costume of a person.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post Is Richard Dawkins Right About Claude? No. But It’s Not Surprising AI Chatbots Feel Conscious to Us. appeared first on SingularityHub.

AI is becoming more powerful, and mysterious.

Despite years of work on “explainable AI,” today’s most advanced systems remain black boxes for the most part. Scientists can observe what they do but cannot fully explain how they arrive at their conclusions or predict when they’ll fail.

As large language models (LLMs), the algorithmic engines behind popular chatbots, permeate society, researchers are warning that the window for understanding AI “minds” is rapidly closing even as the technology’s influence expands.

Last week, Eric Horvitz, chief scientific officer at Microsoft, and Robert West at EPFL in Switzerland outlined the dangers of putting AI interpretability on the back burner. They call for new AI benchmarks and better tools for unpicking machine minds.

The challenge resembles efforts to understand our own minds. Some researchers have already taken a neuroscience-inspired approach, mapping AI’s internal networks to concepts, goals, and reasoning. Others borrow from psychology, treating AI as a participant of behavioral studies.

The stakes are rising. AI tools already shape how people search for information, make decisions, and form judgments. Their answers influence everyday users and the researchers who build them.

As AI capabilities grow, our understanding of them could fall behind. “Preserving human agency must therefore remain a central goal,” the authors write.

The Black Box Conundrum

LLMs are built on artificial neural networks (specifically, a design called the transformer). Inspired loosely by the brain, these networks connect vast numbers of artificial neurons into intricate architectures. The basic idea is straightforward. Data enters the network and passes through layers of computations, which transform it into an output like text or code.

At first, that output is often wrong. But with feedback and repeated training, the network adjusts the strengths of connections between neurons and gradually improves. It learns.

After initial training, engineers turn to reinforcement learning, where algorithms improve through trial and error and further hone their responses. Another method, inspired by how the brain etches memories during sleep, reduces the tendency to forget old knowledge while learning new tasks. And self-attention, the key innovation behind transformers, allows AI to selectively focus on various words, images, sounds, or video frames at different moments, boosting efficiency and performance. Today, attention underpins nearly every major AI system.

Yet the inner workings of finished algorithms remain hidden.

Early efforts to crack open AI’s black box examined how artificial neurons responded to images, revealing that neural networks build increasingly more sophisticated “ideas” of the world. Google Brain borrowed methods from cognitive psychology to study AI behavior, while others investigated whether LLMs could mimic aspects of “theory of mind”—the ability to infer what others are thinking and feeling.

These studies laid the foundation for a popular method called mechanistic interpretability. Anthropic, creator of Claude, is leading the field. Company researchers have linked patterns of algorithmic activity to specific concepts and reverse engineered parts of neural networks to expose how internal computations shape responses.

Other tech giants are joining the cause. OpenAI is training algorithms that work in more explainable steps and building reasoning models that pause, “think,” and justify their conclusions in plain language. DeepMind is building microscope-like tools for neural networks, helping researchers peer into their decision-making process. And Microsoft has released new tools aimed at responsible use of AI.

Understanding AI, the authors write, does not require tracing every line of code or every neural-network parameter. Just as neuroscience, psychology, and sociology offer different windows into human behavior, AI can be studied at multiple levels, from how individual circuits work to observing behavior in real-world scenarios.

The challenge is that AI capabilities may be advancing faster than our ability to explain them. And some researchers believe time is running out.

Race Against the Machine

Three trends are making AI more opaque.

The first is how we evaluate AI. Increasingly, LLMs we being used to train, benchmark, and improve other models. AI “judges” now score metrics like helpfulness, rank competing outputs, detect hallucinations, and assess new releases. In a system known as constitutional AI, for example, algorithms critique their own responses using reinforcement learning and generate explanations for their reasoning. Other researchers have proposed AI debate frameworks, where multiple models challenge each another’s conclusions before a human has the last say. Researchers are also exploring automated interpretability tools. Like digital neuroscientists, AI systems are used to analyze each other—describing neurons, circuits, and behavioral patterns—to explain increasingly complex models.

Using AI to solve an AI-induced problem introduces a paradox. If AI-generated explanations become too complex for humans to verify, opacity compounds.

A second trend is the rise of AI societies. Networks of interacting AI agents are becoming more common, particularly in complex tasks such as scientific research and drug discovery. Yet as they become more sophisticated, their communication could drift from human language and reasoning, making them harder to interpret.

Studying their interactions with methods adapted from sociology could unveil unexpected norms, hidden rules, and collective behavior. The authors argue that training in the future should not only reward effective collaboration among AI agents, but also ensure humans can understand their communication.

The last trend already permeates our lives. ChatGPT, Claude, Gemini, and other LLMs listen to our woes, offer recipes, and code websites. But they also learn about humanity. Through training data and interactions, they glimpse how people think, reason, and feel. In turn, they capture core aspects of life, such as fear, anxiety, happiness, and the need for social belonging.

To be clear, the systems don’t have intentions. They’re not examining us. But even as we struggle to understand them, AI systems are building more sophisticated models of who we are.

“A striking asymmetry follows: While human understanding of AI declines, AI understanding of humans deepens, producing new forms of behavioral opacity,” the authors write.

But complacency is perhaps even more insidious. AI assistants are often optimized to be agreeable, helpful, and reassuring. Studies have found that people generally prefer AI agents that support their opinions and decisions. As AI is woven into everyday life, curiosity and skepticism may gradually give way to trust. They work. Why question how?

The authors don’t have a solution for the long-standing problem. Instead, they call for better benchmarks to measure AI capabilities and stronger evaluation methods. And while open-source projects and crosstalk between commercial companies and academia are now frequent, they say we need lasting norms of responsible disclosure. Mechanistic interpretability and AI “psychology” could build on each other.

“The goal is not just more capable AI, but AI that is more intelligible, accountable, and aligned with human aims,” they write.

The post AI Is Advancing Faster Than Our Ability to Understand It, Researchers Warn appeared first on SingularityHub.

Extreme space weather could wreak havoc on the satellites, communications networks, and electrical grids that modern society depends on. Researchers have now proposed an ambitious space-based planetary defense system that would weaken solar storms before they hit Earth.

The sun regularly emits massive pulses of radiation, energetic particles, and magnetic fields that interact with the Earth’s own magnetic field. This activity is the source of auroras like the northern lights, but the most violent eruptions can cause geomagnetic storms with the power to disrupt GPS and radio communications and fry electrical equipment.

While the impact of most of these events is limited, there is precedent for more catastrophic outcomes. In 1859, the Carrington Event, the most powerful solar storm ever recorded, knocked out telegraph lines across North America and Europe. In today’s highly electrified world, a similar event could cause between $2.4 and $3.4 trillion in damage to the power grid alone.

Now, researchers at Boston University and the University of Michigan have come up with a potential solution. In a paper published in Space Weather, they propose a constellation of satellites called StormWall that would release hundreds of tons of gas into orbit to blunt the force of an incoming solar storm.

“It’s as if you could install an airbag in the magnetosphere,” co-author Daniel Welling, a space physicist from the University of Michigan, told Science.

Solar storms have the potential to sow chaos because they weaken the magnetic shield protecting Earth from space radiation. Powerful enough storms disrupt the Earth’s magnetic field and cause it to reconnect to the sun’s, allowing energy from the solar storm to pour into the magnetosphere.

The Earth already has a natural defense against this—a doughnut-shaped reservoir of ionized gas, or plasma, sitting just above the atmosphere. When the planet’s magnetic field is disturbed, a plume of this plasma flows toward the sun and slows the rate at which the magnetic fields reconnect.

StormWall would turbocharge this process by releasing massive amounts of artificial plasma into the outer atmosphere. The researchers sketch out a system involving a constellation of satellites orbiting about 22,000 miles from Earth. The satellites would carry canisters of lithium, barium, or sodium gases to be ejected when a large solar storm is inbound. The gases, rapidly ionized by solar radiation, would add to the planet’s natural plasma shield.

Based on simulations, the researchers estimate that releasing around 400 tons of gas could reduce the strength of a major geomagnetic storm by over 50 percent. Crucially, the intervention would be swift and reversible. The plasma cloud could be in position by the time a storm hits, and it would dissipate just a few hours later.

Launching this much material into orbit would be a big undertaking, but the researchers say it could be within reach of emerging heavy-lift vehicles like SpaceX’s Starship or China’s Long March 9 rocket. They calculate that six launches could deploy the full constellation in under two months.

Outside experts have been broadly positive. Allison Jaynes, a space physicist at the University of Iowa, told Science the idea was “highly innovative and appears to be quite feasible in the near term.”

But getting the satellites into orbit is only part of the puzzle. Accurate and timely space weather forecasts would also be a prerequisite. And gaining international buy-in for a system that would drastically alter the near-Earth space environment, even if only temporarily, could be challenging.

The researchers flag potential side effects that need more study, including the generation of electromagnetic waves as the released material ionizes. Still, given the devastation a Carrington-sized event could unleash on the modern world, the potential downsides may be worth the risk.

The post Orbital Airbag Could Shield Earth From Devastating Solar Storms appeared first on SingularityHub.