How Amazon gets benefits from Artificial Intelligence

“Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before — as long as we manage to keep the technology beneficial.”

What is Artificial Intelligence (AI) ?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically by recognizing patterns in the data.

AI includes many methods and continuously evolving range of technologies, as well as the following major subfields:

• Machine Learning (ML) is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it learn for themselves.
• Deep Learning (DL) is a variation of machine learning — it involves the ability of machines to develop self-learning capabilities from large amounts of data using huge neural networks with many layers of processing units. Common applications include image and speech recognition.
• Natural Language Processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech.

Why Artificial Intelligence is Important ?

Artificial intelligence and machine learning technologies can automate important, but manual and time-consuming tasks, allowing employees to focus on higher-value work. AI will be used to extract new insights, transform decision making and drive improved business outcomes. A recent PWC report indicates that an overwhelming 72% of business decision makers believe that AI provides a competitive edge on the business front.

With Google, Amazon, and Microsoft Azure launching their Cloud Machine learning platforms, we have seen artificial intelligence and ML gaining prominence in the recent years. Surprisingly, we all have witnessed ML without actually knowing it. Some of the most common instances are ‘Spam’ detection by your email provider, and ‘Image’ or ‘Face’ tagging done by Facebook. While Gmail recognizes the selected words or the pattern to filter out spam, Facebook automatically tags uploaded images using image (face) recognition technique. Business benefits of AI and ML are numerous.

How Amazon gets Benefits from AI/ML ?

IN EARLY 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who’d left IBM in 2005 to head Amazon’s recommendations team, had come to propose a sweeping new plan for incorporating the latest advances in artificial intelligence into his division.

He arrived armed with a “six-pager.” Bezos had long ago decreed that products and services proposed to him must be limited to that length, and include a speculative press release describing the finished product, service, or initiative. Now Bezos was leaning on his deputies to transform the company into an AI powerhouse. Amazon’s product recommendations had been infused with AI since the company’s very early days, as had areas as disparate as its shipping schedules and the robots zipping around its warehouses. But in recent years, there has been a revolution in the field; machine learning has become much more effective, especially in a supercharged form known as deep learning. It has led to dramatic gains in computer vision, speech, and natural language processing.

Thirumalai came to Bezos for his annual planning meeting with ideas on how to be more aggressive in machine learning. But he felt it might be too risky to wholly rebuild the existing system, fine-tuned over 20 years, with machine-learning techniques that worked best in the unrelated domains of image and voice recognition. “No one had really applied deep learning to the recommendations problem and blown us away with amazingly better results,” he says. “So it required a leap of faith on our part.” Thirumalai wasn’t quite ready — but Bezos wanted more. So Thirumalai shared his edgier option of using deep learning to revamp the way recommendations worked. It would require skills that his team didn’t possess, tools that hadn’t been created, and algorithms that no one had thought of yet. Bezos loved it (though it isn’t clear whether he greeted it with his trademark hyena-esque laugh), so Thirumalai rewrote his press release and went to work.

Thirumalai was only one of a procession of company leaders who trekked to Bezos a few years ago with six-pagers in hand. The ideas they proposed involved completely different products with different sets of customers. But each essentially envisioned a variation of Thirumalai’s approach: transforming part of Amazon with advanced machine learning. Some of them involved rethinking current projects, like the company’s robotics efforts and its huge data-center business, Amazon Web Services (AWS).

The results have had an impact far beyond the individual projects. Thirumalai says that at the time of his meeting, Amazon’s AI talent was segregated into isolated pockets. “We would talk, we would have conversations, but we wouldn’t share a lot of artifacts with each other because the lessons were not easily or directly transferable,” he says. They were AI islands in a vast engineering ocean. The push to overhaul the company with machine learning changed that.

While each of those six-pagers hewed to Amazon’s religion of “single-threaded” teams — meaning that only one group “owns” the technology it uses — people started to collaborate across projects. In-house scientists took on hard problems and shared their solutions with other groups. Across the company, AI islands became connected. As Amazon’s ambition for its AI projects grew, the complexity of its challenges became a magnet for top talent, especially those who wanted to see the immediate impact of their work. This compensated for Amazon’s aversion to conducting pure research; the company culture demanded that innovations come solely in the context of serving its customers.

“If you asked me seven or eight years ago how big a force Amazon was in AI, I would have said, ‘They aren’t,’” says Pedro Domingos, a top computer science professor at the University of Washington. “But they have really come on aggressively. Now they are becoming a force.”

Amazon Uses An AI Management Strategy Called The Flywheel

Amazon’s approach to AI is called a flywheel .In engineering terms, a flywheel is a deceptively simple tool designed to efficiently store rotational energy. It works by storing energy when a machine isn’t working at a constant level. Instead of wasting energy turning on and off, the flywheel keeps the energy constant and spreads it to other areas of the machine.

At Amazon, the flywheel approach keeps AI innovation humming along and encourages energy and knowledge to spread to other areas of the company. Amazon’s flywheel approach means that innovation around machine learning in one area of the company fuels the efforts of other teams. Those teams use the technology to drive their products, which impacts innovation throughout the entire organization. Essentially, what is created in one part of Amazon acts as a catalyst for AI and machine learning growth in other areas. Amazon is no stranger to AI. The company was one of the first to use the technology to drive its product recommendations. But as AI and machine learning grow, the flywheel approach has become a keystone to Amazon’s expanding business — a central stone at the summit of the company, connecting the organization together. This is particularly unique at a time when many companies silo their AI efforts and don’t integrate them into the overall company.

AI Is Not Located In One Particular Office At Amazon — It’s Everywhere

The Amazon Echo, which features AI bot Alexa, has been one of the company’s most popular forays into machine learning. Amazon faced an uphill battle at the beginning, especially as it was one of the first companies to try its hand at creating a voice-powered virtual assistant that could fit on a countertop. Once the technology started to come together, divisions across the company realized that Alexa could be beneficial for their products. Some of the first skills for Alexa were integrations with Amazon Music, Prime Video, and personalized product recommendations from an Amazon account.

AI also plays a huge role in Amazon’s recommendation engine, which generates 35% of the company’s revenue. Using data from individual customer preferences and purchases, browsing history and items that are related and regularly bought together, Amazon can create a personalized list of products that customers actually want to buy.

How AWS could become the throbbing center of machine-learning activity ?

In a sense, offering machine learning to the tens of thousands of Amazon cloud customers was inevitable. “When we first put together the original business plan for AWS, the mission was to take technology that was only in reach of a small number of well-funded organizations and make it as broadly distributed as possible” says Wood, the AWS machine-learning manager. “We’ve done that successfully with computing, storage, analytics, and databases — and we’re taking the exact same approach with machine learning.” What made it easier was that the AWS team could draw on the experience that the rest of the company was accumulating.

AWS’s Amazon Machine Learning, first offered in 2015, allows customers like C-Span to set up a private catalog of faces, Wood says. Zillow uses it to estimate house prices. Pinterest employs it for visual search. And several autonomous driving startups are using AWS machine learning to improve products via millions of miles of simulated road testing.

In 2016, AWS released new machine-learning services that more directly drew on the innovations from Alexa — a text-to-speech component called Polly and a natural language processing engine called Lex. These offerings allowed AWS customers, which span from giants like Pinterest and Netflix to tiny startups, to build their own mini Alexas. A third service involving vision, Rekognition, drew on work that had been done in Prime Photos, a relatively obscure group at Amazon that was trying to perform the same deep-learning wizardry found in photo products by Google, Facebook, and Apple.

These machine-learning services are both a powerful revenue generator and key to Amazon’s AI flywheel, as customers as disparate as NASA and the NFL are paying to get their machine learning from Amazon. As companies build their vital machine-learning tools inside AWS, the likelihood that they will move to competing cloud operations becomes ridiculously remote. It recently released an extensive new application called Coleman (named after the NASA mathematician in Hidden Figures) that allows its customers to automate various processes, analyze performance, and interact with data all through a conversational interface. Instead of building its own bot from scratch, it uses AWS’s Lex technology.

AWS’s dominant role in the ether also gives it a strategic advantage over competitors, notably Google, which had hoped to use its machine-learning leadership to catch up with AWS in cloud computing. Yes, Google may offer customers super-fast, machine-learning-optimized chips on its servers. But companies on AWS can more easily interact with — and sell to — firms that are also on the service. DigitalGlobe CTO Walter Scott says “We use AWS for machine learning because that’s where our customers are.”

In a world where so many companies are hung up with bureaucracy and silos, it is refreshing to see Amazon break down the walls to encourage innovation and growth throughout its entire organization. If other companies want to succeed and stay on the cutting edge of new technology, they might also want to consider a new organizational approach like the flywheel.

AWS Case Studies

AWS Cloud and It’s Case Study

Benefits of Artificial Intelligence for Business

• Automating Customer Interactions — By analysing data from previous communications, computers can be programmed to accurately respond to Customers and deal with their enquiries.
• Data Mining — Cloud-based AI apps are so advanced that they can quickly discover important information and relevant findings while processing big data.
• Real-time Assistance — Artificial Intelligence is fantastic for businesses that need to continually communicated with high volumes of customers during the course of each day.
• Improved Personalised Shopping Experience — Companies are taking advantage of AI because it enables them to provide their customers with personalised marketing, which in turn increases engagement, helps to enhance customer loyalty and improve sales.Another advantage of AI is that it is able to identify patterns in customers’ browsing habits and buying behaviour, thus enabling companies to craft highly accurate offers to individuals customers.
• Predicting Outcomes — Finally, AI is great in the sense that it can predict outcomes based on data analysis. For instance, it detects patterns in customer data that show whether the products currently on sale are likely to sell, and the volume in which they will do so. It can also predict when demand for such products will decrease. This is fundamental information in helping a company purchase the right stock — and in the right amounts.

Applications of AI & ML

• Fraud Detection
• Data Security
• Marketing
• Recommendations
• Security Screening
• Online Search
• Language Mining

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