ARTIFICIAL INTELLIGENCE — THE FUTURE OF EVERYTHING

22 min readNov 19, 2020

🎯Origin of Artificial Intelligence

Since the 1st century BCE, human beings have been intrigued by the chances of manufacturing machines that represent the human brain. Currently, the term artificial intelligence was invented in 1955 by John McCarthy. In 1956, McCarthy and others formed a conference titled “Dartmouth Summer Research Project on Artificial Intelligence.” This launch led to the creation of machine learning, predictive analysis, deep analysis, and now perspective analysis. It also discovered a new area of data science and study.

🎯What is Artificial Intelligence?🤔

Artificial Intelligence (AI) makes it possible for machines to learn from experience, adapt to new inputs and perform human-like tasks. 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. Forms of AI in use today include, among others, digital assistants, chatbots and robots.

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

Machine Learning (ML) uses neural networks and statistical analysis to find hidden insights in data without explicitly being programmed for where to look or what to conclude. It automates building analytical models.

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.

🎯Major Use Cases Of Artificial Intelligence

Artificial Intelligence is used almost everywhere today, in systems such as Mail spam filtering, Credit-Card fraud detection systems, Virtual Assistance and so on.

I believe there is no end or limitation to the number of applications we have with Artificial Intelligence to make our lives better!

Next up, in this “What is Artificial Intelligence” blog, let’s go through some of the use cases that I believe stand out.

Artificial Intelligence In Sports — A Computer System That Defeats A World Champion — Deep Blue

Well, in the late 90’s when the common man was still wondering — what is Artificial Intelligence? We had computers trained to play games and solve basic problems.

Deep Blue was a chess-playing computer developed by IBM.

It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls.

Deep Blue won its first game against a world champion on 10 February 1996, when it defeated Garry Kasparov in game one of a six-game match.
However, Deep Blue was then heavily upgraded and played Kasparov again in May 1997.
Deep Blue became the first computer system to defeat a reigning world champion in a match under standard chess tournament time controls.

Today, the Artificial Intelligence available on the free chess games on your phones are exponentially faster and better than Deep Blue.

Artificial Intelligence For Rescue Missions

What we majorly require is the use of Artificial Intelligence and technology to ensure that help arrives faster. We can start by developing systems which help first responders find victims of earthquakes, floods, and any other natural disasters.

Normally, responders need to examine aerial footage to determine where people could be stranded. However, examining a vast number of photos and drone footage is very time and labour-intensive.

This is a time-critical process and it might very well be the difference between life and death for the victims.

An Artificial Intelligence system developed at Texas A&M University permits computer programmers to write basic algorithms that can examine extensive footage and find missing people in under two hours.

Artificial Intelligence For Wildlife Poaching Prevention

Hunting of Wildlife species and poaching is a global problem as it leads to extinction.

For example, the latest African census showed a 30% decline in elephant populations between 2007 and 2014. Wildlife conservation areas have been established to protect these species from poachers, and these areas are protected by park rangers. The Rangers, however, do not always have the resources to patrol the vast areas efficiently.

Uganda’s Queen Elizabeth National Park uses Predictive modelling to predict poaching threat levels. Such models can be used to generate efficient and feasible patrol routes for the park rangers.

Artificial Intelligence For Smart Agriculture

In my opinion, Neural networks work well to provide smart agricultural solutions.

Everything ranging from complete monitoring of the soil and crop yield to providing predictive analytic models to track and predict various factors and variables that could affect future yields.

For example, the Berlin-based agricultural tech startup PEAT has developed a deep learning algorithm-based application called Plantix which can identify defects and nutrient deficiencies in the soil.

Their algorithms correlate particular foliage patterns with certain soil defects, plant pests and diseases.

Artificial Intelligence In Healthcare — Better Surgeries And Prosthetics

Well, one day you’re wondering — ‘What is Artificial Intelligence’ and later robots are ready to perform surgical procedures on you?

Robots today are machine learning-enabled tools that provide doctors with extended precision and control. These machines enable shortening the patients’ hospital stay, positively affecting the surgical experience and reducing medical costs all at once.

Similarly, mind-controlled robotic arms and brain chip implants have begun helping paralyzed patients regain mobility and sensations of touch.

Overall, Machine learning and Artificial Intelligence are helping improve patient experience on the whole.

Artificial Intelligence Tracking Wildlife Populations

It is amazing to see that applications like iNaturalist and eBirds collect data on the species encountered. This helps keep track of species populations, ecosystems and migration patterns.

As a result, these applications also have an important role in the better identification and protection of marine and freshwater ecosystems as well.

🎯 How Artificial Intelligence Works

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:

Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
Cognitive computing is a subfield of AI that strives for natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech — and then speak coherently in response.
Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real-time and interpret their surroundings.
Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

Additionally, several technologies enable and support AI:

Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks require big data plus compute power.
The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.
Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
APIs, or application programming interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.

🎯Artificial Intelligence Case Study

Artificial intelligence has become the backbone of everything Google does

Hours before Google CEO Sundar Pichai stepped onto the stage at his company’s annual I/O developer conference, a big clue was given about the direction of both his keynote address and the company as a whole. The entirety of the Google Research division was renamed Google AI, a move the company said was a result of it “implementing machine learning techniques in nearly everything we do”.

Indeed, Google uses artificial intelligence and machine learning in everything from Gmail and battery management in Android, to gathering news headlines, creating robotic voices which sound human, adding colour to century-old photos, and teaching autonomous cars to drive in the snow.

Google’s use of artificial intelligence is spreading wide and far, so here is a rundown of every AI development mentioned at this year’s I/O keynote.

AI means Assistant can make phone calls on your behalf

The star of the show was Google Assistant and its new Duplex feature. Although not available to the public just yet, Google showed off how the Assistant’s ability to hold natural conversations meant it could book a hair appointment at a salon and make a restaurant reservation, speaking to real humans, without any help.

Using recordings of human conversations, Google has taught its AI how to speak with a person — a person, if the demonstrations are to be believed, who may not even know they are talking to a computer. The Assistant uses discourse markers and filler words like ‘um’ to sound more natural, and although it can only operate in a few set scenarios, for now, Google says its intelligence will help us save time and get on with the more important parts of our day. The customer services sector must also surely be interested in this technology.

Aware that such a powerful tool could pose ethical concerns over people not knowing who (or what) they are speaking to, Google says: “It’s important to us that users and businesses have a good experience with this service, and transparency is a key part of that. We want to be clear about the intent of the call so businesses understand the context. We’ll be experimenting with the right approach over the coming months.”

Google’s AI efforts have also resulted in six new voices coming to Assistant, and a seventh in the form of singer John Legend will arrive later in 2018.

AI adds colour to old black-and-white photographs

Another applause-worthy demonstration of AI was when Google showed off how an update to its Photos smartphone app can add colour to old black-and-white images.

AI is already used by Photos to automatically create animations, stylized photos, movies and other content, but now the app will suggest AI-powered fixes to underexposed photos, offer to share photos with friends Google recognizes in the photos, and add colour to old images.

Called Colorize, the new tool isn’t ready just yet, but a demonstration at I/O showed how it uses AI to add some colour to an old black-and-white photograph. The grass is turned green and, while not magically turned into a brand new photo, the image is given extra life through the addition of basic colour.

Gmail uses AI to write your emails for you

Gmail completes whole sentences after you type a couple of lettersGoogle

An AI feature ready today is Smart Compose, which will roll out to Gmail users over the coming weeks. Smart Compose builds on Gmail’s current ability to suggest quick one- or two-word replied based on phrases you commonly use but is almost capable of writing entire emails.

Smart Compose uses AI to understand the context of the email you are replying to, or the one you are writing based on its subject line and who you are sending it to. The AI then acts like a highly sophisticated predictive text system to complete sentences after you type just a couple of words. As well as making informed guesses about what you want to say, data like your street address is automatically inserted at the right time, like when you type the first few characters of “my address is”.

Google is already known for using AI in its Android operating system, particularly with the camera of the Pixel 2 smartphone, which manages to take better photos than its rivals despite using just one lens compared to their two. Google’s AI is far better at producing ‘bokeh’ background blur in portraits than the multi-lens systems of the iPhone X and Samsung Galaxy S9.

Android P uses AI to save battery

Yet to be fully named, Android ‘P’ uses AI to improve battery lifeGoogle

For Android P, which is due later this summer but can be downloaded as a beta now, Google uses AI for a new feature called Adaptive Battery. Using technology developed by DeepMind, an AI company owned by Google parent Alphabet, Adaptive Battery only draws power to run apps it knows you are using or are likely to use while reducing the power demands of apps running (but not recently used) in the background.

AI is also used by Android P to learn how you manually adjust the screen brightness, then take over this task for you in a bid to save battery life and make viewing the screen more comfortable. Finally, Android P shows a carousel of apps when you swipe up from the home screen; these apps are chosen because the phone’s AI thinks they are what you’re looking for, based on user habits.

Updated Google Lens has AI for recognizing dogs, translating menus

Lens app identifies a wide range of objects, including dogsGoogle

Coming in a few weeks, an update to the Google Lens app uses AI to recognize objects, pictures, buildings, animals, paintings, text and much more. The lens is already in some LG phones, including the recently launched LG G7 ThinQ.

The app uses your smartphone camera and the Assistant to offer places to buy a piece of clothing you show it or information on the breed of dog you just took a photo of. The lens can also translate writing, such as menus while on vacation, or take text from a physical book and let you search, copy, paste, share and translate it on your phone. You can also use Lens to input text from a restaurant menu then find images, recipes and YouTube videos of each meal.

Google News uses AI to pick stories relevant to you

A redesigned Google News app uses AI to pick articles it thinks you might be interested in reading, and this AI improves the more you use the app. The company says: “The reimagined Google News uses a new set of AI techniques to take a constant flow of information as it hits the web, analyze it in real-time and organize it into storylines…Google News understands the people, places and things involved in a story as it evolves, and connects how they relate to one another.”

Waymo drives through the snow with help from AI

Pink sensor noise caused by snow is removed with deep learning technologyWaymo

Finally, Google gave an update on its autonomous car division, Waymo. Using artificial intelligence and deep learning, the company showed how its cars are being taught to drive in snow.

Wintery conditions don’t just make roads slippery and white lines tricky to identify. Falling snow also impacts the car’s ability to see ahead. Machine learning is used to filter out raindrops and snowflakes, which cause a great deal of sensor noise (the pink in the image above) and give a clearer picture of the surrounding environment.

Here are some more artificial intelligence case studies:-

COCA-COLA: The Coca Cola Company is the world’s largest beverage company selling more than 500 brands of soft drink to customers in over 200 countries. Every single day the world consumes more than 1.9 billion servings of their drinks including brands like Coca Cola (including Diet and Zero) as well as Fanta, Sprite, Dasani, Powerade, Schweppes, Minute Maid and others.

Of course, this also means that it generates mountains of data — from production and distribution to sales and customer feedback, the company relies on a solid data-driven strategy to inform business decisions at a strategic level.

In fact, Coca Cola was one of the first globally-recognized brands outside of the IT market to speak about Big Data, when in 2012 their chief big data officer, Esat Sezer, said “Social media, mobile applications, cloud computing and e-commerce are combining to give companies like Coca-Cola an unprecedented toolset to change the way they approach IT. Behind all this, big data gives you the intelligence to cap it all off.”

More recently, Greg Chambers, global director of digital innovation, has said “AI is the foundation for everything we do. We create intelligent experiences. AI is the kernel that powers that experience.”

Product development

Coca Cola is known to have ploughed extensive research and development resources into artificial intelligence (AI) to ensure it is squeezing every drop of insight it can from the data it collects.

Fruits of this research were unveiled earlier this year when it was announced that the decision to launch Cherry Sprite as a new flavour was based on monitoring data collected from the latest generation of self-service soft drinks fountains, which allow customers to mix their own drinks.

As the machines allow customers to add their own choice from a range of flavour “shots” to their drinks while they are mixed, this meant they were able to pick the most popular combinations and launch it as a ready-made, canned drink.

Coca Cola is also looking to follow the lead of tech giants by developing something similar to their “virtual assistant” AI bots such as Alexa and Siri. The AI will reside in vending machines, allowing greater personalization — for example, users will be able to order their favourite blend from any vending machine, with the machine mixing it to their individual preference. The AI will also adapt to the machines’ behaviour depending on its location. This could mean more lively and excitable vending machines in malls or entertainment complexes, and more sombre, functional behaviour in a hospital.

Healthy options

As sales of sugary, fizzy drink products have declined in recent years Coca Cola has also hooked into data to help produce and market some of its healthier options, such as orange juice, which the company sells under several brands around the world (including Minute Maid and Simply Orange).

The company combines weather data, satellite images, information on crop yields, pricing factors and acidity and sweetness ratings, to ensure that orange crops are grown in an optimum way, and maintain a consistent taste.

The algorithm then finds the best combination of variables to match products to local consumer tastes in the 200-plus countries around the world where its products are sold.

Augmented reality

Augmented reality (AR) where computer graphics are overlaid on the user’s view of the real world, using glasses or a headset, is being trialled in a number of the company’s bottling plants around the world.

This allows technicians to receive information about the equipment they are servicing, and get back up from experts at remote locations who can see what they are seeing and help to diagnose and solve technical problems. It is also used to inspect problems with vending machines and dispensers in remote or difficult-to-reach locations, including cruise ships while they are at sea.

Social data mining

With 105 million Facebook fans and 35 million Twitter followers, social media is another hugely important source of data for the company.

Coca Cola closely tracks how its products are represented across social media, and in 2015 was able to calculate that its products were mentioned somewhere in the world an average of just over once every two seconds.

Knowing this gives insight into who is consuming their drinks, where their customers are, and what situations prompt them to talk about their brand. The company has used AI-driven image recognition technology to spot when photographs of its products, or those of competitors, are uploaded to the internet and uses algorithms to determine the best way to serve them advertisements. Ads targeted in this way have a four times greater chance of being clicked on than other methods of targeted advertising, the company has said.

Looking further ahead, the company is also interested in the idea of using AI to create adverts.

Speaking at Mobile World Congress this year, global senior digital director Mariano Bosaz said: “content creation is something that we have been doing for a very long time — we brief creative agencies and then they come up with stories … what I want to start experimenting with is automated narratives.”

Digital transformation

The Coca Cola company is a shining example of a business which has re-ordered itself based on data and intelligence. It has long shown an appreciation of the fact that today’s technology offers unprecedented opportunity to reassess just about every aspect of how business is conducted. Rethinking itself as a technology-driven company with a focus on strategic implementation of data and AI means it is likely to retain its place at the head of the pack for the foreseeable future.

ALIBABA GROUP: 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.

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 fare and a human driver still sits behind the wheel during the pilot program. Google signalled 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.

AMAZON: Not only is Amazon in the artificial intelligence game with its digital voice assistant, Alexa, but artificial intelligence is also part of many aspects of its business. Another innovative way Amazon uses artificial intelligence is to ship things to you before you even think about buying it. They collect a lot of data about each person’s buying habits and have such confidence in how the data they collect helps them recommend items to its customers and now predict what they need even before they need it by using predictive analytics. In a time when many brick-and-mortar stores are struggling to figure out how to stay relevant, America’s largest e-tailer offers a new convenience store concept called Amazon Go. Unlike other stores, there is no checkout required. The stores have artificial intelligence technology that tracks what items you pick up and then automatically charges you for those items through the Amazon Go app on your phone. Since there is no checkout, you bring your own bags to fill up with items, and cameras are watching your every move to identify every item you put in your bag to ultimately charge you for it.

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 its 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

IBM:- 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 another 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.

JD.com:- JD.com is the Chinese version of Amazon. Its founder Richard Liu expects and is driving toward having his company be 100% automated in the future. Right now, its warehouse is already fully automated, and they have been making drone deliveries of packages for the last four years. JD.com is driving business with artificial intelligence revolution, big data, and robotics while building the retail infrastructure for the 4th industrial revolution.

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.

TENCENT:- Chinese social media company Tencent has incorporated artificial intelligence into its operations in its quest to become “the most respected internet enterprise,” Tencent relies on artificial intelligence. It has 1 billion users on its app WeChat but has extended its reach to gaming, digital assistants, mobile payments, cloud storage, live streaming, sports, education, movies, and even self-driving cars. One of the company’s slogans is “AI in all.” Tencent acquires huge amounts of information and insights about its customers that it processes and leverages to the company’s advantage.

COGNIZANT: Is a multinational corporation that provides IT services. Cognizant Digital Business has developed an AI-driven machine learning solution for the compliance function at a leading healthcare services provider that parses doctors’ notes entered into the organization’s electronic medical records (EMR) to identify potential drug-seeking behaviour. Opioid dependency is devastating for patients and their families. In our artificial intelligence case studies, Cognizant’s system uses text analytics and an advanced machine-learning algorithm to mine physicians’ notes and electronic medical records. alerts doctors during patients’ visits when a pattern of at-risk behaviour is identified. So far, 85,000 at-risk patients have been identified through this system with savings to organizations of $60 million.

NETFLIX:- Use Cases of AI/Data/Machine Learning at Netflix
Personalization of Movie Recommendations — Users who watch A are likely to watch B. This is perhaps the most well-known feature of Netflix. Netflix uses the watching history of other users with similar tastes to recommend what you may be most interested in watching next so that you stay engaged and continue your monthly subscription for more.
Auto-Generation and Personalization of Thumbnails / Artwork — Using thousands of video frames from an existing movie or show as a starting point for thumbnail generation, Netflix annotates these images then ranks each image to identify which thumbnails have the highest likelihood of resulting in your click. These calculations are based on what others who are similar to you have clicked on. One finding could be that users who like certain actors/movie genres are more likely to click thumbnails with certain actors/image attributes.
Location Scouting for Movie Production (Pre-Production) — Using data to help decide on where and when best to shoot a movie set — given constraints of scheduling (actor/crew availability), budget(venue, flight/hotel costs), and production scene requirements (day vs night shoot, the likelihood of weather event risks in a location). Notice this is more of a data science optimization problem rather than a machine learning model that makes predictions based on past data.
Movie Editing (Post-Production) — Using historical data of when quality control checks have failed in the past (when syncing of subtitles to sound/movements were off in the past) — to predict when a manual check is most beneficial in what could otherwise be a very time-intensive and laborious process.
Streaming Quality — Using past viewing data to predict bandwidth usage to help Netflix decide when to cache regional servers for faster load times during peak (expected) demand.