Google Research’s Post

We humans resist change but once we accept it why do we normalize it so fast? Like we now see it as totally normal that  if you organize rocks in a specific way and pass electricity between them we can make them think and draw images at this quality??? Just some random thoughts while reading papers on early-fusion multimodal models. The image is from Meta's Chameleon paper, which I think is a little sneak-peak of future Llama models.

Today I earned my "Classify types of space rocks in random photos by using artificial intelligence" badge!🎉 I’m so proud to be celebrating this achievement and hope this inspires you to start your own Microsoft Learn journey!❤ #PythonAI #ML #DataScience #AIwithPython #MachineLearning #DataAnalysis #PythonDeveloper #AIEngineer #DataScientist #ai #machinelearning #artificialintelligence #deeplearning #neuralnetworks #dataanalysis #datamining #microsoftlearn

📍Come at poster board A58, Hall A 🟥, on Thursday morning for further insights on this great work led by Arash Rahi. 📊Topic: modeling #runoff coefficient - a key indicator of #freshwater availability - through #LSTM. #AI #deeplearning #hydrology #EGU2024

I'm really excited to share this news article about BLASTNet, the first large dataset for fundamental fluid dynamics. 🔺This dataset can enable machine learning models to tackle complex problems in fluid flows, such as turbulence, combustion, and multiphase flows. These problems are relevant for many scientific and engineering domains, such as biomedical science, physics, energy, and neuroscience. 🔺I believe this dataset can lead to exponential breakthroughs in these fields and help us better understand the natural phenomena around us. 👨🏾💻Check out the article and the dataset on GitHub. Kudos to the Stanford HAI team for creating this amazing resource! Shout out to Paul Golding for finding this.

The future is NeuroSymbolic. The inefficiency of linear approximation by brute force is just too overwhelming. Trying to multiply that inefficiency by using a system that only uses the most inefficient information encoding, namely "Human Language", only amplifies that problem. Its like inefficiency on top of inefficiency. This is why intelligence or autonomous systems will get more and more out of reach, the more people try to solve it end-to-end gradient-based. The future is intelligently designed cognitive architectures.

The Frontiers journal reacts to the AI rat image scandal In this episode, we discuss.. ✅Published papers disappearing from the internet ✅AI-generated image of rat retracted  ✅Climate scientists modelling the Planet of Dune 😱 ✅+ much more 😁 Listen to episode 77: (Link in the comments)

We're thrilled to share a groundbreaking piece of research we've come across - an innovative approach to applying deep reinforcement learning in the complex domain of fluid dynamics. This study unveils a system, dubbed "Box o Flows," designed to navigate the intricacies of fluid behaviors in the physical world, a challenge often deemed too complex for accurate simulation. What stands out to us is the system's ability to derive intricate behaviors from straightforward rewards, highlighting the potential of offline learning to maximize data utility. This research resonates deeply with our ethos at Pivot-al, where we're passionate about harnessing the transformative power of data to solve real-world problems. Check out the full study here https://lnkd.in/exRniXSb

As we have spoken about in great detail on MLST over the years, basic NN architectures have severe computational limitations. They are not Turing machines, they can not perform the types of computation a simple calculator can perform. But what if we could have our cake and eat it? The future might just be neurosymbolic! Dr. Petar Veličković is a rising star scientist at DeepMind and is taking his Geometric Deep Learning framework to the next level in collaboration with his collegues. This is just a teaser clip but there will be lots more info coming.

Proud to have reached 100 citations in my first 3 years of publications! The first of many milestones. Next, the PhD. P.S.: the 100th citation was from "A Study of Current Socio-Technical Design Practices in the Industry 4.0 Context among Small, Medium, and Large Manufacturers in Minnesota and North Dakota", a pleasant sign of how our research reaches beyond the field of AI.

Hi! Here is my first talk at the University of Potsdam in the Seminar of AI in Software Engineering. The presentation is based in the article "Is Deep Learning Good Enough in SDP?". The focus is on two CNN models: SqueezeNet and Bottleneck. The authors conducted a detailed comparative study across seven datasets from the NASA repository, comparing the performance of these models against baseline models. Paper's link: https://lnkd.in/e88-rp_v