The context ofc came from me and it took several revisions but it already gives you the power to act rather as an editor and not as a writer
AI
-
ATT Business BizTalks: Cybersecurity Networks IoT Industry 4.0
By
–
Check out on-demand @ATTBusiness #BizTalks LinkedIn Live with us @YuHelenYu @Kevin_Jackson @sallyeaves https://
linkedin.com/events/7050195
556396105728/comments/
…… #ATTInfluencer #networks #Cybersecurity #networksecurity #RSAC #IoT #Industry40 -
Modularity Approach Enables Faster Development and Collaboration
By
–
Thanks for sharing @JimMarous
. Modularity approach enables faster development, better collaboration and increased flexibility. -
Artificial Intelligence Transforming Energy Sector Solutions
By
–
Excited that my episode of the @PBS talk show #energyswitch on #AI for energy is out! https://
pbs.org/video/could-ar
tificial-intelligence-change-energy-3fwhmo/
… -
Scale AI Team Honored for Innovation in Artificial Intelligence
By
–
kinda surreal but this is very much a team honor for one of the best teams in ai (the @scale_AI team) and the incredible innovation of our customers, who inspire us every day and the journey is nowhere close to over 🙂 here’s to the next leg!
-
Exemplar SVM: Simple but Counterintuitive in High Dimensions
By
–
Yep exactly! 🙂 The first time I saw the Exemplar SVM idea. It's so simple but also a bit counter-intuitive, I think because low dimensional intuition fails us. A classifier with a single example? In low dimensions it sounds weird. In high dimensions it works great.
-
Customizing Embeddings and Comparing Distance Metrics in AI
By
–
Nice — curious how this compares to learning a modified distance metric as shown here: https://
github.com/openai/openai-
cookbook/blob/main/examples/Customizing_embeddings.ipynb
… -
CIOs warn businesses lack sufficient process maturity
By
–
“CIOs caution me that many businesses aren’t as process driven as one might think.” #CIO @MylesSuer
-
SVMs Outperform k-Nearest Neighbor for Embedding Lookups
By
–
Random note on k-Nearest Neighbor lookups on embeddings: in my experience much better results can be obtained by training SVMs instead. Not too widely known. Short example: https://
github.com/karpathy/rando
mfun/blob/master/knn_vs_svm.ipynb
… Works because SVM ranking considers the unique aspects of your query w.r.t. data.