Status of Artificial Intelligence development

In my last post, I wrote about the handful of household brand name companies actually involved in Artificial Intelligence development, the usual suspects such as IBM, Microsoft, Intel, Apple and more – in a very quick guesstimate I threw out the number of tech professionals at work on AI at 300,000. I received some comments in that I was way short on my estimate and should take another look, so I did, but before I move on to a list of companies actively involved in AI one caveat, I am sure I will be short again.

Venture Scanner is tracking 957 Artificial Intelligence companies across 13 categories, with a combined funding amount of $4.8 Billion. Here are some of the names:

Baidu, Inc., Portland; Kasisito, Inc., New York City; Artivatic Data Labs, Bangalore; Ignite Tech, Vellore; Natxis, Paris; King, Stockholm; Fujitsu, Slovenia; Qikspace, Seattle; Lola, Boston; Pavlov, San Francisco; Cloud (Google), San Francisco; Next AI, Kitchner; Amazon, Seattle; Yik Yak, Atlanta; Operator, Inc., San Francisco; Chorus AI, San Francisco; AI Cities, San Francisco;, San Francisco; Facebook, New York City; Intel, Portland; NVIDIA, San Francisco; Autodesk, San Francisco; Alexa, San Francisco; Degree Six, Chicago; Kount, Boise; UBER, San Francisco; X.AI, New York City; Microsoft, Seattle; OTSAW Digital, San Francisco; Cognitive AI, New York City; Google, San Francisco; All West/Select Sires, Portland; Vecna, Boston; Arkane Studios, Austin; Rockstar New England, Boston; Hex Entertainment, Orange County; Treyarch, Los Angeles; Knexus Research, Allentown; ABS Global, Madison; GGD Groningen, Groningen; Open AI (Tesla), San Francisco; AI + Club, San Francisco; Apple, San Francisco; Drive AI, San Francisco; LinkedIn, San Francisco; Bonsai AI, San Francisco; Voice Box Technologies, Seattle; Accenture, San Francisco; Knexus Research, Washington, DC; Yahoo!, San Francisco; Elemental Cognition, New York City; Mezi, San Francisco; Philips Research, Boston; MITRE, Albany; PWC Analytics, Boston; Artificial Brilliance, Los Ángeles; Zvelo, Inc., Miami/Ft. Lauderdale; and GE Global Research, San Francisco.

Venture Scanner organizes Artificial Intelligence into the 13 categories listed below, the full article can be seen here:

Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data.

Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases. Examples include using machine learning technology to detect banking fraud or to identify the top retail leads.

Natural Language Processing (General): Companies that build algorithms that process human language input and convert it into understandable representations. Examples include automated narrative generation and mining text into data.

Natural Language Processing (Speech Recognition): Companies that process sound clips of human speech, identify the exact words, and derive meaning from them. Examples include software that detects voice commands and translates them into actionable data.

Computer Vision/Image Recognition (General): Companies that build technology that process and analyze images to derive information and recognize objects from them. Examples include visual search platforms and image tagging APIs for developers.

Computer Vision/Image Recognition (Applications): Companies that utilize technology that process images in vertically specific use cases. Examples include software that recognizes faces or enables one to search for a retail item by taking a picture.

Gesture Control: Companies that enable one to interact and communicate with computers through their gestures. Examples include software that enables one to control video game avatars through body motion, or to operate computers and television through hand gestures alone.

Virtual Personal Assistants: Software agents that perform everyday tasks and services for an individual based on feedback and commands. Examples include customer service agents on websites and personal assistant apps that help one with managing calendar events, etc.

Smart Robots: Robots that can learn from their experience and act autonomously based on the conditions of their environment. Examples include home robots that could react to people’s emotions in their interactions and retail robots that help customers find items in stores.

Recommendation Engines and Collaborative Filtering: Software that predicts the preferences and interests of users for items such as movies or restaurants, and delivers personalized recommendations to them. Examples include music recommendation apps and restaurant recommendation websites that deliver their recommendations based on one’s past selections.

Context Aware Computing: Software that automatically becomes aware of its environment and its context of use, such as location, orientation, lighting and adapts its behavior accordingly. Examples include apps that light up when detecting darkness in the environment.

Speech to Speech Translation: Software which recognizes and translates human speech in one language into another language automatically and instantly. Examples include software that translates video chats and webinars into multiple languages automatically and in real-time.

Video Automatic Content Recognition: Software that compares a sampling of video content with a source content file to identify the content through its unique characteristics. Examples include software that detects copyrighted material in user-uploaded videos by comparing them against copyrighted material.

Full article can be seen here:

Voice Digital Assistants