Six planets are hanging out in early morning skies this month. Here's how to spot them
These linkups happen when several planets appear to line up in the night sky at once. Such parades are fairly common, happening around every year depending on the number of planets. At least one bright planet can be spotted on most nights, weather permitting, according to NASA.
Six planets were visible in January skies and every planet of our solar system was visible in February, but not all could be spotted with the naked eye.
Venus, Jupiter, Saturn and a faint Mercury are visible this month without any special equipment, and the best chances to spot them are over the next week. Uranus and Neptune can only be glimpsed through binoculars and telescopes.
Jupiter and Venus made a close brush earlier this week and are still near each other in the eastern sky, 'close together like cat's eyes,' said Carolyn Sumners at the Houston Museum of Natural Science.
Mercury will be at its farthest point from the sun on Tuesday morning, making it easier to spot before it disappears into the sun's glare.
To catch the planets, go out in the morning shortly before sunrise and look east. Try to find Jupiter and Venus clustered together first. Saturn is off to the side and Mercury will be close to the horizon, trying to rise before the sun.
'You're looking for little tiny pinpoints of light, but they are the brightest ones,' said Justin Bartel with the Science Museum of Virginia. 'They don't really twinkle like the stars do.'
Before heading out, make sure it is a clear, cloudless morning and try to get away from tall buildings that could block the view.
Mercury will hide behind the sun again toward the end of the month, but a crescent moon will then join the parade. The next big planetary hangout is in February.
___
The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute's Department of Science Education and the Robert Wood Johnson Foundation. The AP is solely responsible for all content.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
an hour ago
- Forbes
Psychology Aims For A Unified Theory Of Cognition And AI Will Be A Big Help To Get There
In today's column, I examine the ongoing pursuit by psychology to devise a unified theory of cognition. The deal is this. There have been numerous attempts that have been floated regarding proposed unified theories or models of cognition. Subsequently, by and large, those theories or models have been sharply criticized as being at times incomplete, illogical, unfounded, and otherwise not yet fully developed. The desire and need for a true and comprehensive unified theory of cognition persists and remains exasperatingly elusive. Into this pursuit comes the use of AI, especially modern-era AI such as generative AI and large language models (LLMs). Can we make a substantive forward leap on devising a unified theory of cognition via leaning into contemporary AI and LLMs? Some say abundantly yes, others wonder if doing so will be a distraction and lead us down a primrose path. Let's talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). AI And Psychology As a quick background, I've been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that entails the field of psychology, such as providing AI-driven mental health advice and performing AI-based therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For a quick summary of some of my posted columns on this evolving topic, see the link here, which briefly recaps about forty of the over one hundred column postings that I've made on the subject. There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors too. I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS's 60 Minutes, see the link here. You might find of keen interest that AI and psychology have had a longstanding relationship with each other. There is a duality at play. AI can be applied to the field of psychology, as exemplified by the advent of AI-powered mental health apps. Meanwhile, psychology can be applied to AI, such as aiding us in exploring better ways to devise AI that more closely approaches the human mind and how we think. See my in-depth analysis of this duality encompassing AI-psychology and psychology-AI at the link here. The Enigma Of Human Cognition The American Psychological Association (APA) defines cognition this way: One nagging mystery underlies how it is that we can think and embody cognition. All sorts of biochemical elements in our brain seem to work in a manner that gives rise to our minds and our ability to think. But we still haven't cracked the case on how those neurons and other elements in our noggin allow us to do so. Sure, you can trace aspects at a base level, yet explaining how that produces everyday cognition is a puzzle that won't seem to readily be solved. This certainly hasn't stopped researchers from trying dearly to figure things out. Hope springs eternal that the mysteries of cognition will be unraveled and we will one day know precisely the means by which cognition happens. Nobel prizes are bound to be awarded. Fame and fortune are in the cards. And imagine what else we might do to help and overcome cognitive disorders, along with potentially enhancing cognition to nearly unimaginably heightened levels. This is undoubtedly one of the most baffling mysteries of all time, and there is a purist sense of absolute joy and satisfaction in solving it. Various Types Of Models When seeking to come up with a unified theory of cognition, the route taken usually entails these four major paths: You can use only one of those approaches, or you can use two or more. If you opt to use two or more, your best bet is to make sure each model aligns with the other models being utilized. Any misalignment will indubitably bring criticism and skepticism raining down upon you. For example, if you propose a conceptual model and a mathematical model, but those two don't sync up, it becomes an easy line of attack to suggest that your theory is hogwash. AI And Computational Models A tempting avenue for cognition modeling these days is to rely upon an AI-based computational model that leverages the latest generative AI and LLMs. You can essentially repurpose a popular LLM, i.e., OpenAI's ChatGPT, which garners 400 million weekly active users, or Anthropic Claude, Google Gemini, Meta Llama, and so on. Those off-the-shelf LLMs are ready-made for experimenting on psychology-based premises. I recently explained how contemporary generative AI is devised to react to psychological ploys and techniques, an intriguing facet that is both helpful and potentially hurtful, see my coverage at the link here. One monumental wrinkle is whether a conventional LLM is suitable for representing a semblance of human cognition. Allow me to elaborate on this vital point. The mainstay of LLMs makes use of an artificial neural network (ANN). This is a series of mathematical functions that are computationally rendered in a computer system. I refer to this as an artificial neural network to try and distinguish it from a true neural network (NN) or wetware that is inside your head. Please be aware that ANNs are an exceedingly loosely contrived variation of true NNs. They are not the same. An ANN is quite far from real NNs and, in contrast, is many magnitudes simpler. For my detailed explanation about ANNs versus NNs, see the link here. The bottom line is that an instant criticism of any cognition research that dovetails into LLMs is that you are starting at a recognized point of heated contention. Namely, a cogent argument is that since ANNs are not the same as true NNs, you are building your cognition hopes on somewhat of a house of cards. The counterargument is to acknowledge that ANNs are indeed not an isomorphic match, and instead, you are merely engaging them to aid in a broad-based simulation that doesn't have to be a resolute match. In any case, I stridently support using LLMs as insightful exploratory vehicles and assert that we can gain a great deal of progress about cognition in doing so, assuming we proceed mindfully and alertly. LLMs And Intrinsic Human Behavior Suppose you decide to use an off-the-shelf LLM to perform a cognitive modeling investigation. There is something important that you need to be thinking about. I shall unpack the weighty consideration. First, be aware that LLMs are developed via pattern-matching on human writing that is scanned across the Internet. That's how the fluency of LLMs comes about. The ANN is used to pattern-match on how we use words. In turn, when you enter a prompt into generative AI, the generated response produces words composed into sentences that appear to be on par with human writing. They reflect the computational mimicry of extensive computational pattern-matching based on words (actually, it is based on tokens, see the details in my discussion at the link here). You can't especially declare that the LLM is thinking like humans. The AI is using words and patterns about the usage of words. That's not necessarily a direct embodiment of human thinking per se, and more so, presumably, the indirect outcomes of human thinking. One clever idea is to augment an off-the-shelf LLM by aiming to further data-train the AI on veritable traces of human thinking (well, kind of, as you'll see momentarily). Perhaps that will enable the LLM to be more closely aligned with what human cognition consists of. For example, I fed transcripts of therapist-patient sessions into a major LLM to see if it might be feasible to augment its data training and guide the AI toward behaving more like a versed human therapist, see my experiment at the link here. Psych Experimental Results As Rich Data What other kinds of data could we potentially use to perform augmented data training of an LLM so that it can be more readily suited for cognition experimentation? Easy-peasy, tap into the vast tome of psychology experiments that have been performed endlessly on all sorts of people for many decades. Here are the steps. Collect together that data. Work the data into a readable and usable shape. Feed it into an existing LLM, doing so via a method such as RAG (retrieval-augmented generation), see my RAG elicitation at the link here. Voila, perhaps you've tuned up conventional generative AI to better simulate human behavior. A recent research study took that innovative approach. In an article entitled 'A Foundation Model To Predict And Capture Human Cognition' by Marcel Binz et al, Nature, July 2, 2025, the paper made these key points (excerpts): Details Of The Approach The researchers chose to use Meta Llama as their base LLM. The data augmentation was done via the use of the increasingly popular technique known as QLoRA (quantized low-rank adaptation), a distant cousin of RAG. They transcribed 160 experiments into natural language data. It was publicly available data. The types of experiments included many of the classics in psychology, such as memory recall, supervised learning, decision-making, multi-armed bandits, Markov decision processes, and others. To give you a sense of what those experiments are like, consider these two examples: Handily, the researchers have opted to make the dataset available, known as Psych-101, and can be accessed freely on Huggingface. In addition, they have nicely made available the augmented Meta Llama model, which they refer to as Centaur, and which is also freely available on Huggingface. It is a welcome touch because other researchers can now come along and do not need to begin from scratch. They can reuse the arduous and time-consuming work that went into devising Psych-101 and Centaur. Thus, the dataset and the model are ready-made for launching new investigations and serve as a springboard accordingly. The Results In Brief A commonly utilized means of validating an LLM consists of holding back some of the training data so that you can use the holdback for testing purposes. This is a longstanding technique that has been used for statistical model validations. You might use, say, 90% of the data to do the augmented data training and keep the remaining 10% in reserve. When you are ready to test the LLM, you give it the data that was aside to see if the AI can adequately predict the presumed unseen data. They did this and indicated that their Centaur LLM did a bang-up job on the hold-out data. The next step typically undertaken is to employ a make-or-break test when aiming to devise a generalizable model. You give the LLM data that is considered outside the initial scope of the augmentation. The handwringing question is whether the LLM will generalize sufficiently to contend with so-called out-of-distribution (OOD) circumstances. The researchers opted to select a handful of OOD settings, including economic games, deep sequential decision tasks, reward learning, etc. Their reported results indicate that Centaur LLM did quite well at making predictions associated with those previously unseen experimental transcripts. Overall, kudos to the researchers for thinking outside the box on AI and psychology. Some Thoughts To Ponder I'd like to cover a few quick thoughts overall. First, one agonizing difficulty with gauging an off-the-shelf pre-cooked LLM for any kind of newly encountered circumstances is that it is challenging to know whether such data or similar data might have been scanned during the initial setup of the LLM. Usually, only the AI maker knows precisely what data was initially scanned. Ergo, it is worthwhile to be mindful in interpreting generalizability since an LLM might have already had an unknown leg-up previously. Second, and perhaps more importantly, the desire to push toward a semblance of cognitive realism by further data training of an LLM is a laudable idea. Will the AI be more human-like in its reasoning patterns? Maybe, maybe not. One important determinant is whether the AI is still resorting to human-like language and not necessarily patterning on human reasoning. There is a huge debate going on regarding LLM foundational models that are claimed to be using 'reasoning' versus whether they are still potentially doing heads-down next-token prediction, see my coverage on the lively dispute at the link here. Taking Next Steps The overarching aim to see if we can properly ground cognitive computer-based computational simulations in a more psychologically plausible way is exciting. No doubt about that. The researchers also noted that there might be entirely different AI architectural approaches that might be better for us to pursue, beyond the somewhat conventional infrastructures currently dominating the AI realm right now. As a heads-up, some ardently believe that our prevailing LLMs and AI architecture are not going to get us to artificial general intelligence (AGI) or artificial superintelligence (ASI). You see, the trend right now is to mainly power up prevailing designs with faster hardware and more computational running time. But the incremental benefits could be misleadingly tying us to a road that leads to a dead-end. Could the desire to attain a unified model of cognition be the kick in the pants to the AI field to look beyond the groupthink of today's AI and LLMs? I certainly hope so. As General George S. Patton once proclaimed: 'If everyone is thinking alike, then somebody isn't thinking.'
Yahoo
3 hours ago
- Yahoo
Moving to a more walkable city pays off for health, scientists find
People who moved to a walking friendly city walked more, and at the brisk pace favoured to improve health and prevent disease, a new natural experiment shows. Walkable cities allow you to access more amenities on foot for daily life, like going to school or work, buying ingredients to make dinner or heading to the park to play. But that's not available to everyone, given many cities and suburbs in Canada and the U.S. have been designed to emphasize transit by car, urban planners say. Teasing out cause from effect — whether walkable cities lead people to move more or if people who like to walk tend to live in more pedestrian-friendly cities — is important to nail down because it could help encourage more investment in safe sidewalks and zoning to encourage physical activity, medical researchers say. To find out, Tim Althoff, a computer science professor at the University of Washington in Seattle, used data from a step-tracking app to compare daily steps of more than 5,400 people who moved between major U.S. cities. "In short, we found that your city can help make you healthier," Althoff said. "The design of your city impacts how much you walk and as a result, your health." Althoff and his co-authors said in this week's issue of the journal Nature that moving from a less walkable city to a more walkable one adds about 1,100 steps a day on average. It's something the computer scientist has seen first-hand. "I grew up in a rural area in Germany," Althoff said. "I've lived in California and now in Seattle. Personally, I'm a really big fan of public transit, but I also, for instance, intentionally move to a neighborhood where it would be close to a bus stop." Canadian and international guidelines recommend adults get 150 minutes or more a week of moderate to vigorous physical activity a week. The researchers found people who lived in more walkable cities were about twice as likely to accumulate those steps. Dr. Laura Rosella, a professor of epidemiology at the Dalla Lana School of Public Health at the University of Toronto, said several high-quality Canadian studies point to decreased risk of Type 2 diabetes, heart disease and prediabetes as well as mental health benefits from more walkable cities. "We spend lots of money on the health care system," Rosella. "This [walkability] is something that literally we could make small tweaks that could have a huge difference." To apply the findings in Canada's climate, Rosella said, policy makers would also need to take into account safety considerations, like clearing snow and ice on sidewalks. Benefits of mixed density Ahmed El-Geneidy, a professor in urban planning at McGill University, said it takes about 15 years to gradually make changes to neighbourhoods and change the culture so people walk around cities more. "The whole idea is that you need to build high density beside the single family" homes, El-Geneidy said. That's how areas like Montreal's Plateau neighbourhood encouraged people to walk to more destinations, like grocery shops, he said. WATCH | Urban renewal for Edmonton residents: Paul Sharma, director of chronic disease and injury prevention at Peel Public Health, said Mississauga and Brampton's sprawling suburban neighbourhoods are sedentary places. Residents tend to have longer commutes compared to elsewhere in Ontario, according to the region's data. To design more walkable communities, Peel officials say they're working on factors like increasing density, proximity to services, and making wider sidewalks with better lighting and shade. "This is where public health and local planners need to work closely together to improve the health of the residents," Sharma said. At a playground in Calgary's Crestmount residential neighbourhood, Jarek Soltys said the family chose the location to be close to the mountains and walking paths, where they get steps in for enjoyment, but not transportation for errands. "When we moved here seven years ago there really wasn't anything here at all, not even a convenience store so we knew we would be driving places," Soltys said. "That is the reality of the suburbs in a big city."
CNBC
3 hours ago
- CNBC
Investing in Space: All aboard the SpaceX Mars express
It was a matter of time, with governments racing to clinch the first Mars laurels, that private companies would start offering rides to the red planet. Italy's Agenzia Spaziale Italiana (ASI) became the first client who's signed on to send scientific experiments aboard SpaceX's first commercial flights to Mars — where Elon Musk's space company has yet to land. "Italy is going to Mars!" ASI President Teodoro Valente announced on social media, with Italian news outlet ANSA reporting the agency's payloads will feature a plant growth experiment, a weather surveillance station and a radiation sensor for data collection. "#MadeinItaly on #Mars," Italian Industry Minister Adolfo Urso celebrated, while SpaceX President and COO Gwynne Shotwell declared open season for the company's Mars launch bookings. "Get on board! We are going to Mars! SpaceX is now offering Starship services to the red planet," she said on the X social media platform. Notably, no timeline was given for the launch dates of these flights — with the odds of short-term travel to Mars increasingly under question. Both parties to the arrangement have been committed to Mars ventures. ASI barely just made headlines at the end of last month, when it inked a deal to develop the first human lunar outpost with Thales Alenia Space — building on the Italian space agency's 2020 partnership with NASA to coordinate bringing astronauts back to the Moon under the Artemis Accords. Coming in third after France and Germany, Italy contributed 800 million euros ($935 million) — or 15.8% - to the European Space Agency's 7.68 billion euro adopted budget for 2025. It's also been heavily involved in the ExoMars mission, which seeks to launch the Rosalind Franklin rover around 2028. It's meanwhile at once surprising and predictable that SpaceX, which made a name for itself out of commercializing space launches, is already leaping to book Mars excursions. A longtime NASA contractor, the firm's also been offering satellite launch services to Eutelsat's OneWeb and AST SpaceMobile. A few days back, Amazon, whose chief Jeff Bezos owns his own rival rocket company Blue Origin, tapped Musk's company for the second time and launched its fourth batch of Kuiper satellites on SpaceX's 100th mission this year. And Musk has certainly been vocal about his plans to pursue Mars colonization, once echoed by U.S. President Donald Trump's administration. But there's no escaping one (nearly 400-feet) problem. SpaceX's reusable mammoth rocket Starship — the key to materializing Musk's Mars ambitions — has had a long-storied string of publicized test flight failures this year amid technical and refueling woes. We're about to see during its next attempt later this month if it's overcome these challenges in the three months since its last explosive stint. That's skipping over a June incident when a Starship rocket exploded while being loaded with methane and liquid oxygen propellant ahead of its launch — due, Musk later said, citing preliminary data, to problems in the payload bay. Critically for our conversation, Starship is not yet rated crew-ready, and Musk himself has now pushed back his initial targets, flagging a "slight change" of a crewed flight during the next window in 2026, when Earth and the red planet are optimally aligned for travel to Mars. "Slight chance of Starship flight to Mars crewed by Optimus in Nov/Dec next year. A lot needs to go right for that," he said last week on social media. "More likely, first flight without humans in ~3.5 years, next flight ~5.5 years with humans. Mars city self-sustaining in 20 to 30 years." It may seem too early to start selling tickets to Mars, it's no secret that launch capacity worldwide has been struggling to keep up with demand for space access. Time will tell whether ASI's enthusiasm was ultimately strategic or premature. The space law take on NASA's plans for a lunar nuclear reactor — A lawyer reviews the legal backdrop of NASA's next steps to set up a nuclear reactor on the Moon to power satellites and equipment for Mars exploration. — The Conversation Apollo 13 commander dies at 97 — Astronaut Jim Lovell, who commanded the Apollo 13 mission to the Moon and undertook four spaceflights, has passed away. — NBC News What would outer space sound like? — Some scientists have been equating some of the faint sounds in space into rumblings discernible by humans, looking to "sonify" space. — Live Science NASA launches next round of LunaRecycle Challenge — NASA has kicked off phase 2 of the LunaRecycle Challenge asking firms to create recycling systems to convert deep-space mission waste into fabrics, plastics, foam and metals. Submissions are due in January 2026. — Space Daily The return of space insurers — Space insurers are making their way back to the industry, after getting burned on substantial losses years prior. — Space News Data centers set sights on space – Rising data traffic demand and climate risks have compelled tech giants to look skyward for orbital and lunar data server sites. – U.S. to tout new rocket artillery system — The U.S. is set to welcome the new Global Mobile Artillery Rocket System, which completed its initial test firing in New Mexico last week and has a shoot-and-scoot capability allowing it to fire and depart. — The National Interest China unveils rocket recovery ship — Beijing has launched its first ship that will recover reusable rockets, the Xingji Guihang ("Interstellar Return") developed by Chinese private firm iSpace. — South China Morning Post United Launch Alliance CEO outlines plans to reuse tech — United Launch Alliance's Tory Bruno said test flights with the SMART Reuse system aimed at recovering and reusing booster components could begin as early as 2026. — NASA Spaceflight Why does Amazon keep contracting SpaceX launches? — SpaceX's 100th launch of the year carried satellites for Amazon, raising the question of why Jeff Bezos, owner of rocket company Blue Origin, is still employing his rival's services. Ars Technica takes a look. — Ars Technica Northrop Grumman, U.S. Space Force integrate antennas in DARC test — Northrop Grumman and the United States Space Force succeeded to integrate several antennas at the Deep-Space Advanced Radar Capability (DARC) Site 1 for satellite multiple satellites. — Defence Industry Europe Ariane 6 takes off with climate monitoring satellite — Europe's Ariane 6 rocket undertook its third launch, this time to deliver a weather forecasting and climate monitoring satellite. The rocket is critical to lessen Europe's reliance on SpaceX. — EuroNews Aug. 14 — SpaceX's Falcon 9 to take off with Starlink satellites out of Florida Aug. 15 — Landscape's Zhuque-2E rocket to head out with an unknown payload out of Jiuquan Aug. 15 — SpaceX's Falcon 9 to launch with Starlink satellites out of California Aug. 16 — CAS Space's Kinetica-1 to depart with an unknown payload out of Jiuquan Aug. 16 — SpaceX Falcon 9 to leave with Starlink satellites out of Florida Aug. 17 — China Aerospace Science and Technology Corporation's Long March 4C to take off with an unknown payload out of Xichang Aug. 20 — Roscosmos' Soyuz 2.1a to launch with a Bion-M satellite out of Kazakhstan
