Most major tech companies are use Deep Learning techniques in one way or another, and many have new initiatives on the way. Self-driving cars use Deep Learning to model their environment. Siri, Cortana and Google Now use it for speech recognition, Facebook for facial recognition, and Skype for real-time translation.
Naturally there are a lot of startups doing cool things in the space. I tried to do my best to categorize the companies below based on where their main focus seems to be. If you’re a Deep Learning company and I forgot you, please do let me know!
General / Infrastructure
Because Deep Learning is such a generic approach, some companies are focusing on creating infrastructure, algorithms, and tools that can be applied across a variety of domains.
DeepMind, which was acquired by Google for more than $500M in 2014, is working on general-purpose AI algorithms using a combination of Deep Learning and Reinforcement Learning. DeepMind is the company behind an algorithm that learns to play Atari games better than humans. It is a largely a research company and does not provide products for use by businesses or consumers.
MetaMind focuses on providing cutting-edge performance for image and natural language classification tasks. Richard Socher, the founder of Metamind, is very active in the academic community and teaches Stanford’s Deep Learning for Natural Language Processing class. The company offers a cloud service to train Deep Learning classifiers.
Nervana is the company behind the open source Python-based neon framework, a GPU-optimized library to build Deep Learning architectures. Nervana also provides a cloud services where it runs algorithms on proprietary hardware specifically designed for Deep Learning. Nervana raised $20.5M in a June 2015 round led by Data Collective.
Skymind is the company behind the Deeplearning4j framework. Deeplearning4j makes efficient use of GPUs and integrates with distributed systems such as Hadoop and Spark to scale to large data sets. Skymind sells an enterprise editions of its software together with training and support.
It would be fair to say that Deep Learning gained most of its popularity through excellent performance on a variety of computer visions tasks: Recognizing objects in images, understanding scenes, and finding semantically similar images. Convolutional Neural Networks (CNNs), a popular type of Deep Learning architecture, are now considered the standard for most of the above. The rapid success of Deep Learning in Computer Vision has spurred a lot of startup activity.
Madbits was acquired by Twitter in 2014 before it got a chance to launch publicly. In its own words, it “built visual intelligence technology that automatically understands, organizes and extracts relevant information from raw media (images)”.
Perceptio was acquired by Apple In October 2015 while still in stealth mode. The website was shut down after the acquisition, but Perceptio seems to have been developing technology to run image-classifications algorithms on smartphones.
Lookflow was acquired by Yahoo/Flickr in October 2013. It’s unclear what exactly Lookflow was offering, but it was using Deep Learning algorithms for image classifications to help organize photos.
HyperVerge builds technology for a range of visual recognition tasks, including facial recognition, scene recognition, and image similarity search. HyperVerge is also working on a smart photo organization app called Silver. The company came out of IIT and raised a $1M seed round from NEA in August 2015.
Deepomatic builds object recognition technology to identify products (e.g. shoes) in images, which can then be monetized through e-commerce links. It focuses on the fashion vertical and has raised $1.4M from Alven Capital (a French VC) and Angels in September 2015.
Descartes Labs focuses on understanding large datasets of images, such as satellite images. An example use case is tracking agriculture development across the country. Descartes Labs came out of the Los Alamos National Laboratory and has raised $3.3M of funding to date.
Clarifai uses CNNs to provide an API for image and video tagging. In April 2015, Clarifai raised a $10M Series A led by USV.
Tractable trains image classifiers to automate inspection tasks currently done by humans, for example detecting cracks on industrial pipes or inspecting cars.
Affectiva classifies emotional reactions based on facial images. It raised $12 million in Series C funding Horizon Ventures and Mary Meeker and Kleiner Perkins in 2012.
Orbital Insight uses Deep Learning to analyze satellite imagery and understand global and national trends.
After the rapid success in Computer Vision, researchers were quick in adopting Deep Learning techniques for Natural Language Processing (NLP) tasks. In fact, the exact same algorithm that categorizes images can be used to analyze text. Since then, new Deep Learning techniques specifically for NLP have been developed, and are being applied to tasks such as categorizing text, finding content themes, analyzing sentiment, recognizing entities, or answering free-form questions.
AlchemyAPI was acquired by IBM (Watson group) in March 2015. It provides a range of Natural Language Processing APIs, including Sentiment Analysis, Entity Extraction and Concept Tagging. (AlchemyAPI also provides computer vision APIs, but their primary product seems to be language-related so I decided to put them in this category).
VocalIQ was working on a conversational voice-dialog system before being acquired by Apple in October 2015.
Idibon develops general-purpose NLP algorithms that can be applied to any language. Idibon’s public API does Sentiment Analysis for English, but more languages, and support for Named Entity Recognition are coming soon. Idibon raised a $5.5M Series A led by Altpoin, Khosla, and Morningside Ventures in October 2014.
Indico provides a variety of Natural Language APIs based on Deep Learning models. APIs include Text Tagging, Sentiment Analysis, Language Prediction, and Political Alignment Prediction.
Semantria provides APIs and Excel plugins to perform various NLP tasks in 10+ languages. Pricing starts at $1,000/month for both Excel plugins and API access. Lexalytics, an on-premise NLP platform, acquired Semantria in 2014.
ParallelDots provides APIs for Semantic Proximity, Entity Extraction, Taxonomy Classification and Sentiment Analysis, as well as tools for social media analytics and automated timeline construction.
Xyggy is a search engine for all data types (text and non-text) represented by deep-learning vectors. With text for example, a search can be with keywords, snippets or entire documents to find documents with similar meaning.
Instead of focusing on general-purpose vision or language applications, some companies are applying Deep Learning techniques to specific verticals. My research surfaced mostly Healthcare companies, but It’s likely that many others are using Deep Learning without explicitly mentioning in on their website.
Enlitic applies deep learning techniques to medical diagnostics. By classifying x-rays, MRIs and CT scans, Enlitic can recognize early signs of cancer more accurately than humans. The company raised $3M from undisclosed investors in February 2015.
Quantified Skin uses selfies to track and analyze a person’s skin and recommends beneficial products and activities. The company raised a total of $280k in 3 rounds.
Deep Genomics uses Deep Learning to classify and interpret genetic variants. Its first product is SPIDEX, a dataset of genetic variants and their predicted effects.
StocksNeural uses Recurrent Neural Networks to predict stock prices based on historical time-series data.
Analytical Flavor Systems use Deep Learning to understand what people taste and optimize food and beverage production.
Artelnics builds open source libraries and graphical users interfaces to train Deep Learning models for a variety of industries.
Are there any Deep Learning startups I missed? I’d love to hear about them in the comments.