Benefits which MNCs aregetting from AI/ML and emphasize the enhancement of AI provided to their products and make them the top notch companies of this generation…

Let’s first talk about AI and ML…

What is Artificial Intelligence?

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.

What is Machine Leaning?

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Business Benefits of Machine Learning

• Simplifies Product Marketing and Assists in Accurate Sales Forecasts
• Facilitates Accurate Medical Predictions and Diagnoses
• Simplifies Time-Intensive Documentation in Data Entry
• Improves Precision of Financial Rules and Models
• Easy Spam Detection
• Increases the Efficiency of Predictive Maintenance in the Manufacturing Industry
• Better Customer Segmentation and Accurate Lifetime Value Prediction
• Recommending the Right Product

Artificial intelligence impact on business

By deploying the right AI technology, your business may gain an ability to:

• save time and money by automating and optimizing routine processes and tasks
• increase productivity and operational efficiencies
• make faster business decisions based on outputs from cognitive technologies
• avoid mistakes and ‘human error’, provided that AI systems are set up properly
• use insight to predict customer preferences and offer them better, personalized experience
• mine vast amount of data to generate quality leads and grow your customer base
• increase revenue by identifying and maximizing sales opportunities
• grow expertise by enabling analysis and offering intelligent advice and support

How Google is using AI and ML?

The biggest buzzword of all times in the overall technology market is machine learning. With no surprise, Google has mastered it, and with all its applications it has made our life easy!

It takes advantage of machine learning algorithms and provides customers with a valuable and personalized experience. Machine learning is already embedded in its services like Gmail, Google Search and Google Maps.

All that is fine but, how does Google take advantage of it?

Google knows how usage can be optimized. It uses artificial intelligence and satellite data to prevent Illegal work or trade, like illegal Fishing. Wow!

Some of the examples are…

• Gmail

My social, promotional and primary mails might be different than what you have in your mailbox. This is filtered through Google as it labels the email accordingly. This is where machine learning plays a crucial part. The user intervention is used to tune to its threshold and when a user marks a message in a consistent direction, Gmail itself performs a real-time increment to its threshold and that’s how Gmail learns for future and later uses those results for categorization.

Smart replies:

This is really a smart move made by Google. Now, with the help of this feature, you can reply instantly in a second. With the suggested replies given by Gmail.’ Smart Replies’ and ‘Smart Compose’ are indeed the best products that Google has given to its customers. This is also a major reason why Google stands as one of the leading companies today.

Also, it is not just in English. It will bring support in four new languages: Spanish, French, Italian and Portuguese.

• Google Search and Google Maps

This also employs machine learning and while you start typing in the search box it automatically anticipates what you are looking for. It then provides suggested search terms for the same. These suggestions are showcased because of past searches (Recommendations), trend (which everyone is looking for), or from your present location.

For example — Bus traffic delays — Hundreds of major cities around the world, thousands of people traveling, One machine that is learning and informing :P. Google gets all the real-time data on bus locations and forecasts it in a jiffy. So, now you don’t have to wait long hours for your bus.

With the combination of time, distance traveled, and individual events as datasets, it is now possible for Google to provide predictions. Now, there is no need to rely on bus schedules provided by public transportation agencies.

With the help of your location, day of the week, and time of day, your estimated time of arrival (ETA) can be understood.

• Google Assistant

It helps one to assist in everyday tasks, be it household chores or a deal worth crores. The Google Assistant makes it easy for you to search for nearby restaurants when it’s raining heavily, helps you to buy movie tickets while on the go and find the nearest theatre from your place. Also, helps you to navigate to the theater. In short, you don’t have to worry when you have a smartphone, because Google takes care of everything.

How Amazon uses AI and ML?

Being an early adopter of artificial intelligence and automation, Amazon always had an edge in using AI to improve its business efficiencies. Not only has it been using AI to enhance its customer experience but has been heavily focused internally.

According to Amazon, predicting the intent of the query is a significant component of information retrieval which in turn, improves the relevance of the results through an understanding of latent user intents in addition to explicit query keywords. The researchers believe this might improve people’s shopping experience by matching only high-quality products to search queries.

Training The System

The first step of the process was to train the system for which the team had to build a data set. In order to build the data set, the team assembled a list of 173 context-of-use categories divided into 112 activities — such as reading, cleaning, and running — and 61 audiences — like a child, daughter, man, and professional — based on common product queries. They used standard reference texts to create aliases for the terms they used to denote the categories. Such as for the category ‘father’ they included ‘dad’, ‘daddy’, ‘pops’ etc. or for ‘mother’ they included ‘mum’, ‘mommy’, ‘mom’ etc. and then they used their in-house dataset to co-relate million of their products to particular query strings. They also scoured online reviews of their products to label them with their category terms and their aliases — also known as simple binary classification.

The in-house dataset that Amazon used, correlates their query strings with products according to an affinity score — from 1 to 15, where a low score indicates a weak correlation. But, to train their context-of-use predictor system, Amazon researchers created another data set, where each entry was labelled with three data items — a query; a product ID, which has been added by context-of-use categories; and the affinity score derived from the in-house dataset. This data set was then divided into two smaller sets — one annotated according to activity and one according to the audience, and from each of those smaller datasets they constructed two more — one with high-affinity score of 15 and one which was low as 8. This resulting data set was then used to train six different machine learning models.

Multiple Metrics

Once decided to train six different models, the system segregated the models in terms of the affinity score of their training data. The ones had an affinity threshold of 15 were trained using binary cross-entropy, which imposes particularly stiff penalties on incorrect classifications that get high confidence scores. But the ones that had an affinity threshold of 8, Amazon researchers used both binary cross-entropy and B-weighted binary cross-entropy — where it also weights the penalty incurred by each data item according to its affinity score.

The resulted six models were trained to predict context-of-use based on customers’ query strings. In tests, the best-performing model managed to anticipate product annotations with 97% accuracy for activity categories and 92% for audience categories. And, when asked by human reviewers to indicate the classifications they agreed on, they said, an average of 81% of the time the system’s per-item predictions have been correct.

“This suggests, according to Adrian Boteanu, an applied scientist in the customer experience division of Amazon Search that, “The contexts-of-use identified by Amazon’s system could help product discovery algorithms to deliver more relevant results, improving the customer experience. Moreover, the minimal human supervision required to produce training data means that the method could be expanded to new categories with relatively little effort.”

Wrapping Up

As Amazon continues to improve its algorithms, customers shopping on Amazon will see increasingly relevant shopping recommendations. According to Amazon, such research could open a whole new prospect for personalized digital shopping assistants. In this dynamic world where the tech giants are still struggling with their internal bureaucracy and technology silos, it is exceptional to see how Amazon keeps emerging with encouraging innovations to enhance the customer experience.

However, AI-powered technology and deep learning power one of Amazon’s most critical elements of its business — delivery, which is fully dependent on a fluid warehouse operation.

AWS

AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner. AWS is helping more than one hundred thousand customers accelerate their machine learning journey.

One of the primary drivers of Amazon’s rise to a near-trillion-dollar company has been Amazon Web Services, its massive cloud-based storage and server business. AWS has become a cloud standard for companies and developers wanting access to the same kind of AI and machine learning technology that powers Amazon offerings such as Alexa, Amazon Go, Amazon Prime Video’s X-Ray feature, estimates for product delivery times on Amazon.com

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