WHATS IMPORTANT 

📍 The 35th AAAI conference was held from February 2-9th and received a record-high 9034 submissions compared to last year’s 8800. The AAAI 2021 Best Paper awards went to the teams behind: 

• Haoyi Zhou et al., for applying Transformers for long sequence time series forecasting in their "Informer" paper (arxiv.org).
• Stefanos Leonardos et al. for Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory (arxiv.org).
• Ruibo Liu et al., who explained how political bias in language models might be mitigated (paper and keynote on dartmouth.edu).

📍 The Federal Government of Germany updated its National AI Strategy (bmbf.de) in late December, promising 5 billion Euro investments until 2025. Recently, this was followed by a National Data Strategy (bundesregierung.de).

While still addressing privacy and personal data, it is primarily focused on how research can be improved and how companies and other stakeholders can be brought to cooperate on data. A notable shift in the famously technophobic, privacy-focused country - (More in German on netzpolitik.org).

 9 THINGS WE FOUND WORTH SHARING 

📄 1. Papers – DeepMind researchers Brock et al. introduced Normalizer-Free Networks (NFNets) in their paper "High-Performance Large-Scale Image Recognition Without Normalization" (arxiv.org). By getting rid of batch normalization they drastically speed-up the model training, achieving remarkable 89.2% Top-1-Accuracy on Imagenet, but 8.7x faster training compared to Efficientnet-B7.

Yannic Kilcher goes into further detail in his one-man paper discussion group (34 min on youtube.com).

💡 2. Use Cases – Game developer BioWare used AI-powered texture upscaling to remaster their Mass Effect series. This saved them a lot of time when they visually upgraded their 2007 title for current-gen consoles like the PlayStation 5 (venturebeat.com).

💻 3. Libraries & Code – Lightning Flash is a high-level framework for fast prototyping of Deep Learning tasks, built on PyTorch Lightning. It aims to simplify fine-tuning and deployment of state-of-the-art models on custom tasks. Find the release article on Medium (medium.com/pytorch) and code available on GitHub (github.com).

🎨 4. Showcase – Jina released version 1.0 of their open-source Neural Search Engine for enterprises and developers. Their multi-modal search framework can easily find text, graphics, audio, or video in large-scale cloud operations. Try it yourself with code available on (jina.ai & github.com).

💭 4.2 Articles & Tutorials – Or start from scratch and build you own AI-assisted search engine with Elasticsearch, Kubeflow and Katib following this Topbots Tutorial (topbots.com).

👁 5. Miscellaneous – To train Facial Recognition Systems one needs large amounts of input images. Some model trainers took the freedom to scrape images from websites like Flickr - and your picture might be one of them. With Exposing.ai two tech artists built a platform for you to check whether your face was used to train common face recognisers (exposing.ai).

🛠 6. Tools – Papers with Code just became Papers + Code + Datasets. The beloved database for code implementation, benchmarks and academic papers, now also features 3000+ research datasets, sorted by task and modality and usage statistics (paperswithcode.com/datasets).

💡 7. Use Cases – Researchers from Tartu, Estonia trained a GAN to create “artificial genomes”. Instead of creating DeepFakes of human faces, they fake DNA sequences that are 100% synthetic but indistinguishable from human genomes. This could relieve institutions of privacy measures they currently have to ensure with real DNA data (thenextweb.com).

🎨 8. Showcase – Automated Paper Reviews with AI: Waiting for a scientific paper review? Wait no longer. This system can judge your or anybody else's paper automatically (review.nlpedia.ai).

📄 9. Papers – Dutch researchers Le et al. have attempted to reconstruct Dr. Who scenes from viewers' fMRI brain scans, in "Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity" (biorxiv.org).

 IN CASE YOU MISSED IT 

📍 The Enterprise AI Consulting team from AMAI launched a new website. An updated careers page is accompanied by a newly started AI Expert developer blog.

📅 February 24 (online, 11:00 CET) – Boosting High Energy Physics with Generative Networks: In this Data Science Seminar Anja Butter from the Institute for Theoretical Physics in Heidelberg demonstrates how she uses generative neural networks to improve simulations for the CERN Large Hadron Collider – Register at meetup.com.

📅 March 3 (online) – Robert Bosch AI CON 2021 – See the list of speakers and panels early on bosch-ai.com.