We have recently proposed a novel take on xAI. Taking advantage of the duality between xAI and Causality, we have proposed a Causality-inspired taxonomy for explainable artificial intelligence. https://t.co/b2CSHZuGkR
Follow the thread 🧵 to know more!
The new AI Camera Assistant* with Xperia Intelligence brings stories to life. Using subject, scene and weather, it suggests expressive options with adjustments of colour, exposure, bokeh, and lens for breathtaking photos*.
https://t.co/zgSQ9MLWFP
#SonyXperia#Xperia1VIII
I love Geoff.
But he understands even less than Dario about the effects of technological revolutions on the labor market.
Again, don't listen to AI scientists, as brilliant as they might be, and even less to AI CEOs, as successful as they might be, for questions of labor economics.
Listen to reputable economists who have studied these things like @Ph_Aghion , @DAcemogluMIT , @erikbryn , @amcafee , @davidautor , etc.
"Before Transformers, RNNs were the thing. These were a big breakthrough. Suddenly, everyone started to work on improving RNNs. But the results were always these slight modifications on the same architecture, like putting the gate in a different spot, with improvements to 1.26, 1.25 bits per character on language modeling."
"After the Transformer, when we applied very deep decoder-only Transformers to the same task, we immediately got 1.1 bits per character. So all that research on RNNs suddenly seemed a waste of time".
"We're currently in the same situation where a lot of papers are taking the same architecture (Transformer) and making these endless tweaks, in a local minimum, and we might be wasting time in exactly the same way."
- Llion Jones, co-author of the Transformer on @MLStreetTalk
@iScienceLuvr@SophontAI Lot of interesting works @iScienceLuvr
2026 will be even fuller 👍
I feel like we are in an era similar to the beginning of DL. Pushing the metrics is the goal, ensuring fairness is secondary. How are you tackling this at @SophontAI ?
@giffmana The thing with working on ML is that a lot of the bugs are silent bugs, and it takes some time for you to become naturally skeptical and start double or triple checking your results
@nextinsurgery One rarely good take on explainable AI. Showing comparative situations is, IMO, one of the best interpretations on xAI and how explanations might be useful
@iScienceLuvr Tbf AI didn’t even reach properly a lot of domains, medical being one of them!
Yet doctors seem excited by gains in efficiency, and less fatigue-prone analysis with the AI-Doctor combo
The first task of 2026 checklist is cleared: Update my personal website
Figured out that I needed to start the year with a refreshed look, instead of going for a crazy haircut, just changed my entire website.
Let me know what you think: https://t.co/zEtFjvwkkf
#AI#MedicalAI
Quão loucos somos para confiar a nossa saúde a sistemas que não foram desenhados a pensar nela, que carecem de consistência de anos de investigação que fundamentam o conhecimento humano especializado? Crónica de Pedro C. Neto https://t.co/jk9cS0fgb1
Quão loucos somos para confiar a nossa saúde a sistemas que não foram desenhados a pensar nela, que carecem de consistência de anos de investigação que fundamentam o conhecimento humano especializado? Crónica de Pedro C. Neto https://t.co/jk9cS0fgb1