This week we look at the OpenAI + DeepDrive project that helps developers train self-driving car AI’s using Grand Theft Auto V. Plus, CB Insights releases their AI 100 landscape. Read More…
With over 317 million active users a month, Twitter has become a wealth of data for those trying to understand how people feel about brands, topics, and more. Mining Twitter data for insights is one of the most common natural language processing tasks.
We’re excited to announce that Algorithmia has been selected as a CB Insights AI 100 company. This prestigious list is comprised of a select group of the 100 most promising companies breaking ground on artificial intelligence algorithms and technology.
And now back to our regularly scheduled programming… In the last issue of 2016, we looked at self-driving cars, Zuckerberg’s AI assistant, and what’s new with IoT, drones, and chat bots.
This week, we recap the year in artificial intelligence before looking ahead to what 2017 holds for AI, data science, and the shape of things to come.
Plus, we check out the latest White House report on Artificial Intelligence, Automation, and the Economy. Read More…
Sifting through lots of documents can be difficult and time consuming. Without an abstract or summary, it can take minutes just to figure out what the heck someone is talking about in a paper or report.
And, if you need to get through hundreds of documents – good luck.
In an earlier post, we introduced the Sentiment Analysis algorithm and showed how easy it was to retrieve the sentiment score from text content through an API call.
In this post, we’ll show how to build a sentiment analysis pipeline that grabs all the links from a web page, extracts the text content from each URL, and then returns the sentiment of each page.
Dear Friends of Algorithmia,
Every year we like to take a small step back and reflect on what we achieved in 2016. Last year we went from private beta to a top Seattle startup.
2016, as we hoped it would be, was a big year for us. We provided more than 30,000 developers with access to over 2,700 algorithmic microservices – a humbling achievement for our small, dedicated team.
Sentiment analysis is the process of identifying the underlying opinion or subjectivity of a given text. It generally categorizes these opinions on a scale from negative to positive. Some sentiment analysis algorithms include the neutral sentiment, too.
These sentiments scores are generally used to identify the level of satisfaction of a given product or service. This helps companies and organizations better understand their users, and make impactful changes to their products.
In the last issue of Emergent // Future for 2016, we look at Uber’s self-driving cars in San Francisco, Google’s new autonomous car company, how Mark Zuckerberg built an AI assistant for himself, what Amazon, Google, and Microsoft are up to.
Reading emotional expression is one of the most difficult tasks for humans, let alone computers. Two people looking at the same photo might not agree whether someone is grimacing or grinning. Until recently, computers weren’t much better at the job, either.
Fortunately advances in deep learning has brought us speed, efficiency and accuracy in detecting people’s emotions in photos.