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I previously spent hours sifting through research repos such as arXiv.org to find the information I needed, but I stopped that. Also, I no longer use ChatGPT or Bard for in-depth research.

Instead, with ScholarAI, I have access to insights and citations from 200M+ studies. It summarizes the findings in an understandable way. Hands down

Example of how to improve eyesight (but it can be any topic)

For example, in my quest to improve my eyesight this year—believing it must be possible—I utilized ScholarAI to see what fresh research says. Within ca. 45 min., I devised a daily routine. (LMK, if you would like to have my daily routine.)

Essentially, cutting-edge research is at our fingertips. You don’t have to be an expert/ academic to benefit from it, as you can always start with “Explain XYZ to me step-by-step, “

It democratizes research knowledge from all fields, and I am going to use it for researching:

• AI-related topics

• How to improve my hair 😉 

• On new methods for diseases that run in my family, including pets

For me, it is hands-down the most significant enabler in 2024.

See quick short on it:

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Developed by Google Research. Lumiere is designed to synthesize videos that portray realistic, diverse, and coherent motion using simple text prompts.

The tech

It has an architecture that blends a (1) space-time unit structure, (2) temporal downsampling and upsampling, and (3) a foundation rooted in pre-trained image diffusion models. This architecture allows the model to generate videos in a single pass, considering the position of objects and their motion over time within a scene.

Lumiere’s performance

Lumiere has a superior performance to other text-to-video diffusion models, such as ImagenVideo, Pika, and even Runway ML - Gen2.

What are future applications?

Pending public release, it could boost Google Bard's multimodal features and be used for impactful training videos or enhancing YouTube Shorts.

I would love to hear your thoughts, below.

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A collaboration between open-source and closed-source? Looking at it more nuancedly, Hugging Face (HF) has a 100% open-source philosophy, yes, but Google is not fully closed-source. Google has open-sourced Tenserflow, Bert (initial transformer paper), and Kubernetes, to name a few.

The aim

Enable companies to build their own AI using the latest open models from Hugging Face and the newest cloud and hardware features from Google Cloud. This includes training and deploying Hugging Face models within Google Kubernetes Engine (GKE) and Vertex AI. Google Cloud hardware capabilities include

• TPU instances

• A3 VMs powered by NVIDIA H100 Tensor Core GPUs

• C3 VMs powered by Intel Sapphire Rapid CPUs

What’s next?

HF and Google have a shared vision: enhance generative AI accessibility for developers.

It’ll lead to more rapid advancements in AI research; developers will build more powerful AI applications faster. It will stimulate further growth in the AI ecosystem, attracting new talents and investments, and encouraging the development of new, better startups and business models centered around GenAI.

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In collaboration with the European AI Startup Landscape consortium, the applied Institute for Europe's report reveals facts about the nascent state of generative AI in Europe. The findings include:

• Europe hosts 6300 AI startups, 10.6% in GenAI, predominantly in Germany (19.9%), France (17.5%), and the Netherlands (10.9%).

• Their primary focus is on foundation models, dev tools, and infrastructure, with an average of €1.5M in funding.

• Challenges include financing, data access, computing power, talent, and regulation.

• Over half seek access to EuroHPC JU's supercomputers, highlighting the critical need for computational resources.

It may be obvious for some, but seeing actual numbers is interesting. See the full report in the source link.