Cander

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Star Wars Knights of the Old Republic II: The Sith Lords (Kotor 2) is one of the most popular RPGs of all time. The famous Star Wars game is the direct sequel to the original Kotor. After you reach a certain point in the story, you are given the option of exploring Kotor 2's galaxy however you want, with the opportunity to choose which planets you will traverse. Here is the best Kotor 2 planet order and the best way to travel the stars. The Best Kotor 2 Planet Order is: Na Shaddaa Ondoron/ Dxun Part 1 Dantooine Ondoron/ Dxun Part 2 Korriban Kotor 2 has a vast range of planets available to you to explore. After you make it through a specific part of the game, the world of Kotor 2 opens up, allowing you to travel to a vast range of planets in any order you want. Such planets include the Sith homeworld Korriban, Na Shaddaa, Onderon, Dxun, and Dantooine. It is essential to traverse these planets properly so that you can enjoy the story better and maximise your characters' abilities before you reach the end of the game. Kotor 2 Planet Order Guide 1.Na Shaddaa The first planet you visit should be Na Shaddaa. Na Shaddaa is a moon that orbits the Hutt homeworld of Nal Hutta. The planet is called the smuggler's moon and is run by the infamous Hutt Cartel. Thus, the planet is full of corruption and illegal activities. Once on the planet, the game has your character defeat bounty hunters, save a Jedi master, and stop a criminal organisation. Thus, it provides an excellent introduction to the world of Kotor 2 without being super challenging, allowing you to level up for more complicated parts of the game. This planet should be the first place you visit on your journey due to your activities on the planet setting up nicely your future endeavours in-game. 2.Ondoron/ Dxun Part 1 After leaving Na Shaddaa, you should make your way to Dxun, a jungle moon located near Ondoron. Dxun is a moon inhabited by dangerous wildlife; after landing on the moon, your character is quickly mobbed by creatures such as the Zakkeg. After making your way through the moon and meeting Mandalore Canderous Ordo, the game has your character find a Jedi master in hiding on the planet of Onderon. After completing this mission, Canderous Ordo decides to join your team, aiding you in your adventure. Thus, traveling to these two planets first is the best way to travel, allowing you to not only level up but gain a powerful new ally in the fight against the Sith. Canderous Ordo is one of the best shooters in the game and will be vital in helping you to defeat the game's most complex challenges. 3.Dantooine To follow the best Kotor 2 planet order, you should next travel to Dantooine after recruiting Canderous on Dxun/Ondoron. Dantooine is a planet initially introduced in the first Kotor game. The world was home to a Jedi enclave that helped turn Darth Revan toward the light side of the force. However, it was destroyed by Darth Malek in subsequent years. Once on the planet, the game has you and your companions help defend against a Sith onslaught alongside Jedi Master Vrook Lamar. Dantooine is the next best place to visit on this list due to not only offering a moderate level of challenge for the final game boss but bridging the gap between the first and the second game in preparation for Kotor 2's final story reveals. 4.Ondoron/ Dxun Part 2 After saving Dantooine from the Sith, you should revisit the Ondoron/Dxun story arc. Once back on the two planets, the game has you defeat several Sith and stop a civil war on Onderon. Revisiting the two planets is the best place to set up several story arcs that will play out later in the story. The story arc also offers considerable challenge making it the ideal place to visit near the end of the game. 5. Korriban The final planet in our Kotor 2 planet order is Korriban; it should be the last place you visit in the game. Korriban was initially present in the first Kotor game and is known as the homeworld of the Sith. The game has you fight ancient Sith lords searching for a Jedi master, which quickly leads into an ambush by Darth Sion, one of the game's main villains. Leading Korriban to last is ideal due to the planet’s difficulty and the boss fight with Sion. Also, it perfectly sets up the game’s end sequence and story arc. Final Thoughts Overall, the Kotor 2 planet order above is the best way to traverse the stars and story. With the likely release of a Kotor remake shortly, inevitably, a Kotor 2 may also get remade, making this guide even more beneficial. For more Star Wars gaming content, . subscribe to our newsletter below Also, check out, 5 Best Minecraft Hacks and Cheats: How to Enable and Use Them 3 Best Kotor Builds Even Vader Would Approve of How to Get Anarchy in Destiny 2 (Destiny Exotic Guide)

Ultimate KOTOR 2 Planet Order: The Best Way to Traverse the Stars 

Ultimate KOTOR 2 Planet Order: la mejor manera de atravesar las estrellas

The U.S. saw close to 5,000 more cancer deaths in 2023 compared with 2022, marking the only cause of death among the top 10 to see an increase year over year, according to a U.S. News and World report. After heart disease, which comes in as the No. 1 killer of Americans, cancer remained the No. 2 killer in 2023, accounting for 19.8% of all deaths. What this means is that we need to find solutions to help prevent and detect these top causes of death in America. And this is where AI comes in. With AI, doctors can create 3D models from 2D images, providing a more comprehensive view of tumors and their potential spread, improving the accuracy of diagnosis and treatment planning. AI can be used to assist in analyzing tissue samples by examining microscopic images of biopsy samples. It can recognize cancerous cells and differentiate between different types of cancer with a high degree of accuracy. By using advanced AI tools, doctors will be able to automate the counting and classification of cells in tissue samples, speeding up the diagnostic process and reducing the workload on pathologists. Where AI can truly shine is through predictive analytics. AI models can be trained on large datasets to predict an individual’s risk of developing certain types of cancer-based on their genetic, lifestyle, and environmental factors. Using these large datasets as a basis, AI can then analyze a patient’s clinical data to predict the likely course of the disease and the patient’s response to treatment, helping clinicians tailor treatment plans more effectively. Naturally, AI will reduce errors and bias by reducing diagnostic errors caused by human fatigue or oversight. This leads to more accurate and consistent cancer detection. When it comes to research and development, AI can accelerate the discovery of new cancer treatments by analyzing vast amounts of biological and chemical data, identifying potential drug candidates faster than traditional methods. This isn’t just speculation. AI is “outpacing doctors when it comes to detecting a common cancer in men,” according to a recent report. A new study from UCLA found that “AI identified prostate cancer with 84% accuracy — compared to 67% accuracy for cases detected by physicians.” The AI software program, Unfold AI, made by Avenda Health in California, “uses an AI algorithm to visualize the likelihood of cancer based on various types of clinical data.” In further news, an AI model has identified certain breast tumor stages likely to progress to invasive cancer. According to a report in MIT News, “Ductal carcinoma in situ (DCIS) is a type of preinvasive tumor that sometimes progresses to a highly deadly form of breast cancer. It accounts for about 25 percent of all breast cancer diagnoses. “Because it is difficult for clinicians to determine the type and stage of DCIS, patients with DCIS are often overtreated. To address this, an interdisciplinary team of researchers from MIT and ETH Zurich developed an AI model that can identify the different stages of DCIS from a cheap and easy-to-obtain breast tissue image.” One AI model even helps researchers detect disease based on coughs. Google has developed a foundation model called Health Acoustic Representations (HeAR), which is trained on a massive dataset of health-related sounds. The technology described involves using bio-acoustic sounds—such as coughs, speech, and breathing—as indicators of health conditions, analyzed through AI. HeAR is designed to recognize patterns in these sounds, which can be used to detect diseases like tuberculosis (TB) and chronic obstructive pulmonary disease (COPD). The key advantage of HeAR is its ability to accurately analyze health-related sounds using less training data, making it highly efficient in the data-scarce field of healthcare. This model can be generalized across different microphones, enhancing its usability in various settings. HeAR's strength lies in its ability to accelerate the development of custom bio-acoustic models for specific health conditions, even in cases where data is limited, or resources are scarce. An example of its application is seen in the work of Salcit Technologies, which uses HeAR to improve its AI-based tool, Swaasa®, for the early detection of TB through cough analysis. This approach could significantly enhance the accessibility and affordability of TB screening, particularly in regions like India, where TB remains a major public health challenge. By leveraging AI to analyze sounds produced by the human body, HeAR has the potential to revolutionize disease screening and monitoring, offering a low-cost, scalable solution that can be deployed widely, even in resource-limited settings. This technology could play a crucial role in global health initiatives, such as the effort to eradicate TB by 2030, by making early diagnosis more accessible and improving health outcomes on a large scale. The future is already here with AI being used on numerous applications to predict or detect cancer. Further technological developments will only assist us in preventing more deaths and offering more Americans the quality of life they might not have had until now. All thanks to AI. The U.S. saw close to 5,000 more cancer deaths in 2023 compared with 2022, marking the only cause of death among the top 10 to see an increase year over year, according to a U.S. News and World report . After heart disease, which comes in as the No. 1 killer of Americans, cancer remained the No. 2 killer in 2023, accounting for 19.8% of all deaths. according to a U.S. News and World report What this means is that we need to find solutions to help prevent and detect these top causes of death in America. And this is where AI comes in. With AI, doctors can create 3D models from 2D images, providing a more comprehensive view of tumors and their potential spread, improving the accuracy of diagnosis and treatment planning. AI can be used to assist in analyzing tissue samples by examining microscopic images of biopsy samples. It can recognize cancerous cells and differentiate between different types of cancer with a high degree of accuracy. By using advanced AI tools, doctors will be able to automate the counting and classification of cells in tissue samples, speeding up the diagnostic process and reducing the workload on pathologists. Where AI can truly shine is through predictive analytics. AI models can be trained on large datasets to predict an individual’s risk of developing certain types of cancer-based on their genetic, lifestyle, and environmental factors. Using these large datasets as a basis, AI can then analyze a patient’s clinical data to predict the likely course of the disease and the patient’s response to treatment, helping clinicians tailor treatment plans more effectively. Naturally, AI will reduce errors and bias by reducing diagnostic errors caused by human fatigue or oversight. This leads to more accurate and consistent cancer detection. When it comes to research and development, AI can accelerate the discovery of new cancer treatments by analyzing vast amounts of biological and chemical data, identifying potential drug candidates faster than traditional methods. This isn’t just speculation. AI is “outpacing doctors when it comes to detecting a common cancer in men,” according to a recent report. A new study from UCLA found that “AI identified prostate cancer with 84% accuracy — compared to 67% accuracy for cases detected by physicians.” new study from UCLA The AI software program, Unfold AI, made by Avenda Health in California, “uses an AI algorithm to visualize the likelihood of cancer based on various types of clinical data.” In further news, an AI model has identified certain breast tumor stages likely to progress to invasive cancer. According to a report in MIT News, “Ductal carcinoma in situ (DCIS) is a type of preinvasive tumor that sometimes progresses to a highly deadly form of breast cancer. It accounts for about 25 percent of all breast cancer diagnoses. “Because it is difficult for clinicians to determine the type and stage of DCIS, patients with DCIS are often overtreated. To address this, an interdisciplinary team of researchers from MIT and ETH Zurich developed an AI model that can identify the different stages of DCIS from a cheap and easy-to-obtain breast tissue image.” One AI model even helps researchers detect disease based on coughs. Google has developed a foundation model called Health Acoustic Representations (HeAR), which is trained on a massive dataset of health-related sounds. The technology described involves using bio-acoustic sounds—such as coughs, speech, and breathing—as indicators of health conditions, analyzed through AI. Google has developed a foundation model HeAR is designed to recognize patterns in these sounds, which can be used to detect diseases like tuberculosis (TB) and chronic obstructive pulmonary disease (COPD). The key advantage of HeAR is its ability to accurately analyze health-related sounds using less training data, making it highly efficient in the data-scarce field of healthcare. This model can be generalized across different microphones, enhancing its usability in various settings. HeAR's strength lies in its ability to accelerate the development of custom bio-acoustic models for specific health conditions, even in cases where data is limited, or resources are scarce. An example of its application is seen in the work of Salcit Technologies, which uses HeAR to improve its AI-based tool, Swaasa®, for the early detection of TB through cough analysis. This approach could significantly enhance the accessibility and affordability of TB screening, particularly in regions like India, where TB remains a major public health challenge. By leveraging AI to analyze sounds produced by the human body, HeAR has the potential to revolutionize disease screening and monitoring, offering a low-cost, scalable solution that can be deployed widely, even in resource-limited settings. This technology could play a crucial role in global health initiatives, such as the effort to eradicate TB by 2030, by making early diagnosis more accessible and improving health outcomes on a large scale. The future is already here with AI being used on numerous applications to predict or detect cancer. Further technological developments will only assist us in preventing more deaths and offering more Americans the quality of life they might not have had until now. All thanks to AI.

This Is How AI Can Help Detect Cancer

SAN FRANSCISCO, United States, May 24th, 2024/Chainwire/ Rollup-as-a-Service platform Caldera is launching Guardian Nodes, a production-ready node system that enables network users to verify rollup blocks for rewards.
HYCHAIN, the first team to leverage this system with Caldera, generated $8 million from their initial node sale. Caldera, the leading Rollup-as-a-Service (RaaS) platform, currently powers over fifty EVM rollups (including Manta, Injective inEVM, Kinto, HYCHAIN, Treasure, and ApeChain) that secure ~$1B in total value locked. Along with their core rollup infrastructure, Caldera offers a catalog of over 50 integrations across the modular stack and an assortment of other custom user-facing tools for chains to leverage. Guardian Nodes are the latest addition to that list. The Background Today, the majority of L1 blockchains operate using a proof-of-stake model, where a chain’s participants are incentivized via network rewards to lend their stake and validate the chain. This incentive structure fosters decentralization by encouraging individual stakers to operate honest validators, while punishing those who don't, making for an extremely high cost to attack a network and compromise its security. Decentralizing Rollups EVM rollups have all but solved the scalability problem, with L2s and L3s on Ethereum enabling virtually infinite scale. But, there's still no incentive for honest network participants to monitor these rollups. Caldera’s Guardian Nodes allow teams to decentralize their rollups by enabling users to verify blocks and secure the network in exchange for rewards. Under the hood, this is accomplished by introducing a novel “light verifier'' to Arbitrum rollups that allows Guardian Node operators to verify Nitro batches on everyday hardware without needing to run a full node. Teams can launch Guardian Nodes to their users through a “Node Sale”, which distributes “keys” that authenticate a node's eligibility to submit claims and earn rewards, granting purchasers the ability to operate a Guardian Node on a given rollup. HYCHAIN, the first team to leverage this system, raised over $8m across 16,000+ node keys in just 2 weeks, completely supercharging their community while generating significant revenue for their project. By enabling more parties to watch over a rollup and identify malicious behavior, the network’s security grows more robust— a crucial step to establishing trust in the chain’s correctness. This in turn generates more demand for a rollup’s native token, which is required for users to participate in validation and helps provide practical cryptoeconomic security for the network. Guardians Nodes are another notch in the belt of Caldera’s impressive infrastructure solution. With the success of HYCHAIN's launch, we expect more teams building rollups to leverage this innovative product in the coming months. To get started with a high performance rollup, visit Caldera’s website here and/or book a call here. Contact Growth Alex Gu Caldera alex@caldera.xyz 4844329611 This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SAN FRANSCISCO, United States, May 24th, 2024/Chainwire/ SAN FRANSCISCO, United States, May 24th, 2024/Chainwire/ Rollup-as-a-Service platform Caldera is launching Guardian Nodes, a production-ready node system that enables network users to verify rollup blocks for rewards. HYCHAIN, the first team to leverage this system with Caldera, generated $8 million from their initial node sale. Rollup-as-a-Service platform Caldera is launching Guardian Nodes, a production-ready node system that enables network users to verify rollup blocks for rewards. HYCHAIN, the first team to leverage this system with Caldera, generated $8 million from their initial node sale. Caldera, the leading Rollup-as-a-Service (RaaS) platform, currently powers over fifty EVM rollups (including Manta, Injective inEVM, Kinto, HYCHAIN, Treasure, and ApeChain) that secure ~$1B in total value locked. Along with their core rollup infrastructure, Caldera offers a catalog of over 50 integrations across the modular stack and an assortment of other custom user-facing tools for chains to leverage. Guardian Nodes are the latest addition to that list. The Background Today, the majority of L1 blockchains operate using a proof-of-stake model, where a chain’s participants are incentivized via network rewards to lend their stake and validate the chain. This incentive structure fosters decentralization by encouraging individual stakers to operate honest validators, while punishing those who don't, making for an extremely high cost to attack a network and compromise its security. Decentralizing Rollups EVM rollups have all but solved the scalability problem, with L2s and L3s on Ethereum enabling virtually infinite scale. But, there's still no incentive for honest network participants to monitor these rollups. Caldera’s Guardian Nodes allow teams to decentralize their rollups by enabling users to verify blocks and secure the network in exchange for rewards. Under the hood, this is accomplished by introducing a novel “light verifier'' to Arbitrum rollups that allows Guardian Node operators to verify Nitro batches on everyday hardware without needing to run a full node. Teams can launch Guardian Nodes to their users through a “Node Sale”, which distributes “keys” that authenticate a node's eligibility to submit claims and earn rewards, granting purchasers the ability to operate a Guardian Node on a given rollup. HYCHAIN , the first team to leverage this system, raised over $8m across 16,000+ node keys in just 2 weeks, completely supercharging their community while generating significant revenue for their project. HYCHAIN HYCHAIN By enabling more parties to watch over a rollup and identify malicious behavior, the network’s security grows more robust— a crucial step to establishing trust in the chain’s correctness. This in turn generates more demand for a rollup’s native token, which is required for users to participate in validation and helps provide practical cryptoeconomic security for the network. Guardians Nodes are another notch in the belt of Caldera’s impressive infrastructure solution. With the success of HYCHAIN's launch, we expect more teams building rollups to leverage this innovative product in the coming months. To get started with a high performance rollup, visit Caldera’s website here and/or book a call here . here here here here Contact Growth Alex Gu Caldera alex@caldera.xyz 4844329611 This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Caldera Launches Guardian Nodes, Enabling Teams To Raise Funds And Decentralize Their Network

Why Everyone is Wrong About the Future of Work™

কেন সবাই কাজের ভবিষ্যত সম্পর্কে ভুল ™

Warum alle falsch liegen, wenn sie über die Zukunft der Arbeit sprechen™

Por qué todo el mundo está equivocado sobre el futuro del trabajo™

Pourquoi tout le monde se trompe sur l’avenir du travail™

काम के भविष्य के बारे में हर कोई गलत क्यों है™

誰もが仕事の未来について間違っている理由™

왜 모든 사람이 미래의 일에 대해 틀렸는가?™

Por que todos estão errados sobre o futuro do trabalho™

Почему все ошибаются относительно будущего работы™

Neden Herkes Çalışmanın Geleceği Hakkında Yanılıyor?™

Tại sao mọi người đều sai về tương lai của công việc™

为什么每个人对工作的未来都有误解™

The artificial intelligence landscape is undergoing a seismic shift. While text-based Large Language Models (LLMs) have dominated headlines and captured our imagination, a new paradigm is emerging that promises to revolutionize how we interact with AI: multimodal artificial intelligence. This evolution marks a fundamental change in how machines process and understand our world. The Natural Evolution: From Text to Multiple Modalities Humans don't communicate solely through text – we interpret facial expressions, analyze tone of voice, and process visual information simultaneously. This multifaceted approach to communication is what makes human interaction so rich and nuanced. Now, AI is following suit, evolving beyond the constraints of text-only models to embrace a more holistic approach to understanding and generating content. Multimodal AI systems can process and generate multiple forms of data – text, images, audio, video, and documents – in unified models that more closely mirror human cognitive processes. We're already seeing impressive demonstrations of this technology, from foundational models that can generate videos or short films from written descriptions such as Meta’s Movie Gen, Pika Labs, or Runway’s Gen-3 Alpha, to platforms capable of real-time voice conversations that understand both words and emotional undertones like OpenAI’s GPT-4o. These developments aren't just technical achievements – they're reshaping how we think about artificial intelligence and its capabilities. Multimodal AI Potential to Transforming Industries The potential applications of multimodal AI are vast and transformative. In healthcare, systems that can simultaneously analyze medical images, patient voice recordings, and clinical documentation could revolutionize diagnostic processes. Early research shows promising results in detecting cognitive conditions like Alzheimer's disease by combining analysis of speech patterns with medical imaging data. Multimodal models utilizing data from radiology images, histopathology slides, genomics, and clinical data have shown potential to outperform unimodal approaches in predicting radiotherapy treatment outcomes for non-small-cell lung cancer patients. Multimodal AI has the potential to revolutionize robotics by enabling machines to perceive, understand, and interact with the world in ways that more closely mimic human capabilities. By integrating data from multiple sensors such as cameras, microphones, and touch sensors, robots can achieve enhanced environmental awareness, more natural human-robot interactions, and improved decision-making in complex scenarios. This technology paves the way for more adaptable and versatile robots capable of learning from demonstration, performing advanced manipulation tasks, and navigating diverse environments with greater ease. The creative industry stands to benefit significantly as well. Content creators can now envision AI platforms that generate coordinated audio-visual experiences from simple text descriptions. Film production workflows could be streamlined with AI-generated b-roll footage, while virtual assistants could become more empathetic by understanding not just words, but tone and facial expressions. The Data Challenge: Managing Multimodal Complexity However, the journey toward truly effective multimodal AI isn't without its hurdles. The primary challenge lies in data management – specifically, the need for diverse, high-quality datasets that span multiple modalities. While traditional LLMs could rely on text sources like Wikipedia and books, multimodal systems require a much broader spectrum of data, including podcasts, videos, medical imaging, and even biometric data from wearable devices. This expanded data requirement introduces new complexities in data quality control. Poor-quality inputs in any modality can compromise the entire system's performance. For instance, mislabeled video data might confuse visual recognition capabilities, while degraded audio could impair speech recognition accuracy. The old programming adage "garbage in, garbage out" takes on new significance in the multimodal era.\ Introducing Encord - the multimodal data development platform As the industry evolves, new platforms are emerging to address the unique challenges of multimodal AI development. At Encord, we're excited to announce our contribution to this revolution by launching a truly multimodal AI data development platform that enables teams to manage, curate, and label multiple data types within a single unified interface. Our platform addresses one of the most significant pain points in multimodal AI development: the fragmentation of data preparation workflows. Instead of juggling multiple tools for different data types, teams can now handle images, videos, audio files, documents, and DICOM files in one seamless environment. This consolidation significantly reduces development time and improves data quality consistency. A standout feature is our multimodal annotation and workflow orchestration capability, which allows teams to view and annotate multiple file types simultaneously, all in one platform. This unlocks a variety of use cases that previously were only possible through cumbersome workarounds.  A few of these include analyzing PDF reports alongside images, videos, or DICOM files to improve the accuracy and efficiency of annotation workflows by empowering labelers with extreme context. The platform also supports human-in-the-loop workflows and natively integrates with state-of-the-art models like SAM for automated labeling, making it ideal for both traditional AI development and emerging applications like RLHF (Reinforcement Learning from Human Feedback) for generative AI. The Road Ahead The transition to multimodal AI represents more than just a technological advancement – it's a fundamental shift in how we think about artificial intelligence. As these systems become more sophisticated in processing multiple data streams simultaneously, they move us closer to achieving more general forms of artificial intelligence that can understand and interact with the world in ways that feel more natural and human-like. However, success in this new era will depend heavily on our ability to manage and curate diverse data types effectively. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. The future of AI is multimodal, and it's arriving faster than many might have anticipated. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. How about you and your AI team - are you ready? The artificial intelligence landscape is undergoing a seismic shift. While text-based Large Language Models (LLMs) have dominated headlines and captured our imagination, a new paradigm is emerging that promises to revolutionize how we interact with AI: multimodal artificial intelligence. This evolution marks a fundamental change in how machines process and understand our world. The Natural Evolution: From Text to Multiple Modalities The Natural Evolution: From Text to Multiple Modalities Humans don't communicate solely through text – we interpret facial expressions, analyze tone of voice, and process visual information simultaneously. This multifaceted approach to communication is what makes human interaction so rich and nuanced. Now, AI is following suit, evolving beyond the constraints of text-only models to embrace a more holistic approach to understanding and generating content. Multimodal AI systems can process and generate multiple forms of data – text, images, audio, video, and documents – in unified models that more closely mirror human cognitive processes. We're already seeing impressive demonstrations of this technology, from foundational models that can generate videos or short films from written descriptions such as Meta’s Movie Gen, Pika Labs, or Runway’s Gen-3 Alpha, to platforms capable of real-time voice conversations that understand both words and emotional undertones like OpenAI’s GPT-4o. These developments aren't just technical achievements – they're reshaping how we think about artificial intelligence and its capabilities. Multimodal AI Potential to Transforming Industries The potential applications of multimodal AI are vast and transformative. In healthcare, systems that can simultaneously analyze medical images, patient voice recordings, and clinical documentation could revolutionize diagnostic processes. Early research shows promising results in detecting cognitive conditions like Alzheimer's disease by combining analysis of speech patterns with medical imaging data . Multimodal models utilizing data from radiology images, histopathology slides, genomics, and clinical data have shown potential to outperform unimodal approaches in predicting radiotherapy treatment outcomes for non-small-cell lung cancer patients. by combining analysis of speech patterns with medical imaging data by combining analysis of speech patterns with medical imaging data predicting radiotherapy treatment outcomes predicting radiotherapy treatment outcomes Multimodal AI has the potential to revolutionize robotics by enabling machines to perceive, understand, and interact with the world in ways that more closely mimic human capabilities. By integrating data from multiple sensors such as cameras, microphones, and touch sensors, robots can achieve enhanced environmental awareness, more natural human-robot interactions, and improved decision-making in complex scenarios. This technology paves the way for more adaptable and versatile robots capable of learning from demonstration, performing advanced manipulation tasks, and navigating diverse environments with greater ease. revolutionize robotics revolutionize robotics The creative industry stands to benefit significantly as well. Content creators can now envision AI platforms that generate coordinated audio-visual experiences from simple text descriptions. Film production workflows could be streamlined with AI-generated b-roll footage, while virtual assistants could become more empathetic by understanding not just words, but tone and facial expressions. The Data Challenge: Managing Multimodal Complexity However, the journey toward truly effective multimodal AI isn't without its hurdles. The primary challenge lies in data management – specifically, the need for diverse, high-quality datasets that span multiple modalities. While traditional LLMs could rely on text sources like Wikipedia and books, multimodal systems require a much broader spectrum of data, including podcasts, videos, medical imaging, and even biometric data from wearable devices. This expanded data requirement introduces new complexities in data quality control. Poor-quality inputs in any modality can compromise the entire system's performance. For instance, mislabeled video data might confuse visual recognition capabilities, while degraded audio could impair speech recognition accuracy. The old programming adage "garbage in, garbage out" takes on new significance in the multimodal era.\ Introducing Encord - the multimodal data development platform Encord Encord As the industry evolves, new platforms are emerging to address the unique challenges of multimodal AI development. At Encord, we're excited to announce our contribution to this revolution by launching a truly multimodal AI data development platform that enables teams to manage, curate, and label multiple data types within a single unified interface. Our platform addresses one of the most significant pain points in multimodal AI development: the fragmentation of data preparation workflows. Instead of juggling multiple tools for different data types, teams can now handle images, videos, audio files, documents, and DICOM files in one seamless environment. This consolidation significantly reduces development time and improves data quality consistency. A standout feature is our multimodal annotation and workflow orchestration capability, which allows teams to view and annotate multiple file types simultaneously, all in one platform. This unlocks a variety of use cases that previously were only possible through cumbersome workarounds.  A few of these include analyzing PDF reports alongside images, videos, or DICOM files to improve the accuracy and efficiency of annotation workflows by empowering labelers with extreme context. The platform also supports human-in-the-loop workflows and natively integrates with state-of-the-art models like SAM for automated labeling, making it ideal for both traditional AI development and emerging applications like RLHF (Reinforcement Learning from Human Feedback) for generative AI. The Road Ahead The transition to multimodal AI represents more than just a technological advancement – it's a fundamental shift in how we think about artificial intelligence. As these systems become more sophisticated in processing multiple data streams simultaneously, they move us closer to achieving more general forms of artificial intelligence that can understand and interact with the world in ways that feel more natural and human-like. However, success in this new era will depend heavily on our ability to manage and curate diverse data types effectively. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. The future of AI is multimodal, and it's arriving faster than many might have anticipated. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. How about you and your AI team - are you ready?

Welcome to the Multimodal AI Era

Is Artificial Intelligence in Healthcare a Ticking Time Bomb or Timely Solution?

Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Roughly 129 million people in the U.S. have a chronic health condition. Despite many of these diseases being treatable, they’re also behind five of the 10 most common causes of death. Consequently, managing them is becoming increasingly crucial, and AI can help in several ways. Improved Diagnoses and Prognoses The use of AI to treat chronic conditions starts with a patient’s diagnosis. Because machine learning excels at detecting subtle trends in data, it can often identify diseases early and with impressive accuracy. AI even outperformed doctors at detecting skin cancer in 61.2% of studies and matched human experts in another 29% of cases. Similarly, predictive analytics models can predict how an individual patient’s case may progress. This insight into the future helps patients and doctors understand the situation so they can better decide how to treat the condition. These predictions become increasingly reliable as AI gathers additional data. Personalized Healthcare Once treatment begins, AI can improve health outcomes by tailoring care plans to individual patients. The same disease may play out entirely differently between patients with varying biologies, lifestyles and socioeconomic backgrounds. Consequently, healthcare is most effective when it’s specific to the individual, and AI provides the granular data analysis necessary to personalize it. AI-powered personalized care apps already exist. Some generate meal plans for individual users to help them meet unique nutritional goals based on their background and biology. Others predict how different medicines may perform for varying patients. Across all cases, machine learning can take the trial and error out of finding the best treatment, leading to more effective long-term care. Automated Reminders and Updates As patients try to stick to their nutrition and medicine plans, AI apps can offer automated reminders to help. Many people don’t take their meds as prescribed, leading to 125,000 deaths annually in the U.S. AI can monitor data from wearables to detect when patients may be veering off their treatment plans and alert them so they can respond before it’s too late. Similarly, AI monitoring tools can provide doctors with real-time updates on how their patients’ conditions are progressing. In some cases, machine learning algorithms could then use this data to predict if a change of course would yield better results. Even without that level of automation, AI’s timely insights enable faster, effective care changes. Streamlined Administrative Tasks There are behind-the-scenes benefits to AI in chronic disease management, too. Smart algorithms can automate administrative work like scheduling, data entry and health documentation compliance. As a result, doctors and nurses get more time to spend with patients. Improving efficiency and accuracy in these tasks also produces financial advantages. Administrative work accounts for up to 25% of healthcare spending in the U.S., so removing errors and minimizing the time spent on it leads to considerable savings. As more hospitals take advantage of this opportunity, the industry could justify lower costs to become increasingly accessible. Potential Downsides to AI in Chronic Disease Management While the upsides to AI in chronic disease management show significant promise, there are some complications to consider, too. Before the technology can reach its full potential, medical organizations must grapple with issues of bias, hallucinations and data security. Bias One of the biggest challenges of AI in healthcare is its tendency to replicate and even exaggerate human prejudices. Machine learning’s accuracy varies between demographics — facial recognition misidentifies Asian and Black faces up to 100 times more often than white faces. Similar issues could make AI diagnoses far less reliable for patients of color. Human bias throughout history means there’s less data on Black patients and other minorities. Consequently, AI may be unable to accurately diagnose their conditions or determine which treatments will be most effective. Failing to recognize this gap could worsen the inequality in care already plaguing the U.S. medical system. Hallucinations There’s also the hallucination issue to deal with. Even the most accurate machine learning models can hallucinate, thanks to various data-related problems and the fact that AI cannot identify facts — it only sees trends in data. In many applications, hallucinations cause minor hiccups, but they could be dangerous in a chronic disease context. Doctors could over-rely on AI and fail to account for its tendency to hallucinate. Once that happens, they could prescribe treatments based on false or misleading predictions, possibly leading to risky health complications. Human experts must always have the final say, but the mere presence of AI suggestions can lead to complacency and overreliance. Data Privacy and Security AI models also require a significant amount of data, and medical information is highly sensitive. This combination raises questions about patient privacy and cybersecurity. What if an AI service accidentally discloses personal information about patients? What if AI datasets make hospitals a bigger target for cybercriminals? Healthcare has already seen a troubling rise in cybercrime. The industry suffered 809 data compromises in 2023, more than double the year before. Security standards will likely need to evolve before hospitals can safely feed large volumes of patient information to AI models. AI Could Revolutionize Chronic Disease Care While obstacles remain, AI has big potential in the realm of managing chronic diseases. As the industry develops regulations and best practices around using this technology safely, its promise will only grow. Increasing use and technological development will lead to better patient outcomes, lower costs and streamlined medical workflows, benefiting everyone involved. Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy. Roughly 129 million people in the U.S . have a chronic health condition. Despite many of these diseases being treatable, they’re also behind five of the 10 most common causes of death. Consequently, managing them is becoming increasingly crucial, and AI can help in several ways. 129 million people in the U.S 129 million people in the U.S Improved Diagnoses and Prognoses The use of AI to treat chronic conditions starts with a patient’s diagnosis. Because machine learning excels at detecting subtle trends in data, it can often identify diseases early and with impressive accuracy. AI even outperformed doctors at detecting skin cancer in 61.2% of studies and matched human experts in another 29% of cases. in 61.2% of studies in 61.2% of studies Similarly, predictive analytics models can predict how an individual patient’s case may progress. This insight into the future helps patients and doctors understand the situation so they can better decide how to treat the condition. These predictions become increasingly reliable as AI gathers additional data. Personalized Healthcare Once treatment begins, AI can improve health outcomes by tailoring care plans to individual patients. The same disease may play out entirely differently between patients with varying biologies, lifestyles and socioeconomic backgrounds. Consequently, healthcare is most effective when it’s specific to the individual, and AI provides the granular data analysis necessary to personalize it. AI-powered personalized care apps already exist. Some generate meal plans for individual users to help them meet unique nutritional goals based on their background and biology. Others predict how different medicines may perform for varying patients. Across all cases, machine learning can take the trial and error out of finding the best treatment, leading to more effective long-term care. generate meal plans for individual users generate meal plans for individual users Automated Reminders and Updates As patients try to stick to their nutrition and medicine plans, AI apps can offer automated reminders to help. Many people don’t take their meds as prescribed, leading to 125,000 deaths annually in the U.S. AI can monitor data from wearables to detect when patients may be veering off their treatment plans and alert them so they can respond before it’s too late. leading to 125,000 deaths annually leading to 125,000 deaths annually Similarly, AI monitoring tools can provide doctors with real-time updates on how their patients’ conditions are progressing. In some cases, machine learning algorithms could then use this data to predict if a change of course would yield better results. Even without that level of automation, AI’s timely insights enable faster, effective care changes. Streamlined Administrative Tasks There are behind-the-scenes benefits to AI in chronic disease management, too. Smart algorithms can automate administrative work like scheduling, data entry and health documentation compliance. As a result, doctors and nurses get more time to spend with patients. Improving efficiency and accuracy in these tasks also produces financial advantages. Administrative work accounts for up to 25% of healthcare spending in the U.S., so removing errors and minimizing the time spent on it leads to considerable savings. As more hospitals take advantage of this opportunity, the industry could justify lower costs to become increasingly accessible. 25% of healthcare spending 25% of healthcare spending Potential Downsides to AI in Chronic Disease Management While the upsides to AI in chronic disease management show significant promise, there are some complications to consider, too. Before the technology can reach its full potential, medical organizations must grapple with issues of bias, hallucinations and data security. Bias One of the biggest challenges of AI in healthcare is its tendency to replicate and even exaggerate human prejudices. Machine learning’s accuracy varies between demographics — facial recognition misidentifies Asian and Black faces up to 100 times more often than white faces. Similar issues could make AI diagnoses far less reliable for patients of color. up to 100 times more up to 100 times more Human bias throughout history means there’s less data on Black patients and other minorities. Consequently, AI may be unable to accurately diagnose their conditions or determine which treatments will be most effective. Failing to recognize this gap could worsen the inequality in care already plaguing the U.S. medical system. Hallucinations There’s also the hallucination issue to deal with. Even the most accurate machine learning models can hallucinate, thanks to various data-related problems and the fact that AI cannot identify facts — it only sees trends in data. In many applications, hallucinations cause minor hiccups, but they could be dangerous in a chronic disease context. Doctors could over-rely on AI and fail to account for its tendency to hallucinate. Once that happens, they could prescribe treatments based on false or misleading predictions, possibly leading to risky health complications. Human experts must always have the final say, but the mere presence of AI suggestions can lead to complacency and overreliance. Data Privacy and Security AI models also require a significant amount of data, and medical information is highly sensitive. This combination raises questions about patient privacy and cybersecurity. What if an AI service accidentally discloses personal information about patients? What if AI datasets make hospitals a bigger target for cybercriminals? Healthcare has already seen a troubling rise in cybercrime. The industry suffered 809 data compromises in 2023 , more than double the year before. Security standards will likely need to evolve before hospitals can safely feed large volumes of patient information to AI models. 809 data compromises in 2023 809 data compromises in 2023 AI Could Revolutionize Chronic Disease Care While obstacles remain, AI has big potential in the realm of managing chronic diseases. As the industry develops regulations and best practices around using this technology safely, its promise will only grow. Increasing use and technological development will lead to better patient outcomes, lower costs and streamlined medical workflows, benefiting everyone involved.

How AI is Transforming Chronic Disease Management

The Useless Skills You're Still Learning 

You know an emerging technology is getting real when Big Tech firms try to own it. That's what’s happening as Salesforce attempts to seize leadership of the autonomous AI agents market. AI agents are intelligent systems designed to perform tasks autonomously. At its annual Dreamforce event, Salesforce introduced AgentForce, its autonomous AI agent for the workplace. Afterward, Salesforce CEO Marc Benioff became awfully busy attacking the value of large language models (LLMs) and taking shots at Microsoft for good measure. To wit: He recently said LLM-based generative AI assistants, such as Microsoft Copilot, are overhyped. On the Rapid Response podcast, he said, “We may have heard from these AI priests and priestesses of these LLM model companies and Microsoft and others about AI curing cancer and climate change, and we all have to plug into these nuclear power plants to get these data centers. None of this is true.” And After Microsoft said it will allow organizations to create their own autonomous AI agents within the Copilot Studio platform, Benioff issued a brutal take-down on X: "Microsoft rebranding Copilot as 'agents'? That's panic mode. Let's be real — Copilot's a flop because Microsoft lacks the data, metadata, and enterprise security models to create real corporate intelligence." https://x.com/Benioff/status/1849506221931287013?embedable=true Why would a busy CEO of a multi-billion dollar global Big Tech company lash out at LLMs and Microsoft? Because Benioff knows how important AI agents are becoming. This means: He wants to move the conversation about AI away from LLMs and toward AI agents.


He sees Microsoft’s foray into AI agents as a threat. If he didn’t, he would have ignored Microsoft’s announcement. In doing so, he underlined the importance of AI agents. And propped Microsoft up. The Importance of AI Agents The market for autonomous AI agents is experiencing rapid growth.  The AI agents market is expected to expand from $5.1 billion in 2024 to $47.1 billion by 2030, reflecting a compound annual growth rate of 44.8%. Salesforce wants a piece of that market. But the numbers don’t quite capture their potential impact. In fact, AI agents have become a hot topic representing a significant leap in the AI ecosystem. Unlike traditional AI systems that react to commands, AI agents can proactively pursue objectives, learn from their experiences, and even interact with other agents and humans in complex ways. As a result, AI agents may do mundane, tedious tasks that people don’t want to do – like data entry, managing calendars, automating the creation of reports, and a lot more. In other words, what AI has been promising all along. This autonomy has sparked both excitement and apprehension. Venture capitalist Jeremiah Owyang has speculated that AI agents will upend websites because businesses will need to figure out how to re-tool their sites for AI agents doing web searches instead of people. Chris Mattmann, NASA JPL’s former chief technology and innovation officer, echoes the concerns of many by asking if we’re ready for a world in which AI functions without human oversight. Gartner has identified “Agentic AI” as a top tech trend for 2025, emphasizing its potential to transform enterprise operations. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously. However, Gartner warns of the need for governance to prevent risks such as bias and privacy issues. Gartner stresses the importance of establishing guardrails to ensure AI aligns with human values and intentions. Marc Benioff versus Microsoft This brings us back to Marc Benioff. He has read the tea leaves. He sees autonomous agents as the future, and he’s placed his bets accordingly. Salesforce has embedded itself in 150,000 organizations. When you’re so deeply embedded across corporate America, your product becomes a Trojan Horse for new technology. For instance, Salesforce AgentForce will deploy autonomous AI agents that enhance customer service, sales, marketing, and commerce operations. Need to automate routine tasks such as answering customer inquiries, qualifying sales leads, and optimizing marketing campaigns? Hey, there’s an agent for that – AgentForce. Need a more efficient way to get data-driven insights from Salesforce? Well, AgentForce uses the Atlas Reasoning Engine to analyze data, make decisions, and execute tasks autonomously. Microsoft isn’t taking all of this sitting down. On October 21, Microsoft announced: The ability to create autonomous agents with Copilot Studio.
The introduction of 10 new autonomous agents in Dynamics 365 to build capacity for sales, service, finance and supply chain functions. Here’s the heady way that Microsoft described on its blog the potential impact of AI agents: Copilot isyour AI assistant — it works for you — and Copilot Studio enables you to easily create, manage and connect agents to Copilot. Think of agents as the new apps for an AI-powered world. Every organization will have a constellation of agents — ranging from simple prompt-and-response to fully autonomous. They will work on behalf of an individual, team or function to execute and orchestrate businesses process. Copilot is how you’ll interact with these agents, and they’ll do everything from accelerating lead generation and processing sales orders to automating your supply chain. In effect, Microsoft is going to war with its own Trojan Horse: 60 percent of the Fortune 500 are using Microsoft 365 Copilot (per Microsoft). Microsoft CEO has avoided a war of words with Benioff. Instead, he’s relying on Jared Spataro to answer Benioff’s dismissive remarks by telling CRN, “Every customer is at a different place in their journey, but overall we are hearing something quite different from our Copilot for Microsoft 365 customers . . . Last quarter alone, we saw a customer increase of over 60 percent, and daily users have more than doubled – a clear indicator of Copilot's value in the market.” Marc Benioff versus LLMs I don’t know how the war between Salesforce and Microsoft will turn out. However, I believe Benioff is wrong to attack LLM-based AI applications that are based on human prompts. ChatGPT has been in the market for only two years, and already the off-the-shelf generative AI products based on LLMs are changing our lives. In addition, we’re still in the early days of businesses developing their own generative AI models based on private data. Consider two industries that affect all of us: financial services and healthcare. Financial services: LLMs are changing how financial services do their work, with firms like BlackRock using them to analyze market data and identify investment opportunities. BlackRock’s systematic active equity team uses LLMs to process vast datasets, uncovering patterns that traditional methods might miss. Hedge fund Bridgewater Associates, managing a $19 billion portfolio, uses LLMs in its AIA Platform for complex tasks like market research.
Healthcare: hospitals are figuring out how to use LLMs to improve efficiency across the board, from streamlining emergency room intake to improving clinical care. For example, HCA Healthcare in Tennessee is integrating LLMs into its hospitals, focusing on improving time-consuming tasks like documenting notes from conversations with patients during visits. The above examples illustrate how technology changes the world: one task at a time. Sometimes in visible ways. Oftentimes behind the scenes.Benioff is correct to pressure tech firms to improve LLM-based AI models. They make mistakes and face criticism over issues like bias and intellectual copyright. But on the other hand, so do AI agents. It’s vital to train them properly and keep humans in the loop to correct errors and prevent bias. But he is overlooking the fact that LLMs learn from their mistakes. They improve. Quickly. If anything, the impact of what LLMs can and will do is understated. You know an emerging technology is getting real when Big Tech firms try to own it. That's what’s happening as Salesforce attempts to seize leadership of the autonomous AI agents market. AI agents are intelligent systems designed to perform tasks autonomously. At its annual Dreamforce event, Salesforce introduced AgentForce, its autonomous AI agent for the workplace . Afterward, Salesforce CEO Marc Benioff became awfully busy attacking the value of large language models (LLMs) and taking shots at Microsoft for good measure. To wit: its autonomous AI agent for the workplace its autonomous AI agent for the workplace He recently said LLM-based generative AI assistants, such as Microsoft Copilot, are overhyped. On the Rapid Response podcast, he said, “We may have heard from these AI priests and priestesses of these LLM model companies and Microsoft and others about AI curing cancer and climate change, and we all have to plug into these nuclear power plants to get these data centers. None of this is true.” He recently said LLM-based generative AI assistants, such as Microsoft Copilot, are overhyped. On the Rapid Response podcast, he said, “We may have heard from these AI priests and priestesses of these LLM model companies and Microsoft and others about AI curing cancer and climate change, and we all have to plug into these nuclear power plants to get these data centers. None of this is true.” And After Microsoft said it will allow organizations to create their own autonomous AI agents within the Copilot Studio platform, Benioff issued a brutal take-down on X: "Microsoft rebranding Copilot as 'agents'? That's panic mode. Let's be real — Copilot's a flop because Microsoft lacks the data, metadata, and enterprise security models to create real corporate intelligence." And After Microsoft said it will allow organizations to create their own autonomous AI agents within the Copilot Studio platform, Benioff issued a brutal take-down on X : "Microsoft rebranding Copilot as 'agents'? That's panic mode. Let's be real — Copilot's a flop because Microsoft lacks the data, metadata, and enterprise security models to create real corporate intelligence." create their own autonomous AI agents create their own autonomous AI agents brutal take-down on X brutal take-down on X https://x.com/Benioff/status/1849506221931287013?embedable=true https://x.com/Benioff/status/1849506221931287013?embedable=true Why would a busy CEO of a multi-billion dollar global Big Tech company lash out at LLMs and Microsoft? Because Benioff knows how important AI agents are becoming. This means: He wants to move the conversation about AI away from LLMs and toward AI agents. He sees Microsoft’s foray into AI agents as a threat. If he didn’t, he would have ignored Microsoft’s announcement. He wants to move the conversation about AI away from LLMs and toward AI agents. He wants to move the conversation about AI away from LLMs and toward AI agents. He sees Microsoft’s foray into AI agents as a threat. If he didn’t, he would have ignored Microsoft’s announcement. He sees Microsoft’s foray into AI agents as a threat. If he didn’t, he would have ignored Microsoft’s announcement. In doing so, he underlined the importance of AI agents. And propped Microsoft up. Microsoft The Importance of AI Agents The Importance of AI Agents The market for autonomous AI agents is experiencing rapid growth.  The AI agents market is expected to expand from $5.1 billion in 2024 to $47.1 billion by 2030, reflecting a compound annual growth rate of 44.8% . Salesforce wants a piece of that market. compound annual growth rate of 44.8% But the numbers don’t quite capture their potential impact. In fact, AI agents have become a hot topic representing a significant leap in the AI ecosystem. Unlike traditional AI systems that react to commands, AI agents can proactively pursue objectives, learn from their experiences, and even interact with other agents and humans in complex ways. As a result, AI agents may do mundane, tedious tasks that people don’t want to do – like data entry, managing calendars, automating the creation of reports, and a lot more. In other words, what AI has been promising all along. This autonomy has sparked both excitement and apprehension. Venture capitalist Jeremiah Owyang has speculated that AI agents will upend websites because businesses will need to figure out how to re-tool their sites for AI agents doing web searches instead of people. Chris Mattmann, NASA JPL’s former chief technology and innovation officer, echoes the concerns of many by asking if we’re ready for a world in which AI functions without human oversight . upend websites upend websites ready for a world in which AI functions without human oversight ready for a world in which AI functions without human oversight Gartner has identified “Agentic AI” as a top tech trend for 2025 , emphasizing its potential to transform enterprise operations. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously. However, Gartner warns of the need for governance to prevent risks such as bias and privacy issues. Gartner stresses the importance of establishing guardrails to ensure AI aligns with human values and intentions. top tech trend for 2025 Marc Benioff versus Microsoft Marc Benioff versus Microsoft This brings us back to Marc Benioff. He has read the tea leaves. He sees autonomous agents as the future, and he’s placed his bets accordingly. Salesforce has embedded itself in 150,000 organizations . When you’re so deeply embedded across corporate America, your product becomes a Trojan Horse for new technology. For instance, Salesforce AgentForce will deploy autonomous AI agents that enhance customer service, sales, marketing, and commerce operations. 150,000 organizations 150,000 organizations Need to automate routine tasks such as answering customer inquiries, qualifying sales leads, and optimizing marketing campaigns? Hey, there’s an agent for that – AgentForce. Need a more efficient way to get data-driven insights from Salesforce? Well, AgentForce uses the Atlas Reasoning Engine to analyze data, make decisions, and execute tasks autonomously. Microsoft isn’t taking all of this sitting down. On October 21, Microsoft announced : announced announced The ability to create autonomous agents with Copilot Studio. The introduction of 10 new autonomous agents in Dynamics 365 to build capacity for sales, service, finance and supply chain functions. The ability to create autonomous agents with Copilot Studio. The introduction of 10 new autonomous agents in Dynamics 365 to build capacity for sales, service, finance and supply chain functions. Here’s the heady way that Microsoft described on its blog the potential impact of AI agents: Copilot isyour AI assistant — it works for you — and Copilot Studio enables you to easily create, manage and connect agents to Copilot. Think of agents as the new apps for an AI-powered world. Every organization will have a constellation of agents — ranging from simple prompt-and-response to fully autonomous. They will work on behalf of an individual, team or function to execute and orchestrate businesses process. Copilot is how you’ll interact with these agents, and they’ll do everything from accelerating lead generation and processing sales orders to automating your supply chain. Copilot is your AI assistant — it works for you — and Copilot Studio enables you to easily create, manage and connect agents to Copilot. Think of agents as the new apps for an AI-powered world . Every organization will have a constellation of agents — ranging from simple prompt-and-response to fully autonomous. They will work on behalf of an individual, team or function to execute and orchestrate businesses process. Copilot is how you’ll interact with these agents, and they’ll do everything from accelerating lead generation and processing sales orders to automating your supply chain. your agents as the new apps for an AI-powered world In effect, Microsoft is going to war with its own Trojan Horse: 60 percent of the Fortune 500 are using Microsoft 365 Copilot (per Microsoft). Microsoft CEO has avoided a war of words with Benioff. Instead, he’s relying on Jared Spataro to answer Benioff’s dismissive remarks by telling CRN , “Every customer is at a different place in their journey, but overall we are hearing something quite different from our Copilot for Microsoft 365 customers . . . Last quarter alone, we saw a customer increase of over 60 percent, and daily users have more than doubled – a clear indicator of Copilot's value in the market.” telling CRN telling CRN Marc Benioff versus LLMs Marc Benioff versus LLMs I don’t know how the war between Salesforce and Microsoft will turn out. However, I believe Benioff is wrong to attack LLM-based AI applications that are based on human prompts. ChatGPT has been in the market for only two years, and already the off-the-shelf generative AI products based on LLMs are changing our lives. In addition, we’re still in the early days of businesses developing their own generative AI models based on private data. Consider two industries that affect all of us: financial services and healthcare. Consider two industries that affect all of us: financial services and healthcare. Financial services: LLMs are changing how financial services do their work, with firms like BlackRock using them to analyze market data and identify investment opportunities. BlackRock’s systematic active equity team uses LLMs to process vast datasets, uncovering patterns that traditional methods might miss. Hedge fund Bridgewater Associates, managing a $19 billion portfolio, uses LLMs in its AIA Platform for complex tasks like market research. Healthcare: hospitals are figuring out how to use LLMs to improve efficiency across the board, from streamlining emergency room intake to improving clinical care. For example, HCA Healthcare in Tennessee is integrating LLMs into its hospitals, focusing on improving time-consuming tasks like documenting notes from conversations with patients during visits. Financial services: LLMs are changing how financial services do their work, with firms like BlackRock using them to analyze market data and identify investment opportunities. BlackRock’s systematic active equity team uses LLMs to process vast datasets, uncovering patterns that traditional methods might miss. Hedge fund Bridgewater Associates, managing a $19 billion portfolio, uses LLMs in its AIA Platform for complex tasks like market research. Healthcare: hospitals are figuring out how to use LLMs to improve efficiency across the board, from streamlining emergency room intake to improving clinical care. For example, HCA Healthcare in Tennessee is integrating LLMs into its hospitals, focusing on improving time-consuming tasks like documenting notes from conversations with patients during visits. The above examples illustrate how technology changes the world: one task at a time. Sometimes in visible ways. Oftentimes behind the scenes. Benioff is correct to pressure tech firms to improve LLM-based AI models. They make mistakes and face criticism over issues like bias and intellectual copyright. But on the other hand, so do AI agents. It’s vital to train them properly and keep humans in the loop to correct errors and prevent bias. But he is overlooking the fact that LLMs learn from their mistakes. They improve. Quickly. If anything, the impact of what LLMs can and will do is understated.

Why Salesforce and Microsoft Are Battling for the Future of AI Agents

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Warum Salesforce und Microsoft um die Zukunft der KI-Agenten kämpfen

Por qué Salesforce y Microsoft luchan por el futuro de los agentes de IA

چرا Salesforce و Microsoft برای آینده نمایندگان هوش مصنوعی می جنگند؟

Salesforce と Microsoft が AI エージェントの未来をめぐって争う理由

Pourquoi Salesforce na Microsoft Bazali Kobunda pona Avenir ya ba Agents ya AI

Почему Salesforce и Microsoft борются за будущее агентов ИИ

Prečo Salesforce a Microsoft bojujú o budúcnosť agentov AI

Varför Salesforce och Microsoft kämpar för framtiden för AI-agenter

Salesforce ve Microsoft Neden Yapay Zeka Temsilcilerinin Geleceği İçin Mücadele Ediyor?

Kutheni iSalesforce kunye neMicrosoft zisilwela ikamva leearhente ze-AI

Kungani i-Salesforce ne-Microsoft Balwela Ikusasa Lama-AI Agents

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پروڈکٹ کی بصیرت کی تلاش: پروڈکٹ ریسرچ ایجنسی کے بانی، وادیم گلازکوف کے ساتھ انٹرویو

Mahsulot tushunchalarini o'rganish: Mahsulot tadqiqot agentligi asoschisi Vadim Glazkov bilan suhbat

Ukuphonononga ulwazi lweMveliso: Udliwano-ndlebe noVadim Glazkov, uMseki we-Arhente yoPhando lweMveliso

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