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Episode 64: Google IO and Claude 4
In dieser Episode reden Florian und Ich über die Google IO und viele neue Modelle wie zum Beispiel Gwen 3. Außerdem gibt es einen sneak peak auf den baldigen Blog post. Mehr Informationen auf dem Discord Server https://discord.gg/3YzyeGJHth oder auf https://mkannen.tech — read more
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Episode 63: o3 und die Ferne Zukunft
In dieser Folge reden Florian und Ich über die neuen Reasoning Modelle von OpenAI und Google. Außerdem reden wir über einige mögliche Zukunftstechnologien. Mehr Informationen auf dem Discord Server https://discord.gg/3YzyeGJHth oder auf https://mkannen.tech — read more
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Episode 62: Gemini 2.5 Pro ist King
In dieser besonderen Folge reden Florian und Ich in person und interviewen ChatGPT. Wir reden über neue Open source Modelle und Gemini 2.5 Pro, das neue Top Model von Google. Mehr Informationen auf dem Discord Server https://discord.gg/3YzyeGJHth oder auf https://mkannen.tech — read more
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Episode 34: Gemini ist da!
Gemini ist endlich raus und Florian und Ich reden über das model und was sonst noch diese Woche passiert ist. GPT Visualisierung: https://bbycroft.net/llm Mehr Informationen auf dem Discord Serverhttps://discord.gg/3YzyeGJHthoder auf https://mkannen.tech — read more
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Gemini is here
Google Deepmind just released their new Gemini models. They come in 3 sizes. Nano will be used on devices like the Pixel phones, and Pro will be used in their products such as Bard, and Ultra is going to be released at the beginning of next year. The models are multimodal and can input, audio, — read more
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Google found a way to improve math skills in LLMs
LLMs are powerful tools, but they often struggle with tasks that require logical and algorithmic reasoning, such as arithmetic. A team of researchers from Google has developed a new technique to teach LLMs how to perform arithmetic operations by using in-context learning and algorithmic prompting. Algorithmic prompting means that the model is given detailed explanations — read more
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Episode 5: Google IO und neue Models
In dieser Episode reden Florian und Ich über die Google IO, PaLM 2, Gamini und viele andere News der letzten Woche. GPT-2 Neuronen: https://openaipublic.blob.core.windows.net/neuron-explainer/neuron-viewer/index.html Mehr informationen auf dem Discord server https://discord.gg/3YzyeGJHthoder auf https://mkannen.tech/ — read more
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Google IO Summary
Google IO happened yesterday and the keynote focused heavily on AI. Some of the things that I found most important are: PaLM 2 is their new LLM. It comes in different sizes from small enough for pixel phones, to big enough to beat ChatGPT-3.5. It is used in Bard and many of their productivity tools. — read more
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Google and DeepMind Team Up
Google and DeepMind just announced that they will unite Google Brain and Deepmind into Google DeepMind. This is a good step for both sites since Deepmind really needs the computing power of Google to make further progress on AGI and Google needs the Manpower and knowledge of the Deepmind team to quickly catch up to — read more
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Zip-NeRF: the next step towards the Metaverse
Neural Radiance Fields (NeRFs), which are used for synthesizing high-quality images of 3D scenes are a class of generative models that learn to represent scenes as continuous volumetric functions, mapping 3D spatial coordinates to RGB colors and volumetric density. Grid-based representations of NeRFs use a discretized grid to approximate this continuous function, which allows for — read more
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Stanford and Google let AI roleplay
In a new research paper, Google and Stanford University created a sandbox world where they let 25 AI agents role-play. The agents are based on chatGPT-3.5 and behave more believably than real humans. Future agents based on GPT-4 will be able to act even more realistically and intelligently. This could not only mean that we — read more
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Deepmind follows OpenAI
Similar to OpenAI, Deepmind started to work together with other companies to build more commercial products. In their recent blog post they explained how they developed a new Video codec and improved auto chapters for Youtube. If this trend continues we will see more products for other Alphabet companies developed by Deepmind. — read more
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New Biggest Vision Transformer
Google’s new ViT-22B is the largest Vision Transformer model by far, with 22 billion parameters. It has achieved SOTA in numerous benchmarks such as depth estimation, image classification, and semantic segmentation. ViT-22B has been trained on four billion images and can be used for all kinds of computer vision tasks. This result shows that further — read more
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Google opens Bard
Google’s GPT alternative Bard is now available in the US and UK. Early testers already speak out in favor of Bing which also launched image generation this week. Bard is based on LaMDA, an older Language model that is not as capable as GPT-4. — read more
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Google is publishing its new AI tools and APIs
In a new blog post, Google presents their Generative AI App Builder, PaLM API, and MakerSuite which works similarly to OpenAI’s playground. This announcement is happening shortly before the Microsoft presentation on Thursday. Similar to how they did it with their Bard presentation just before the Bing chat announcement. — read more
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Google crushes Speech recognition
Google released the Universal Speech Model (USM), which can transcribe over 300 languages. It outperforms the state-of-the-art model Whisper in the 18 languages that Whisper supports. This is part of Google’s plan to support the 1000 most spoken languages. The model is with 2B parameters slightly bigger than Whisper and was pre-trained mostly on unlabeled — read more
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Google presents PaLM-E. An Embodied Multimodal Language Model
PaLM-E has 562B parameters which make it one of the largest models today. It combines sensory data from a robot with text and image data. It is based on PaLM and was fine-tuned on input & scene representations for different sensor modalities. These kinds of more general models are the way to more powerful and — read more
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Google found a way to make Qubits more stable which scales well
A new paper was published by Google where they present their advancements in quantum error correction. By scaling to larger numbers of Qubits and combining them to logical Qubits they can reduce the quantum error rate significantly. This opens up a clear path to better quantum computers by just scaling them up. — read more
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New Paper by Google uses Generative AI to train Robots
Google just published the paper “Scaling Robot Learning with Semantically Imagined Experience” showing how to use generated images like Imagen to generate Training data for their robot system. This allows the robot to have a more diverse data set and therefore be more robust and able to solve unseen tasks. We saw similar approaches using simulations for — read more