１．2022年のGoogleのAI研究の成果と今後の展望～MLとコンピュータシステム編～(1/3)まとめ・複雑なモデルの提供とトレーニングをサポートすることを可能にするML用システムの昨年の進歩の概要の説明・大規模モデルを効率的に規模拡大し

１．2022年のGoogleのAI研究の成果の振り返りと今後の展望(3/11)まとめ・次世代のAIは特定のデータ形式しか扱えない従来のAIと異なり複数のデータ形式を扱えう事が可能・マルチモーダルモデルと呼ばれるこれらのAIは音声、動画、画像

１．Connect the Dots:差分プライバシーのより良いプライバシーコスト推定(1/2)まとめ・差分プライバシーはプライバシーを保証した上で分析や機械学習を可能にする・差分プライバシーでは個々のアルゴリズムを合成した際の特性が重要と

This is part of a series of articles in AI Codecs. While digital media are transmitted in a wide variety of settings, the available codecs are "one-size-fits-all": they are hard-coded, and cannot be customized to particular use cases beyond high-level hyperparameter tuning [10]. In the last few years, deep learning has revolutionized many tasks such as machine translation, speech recognition, face recognition, natural language processing and photo-realistic image generation. Given unlabeled training data, deep learning based models generate new samples from the input data distribution [6]. These are called deep generative models and have powerful capabilities such as extracting features by learning a low-dimension feature representation of the input space and sampling to generate, restore, predict or compress data.

１．SmeLU：ディープラーニングの再現性を悪化させている犯人はReLU関数(3/3)まとめ・SmeLUは推薦システムにおいてその再現性を高める事や学習と推論の効率を向上させる・滑らかな活性化を用いる事で精度など他の重要な指標を低下させるこ

Artificial intelligence (AI) is already a part of our everyday lives – from search, to translate, to finding all the dog photos we've ever taken. Soon, it will also have a major impact on our health and wellbeing. For the past few years, Google researchers have been exploring ways these same technologies could help advance the fields of medicine and science, working with scientists, doctors, and others in the field. In this video, we share a few early research projects that have big potential. Check out the description below for more info on each project.

"Learning", "thinking", "intelligence", even "cognition"… Such words were once reserved for humans (and to a lesser extent, other highly complex animals), but have now seemingly been extended to a "species" of machines, machines infused with artificial intelligence or "AI". In October 2015, a computer program developed by Google DeepMind, named AlphaGo, defeated the incumbent European champion at the complex ancient Chinese board game of Go. In March 2016, AlphaGo went on to defeat the world champion, Lee Sedol. This seminal moment caught the world's attention, the media have since been incessantly covering every AI-related story, and companies from all walks of life have since been on a mission to add "artificial intelligence" to their business description. At Platinum we have been closely following the major technological trends for many years.

We hope you find it useful in your machine learning projects.