🏭 Industrial IoT + AI / Generative AI 🤖: Actual Case Studies, Insights and Perspectives >>> 🚀🚀🚀 | November 2024

Ciao a tutti,

Last newsletter of the year (𝘸𝘩𝘢𝘵 𝘢 𝘺𝘦𝘢𝘳!😎), in which 𝐀𝐈 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐚𝐠𝐞𝐧𝐭𝐬 [𝑨𝒈𝒆𝒏𝒕𝒊𝒄 𝑨𝑰] are definitely taking the whole stage of both 𝐬𝐭𝐚𝐫𝐭𝐮𝐩𝐬' 𝐥𝐮𝐝𝐢𝐜𝐫𝐨𝐮𝐬 𝐟𝐮𝐧𝐝𝐢𝐧𝐠 and overall market interest...

Are we actually at the cusp of a 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧, or it will take some time - 𝘮𝘰𝘯𝘵𝘩𝘴, 𝘺𝘦𝘢𝘳𝘴? - for the enterprises to start grasping the benefits, at scale, of such revolutionary business applications/solutions?

Last, but not least, a primer on actual 𝐀𝐈/𝐌𝐋 vs. 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬/𝐦𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 different 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬/𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬, which nowadays is often (𝘰𝘯 𝘱𝘶𝘳𝘱𝘰𝘴𝘦?) 𝒏𝒐𝒕 𝒆𝒙𝒑𝒍𝒊𝒄𝒊𝒕, riding the 𝐀𝐈/𝐌𝐋 & 𝐆𝐞𝐧𝐀𝐈 hype, to guarantee exceptional funding and astonishing results, when sometimes is "𝐣𝐮𝐬𝐭" 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬/𝐦𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬...😉

Enjoy your holidays and I'll see you all - hopefully recharged - in 2025!

Ciao, Fabio

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

#industrial #iot #iiot #ai #genai #generativeai #casestudies #insights #perspectives

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𝐅𝐨𝐫𝐦𝐞𝐫 𝐆𝐨𝐨𝐠𝐥𝐞, 𝐒𝐭𝐫𝐢𝐩𝐞 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞𝐬 𝐑𝐚𝐢𝐬𝐞 $𝟓𝟔 𝐌𝐢𝐥𝐥𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐒𝐭𝐚𝐫𝐭𝐮𝐩: 𝘛𝘩𝘦 𝘤𝘰𝘮𝘱𝘢𝘯𝘺 𝘪𝘴 𝘭𝘰𝘰𝘬𝘪𝘯𝘨 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘢𝘯 𝘰𝘱𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘴𝘺𝘴𝘵𝘦𝘮 𝘵𝘰 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘢 𝘯𝘦𝘸 𝘤𝘳𝘰𝘱 𝘰𝘧 𝘢𝘳𝘵𝘪𝘧𝘪𝘤𝘪𝘢𝘭 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴 🚀🚀🚀

Via Bloomberg | November 26, 2024 | @VINCI_Digital

A team of former Google , Meta , and Stripe executives just emerged from stealth mode to launch a new startup called /dev/agents with $56M in seed funding, aiming to create what they are calling an 𝘈𝘯𝘥𝘳𝘰𝘪𝘥 𝘮𝘰𝘮𝘦𝘯𝘵 for 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 /dev/agents 𝘱𝘭𝘢𝘯𝘴 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘢 𝘤𝘭𝘰𝘶𝘥-𝘣𝘢𝘴𝘦𝘥 𝘰𝘱𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘴𝘺𝘴𝘵𝘦𝘮 𝘵𝘩𝘢𝘵 𝘢𝘭𝘭𝘰𝘸𝘴 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 𝘵𝘰 𝘳𝘶𝘯 𝘴𝘦𝘢𝘮𝘭𝘦𝘴𝘴𝘭𝘺 𝘰𝘯 𝘱𝘩𝘰𝘯𝘦𝘴, 𝘭𝘢𝘱𝘵𝘰𝘱𝘴, 𝘤𝘢𝘳𝘴, 𝘢𝘯𝘥 𝘰𝘵𝘩𝘦𝘳 𝘥𝘦𝘷𝘪𝘤𝘦𝘴

🔹The founding team includes Android's former VP of Engineering David Singleton, Oculus VR VP Hugo Barra, and Google Chrome OS design lead Nicholas Jitkoff

🔹The company hopes to tackle major barriers in 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭, including:

>>> 𝘯𝘦𝘸 𝘜𝘐 𝘱𝘢𝘵𝘵𝘦𝘳𝘯

>>> 𝘱𝘳𝘪𝘷𝘢𝘤𝘺 𝘮𝘰𝘥𝘦𝘭𝘴

>>> 𝘴𝘪𝘮𝘱𝘭𝘪𝘧𝘪𝘦𝘥 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳 𝘵𝘰𝘰𝘭𝘴

🔹Index Ventures and Alphabet Inc.’s funding arm led the raise, with other investors including OpenAI co-founder 𝐀𝐧𝐝𝐫𝐞𝐣 𝐊𝐚𝐫𝐩𝐚𝐭𝐡𝐲 and Scale AI’s Alexandr Wang

𝑾𝒉𝒊𝒍𝒆 𝒆𝒗𝒆𝒓𝒚𝒐𝒏𝒆 𝒓𝒂𝒄𝒆𝒔 𝒕𝒐 𝒃𝒖𝒊𝒍𝒅 𝑨𝑰 𝒂𝒈𝒆𝒏𝒕𝒔, 𝒇𝒆𝒘 𝒂𝒊𝒎 𝒕𝒐 𝒄𝒓𝒂𝒄𝒌 𝒕𝒉𝒆 𝒇𝒐𝒖𝒏𝒅𝒂𝒕𝒊𝒐𝒏 𝒕𝒉𝒆𝒚'𝒍𝒍 𝒓𝒖𝒏 𝒐𝒏

🔹With a powerhouse Android team that helped accelerate mobile apps, /dev/agents could help lay the groundwork for how we'll all interact with AI in the future — with specialized agents as plentiful as the apps on our phones

Source: https://bloom.bg/3VfQUSh

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#iot #ai #genai #agents #aiagents #os

𝐌𝐈𝐓 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫𝐬 𝐝𝐞𝐯𝐞𝐥𝐨𝐩 𝐚𝐧 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐰𝐚𝐲 𝐭𝐨 𝐭𝐫𝐚𝐢𝐧 𝐦𝐨𝐫𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬: 𝘛𝘩𝘦 𝘵𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦 𝘤𝘰𝘶𝘭𝘥 𝘮𝘢𝘬𝘦 𝘈𝘐 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘣𝘦𝘵𝘵𝘦𝘳 𝘢𝘵 𝘤𝘰𝘮𝘱𝘭𝘦𝘹 𝘵𝘢𝘴𝘬𝘴 𝘵𝘩𝘢𝘵 𝘪𝘯𝘷𝘰𝘭𝘷𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘪𝘭𝘪𝘵𝘺🚀🚀🚀

Via Massachusetts Institute of Technology News | November 22, 2024 | @VINCI_Digital

Fields ranging from robotics to medicine to political science are attempting to train AI systems to make meaningful decisions of all kinds.

🔹For example, using an AI system to intelligently control traffic in a congested city could help motorists reach their destinations faster, while improving safety or sustainability.

Unfortunately, teaching an AI system to make good decisions is no easy task

🔹Reinforcement learning models, which underlie these AI decision-making systems, still often fail when faced with even small variations in the tasks they are trained to perform. In the case of traffic, a model might struggle to control a set of intersections with different speed limits, numbers of lanes, or traffic patterns.

To boost the reliability of reinforcement learning models for complex tasks with variability, MIT researchers have introduced a more efficient algorithm for training them.

🔹The algorithm strategically selects the best tasks for training an AI agent so it can effectively perform all tasks in a collection of related tasks. In the case of traffic signal control, each task could be one intersection in a task space that includes all intersections in the city.

🔹By focusing on a smaller number of intersections that contribute the most to the algorithm’s overall effectiveness, this method maximizes performance while keeping the training cost low.

The researchers found that their technique was between five and 50 times more efficient than standard approaches on an array of simulated tasks. This gain in efficiency helps the algorithm learn a better solution in a faster manner, ultimately improving the performance of the AI agent.

“We were able to see incredible performance improvements, with a very simple algorithm, by thinking outside the box. An algorithm that is not very complicated stands a better chance of being adopted by the community because it is easier to implement and easier for others to understand,”

says senior author Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS), and a member of the Laboratory for Information and Decision Systems (LIDS).

Source: https://bit.ly/3ZeKbsT

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#iot #ai #genai #aiagents #rlm #algorithm

𝐣𝐮𝐧𝐚.𝐚𝐢 𝐰𝐚𝐧𝐭𝐬 𝐭𝐨 𝐮𝐬𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐟𝐚𝐜𝐭𝐨𝐫𝐢𝐞𝐬 𝐦𝐨𝐫𝐞 𝐞𝐧𝐞𝐫𝐠𝐲-𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭: 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 𝘢𝘳𝘦 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘳𝘢𝘨𝘦, 𝘢 𝘵𝘳𝘦𝘯𝘥 𝘥𝘳𝘪𝘷𝘦𝘯 𝘣𝘺 𝘵𝘩𝘦 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘈𝘐 𝘢𝘯𝘥 𝘭𝘢𝘳𝘨𝘦 𝘭𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘮𝘰𝘥𝘦𝘭 (𝘓𝘓𝘔) 𝘣𝘰𝘰𝘮 𝘵𝘩𝘦𝘴𝘦 𝘱𝘢𝘴𝘵 𝘧𝘦𝘸 𝘺𝘦𝘢𝘳𝘴🚀🚀🚀

Via TechCrunch | November 18, 2024 | @VINCI_Digital

Getting people to agree on what exactly AI agents are is a challenge, but most contend they are software programs that can be assigned tasks and given decisions to make — with varying degrees of autonomy.

In short, AI agents go beyond what a mere chatbot can do: They help people get things done.

🔹It’s still early days, but the likes of Salesforce and Google are already investing heavily in AI agents. Amazon CEO Andy Jassy recently hinted at a more “agentic” Alexa in the future, one that’s as much about action as it is words.

🔹In tandem, startups are also raising cash off the hype:

The latest of these is German company juna.ai , which wants to help factories be more efficient by automating complex industrial processes to “maximize production throughput, increase energy efficiency and reduce overall emissions.”

🔹And to pull that off, the Berlin-based startup today said that it has raised $7.5 million in a seed round from Silicon Valley venture capital firm Kleiner Perkins, Sweden-based Norrsken VC, and Kleiner Perkins’ chairman John Doerr.

Self-learning is the way

🔹Founded in 2023, @juna ai is the handiwork of Matthias Auf der Mauer (pictured above, left) and Christian Hardenberg. Der Mauer previously founded a predictive machine maintenance startup called AiSight and sold it to Swiss smart sensor company Sensirion in 2021, while Hardenberg is the former chief technology officer at European food delivery giant Delivery Hero.

At its core, juna.ai wants to help manufacturing facilities transform into smarter, self-learning systems that can deliver better margins and, ultimately, a lower carbon footprint. The company focuses on “heavy industries” — industries such as steel, cement, paper, chemicals, wood and textile with large-scale production processes that consume lots of raw materials.

🔹“We work with very process-driven industries, and it mostly involves use cases that use a lot of energy,” Matthias Auf der Mauer told TechCrunch. “So, for example, chemical reactors that use a lot of heat in order to produce something.”

🔹juna.ai’s software integrates with manufacturers’ production tools, like industrial software from Aveva or SAP, and looks at all its historical data garnered from machine sensors: This might involve temperate, pressure, velocity, and all the measurements of the given output, such as quality, thickness, and color.

Using this information, juna.ai helps companies train their in-house agents to figure out the optimal settings for machinery, giving operators real-time data and guidance to ensure everything is running at peak efficiency with minimal waste.

Source: https://tcrn.ch/3B6QS8m

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#industrial #iot #iiot #ai #genai #aiagents #throughput #energy #efficiency

𝐀𝐖𝐒 𝐋𝐚𝐮𝐧𝐜𝐡𝐞𝐬 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐈𝐨𝐓 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭: 𝘈𝘞𝘚 𝘐𝘰𝘛 𝘚𝘪𝘵𝘦𝘞𝘪𝘴𝘦 𝘤𝘰𝘭𝘭𝘦𝘤𝘵𝘴, 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘦𝘴 𝘢𝘯𝘥 𝘮𝘰𝘯𝘪𝘵𝘰𝘳𝘴 𝘪𝘯𝘥𝘶𝘴𝘵𝘳𝘪𝘢𝘭 𝘦𝘲𝘶𝘪𝘱𝘮𝘦𝘯𝘵 𝘥𝘢𝘵𝘢 𝘢𝘵 𝘵𝘩𝘦 𝘧𝘢𝘤𝘵𝘰𝘳𝘺 𝘧𝘭𝘰𝘰𝘳🚀🚀🚀

Via IoT World Today | November 27, 2024 | @VINCI_Digital

Amazon Web Services (AWS) has launched AWS IoT SiteWise Assistant, a generative AI tool designed to enable industrial users to access and understand their operational data.

🔹AWS IoT SiteWise Assistant uses generative AI to enable users to understand and interact with their industrial data in a conversational manner, simply by typing in questions using natural language.

For example, they could click on alarms in the dashboard and ask questions like: “What assets have active alarms?” or “How do I fix the wind turbine's low RPM issue?”

🔹The assistant understands the context of the industrial data, taking into consideration asset relationships, historical data and operational processes.

🔹It integrates with Amazon’s Kendra enterprise search service to look up additional relevant information, such as user manuals, standard operating procedures and other relevant documents.

Only the data and documentation managed within the user's account are used for analysis to maintain data security and privacy.

🔹Developers can integrate the assistant's capabilities into existing industrial applications using new APIs and updated IoT AppKit widgets, such as chatbots, line charts and KPI gauges.

According to Amazon Web Services (AWS) , the assistant aims to reduce downtime, facilitate process optimization and boost productivity across industrial settings by providing rapid access to data summaries, insights and solutions.

Sources: IoT WT > https://bit.ly/3Zc9mMF / AWS > https://go.aws/49exXFm

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#industrial #iot #iiot #ai #genai #industrialcopilot #industrialdata

𝐇𝐲𝐩𝐞𝐝 𝐀𝐈 𝐬𝐭𝐚𝐫𝐭𝐮𝐩 𝟏𝟏𝐱 𝐫𝐚𝐢𝐬𝐞𝐬 $𝟓𝟎𝐦 𝐟𝐫𝐨𝐦 Andreessen Horowitz : 𝘐𝘵 𝘤𝘰𝘮𝘦𝘴 𝘭𝘦𝘴𝘴 𝘵𝘩𝘢𝘯 𝘵𝘸𝘰 𝘮𝘰𝘯𝘵𝘩𝘴 𝘢𝘧𝘵𝘦𝘳 𝘵𝘩𝘦 𝘤𝘰𝘮𝘱𝘢𝘯𝘺 𝘢𝘯𝘯𝘰𝘶𝘯𝘤𝘦𝘥 𝘢 $24𝘮 𝘚𝘦𝘳𝘪𝘦𝘴 𝘈 🚀🚀🚀

Via Sifted / Financial Times | November 11, 2024 | @VINCI_Digital

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬

UK-founded 11x is building “𝒅𝒊𝒈𝒊𝒕𝒂𝒍 𝒘𝒐𝒓𝒌𝒆𝒓𝒔” — AI-powered tools that do the job of salespeople, as well as some customer support functions

🔹In the next year, the company says it plans to launch multiple new AI agents and make key hires in San Francisco

“𝘌𝘢𝘤𝘩 𝘯𝘦𝘸 𝘥𝘪𝘨𝘪𝘵𝘢𝘭 𝘸𝘰𝘳𝘬𝘦𝘳 𝘸𝘪𝘭𝘭 𝘳𝘦𝘱𝘭𝘢𝘤𝘦 𝘵𝘩𝘦 𝘸𝘰𝘳𝘬 𝘰𝘧 11 𝒇𝒖𝒍𝒍-𝒕𝒊𝒎𝒆 𝒆𝒎𝒑𝒍𝒐𝒚𝒆𝒆𝒔, 𝘮𝘢𝘯𝘢𝘨𝘪𝘯𝘨 𝘢𝘯 𝘦𝘷𝘦𝘯 𝘣𝘳𝘰𝘢𝘥𝘦𝘳 𝘳𝘢𝘯𝘨𝘦 𝘰𝘧 𝘨𝘰-𝘵𝘰-𝘮𝘢𝘳𝘬𝘦𝘵 𝘵𝘢𝘴𝘬𝘴 𝘧𝘳𝘰𝘮 𝘭𝘦𝘢𝘥 𝘮𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵 𝘵𝘰 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴,” says CEO and founder Hasan Sukkar

Sources: Sifted > https://bit.ly/48Zf610 / 11x AI > https://www.11x.ai/

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#iot #ai #genai #aiagents #digitalworkers

𝐍𝐞𝐬𝐭𝐥é 𝐏𝐮𝐫𝐢𝐧𝐚’𝐬 𝐑𝐨𝐛𝐨𝐭 𝐃𝐨𝐠 𝐏𝐚𝐜𝐤: 𝘛𝘩𝘦 𝘲𝘶𝘢𝘥𝘳𝘶𝘱𝘦𝘥 𝘚𝘱𝘰𝘵 𝘳𝘰𝘣𝘰𝘵𝘴 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘷𝘦 𝘮𝘢𝘪𝘯𝘵𝘦𝘯𝘢𝘯𝘤𝘦 𝘱𝘳𝘰𝘨𝘳𝘢𝘮𝘴 𝘢𝘯𝘥 𝘮𝘦𝘵 𝘙𝘖𝘐 𝘪𝘯 𝘩𝘢𝘭𝘧 𝘵𝘩𝘦 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘦𝘥 𝘢𝘮𝘰𝘶𝘯𝘵 𝘰𝘧 𝘵𝘪𝘮𝘦 🚀🚀🚀

Via IndustryWeek | November 22, 2024 | @VINCI_Digital

Predictive maintenance technicians have better things to do than walk up and down countless stairwells across a plant to point acoustic sensors and thermal cameras at equipment for hours at a time: <<< 𝘐𝘧 𝘰𝘯𝘭𝘺 𝘢 𝘳𝘰𝘣𝘰𝘵 𝘦𝘹𝘤𝘦𝘭𝘭𝘦𝘥 𝘢𝘵 𝘤𝘭𝘪𝘮𝘣𝘪𝘯𝘨 𝘶𝘱 𝘢𝘯𝘥 𝘥𝘰𝘸𝘯 𝘴𝘵𝘢𝘪𝘳𝘴… >>>

🔹Oh. Wait. Boston Dynamics has a bunch of those. They’re called 𝐒𝐩𝐨𝐭 <<< 𝘈𝘯𝘥 𝘸𝘩𝘰 𝘣𝘦𝘵𝘵𝘦𝘳 𝘵𝘰 𝘢𝘴𝘴𝘪𝘴𝘵 𝘪𝘯 𝘵𝘩𝘦 𝘮𝘢𝘯𝘶𝘧𝘢𝘤𝘵𝘶𝘳𝘦 𝘰𝘧 𝘥𝘰𝘨 𝘧𝘰𝘰𝘥 (𝘢𝘮𝘰𝘯𝘨 𝘰𝘵𝘩𝘦𝘳 𝘱𝘦𝘵 𝘤𝘢𝘳𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴) 𝘵𝘩𝘢𝘯 𝘢 𝘳𝘰𝘣𝘰𝘵 𝘥𝘰𝘨 >>>

🔹Roger Brecht, vice president of digital manufacturing, in 2021 took the leash of Nestlé / Nestlé Purina North America’s digital transformation program for its North American pet care plants and presented options for the company’s next project, including an automated cleaning solution akin to industrialized Roombas and industrial exoskeletons, but the pitch for a 𝐪𝐮𝐚𝐝𝐫𝐮𝐩𝐞𝐝 𝐫𝐨𝐛𝐨𝐭 won, hands-down.

🔹Nestlé Purina North America’s nascent 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 system used IoT devices to gather data on critical OT assets.

<<<𝘛𝘩𝘦 𝘤𝘰𝘮𝘱𝘢𝘯𝘺 𝘥𝘪𝘥 𝘯𝘰𝘵 𝘤𝘰𝘯𝘴𝘪𝘥𝘦𝘳 𝘦𝘹𝘱𝘢𝘯𝘥𝘪𝘯𝘨 𝘐𝘰𝘛 𝘤𝘰𝘷𝘦𝘳𝘢𝘨𝘦 𝘵𝘰 𝘯𝘰𝘯-𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘢𝘴𝘴𝘦𝘵𝘴 𝘧𝘪𝘯𝘢𝘯𝘤𝘪𝘢𝘭𝘭𝘺 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘭𝘦, 𝘢𝘯𝘥 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘪𝘢𝘯𝘴 𝘥𝘪𝘥𝘯’𝘵 𝘢𝘭𝘸𝘢𝘺𝘴 𝘩𝘢𝘷𝘦 𝘵𝘪𝘮𝘦 𝘵𝘰 𝘮𝘢𝘬𝘦 𝘵𝘩𝘦 𝘳𝘰𝘶𝘯𝘥𝘴 𝘢𝘯𝘥 𝘨𝘢𝘵𝘩𝘦𝘳 𝘪𝘯𝘴𝘱𝘦𝘤𝘵𝘪𝘰𝘯 𝘥𝘢𝘵𝘢 𝘵𝘰 𝘧𝘦𝘦𝘥 𝘵𝘩𝘦 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘷𝘦 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮.>>>

🔹Brecht based his 𝐑𝐎𝐈 for the 𝐒𝐩𝐨𝐭 deployments on 𝐞𝐧𝐞𝐫𝐠𝐲 𝐬𝐚𝐯𝐢𝐧𝐠𝐬 and overall equipment efficiency (𝐎𝐄𝐄) improvement, reducing equipment stops or failures based on improving Nestlé Purina North America’s 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 program.

“𝘞𝘦 𝘥𝘪𝘥𝘯’𝘵 𝘦𝘷𝘦𝘯 𝘧𝘢𝘤𝘵𝘰𝘳 𝘪𝘯 𝘢𝘯𝘺 𝘴𝘰𝘳𝘵 𝘰𝘧 𝘭𝘢𝘣𝘰𝘳 𝘴𝘢𝘷𝘪𝘯𝘨𝘴, 𝘢𝘭𝘭𝘰𝘸𝘪𝘯𝘨 𝘵𝘩𝘦 𝘳𝘰𝘣𝘰𝘵 𝘵𝘰 𝘥𝘰 𝘵𝘩𝘦 𝘸𝘰𝘳𝘬 𝘷𝘦𝘳𝘴𝘶𝘴 𝘵𝘩𝘦 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘪𝘢𝘯 𝘥𝘰𝘪𝘯𝘨 𝘵𝘩𝘦 𝘸𝘢𝘭𝘬𝘪𝘯𝘨 𝘸𝘰𝘳𝘬. 𝘐𝘵’𝘴 𝘢𝘭𝘭 𝘣𝘦𝘦𝘯 𝘱𝘳𝘦𝘥𝘪𝘤𝘢𝘵𝘦𝘥 𝘰𝘯 𝒉𝒂𝒓𝒅 𝒔𝒂𝒗𝒊𝒏𝒈𝒔 𝘧𝘳𝘰𝘮 𝒄𝒐𝒔𝒕 𝒂𝒗𝒐𝒊𝒅𝒂𝒏𝒄𝒆 𝒘𝒊𝒕𝒉 𝒏𝒐𝒏-𝒇𝒂𝒊𝒍𝒖𝒓𝒆𝒔,” Roger Brecht says.

🔹While he won’t throw hard numbers on an assessment, Brecht does say that he anticipated 𝐒𝐩𝐨𝐭 to meet 𝐑𝐎𝐈 projections in two years. For most of the deployments thus far, 𝐢𝐭 𝐭𝐚𝐤𝐞𝐬 𝐨𝐧𝐥𝐲 𝐨𝐧𝐞 𝐲𝐞𝐚𝐫 𝐭𝐨 𝐡𝐢𝐭 𝐑𝐎𝐈.

Source: https://bit.ly/4eVWRuo

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#iot #ai #genai #predictivemaintenance #robotdog #roi

𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐂𝐥𝐨𝐄𝐄: 𝘊𝘭𝘰𝘌𝘌 𝘪𝘴 𝘢𝘯 𝘈𝘐 𝘥𝘪𝘨𝘪𝘵𝘢𝘭 𝘢𝘥𝘷𝘪𝘴𝘰𝘳 𝘧𝘰𝘳 𝘥𝘪𝘴𝘤𝘳𝘦𝘵𝘦 𝘮𝘢𝘯𝘶𝘧𝘢𝘤𝘵𝘶𝘳𝘪𝘯𝘨 𝘵𝘩𝘢𝘵 𝘣𝘰𝘰𝘴𝘵𝘴 𝘴𝘶𝘴𝘵𝘢𝘪𝘯𝘢𝘣𝘪𝘭𝘪𝘵𝘺, 𝘪𝘮𝘱𝘳𝘰𝘷𝘦𝘴 𝘰𝘷𝘦𝘳𝘢𝘭𝘭 𝘦𝘲𝘶𝘪𝘱𝘮𝘦𝘯𝘵 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺 (𝘖𝘌𝘌), 𝘢𝘯𝘥 𝘥𝘳𝘪𝘷𝘦𝘴 𝘢𝘯𝘯𝘶𝘢𝘭 𝘳𝘦𝘷𝘦𝘯𝘶𝘦 𝘨𝘳𝘰𝘸𝘵𝘩 𝘰𝘧 𝘶𝘱 𝘵𝘰 $1 𝘮𝘪𝘭𝘭𝘪𝘰𝘯🚀🚀🚀

Via Tech.eu / By Cate Lawrence | November 20, 2024 | @VINCI_Digital

𝐂𝐥𝐨𝐄𝐄 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐬 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬

🔹𝐂𝐥𝐨𝐄𝐄 brings highly accessible digitisation to 𝐦𝐢𝐝-𝐬𝐢𝐳𝐞 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 companies. Companies can start with one facility and then scale it to their other operations.

𝑳𝒆𝒗𝒆𝒓𝒂𝒈𝒊𝒏𝒈 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝑨𝑰, 𝑪𝒍𝒐𝑬𝑬 𝒅𝒆𝒍𝒊𝒗𝒆𝒓𝒔 𝒄𝒐𝒏𝒕𝒊𝒏𝒖𝒐𝒖𝒔 𝒊𝒎𝒑𝒓𝒐𝒗𝒆𝒎𝒆𝒏𝒕 𝒖𝒔𝒊𝒏𝒈 𝒓𝒆𝒂𝒍-𝒕𝒊𝒎𝒆 𝒅𝒂𝒕𝒂 𝒇𝒓𝒐𝒎 𝒎𝒂𝒏𝒖𝒇𝒂𝒄𝒕𝒖𝒓𝒊𝒏𝒈 𝒆𝒒𝒖𝒊𝒑𝒎𝒆𝒏𝒕, 𝑴𝑬𝑺, 𝒂𝒏𝒅 𝑬𝑹𝑷 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

It reduces energy consumption by 𝟑𝟎% and emergency stops by 𝟗𝟓%, while providing quick, cost-free project implementation to save manufacturers time and money.

🔹With CloEE | Berkeley SKYDECK'24, workers don't require deep domain expertise in data analytics, AI, advanced automation or advanced upskilling.

"𝘞𝘦 𝘳𝘦𝘢𝘭𝘪𝘴𝘦𝘥 𝘵𝘩𝘢𝘵 𝘦𝘷𝘦𝘳𝘺𝘰𝘯𝘦 𝘬𝘯𝘦𝘸 𝘢𝘣𝘰𝘶𝘵 𝘥𝘢𝘵𝘢 𝘤𝘰𝘭𝘭𝘦𝘤𝘵𝘪𝘰𝘯 𝘴𝘺𝘴𝘵𝘦𝘮𝘴, 𝘣𝘶𝘵 𝘯𝘰 𝘰𝘯𝘦 𝘶𝘴𝘦𝘥 𝘵𝘩𝘦𝘮 𝘱𝘳𝘰𝘱𝘦𝘳𝘭𝘺. 𝘉𝘶𝘵 𝘸𝘦 𝘤𝘰𝘶𝘭𝘥 𝘣𝘶𝘪𝘭𝘥 𝘢 𝘴𝘪𝘮𝘱𝘭𝘦-𝘵𝘰-𝘶𝘴𝘦, 𝘦𝘢𝘴𝘺-𝘵𝘰-𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘢𝘯𝘥 𝘥𝘦𝘱𝘭𝘰𝘺 𝘱𝘭𝘶𝘨-𝘢𝘯𝘥-𝘱𝘭𝘢𝘺 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘸𝘪𝘵𝘩 𝘯𝘰 𝘤𝘩𝘢𝘳𝘨𝘦 𝘧𝘰𝘳 𝘪𝘮𝘱𝘭𝘦𝘮𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯 𝘵𝘩𝘢𝘵 𝘤𝘰𝘶𝘭𝘥 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 𝘳𝘦𝘴𝘶𝘭𝘵𝘴 𝘢𝘯𝘥 𝘙𝘖𝘐 𝘪𝘯 𝘭𝘦𝘴𝘴 𝘵𝘩𝘢𝘯 𝘵𝘩𝘳𝘦𝘦 𝘮𝘰𝘯𝘵𝘩𝘴 𝘧𝘳𝘰𝘮 𝘪𝘯𝘴𝘵𝘢𝘭𝘭𝘢𝘵𝘪𝘰𝘯." ─ Julia Sabitova, co-founder, COO, and Chief AI Advisor Nick Gushchin @CloEE | Berkeley SKYDECK'24

Video: https://youtu.be/4rgnq9qgndc?si=S59MUhYw45Nw9j4l

Source: https://bit.ly/49eT768

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#industrial #iot #ai #genai #aidigitaladvisor #manufacturing #oee

𝐓𝐡𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐒𝐭𝐚𝐜𝐤: 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘵𝘩𝘦 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 𝘭𝘢𝘯𝘥𝘴𝘤𝘢𝘱𝘦 🚀🚀🚀

Via Letta / By Charles Packer & Sarah Wooders | November 14, 2024 | @VINCI_Digital

Breaking down today’s tech stack for building 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 into 3 key layers

(1) 𝘈𝘨𝘦𝘯𝘵 𝘩𝘰𝘴𝘵𝘪𝘯𝘨/𝘴𝘦𝘳𝘷𝘪𝘯𝘨

(2) 𝘈𝘨𝘦𝘯𝘵 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬𝘴

(3) 𝘓𝘓𝘔 𝘮𝘰𝘥𝘦𝘭𝘴 & 𝘴𝘵𝘰𝘳𝘢𝘨𝘦

🔹A 𝐦𝐚𝐫𝐤𝐞𝐭 𝐦𝐚𝐩 diagrams that actually reflects real world usage by today’s developers building AI agents

𝑰𝒇 𝒚𝒐𝒖’𝒓𝒆 𝒔𝒕𝒂𝒓𝒕𝒊𝒏𝒈 𝒂 𝒗𝒆𝒓𝒕𝒊𝒄𝒂𝒍 𝒂𝒈𝒆𝒏𝒕𝒔 𝒄𝒐𝒎𝒑𝒂𝒏𝒚 𝒕𝒐𝒅𝒂𝒚 (𝑵𝒐𝒗 2024), 𝒘𝒉𝒂𝒕 𝒔𝒐𝒇𝒕𝒘𝒂𝒓𝒆 𝒂𝒓𝒆 𝒚𝒐𝒖 𝒎𝒐𝒔𝒕 𝒍𝒊𝒌𝒆𝒍𝒚 𝒕𝒐 𝒖𝒔𝒆 𝒕𝒐 𝒃𝒖𝒊𝒍𝒅 𝒐𝒖𝒕 𝒚𝒐𝒖𝒓 “𝒂𝒈𝒆𝒏𝒕𝒔 𝒔𝒕𝒂𝒄𝒌”?

🔹In our opinion, the 𝐀𝐈/𝐋𝐋𝐌 𝐚𝐠𝐞𝐧𝐭𝐬 𝐬𝐭𝐚𝐜𝐤 is a significant departure from the standard LLM stack.

The key difference between the two lies in 𝐦𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐬𝐭𝐚𝐭𝐞:

>>> 𝐋𝐋𝐌 𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐚𝐫𝐞 𝐠𝐞𝐧𝐞𝐫𝐚𝐥𝐥𝐲 𝐬𝐭𝐚𝐭𝐞𝐥𝐞𝐬𝐬

>>> 𝐰𝐡𝐞𝐫𝐞𝐚𝐬 𝐚𝐠𝐞𝐧𝐭 𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐛𝐞 𝐬𝐭𝐚𝐭𝐞𝐟𝐮𝐥 (retain the state of the agent server-side)

🔹Building 𝐬𝐭𝐚𝐭𝐞𝐟𝐮𝐥 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 is a lot harder of an engineering challenge compared to building developer SDKs, 𝘴𝘰 𝘶𝘯𝘴𝘶𝘳𝘱𝘳𝘪𝘴𝘪𝘯𝘨𝘭𝘺 𝘷𝘦𝘳𝘺 𝘧𝘦𝘸 𝘢𝘨𝘦𝘯𝘵 𝘴𝘦𝘳𝘷𝘪𝘯𝘨 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮𝘴 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘦𝘹𝘪𝘴𝘵 𝘵𝘰𝘥𝘢𝘺

Source: https://bit.ly/3OwCWb1

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#iot #ai #genai #llms #aiagents #stack #marketmap

𝐓𝐡𝐞 𝐁𝐚𝐭𝐭𝐥𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚: 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐯𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: 𝘊𝘰𝘮𝘱𝘢𝘳𝘦 𝘴𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘴 𝘢𝘯𝘥 𝘮𝘢𝘤𝘩𝘪𝘯𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨, 𝘥𝘪𝘴𝘤𝘶𝘴𝘴𝘪𝘯𝘨 𝘵𝘩𝘦𝘪𝘳 𝘧𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯𝘴, 𝘮𝘦𝘵𝘩𝘰𝘥𝘴, 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴, 𝘢𝘯𝘥 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦𝘴 𝘪𝘯 𝘢𝘯𝘢𝘭𝘺𝘻𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘧𝘰𝘳 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘰𝘯𝘴 🚀🚀🚀

Via DZone / By Vasanthi Govindaraj | October 14, 2024 | @VINCI_Digital

The goal of this article is to investigate the fields of 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 and 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 and look at the differences, similarities, usage, and ways of analyzing data in these two branches

🔹Both branches of science allow 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚, however, they are based on different pillars:

>>> 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐨𝐧 𝐦𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬

>>> 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐨𝐭𝐡𝐞𝐫 𝐨𝐧 𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 — 𝐭𝐡𝐞 𝐟𝐨𝐜𝐮𝐬 𝐨𝐟 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠

𝑨𝒓𝒕𝒊𝒇𝒊𝒄𝒊𝒂𝒍 𝒊𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆 𝒕𝒐𝒈𝒆𝒕𝒉𝒆𝒓 𝒘𝒊𝒕𝒉 𝒎𝒂𝒄𝒉𝒊𝒏𝒆 𝒍𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝒊𝒔 𝒑𝒓𝒆𝒔𝒆𝒏𝒕𝒍𝒚 𝒕𝒉𝒆 𝒕𝒆𝒄𝒉𝒏𝒐𝒍𝒐𝒈𝒊𝒄𝒂𝒍𝒍𝒚 𝒂𝒅𝒗𝒂𝒏𝒄𝒆𝒅 𝒎𝒆𝒂𝒏𝒔 𝒐𝒇 𝒆𝒙𝒕𝒓𝒂𝒄𝒕𝒊𝒏𝒈 𝒖𝒔𝒆𝒇𝒖𝒍 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 𝒇𝒓𝒐𝒎 𝒕𝒉𝒆 𝒓𝒂𝒘 𝒅𝒂𝒕𝒂 𝒕𝒉𝒂𝒕 𝒊𝒔 𝒄𝒉𝒂𝒏𝒈𝒊𝒏𝒈 𝒆𝒗𝒆𝒓𝒚 𝒅𝒂𝒚 𝒂𝒓𝒐𝒖𝒏𝒅 𝒖𝒔

𝑶𝒏 𝒕𝒉𝒆 𝒄𝒐𝒏𝒕𝒓𝒂𝒓𝒚, 𝒔𝒕𝒂𝒕𝒊𝒔𝒕𝒊𝒄𝒔 — 𝒂 𝒗𝒆𝒓𝒚 𝒐𝒍𝒅 𝒇𝒊𝒆𝒍𝒅 𝒐𝒇 𝒓𝒆𝒔𝒆𝒂𝒓𝒄𝒉 𝒐𝒇 𝒐𝒗𝒆𝒓 3 𝒄𝒆𝒏𝒕𝒖𝒓𝒊𝒆𝒔 — 𝒉𝒂𝒔 𝒂𝒍𝒘𝒂𝒚𝒔 𝒃𝒆𝒆𝒏 𝒓𝒆𝒈𝒂𝒓𝒅𝒆𝒅 𝒂𝒔 𝒂 𝒄𝒐𝒓𝒆 𝒅𝒊𝒔𝒄𝒊𝒑𝒍𝒊𝒏𝒆 𝒇𝒐𝒓 𝒕𝒉𝒆 𝒊𝒏𝒕𝒆𝒓𝒑𝒓𝒆𝒕𝒂𝒕𝒊𝒐𝒏 𝒐𝒇 𝒕𝒉𝒆 𝒄𝒐𝒍𝒍𝒆𝒄𝒕𝒆𝒅 𝒅𝒂𝒕𝒂 𝒂𝒏𝒅 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏-𝒎𝒂𝒌𝒊𝒏𝒈

🔹Even though both of them share one goal of studying data, how the goal is achieved and where the focus is varies in statistics and machine learning.

🔹This article, however, seeks to relate the two fields and how they address the needs of contemporary society as the field of data science expands

Source: https://bit.ly/3YSWSuz

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

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#iot #ai #genai #data #statistics #machinelearning #ml

Bio:🔹Fabio Bottacci is a relationship builder, creative problem solver, and strategic thinker. Senior industrial executive and former strategic consultant, he acquired a solid background in large multinational organizations across Brazil, US, and Western Europe. He is known for his ability to deliver results despite ambiguity and obstacles, to build bridges between people, and to manage conflict and negotiations.

Fabio graduated in Economics & Business Administration from Università Bocconi and from Stanford University School of Engineering in Internet of Things [IoT]. Recently, Fabio has successfully completed a Private Equity & Venture Capital course by SDA Bocconi [Final Grade: 94%], and two accredited certifications: Generative AI by Databricks Academy and Generative AI by Andrew Ng, founder DeepLearning.AI [Final Grade: 96%].

He began his career at Accenture Italia/Accenture, strategy practice, while attending MBA courses. He then moved to Brazil, where he consistently proved, during more than 20 years of professional experience, strong clients' network, industry knowledge and business development expertise in the automotive, oil and gas, energy, and utilities verticals.

Since 2015, he has been the founder & CEO @VINCI Digital | IIoT + AI/GenAI Strategic Advisory 🚀 , being recognized internationally as a thought leader by well-known organizations, such as the World Economic Forum, the IOT Solutions World Congress, and the BNDES (Brazilian Federal Development Bank), and - lately - the Harvard Business Review Advisory Council.

Recently, Fabio has been contracted by Bain & Company as Senior Advisor to its Advanced Manufacturing & Services and Energy & Natural Resources global practices, with a particular focus on Industrial IoT + AI / Generative AI's end-to-end solutions and their actual deployment, at-scale, within the Oil & Energy / Utilities, Metals & Mining, Automotive, Agriculture, and Pulp & Paper verticals.

In 2023, he had joined the European Research Executive Agency (REA) as an Industrial IoT + AI / Generative AI Expert and Evaluator for the HORIZON Europe Programme of the European Commission, and Kobo Funds as Venture Partner, focused on Deeptech Seed/Series A startups, helping investors work successfully with entrepreneurs.

Fabio's mission is to help startups/scaleups, SME, and corporations to thrive within the current digital transformation environment, by increasing productivity, developing new business models, and delivering actual results / ROI in months, not years!

VINCI Digital | IIoT + AI / GenAI Strategic Advisory 🚀

#industrial #iot #iiot #ai #genai #generativeai #casestudies #insights #perspectives

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