Internal Microsoft memo lays out its new strategy for selling AI as the company cuts salespeople
Althoff, the company's chief commercial officer, sent the memo to the sales unit, called Microsoft Customer and Partner Solutions (MCAPS), a day before the company announced a significant round of layoffs.
Those layoffs affected many salespeople in Althoff's organization, sources familiar with them said. The memo did not mention the layoffs, announced beginning July 2 in separate communications to employees.
Althoff's memo called for "continued agility" and "reinventing Microsoft and MCAPS" to become "the Frontier AI Firm," and outlined the five priorities of the sales organization:
Establish a Copilot on every device and across every role
Strengthen our M365 and D365 execution and penetration across all segments
Create meaningful AI design wins
Grow our cloud platform business by migrating and modernizing workloads to Azure
Build a cybersecurity foundation to enable secure AI Transformation
Althoff in April unveiled plans to slash the number of the sales team's "solutions areas" by half during the next fiscal year, which started July 1.
BI obtained copies of slides from Althoff's April presentation, showing the company planned to condense its six previous areas into three: AI Business Solutions, Cloud & AI Platforms, and Security, according to those slides.
AI Business Solutions will focus on getting "Copilots on every device across every role" and on selling Microsoft 365's suite of business applications and Dynamics 365 customer relationship management service, according to the July 1 memo.
Cloud & AI Platform will include the company's Azure business, its AI "agent factory" Foundry, and data analytics platform Fabric. That group will be focused on frontier AI solutions and migrating and modernizing cloud workloads to Azure.
Security focuses on selling Microsoft's security tools. "We have spent a lot of time playing defense over the last year, and it is now time to compete more aggressively," Althoff said, referring to the security solutions area.
The changes come as Microsoft faces increasing competition for enterprise customers in AI from companies like OpenAI and Google. Microsoft has an advantage in that many large companies already use its other tools, but many of those companies' employees want the more well-known ChatGPT.
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26 minutes ago
Company's carbon credits raise questions about unproven ocean technology to fight global warming
The startup Gigablue announced with fanfare this year that it reached a historic milestone: selling 200,000 carbon credits to fund what it describes as a groundbreaking technology in the fight against climate change. Formed three years ago by a group of entrepreneurs in Israel, the company says it has designed particles that when released in the ocean will trap carbon at the bottom of the sea. By 'harnessing the power of nature,' Gigablue says, its work will do nothing less than save the planet. But outside scientists frustrated by the lack of information released by the company say serious questions remain about whether Gigablue's technology works as the company describes. Their questions showcase tensions in an industry built on little regulation and big promises — and a tantalizing chance to profit. Jimmy Pallas, an event organizer based in Italy, struck a deal with Gigablue last year. He said he trusts the company does what it has promised him — ensuring the transportation, meals, and electricity of a recent 1,000-person event will be offset by particles in the ocean. Gigablue's service is like 'an extra trash can' where Pallas can discard his unwanted emissions, he said. 'Same way I use my trash can — I don't follow where the truck that comes and picks up my trash brings it to,' he said. 'I'll take their word for it.' Gigablue has a grand vision for the future of carbon removal. It was originally named 'Gigaton' after the one billion metric tons of carbon dioxide most scientists say will be necessary to remove from the atmosphere each year to slow global warming. The company began trials in the South Pacific Ocean last year, and says it will work with country authorities to create a 'sequestration field' — a dedicated part of the ocean where 'pulses' of particles will be released on a seasonal basis. Gigablue says its solution is affordable, too — priced to attract investors. 'Every time we go to the ocean, we generate hundreds of thousands of carbon credits, and this is what we're going to do continuously over the upcoming years and towards the future, in greater and greater quantities,' co-founder Ori Shaashua said. Carbon credits, which have grown in popularity over the last decade, are tokens that symbolize the removal of one metric ton of carbon dioxide from the atmosphere. On paper, companies that buy credits achieve a smaller carbon footprint without needing to reduce their own emissions — for instance, by paying another vendor to plant trees or capture carbon dioxide from the air. Only a few countries have required local industries to purchase carbon credits. Most companies that buy them, including Microsoft and Google, do so voluntarily. The credits have helped fund a band of startups like Gigablue that are eager to tackle the climate crisis, but they are also unevenly regulated, scientifically complex, and have in some cases been linked to fraud. Gigablue's 200,000 credits are pledged to SkiesFifty, a newly formed company investing in greener practices for the aviation industry. It's the largest sale to date for a climate startup operating in the ocean, according to the tracking site making up more than half of all ocean-based carbon credits sold last year. And it could beckon a rapid acceleration of the company's work. Gigablue hopes to reach a goal this year of capturing 10 metric tons of carbon dioxide for each ton of particles it deploys, Shaashua said. At that rate, Gigablue would disperse at least 20,000 tons of particles in the ocean. Gigablue wouldn't reveal what it earned in the sale, and SkiesFifty's team declined to be interviewed for this story. Most credits are sold for a few hundred dollars each — but a chart on Gigablue's website suggests its prices are lower than almost any other form of carbon capture on the market. The startup is the brainchild of four entrepreneurs hailing from the tech industry. According to their LinkedIn profiles, Gigablue's CEO previously worked for an online grocery startup, while its COO was vice president of SeeTree, a company that raised $60 million to provide farmers with information on their trees. Shaashua, who often serves as the face of Gigablue, said he specializes in using artificial intelligence to pursue positive outcomes in the world. He co-founded a data mining company that tracked exposure risks during the COVID-19 pandemic, and led an auto startup that brokered data on car mileage and traffic patterns. 'Three years ago, I decided to take the same formula, so to say, to climate,' Shaashua said. Under his guidance, he said, Gigablue created an AI-driven 'digital twin' of the ocean based on dozens of metrics to determine where to release the particles. Chief technology officer Sapir Markus-Alford earned a bachelor's degree in earth and environmental sciences from Israel's Ben-Gurion University in 2021, shortly before founding Gigablue. Markus-Alford said she began her studies and eventual path to Gigablue after seeing bleached coral reefs and other impacts of warming waters on a series of diving trips around the world. 'I understood that the best thing we could do for the ocean is to be able to remove CO2,' Markus-Alford said. A spokesperson for Gigablue did not answer whether the other co-founders have graduate degrees in oceanography or environmental science, but said the company's broader team holds a total of 46 Ph.D.s with expertise in biology, chemistry, oceanography, and environmental science. Markus-Alford said that figure includes outside experts and academics and 'everyone that supports us.' The company's staffing has expanded from Israel to hubs in New York and New Zealand, Shaashua said. In social media posts advertising open jobs, Gigablue employees encouraged applicants to 'Join Our Mission to Save the World!' The particles Gigablue has patented are meant to capture carbon in the ocean by floating for a number of days and growing algae, before sinking rapidly to the ocean floor. 'We are an elevator for carbon,' Shaashua said. 'We are exporting the carbon from the top to the bottom.' Algae — sometimes referred to as phytoplankton — has long been attractive to climate scientists because it absorbs carbon dioxide from the surrounding water as it grows. If the algae sinks to the deep sea or ocean floor, Gigablue expects the carbon to be trapped there for hundreds to thousands of years. The ultimate goal is to lower carbon dioxide levels so drastically that the ocean rebalances with the atmosphere by soaking up more CO2 from the air. It's a feat that would help slow climate change, but one that is still under active study by climate scientists. Gigablue's founders have said the company's work is inspired by nature and 'very, very environmentally safe.' The company's particles and sinking methods simply recreate what nature has been doing 'since forever,' Shaashua said. Gigablue ran its first trial sinking particles in the Mediterranean in March last year. Later, on two voyages to the South Pacific, the company released 60 cubic meters — about two shipping containers — of particles off the coast of New Zealand. While Gigablue has made several commercial deals, it has not yet revealed what its particles are made of. Partly this is because the company says it will build different particles tailored to different seasons and areas of the ocean. 'It's proprietary,' Markus-Alford said. Documents provide a window into the possible ingredients. According to information on the permit, Gigablue's first New Zealand trial last year involved releasing particles of pure vermiculite, a porous clay often used in potting soil. In the second New Zealand trial, the company released particles made of vermiculite, ground rock, a plant-based wax, as well as manganese and iron. A patent published last year hints the particles could also be made of scores of other materials, including cotton, rice husks or jute, as well as synthetic ingredients like polyester fibers or lint. The particles contain a range of possible binding agents, and up to 18 different chemicals and metals, from iron to nickel to vanadium. Without specifying future designs, Markus-Alford said Gigablue's particles meet certain requirements: 'All the materials we use are materials that are natural, nontoxic, nonhazardous, and can be found in the ocean,' she said. She wouldn't comment on the possible use of cotton or rice, but said the particles won't include any kind of plastic. When asked about vermiculite, which is typically mined on land and heated to expand, Markus-Alford said rivers and erosion transport most materials including vermiculite to the ocean. 'Almost everything, basically, that exists on land can be found in the ocean,' she said. The company said it had commissioned an environmental institute to verify that the particles are safe for thousands of organisms, including mussels and oysters. Any materials in future particles, Gigablue said, will be approved by local authorities. Shaashua has said the particles are so benign that they have zero impact on the ocean. 'We are not changing the water chemistry or the water biology,' Shaashua said. Ken Buesseler, a senior scientist with the Woods Hole Oceanographic Institution who has spent decades studying the biological carbon cycle of the ocean, says that while he's intrigued by Gigablue's proposal, the idea that the particles don't alter the ocean is 'almost inconceivable.' 'There has to be a relationship between what they're putting in the ocean and the carbon dioxide that's dissolved in seawater for this to, quote, work,' Buesseler said. Buesseler co-leads a nonprofit group of scientists hoping to tap the power of algae in the ocean to capture carbon. The group organizes regular forums on the subject, and Gigablue presented in April. 'I left with more questions than answers,' Buesseler said. Several scientists not affiliated with Gigablue interviewed by The Associated Press said they were interested in how a company with so little public information about its technology could secure a deal for 200,000 carbon credits. The success of the company's method, they said, will depend on how much algae grows on the particles, and the amount that sinks to the deep ocean. So far, Gigablue has not released any studies demonstrating those rates. Thomas Kiørboe, a professor of ocean ecology at the Technical University of Denmark, and Philip Boyd, an oceanographer at the University of Tasmania who studies the role of algae in the Earth's carbon cycle, said they were doubtful algae would get enough sunlight to grow inside the particles. It's more likely the particles would attract hungry bacteria, Kiørboe said. 'Typical phytoplankton do not grow on surfaces, and they do not colonize particles,' Kiørboe said. 'To most phytoplankton ecologists, this would just be, I think, absurd.' The rates at which Gigablue says its particles sink — up to a hundred meters (yards) per hour — might shear off algae from the particles in the quick descent, Boyd said. It's likely that some particles would also be eaten by fish — limiting the carbon capture, and raising the question of how the particles could impact marine life. Boyd is eager to see field results showing algae growth, and wants to see proof that Gigablue's particles cause the ocean to absorb more CO2 from the air. 'These are incredibly challenging issues that I don't think, certainly for the biological part, I don't think anyone on the planet has got solutions for them,' he said. James Kerry, a senior marine and climate scientist for the conservation group OceanCare and senior research fellow at Australia's James Cook University, has closely followed Gigablue's work. 'What we've got is a situation of a company, a startup, upfront selling large quantities of credits for a technology that is unproven,' he said. In a statement, Gigablue said that bacteria does consume the particles but the effect is minimal, and its measurements will account for any loss of algae or particles as they sink. The company noted that a major science institute in New Zealand has given Gigablue its stamp of approval. Gigablue hired the National Institute of Water and Atmospheric Research, a government-owned company, to review several drafts of its methodology. In a recent letter posted to Gigablue's website, the institute's chief ocean scientist said his staff had confidence the company's work is 'scientifically sound' and the proposed measurements for carbon sequestration were robust. Whether Gigablue's methods are deemed successful, for now, will be determined not by regulators — but by another private company. is one of several companies known as registries that serve the carbon credit market. Amid the lack of regulation and the potential for climate startups to overstate their impact, registries aim to verify how much carbon was really removed. The Finnish has verified more than a million carbon credits since its founding seven years ago. But most of those credits originated in land-based climate projects. Only recently has it aimed to set standards for the ocean. In part, that's because marine carbon credits are some of the newest to be traded. Dozens of ocean startups have entered the industry, with credit sales catapulting from 2,000 in 2021 to more than 340,000, including Gigablue's deal, last year. But the ocean remains a hostile and expensive place in which to operate a business or monitor research. Some ocean startups have sold credits only to fold before they could complete their work. Running Tide, a Maine-based startup aimed at removing carbon from the atmosphere by sinking wood chips and seaweed, abruptly shuttered last year despite the backing of $50 million from investors, leaving sales of about 7,000 carbon credits unfulfilled. In June, published a draft methodology that will be used to verify Gigablue's work, which it designed in consultation with Gigablue. Once finalized, Gigablue will pay the registry for each metric ton of carbon dioxide that it claims to remove. Marianne Tikkanen, head of standards at said that although this methodology was designed with Gigablue, her team expects other startups to adopt the same approach. 'We hope that there will be many who can do it and that it stimulates the market,' she said. It remains to be seen whether New Zealand officials will grant permission for the expanded 'sequestration field' that Gigablue has proposed creating, or if the company will look to other countries. New Zealand's environmental authority has so far treated Gigablue's work as research — a designation that requires no formal review process or consultations with the public. The agency said in a statement that it could not comment on how it would handle a future commercial application from Gigablue. But like many climate startups, Gigablue was involved in selling carbon credits during its research expeditions — not only inking a major deal, but smaller agreements, too. Pallas, the Italian businessman, said he ordered 22 carbon credits from Gigablue last year to offset the emissions associated with his event in November. He said Gigablue gave them to him for free — but says he will pay for more in the future. Pallas sought out carbon credits because he sees the signs of climate change all around him, he says, and expects more requirements in Italy for businesses to decarbonize in coming years. He chose Gigablue because they are one of the largest suppliers: 'They've got quantity,' he said. How authorities view Gigablue's growing commercial activity could matter in the context of an international treaty that has banned certain climate operations in the ocean. More than a decade ago, dozens of countries including New Zealand agreed they should not allow any commercial climate endeavor that involves releasing iron in the ocean, a technique known as 'iron fertilization.' Only research, they said, with no prospect of economic gain should be allowed. Iron is considered a key ingredient for spurring algae growth and was embedded in the particles that Gigablue dispersed in October in the Pacific Ocean. Several scientific papers have raised concerns that spurring iron-fueled algae blooms on a large scale would deplete important nutrients in the ocean and harm fisheries. The startup denies any link to iron dumping on the basis that its particles don't release iron directly into the water and don't create an uncontrolled algae bloom. 'We are not fertilizing the ocean,' Markus-Alford said. 'In fact, we looked at iron fertilization as an inspiration of something to avoid,' Shaashua said. But the draft methodology that will use to verify Gigablue's work notes many of the same concerns that have been raised about iron fertilization, including disruptions to the marine food web. Other scientists who spoke with AP see a clear link between Gigablue's work and the controversial practice. 'If they're using iron to stimulate phytoplankton growth,' said Kerry, the OceanCare scientist, 'then it is iron fertilization.' For now, scientific concerns don't seem to have troubled Gigablue's buyers. The company has already planned its next research expedition in New Zealand and hopes to release more particles this fall. 'They mean well, and so do I,' said Pallas, of his support for Gigablue. 'Sooner or later, I'll catch a plane, go to New Zealand, and grab a boat to see what they've done.'
Forbes
34 minutes ago
- Forbes
AGI And AI Superintelligence Are Going To Sharply Hit The Human Ceiling Assumption Barrier
Is there a limit or ceiling to human intelligence and how will that impact AI? In today's column, I examine an unresolved question about the nature of human intelligence, which in turn has a great deal to do with AI, especially regarding achieving artificial general intelligence (AGI) and potentially even reaching artificial superintelligence (ASI). The thorny question is often referred to as the human ceiling assumption. It goes like this. Is there a ceiling or ending point that confines how far human intellect can go? Or does human intellect extend indefinitely and nearly have infinite possibilities? Let's talk about it. This analysis of an innovative AI breakthrough 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). Heading Toward AGI And ASI First, some fundamentals are required to set the stage for this weighty discussion. There is a great deal of research going on to further advance AI. The general goal is to either reach artificial general intelligence (AGI) or maybe even the outstretched possibility of achieving artificial superintelligence (ASI). AGI is AI that is considered on par with human intellect and can seemingly match our intelligence. ASI is AI that has gone beyond human intellect and would be superior in many if not all feasible ways. The idea is that ASI would be able to run circles around humans by outthinking us at every turn. For more details on the nature of conventional AI versus AGI and ASI, see my analysis at the link here. We have not yet attained AGI. In fact, it is unknown as to whether we will reach AGI, or that maybe AGI will be achievable in decades or perhaps centuries from now. The AGI attainment dates that are floating around are wildly varying and wildly unsubstantiated by any credible evidence or ironclad logic. ASI is even more beyond the pale when it comes to where we are currently with conventional AI. Human Intellect As A Measuring Stick Have you ever pondered the classic riddle that asks how high is up? I'm sure that you have. Children ask this vexing question of their parents. The usual answer is that up goes to the outer edge of Earth's atmosphere. After hitting that threshold, up continues onward into outer space. Up is either a bounded concept based on our atmosphere or it is a nearly infinite notion that goes as far as the edge of our expanding universe. I bring this riddle to your attention since it somewhat mirrors an akin question about the nature of human intelligence: In other words, the intelligence we exhibit currently is presumably not our upper bound. If you compare our intelligence with that of past generations, it certainly seems relatively apparent that we keep increasing in intelligence on a generational basis. Will those born in the year 2100 be more intelligent than we are now? What about being born in 2200? All in all, most people would speculate that yes, the intelligence of those future generations will be greater than the prevailing intelligence at this time. If you buy into that logic, the up-related aspect rears its thorny head. Think of it this way. The capability of human intelligence is going to keep increasing generationally. At some point, will a generation exist that has capped out? The future generation represents the highest that human intellect can ever go. Subsequent generations will either be of equal human intellect, or less so and not more so. The reason we want to have an answer to that question is that there is a present-time pressing need to know whether there is a limit or not. I've just earlier pointed out that AGI will be on par with human intellect, while ASI will be superhuman intelligence. Where does AGI top out, such that we can then draw a line and say that's it? Anything above that line is going to be construed as superhuman or superintelligence. Right now, using human intellect as a measuring stick is hazy because we do not know how long that line is. Perhaps the line ends at some given point, or maybe it keeps going infinitely. Give that weighty thought some mindful pondering. The Line In The Sand You might be tempted to assume that there must be an upper bound to human intelligence. This intuitively feels right. We aren't at that limit just yet (so it seems!). One hopes that humankind will someday live long enough to reach that outer atmosphere. Since we will go with the assumption of human intelligence as having a topping point, doing so for the sake of discussion, we can now declare that AGI must also have a topping point. The basis for that claim is certainly defensible. If AGI consists of mimicking or somehow exhibiting human intelligence, and if human intelligence meets a maximum, AGI will also inevitably meet that same maximum. That's a definitional supposition. Admittedly, we don't necessarily know yet what the maximum point is. No worries, at least we've landed on a stable belief that there is a maximum. We can then draw our attention toward figuring out where that maximum resides. No need to be stressed by the infinite aspects anymore. Twists And Turns Galore AI gets mired in a controversy associated with the unresolved conundrum underlying a ceiling to human intelligence. Let's explore three notable possibilities. First, if there is a ceiling to human intelligence, maybe that implies that there cannot be superhuman intelligence. Say what? It goes like this. Once we hit the top of human intelligence, bam, that's it, no more room to proceed further upward. Anything up until that point has been conventional human intelligence. We might have falsely thought that there was superhuman intelligence, but it was really just intelligence slightly ahead of conventional intelligence. There isn't any superhuman intelligence per se. Everything is confined to being within conventional intelligence. Thus, any AI that we make will ultimately be no greater than human intelligence. Mull that over. Second, well, if there is a ceiling to human intelligence, perhaps via AI we can go beyond that ceiling and devise superhuman intelligence. That seems more straightforward. The essence is that humans top out but that doesn't mean that AI must also top out. Via AI, we might be able to surpass human intelligence, i.e., go past the maximum limit of human intelligence. Nice. Third, if there isn't any ceiling to human intelligence, we would presumably have to say that superhuman intelligence is included in that infinite possibility. Therefore, the distinction between AGI and ASI is a falsehood. It is an arbitrarily drawn line. Yikes, it is quite a mind-bending dilemma. Without some fixed landing on whether there is a human intelligence cap, the chances of nailing down AGI and ASI remain aloof. We don't know the answer to this ceiling proposition; thus, AI research must make varying base assumptions about the unresolved topic. AI Research Taking Stances AI researchers often take the stance that there must be a maximum level associated with human intellect. They generally accept that there is a maximum even if we cannot prove it. The altogether unknown, but considered plausibly existent limit, becomes the dividing line between AGI and ASI. Once AI exceeds the human intellectual limit, we find ourselves in superhuman territory. In a recently posted paper entitled 'An Approach to Technical AGI Safety and Security' by Google DeepMind researchers Rohin Shah, Alex Irpan, Alexander Matt Turner, Anna Wang, Arthur Conmy, David Lindner, Jonah Brown-Cohen, Lewis Ho, Neel Nanda, Raluca Ada Popa, Rishub Jain, Rory Greig, Samuel Albanie, Scott Emmons, Sebastian Farquhar, Sébastien Krier, Senthooran Rajamanoharan, Sophie Bridgers, Tobi Ijitoye, Tom Everitt, Victoria Krakovna, Vikrant Varma, Vladimir Mikulik, Zachary Kenton, Dave Orr, Shane Legg, Noah Goodman, Allan Dafoe, Four Flynn, and Anca Dragan, arXiv, April 2, 2025, they made these salient points (excerpts): You can see from those key points that the researchers have tried to make a compelling case that there is such a thing as superhuman intellect. The superhuman consists of that which goes beyond the human ceiling. Furthermore, AI won't get stuck at the human intellect ceiling. AI will surpass the human ceiling and proceed into the superhuman intellect realm. Mystery Of Superhuman Intelligence Suppose that there is a ceiling to human intelligence. If that's true, would superhuman intelligence be something entirely different from the nature of human intelligence? In other words, we are saying that human intelligence cannot reach superhuman intelligence. But the AI we are devising seems to be generally shaped around the overall nature of human intelligence. How then can AI that is shaped around human intelligence attain superintelligence when human intelligence cannot apparently do so? Two of the most frequently voiced answers are these possibilities: The usual first response to the exasperating enigma is that size might make the difference. The human brain is approximately three pounds in weight and is entirely confined to the size of our skulls, roughly allowing brains to be about 5.5 inches by 6.5 inches by 3.6 inches in respective dimensions. The human brain consists of around 86 billion neurons and perhaps 1,000 trillion synapses. Human intelligence is seemingly stuck to whatever can happen within those sizing constraints. AI is software and data that runs across perhaps thousands or millions of computer servers and processing units. We can always add more. The size limit is not as constraining as a brain that is housed inside our heads. The bottom line is that the reason we might have AI that exhibits superhuman intelligence is due to exceeding the physical size limitations that human brains have. Advances in hardware would allow us to substitute faster processors and more processors to keep pushing AI onward into superhuman intelligence. The second response is that AI doesn't necessarily need to conform to the biochemical compositions that give rise to human intelligence. Superhuman intelligence might not be feasible with humans due to the brain being biochemically precast. AI can easily be devised and revised to exploit all manner of new kinds of algorithms and hardware that differentiate AI capabilities from human capabilities. Heading Into The Unknown Those two considerations of size and differentiation could also work in concert. It could be that AI becomes superhuman intellectually because of both the scaling aspects and the differentiation in how AI mimics or represents intelligence. Hogwash, some exhort. AI is devised by humans. Therefore, AI cannot do better than humans can do. AI will someday reach the maximum of human intellect and go no further. Period, end of story. Whoa, comes the retort. Think about humankind figuring out how to fly. We don't flap our arms like birds do. Instead, we devised planes. Planes fly. Humans make planes. Ergo, humans can decidedly exceed their own limitations. The same will apply to AI. Humans will make AI. AI will exhibit human intelligence and at some point reach the upper limits of human intelligence. AI will then be further advanced into superhuman intelligence, going beyond the limits of human intelligence. You might say that humans can make AI that flies even though humans cannot do so. A final thought for now on this beguiling topic. Albert Einstein famously said this: 'Only two things are infinite, the universe and human stupidity, and I'm not sure about the former.' Quite a cheeky comment. Go ahead and give the matter of AI becoming AGI and possibly ASI some serious deliberation but remain soberly thoughtful since all of humanity might depend on what the answer is.
Medscape
an hour ago
- Medscape
This Model Beats Docs at Predicting Sudden Cardiac Arrest
An artificial intelligence (AI) model has performed dramatically better than doctors using the latest clinical guidelines to predict the risk for sudden cardiac arrest in people with hypertrophic cardiomyopathy. The model, called Multimodal AI for ventricular Arrhythmia Risk Stratification (MAARS), is described in a paper published online on July 2 in Nature Cardiovascular Research . It predicts patients' risk by analyzing a variety of medical data and records such as echocardiogram and radiology reports, as well as all the information contained in contrast-enhanced MRI (CMR) images of the patient's heart. Natalia Trayanova, PhD, director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation at Johns Hopkins University in Baltimore, led the development of the model. She said that while hypertrophic cardiomyopathy is one of the most common inherited heart diseases, affecting 1 in every 200-500 individuals worldwide, and is a leading cause of sudden cardiac death in young people and athletes, an individual's risk for cardiac arrest remains difficult to predict. Current clinical guidelines from the American Heart Association and American College of Cardiology, and those from the European Society of Cardiology, identify the patients who go on to experience cardiac arrest in about half of cases. 'The clinical guidelines are extremely inaccurate, little better than throwing dice,' Trayanova, who is also the Murray B. Sachs Professor in the Department of Biomedical Engineering at Johns Hopkins, told Medscape Medical News . Compared to the guidelines, MAARS was nearly twice as sensitive, achieving 89% accuracy across all patients and 93% accuracy for those 40-60 years old, the group of people with hypertrophic cardiomyopathy most at risk for sudden cardiac death. Building a Model MAARS was trained on data from 553 patients in The Johns Hopkins Hospital, Baltimore, hypertrophic cardiomyopathy registry. The researchers then tested the algorithm on an independent external cohort of 286 patients from the Sanger Heart & Vascular Institute hypertrophic cardiomyopathy registry in Charlotte, North Carolina. The model uses all of the data available from these patients, drawing on electronic health records, ECG readings, reports from radiologists and imaging technicians, and raw data from CMR. 'All these different channels are fed into this multimodal AI predictor, which fuses it together and comes up with the risk for these particular patients,' Trayanova said. The inclusion of CMR data is particularly important, she said, because the imaging test can identify areas of scarring on the heart that characterize hypertrophic cardiomyopathy. But clinicians have yet to be able to make much use of those images because linking the fairly random patterns of scar tissue to clinical outcomes has been a challenge. But that is just the sort of task that deep neural networks are particularly well-suited to tackle. 'They can recognize patterns in the data that humans miss, then analyze and combine them with the other inputs into a single prediction,' Trayanova said. Clinical Benefits Better predictions of the risk for serious adverse outcomes will help improve care, by ensuring people get the right treatments to reduce their risk, and avoid the ones that are unnecessary, Trayanova said The best way to protect against sudden cardiac arrest is with an implantable defibrillator — but the procedure carries potential risks that are best avoided unless truly needed. 'More accurate risk prediction means fewer patients might undergo unnecessary ICD implantation, which carries risks such as infections, device malfunction, and inappropriate shocks,' said Antonis Armoundas, PhD, from the Cardiovascular Research Center at Massachusetts General Hospital in Boston. The model could also help personalize treatment for patients with hypertrophic cardiomyopathy, Trayanova said. 'It's able to drill down into each patient and predict which parameters are the most important to help influence the management of the condition,' she said. Robert Avram, MD, MSc, a cardiologist at the Montreal Heart Institute, Montreal, Quebec, Canada, said the results are encouraging. 'I'm especially interested in how a tool like this could streamline risk stratification and ultimately improve patient outcomes,' he said. But it is not yet ready for widespread use in the clinic. 'Before it can be adopted in routine care, however, we'll need rigorous external validation across diverse institutions, harmonized variable definitions, and unified extraction pipelines for each modality, along with clear regulatory and workflow-integration strategies,' Avram said. Armoundas said he would like to see the model tested on larger sample sizes, with greater diversity in healthcare settings, geographical regions, and demographics, as well as prospective, randomized studies and comparisons against other AI predictive models. 'Further validation in larger cohorts and assessment over longer follow-up periods are necessary for its full clinical integration,' he said. Armoundas, Avram, and Trayanova reported having no relevant financial conflicts of interest.
