Releases

IBM commercialized enterprise conversational-AI tooling that evolved into watsonx Assistant.

IBM launched an AI-powered enterprise search and document-understanding service.

Scale began building distributed contributor platforms and training operations to support large-scale annotation, expert-data, and evaluation programs.

Scale launched Remotasks to train and coordinate distributed contributors performing data annotation and review tasks.

Databricks raised a $60 million Series C led by New Enterprise Associates.

Amazon unveiled a checkout-free store using computer vision, sensor fusion and deep learning.

OpenAI released Universe, a software platform for training intelligent agents across games, websites, and applications.

AWS introduced a managed text-to-speech service with natural-sounding voices.

AWS introduced the conversational AI service built from the speech recognition and language understanding technology behind Alexa.

AWS introduced managed image analysis for object, scene and face detection.

AWS selected MXNet as its preferred deep-learning framework and committed engineering investment to the open-source project.

Microsoft creates the AI and Research Group, combining research, information-platform and AI product teams under Harry Shum.

Oracle introduced the infrastructure platform that became OCI, later forming the base for its managed AI services and GPU cloud.

DeepMind introduces WaveNet, a neural generative model for raw audio waveforms that substantially improves speech synthesis.

Baidu launched its core AI technology engine and open-capability platform.

Baidu opened PaddlePaddle to the global developer community for deep learning, vision, speech and NLP.

Facebook AI Research released PyTorch, a Python-first tensor and dynamic neural-network framework.

Facebook AI Research released fastText, a library for efficient text classification and word representations.

Scale launched the data infrastructure that became its core Data Engine, combining APIs, machine assistance, and human review to create high-quality training data.

Scale launched an API that let developers submit human-powered data tasks with one line of code, establishing the foundation of its AI data business.

Alexandr Wang and Lucy Guo founded Scale in San Francisco and entered Y Combinator to build an API for routing human work, which evolved into AI data infrastructure.

Google publicly reveals the TPU, a custom accelerator designed to speed machine-learning workloads in its data centers.

Amazon open-sourced its Deep Scalable Sparse Tensor Network Engine for recommendation workloads.

Facebook introduced FBLearner Flow, its internal platform for managing the full lifecycle of machine-learning models.

OpenAI released the public beta of Gym, a toolkit for developing and comparing reinforcement-learning algorithms.