What is Multi Modal AI – An Easy Explanation For Anyone Multimodal AI is changing the way machines interact with the world by processing multiple data forms like text, images, and sound simultaneously. From healthcare to creative industries, these systems are revolutionizing
RESEARCH
-
Are Ethics Universal in AI Programs?
By
–
Are ethics universal? I am sharing this insightful HBR article I came across the other day, written by Swanand Deodhar, Favour Borokini, and Ben Waber. The authors discuss that in the race to build #AIEthics programs, many miss a crucial point: #ethics are not universal.
-
AI Builders Gather at AIAI Boston Conference
By
–
We're attending @AI_AInstitute AIAI Boston, one of the East Coast's most prolific gatherings of #AI builders and experts! We can't wait to share the latest updates with what we've been working on. See you there! #GenAI
-
AI Institute AIAI Boston Conference Announcement 2026
By
–
We're attending @AI_AInstitute AIAI Boston, one of the East Coast's most prolific gatherings of #AI builders and experts! We can't wait to share the latest updates with what we've been working on. See you there! #GenAI
-
Open-source model merging technique simplifies parameter tuning
By
–
Hope you'll like this technique. This makes model merging 1/ simpler by removing parameter tuning and 2/ more computationally efficient than alternatives. You can play with it today. The code is open-sourced and available on GitHub
-
DAM Optimization Outperforms Techniques for SQL and German Tasks
By
–
Case study 2: SQL + German + Korean We combined three Llama-based models and optimized coefficients on three different datasets. DAM outperforms other techniques on average: in particular in German and SQL, but falls short in Korean.
-
DAM Model Merging: Japanese Math LLM Optimization Results
By
–
Case study 1: Japanese + Math model We combined shisa-gamma-7b, WizardMath-7B-V1 and Abel-7B-002 and optimized coefficients on Ichikara, MetaMathQA, and Orca-Math. DAM achieved the best average score across various tasks, matching Evolutionary Merging but with less
-
DAM: Scaling Coefficients for Source Model Weight Merging
By
–
DAM learns scaling coefficients for each column in the weight matrices of the source models. The objective function is optimized with gradient descent. It includes: – KL divergence to align the merged model's predictions with each expert source model
– Cosine similarity -
Differentiable Adaptive Merging: Faster Automated Model Fusion
By
–
Differentiable Adaptive Merging I participated in a paper with @arcee_ai about a new automated merging technique (no manual parameter tuning!) Compared to other algorithms, like Evolutionary Merging, it is significantly faster to run and achieves the same level of performance.
-
IBM Think2024: AI Scalability, Security and UK Industry Growth
By
–
Just left #IBM #Think2024 in London! 🇬🇧 Key takeaways:
— Bernard Marr (@BernardMarr) 16 octobre 2024
1. AI scalability is huge (only 1% of corporate data used now!)
2. Security & ethics are crucial
3. Partnerships driving innovation (IBM+AWS+Microsoft)
4. UK AI industry could hit £800B by 2035
5. Building trust in AI is… pic.twitter.com/bjbYMzBjtWJust left #IBM #Think2024 in London! Key takeaways: 1. AI scalability is huge (only 1% of corporate data used now!)
2. Security & ethics are crucial
3. Partnerships driving innovation (IBM+AWS+Microsoft)
4. UK AI industry could hit £800B by 2035
5. Building trust in AI is