This now means that certain chains see a MASSIVE speed up. For example, the map-reduce chain can batch the map calls to the LLM, resulting in a ~75% drop in time
MACHINE LEARNING
-
From Boxing Ring to JPMorgan: Data Science Leadership Journey
By
–
From the ring to The Street, Tiffany Perkins-Munn has always employed data to guide her business success. Learn about her journey from TITLE Boxing Club to @JPmorgan in The Finance and Insurance Data Science Innovator's Playbook. https://
domino.buzz/3UH50sH #MLOps -
30 MLOps Requirements: Abacus AI Simplifies Machine Learning Systems
By
–
Building Machine Learning Systems is hard. Here are 30 requirements for an MLOps environment. At @abacusai we worry about this for you. You bring the data, we do the rest. (Source: "Requirements and Reference Architecture for MLOps: Insights from Industry")
-
ChatGPT Interview Reveals Promising AI Future in Banking
By
–
Our #ChatGPT Interview Shows AI Future in Banking Is Scary-Good
#AI #MachineLearning #digital #banking #DataScience #DataScientist #Python Cc @Khulood_Almani @amalmerzouk @Analytics_699 @Hana_ElSayyed @CurieuxExplorer https://
thefinancialbrand.com/news/data-anal
ytics-banking/artificial-intelligence-banking/interview-with-chatgpt-on-the-future-of-ai-assistants-in-banking-156941/
… -
Measuring Data: Mitchell et al. Research on AI Evaluation
By
–
Measuring Data Mitchell et al.: https://
arxiv.org/abs/2212.05129 #ArtificialIntelligence #DeepLearning #MachineLearning -
Data Lineage Techniques Improve Efficiency in Complex Ecosystems
By
–
→ @Databand_ai Director of Engineering Niv Sluzki illustrates how different data lineage techniques improve operational efficiency and instill confidence across complex data ecosystems by tracking data origin and transformation: https://
ibm.co/3hmtppM -
YOLOR Outperforms YOLOv7 on COCO Test-Dev Dataset
By
–
Did you know YOLOR performs better on the Coco test-dev set than YOLOv7?
— Satya Mallick (@LearnOpenCV) 14 décembre 2022
YOLOR takes inspiration from how humans combine explicit & implicit knowledge to process previously unseen data.
▶️Check out our blog post to learn more.https://t.co/SUXpFNwMvj#yolor #computervision #ai pic.twitter.com/GcIyHlqr02Did you know YOLOR performs better on the Coco test-dev set than YOLOv7?
YOLOR takes inspiration from how humans combine explicit & implicit knowledge to process previously unseen data. Check out our blog post to learn more. https://
learnopencv.com/yolor-paper-ex
planation-inference-an-in-depth-analysis/
… #yolor #computervision #ai -
GPT-3 code-davinci-002 is the most capable model
By
–
“Wait, code-davinci-002 is the most capable GPT-3 model for natural language as well as code, on top of having a context window twice as big??” “Always has been.”
-
AI System Automates Disease Outbreak Detection India
By
–
We have developed an AI-powered system to automate aspects of event-based disease surveillance in India, towards augmenting the Integrated Disease Surveillance Programme established by the @MoHFW_INDIA
. Learn about our technical approach here — https://
wadhwaniai.org/2022/12/using-
ai-to-automate-the-early-detection-of-disease-outbreaks-in-india/
… -
Performance in ML means accuracy, not speed
By
–
(For non-ML folks: “performance” means being right, not being fast — accuracy, F1 score, etc.)