How Big MNC’s Are Using AI (Artificial Intelligence) And How does it Benefit Them

15 min readOct 27, 2020

In this blog you will learn about Artificial Intelligence and how it benefits to MNC’s .

What is Data Science .?
What is AI (Artificial Intelligence)?
What is ML (Machine Learning) ?
How MNC's are using and benefited from AI ..

So Let’s Start :)

What is Data Science .

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.

Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science. Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.

What is AI (Artificial Intelligence) .?

Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Artificial Intelligence, often abbreviated as “AI”, may connote robotics or futuristic scenes, AI goes well beyond the automatons of science fiction, into the non-fiction of modern day advanced computer science. Professor Pedro Domingos, a prominent researcher in this field, describes “five tribes” of machine learning, comprised of symbolists, with origins in logic and philosophy; connectionists, stemming from neuroscience; evolutionaries, relating to evolutionary biology; Bayesians, engaged with statistics and probability; and analogizers with origins in psychology. Recently, advances in the efficiency of statistical computation have led to Bayesians being successful at furthering the field in a number of areas, under the name “machine learning”. Similarly, advances in network computation have led to connectionists furthering a subfield under the name “deep learning”. Machine learning (ML) and deep learning (DL) are both computer science fields derived from the discipline of Artificial Intelligence.

Broadly, these techniques are separated into “supervised” and “unsupervised” learning techniques, where “supervised” uses training data that includes the desired output, and “unsupervised” uses training data without the desired output.

AI becomes “smarter” and learns faster with more data, and every day, businesses are generating this fuel for running machine learning and deep learning solutions, whether collected and extracted from a data warehouse like Amazon Redshift, ground-truthed through the power of “the crowd” with Mechanical Turk, or dynamically mined through Kinesis Streams. Further, with the advent of IoT, sensor technology exponentially adds to the amount of data to be analyzed — data from sources and places and objects and events that have previously been nearly untouched.

What is Machine Learning ?

Machine Learning is the name commonly applied to a number of Bayesian techniques used for pattern recognition and learning. At its core, machine learning is a collection of algorithms that can learn from and make predictions based on recorded data, optimize a given utility function under uncertainty, extract hidden structures from data and classify data into concise descriptions. Machine Learning is often deployed where explicit programing is too rigid or is impractical. Unlike regular computer code that is developed by software developers to try to generate a program code-specific output based on given input, machine learning uses data to generate statistical code (an ML model), that will output the “right result” based on a pattern recognized from previous examples of input (and output, in the case of supervised techniques). The accuracy of an ML model is based mainly on the quality and quantity of the historical data.

With the right data, an ML model can analyze high dimensional problems with billions of examples, to find the optimal function that can predict an outcome with a given input. ML models can usually provide statistical confidence on predictions, as well as on its overall performance. Such evaluation scores are important in the decision if you are to use an ML model or any individual prediction.

What is Deep Learning ?

Deep learning exists within machine learning, which exists within the larger category of artificial intelligence (AI), which refers to techniques that imitate what humans would do with their mind.

What is deep learning? Within the area of artificial intelligence (AI) exists machine learning, and within that, is the area of deep learning. Many different types of artificial intelligence exist. AI is any technique that imitates, at least in part, what a human would do with their mind. It is the ability of machines to mimic human behavior, and usually integrates perception, prediction, reasoning, and decision making, according to Yann LeCun, Vice President and Chief AI Scientist at Facebook.

Deep learning (Figure 1) can be thought of as “programming with data.” In a virtual machine setup with a neural network, the network is initially not programmed to do anything. When this virtual machine is given images, it begins to program itself to perform whatever the task is that is being set up. Deep learning can be used in imaging in many ways, including the recognition of objects in images, flaw detection, sorting and grading products, facial recognition, self-driving cars, and denoising images, among others.

Neural networks are modeled after the human brain. The brain consists of around 100 billion brain cells — neurons. Each neuron has dendrites (inputs), a nucleus, and axons (outputs). The connection between an axon of one neuron and a dendrite of another neuron is a synapse. The synapse contains a small gap separating the axon and dendrite, and when things are going right, there is dopamine in the synapse that strengthens the electrochemical connection the two neurons.

Top 5 Trends in Artificial Intelligence That May Dominate 2020s

Artificial Intelligence is a hot topic for all industries in current times. In fact, 77% of people in the world already use AI in some form (And the rest of 23% will start using it soon!) Artificial Intelligence does not only impact the technology industry but any and all industries you can think of! And with top companies like Google, Facebook, Microsoft, Amazon, etc. working on all possible applications of AI in multiple fields, there is no doubt that it will make a big difference in the future! Adobe even predicts that 80% of all emerging technologies will have some AI foundations by 2021.

And this integration of Artificial Intelligence in all existing and emerging technologies is only increasing year by year. Keeping that in mind, let’s see some of the top Artificial Intelligence trends that might continue in 2020 and shape the future to be more intelligence!!!

1. Artificial Intelligence Enabled Chips

AI-Enabled Chips are the latest trend in Artificial Intelligence. Their popularity can be calculated from the fact that it will reach an estimated revenue of $91,185 Million in 2025 from $6,638 Million in 2018. While some brands have already integrated AI-Enabled Chips, they will soon be added to all the latest smartphones. And this is necessary because AI required specialized processors along with the CPU as just the CPU is not enough. The extra hardware is needed to perform the complex mathematical computations that are needed for AI models. So, these AI-Enabled Chips will make sure that tasks requiring AI such as facial recognition, natural language processing, object detection, computer vision, etc. much faster.

There are many companies like NVIDIA, Qualcomm, AMD, etc. that are creating AI-Enabled Chips that will boost the performance of AI applications. In fact, Qualcomm is launching its new AI-Enabled Snapdragon processors in 2020 that will be able to perform 15 trillion operations per second with efficiency. This will improve all the AI-based services in the phone like real-time AI translation, photography, virtual assistants, etc. And all this while utilizing considerably lower power than expected.

2. Artificial Intelligence and Internet of Things

Artificial Intelligence and the Internet of Things together is a match made in technical heaven!!! These two technologies used together can change the way technologies operate currently. The IoT devices create a lot of data that needs to be mined for actionable insights. On the other hand, Artificial Intelligence algorithms require the data before making any conclusions. So the data collected by IoT can then used by Artificial Intelligence algorithms to create useful results that are further implemented by the IoT devices.

One example of this is Smart Home Devices that are becoming more and more popular. In fact, 28% of all homes in the US could become smart homes by 2021. And businesses are also increasingly adopting smart devices as they reduce costs and are more efficient as well. Google-owned Nest is the most popular name in this market as it produces smart products like thermostats, alarm systems, doorbells, etc.

The integration of Artificial Intelligence and Internet of Things has also led to increasingly smart cities like New York. Here, there are facilities like the Automated Meter Reading (AMR) system to monitor water usage and solar-powered smart bins that can monitor trash levels and schedule the waste pick-up on time. And this integration of intelligence is only set to increase in the future with more and more innovations coming up.

3. Automated Machine Learning

More and more organizations are shifting towards Automated Machine Learning in the coming years. It is quite complicated and expensive to apply traditional machine learning models in the real world for all business problems. So a better solution is to use Automated Machine Learning which allows even ML non-experts to use Machine Learning algorithms and techniques without being an ML tech wizard!

This means that tools like Google Cloud AutoML that can be used to train custom made and high-quality ML models while having the minimum required machine learning expertise will become quite popular in the future. These tools can create as much customization as required without knowing the complex workflow of Machine Learning in detail. However, AutoML is not a total child’s play and some ML expertise is still required to set additional parameters as needed. Many companies in the US that already use AutoML are BlackLocus, Zenefits, Nationstar Mortgage, etc. with many more to follow.

4. Artificial Intelligence and Cloud Computing

Artificial Intelligence and Cloud Computing can totally revolutionalize the current market and create new methods of improvement. Currently, it is obvious that AI has huge potential and it is the technology of the future but the integration of AI also requires experienced employees and enormous infrastructure. This is where Cloud Computing can provide immense help. Even if companies don’t have massive computing power and access to large data sets, they can still avail of the benefits of Artificial Intelligence through the cloud without spending huge sums of money.

At the same time, AI can also be used to monitor and manage issues in the cloud. Some experts predict that AI can first be used to automate the basic workflow of both the private and public cloud computing systems and then eventually it can be used to independently create working scenarios that are more efficient.

Currently, the most famous cloud leaders in the market that incorporate AI into their cloud services are Amazon Web Service (AWS), Google, IBM, Alibaba, Oracle, etc. These are expected to grow even more in the future with the increasing popularity of both Artificial Intelligence and Cloud Computing.

5. Artificial Intelligence Cyber Security

With the rising popularity of AI, it is even becoming a key player in cybersecurity. The addition of Artificial Intelligence can improve the analysis, understanding, and prevention of cybercrime. It can also enhance the cybersecurity measures of companies so that they are safe and secure. However, it is also expensive and difficult to implement in all applications. Moreover, AI is also a tool in the hands of cybercriminals who use this to improve and enhance their cyberattacks.

Despite all this, AI will be a critical cybersecurity element in the future. According to a study conducted by Capgemini Research Institute, AI is necessary for cybersecurity because hackers are already using it for cyberattacks. 75% of the surveyed executives also believe that AI allows for a faster response to security breaches.

So companies can start with Artificial Intelligence CyberSecurity by first implementing AI in their existing CyberSecurity protocols. This can be done by using predictive analytics to detect threats and malicious activity, using natural language processing for security, enhancing biometric-based login techniques, etc.

How do MNC’s Benefit from These Technologies …

All the world’s tech giants from Alibaba to Amazon are in a race to become the world’s leaders in artificial intelligence (AI).

1. 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 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 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, but the AlphaGo program was also 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.

DeepMind, Alphabet Inc.’s artificial intelligence research unit, today detailed new machine learning technology it has developed to make Google Maps more useful. Maps have more than a billion users worldwide who rely on the service to plan their travel routes. One of the service’s most central features is its ability to generate time of arrival estimates, helping drivers view key information such as how soon they need to depart to catch a train. DeepMind teamed up with sister company Google LLC to reduce inaccuracies in the time of arrival estimates. Their collaboration, the unit detailed this morning, has produced a double-digit reduction in the percentage of inaccuracies. In one case, prediction errors dropped by no less than 51%. DeepMind achieved this improvement by implementing a so-called “graph” neural network in Maps to help with arrival time estimation. A graph is a data structure that stores data points and the relationships between them in the form of interconnected dots. This structure, DeepMind has found, lends itself well to capturing the interconnected nature of road systems. But the process wasn’t as simple in the case of Maps because of differences in the way roads are built. An AI that is trained to estimate the duration of highway trips won’t necessarily be capable of doing the same for urban roads, and much smaller differences can cause accuracy issues as well. DeepMind solved the challenge by taking advantage of its neural network’s graph structure. The unit’s engineers organized the road data that the AI processes to estimate arrival times into “Supersegments” also based on a graph structure, much like the AI itself. These Supersegments are sufficiently flexible that DeepMind’s neural network managed to overcome training data differences.

2. IBM (International Business Machines)

IBM is hard at work trying to disentangle the concepts behind artificial intelligence (AI) to clients, explaining to them how the technology makes decisions. Eighty-two percent of C-suite executives it researched said they wanted to use AI but were concerned about unconscious bias and the skills needed. It’s offering AI for a range of services and has implemented it itself in areas such as recruitment where it’s used to make sure there is no bias in how job descriptions are written, according to IBM Senior Vice President and Chief Marketing Officer Michelle Peluso.

“Technology can help to make sure there’s not bias in promotions and the like and so (there is) this grounded belief at IBM that inclusion is part of our ‘brand state’,” she told CNBC’s “Marketing Media Money.”

There are several ways marketers can best use AI, Peluso said. The first is in getting to know customers. “It allows us to understand more about our customers. We can analyze tone. We can listen in on chat bots, we can analyze personality and social (media), so we have the ability to develop a richer understanding of our customers,” she said. AI is also being used in how businesses interact with their customers, allowing chat bots to answer customer service queries, for example. The nature of advertising — where traditionally messages are broadcast to people one-way — could also become more of an interaction. “We can say in a digital ad (for example) what’s in your refrigerator … And (it will) give you a great recipe, or (AI can) tell us why you’re interested in a certain car. And we’ll tailor the content live to make sure you’re getting the answer, so it will change (so the advertising is) actually interacting … with customers,” Peluso said.

3. Amazon

The company that fixes the mode for so many aspects of customer experience is breaking down internal silos and proving how other firms can do the same. Amazon, a leader in leveraging customer-centric innovation, has taken its business to the next level by redesigning the company around its AI and machine learning applications. Some of them involved rethinking current plans, like the company’s robotics space and its huge Amazon Web Services (AWS) business. Others are entirely new businesses, like Amazon Echo, healthcare division, and more.

For some reason, the company always gets my attention, and the Amazon Artificial Intelligence strategy even made me more curious. I love to constantly check how the company launches new products and develop go to market strategy for its innovations.

How does Amazon leverage Artificial Intelligence?

How AI drives Amazon business growth

The role of AI in Amazon’s recommendation engine is enormous, as it generates 35 percent of the company’ company’s revenue. Through collecting data from individual customer preferences and purchases, the company’s recommendation engine tends to personalize the list of products that customers need to purchase. The massive quantity of data formed or gathered is used to organize a “360-degree view” of an individual client. Using that profile, Amazon can find other people who fit into the same criteria based on hundreds of touchpoints (data sources) make recommendations for them as well.

When you visit Amazon Book Store, you can scan a QR code at the register, and then the store associated will offer an optional paper receipt. The purchase record is stored in a user’s app account order history — as simple as that.

Amazon Book Stores displays books face out without a price. What space waste and confusing label!. In fact, this is data. Books are face out with placards displaying reviews and ratings. No price tag! Why? The firm wants you to pull up the app and scan the book to buy it there. Can you imagine the data flow? Not only for merchandise analysis but also for training the algorithms and machine earning infrastructure.

3. Microsoft

Microsoft

AI for Accessibility

According to Bellard, strong customer demand for assistive technologies development platforms catalyzed the launch of AI for Accessibility, Microsoft’s second AI for Good initiative. It followed on the heels of — and was largely modeled after — the company’s AI for Earth program, which provides training and resources to organizations looking to tackle problems relating to climate, water, agriculture, and biodiversity.

“We wondered if a similar [approach] would work for the disability community and also the innovation community that works on the technology for people,” said Bellard.

Proposals are accepted on a rolling basis and are evaluated “on their scientific merit,” Bellard says, in addition to their innovativeness and scalability. Selected applicants receive compute credits for Microsoft’s Azure AI Platform in increments of $10,000, $15,000, or $20,000, depending on their project’s scope and needs, and additional funds to cover costs related to collecting or labeling data, refining models, or other engineering-related work. They also gain access to Microsoft engineers, who work with them to accelerate development and incorporate their innovations into “platform-level” partner services. To that end, InnerVoice combines avatars with written text, pictures, and video to create experiences that help learners identify the connections between speech and language. Its videos abstract concepts, while the avatars label what’s happening using facial expressions and emotional tone of voice, and users practice conversations with the avatars — a superhero, drawing, or photograph of a loved one — and learn words by taking pictures that machine learning algorithms detect and label.