AI is everywhere nowadays. But do you know how you can incorporate it into your commercial real estate business? Avison Young Chair & CEO Mark E. Rose shares how we are leveraging artificial intelligence to drive innovation and stay ahead of the curve in the #CRE investment landscape. Read Commercial Property Executive: https://lnkd.in/eb_McSPU #Tech #AI #AYdifference
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Exciting times for #opensource #AI! 🦙 According to a recent Databricks report, Meta's Llama 3 captured nearly 40% of all open-source model usage within a month of its launch. With 3/4 of enterprises opting for open-source large language models, it's clear that cost and latency are driving this trend. Interestingly, most users of Meta’s Llama 2, Llama 3, and Mistral prefer models with 13 billion parameters or fewer. Open-source AI isn't just a trend—it's becoming a cornerstone of enterprise AI strategy. #AI #OpenSource #Llama3 #EnterpriseAI Follow Ray Estevez Advisory LLC and sign up for our newsletter to keep up with all the AI, Data, and Technology innovations. https://lnkd.in/ggbxXW5U
State of Data + AI
databricks.com
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Driving Real Estate Evolution with AI | CEO at DataBrut | Passionate about Technology and Innovation
We often say, Data Science is 90% of data cleaning, wrangling and feature engineering. The rest is applying models and attending meetings with stakeholders. And these 90% are “pull your sleeves, it’s getting real and dirty”. Now imagine how “dirty” would be to go upstream to data generating sources, analyze, structure and standardize it. Is it even possible in some cases? The algorithms we have at our disposal are easy patch. Wrapping yourself around the data sources, formats, process that generate it… now that is a different beast. #ai #technology #innovation
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
The problem is that most companies are investing 80% of their AI budget into models and only 20% into data. Here’s what needs to change to unlock AI value. Shift investment from gathering data to curating data. Curation builds a dataset for model consumers vs. human consumers. The cost of model training drops because reliable use case support is delivered with less data and less complex models. Shift investment from engineering data pipelines to engineering data-generating processes. Moving data from one place to another creates no value, while each new dataset makes the business more valuable. Data creates more AI opportunities. Unique datasets are the primary competitive advantage and AI moat. Models are only best-in-class for a few months. GPT-4o was upstaged by Gemini 1.5, which was just surpassed by Claude 3.5. The investment required to win on massive models is much too high for enterprise business models to support. Follow me here and click the link under my name to learn more about how to deliver value-centric AI. #AI #Data #AIStrategy #DataQuality
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AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
The problem is that most companies are investing 80% of their AI budget into models and only 20% into data. Here’s what needs to change to unlock AI value. Shift investment from gathering data to curating data. Curation builds a dataset for model consumers vs. human consumers. The cost of model training drops because reliable use case support is delivered with less data and less complex models. Shift investment from engineering data pipelines to engineering data-generating processes. Moving data from one place to another creates no value, while each new dataset makes the business more valuable. Data creates more AI opportunities. Unique datasets are the primary competitive advantage and AI moat. Models are only best-in-class for a few months. GPT-4o was upstaged by Gemini 1.5, which was just surpassed by Claude 3.5. The investment required to win on massive models is much too high for enterprise business models to support. Follow me here and click the link under my name to learn more about how to deliver value-centric AI. #AI #Data #AIStrategy #DataQuality
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Absolutely agree! In the pursuit of maximizing business value through data science and AI, prioritizing data quality over models is paramount. Reliable insights and decisions hinge on the quality of data, making it a foundational asset for success. #DataQuality #BusinessValue
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
The problem is that most companies are investing 80% of their AI budget into models and only 20% into data. Here’s what needs to change to unlock AI value. Shift investment from gathering data to curating data. Curation builds a dataset for model consumers vs. human consumers. The cost of model training drops because reliable use case support is delivered with less data and less complex models. Shift investment from engineering data pipelines to engineering data-generating processes. Moving data from one place to another creates no value, while each new dataset makes the business more valuable. Data creates more AI opportunities. Unique datasets are the primary competitive advantage and AI moat. Models are only best-in-class for a few months. GPT-4o was upstaged by Gemini 1.5, which was just surpassed by Claude 3.5. The investment required to win on massive models is much too high for enterprise business models to support. Follow me here and click the link under my name to learn more about how to deliver value-centric AI. #AI #Data #AIStrategy #DataQuality
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Driving Success in Complex Programmes | Expert in Private Equity Transitions & Startups | Senior Programme Manager delivering business results
A great insight here, about the real value generation from good quality data, thus unlocking value from AI implementation efforts, and making them simpler.
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
The problem is that most companies are investing 80% of their AI budget into models and only 20% into data. Here’s what needs to change to unlock AI value. Shift investment from gathering data to curating data. Curation builds a dataset for model consumers vs. human consumers. The cost of model training drops because reliable use case support is delivered with less data and less complex models. Shift investment from engineering data pipelines to engineering data-generating processes. Moving data from one place to another creates no value, while each new dataset makes the business more valuable. Data creates more AI opportunities. Unique datasets are the primary competitive advantage and AI moat. Models are only best-in-class for a few months. GPT-4o was upstaged by Gemini 1.5, which was just surpassed by Claude 3.5. The investment required to win on massive models is much too high for enterprise business models to support. Follow me here and click the link under my name to learn more about how to deliver value-centric AI. #AI #Data #AIStrategy #DataQuality
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Reliable data availability is a challenge and is harder to get than it seems. Most of the startups struggle with this issue. They prepare their business plans and strategies to launch a superior model compared to their competitions but then struggle at the very first step of data availability. The 80:20 rule for data:model should be a basic thumb-rule consideration.
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
The problem is that most companies are investing 80% of their AI budget into models and only 20% into data. Here’s what needs to change to unlock AI value. Shift investment from gathering data to curating data. Curation builds a dataset for model consumers vs. human consumers. The cost of model training drops because reliable use case support is delivered with less data and less complex models. Shift investment from engineering data pipelines to engineering data-generating processes. Moving data from one place to another creates no value, while each new dataset makes the business more valuable. Data creates more AI opportunities. Unique datasets are the primary competitive advantage and AI moat. Models are only best-in-class for a few months. GPT-4o was upstaged by Gemini 1.5, which was just surpassed by Claude 3.5. The investment required to win on massive models is much too high for enterprise business models to support. Follow me here and click the link under my name to learn more about how to deliver value-centric AI. #AI #Data #AIStrategy #DataQuality
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I innovate by dreaming and meditating. ESG Innovator. Pacanata influencer. CEO at CODIUM. Chief Venture Builder at #1000C startups. PhD Candidate at CUTIP. Part-time artist aka Pacasso. #Treelionaire wanna-be.
Exactly CODIUM’s “Trump card” In fact, our rule is not AI 80/20. It’s “Trusted” Data 100/0. And we started building the infrastructure for this since 2021 even before OpenAI ChatGPT became popular. Heck, even after ChatGPT and a bunch of copycats went mainstream, and amidst the ongoing AI hype, we double downed our penetrating “Truth” insights by never spending a single dime on building ANY AI models. We also don’t sell “AI hype” to any of our customers. Never done it. Ain’t never gonna do it in the future. Our goal has never been about get-rich-quick, get-famous-quick, nor get-Unicorn-quick. Fuck all of that. Since Day 1 (circa 2013), it has ALWAYS been about building something that will fundamentally transform Thailand with technology. That something finally is being manifested on our CODIUM YouTube channel and my own personal #Pacanata YouTube channel now. The time has come to “walk the talk” by storytelling our mission to the mass. And to my dying day, We #will #never #stop #period #โคตรเยี่ยม #AIdata #data #Strategy
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
The problem is that most companies are investing 80% of their AI budget into models and only 20% into data. Here’s what needs to change to unlock AI value. Shift investment from gathering data to curating data. Curation builds a dataset for model consumers vs. human consumers. The cost of model training drops because reliable use case support is delivered with less data and less complex models. Shift investment from engineering data pipelines to engineering data-generating processes. Moving data from one place to another creates no value, while each new dataset makes the business more valuable. Data creates more AI opportunities. Unique datasets are the primary competitive advantage and AI moat. Models are only best-in-class for a few months. GPT-4o was upstaged by Gemini 1.5, which was just surpassed by Claude 3.5. The investment required to win on massive models is much too high for enterprise business models to support. Follow me here and click the link under my name to learn more about how to deliver value-centric AI. #AI #Data #AIStrategy #DataQuality
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It's frustrating to build an AI model , then not able to explain what it does. This is a gap for a lot of senior+ data scientists - we love building models. We love explaining them - but knowing what to pick can be hard. You'll face an even bigger challenge. You realize it's not just stakeholders you need to explain to - it's your team too. Challenges like this is where AI model cards are an advantage. They give a clear summary of your model - what it does, who uses it, and even the data. I saw model cards used firsthand with several organizations. They gave them a huge competitive advantage - models could be iterated, refactored, and turned quickly. Alignment was clear - teams and users knew the model, data, and the business value. To learn more about why AI model cards are an advantage, and how you get started, check out my article here: https://buff.ly/3x3KRqY #datalife360 #ai #datascience #ml #datascientist
Why do you need AI model cards?
pragmaticdatascientist.substack.com
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Companies are implementing artificial intelligence (AI) technologies into their processes to save money, increase efficiency, provide insights, and create new markets. The explosion in data will be a windfall for organizations committed to data-driven decision-making. Large data sets provide the raw material for producing in-depth business insights that drive enhancements in existing company operations and open up new business lines. Organizations cannot profit from these massive data stores unless they use AI. Deep learning, a subfield of AI and machine learning, analyzes enormous data sets to uncover subtle patterns and connections in big data that might provide businesses with a competitive advantage. The strategic importance of artificial intelligence to 21st-century business is in stark contrast to the strategic significance of electricity in the early twentieth century when electrification changed industries such as manufacturing while creating new ones like mass media. The capacity of AI to automate and augment occupations that are currently performed by humans will have the greatest influence on business in the coming years. This synergy isn’t going away anytime soon. The boundary between what is human intelligence and what is AI will continue to diminish. This blurring of human and artificial intelligence is occurring as a result of other technological trends, which have been exacerbated by AI. Finally, our future may be one in which intelligence improvement is bidirectional, making both our systems and ourselves smarter. Learn more about the business value of AI and Human + Machine collaboration and orchestration with the StrategyOps framework: https://lnkd.in/eekGw7Fw #strategyops #strategyopsinstitute #ai #artificialintelligence #workflow #hyperadaptative #operations #exponentialgrowth #disruptivetechnology #workforce #strategicagility #training #analytics #valuecreation #innovation #business #growth #valtics #intelligentbusiness #technology #software #machinelearning #deeplearning #data #manufacturing #bigdata #future
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🔍 Unlocking Insights: The Synergy of AI and Big Data 🤖💡 In today's data-driven world, the marriage of Artificial Intelligence (AI) and Big Data holds immense promise for businesses and industries alike. As organizations grapple with ever-expanding datasets, AI emerges as the key to unlock hidden insights and drive informed decision-making. AI algorithms, fueled by the vast reservoirs of Big Data, excel at extracting valuable patterns, trends, and correlations that might otherwise remain buried. Whether it's optimizing operations, personalizing customer experiences, or predicting market trends, the fusion of AI and Big Data empowers organizations to stay agile and competitive in a fast-paced landscape. Moreover, as AI continues to evolve, it enables dynamic learning and adaptation, refining its analyses over time and uncovering deeper layers of understanding within complex datasets. As we embrace this synergy, it's crucial to prioritize ethical data practices and ensure transparency and accountability in AI-driven initiatives. Let's harness the transformative power of AI and Big Data to drive innovation, foster growth, and create meaningful impact across industries. #AI #BigData #DataAnalytics #Innovation #Technology #BusinessInsights #ai #aiforgood #futureofai
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