Here are some hints and the answers for Connections for June 5, #725.

Published May 31, 2025 6:00am EDT close Brain implant enables ALS patient to communicate using AI ALS patient communicates with the world using only his thoughts. Imagine losing your ability to speak or move, yet still having so much to say. For Brad G. Smith, this became his reality after being diagnosed with ALS, a rare and progressive disease that attacks the nerves controlling voluntary muscle movement. But thanks to a groundbreaking Neuralink brain implant, Smith is now able to communicate with the world using only his thoughts. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Life before NeuralinkBefore receiving the Neuralink implant, Smith relied on eye-tracking technology to communicate. While impressive, it came with major limitations. "It is a miracle of technology, but it is frustrating. It works best in dark rooms, so I was basically Batman. I was stuck in a dark room," Smith shared in a recent post on X. Bright environments would disrupt the system, making communication slow and sometimes impossible. Now, Smith says, "Neuralink lets me go outside and ignore lighting changes."PARALYZED MAN WITH ALS IS THIRD TO RECEIVE NEURALINK IMPLANT, CAN TYPE WITH BRAIN ALS patient Brad G. Smith. (Bradford G. Smith/X)How the Neuralink brain implant worksSmith is the first non-verbal person and only the third individual worldwide to receive the Neuralink Brain-Computer Interface (BCI). The device, about as thick as five stacked coins, sits in his skull and connects to the motor cortex-the part of the brain that controls movement.Tiny wires, thinner than human hair, extend into Smith's brain. These pick up signals from his neurons and transmit them wirelessly to his MacBook Pro. The computer then decodes these signals, allowing Smith to move a cursor on the screen with his thoughts alone.As Smith explains, "The Neuralink implant embedded in my brain contains 1024 electrodes that capture neuron firings every 15 milliseconds generating a vast amount of data. Artificial intelligence processes this data on a connected MacBook Pro to decode my intended movements in real time to move the cursor on my screen. Neuralink does not read my deepest thoughts or words I think about. It just reads how I wanna move and moves the cursor where I want."WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Neuralink brain implant. (Bradford G. Smith/X)Training the brain-computer connectionLearning to use the system took some trial and error. At first, the team tried mapping Smith's hand movements to the cursor, but it didn't work well. After more research, they discovered that signals related to his tongue were the most effective for cursor movement, and clenching his jaw worked best for clicking. "I am not actively thinking about my tongue, just like you don't think about your wrist when you move a mouse. I have done a lot of cursor movements in my life. I think my brain has switched over to subconscious control quickly so I just think about moving the cursor," Smith said. ALS patient Brad G. Smith with his wife and child. (Bradford G. Smith/X)Everyday life: Communication, play, and problem-solvingThe Neuralink implant has given Smith new ways to interact with his family and the world. He can now play games like Mario Kart with his children and communicate more quickly than before. The system includes a virtual keyboard and shortcuts for common actions, making tasks like copying, pasting and navigating web pages much easier.Smith also worked with Neuralink engineers to develop a "parking spot" feature for the cursor. "Sometimes you just wanna park the cursor and watch a video. When it is in the parking spot, I can watch a show or take a nap without worrying about the cursor," he explained. ALS patient Brad G. Smith and his child. (Bradford G. Smith/X)AI assistance: Keeping up with conversationTo speed up communication even more, Smith uses Grok, Elon Musk's AI chatbot. Grok helps him write responses and even suggests witty replies. "We have created a chat app that uses AI to listen to the conversation and gives me options to say in response. It uses Grok 3 and an AI clone of my old voice to generate options for me to say. It is not perfect, but it keeps me in the conversation and it comes up with some great ideas," Smith shared. One example? When a friend needed a gift idea for his girlfriend who loves horses, the AI suggested a bouquet of carrots. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)The human side: Family, faith and perspectiveSmith's journey has been shaped by more than just technology. He credits his wife, Tiffany, as his "best caregiver I could ever imagine," and recognizes the support of his kids, friends and family. Despite the challenges of ALS, Smith finds meaning and hope in his faith. "I have not always understood why God afflicted me with ALS but with time I am learning to trust his plan for me. I'm a better man because of ALS. I'm a better disciple of Jesus Christ because of ALS. I'm closer to my amazing wife, literally and figuratively, because of ALS," he said. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Looking ahead: What does this mean for others?Neuralink's technology is still in its early stages, but Smith's experience is already making waves. The company recently received a "breakthrough" designation from the Food and Drug Administration for its brain implant device, which hopes to help people with severe speech impairments caused by ALS, stroke, spinal cord injury and other neurological conditions.Neuro-ethicists are watching closely, as the merging of brain implants and AI raises important questions about privacy, autonomy and the future of human communication. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Kurt's key takeawaysSmith's story is about resilience, creativity and the power of technology to restore something as fundamental as the ability to communicate. As Smith puts it,CLICK HERE TO GET THE FOX NEWS APPIf you or a family member lost the ability to speak or move, would you consider a brain implant that lets you communicate with your thoughts? Let us know by writing to us atCyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.

Artificial intelligence is a deep and convoluted world. The scientists who work in this field often rely on jargon and lingo to explain what they’re working on. As a result, we frequently have to use those technical terms in our coverage of the artificial intelligence industry. That’s why we thought it would be helpful to put together a glossary with definitions of some of the most important words and phrases that we use in our articles. We will regularly update this glossary to add new entries as researchers continually uncover novel methods to push the frontier of artificial intelligence while identifying emerging safety risks. AGI Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that’s more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman recently described AGI as the “equivalent of a median human that you could hire as a co-worker.” Meanwhile, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Confused? Not to worry — so are experts at the forefront of AI research. AI agent An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so “AI agent” might mean different things to different people. Infrastructure is also still being built out to deliver on its envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks. Chain of thought Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller, a giraffe or a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows). In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be correct, especially in a logic or coding context. Reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning. (See: Large language model) Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW Deep learning A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees. The structure of deep learning algorithms draws inspiration from the interconnected pathways of neurons in the human brain. Deep learning AI models are able to identify important characteristics in data themselves, rather than requiring human engineers to define these features. The structure also supports algorithms that can learn from errors and, through a process of repetition and adjustment, improve their own outputs. However, deep learning systems require a lot of data points to yield good results (millions or more). They also typically take longer to train compared to simpler machine learning algorithms — so development costs tend to be higher. (See: Neural network) Diffusion Diffusion is the tech at the heart of many art-, music-, and text-generating AI models. Inspired by physics, diffusion systems slowly “destroy” the structure of data — e.g. photos, songs, and so on — by adding noise until there’s nothing left. In physics, diffusion is spontaneous and irreversible — sugar diffused in coffee can’t be restored to cube form. But diffusion systems in AI aim to learn a sort of “reverse diffusion” process to restore the destroyed data, gaining the ability to recover the data from noise. Distillation Distillation is a technique used to extract knowledge from a large AI model with a ‘teacher-student’ model. Developers send requests to a teacher model and record the outputs. Answers are sometimes compared with a dataset to see how accurate they are. These outputs are then used to train the student model, which is trained to approximate the teacher’s behavior. Distillation can be used to create a smaller, more efficient model based on a larger model with a minimal distillation loss. This is likely how OpenAI developed GPT-4 Turbo, a faster version of GPT-4. While all AI companies use distillation internally, it may have also been used by some AI companies to catch up with frontier models. Distillation from a competitor usually violates the terms of service of AI API and chat assistants. Fine-tuning This refers to the further training of an AI model to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new, specialized (i.e., task-oriented) data.  Many AI startups are taking large language models as a starting point to build a commercial product but are vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise. (See: Large language model [LLM]) GAN A GAN, or Generative Adversarial Network, is a type of machine learning framework that underpins some important developments in generative AI when it comes to producing realistic data – including (but not only) deepfake tools. GANs involve the use of a pair of neural networks, one of which draws on its training data to generate an output that is passed to the other model to evaluate. This second, discriminator model thus plays the role of a classifier on the generator’s output – enabling it to improve over time.  The GAN structure is set up as a competition (hence “adversarial”) – with the two models essentially programmed to try to outdo each other: the generator is trying to get its output past the discriminator, while the discriminator is working to spot artificially generated data. This structured contest can optimize AI outputs to be more realistic without the need for additional human intervention. Though GANs work best for narrower applications (such as producing realistic photos or videos), rather than general purpose AI. Hallucination Hallucination is the AI industry’s preferred term for AI models making stuff up – literally generating information that is incorrect. Obviously, it’s a huge problem for AI quality.  Hallucinations produce GenAI outputs that can be misleading and could even lead to real-life risks — with potentially dangerous consequences (think of a health query that returns harmful medical advice). This is why most GenAI tools’ small print now warns users to verify AI-generated answers, even though such disclaimers are usually far less prominent than the information the tools dispense at the touch of a button. The problem of AIs fabricating information is thought to arise as a consequence of gaps in training data. For general purpose GenAI especially — also sometimes known as foundation models — this looks difficult to resolve. There is simply not enough data in existence to train AI models to comprehensively resolve all the questions we could possibly ask. TL;DR: we haven’t invented God (yet).  Hallucinations are contributing to a push towards increasingly specialized and/or vertical AI models — i.e. domain-specific AIs that require narrower expertise – as a way to reduce the likelihood of knowledge gaps and shrink disinformation risks. Inference Inference is the process of running an AI model. It’s setting a model loose to make predictions or draw conclusions from previously-seen data. To be clear, inference can’t happen without training; a model must learn patterns in a set of data before it can effectively extrapolate from this training data. Many types of hardware can perform inference, ranging from smartphone processors to beefy GPUs to custom-designed AI accelerators. But not all of them can run models equally well. Very large models would take ages to make predictions on, say, a laptop versus a cloud server with high-end AI chips. [See: Training] Large language model (LLM) Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, or Mistral’s Le Chat. When you chat with an AI assistant, you interact with a large language model that processes your request directly or with the help of different available tools, such as web browsing or code interpreters. AI assistants and LLMs can have different names. For instance, GPT is OpenAI’s large language model and ChatGPT is the AI assistant product. LLMs are deep neural networks made of billions of numerical parameters (or weights, see below) that learn the relationships between words and phrases and create a representation of language, a sort of multidimensional map of words. These models are created from encoding the patterns they find in billions of books, articles, and transcripts. When you prompt an LLM, the model generates the most likely pattern that fits the prompt. It then evaluates the most probable next word after the last one based on what was said before. Repeat, repeat, and repeat. (See: Neural network) Neural network A neural network refers to the multi-layered algorithmic structure that underpins deep learning — and, more broadly, the whole boom in generative AI tools following the emergence of large language models.  Although the idea of taking inspiration from the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates all the way back to the 1940s, it was the much more recent rise of graphical processing hardware (GPUs) — via the video game industry — that really unlocked the power of this theory. These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs — enabling neural network-based AI systems to achieve far better performance across many domains, including voice recognition, autonomous navigation, and drug discovery. (See: Large language model [LLM]) Training Developing machine learning AIs involves a process known as training. In simple terms, this refers to data being fed in in order that the model can learn from patterns and generate useful outputs. Things can get a bit philosophical at this point in the AI stack — since, pre-training, the mathematical structure that’s used as the starting point for developing a learning system is just a bunch of layers and random numbers. It’s only through training that the AI model really takes shape. Essentially, it’s the process of the system responding to characteristics in the data that enables it to adapt outputs towards a sought-for goal — whether that’s identifying images of cats or producing a haiku on demand. It’s important to note that not all AI requires training. Rules-based AIs that are programmed to follow manually predefined instructions — for example, such as linear chatbots — don’t need to undergo training. However, such AI systems are likely to be more constrained than (well-trained) self-learning systems. Still, training can be expensive because it requires lots of inputs — and, typically, the volumes of inputs required for such models have been trending upwards. Hybrid approaches can sometimes be used to shortcut model development and help manage costs. Such as doing data-driven fine-tuning of a rules-based AI — meaning development requires less data, compute, energy, and algorithmic complexity than if the developer had started building from scratch. [See: Inference] Transfer learning A technique where a previously trained AI model is used as the starting point for developing a new model for a different but typically related task – allowing knowledge gained in previous training cycles to be reapplied.  Transfer learning can drive efficiency savings by shortcutting model development. It can also be useful when data for the task that the model is being developed for is somewhat limited. But it’s important to note that the approach has limitations. Models that rely on transfer learning to gain generalized capabilities will likely require training on additional data in order to perform well in their domain of focus (See: Fine tuning) Weights Weights are core to AI training, as they determine how much importance (or weight) is given to different features (or input variables) in the data used for training the system — thereby shaping the AI model’s output.  Put another way, weights are numerical parameters that define what’s most salient in a dataset for the given training task. They achieve their function by applying multiplication to inputs. Model training typically begins with weights that are randomly assigned, but as the process unfolds, the weights adjust as the model seeks to arrive at an output that more closely matches the target. For example, an AI model for predicting housing prices that’s trained on historical real estate data for a target location could include weights for features such as the number of bedrooms and bathrooms, whether a property is detached or semi-detached, whether it has parking, a garage, and so on.  Ultimately, the weights the model attaches to each of these inputs reflect how much they influence the value of a property, based on the given dataset. Topics

The FDA recently cleared the Lumipulse blood test for early diagnosis of Alzheimer's disease in ... More people 55 and over with memory loss. The noninvasive Lumipulse blood test measures the levels of two proteins—pTau 217 and β-Amyloid 1-42—in plasma and calculates the ratio between them. This ratio is correlated with the presence or absence of amyloid plaques, a hallmark of Alzheimer's disease, in the brain.getty Whether you’re noticing changes in your memory that are affecting your daily life, caring for a loved one recently diagnosed with dementia, evaluating a patient as a physician, or simply worried about someone close to you, the recent FDA clearance of the Lumipulse blood test for the early diagnosis of Alzheimer’s disease is a significant development that you should be aware of. Here’s what you need to know about this Breakthrough Alzheimer’s blood test. The Lumipulse G pTau217/β-Amyloid 1-42 Plasma Ratio test is designed for the early detection of amyloid plaques associated with Alzheimer’s disease in adults aged 55 years and older who are showing signs and symptoms of the condition. If you’ve witnessed a loved one gradually lose their memories due to the impact of amyloid plaques in their brain, you understand how important a test like this can be. The Lumipulse test measures the levels of two proteins—pTau 217 and β-Amyloid 1-42—in plasma and calculates the ratio between them. This ratio is correlated with the presence or absence of amyloid plaques in the brain, potentially reducing the need for more invasive procedures like PET scans or spinal fluid analysis. Benefits of testing with Lumipulse Dr. Phillipe Douyon, a neurologist and author of “7 Things You Should Be Doing to Minimize Your Risk of Dementia,” notes that the Alzheimer’s Association has reported that 50-70% of symptomatic patients in community settings are inaccurately diagnosed with Alzheimer’s disease. In specialized memory clinics, this misdiagnosis rate drops to 25-30%. “Having a test that provides early and accurate insights into the cause of someone’s dementia could be a massive game changer,” says Dr. Douyon. Alzheimer's disease. Neurodegeneration. Cross section of normal and Alzheimer brain, with Atrophy of ... More the cerebral cortex, Enlarged ventricles and Hippocampus. Close-up of neurons with Neurofibrillary tangles and Amyloid plaques. Vector illustrationgetty This new test follows the recent FDA approval of two medications, lecanemab and donanemab, which are highly effective in removing amyloid from the brain. Clinical trials have shown that these treatments can slow the progression of dementia. Currently, to qualify for these medications, patients must undergo expensive examinations, such as a brain amyloid PET scan or a lumbar puncture to analyze their spinal fluid. Many patients, however, do not have access to PET imaging or specialist care. “A blood test makes diagnostic procedures more accessible and benefits underserved populations,” says Dr. Haythum Tayeb, a neurologist at WMCHealth. “It also enables earlier and more personalized care planning, even before formal treatment begins. This empowers patients and their families to make informed decisions sooner,” Dr. Tayeb adds. Who Should Be Tested With Lumipulse While this blood test may improve access to care for patients from communities lacking neurology and other specialty services, it is recommended to use it only for individuals experiencing memory problems, rather than for those who are asymptomatic. “Given that there is no specific treatment indicated for asymptomatic persons, there is a risk of introducing psychological harm at this stage,” warns Dr. James Noble who is Professor of Neurology at Columbia University Irving Medical Center and author of Navigating Life With Dementia. “Healthy approaches to lifestyle will remain central in adulthood whether or not someone has a positive test, and that advice will not really change,” adds Dr. Noble. Living a healthy lifestyle can significantly enhance brain health, regardless of whether a person has an abnormal accumulation of amyloid in their brain. Key factors include regular exercise, following a healthy diet such as the Mediterranean diet, getting adequate sleep, engaging in social and cognitive activities. These practices are all essential for maintaining cognitive function. Additionally, taking steps to protect your hearing may help reduce the risk of developing dementia.To reduce your risk of dementia, you can do regular exercise, consume a healthy diet such as the ... More Mediterranean diet, get adequate sleep, and engage regularly in social and cognitive activities.getty Anyone experiencing memory loss should consult their medical provider for an evaluation. The provider can conduct basic cognitive testing and determine if a referral to a specialist is necessary. If the individual meets the criteria for testing, the lumipulse blood test should also be considered. Future Of Alzheimer’s Testing “Looking across the wide landscape of medicine, many other conditions benefit from early detection, diagnosis, and treatment. There is no reason to believe that Alzheimer’s disease will be any different” says Dr. Noble. Indeed, screening for diseases like colon cancer, breast cancer, and high blood pressure has significantly extended the average American lifespan. Imagine how much our lives could change if we could screen for Alzheimer’s dementia in the same way. This would be particularly useful for patients at higher risk due to age or family history. Providing earlier intervention for Alzheimer’s disease could potentially reduce amyloid buildup in the brain, help preserve memories, and allow individuals to live more independently at home, rather than in nursing homes. Another advantage of using a test like the Lumipulse blood test is the ability to inform a patient that their memory loss is not linked to Alzheimer’s disease. While a negative blood test does not entirely rule out an Alzheimer’s diagnosis, it does make it less probable. This could prompt the medical provider to conduct further testing to identify a more accurate cause for the patient’s memory loss. In some instances, the medical provider may conclude that the patient’s memory loss is related to normal aging. This is also important so that patients are not unnecessarily placed on medications that may not help them. It is reasonable to anticipate that additional blood-based biomarkers for diagnosing Alzheimer’s disease and other dementias will be available in the future. Perhaps one day, there will be a dementia panel blood test that can be sent off to provide early diagnosis of a wide range of dementias. Alzheimer’s blood testing is not only beneficial for individuals, but it also represents a significant advancement for research. Doctors and scientists can more easily identify individuals in the early stages of Alzheimer’s disease, which accelerates clinical trials for new medications. This increased diagnostic accuracy can enhance the effectiveness of Alzheimer’s clinical trials, as it ensures that patients enrolled have more reliable diagnoses. Consequently, new and more effective treatments could be developed and made available more quickly. The Lumipulse Alzheimer’s blood test marks a pivotal moment in our approach to this disease. While patients may still need confirmatory testing through brain imaging or spinal fluid analysis, this blood test enables the medical community to adopt a more proactive, precise, and personalized strategy for diagnosing and treating patients with dementia. This simple blood test brings us one step closer to earlier answers, better care, and renewed hope for millions of people facing the uncertainty of dementia.

Worms are decomposers. Many survive by breaking down dead things — dead bacteria, dead plants, dead animals, dead anything. So, they must be accustomed to the stench of death. Not so, a new study suggests — not when the dead organism is another worm.Published in Current Biology, the study states that C. elegans roundworms react adversely to the smell of a deceased counterpart. Not only does this smell invoke a behavioral response of corpse avoidance, but it also invokes a physiological response of increased short-term fertility and decreased long-term fitness and lifespan.“Caenorhabditis elegans prefers to avoid dead conspecifics,” or deceased members of the same species, the authors state in the study, with the worms reacting to death with a range of “aversion” and “survival” responses. Taken together, the results reveal a new signaling mechanism that’s available to worms and possibly other organisms, too, as a means of detecting and responding to death.Read More: These Fruit Flies Aged Faster After Seeing DeathWorms Signal and Detect DeathC. elegans roundworms aren’t the only small organisms that respond to the dead. Ants and bees dispose of the deceased from their colonies, for instance, while fruit flies avoid corpses (and shun flies that have seen corpses themselves). Death-exposed fruit flies even experience faster aging after seeing a deceased counterpart, and have shorter lifespans than those that have had no encounters with death. That these animals respond so strongly to the dead is widely documented. So, when the authors of the new study noticed C. elegans worms wriggle away from corpses, they saw the response as a chance to dig deeper into death signaling and detection. Indeed, while many species’ reactions to death are mediated mainly by sight, that certainly wasn’t the case for wiggling roundworms, which have no eyes and no sense of vision. “We felt this was quite a unique opportunity to start diving into what is happening mechanistically that enables C. elegans to detect a dead conscript,” said Matthias Truttmann, a senior study author and a physiologist at the University of Michigan, according to a press release.To determine how C. elegans worms detect the dead, Truttman and his team exposed the worms to conspecific corpses and to fluids taken from the deteriorating cells of those corpses. The worms responded to both with avoidance, moving away regardless of their age and sex, suggesting that the corpses and fluids carried similar signatures of death. These death cues also resulted in short-term increases in fertility, long-term decreases in fitness (represented by a reduced thrashing rate), and long-term decreases in lifespan. But what were those death cues, exactly, and how did the worms pick up on them?Sounding a Sensory AlarmTo figure out what those cues could be, the study authors recorded the activity in the worms’ sensory neurons as they encountered the corpses and fluids. The recordings revealed that AWB and ASH, two neurons that are responsible for making sense of olfactory stimuli, were activated when the corpses and fluids were present, indicating that the worms were smelling the signature of death.“The neurons we identified are well known to be involved in behavioral responses to a variety of environmental cues,” Truttmann said in the release. According to the study authors, the metabolites AMP and histidine were probably responsible for the signal of death that the C. elegans worms recognized. Though these metabolites are typically contained in living cells, they are released when living cells die and deteriorate — in this case, triggering the behavioral and physiological responses in C. elegans. “They also detect a couple of intracellular metabolites that are not typically found in the environment. If they are around, it indicates that a cell has died, popped open, and that something has gone wrong,” Truttmann said in the release.It is possible that cellular metabolites serve as a signal of death in other organisms, too, Truttmann said, as the release of metabolites from dying and disintegrating cells in one tissue can cause changes in other tissues in humans, for instance. Whether this signal sounds the alarm in other organisms is still uncertain. While further research is required to understand the role of cellular metabolites in detecting death across species, for now, it’s clear that death is a sensitive subject, even for worms like C. elegans.Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:Current Biology. Sam Walters is a journalist covering archaeology, paleontology, ecology, and evolution for Discover, along with an assortment of other topics. Before joining the Discover team as an assistant editor in 2022, Sam studied journalism at Northwestern University in Evanston, Illinois.

Published May 23, 2025 10:00am EDT close Expert warns artificial general intelligence could be more dangerous than a nuclear weapon Tyler Saltsman, founder and CEO of EdgeRunner AI, warns that creating artificial general intelligence could "destroy the world as we know it." Whether it's powering your phone’s autocorrect or helping someone create a new recipe with a few words, artificial intelligence (AI) is everywhere right now. But if you're still nodding along when someone mentions "neural networks" or "generative AI," you're not alone.Today I am breaking down five buzzy AI terms that you've probably seen in headlines, group chats or app updates, minus the tech talk. Understanding these basics will help you talk AI with confidence, even if you’re not a programmer.Stay tuned for more in this series as we dive deeper into privacy-related tech terms and other essential concepts, answering the top questions we get from readers like you.JOIN THE FREE ‘CYBERGUY REPORT’: GET MY EXPERT TECH TIPS, CRITICAL SECURITY ALERTS AND EXCLUSIVE DEALS, PLUS INSTANT ACCESS TO MY FREE ‘ULTIMATE SCAM SURVIVAL GUIDE’ WHEN YOU SIGN UP! Visual Illustration of AI (Kurt "CyberGuy" Knutsson)1. Artificial intelligence (AI)The big umbrella term Artificial Intelligence is a broad term for computer systems that can do tasks normally requiring human intelligence. That includes understanding language, recognizing images, making decisions and even learning from experience.You’re using AI when:Your email suggests repliesYour phone transcribes your voiceNetflix recommends what to watch nextThink of AI as the category; everything else on this list is a branch of it. It’s the foundation for all the smart tools we use today, from voice assistants to facial recognition. As AI continues to evolve, it has the power to make everyday tasks easier, faster and more personalized. But as it becomes more embedded in our lives, understanding the basics is key to using it wisely and protecting your digital autonomy.2. Machine learning (ML)How AI learns patternsMachine Learning is a type of AI that learns from data instead of being explicitly programmed. It improves over time by finding patterns and making predictions.For example: You like action movies. You watch a few. Over time, the algorithm learns your preferences and recommends similar titles, even if you didn’t say anything directly.Common uses of ML:Spam filters in emailFraud detection in bankingPersonalized playlistsML is how AI "gets smarter" by itself, and it's a big part of how tech becomes more helpful and intuitive. From catching suspicious charges on your credit card to curating your favorite music, machine learning can make life more seamless and even safer. But as with any technology, it’s important to stay aware of how your data is being used and who’s doing the learning. The more we understand how it works, the better we can decide how and when to trust it.3. Neural networkThe tech that mimics your brainNeural Networks are a special kind of machine learning designed to mimic how the human brain works, at least loosely. They're made up of layers of "neurons" that process data and make decisions.They’re particularly good at recognizing complex patterns, like identifying faces in photos or translating languages.Use cases include:Face ID unlocking your phoneSpeech-to-text appsTranslating text in real timeIf AI is the brain, neural networks are the brain cells doing the work. Neural networks are the part of AI that actually processes information. They’re designed to mimic how human brains work, taking in data, learning patterns and making decisions. So, when AI recognizes a face, writes a sentence or makes a suggestion, it’s neural networks making that happen behind the scenes. Illustration of humans and machine learning (Kurt "CyberGuy" Knutsson)FOX NEWS AI NEWSLETTER: THE SCHOOL WHERE AI RUNS THE CLASSROOM4. Generative AIAI that creates, not just predictsGenerative AI doesn’t just analyze data, it creates new stuff: text, images, videos, code, music, even voices. It’s trained on huge amounts of content and learns how to generate something new that mimics the original.You’ve seen it in action if you’ve used:ChatGPT or similar bots to write messagesAI art generators like Midjourney or DALL·EAuto-generated captions or social media copyIt’s like giving a machine a vibe and watching it invent something that fits. Generative AI is creative, fast and sometimes uncannily realistic, which is what makes it both exciting and a little unsettling.Think you can tell the difference?Be sure to play my game to guess which photo is AI and which one is real. It’s harder than you think and a good reminder that as these tools get more advanced, staying alert and informed is more important than ever.WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Find the fake kitten. (Kurt "CyberGuy" Knutsson)5. PromptThe magic words that make AI workA prompt is the input you give to an AI system, usually a question, command or description. It’s how you talk to tools like ChatGPT or image generators.The better your prompt, the better the result.Examples:"Write a birthday message in the style of Shakespeare""Create a recipe using only chickpeas and chocolate""Make an image of a robot drinking coffee in Paris, 1920s style"Prompts are to AI what questions are to Google, but with more creativity and conversation. Unlike a search engine that simply points you to existing content, AI can generate entirely new ideas, images and text based on what you ask. It’s more than a search box; it’s a creative tool. Whether you’re drafting a story, designing a logo or planning a vacation, learning how to prompt effectively lets you tap into AI’s full potential as a collaborator, not just an information source.Kurt’s key takeawaysYou don’t need a computer science degree to understand AI, just a few solid definitions. From machine learning and neural networks to generative AI and prompts, these tools are no longer reserved for tech labs; they're becoming part of your everyday life. Whether it’s helping you write an email, organize your photos or get dinner ideas based on what’s in your fridge, AI is already working behind the scenes to make life a little easier (and sometimes a lot more interesting).Now that you’ve got the lingo down, you’ll be better equipped to navigate the AI-powered world with confidence and curiosity.CLICK HERE TO GET THE FOX NEWS APPWant to go deeper? Interested in how AI can improve your daily routine or looking for creative prompt ideas to get the most out of tools like ChatGPT? Let us know by writing us at Cyberguy.com/Contact.For more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/Newsletter.Ask Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved. Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.

Nature, Published online: 22 May 2025; doi:10.1038/d41586-025-01534-wNeurons in a brain region linked with substance-use disorders can affect how much pain is felt when stopping chronic opioid use.

It's shocking! Scientists figure out how the brain forms emotional connections Neural recordings track how neurons link environments to emotional events. Jacek Krywko – May 21, 2025 4:07 pm | 16 Credit: fotografixx Credit: fotografixx Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more Whenever something bad happens to us, brain systems responsible for mediating emotions kick in to prevent it from happening again. When we get stung by a wasp, the association between pain and wasps is encoded in the region of the brain called the amygdala, which connects simple stimuli with basic emotions. But the brain does more than simple associations; it also encodes lots of other stimuli that are less directly connected with the harmful event—things like the place where we got stung or the wasps’ nest in a nearby tree. These are combined into complex emotional models of potentially threatening circumstances. Till now, we didn’t know exactly how these models are built. But we’re beginning to understand how it’s done. Emotional complexity “Decades of work has revealed how simple forms of emotional learning occurs—how sensory stimuli are paired with aversive events,” says Joshua Johansen, a team director at the Neural Circuitry of Learning and Memory at RIKEN Center for Brain Science in Tokyo. But Johansen says that these decades didn’t bring much progress in treating psychiatric conditions like anxiety and trauma-related disorders. “We thought if we could get a handle of more complex emotional processes and understand their mechanisms, we may be able to provide relief for patients with conditions like that,” Johansen claims. To make it happen, his team performed experiments designed to trigger complex emotional processes in rats while closely monitoring their brains. Johansen and Xiaowei Gu, his co-author and colleague at RIKEN, started by dividing the rats into two groups. The first “paired” group of rats was conditioned to associate an image with a sound. The second “unpaired” group watched the same image and listened to the same sound, but not at the same time. This prevented the rats from making an association. Then, one day later, the rats were shown the same image and treated with an electric shock until they learned to connect the image with pain. Finally, the team tested if the rats would freeze in fear in response to the sound. The “unpaired” group didn’t. The rats in the “paired” group did—it turned out human-like complex emotional models were present in rats as well. Once Johansen and Gu confirmed the capacity was there, they got busy figuring out how it worked exactly. Playing tag “Behaviorally, we measured freezing responses to the directly paired stimulus, which was the image, and inferred stimulus which was the sound,” Johansen says. “But we also performed something we called miniscope calcium imaging.” The trick relied on injecting rats with a virus that forced their cells to produce proteins that fluoresce in response to increased levels of calcium in the cells. Increased levels of calcium are the telltale sign of activity in neurons, meaning the team could see in real time which neurons in rats’ brains lit up during the experiments. It turned out that the region crucial for building these complex emotional models was not the amygdala, but the dorsomedial prefrontal cortex (dmPFC), which had a rather specialized role. “The dmPFC does not form the sensory model of the world. It only cares about things when they have emotional relevance,” Johansen explains. He said there wasn’t much change in neuronal activity during the sensory learning phase, when the animals were watching the image and listening to the sound. The neurons became significantly more active when the rats received the electric shock. In the “unpaired” group, the active neurons that held the representations of the electric shock and the image started to overlap. In the “paired” group, this overlap also included the neuronal representation of the sound. “There was a kind of an associative bundle that formed,” Johansen says. After Johansen and Gu pinpointed the neurons that formed those associative bundles, they started looking at how each of these components works. Detraumatizing rodents In the first step, the team identified the dmPFC neurons that sent output to the amygdala. Then they selectively inhibited those neurons and exposed the rats from the “paired” group to the image and the sound again. The result of disconnecting the dmPFC neurons from the amygdala was that rats exhibited a fear response to the image but no longer feared the sound. “It seems like the amygdala can form the simple representations on its own but requires input from the dmPFC to express more complex, inferred emotions,” Johansen says. But there are still a lot of unanswered questions left. The next thing the team wants to take a closer look at is the process that enables the brain to tie an aversive stimulus, like the shock, to one that was not active during the aversive event. In the “paired” group of rats, some multi-sensory neurons responding to both auditory and visual stimuli apparently got recruited. “We haven’t worked that out yet,” Johansen says. "This is a very novel type of mechanism.” Another thing is that the emotional model Johansen and Gu induced in rats was relatively simple. In the real world, especially in humans, we can have many different aversive outcomes tied to the same triggers. A single location could be where you got stung by a wasp, attacked by a dog, robbed of your wallet, and dumped by your significant other—all different aversive representations with myriad inferred, indirect stimuli to go along with them. “Does the dmPFC combine all those representations into sort of a single, overlapping representation? Or is it a really rich environment that bundles different aversive experiences with the individual aspects of these experiences?” Johansen asked. “This is something we want to test more.” Nature, 2025.  DOI: 10.1038/s41586-025-09001-2 Jacek Krywko Associate Writer Jacek Krywko Associate Writer Jacek Krywko is a freelance science and technology writer who covers space exploration, artificial intelligence research, computer science, and all sorts of engineering wizardry. 16 Comments

Humpback Whales Can’t See as Well as Scientists Thought, and It Might Explain Why They Keep Getting Tangled in Fishing Gear Despite having big eyes, the whales can’t make out details of objects more than a few body lengths away, according to a new study Humpback whales have poorer eyesight than previously thought, according to a new study. by wildestanimal via Getty Images With large, grapefruit-sized eyes, it would make sense if humpback whales had decently strong eyesight. So, why do these intelligent cetaceans continue to become entangled in fishing gear? Scientists have dissected the left eye of a juvenile humpback and revealed the species’ eyesight is weaker than biologists previously suspected. Their results, published Wednesday in the journal Proceedings of the Royal Society B, carry implications for how humans can help humpback whales steer clear of fishing nets. When lead author Jacob Bolin, at the time studying marine biology at the University of North Carolina Wilmington, cut open the specimen with his colleagues, they found that the white of the whale’s eye was particularly thick at the back. This made the humpback’s focal length—the distance between the eye’s lens and retina—shorter than expected, as Bolin tells bioGraphic’s Marina Wang. Longer focal length usually indicates sharper eyesight, so this was one sign that humpbacks may have poor vision. Another limitation they discovered involved the number of retinal ganglion cells, which are like “the pixels of the eye,” as Bolin says to the New York Times’ Elizabeth Anne Brown. These neurons are responsible for converting the image on the retina at the back of the eye into electrical signals for the brain. The dissection revealed that humpback whales have a surprisingly low density of these cells, especially compared to humans. The whale had, at most, 180 retinal ganglion cells per square millimeter; humans, meanwhile, have up to about 40,000 in the same area. The researchers also found that the humpback whale could see at 3.95 cycles per degree (CPD), a measure of vision determined by how many pairs of black and white lines an animal can make out in one degree of visual space. Humans have much higher visual acuity, between 60 to 100 CPD, per the paper. Researchers investigate a preserved humpback whale eyeball. Michael Spencer / UNCW The team processed these observations with computer models to simulate how humpback whales see their environment. Their visualizations demonstrated that while the animals can see large, faraway shapes, like schools of fish, they can only detect smaller details within about three to four body lengths of the whale, according to a University of North Carolina Wilmington statement. (That’s about 150 to 200 feet.) Overall, humpback whale eyesight is surprisingly less sharp than what scientists had suggested it might be, given the size of their eyeballs, per a Nature research highlight. That means humpback whales might not see fishing nets until it’s too late. Researchers measuring the dissected humpback whale eye.  Michael Spencer/UNCW “[A low CPD] is bad for a human, but not bad for a whale at all,” says Thomas Cronin, a visual ecologist at the University of Maryland, Baltimore County, who did not participate in the study, to bioGraphic. Whales don’t typically need sharp vision to catch their prey, he adds. In other words, if humans’ boats and nets didn’t get in their way, the whales could get along perfectly well. “This work helps fill a major gap in our understanding of the sensory ecology of large whales, how humpbacks experience their world,” Lori Schweikert, a co-author of the study and neurophysiologist at the University of North Carolina Wilmington, explains in the statement. Elena Vecino Cordero, a biologist at the University of the Basque Country in Spain who has previously analyzed whale eyes but didn’t participate in the study, tells the New York Times that the research might even be overestimating humpback whale eyesight because of how the dissected eye may have changed after spending more than a decade in a jar. Ultimately, the researchers suggest their work could help inspire fishing net designs that are more visible to humpback whales and potentially result in fewer entanglement incidents. Get the latest stories in your inbox every weekday.