In our latest episode of Diaries of a Data Scientist we dive into one of the most pressing challenges we face today: 𝐇𝐨𝐰 𝐝𝐨 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐀𝐈 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬? 👀👀

Kate recently met with a team of data scientists in Brazil, where key questions many of us have faced were raised:

— How do we make business teams trust our models❓

— How can we translate complex technical concepts into something business people 𝐫𝐞𝐚𝐥𝐥𝐲 understand❓

— And how do we ensure our models deliver exactly what’s needed❓

In this episode, we break down 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 you can use to bridge the gap between tech and business, build transparency, and most importantly—gain trust.

Plus, we’re sharing 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞 on making your strategies more accessible and trusted, drawing insights from our experience, the Technology Acceptance Model (TAM), and how explainability is shaping trust in tech.

Trust me, you don’t want to miss this one. 😉

If you like our podcast, please consider leaving a review on Spotify, subscribe on Youtube and leave a comment and/or 👍! This would help us a lot ♥️

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🪽 Follow Jasmin on LinkedIn: https://www.linkedin.com/in/jasmin-weimueller-bsc2018/

🪽 Follow Kate on LinkedIn: https://www.linkedin.com/in/kate-nazarova-data-science/

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𝐖𝐞𝐥𝐜𝐨𝐦𝐞 𝐛𝐚𝐜𝐤 𝐭𝐨 𝐭𝐡𝐞 𝐄𝐩𝐢𝐬𝐨𝐝𝐞 21! 🎙

Today, we’re doing something a little different. We thought we’d take a break from the usual deep dives into data science and just chat with you about 𝗅𝗂𝖿𝖾 𝗎𝗉𝖽𝖺𝗍𝖾𝗌, 𝗇𝖾𝗐 𝗂𝖽𝖾𝖺𝗌 we’re cooking up for the next few months, sharing 𝗌𝗈𝗆𝖾 𝖿𝗎𝗇 𝗌𝗍𝗈𝗋𝗂𝖾𝗌 𝖺𝗇𝖽 𝖼𝗈𝗆𝗉𝖺𝗋𝗂𝗌𝗈𝗇𝗌 𝖻𝖾𝗍𝗐𝖾𝖾𝗇 𝗅𝖺𝗇𝗀𝗎𝖺𝗀𝖾𝗌 𝖺𝗇𝖽 𝖼𝗎𝗅𝗍𝗎𝗋𝖾𝗌.

Let us know if you enjoy this type of content! We’d be happy to sprinkle in more personal stories alongside the tech discussions.

If you like our podcast, please consider leaving a review on Spotify, subscribe on Youtube and leave a comment and/or 👍! This would help us a lot ♥️

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𝙸𝗇 𝖺 𝗐𝗈𝗈𝖽𝗌 𝗈𝖿 𝗍𝗁𝖾 𝖽𝖺𝗍𝖺 𝗌𝖼𝗂𝖾𝗇𝖼𝖾 𝗐𝗈𝗋𝗅𝖽, 𝗈𝗇𝖾 𝖼𝖺𝗇 𝗌𝗎𝗋𝖾𝗅𝗒 𝖿𝗂𝗇𝖽 𝗍𝗁𝖾𝗂𝗋 𝗌𝗉𝗈𝗍! 👾

Whether you're a developer or an entrepreneur, open-source communities 👥 are a game-changer. This #𝟤𝟢𝗍𝗁 𝖾𝗉𝗂𝗌𝗈𝖽𝖾 𝗈𝖿 𝖣𝖺𝗂𝗋𝗂𝖾𝗌 𝗈𝖿 𝖺 𝖣𝖺𝗍𝖺 𝖲𝖼𝗂𝖾𝗇𝗍𝗂𝗌𝗍 is 𝖸𝗈𝗎𝗋 𝖦𝗎𝗂𝖽𝖾 to not just improving your skills but also:

🆒 earning trust

🆒 credibility

🆒 and making a real impact in the tech ecosystem

🎙️ Join @Jasmin/@Kate and Kate/Jasmin as we dive deep into the advantages of contributing to open-source packages, from boosting your career to building valuable connections in both tech and business.

If you like our podcast, please consider leaving a review on Spotify, subscribe on Youtube and leave a comment and/or 👍! This would help us a lot ♥️

#datascience #dods #diariesofadatascientist #aicommunity

👉 P.S. Feel free to check out Episode 15 if you're interested in learning more about interdisciplinary positions with a strong focus on open-source communities

In this episode, we dive into the most significant milestones in generative AI during 2024 and explore how these advancements are transforming industries worldwide.

Whether you're a tech enthusiast, industry professional, or just curious about the future of AI, this episode provides a comprehensive overview of how generative AI is making its mark in 2024 and beyond.

Some links to further check out resources:

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

https://openai.com/index/hello-gpt-4o/

https://www.ibm.com/think/insights/artificial-intelligence-trends

https://www.etoro.com/news-and-analysis/press-releases/etoros-first-ai-generated-ad-campaign-to-be-aired-during-paris-olympics/

If you like our podcast, please consider leaving a review on Spotify, subscribe on Youtube and leave a comment and/or 👍! This would help us a lot ♥️

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Below we attach a list of models that is helpful to catch up with the most relevant ones from 2024 so far:

• Llama 3.1: Meta's latest version in the Llama series, offering significant improvements in both language understanding and generation capabilities. https://llama.meta.com
• GPT-4o and GPT-4o mini: Enhanced versions of OpenAI's GPT series, focusing on more efficient and contextually aware text generation.
• Phi 3: A new model developed for complex multimodal tasks, integrating text, audio, and visual data processing. (SLM): https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/
• Mistral Large 2: Known for its low latency and high performance, particularly in real-time applications (The GitHub Blog) (ThinkPalm).
• DeepDream: An updated version of Google's model that generates visually striking and abstract images using deep neural networks (ThinkPalm).
• GitHub Copilot: A collaborative tool that assists developers by providing context-aware code completions, now enhanced with broader language support and improved interactive documentation suggestions (ThinkPalm) (The GitHub Blog).
• Microsoft 365 CoPilot, Studio
• Complexity of Generative AI Adoption in Corporate

Recent Generative AI Models Developed (January 2024 - July 2024)

“𝗜 𝗔𝗺 𝗡𝗼𝘁 𝗚𝗼𝗼𝗱 𝗘𝗻𝗼𝘂𝗴𝗵” …

You’re not alone. 𝘐𝘮𝘱𝘰𝘴𝘵𝘦𝘳 𝘴𝘺𝘯𝘥𝘳𝘰𝘮𝘦 is a silent struggle for many, especially in the fast-paced 𝘵𝘦𝘤𝘩 𝘸𝘰𝘳𝘭𝘥. In our 18th episode "𝗜𝗺𝗽𝗼𝘀𝘁𝗲𝗿 𝗦𝘆𝗻𝗱𝗿𝗼𝗺𝗲 𝗶𝗻 𝗧𝗲𝗰𝗵”, we dive deep into this phenomenon, sharing insights, our stories and strategies to overcome it (based on our experience).

If you like our podcast, please consider leaving a review on Spotify, subscribe on Youtube and leave a comment and/or 👍! This would help us a lot ♥️

#datascience #dods #diariesofadatascientist #aicommunity

Tune in for our 17th Episode of “Diaries of a Data Scientist”, a bonus episode, where we share our honest summary on our talk at the Frankfurt Tech Show, 23rd of May. We were representing BASF at the Big Data & AI World Show.

If you like our podcast, please consider leaving a review on Spotify, subscribe on Youtube and leave a comment and/or 👍! This would help us a lot ♥️

#datascience #dods #diariesofadatascientist #aicommunity

Did you know? n the past year, 76% of graduating North American PhDs in AI went into industry — up from 44.4% in 2010. This shift highlights the increasingly significant role industry is playing in AI development. By 2023, industry produced 51 notable machine learning models, while academia contributed only 15. 🤯 (Statistics from the AI Index Report, 2024, Stanford)

In this episode, we explore💡:

1️⃣ The Importance of Collaboration: Discover why it's crucial for data scientists in corporate roles to collaborate with universities and the immense benefits these partnerships can bring.

2️⃣ How to Start Collaborations: We share practical ways and channels to initiate and foster collaborations between industry and academia.

3️⃣ The Downsides: We don't shy away from discussing the challenges and downsides of balancing research and corporate responsibilities as a data scientist.

If you like our podcast, please consider leaving a review on Spotify, subscribe on Youtube and leave a comment and/or 👍! This would help us a lot ♥️

#datascience #dods #diariesofadatascientist #aicommunity

🖇️ Useful links (unpaid advertisement, personal recommendations)::

1. AI Index Report by Stanford, 2024: https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf