AI & Machine Learning: Is it Beneficial for the Product Based Companies?

In this article we'll see what's the major benefit of AI to different MNC's and how it is enhancing the products with the blend of AI concepts which make them a top notch companies in this generation.

> Amazon:

Amazon has set the tone for so many aspects of customer experience is breaking down internal barriers and showing how other companies can do the same. Amazon, a leader in customer experience innovation, has taken things to the next level by reorganizing the company around its AI and machine learning efforts. Amazon’s approach to AI is called as 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 isn’t located in a single office at Amazon, and information is spread throughout departments. Machine learning technology is used by the product recommendation team to improve its product forecasts, and those insights are shared throughout the company. AI and machine learning powers three popular Amazon products: Alexa, the Amazon Go Store, and the Amazon recommendation engine.

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. Many companies now have Alexa skills that add value to the customer’s life such as Liberty Mutual and Capital one. Liberty Mutual provides auto insurance information and Capital One allows customers to make a payment through their Amazon device.

The cashier-less Amazon Go store also took advantage of the wealth of data to track customer shopping trends. Data from customers’ smartphone cameras tracks shopping activities and not only helps Amazon Go, but can also be shared with the machine learning team for continued development.

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.

Data from these three main pillars of the company work together to create a cohesive customer experience. A customer can visit the Amazon Go store to get a few items for dinner, ask Alexa to look up a recipe and the product recommendation engine can determine that the customer likely needs to purchase a certain type of sauce pan. Instead of fighting against each other, different divisions share their innovative knowledge to provide a customized and cohesive customer experience.

Amazon has come a long way since its early beginnings in AI and machine learning. The company now sells its machine-learning approach through Amazon Web Services to clients including NASA and the NFL. By taking advantage of AI advancements and applications in other areas of the company, it offers personalized AI solutions to large and small businesses.

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. So that's how amazon uses the key features of AI and has created a deep networks within almost every company in this current market.

> IBM:

With the unemployment rate at a low 3.7% and the skills shortage severe, corporations need to get creative about finding talented job candidates. IBM is among the technology giants testing new methods involving artificial intelligence to overcome the labor market challenges. AI has been applied to the job application process directly as a method to prevent human bias in hiring decisions. Now more companies are using AI assessment tools to reverse-engineer job roles and find candidates often overlooked by recruiters.

The IBM initiative provides jobseekers, including those with long-term unemployment, refugees, asylum seekers and veterans, with career fit assessments, training, personalized coaching and learning needed to reenter the workforce. SkillsBuild has partnered with several and nonprofits to form "a new, sustainable hiring mindset” but it is not currently used as part of the application process for IBM jobs, specifically.

IBM recently helped an energy technology company embrace a new digital business model. Previously, the company wasn’t converting data into insights, and its operations were still entirely manual. We helped them shift from a reactive stance to a proactive one by leveraging data to predict operations and improve the product design process.

The company now takes the sensor data from its products in the field and analyzes it against past performance and predictive models. This deep layer of insight spots equipment problems before they happen, improving not only repairs but also the design and creation of the next line of products. It’s not just a new approach to maintenance — it’s a new way of doing business.

To respond to this change, IBM Research found that 41 percent of electronics companies are launching or modifying new business models in the next two to three years to respond, compared to 17 percent that did so in the past two to three years. That’s a transition to previously unattainable growth, enabled by data, insight and AI.

IBM has been at the forefront of artificial intelligence for years. It's been more than 20 years since IBM's Deep Blue computer became the first to conquer a human world chess champion. The company followed up that feat with other man vs. machine competitions, including its Watson computer winning the game show Jeopardy. The latest artificial intelligence accomplishment for IBM is Project Debater. This AI is a cognitive computing engine that competed against two professional debaters and formulated human-like arguments.

> Alphabet - Google:

Alphabet is Google’s parent company. Waymo, the company’s self-driving technology division, began as a project at Google. Today, Waymo wants to bring self-driving technology to the world to not only to move people around, but to reduce the number of crashes. Its autonomous vehicles are currently shuttling riders around California in self-driving taxis. Right now, the company can’t charge a fare and a human driver still sits behind the wheel during the pilot program. Google signaled its commitment to deep learning when it acquired DeepMind. Not only did the system learn how to play 49 different Atari games, the AlphaGo program was the first to beat a professional player at the game of Go. Another AI innovation from Google is Google Duplex. Using natural language processing, an AI voice interface can make phone calls and schedule appointments on your behalf. Learn even more about how Google is incorporating artificial intelligence and machine learning into operations.

>Apple :

Apple, one of the world’s largest technology companies, selling consumer electronics such as iPhones and Apple Watches, as well as computer software and online services. Apple uses artificial intelligence and machine learning in products like the iPhone, where it enables the FaceID feature, or in products like the AirPods, Apple Watch, or HomePod smart speakers, where it enables the smart assistant Siri. Apple is also growing its service offering and is using AI to recommend songs on Apple Music, help you find your photo in the iCloud, or navigate to your next meeting using Maps.

>Facebook :

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. The company also uses artificial intelligence to automatically catch and remove images that are posted on its site which is not appropriate for the users.

> Alibaba:

Chinese company Alibaba is the world's largest e-commerce platform that sells more than Amazon and eBay combined. Artificial intelligence (AI) is integral in Alibaba’s daily operations and is used to predict what customers might want to buy. With natural language processing, the company automatically generates product descriptions for the site. Another way Alibaba uses artificial intelligence is in its City Brain project to create smart cities. The project uses AI algorithms to help reduce traffic jams by monitoring every vehicle in the city. Additionally, Alibaba, through its cloud computing division called Alibaba Cloud, is helping farmers monitor crops to improve yield and cuts costs with artificial intelligence.

>Google:

As mentioned in a previous post on the impact of artificial intelligence, Google is using artificial intelligence to help their algorithms understand and learn. Google can use AI to make the results more accurate for each user, which provides a better use experience. Google has always been focused on providing the best result for each user.

Besides, using artificial intelligence for their algorithms, Google is using the technology for the self-driving cars too. The goal of self-driving cars will be able to analyze the road ahead of it and make decisions in advance by learning from past experiences. The more experiences the self-driving cars encounter the more it can learn to make sure the people riding in the car are protected. Lastly, like Siri from Apple, Google uses AI to help power its own personal assistant on smartphones. Google will use the technology to learn about certain locations a person typically goes to or eats at. Google also pulls in information from events and emails to show people the information they might want to see as quickly as possible. Google’s personal assistant over time while learn more about the user to help them find the information they are looking for as quickly as possible.

Nest was one of the most famous and successful artificial intelligence startups and it was acquired by Google in 2014 for $3.2 billion. The Nest Learning Thermostat uses behavioral algorithms to save energy based on your behavior and schedule. It employs a very intelligent machine learning process that learns the temperature you like and programs itself in about a week. Moreover, it will automatically turn off to save energy, if nobody is at home. In fact, it is a combination of both – artificial intelligence as well as Bluetooth low-energy because some components of this solution will use BLE services and solutions.

> Netflix:

Netflix also uses AI, infact in the last quarter of 2020 the total number of Netflix subscribers had increased drastically and is estimated at around 195.5 million globally. From teenagers to the elderly now these days everyone is obsessed with Netflix and it has become a part of our entertainment regime, as the age group is different their likable contents must be varied, so Netflix uses advanced technologies for their users’ recommendation, Machine Learning is one of that advanced technology. Below points depicts the ways through which Netflix uses AI and machine learning algorithms to sell it's streamed video's:

-Thumbnail/artwork personalization: Thumbnails projection has made it even more simpler for users to choose the movies they prefer. Most of the users tend to choose movies or series based on the thumbnail to determine whether it is worth watching the movie or not. With time Netflix realizes that the title alone cannot convince the user to watch the movie, thus, their projection toward dynamic personalized thumbnail.

Every thumbnail chosen is algorithm-based, through which the users’ preference is chosen, and based on the past viewing history, the thumbnail selected has the highest rate of converting. For every program in Netflix, there is a diverse range of posters each of which caters to a specific group of viewers. As the algorithm gathers data and information on the user based on the thumbnails, it gives a better response in identifying the users’ genre.

-Optimal streaming quality: The streaming quality is a crucial metric that specifically contributes to bounce rates. With over 140 million subscribers worldwide, it gets challenging for Netflix to offer the best streaming quality to its viewers. However, with the help of AI and machine learning, Netflix can now predict the future demands and position assets at strategic server locations way ahead of time. By pre-positioning the video assets closer to the subscribers, viewers can stream high-quality video even during peak hours without any interruption.

-Tailored movie recommendations made just for you: Despite having two individuals log-in Netflix at the same time, both would be offered different program recommendations. Though this might seem obvious on the surface, however, the inside story is entirely different. Netflix’s recommendation system works on algorithm-based, but the major factor that increases the relevancy of these recommendations is because of machine learning and AI. The algorithm learns as data gets collected. Therefore, the more time you spend on Netflix, the more relevant programs will be recommended. Netflix’s recommended engine worth over $1 Billion per year comes with a personalized grid of suggestions that is catered only to the viewers’ taste.

> Spotify:

Streaming companies have emerged, and each wants to become the best by providing unique content, focusing on artists, and offering affordable price models. For instance, Spotify has different models where people buy Spotify plays. Spotify and Soundcloud are the giants of streaming services. If you decide to buy plays for Spotify, then be sure to do you due diligence and shop around first. A lot of providers of plays are notorious for selling fake plays. Make sure you buy real plays only.

-Collaborative Filtering: This strategy involves comparing a person’s behavioral trends with those of others. Content streaming platforms such as Netflix also use collaborative filtering to improve their services. Netflix uses your star-based movie ratings to come up with recommendations for other similar viewers. Spotify does not have a star rating system. As such, they use implicit feedback. For instance, the number of times you have played a song, your saved music on their list, or the number of times you have clicked on an artist’s page. This information helps them to create recommendations for other users that have similar trends.

-Natural Language Processing: Natural Language Processing or NPL analyses your speech over text. Spotify uses AI to scan a song’s metadata, blog spots, discussions about musicians, news articles about tracks, and artists on the internet. The Spotify AI looks at what consumers are saying about a specific artist or song and the language being used. The technology also looks at which artist that consumers compare and other texts associated with those musicians and tracks. Spotify then categorizes these keywords into two: cultural vectors and top terms. Each artist and song have several top words that can change daily. Spotify assigns a certain weight on each term. This strategy helps them to determine your preferred music genre and create a perfect playlist for you.

-Audio Models: When it comes to audio models, there is no match to Spotify. Spotify uses audio models to categorize music accordingly. This method allows Spotify to evaluate all songs and come up with recommendations. NLP may fail to pick a new song if its coverage on the internet is low. However, audio models may leverage the song’s data, and the collaborative filtering model will be able to analyze the song and recommend it to consumers.

-Convolutional Neural Networks: Spotify has also adopted technology similar to the one used for facial recognition. However, the platform uses this technology on audio data instead of on pixels. Here, Spotify portrays itself more than just a platform for famous musicians. Spotify now offers opportunities to musicians who are trying to gain recognition.

>Microsoft:

Artificial intelligence is a term that appears on Microsoft's vision statement, which illustrates the company’s focus on having smart machines central to everything they do. They are incorporating intelligent capabilities to all its products and services, including Cortana, Skype, Bing, and Office 365, and are one of the world's biggest AI as a Service (AIaaS) vendors.

Microsoft is opening up limited access to a text-to-speech AI called Custom Neural Voice, which allows developers to create custom synthetic voices. The tech is part of an Azure AI service called Speech. Companies can use the tech for things like voice-powered smart assistants and devices, chatbots, online learning and reading audiobooks or news. They’ll have to apply for access and gain approval from Microsoft before they can harness Custom Neural Voice. The tech can deliver more natural-sounding voices than many other text-to-speech services, according to Microsoft. Custom voices use a bank of sounds, or phonemes, to create voice fonts. Custom Neural Voice uses multiple neural networks in an attempt to make sure the prosody (the tone and duration of each phoneme) and pronunciation is accurate. That helps the AI to mimic an actor's voice correctly or use a realistic-sounding synthetic voice.

The Microsoft AI platform provides a suite of powerful tools, such as the Bot Framework, Cognitive Services, Azure Machine Learning and many more. These tools allow developers to easily and quickly infuse AI into their applications and scenarios, enabling new, intelligent experiences for their users.

Powered by the enterprise-ready capabilities of Azure, the Microsoft AI platform presents a rich set of interoperable services, APIs, libraries, frameworks and tools that developers can leverage to build smart applications. The Microsoft AI platform consists of three core areas: AI Services, AI Infrastructure and AI Tools.

Cognitive Services: Trained services like Cognitive Services enable you to jumpstart development of your AI applications, without requiring you or your data science team to develop and train the models. Cognitive Services features a rich set of instant AI capabilities that you can use. These AI capabilities are organized into the following categories: vision, speech, language, knowledge and search. The Cognitive Services APIs enable you to leverage powerful computer vision algorithms that have been pre-trained to recognize things like different face attributes, landmarks, celebrities, gender, emotion, and printed or written words (Optical Character Recognition, or OCR). Powerful language capabilities can recognize commands from users, analyze key phrases, perform translations and spell check, and more.

-Customized Computer Vision Models: As you explore Cognitive Services to develop your AI applications, you may find that you need to further customize the models using your own data. You can do that with Custom Vision services (customvision.ai). Custom Vision lets you bring your own data, and use it to train your computer vision models. Underneath the hood, state-of-the-art transfer learning techniques leverage existing pre-trained computer vision models, and evolve them to learn about the new images you upload to the Custom Vision service.

-Custom Machine Learning and Deep Learning Models: As you work on various use cases, data scientists in your organization might need to develop and customize deep learning models, using various deep learning toolkits. The Microsoft AI platform provides an open and flexible environment for that deep learning. Azure Machine Learning empowers data scientists to build, develop and manage models at scale, while data stores like CosmosDB, SQL DB, SQL Data Warehouse (DW) and Azure Data Lake (ADL) provide access to the structured and unstructured data that inform your ML and deep learning models. With Azure Machine Learning, you can easily train your models in Spark, run them on Azure Deep Learning Virtual Machines (DLVM), or process them on a managed GPU cluster with Batch AI, and more. Azure Machine Learning experimentation and model services boost productivity by helping you keep track of your projects, enabling you to train on both local and remote compute infrastructures, create containers for model deployment, and manage and monitor the behavior of models. Bots provide exciting new ways to engage with customers and employees, helping them complete tasks. The Bot Framework provides a rich set of capabilities for conversational AI, so you can develop powerful new bots that interact with your customers and employees via Web sites, applications, text/SMS, Skype and more. Tools like Visual Studio Tools for AI, Azure Machine Learning Studio and Azure Machine Learning Workbench provide a great starting point to get started building innovative, intelligent AI applications. Let’s get started with Microsoft AI by using the various services to build an AI application that leverages the intelligent cloud and can be deployed to the intelligent edge. I’ll start with Cognitive Services, then move on to building custom models with Azure Machine Learning. I’ll finish with a dive into the Bot Framework and show how you can turn any bot into an intelligent bot powered by Microsoft AI.

> Key Benefits of Artificial Intelligence

1. Reducing Human Intensive Labour

AI has been instrumental in reducing human-intensive labour by leveraging on Smart Automation. As per the Oxford Economics Report in June 2019, more than 2.25 million Robots are deployed worldwide (Threefold increase from last decade). Now in many factories, all the heavy lifting, carrying, transporting and other mundane activities are carried out by AI-enabled robots. This saves a lot of human efforts which can be better utilized in more productive activities. For Example, Amazon deploys more than 100,000 AI-based Kiva robots in their fulfilment centre. The use of AI-enabled robots not only reduces human efforts in performing physically intensive work like carrying large inventory quantities from one shelf to another but also enhances safety at the workplace. These Cyborgs can load and unload one full trailer of stocks in less than 30 minutes which took more than a couple of hours for human workers.

2. Increasing Efficiency in Pharma Industry

AI has been a boon to the Pharma and Healthcare Industry. As per the study by MIT, merely 13% of the drugs pass the clinical trial stages, further, it costs Pharma companies millions of dollars for any of its drugs to pass the clinical trials. Therefore Pharma companies in order to ensure better utilization of their R&D Budget deploy AI to increase the chances of their drugs clearing the clinical trials. Different Machine Learning algorithms aid scientists in finding the right composition of different salts in the drugs by analysing historical data related to Genes, chemical reactions, and other attributes.

Example: Novartis, A leading Pharma Company, has been using Machine Learning Algorithm to find out which compound is best at fighting the diseased cells under examination. Previously, this procedure involved the manual microscopic investigation for each sample which was both time consuming and prone to human errors. With Machine Learning based algorithms, they can run real-time simulations and get more accurate results sooner.

3. Transforming the Financial Sector

Most of the Financial Applications revolve around analysing past data to get better results. There is no surprise that Artificial Intelligence whose USP is analysing past data enjoys huge success in Finance Sector. AI has wide-ranging applications in the Finance Industry ranging from Risk Assessment, Fraud Detection, Algorithm based Trading, Financial Advisory, and Finance Management among several others. For Example: Paypal has been using advanced Deep Learning Algorithm to detect fraudulent transactions. Paypal processes humongous amount of transaction data, it processed more than $235 billion in payments from 4 billion transactions done by more than 170 million users. Paypal uses Deep learning algorithm to analyse the large scale of data and compare transactions with fraud transaction pattern stored in their database. Based on this pattern comparison it can detect fraudulent transactions from normal transactions.

4. Quicker and Easier Customer Service using AI Chat-Bots

An earlier version of Chat-Bots interactions was very time consuming and frustrating. The bots used to run into loops and could assist only in pre-defined tasks. The AI-powered chat-bots using Natural Language Processing have a better understanding of human interactions and can learn on its own and hence are far more adept in providing an adequate response to the customers. For Example: Bank of America virtual assistant Erica is one such example of AI-enabled chat-bot. It has already helped 7 million clients since its roll out in June 2018. Erica uses Artificial Intelligence, Predictive Analytics and Artificial Neural Network to serve more than 50 million client requests it received last year. The request ranges from normal banking tasks like Bank balance information, Bill Payment to complex tasks like Investment planning and budgeting suggestions.

5. Enhancing Safety on Roads

As per World Health Organization Report, more than a million people die in road accidents every year. Artificial Intelligence is playing a major role in reducing such fatalities. Many companies have started using AI to record and analyse every minute details regarding the driving pattern of different drivers ranging from lane discipline, Traffic rules abidance, distance maintained with other vehicles on the road. The details so collected is used by AI applications to provide safety recommendations to the driver and help automobile companies to come up with safer vehicles. For Example: Microsoft has been experimenting with HAMS (Harnessing Auto-Mobiles for Safety) to enhance safety in Indian roads. It takes into account two factors- the driver’s state and his/her vehicle’s position relative to other vehicles. It makes use of Front and Rear camera mounted in front of Driver’s seat. The front camera is used to gauge the driver’s physical state like fatigue by detecting eye movement and yawning frequency. These are detected using Mouth Aspect Ratio. Rear camera analyses lane discipline and distance with other vehicles. All this data is analyzed using AI applications using Edge-based processing and safety based recommendation alerts are generated in real-time.

6. Predicting and Enabling Quicker Response to Disaster

Artificial Intelligence has turned out to be a silver lining for us in the face of calamity. Now-days, Artificial Intelligence applications are being deployed to pre-empt natural disasters using different pattern recognition algorithm. It is also being used to mitigate the losses after such disasters by aiding in disaster relief work. AIDR (Artificial Intelligence for Disaster Response) is widely used for this purpose. For Example: AIDR was deployed in rescue effort post the earthquake in Nepal (2015). Volunteers and rescue workers were able to reach out to the affected victims quickly with the help of AIDR. AIDR uses Social Media analytics to categorize all the tagged tweets. The insights from these tweets not only helped rescuers to reach the affected area quickly but also helped them in categorizing areas based on urgency to better channelize the rescue effort.

Artificial Intelligence and the technology are one side of the life that always interest and surprise us with the new ideas, topics, innovations, products …etc. AI is still not implemented as the films representing it(i.e. intelligent robots), however there are many important tries to reach the level and to compete in market, like sometimes the robots that they show in TV. Nevertheless, the hidden projects and the development in industrial companies.  At the end, we’ve been in this research through the AI definitions, brief history, applications of AI in public, applications of AI in military, ethics of AI, and the three rules of robotics. This is not the end of AI, there is more to come from it, who knows what the AI can do for us in the future, maybe it will be a whole society of robots.