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How AI brain mapping can improve disease detection

How AI brain mapping can improve disease detection

Fast Company2 days ago
Traditional brain scans only show part of the picture. They can't fully capture how different regions of the brain communicate—an essential factor in detecting neurological diseases early. Dr. Rahul Biswas, a neurologist at the University of California–San Francisco, is working to change that with AI -powered tools that map these hidden neural connections.
His groundbreaking research reveals how Alzheimer's disrupts brain communication in unexpected areas, challenging long-held assumptions about the disease. Now, through his company, Kaneva Consulting, Dr. Biswas is focused on transforming this science into practical diagnostic tools that can identify brain disorders long before symptoms emerge.
Fast Company spoke with Biswas about how AI is revolutionizing brain health, from early disease detection to personalized treatments and everyday tech. The conversation has been edited for length and clarity.
How are AI models revealing new insights about the brain that weren't possible with traditional neuroscience methods?
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Veteran Inventor Announces New Air Purification System That Splits CO₂, Produces Oxygen, and May Support Health and Ozone Recovery
Veteran Inventor Announces New Air Purification System That Splits CO₂, Produces Oxygen, and May Support Health and Ozone Recovery

Yahoo

time8 minutes ago

  • Yahoo

Veteran Inventor Announces New Air Purification System That Splits CO₂, Produces Oxygen, and May Support Health and Ozone Recovery

Veteran-led innovation introduces a multi-functional air filter system using Conducted Direct Current (CDC) to address environmental and wellness concerns Seminole, FLORIDA , July 20, 2025 (GLOBE NEWSWIRE) -- Clean Air Electric Filter announces the launch of its Conducted Direct Current (CDC) air purification system, a technology developed over 12 years by disabled American veteran Gal Joe Nahum. The system is designed to split carbon dioxide (CO₂) into carbon and oxygen, generate ozone through negative ions, and may offer additional wellness benefits, including support for nutrient absorption. Gal Joe Nahum Photo The CDC filtration system works by passing direct current through the air, targeting CO₂ molecules. As the air flows through the unit, the current splits the carbon from the oxygen atoms. Carbon is internally collected, and purified oxygen is released, contributing to cleaner indoor and outdoor environments. A unique feature of this system is its generation of negative ions (–ions). These ions are naturally found in environments like waterfalls, where the friction of water hitting air produces a charge. 'Negative ions bind to positively charged particles, such as dust and pollutants, pulling them to the ground and purifying the air,' said Gal Joe. Nahum. 'If they reach the upper atmosphere, they may convert into ozone. Given the ozone depletion over Antarctica, restoring this layer is a priority for global health.' Beyond air purification, Gal Joe Nahum suggests the CDC technology may also have future use in health and wellness. 'There is a theory that this technology could assist the body in absorbing essential nutrients like vitamin D and calcium, potentially benefiting bones, nails, and teeth,' he noted. While this aspect remains under exploration, the concept reflects Nahum's commitment to broader applications for public benefit. The system is also being paired with a renewable energy source. Nahum is developing a transformer that harvests atmospheric energy, such as lightning and thunder, and converts it into direct current, allowing the filtration system to operate sustainably off-grid. Gal Joe Nahum, who served during the Desert Storm conflict, has dedicated over a decade of his life to the development of this technology. However, his efforts have not always been met with support. 'I came to NASA with this technology in good faith, hoping to collaborate,' he said. 'But I was dismissed simply because I wasn't part of the system. They forgot that some of the greatest innovators, like the Wright brothers, didn't have formal credentials. Without them, NASA wouldn't even exist.' His work is also chronicled in his book, which explores his scientific innovations and space-related concepts. Learn more at Gal Joe Nahum Clean Air Electric Filter galnahum1971@ in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data

Why Machines Aren't Intelligent
Why Machines Aren't Intelligent

Forbes

time10 minutes ago

  • Forbes

Why Machines Aren't Intelligent

Abstract painting of man versus machine, cubism style artwork. Original acrylic painting on canvas. OpenAI has announced that its latest experimental reasoning LLM, referred to internally as the 'IMO gold LLM', has achieved gold‑medal level performance at the 2025 International Mathematical Olympiad (IMO). Unlike specialized systems like DeepMind's AlphaGeometry, this is a reasoning LLM, built with reinforcement learning and scaled inference, not a math-only engine. As OpenAI researcher Noam Brown put it, the model showed 'a new level of sustained creative thinking' required for multi-hour problem-solving. CEO Sam Altman said this achievement marks 'a dream… a key step toward general intelligence', and that such a model won't be generally available for months. Undoubtedly, machines are becoming exceptionally proficient at narrowly defined, high-performance cognitive tasks. This includes mathematical reasoning, formal proof construction, symbolic manipulation, code generation, and formal logic. Their capabilities also extend significantly to computer vision, complex data analysis, language processing, and strategic problem-solving, because of significant advancements in deep learning architectures (such as transformers and convolutional neural networks), the availability of vast datasets for training, substantial increases in computational power, and sophisticated algorithmic optimization techniques that enable these systems to identify intricate patterns and correlations within data at an unprecedented scale and speed. These systems can accomplish sustained multi-step reasoning, generate fluent human-like responses, and perform under expert-level constraints similar to humans. With all this, and a bit of enthusiasm, we might be tempted to think that this means machines are becoming incredibly intelligent, incredibly quickly. Yet this would be a mistake. Because being good at mathematics, formal proof construction, symbolic manipulation, code generation, formal logic, computer vision, complex data analysis, language processing, and strategic problem-solving, is neither a necessary nor a sufficient condition for 'intelligence', let alone for incredible intelligence. The fundamental distinction lies in several key characteristics that machines demonstrably lack. Machines cannot seamlessly transfer knowledge or adapt their capabilities to entirely novel, unforeseen problems or contexts without significant re-engineering or retraining. They are inherently specialized. They are proficient at tasks within their pre-defined scope and their impressive performance is confined to the specific domains and types of data on which they have been extensively trained. This contrasts sharply with the human capacity for flexible learning and adaptation across a vast and unpredictable array of situations. Machines do not possess the capacity to genuinely experience or comprehend emotions, nor can they truly interpret the nuanced mental states, intentions, or feelings of others (often referred to as "theory of mind"). Their "empathetic" or "socially aware" responses are sophisticated statistical patterns learned from vast datasets of human interaction, not a reflection of genuine subjective experience, emotional resonance, or an understanding of human affect. Machines lack self-awareness and the ability for introspection. They do not reflect on their own internal processes, motivations, or the nature of their "knowledge." Their operations are algorithmic and data-driven; they do not possess a subjective "self" that can ponder its own existence, learn from its own mistakes through conscious reflection, or develop a personal narrative. Machines do not exhibit genuine intentionality, innate curiosity, or the capacity for autonomous goal-setting driven by internal desires, values, or motivations. They operate purely based on programmed objectives and the data inputs they receive. Their "goals" are externally imposed by their human creators, rather than emerging from an internal drive or will. Machines lack the direct, lived, and felt experience that comes from having a physical body interacting with and perceiving the environment. This embodied experience is crucial for developing common sense, intuitive physics, and a deep, non-abstracted understanding of the world. While machines can interact with and navigate the physical world through sensors and actuators, their "understanding" of reality is mediated by symbolic representations and data. Machines do not demonstrate genuine conceptual leaps, the ability to invent entirely new paradigms, or to break fundamental rules in a truly meaningful and original way that transcends their training data. Generative models can only produce novel combinations of existing data, Machines often struggle with true cause-and-effect reasoning. Even though they excel at identifying correlations and patterns, correlation is not causation. They can predict "what" is likely to happen based on past data, but their understanding of "why" is limited to statistical associations rather than deep mechanistic insight. Machines cannot learn complex concepts from just a few examples. While one-shot and few-shot learning have made progress in enabling machines to recognize new patterns or categories from limited data, they cannot learn genuinely complex, abstract concepts from just a few examples, unlike humans. Machines still typically require vast datasets for effective and nuanced training. And perhaps the most profound distinction, machines do not possess subjective experience, feelings, or awareness. They are not conscious entities. Only when a machine is capable of all (are at least most of) these characteristics, even at a relatively low level, could we then reasonably claim that machines are becoming 'intelligent', without exaggeration, misuse of the term, or mere fantasy. Therefore, while machines are incredibly powerful for specific cognitive functions, their capabilities are fundamentally different from the multifaceted, adaptable, self-aware, and experientially grounded nature of what intelligence is, particularly as manifested in humans. Their proficiency is a product of advanced computational design and data processing, not an indication of a nascent form of intelligence in machines. In fact, the term "artificial general intelligence" in AI discourse emerged in part to recover the meaning of "intelligence" after it had been diluted through overuse in describing machines that are not "intelligent" to clarify what these so-called "intelligent" machines still lack in order to really be, "intelligent". We all tend to oversimplify and the field of AI is contributing to the evolution of the meaning of 'intelligence,' making the term increasingly polysemous. That's part of the charm of language. And as AI stirs both real promise and real societal anxiety, it's also worth remembering that the intelligence of machines does not exist in any meaningful sense. The rapid advances in AI signal that it is beyond time to think about the impact we want and don't want AI to have on society. In doing so, this should not only allow, but actively encourage us to consider both AI's capacities and its limitations, making every effort not to confuse 'intelligence' (i.e. in its rich, general sense) with the narrow and task-specific behaviors machines are capable of simulating or exhibiting. While some are racing for Artificial General Intelligence (AGI), the question we should now be asking is not when they think they might succeed, but whether what they believe they could make happen truly makes sense civilisationally as something we should even aim to achieve, while defining where we draw the line on algorithmic transhumanism.

Burgeoning Hedge Strategy Amplifies Commercial Technology In Defense
Burgeoning Hedge Strategy Amplifies Commercial Technology In Defense

Forbes

time10 minutes ago

  • Forbes

Burgeoning Hedge Strategy Amplifies Commercial Technology In Defense

ATLANTIC OCEAN - MAY 14: In this handout released by the U.S. Navy, An X-47B Unmanned Combat Air ... More System (UCAS) demonstrator launches from the flight deck of the aircraft carrier USS George H.W. Bush (CVN 77) May 14, 2013 in the Atlantic Ocean. George H.W. Bush is the first aircraft carrier to sucessfully catapult-launch an unmanned aircraft from its flight deck. The Navy plans to have unmanned aircraft on each of its carriers to be used for surveillance and be armed and used in combat roles. (Photo by U.S. Navy via Getty Images)Advancing the Mix of Defense Capabilities The President's defense budget, released in June, embraces a vision of balancing traditional platforms with new digital technologies—a 'Hedge Strategy'—at a pivotal moment for military modernization. This "high/low" approach, enables the U.S. military to complement its arsenal of (high) expensive defense platforms of ships, tanks and planes with new capabilities such as small, inexpensive, AI-enabled and upgradeable unmanned systems (low). Hedge Strategy is a term coined by Rear Admiral (retired) Lorin Selby and me in a paper we co-authored in 2022. Later that year, Rep. Ken Calvert, Chairman of the House Appropriations Committee—Defense (HAC-D), called for adopting new technologies—predominantly commercial technologies—as a hedge strategy and provided increased funding for the concept. Today, the vision is becoming a reality on a much larger scale. America's military arsenal now includes an increasing inventory of unmanned systems across air, sea, and ground, along with a proliferated constellation of small satellites. Harnessing leading commercial technologies, these capabilities weren't mature enough to be on the battlefield a decade ago but have proven instrumental in Ukraine and other recent conflicts. In fact, in describing the War in Ukraine a few weeks ago, former Undersecretary of Defense for Policy, Colin Kahl, observed that this war is a surprising mix of the trenches and artillery shells of World War I with World War III. We haven't left the industrial age of armaments behind, but armaments are now complemented by drones and emerging digital technologies which, in combination, are game-changing capabilities a modern military cannot live without. This photograph shows the first batch of Ukrainian made drone missiles "Peklo" (Hell) delivered to ... More the Defence Forces of Ukraine in Kyiv on December 6, 2024, amid the Russian invasion of Ukraine. December 6, 2024 marks the 33th anniversary of the Armed Forces of Ukraine. (Photo by Genya SAVILOV / AFP) (Photo by GENYA SAVILOV/AFP via Getty Images) Recognizing this, Congress has strongly supported the organizations within the Defense Department which focus on commercial capabilities such as the Defense Innovation Unit (DIU), whose budget has increased ten-fold in the last three years to $2 billion for FY26, and the Office of Strategic Capital, which now can offer up to $4 billion in loans for component technologies like batteries and rare-earth magnets that are critical for defense. The Defense Department must pivot quickly to adopt new capabilities to augment what's in place and do so with more cost-effective technologies. In other words, our military must shift from expending the U.S. Navy's multi-million dollar missiles to neutralize Houthi low-cost drone attacks. Additionally, we must embrace more nimble and asymmetric warfare like Ukraine's recent Operation Spider's Web. Systems that provide low-cost, attritable mass, better tactical situational awareness, and optimized decision-making by fusing multi-modal data in real-time all reinforce the mix shift towards new capabilities. Traditional primes like Lockheed Martin and L3Harris, along with emerging primes like Anduril, are advancing lower-cost, mass produced munitions to improve defense-offense cost ratios. SANA'A, YEMEN - NOVEMBER 13: Mock drones and missiles are displayed at an exhibition on November 13, ... More 2024, in Sana'a, Yemen. Yemen's Iran-allied Houthis announced on Tuesday that they launched a significant attack by drone and ballistic and cruise missiles on US Navy vessels, while they were navigating at the Bab al-Mandeb strait off the Yemeni coast. (Photo by)$1 Trillion for Defense The appropriations landscape today is multifaceted. For the current fiscal year—FY25–Congress did not pass a defense budget, so the Defense Department operates under a Continuing Resolution which is less efficient than an on-time budget since a CR usually means no annual spending increases (regardless of inflation) and no new program starts. For next fiscal year—FY26, which begins this October—the President submitted his budget four months late which puts a burden on Congress to appropriate funds on time. The FY26 budget request at $831.5 billion is only slightly larger than last year's $825 billion. WASHINGTON, DC - JULY 03: Members of the House walk up the steps outside the U.S. Capitol during the ... More procedural vote on the One Big Beautiful Bill Act on July 03, 2025 in Washington, DC. Speaker of the House Mike Johnson (R-LA), President Donald Trump and other Republicans are scrambling to gather enough support to begin debate on Trump's sweeping tax and spending bill. (Photo by) However, Congress recently passed the Big Beautiful Bill (or Reconciliation) which includes $150 billion of additional and multi-year defense spending for the next five fiscal years with $133 billion planned in FY26. Defense spending in FY26 might include the regular appropriation of $831.5 billion plus $133 billion plus around $40 billion for nuclear refurbishment executed by the Department of Energy. This combination pushes top-line defense spending to a record $1 trillion. However, this level of spending, as a percentage of U.S. GDP (estimated at $30 trillion for 2026), is only 3.4% of the economy. Historically, the U.S. has spent much more on defense: 6% in the Reagan build-up, 9% in the Vietnam War and routinely 8-10% in the 1950s. The cost of fighting (and potentially losing) a major war with China is far more costly than what the U.S. spends on defense. With a nod to Ronald Reagan, President Trump's 'Peace through strength' policy means the U.S. must continue to invest to deter adversaries from initiating future wars. A $1 Trillion defense budget is likely to become the norm as geopolitical tensions suggest we will be in a great power competition with multiple capable adversaries for years to come. Our adversaries, especially China, recognize that new technologies can deliver battlefield advantage and our adversaries are sharing military technology and strengthening each other's supply chains. As a result, the race is on to invest in new capabilities and adopt them rapidly to yield a military edge. In addition, the U.S. must rebuild with its allies the ability to sustain manufacturing for the materiel needed in a conflict. Golden Dome, All Types of Unmanned Systems, and AI HawkEye 360 satellites gather signals intelligence data to provide governments with better ... More understanding of activity in regions such as the South China Sea. HAWKEYE 360, HERNDON, VIRGINIA Specifically, there are several initiatives fueling large spending increases that should benefit commercial technology vendors. Perhaps most visible is the President's Golden Dome estimated at three years and $175 billion or more to develop. The FY26 down payment on this initiative is $25 billion with two-thirds for more and better sensors from space that could detect enemy missiles, improvements in standardized satellite manufacturing, and better ways to search and process the increasing amount of space-based imagery. DoD will heavily leverage commercial solutions in delivering Golden Dome. Rendering of Albedo's Clarity-1 in Very Low Earth Orbit (VLEO) capturing exquisite imagery For the first-time, there are large, dedicated budget lines for aerial, ground-based, maritime surface and underwater autonomous systems, totaling $16 billion including AI-based software to control them. A partial list includes $1.4 billion for small unmanned aerial vehicles like the first-person view drones in Ukraine, $2 billion for medium maritime surface autonomy, $.7 billion for underwater autonomy, $1 billion for one-way aerial attack drones, and $3 billion for systems to counter drones. Lightfish Security ASV features a small solid state radar and a high resolution day/night camera to ... More detect and identify vessels within a few miles and transmit the radar image and video real time to the user. Designed to be used in a constellation of multiple vessels to counter Illegal, Unreported and Unregulated Fishing, monitor protected marine areas, secure our maritime borders and maintain overwatch on critical maritime infrastructure In AI-based software, spending will increase 50% from $1.5 billion to $2.2 billion according to defense data provider, Obviant. This should significantly expand the military's adoption of AI-based commercial applications such as modeling and simulation for logistics, analyzing the electromagnetic spectrum, code generation and modernization, and other AI-native applications like agentic workflows. The Pentagon's Chief Digital and Artificial Intelligence Office recently awarded four contracts (each worth up to $200 million) to the leading large language model providers so that, analogous to the Department's multi-cloud strategy, the Pentagon can make use of multiple models for better decision What? The upshot of this shifting mix of capabilities is a much more favorable environment for commercial technology adoption with opportunities for new vendors. The defense primes have consolidated 90% over the last 30 years to only five companies. While there are only one to three primes competing for major weapon systems, there are dozens of companies producing unmanned systems, software. and space solutions. As the Department buys these new capabilities, there is a much-needed opportunity to expand the supply base. In addition, both the Congress and the Administration have been rapidly ordering changes to streamline the requirements, budget and acquisition processes that underlie the historically long timeframes to deliver new warfighter capabilities. The SPEED Act and FoRGED Act will include some of the biggest reforms in decades much of which will be incorporated in this year's National Defense Authorization Act. The President has already issued six Executive Orders to modernize defense acquisition, realize American drone dominance and rapidly increase shipbuilding capacity. The process changes incorporated in these Orders reinforce existing law that the military should buy commercial first wherever possible (rather than unique military items) and emphasize commercial methods of procurement (leveraging Other Transaction Authority vs. Federal Acquisition Regulations). Additionally, the Department should use a modular open-systems approach when designing large platforms that allows for substituting more capable subsystems on a faster cycle (rather than buying a set specification from a single vendor for decades). These changes create an inflection point for commercial technology adoption. UNITED STATES - MAY 06: Full committee hearing on the "Department of Defense at High Risk: The ... More Chief Management Office's Recommendations for Acquisition Reform and Related High Risk Areas." Witnesses: Deputy Defense Secretary William Lynn testifies Location: 2118 Rayburn House Office Building. May 6, 2009. (Photo By Douglas Graham/) In an increasingly dangerous world, we are entering a new era of record defense spending and the adoption of commercial technologies to augment current capabilities. The Trump Administration is striving for real reform in what's being bought and how it's being bought—doubling down on acquisition tradecraft pioneered by the Defense Innovation Unit as described in the book Unit X. Consequently, these process simplifications make it more attractive for venture-backed companies to develop a defense business. Hedging existing military capabilities with solutions from defense tech vendors not only complements what defense primes can do with large platforms (ships, planes and tanks) but also brings more competition to defense procurement with companies that can ramp manufacturing quickly. Today's arsenal of democracy needs to not only be better stocked but also stocked with more modern capabilities. Additionally, the arsenal should include companies that can iterate capabilities with warfighter feedback since improvements on the battlefield now happen in hours not years. Ukraine demonstrates that wars are never won as rapidly as aggressors imagine before the conflict. Consequently, success requires new capabilities that evolve rapidly plus sufficient industrial capacity to sustain a war effort for years. A SpaceX Falcon Heavy rocket carrying the National Oceanic and Atmospheric Administration's (NOAA) ... More weather satellite Geostationary Operational Environmental Satellite U (GOES-U) lifts off from Launch Complex 39A at NASA's Kennedy Space Center, Florida, June 25, 2024. The United States on June 25 launched a new satellite expected to significantly improve forecasts of solar flares and coronal mass ejections — huge plasma bubbles that can crash into Earth, disrupting power grids and communications. A SpaceX Falcon Heavy rocket carrying the satellite into orbit took off from NASA's Kennedy Space Center in Florida at 5:26 pm (2126 GMT), the US space agency announced. (Photo by Miguel J. Rodriguez Carrillo / AFP) (Photo by MIGUEL J. RODRIGUEZ CARRILLO/AFP via Getty Images)

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