Getting Started With AlchemyAPI

AlchemyAPI is a collection of APIs that help you understand text and images.

Here are three links to checkout before moving on:

Get your free API key
API Documentation
API Demos

Alright, now that you have your API key, you can continue on with these examples. Simply replace your API key and copy into a browser tab.

Working with Text:

Using the “combined” call, you can extract a large amount of meta data from any document or URL including entities, relations, concepts, sentiment, taxonomy and more.

// replace YOUR API KEY,image-kw,feed,entity,keyword,title,author,taxonomy,concept,relation,pub-date,doc-sentiment&url=

To get “clean” text from a web page by removing ads and other unnecessary content:

// replace YOUR API KEY API KEY&url=

Working with Images:

To find objects and text within an image, combine these two AlchemyVision API calls:

// replace YOUR API KEY API KEY&outputMode=json&url= API KEY&outputMode=json&url=

To find demographics of the people within an image, use the Face call:

// replace YOUR API KEY API KEY&outputMode=json&knowledgeGraph=1&url=

Querying the News:

To get news articles about IBM over the past 24 hours:

// replace YOUR API KEY API KEY&outputMode=json&start=now-1d&end=now-0d&maxResults=10&q.enriched.url.enrichedTitle.entities.entity=|text=ibm,type=company|&label_format_string=enriched.url.url,enriched.url.title

and to find approximately how many articles were published by the WSJ over the past 30 days grouped by day:

// replace YOUR API KEY API KEY&outputMode=json&start=now-30d&end=now&timeSlice=1d&q.enriched.url.url=wsj

About Language Support:
AlchemyAPI provides named entity extraction capabilities in 8 different languages: English, French, German, Italian, Portuguese, Russian, Spanish, and Swedish.  View full Language Support

A Deep Learning Primer for Product Managers

Most of my discussions lately with CTOs and Product Managers have ended with me emailing some links and info about Deep Learning. I’m a relative newbie to the space myself so hopefully this is a good primer.

For Product Managers and CTOs, having an understanding of what’s possible with Deep Learning and how the technology could impact your features and stack is quickly becoming very important.

A great summary of Deep Learning from Lee Gomes taken from this article:

The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as “convolutional neural networks.” This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: “Deep Learning.”

Here are few more article I like:

Albert Wenger from USV talks about Machine Intelligence
Shivon Zilis from Bloomberg Beta created a Machine Intelligence Landscape

And, here’s a list of terms to explore:

Word Embeddings
Nodes and edges
Precision and recall
Frame semantics
Knowledge graph
Hypernym and Hyponym
Word sense disambiguation
Hearst Patterns
GPU computing
Stochastic Pooling
Cognitive Computing

And, some good people to follow in the Deep Learning space:

Yann LeCun – Facebook’s Director of AI Research
Geoffrey Hinton – Distinguished Researcher at Google and Distinguished Professor at University of Toronto
Yoshua Bengio – Full Professor Department of Computer Science and Operations Research
and Canada Research Chair in Statistical Learning Algorithms
Elliot Turner – Founder AlchemyAPI
Derrick Harris – Senior Writer at GigaOm
Seth Grimes – Industry Analyst

And, some websites to explore:

Google Deep Learning Community

And finally, some awesome AI videos:

Introducing Spot
Space Invaders