AWS to offer AI to client’s of its platform

Amazon Web Services, the company’s cloud-computing division, will offer affordable tools so clients can incorporate artificial intelligence and machine learning into their own operations. Such tools have already been used by to detect diseases and increase crop yields, Bezos wrote.

How Amazon uses AI to drive its growth?

“But much of what we do with machine learning happens beneath the surface,” he wrote. “Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations.”

“Watch this space,” Bezos said. “Much more to come.”

Wanna know what is NLP?

This is a great serie of lecture about NLP which is given by Stanford.

Natural Language Processing with Deep Learning

– Chris Manning
– Richard Socher

Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component.

Shall we thank AI for its work?

Haha! This grandma didn’t know that behind Google were some powerful algorithms that try to predict the best answer to your request. So she say “please” and”thanks”. Yet, this is really what AI is doing, it is rendering us services, and we shall be more grateful for this.

In the world where AI Assistant is designed to be the perfect 24/7 employee, maybe we could add the rule that the politeness matters; like this grandma : )



Behind the algorithms

Here are two articles that I discovered today:


77% percent of the time spent by a data scientist is related to non-machine learning algorithms selection, testing and refinement.

  1. Data Processing
  2. Training Sets creation
  3. Machine Learning Algorithms testing, evaluation and selection
  4. Deployment and A/B testing


An really comprehensible tour of datascience implementation at Stichfix.


“Our business model enables unprecedented data science, not only in recommendation systems, but also in human computation, resource management, inventory management, algorithmic fashion design and many other areas. Experimentation and algorithm development is deeply engrained in everything that Stitch Fix does. We’ll describe a few examples in detail as you scroll along.”

Automatons: the Ancestors of the Current Robots – OpenMind

This article lists some very interesting first attempts to make “automatons” ancestors of robots. Far before informatics existed, our ancestors were using disks, gears, etc to make those extremely cute “automatons”moving with amazing grace on very few movements. It is amazing to think that humans have been fascinated by imitating nature with no other purpose that to better understand what is hidden behind the body.

Nowadays, you can still buy an automaton bird made with finely cut pieces where the beauty is the mechanism itself and the almost perfect imitation of the natural grace of the epitome of grace: the bird.

CISCO launches chatbot for Project Management

You get what was announced at Cisco’s (NASDAQ: CSCO) annual IT and communications conference, Cisco Live. It’s an integration between Redbooth, a project management platform, and Cisco Spark, a cloud-based messaging platform, that utilizes AI-inspired natural language processing to communicate.

Think of it as Apple’s Siri applied to business. Instead of asking about the weather, users ask about various aspects of their project portfolio and team’s status in their own words. Rather than talking to the interface, however, users type their questions: