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NEW! The Edge browser now has a "Learning Toolkit" mode 🎓 Make it easy to find #EdgeEDU features! Just type "edge://learning" ➗ Math Solver 📌 Citations 📃 PDF reader 📖 Immersive Reader 🔊 Read Aloud More details: #edtech #MIEExpert #MicrosoftEDU

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Mike Tholfsen

65,719 subscribers

18,721 просмотров • 3 лет назад •via X (Twitter)

Образование#EdgeEDU#edtech#MIEExpert#MicrosoftEDU
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Комментарии: 4

Фото профиля TricksyBell❣
TricksyBell❣3 лет назад

Nearly as good as edge://surf 🏄‍♀️ 🤣

Фото профиля Javier Boncompte G.
Javier Boncompte G.3 лет назад

Are there any coming improvements for ink in PDFs? Like multiple pens, styles, better settings like office, etc?

Фото профиля Ajay
Ajay3 лет назад

@MicrosoftEdge

Фото профиля Mike Tholfsen
Mike Tholfsen3 лет назад

@MrKavanagh Yup. Not sure if it has rolled to main or still in beta builds there

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54,575 просмотров • 10 дней назад

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52,110 просмотров • 7 месяцев назад

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Just when I thought I couldn’t hate Monarch more. Guy announces a “Zero Edge” slot on Duel and a “Zero Edge” live blackjack game. You can’t just slap zero edge on something and call it revolutionary. It’s the same type of marketing buzzword as “provably fair,” which is already a running joke in this industry. Slapping ‘zero edge’ on a game doesn’t magically make it innovative. Without real transparency around in house server seeds that can be manipulated in the house’s favor, it’s hard for players to know what they’re actually getting. Nobody should touch them with a 10 foot pole. Probably fair is mostly a scam. There are some exceptions that actually implement true provably fair but most casinos slap the label on and have no idea what it means. I can’t believe this guy used to be one of my heroes. He’s blatantly launching garbage and he knows it. His team behind the scenes is trash and he admits it, and Duel has only gone downhill ever since Ossi got perma banned on Twitter. He was the only one carrying marketing and all things Duel on his back. Right now there’s no truly superior product in this space except Stake and honestly, that’s pretty disappointing.

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Robots aren’t just the future — they’re already reshaping our world. At IIT Madras, the new in Robotics, offered under the Department of Engineering Design, is building the brains behind tomorrow’s intelligent machines. From motion planning and control to AI/ML and hands-on labs, this two-year program blends cutting-edge research with real-world industry relevance — powered by a strong robotics faculty team and a legacy of interdisciplinary innovation. Rooted in the success of the Inter-Disciplinary Dual Degree (IDDD) Robotics program, this two-year program brings together expert faculty members and a powerful blend of theory and hands-on learning. From robot design and analysis to motion planning, control systems, and AI/ML, students dive deep into the science and engineering behind next-generation robotic systems. Add immersive lab work and cutting-edge projects, and you have a curriculum that’s as practical as it is pioneering. Manufacturing, healthcare, logistics, and defence — robotics is reshaping them all. This program is about building the minds that will lead to that transformation. If the future runs on robots, it starts here. For more details visit: IIT Madras

IIT Madras

22,421 просмотров • 3 месяцев назад

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I’m really proud of the results Wayve released today. 📊 For the first time we share quantitative results of our global generalisation journey. What did we learn? 💡 Over our first 8-weeks training in the US, our AI has rapidly adapted to its unique driving environment with just 500 hours worth of incremental US-specific training data, on top of our large scale foundation model's learning. What’s more, we just launched in Germany and first results are already 3x better — generalisation just keeps improving with new data. It is clear to me the future of autonomy will be unlocked by Embodied AI capable of generalising to new countries, vehicles and able to safely navigate challenging edge cases it has never seen before during training. Now we’re excited to kick things up a gear and work with the world’s leading automotive manufacturers to collect the data necessary to validate this technology and deploy it in consumer vehicles worldwide. Embodied AI is a generational opportunity and the pace of progress I'm seeing is incredible. We're building the strongest team in AI for automated driving. If you're excited about this mission, join us!

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Excited to release a tool that makes the joy of live music creation more accessible 🎸 “Music FX DJ” is new and improved: Featuring a whole suite of new controls, sharing and remix features, and improved audio quality and diversity of outputs, to let you blend and warp genres and instruments in an ever evolving stream of music. It’s been an absolute pleasure to work with Jacob Collier over the past year on this. It was very affirming to find him pushing for the same things Google Magenta Project has always valued: making tools that intrinsically value creativity and agency over just imitation, embrace the “weird corners” of AI, encouraging veterans to express themselves in new ways, while empowering novices to express themselves musically maybe for the first time. Read details at: And in the 🧵 below…

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20,093 просмотров • 1 год назад

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2:35

Today at Meta FAIR we’re announcing three new cutting-edge developments in robotics and touch perception — and releasing a collection of artifacts to empower the community to build on this work. Details on all of this new work ➡️ 1️⃣ Meta Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks. Trained on 460K+ tactile images using self-supervised learning. 2️⃣ Meta Digit 360 is a breakthrough artificial fingertip-based tactile sensor, equipped with 18+ sensing features to deliver detailed touch data with human-level precision and touch-sensing capabilities. 3️⃣ Meta Digit Plexus is a standardized platform for robotic sensor connections and interactions. It provides a hardware-software solution to integrate tactile sensors on a single robot hand and enables seamless data collection, control and analysis over a single cable. The potential impact of expanding capabilities and components like these for the open source community ranges from medical research to supply chain, manufacturing and much more. We’re excited to continue this work with the broader community.

AI at Meta

453,218 просмотров • 1 год назад

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Tainted Grail

34,682 просмотров • 2 месяцев назад

We've all finished a chapter only to realize we can't remember what we just read. This points to something decades of research has confirmed: passive reading is surprisingly ineffective for learning. # The control effect Markant et al. (2014) stripped away different aspects of self-directed learning to find what actually matters. They have found that even just pressing a button when ready to see the next item—with no control over content or duration—significantly enhanced recognition memory. The researchers suggested this works because controlling when you see new information lets you coordinate stimulus presentation with your own attentional state. When you decide when to advance, your brain is better prepared to encode what comes next. # Self-pacing and strategy Tullis and Benjamin (2011) showed that self-paced learners outperformed those who studied for the same total time but couldn't control their pace. The benefit was strongest for learners who spent more time on difficult material—a "discrepancy reduction" strategy. Self-pacing isn't just about having control; it's about using it to allocate attention where it's needed. # Why agency matters DuBrow et al. (2019) found that choice are inherently rewarding. Items learned when participants could make a choice (even an inconsequential one) were remembered better, and there was a correlation between how much someone's preference increased for chosen items and how much their memory improved. Agency engages value-based brain systems that strengthen consolidation. Ding et al. (2021) extended this to incidental memory—showing that even when participants weren't trying to memorize, having control over the task improved later recognition, particularly for items processed quickly. # ChapterPal I programmed ChapterPal to implement these principles: gradual text reveal controlled by the reader, AI-generated comprehension quizzes inserted during reading, regular guess-this-blurred-term puzzles, and contextual Q&A for engaging with difficult passages. The quizzes and puzzles align with research showing that testing and guessing strengthens retention, while the self-paced reveal directly implements the minimal agency sufficient to enhance memory. # References - Tullis & Benjamin (2011). On the effectiveness of self-paced learning. Journal of Memory and Language. - Markant et al. (2014). Deconstructing the effect of self-directed study on episodic memory. Memory & Cognition. - DuBrow et al. (2019). A common mechanism underlying choice's influence on preference and memory. Psychonomic Bulletin & Review. - Ding et al. (2021). The effect of choice on intentional and incidental memory. Learning & Memory. A demo of puzzles and Q&A's on ChapterPal:
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We've all finished a chapter only to realize we can't remember what we just read. This points to something decades of research has confirmed: passive reading is surprisingly ineffective for learning. # The control effect Markant et al. (2014) stripped away different aspects of self-directed learning to find what actually matters. They have found that even just pressing a button when ready to see the next item—with no control over content or duration—significantly enhanced recognition memory. The researchers suggested this works because controlling when you see new information lets you coordinate stimulus presentation with your own attentional state. When you decide when to advance, your brain is better prepared to encode what comes next. # Self-pacing and strategy Tullis and Benjamin (2011) showed that self-paced learners outperformed those who studied for the same total time but couldn't control their pace. The benefit was strongest for learners who spent more time on difficult material—a "discrepancy reduction" strategy. Self-pacing isn't just about having control; it's about using it to allocate attention where it's needed. # Why agency matters DuBrow et al. (2019) found that choice are inherently rewarding. Items learned when participants could make a choice (even an inconsequential one) were remembered better, and there was a correlation between how much someone's preference increased for chosen items and how much their memory improved. Agency engages value-based brain systems that strengthen consolidation. Ding et al. (2021) extended this to incidental memory—showing that even when participants weren't trying to memorize, having control over the task improved later recognition, particularly for items processed quickly. # ChapterPal I programmed ChapterPal to implement these principles: gradual text reveal controlled by the reader, AI-generated comprehension quizzes inserted during reading, regular guess-this-blurred-term puzzles, and contextual Q&A for engaging with difficult passages. The quizzes and puzzles align with research showing that testing and guessing strengthens retention, while the self-paced reveal directly implements the minimal agency sufficient to enhance memory. # References - Tullis & Benjamin (2011). On the effectiveness of self-paced learning. Journal of Memory and Language. - Markant et al. (2014). Deconstructing the effect of self-directed study on episodic memory. Memory & Cognition. - DuBrow et al. (2019). A common mechanism underlying choice's influence on preference and memory. Psychonomic Bulletin & Review. - Ding et al. (2021). The effect of choice on intentional and incidental memory. Learning & Memory. A demo of puzzles and Q&A's on ChapterPal:

BURKOV

39,849 просмотров • 6 месяцев назад

Mark Zuckerberg just argued that AI will force companies to hire more people. Not fewer. Three and a half billion people use Meta every day. Not one of them has a phone number to call. Mark Zuckerberg: “It’s clearly just going to automate jobs and like all these jobs are going to go away… that has not really been how the history of technology has worked.” The entire media cycle runs the same story. AI replaces workers. Industries hollow out. The human becomes unnecessary. History has never once cooperated. Voice support for 3.5 billion daily users costs between ten and twenty billion dollars a year. The math made it untouchable. So Meta never built it. AI changed the math. Zuckerberg: “Let’s say the AI can handle 90 percent of that… you’ve gotten the cost of providing that service down to one 10th.” A service that could not exist becomes standard. Overnight. The moment it goes live, the edge cases arrive. The escalations. The problems no model can close alone. Every one needs a human on the other end. Zuckerberg: “I actually think we’re probably going to go hire more customer support people.” The AI did not kill the jobs. It unlocked a service so vast the company now needs people it never would have hired. When execution costs crater, companies do not pocket the savings. They go after problems they could never afford to touch. New markets. New products. New services that were economically impossible twelve months ago. Every one creates roles that did not exist before the machine arrived. The people terrified of automation are tracking the wrong number. They count the jobs that disappear. They have no framework for the ones that haven’t been invented yet.
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Mark Zuckerberg just argued that AI will force companies to hire more people. Not fewer. Three and a half billion people use Meta every day. Not one of them has a phone number to call. Mark Zuckerberg: “It’s clearly just going to automate jobs and like all these jobs are going to go away… that has not really been how the history of technology has worked.” The entire media cycle runs the same story. AI replaces workers. Industries hollow out. The human becomes unnecessary. History has never once cooperated. Voice support for 3.5 billion daily users costs between ten and twenty billion dollars a year. The math made it untouchable. So Meta never built it. AI changed the math. Zuckerberg: “Let’s say the AI can handle 90 percent of that… you’ve gotten the cost of providing that service down to one 10th.” A service that could not exist becomes standard. Overnight. The moment it goes live, the edge cases arrive. The escalations. The problems no model can close alone. Every one needs a human on the other end. Zuckerberg: “I actually think we’re probably going to go hire more customer support people.” The AI did not kill the jobs. It unlocked a service so vast the company now needs people it never would have hired. When execution costs crater, companies do not pocket the savings. They go after problems they could never afford to touch. New markets. New products. New services that were economically impossible twelve months ago. Every one creates roles that did not exist before the machine arrived. The people terrified of automation are tracking the wrong number. They count the jobs that disappear. They have no framework for the ones that haven’t been invented yet.

Dustin

369,810 просмотров • 3 месяцев назад

We’re thrilled to announce the launch of a brand-new Seven Screen's Cinemas located at Sky Hub, right opposite to Kilambakkam Bus Terminal. After years of crafting engaging, distinctive films, we now take a bold step forward—venturing into the multiplex space to bring audiences a cinema experience crafted by people who truly understand storytelling, sound, and spectacle. This next-generation multiplex brings together cutting-edge technology and immersive design to redefine the movie-going experience: For Pure Cinema Magic 🎥 5 Screens powered by 4K RGB Laser Projection for ultra-sharp visuals, deeper contrasts, and stunning color accuracy 🔊 Q-SYS Dolby Atmos sound delivering breathtaking, 360° audio that surrounds you from every angle First in India All screens are fully integrated with Q-SYS Dolby Atmos technology, enabling high-performance power amplification that delivers a truly seat-shaking, immersive cinematic experience—you don’t just watch the movie, you feel it. 🎟️ Bookings Opening Soon on District/Ticketnew — For Tomorrow! Get ready to experience cinema like never before. 📲 For updates, showtimes, and more information, follow us on: X : Instagram :
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We’re thrilled to announce the launch of a brand-new Seven Screen's Cinemas located at Sky Hub, right opposite to Kilambakkam Bus Terminal. After years of crafting engaging, distinctive films, we now take a bold step forward—venturing into the multiplex space to bring audiences a cinema experience crafted by people who truly understand storytelling, sound, and spectacle. This next-generation multiplex brings together cutting-edge technology and immersive design to redefine the movie-going experience: For Pure Cinema Magic 🎥 5 Screens powered by 4K RGB Laser Projection for ultra-sharp visuals, deeper contrasts, and stunning color accuracy 🔊 Q-SYS Dolby Atmos sound delivering breathtaking, 360° audio that surrounds you from every angle First in India All screens are fully integrated with Q-SYS Dolby Atmos technology, enabling high-performance power amplification that delivers a truly seat-shaking, immersive cinematic experience—you don’t just watch the movie, you feel it. 🎟️ Bookings Opening Soon on District/Ticketnew — For Tomorrow! Get ready to experience cinema like never before. 📲 For updates, showtimes, and more information, follow us on: X : Instagram :

Seven Screen Studio

164,295 просмотров • 6 месяцев назад

⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute
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⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute

Faisal Mahmood

65,520 просмотров • 2 лет назад