Here's what caught my eye in the world of AI this week:

1. Starting an Analytics Org From Scratch — Lessons From a Decade at DoorDash

Packed with insights, this must-read article by Jessica Lachs highlights:

• The teams you need will change based on the types of problems you have, the amount of data you have, and how you plan to use data for decision-making.

• There are three core elements of analytics: creating the data you need, curating that data for usability, and helping users accurately interpret and consume that data to make decisions.

• Ask yourself if you’re willing to change decisions based on the outcomes of experiments. If you’re not, building an experiment culture may not be the best use of your resources. Experiments shouldn’t support confirmation biases and be ignored when they present you with contradictory evidence.

2. In memory of an early mentor, Brant Cooper, here’s a quote that inspires deep thinking and motivation. You are already deeply missed Brant Cooper.

3. Layering AI into your B2B Product and GTM

Anurag and Ibrahim stress the importance of a strategic approach to AI integration, focusing on enhancing value across user productivity, team collaboration, and buyer decision-making. They also highlight the potential for AI to disrupt traditional frameworks, urging businesses to prepare for the evolving landscape. I agree it is not if but when and these muscles take time for corporations to build so start investing now before it is too late.

Article by: ANURAG WADEHRA AND IBRAHIM BASHIR

4. DataComp, the machine learning benchmark where the models are fixed and the challenge is to find the best possible data!

This is type of human ingenuity that gives me strong faith that together we will be able to solve any challenge that comes our way. One team, one dream - Go Human Race!

Developing datasets for better models that are cheaper to train. Using DataComp-LM, they are working on developing a high-quality dataset, DCLM-BASELINE, which they use to train models with strong compute performance tradeoffs. We compare on both a Core set of tasks (left) and on MMLU 5-shot (right). DCLM-BASELINE (orange) shows favorable performance relative to both close-source models (crosses) and other open-source datasets and models (circles).

Want to get really wonky? Check out the Research Paper.

5. I think I just found my new bestie for Responsible AI, so excited

Irene shares great info on AI Policy by the BRG Research Group in the above and trackers and resources that can help you stay informed. I look forward to learning more and helping to spread the Responsible AI love.

Great Posts Irene Liu!

Million dollar AI strategies packed in this free 3 hour AI Masterclass – designed for founders & professionals. Act fast because It’s free only for the first 100.

Join it here for $0. 🎁

6. Friday Funnies 🤣 .

Send me your best AI meme and I will post the Top 5 and put it to a vote by our readership! I am sure anyone can do better! Winner gets a bottle Moet Champagne! Have to get at least five memes for there to be a competition.

7. Integrating AI with Your Core Product - Great Example

Come see Wyl talk about how Tability is mastering this challenging area of AI at our meetup. I tried it and you can see the powerful results I will be implementing above!

8. Artificial Intelligence -powered interface concepts

   Was trying to explain the difference between Bolt-on AI and Native AI to a friend and did not land well. Then I sent the this awesome site link and things became a little clearer. A picture is worth a 1,000 words :-)

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