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for incremental efficiency gains to create transformative impact i write from the world economic forum wef in davos switzerland where i ve been speaking with many ceos about how to use ai for growth a recurring theme is that running many experimental bottom up ai projects letting a thousand flowers bloom has failed to lead to significant payoffs instead bigger gains require workflow redesign taking a broader perhaps top down view of the multiple steps in a process and changing how they work together from end to end consider a bank issuing loans the workflow consists of several discrete stages marketing application preliminary approval final review execution suppose each step used to be manual preliminary approval used to require an hour long human review but a new agentic system can do this automatically in 10 minutes swapping human review for ai review but keeping everything else the same gives a minor efficiency gain but isn t transformative here s what would be transformative instead of applicants waiting a week for a human to review their application they can get a decision in 10 minutes when that happens the loan becomes a more compelling product and that better customer experience allows lenders to attract more applications and ultimately issue more loans however making this change requires taking a broader business or product perspective not just a technology perspective further it changes the workflow of loan processing switching to offering a 10 minute loan product would require changing how it is marketed applications would need to be digitized and routed more efficiently and final review and execution would need to be redesigned to handle a larger volume even though ai is applied only to one step preliminary approval we end up implementing not just a point solution but a broader workflow redesign that transforms the product offering at ai aspire an advisory firm i co lead here s what we see bottom up innovation matters because the people closest to problems often see solutions first but scaling such ideas to create transformative impact often requires seeing how ai can transform entire workflows end to end not just individual steps and this is where top down strategic direction and innovation can help this year s wef meeting as in previous years has been an energizing event among technologists frequent topics of discussion include agentic ai when i coined this term i was not expecting to see it plastered on billboards and buildings sovereign ai how nations can control their own access to ai talent the challenging job market for recent graduates and how to upskill nations and data center infrastructure how to address bottlenecks in energy talent gpu chips and memory i will address some of these topics in future posts original text https lnkd in gbirs2mi more self driving reasoning models chatgpt adds ads apple s deal with google and more deeplearning ai 2 377 270 comments like comment share copy linkedin facebook x demis hassabis demis hassabis is an influencer co founder ceo google deepmind 268 036 followers 5mo report this post i ve worked on ai my whole life because i ve always believed it could unlock the ability to answer some of the biggest and most intractable problems in science our first big science breakthrough happened five years ago when we announced our solution to the protein structure prediction problem alphafold 2 it has been incredible to see its impact since then more than 3 million researchers across 190 countries have used this tool for disease understanding drug discovery and more and it was an honour of a lifetime for our work to be recognised last year with a nobel prize one of our greatest ambitions is for ai to aid in accelerating drug design and help cure all diseases this is what led me to found isomorphic labs which is already making amazing progress we ve also expanded alphafold to predict the interactions of all of life s molecules but alphafold represents more than a solution to a biological puzzle it demonstrated how ai can crack root node problems where a single breakthrough unlocks entire new avenues of research it is a critical step towards a long held dream of mine building a virtual cell imagine running in silico experiments orders of magnitude faster than in a wet lab scientists could rapidly test hypotheses model complex pathways and see how a drug affects a cell it would be an incredible boon not only for fundamental biology but also for medicine although for me alphafold was never just about biology it was the first major proof point for a much larger thesis that ai could be the ultimate tool for advancing science by processing data or helping us come up with new hypotheses i think ai will help us tackle some of humanity s greatest challenges and answer fundamental questions about the universe from materials design to fusion energy to mathematics i believe we re on the cusp of a new golden age of discovery we re just getting started read more about alphafold s impact https lnkd in eneqxqqp more alphafold five years of impact deepmind google 8 647 351 comments like comment share copy linkedin facebook x arianna huffington arianna huffington is an influencer founder and ceo at thrive global passionate about health and ai 9 600 828 followers 5mo report this post a study by researchers from stanford and carnegie mellon has found that ai models are 50 more sycophantic than humans not only that participants rated flattering responses as higher quality and wanted to use them more it gets even worse the flattery made participants less likely to admit they were wrong even when confronted with evidence this suggests that people are drawn to ai that unquestioningly validates even as that validation risks eroding their judgment and reducing their inclination toward prosocial behavior the researchers wrote these preferences create perverse incentives both for people to increasingly rely on sycophantic ai models and for ai model training to favor sycophancy https lnkd in dbtjwe f in other words humans are hard wired for approval and so is ai it s a win win for both sides a flattery perpetual motion machine ai models have turned into high tech versions of courtesans mistresses or prostitutes found in royal and aristocratic courts in europe and asia over the centuries among other talents they often used flattery to seduce and gain status now we re all royals being sweet talked by courtesans at the touch of a button as disraeli said everyone likes flattery and when you come to royalty you should lay it on with a trowel there are some obvious pitfalls to having flattery applied to our queries with a digital trowel openai now has 800 million weekly users and people increasingly trust ai to give them advice on more and more aspects of their lives surveys have found that 66 of americans have used ai for financial advice that nearly 40 trust ai on medical advice and that 72 of teens have used ai companions sure ai will answer any question but how useful is that when it s just telling us what we want to hear what it s not doing is what a human advisor or a trusted friend would do tell us when we re wrong nor in its eagerness to please will it tell us when we re asking the wrong question altogether as plutarch put it i do not need a friend who changes when i change and nods when i nod my shadow does that much better the risk is that instead of being a defense against the echo chambers of social media ai just becomes a more powerful version more no more previous content no more next content 1 901 143 comments like comment share copy linkedin facebook x andy jassy andy jassy is an influencer 1 036 470 followers 5mo report this post every cloud provider faces the same ai infrastructure challenge chips need to be positioned close together to exchange data quickly but they generate intense heat creating unprecedented cooling demands we needed a strategic solution that allowed us to use our existing air cooled data centers to do liquid cooling without waiting for new construction and it needed to be rapidly deployed so we could bring customers these powerful ai capabilities while we transition towards facility level liquid cooling think of a home where only one sunny room needs ac while the rest stays naturally cool that s what we wanted to achieve allowing us to efficiently land both liquid and air cooled racks in the same facilities with complete flexibility the available options weren t great either we could wait to build specialized liquid cooled facilities or adopt off the shelf solutions that didn t scale or meet our unique needs neither worked for our customers so we did what we often do at amazon we invented our own solution our teams designed and delivered our in row heat exchanger irhx which uses a direct to chip approach with a cold plate on the chips the liquid runs through this sealed plate in a closed loop continuously removing heat without increasing water use this enables us to support traditional workloads and demanding ai applications in the same facilities by 2026 our liquid cooled capacity will grow to over 20 of our ml capacity which is at multi gigawatt scale today while liquid cooling technology itself isn t unique our approach was creating something this effective that could be deployed across our 120 availability zones in 38 regions was significant because this solution didn t exist in the market we developed a system that enables greater liquid cooling capacity with a smaller physical footprint while maintaining flexibility and efficiency our irhx can support a wide range of racks requiring liquid cooling uses 9 less water than fully air cooled sites and offers a 20 improvement in power efficiency compared to off the shelf solutions and because we invented it in house we can deploy it within months in any of our data centers creating a flexible foundation to serve our customers for decades to come reimagining and innovating at scale has been something amazon has done for a long time and one of the reasons we ve been the leader in technology infrastructure and data center invention sustainability and resilience we re not done there s still so much more to invent for customers more no more previous content no more next content 6 717 212 comments like comment share copy linkedin facebook x saanya ojha saanya ojha is an influencer partner at bain capital ventures 80 201 followers 8mo report this post this week mit dropped a stat engineered to go viral 95 of enterprise genai pilots are failing markets predictably had a minor existential crisis pundits whispered the b word bubble traders rotated into defensive stocks and your colleague forwarded you a link with is ai overhyped in the subject line let s be clear the 95 failure rate isn t a caution against ai it s a mirror held up to how deeply ossified enterprises are two truths can coexist 1 the tech is very real 2 most companies are hilariously bad at deploying it if you re a startup ai feels like a superpower no legacy systems no 17 step approval chains no legal team asking whether chatgpt has been soc2 audited you ship you iterate you win if you re an enterprise your org chart looks like a game of twister and your workflows were last updated when friendswas still airing you don t need a better model you need a cultural lobotomy this isn t an ai bubble popping it s the adoption lag every platform shift goes through cloud in the 2010s endless proofs of concept before actual transformation mobile in the 2000s enterprises thought an iphone app was strategy spoiler it wasn t internet in the 90s half of fortune 500 ceos declared this is just a fad some of those companies no longer exist history rhymes the lag isn t a bug it s the default setting buried beneath the viral 95 headline are 3 lessons enterprises can actually use ️ back office front office the biggest roi comes from back office automation finance ops procurement claims processing yet over half of ai dollars go into sales and marketing the treasure s just buried in a different part of the org chart ️buy build success rates hit 67 when companies buy or partner with vendors diy attempts succeed a third as often unless it s literally your full time job to stay current on model architecture you ll fall behind your engineers don t need to reinvent an llm powered wheel they need to build where you re actually differentiated ️integration innovation pilots flop not because ai doesn t work but because enterprises don t know how to weave it into workflows the learning gap is the real killer spend as much energy on change management process design and user training as you do on the tool itself without redesigning processes ai adoption is just a peloton bought in january and used as a coat rack by march you didn t fail at fitness you failed at follow through in five years genai will be as invisible and indispensable as cloud is today the difference between the winners and the laggards won t be access to models but the courage to rip up processes and rebuild them the 95 failure stat doesn t mean ai is snake oil it means enterprises are in year 1 of a 10 year adoption curve the market just confused growing pains for terminal illness more 3 049 382 comments like comment share copy linkedin facebook x felix haas design at lovable angel investor 97 725 followers 9mo report this post invisible ux is coming and it s going to change how we design products forever for decades ux design has been about guiding users through an experience we ve done that with visible interfaces menus buttons cards sliders we ve obsessed over layouts states and transitions but with ai a new kind of interface is emerging one that s invisible one that s driven by intent not interaction think about it you used to open spotify scroll through genres click into focus pick a playlist now you just say play deep focus music no menus no tapping no ui just intent output you used to search on airbnb pick dates guests filters scroll through 50 listings now we re entering a world where you guide with words find me a cabin near oslo with a sauna available next weekend so the best ux becomes barely visible why does this matter because traditional ux gives users options ai native ux gives users outcomes old ux here are 12 ways to get what you want new ux just tell me what you want we ll handle the rest and this goes way beyond voice or chat it s about reducing friction designing systems that understand intent respond instantly and get out of the way the ui isn t disappearing it s mainly dissolving into the background so what should designers do rethink your role going forward you ll not just lay out screens you ll design interactions without interfaces that means understanding how people express goals guiding model behavior through prompt architecture creating invisible guardrails for trust speed and clarity you are basically designing for understanding the future of ux won t be seen it will be felt welcome to the age of...