Artificial Intelligence and Machine Learning

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. 

The scientific field of  machine learning (ML) is a branch of artificial intelligence, as defined by Computer Scientist and machine learning pioneer Tom M. Mitchell .Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience 

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

Applications of Machine Learning from Day-to-Day Life :

1.Predictions while Commuting:

Traffic Predictions: We all have been using GPS navigation services. While we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of current traffic. 

Online Transportation Networks: When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the detours? The answer is machine learning. Jeff Schneider, the engineering lead at Uber ATC reveals in a an interview that they use ML to define price surge hours by predicting the rider demand. In the entire cycle of the services, ML is playing a major role.

2. Virtual Personal Assistants:

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. 

Virtual Assistants are integrated to a variety of platforms. For example:

• Smart Speakers: Amazon Echo and Google Home
• Smartphones: Samsung Bixby on Samsung S8
• Mobile Apps: Google Allo

3. Videos Surveillance:

Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. This is why the idea of training computers to do this job makes sense.

4. Social Media Services:

From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits. Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are nothing but the applications of ML.

5. Email Spam and Malware Filtering:

• There are a number of spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning. When rule-based spam filtering is done, it fails to track the latest tricks adopted by spammers. Multi Layer Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that are powered by ML.

6. Online Customer Support:

A number of websites nowadays offer the option to chat with customer support representative while they are navigating within the site. However, not every website has a live executive to answer your queries. In most of the cases, you talk to a chatbot.

7. Search Engine Result Refining:

Google and other search engines use machine learning to improve the search results for you. Every time you execute a search, the algorithms at the backend keep a watch at how you respond to the results. If you open the top results and stay on the web page for long, the search engine assumes that the the results it displayed were in accordance to the query.

8. Product Recommendations:

You shopped for a product online few days back and then you keep receiving emails for shopping suggestions. If not this, then you might have noticed that the shopping website or the app recommends you some items that somehow matches with your taste.

9. Online Fraud Detection:

Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples. For example: Paypal is using ML for protection against money laundering.

Machine Learning Workflow Defined

Primary Infrastructure Challenges

How Facebook Uses Machine Learning And Artificial intelligence

One of the primary ways Facebook uses artificial intelligence and deep learning is to add structure to its unstructured data . They use DeepText, a text understanding engine, to automatically understand and interpret the content and emotional sentiment of the thousands of posts (in multiple languages) that its users publish every second. With DeepFace, the social media giant can automatically identify you in a photo that is shared on their platform. In fact, this technology is so good, it’s better at facial recognition than humans.

Facebook was nice enough to show us the inner workings of how they build and scale ML infrastructure to support over 2 billion users. If you follow Facebook (in real life, not on their social media platforms) you’ll know their openness and willingness to share internal technical details is nothing new as they have a history of  sharing innovations and their data center designs with the public through  opencompute.org. Their AI platform can be categorized with these primary pillars:

1.the Frameworks needed to create, migrate and train models

2.Platforms for model deployment and management and

3.the Infrastructure needed to compute workloads and store data

FB Learner

Even after establishing the frameworks you want to use, you still need an orchestration engine to assist with the entire ML workflow. To manage the ML workflow Facebook created  FB Leaner.

FB Leaner has three primary components for processing Facebook’s Machine Learning Workflow:

• Feature Store. Helpful for data manipulation and feature extraction. Has an API for developers to use to interact with the feature store. Reduces development time as common features and model attributes can be stored here with associated metadata.
• FB Learner Flow. Manages workflow processes required during training. Takes care of requesting required hardware, setting machines up in a cluster for training and packaging the models. Capable of easily reusing algorithms in different products and scaling to run thousands of simultaneous custom experiments.
• FB Learner Predictor. Used for serving the models that other applications use to make inferences against. Provides an API to make inferences against the models easier.