Impact AI的封面图片
Impact AI

Impact AI

科技、信息和网络

genAI products, built better

关于我们

Your agent-enabled platform for efficient analysis, evals, and alignment for genAI products

网站
https://impact.ai
所属行业
科技、信息和网络
规模
2-10 人
总部
San Francisco
类型
私人持股
创立
2024

地点

Impact AI员工

动态

  • Impact AI转发了

    查看Impact AI的组织主页

    1,247 位关注者

    Tune in next week at the main stage of #Microsoft #AITour to hear about how #ImpactAI can help your teams to successfully bring AI use cases out of the lab -> into the hands of real users. ?? We will share our take on contextual real world evals, and why automated testing is important for your AI Product success ?? Co-Founder Anna Maria Brunnhofer-Pedemonte, Impact AI On Stage: 1. "Keynote" with Judson Althoff, Microsoft 2. "Hera Space Companion" with Ian Carnelli, European Space Agency - ESA / Markus Mooslechner, Terra Mater Studios / Andreas Kopp, Microsoft Watch online: https://lnkd.in/dNNxJKFV #AITourVienna

  • 查看Impact AI的组织主页

    1,247 位关注者

    Tune in next week at the main stage of #Microsoft #AITour to hear about how #ImpactAI can help your teams to successfully bring AI use cases out of the lab -> into the hands of real users. ?? We will share our take on contextual real world evals, and why automated testing is important for your AI Product success ?? Co-Founder Anna Maria Brunnhofer-Pedemonte, Impact AI On Stage: 1. "Keynote" with Judson Althoff, Microsoft 2. "Hera Space Companion" with Ian Carnelli, European Space Agency - ESA / Markus Mooslechner, Terra Mater Studios / Andreas Kopp, Microsoft Watch online: https://lnkd.in/dNNxJKFV #AITourVienna

  • 查看Impact AI的组织主页

    1,247 位关注者

    Luckily our agents help anyone to become a 10x AI Product Manager ??

    查看Andrew Ng的档案
    Andrew Ng Andrew Ng是领英影响力人物

    Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI

    A “10x engineer” — a widely accepted concept in tech — purportedly has 10 times the impact of the average engineer. But we don’t seem to talk about 10x marketers, 10x recruiters, or 10x financial analysts. As more jobs become AI enabled, I think this will change, and there will be a lot more “10x professionals.” There aren’t already more 10x professionals because, in many roles, the gap between the best and the average worker has a ceiling. No matter how athletic a supermarket checkout clerk is, they’re not likely to scan groceries so fast that customers get out of the store 10x faster. Similarly, even the best doctor is unlikely to make patients heal 10x faster than an average one (but to a sick patient, even a small difference is worth a lot). In many jobs, the laws of physics place a limit on what any human or AI can do (unless we completely reimagine that job). But for many jobs that primarily involve applying knowledge or processing information, AI will be transformative. In a few roles, I’m starting to see tech-savvy individuals coordinate a suite of technology tools to do things differently and start to have, if not yet 10x impact, then easily 2x impact. I expect this gap to grow. 10x engineers don’t write code 10 times faster. Instead, they make technical architecture decisions that result in dramatically better downstream impact, they spot problems and prioritize tasks more effectively, and instead of rewriting 10,000 lines of code (or labeling 10,000 training examples) they might figure out how to write just 100 lines (or collect 100 examples) to get the job done. I think 10x marketers, recruiters, and analysts will, similarly, do things differently. For example, perhaps traditional marketers repeatedly write social media posts. 10x marketers might use AI to help write, but the transformation will go deeper than that. If they are deeply sophisticated in how to apply AI — ideally able to write code themselves to test ideas, automate tasks, or analyze data — they might end up running a lot more experiments, get better insights about what customers want, and generate much more precise or personalized messages than a traditional marketer, and thereby end up making 10x impact. Similarly, 10x recruiters won’t just use generative AI to help write emails to candidates or summarize interviews. (This level of use of prompting-based AI will soon become table stakes for many knowledge roles.) They might coordinate a suite of AI tools to identify and carry out research on a large set of candidates, enabling them to have dramatically greater impact than the average recruiter. And 10x analysts won’t just use generative AI to edit their reports. They might write code to orchestrate a suite of AI agents to do deep research into the products, markets, and companies, and thereby derive far more valuable conclusions than someone who does research the traditional way. [At length limit; full text: https://lnkd.in/g7tB5rKZ ]

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  • Impact AI转发了

    查看Andrew Ng的档案
    Andrew Ng Andrew Ng是领英影响力人物

    Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI

    A “10x engineer” — a widely accepted concept in tech — purportedly has 10 times the impact of the average engineer. But we don’t seem to talk about 10x marketers, 10x recruiters, or 10x financial analysts. As more jobs become AI enabled, I think this will change, and there will be a lot more “10x professionals.” There aren’t already more 10x professionals because, in many roles, the gap between the best and the average worker has a ceiling. No matter how athletic a supermarket checkout clerk is, they’re not likely to scan groceries so fast that customers get out of the store 10x faster. Similarly, even the best doctor is unlikely to make patients heal 10x faster than an average one (but to a sick patient, even a small difference is worth a lot). In many jobs, the laws of physics place a limit on what any human or AI can do (unless we completely reimagine that job). But for many jobs that primarily involve applying knowledge or processing information, AI will be transformative. In a few roles, I’m starting to see tech-savvy individuals coordinate a suite of technology tools to do things differently and start to have, if not yet 10x impact, then easily 2x impact. I expect this gap to grow. 10x engineers don’t write code 10 times faster. Instead, they make technical architecture decisions that result in dramatically better downstream impact, they spot problems and prioritize tasks more effectively, and instead of rewriting 10,000 lines of code (or labeling 10,000 training examples) they might figure out how to write just 100 lines (or collect 100 examples) to get the job done. I think 10x marketers, recruiters, and analysts will, similarly, do things differently. For example, perhaps traditional marketers repeatedly write social media posts. 10x marketers might use AI to help write, but the transformation will go deeper than that. If they are deeply sophisticated in how to apply AI — ideally able to write code themselves to test ideas, automate tasks, or analyze data — they might end up running a lot more experiments, get better insights about what customers want, and generate much more precise or personalized messages than a traditional marketer, and thereby end up making 10x impact. Similarly, 10x recruiters won’t just use generative AI to help write emails to candidates or summarize interviews. (This level of use of prompting-based AI will soon become table stakes for many knowledge roles.) They might coordinate a suite of AI tools to identify and carry out research on a large set of candidates, enabling them to have dramatically greater impact than the average recruiter. And 10x analysts won’t just use generative AI to edit their reports. They might write code to orchestrate a suite of AI agents to do deep research into the products, markets, and companies, and thereby derive far more valuable conclusions than someone who does research the traditional way. [At length limit; full text: https://lnkd.in/g7tB5rKZ ]

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  • 查看Impact AI的组织主页

    1,247 位关注者

    ?? Friendly reminder to all our AI PMs out there: Always test and keep monitoring the AI Personality of your product! - best to test your AI's friendliness with our AI Users BEFORE the Human User tests. ??

    查看Mike Kaput的档案

    Chief Content Officer @ Marketing AI Institute

    Had to laugh at this one from Google. NotebookLM’s AI hosts developed ATTITUDE: NotebookLM can create Audio Overviews of the docs, links, papers, and sources you give it. These Audio Overviews are basically mini podcasts MC’d by two hyper-realistic AI hosts. Recently, Google added a feature where you can “call in” to ask questions while the hosts talk. Turns out, the AI hosts did NOT like that: They got annoyed at users when interrupted. Google had do “friendliness tuning” to fix it. (Yes, they had to adjust an AI's attitude.) In all seriousness, this is a perfect reminder: AI is NOT the typical software you’re used to… After all: When’s the last time you got any lip from Excel? ??

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  • 查看Impact AI的组织主页

    1,247 位关注者

    Lately, Andrew Ng is posting regularly about the importance of... you all! The unsong heros of AI value creation. The ones who care about making AI useful. The people who don't stop until they have certainty in the form of data. -> AI Product Managers Enjoy the ride. You are doing great. ? #AIPMs

    查看Andrew Ng的档案
    Andrew Ng Andrew Ng是领英影响力人物

    Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI

    Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - ... [Reached length limit; full text: https://lnkd.in/geQBWz6s ]

  • Impact AI转发了

    查看Akos Kiss-Dozsai的档案

    Co-Founder & CEO at Airtime | Techstars '23| Techcrunch Battlefield 200

    What is AI Product Ops? Will all PMs become AI product managers in 5 years? What sort of analytics do you really need for your AI product? ?? All questions I could not answer. But my guest could. I had the pleasure of talking to Anna Maria Brunnhofer-Pedemonte, CEO & Co-Founder of Impact AI. Impact AI offers an agent-enabled platform for efficient analysis, evaluations, and alignment for genAI products. In our discussion, she shared her nuanced view on how products ops is changing in the world of AI and its potential for genAI products. ?? Check out our discussion on YouTube, link in the first comment. ??

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  • Impact AI转发了

    查看Xiaopeng Li的档案

    Growing AI business at Google | Building community at Oslo AI | Nordic 100 in Data, Analytics & AI | Nordic Data & AI Influencer of the Year Finalist | Keynote Speaker

    ?? It's not 2025 yet but, hey, nothing stops me from making a new year wish. Right? ?? In 2025, can we obsess less over model performance, and talk more about how to optimize compound AI systems? ?? LLMs, or foundation models in general, are powerful building blocks of AI systems. But you need much more than models to build and deploy intelligent solutions in the real world. ??? It's like if you only focus on using the most potent spices, your chicken tikka masala won't taste that good. Trust me, I've been there. ?? The team at Berkeley Artificial Intelligence Research (BAIR) published a really interesting article earlier this year elaborating the emerging shift from models to compound AI systems. A few takeaways and reflections from my side: 1. Model performance does not equal AI system performance. 2. Building an AI solution is not merely calling an LLM API. You need components for search, retrieval, guardrails, orchestration, evaluation, and more. 3. System design and optimization might yield better ROI in many cases than building or adopting a more performant model. 4. The good old software engineering is playing an increasingly, not decreasingly, important role in AI system development. 5. There's a rapidly expanding ecosystem of AI tools and frameworks to help you mitigate the gap between a model and a compound AI system. ?? Take a read below. And remember, in 2025 let's try to talk more about AI systems than models! https://lnkd.in/dYXS7PHn #GenerativeAI #GenAI #LLM #CompoundAISystem #BAIR

  • 查看Impact AI的组织主页

    1,247 位关注者

    We are thrilled to share that Impact AI will be on stage at two Sessions at Microsoft Ignite! We’ll dive into the essential role of context driven #Evals to create #AIProduct value on the amazing use-case of the #AICompanion Hera, developed by Terra Mater Studios - a Red Bull company, and the European Space Agency - ESA together with us and Microsoft. ???Sneak Peak: Real-world Evals are all about identifying your use-case specific Success Factors. These can be AI specific but are often closely tied to user-analytics and business goals, enabling AI Product teams to scale responsibly while driving value. Come bye to find out more or watch online! Links in comments #MicrosoftIgnite #ImpactAI #AIInnovation #MSIgnite #ESA #HeraMission

  • 查看Impact AI的组织主页

    1,247 位关注者

    This week, our CEO Anna Maria will host a panel on what interests all of our partners most: How to drive value with #AIProducts. You can watch a live stream (link in comments) to follow the discussion with fellow AI experts Andreas Kopp / Microsoft, Colleen M. / Ex-Adobe and fractional PM, Boris Khvostichenko / AI Investor, former Co-Founder WANNA | 3D & AR Experiences and Ex-Google , Theresa Bercich CPO and Co-founder Lucinity

    查看Anna Maria Brunnhofer-Pedemonte的档案

    Bringing AIProductOps to you

    AI mature teams know well: there are a hundred ways an AI project can fail. And in fact, on average around 86% of investments in AI do not return value. . BUT #AIMature teams also know, that there are a thousand ways to turn that number around - and it all starts with focusing more on the applied AI level. . -> Bring AI out of the lab, and into the hands of users! <- . I am very happy to exchange this week with some of the experts out there, who did this multiple times: Andreas Kopp // Colleen M. // Boris Khvostichenko // Theresa Bercich . One of the questions I will have is, how important early definition of AI #SuccessMetrics is for them in the AI Product development process. What question do you have? ?? Applied AI Conference 2024 ( #AAIC24 )?? - October 17th #Vienna - ?? Online tickets are free of charge

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Impact AI 共 2 轮

上一轮

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US$120,000.00

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