How Artificial Intelligence (AI) & Machine Learning (ML) making our daily life better

It can be difficult to see how AI is affecting the lives of common people every given moment. What are examples of artificial intelligence and machine learning that you’re already using — right now?

To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning (ML) as the specific scientific methods currently in vogue for building AI.

Today you used AI on your way to work, communicating online with friends, searching on the web, and making online purchases and AI will continue to transform our daily lives in the near future.

1) Ridesharing Apps Like Uber and Lyft

How do they determine the price of your ride? How do they minimize the wait time once you hail a car? How do these services optimally match you with other passengers to minimize detours? The answer to all these questions is ML.

Uber uses ML to predict rider demand to ensure that “surge pricing”(short periods of sharp price increases to decrease rider demand and increase driver supply) will soon no longer be necessary. Also, Uber uses ML for ETAs for rides, estimated meal delivery times on UberEATS, computing optimal pickup locations, as well as for fraud detection. In future, we will have more efficient ride sharing to reduce the number of cars on the road by up to 75%,

2) Commercial Flights Use an AI Autopilot

AI autopilot in commercial airlines is a surprisingly early use of AI technology that dates as far back as 1914, depending on how loosely you define autopilot. The average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for takeoff and landing.

3) Autonomous Cars

Just like a human, self-driving cars need to have sensors to understand the world around them and a brain that collects, processes and chooses specific actions based on information gathered. The same goes for self-driving cars, and each autonomous vehicle is outfitted with advanced tools to gather information, including long-range radar, LIDAR, cameras, short/medium-range radar, and ultrasound

Each of these technologies is used in different capacities, and each collects different information. However, this information is useless unless it is processed and some form of action is taken based on the gathered information. This is where Artificial Intelligence comes into play and can be compared to the human brain, and the actual goal of Artificial Intelligence is for a self-driving car to conduct in-depth learning.

In the future, AI will shorten your commute even further via self-driving cars that result in up to 90% fewer accidents. The timeline for self-driving cars is “a year’s thing, not a decade’s thing.

4) Email -

I — Spam Filters

Your email inbox seems like an unlikely place for AI, but the technology is largely powering one of its most important features: the spam filter. Simple rules-based filters aren’t effective against spam, because spammers can quickly update their messages to work around them. Instead, spam filters must continuously learn from a variety of signals, such as the words in the message, message metadata (where it’s sent from, who sent it, etc.).

Through the use of machine learning algorithms, Gmail successfully filters 99.9% of spam.

II — Smart Email Categorization

Gmail uses a similar approach to categorize your emails into primary, social, and promotion inboxes, as well as labelling emails as important. Every time you mark an email as important, Gmail learns. The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with Priority Inbox “spent 6% less time reading email overall, and 13% less time reading unimportant email.

5) AI in Banking/Personal Finance

Your regular, everyday financial transactions are also heavily reliant on machine learning.

I — Mobile Check Deposits

Most large banks offer the ability to deposit checks through a smartphone app, eliminating a need for customers to physically deliver a check to the bank. According to a 2014 SEC filing, the vast majority of major banks rely on technology developed by Mitek, which uses AI and ML to decipher and convert handwriting on checks into text via OCR.

II — Fraud Prevention

Have you ever gotten an email or a letter asking you if you made a specific purchase on your credit card? Many banks send these types of communications if they think there’s a chance that fraud may have been committed on your account, and want to make sure that you approve the purchase before sending money over to another company. Artificial intelligence is often the technology deployed to monitor for this type of fraud.

How can a financial institution determine if a transaction is fraudulent? In most cases, the daily transaction volume is far too high for humans to manually review each transaction. Instead, AI is used to create systems that learn what types of transactions are fraudulent. Today, creditworthiness is too being determined using neural networks to predict fraudulent transactions.

III — Credit Decisions

Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer. ML is used to make credit decisions, and in determining the specific risk assessment for individual customers.

6) Social Networking

I — Facebook

When you upload photos to Facebook, the service automatically highlights faces and suggests friends to tag. How can it instantly identify which of your friends is in the photo? Facebook uses AI to recognize faces. Facebook also uses AI to personalize your newsfeed and ensure you’re seeing posts that interest you. And, of particular business interest to Facebook is showing ads that are relevant to your interests. Better targeted ads mean you’re more likely to click them and buy something from the advertisers — and when you do, Facebook gets paid.

In June 2016, Facebook announced a new AI initiative: DeepText, a text understanding engine that, the company claims “can understand with near-human accuracy the textual content of several thousand posts per second, spanning more than 20 languages.” DeepText is used in Facebook Messenger to detect intent — for instance, by allowing you to hail an Uber from within the app when you message “I need a ride” but not when you say, “I like to ride donkeys.”

II — Pinterest

Pinterest uses computer vision, an application of AI where computers are taught to “see”, in order to automatically identify objects in images (or “pins”) and then recommend visually similar pins. Other applications of machine learning at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.

III — Instagram

Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”)

By algorithmically identifying the sentiments behind emojis, Instagram can create and auto-suggest emojis and emoji hashtags. This may seem like a trivial application of AI, but Instagram has seen a massive increase in emoji use among all demographics, and being able to interpret and analyze it at large scale via this emoji-to-text translation sets the basis for further analysis on how people use Instagram.

IV — Snapchat

Snapchat introduced facial filters, called Lenses, in 2015. These filters track facial movements, allowing users to add animated effects or digital masks that adjust when their faces moved.

7) Online Shopping

I — Search

Your Amazon searches (“ironing board”, “pizza stone”, “Android charger”, etc.) quickly return a list of the most relevant products related to your search. Amazon doesn’t reveal exactly how its doing this, but in a description of its product search technology, Amazon notes that its algorithms “automatically learn to combine multiple relevance features. Our catalog’s structured data provides us with many such relevance features and we learn from past search patterns and adapt to what is important to our customers.”

II — Recommendations

You see recommendations for products you’re interested in as “customers who viewed this item also viewed” and “customers who bought this item also bought”, as well as via personalized recommendations on the home page, bottom of item pages, and through email. Amazon uses artificial neural networks to generate these product recommendations.

8) Mobile Use

I — Voice-to-Text

A standard feature on smart phones today is voice-to-text. By pressing a button or saying a particular phrase (“Ok Google”, for example), you can start speaking and your phone converts the audio into text. Nowadays, this is a relatively routine task, but for many years, accurate automated transcription was beyond the abilities of even the most advanced computers. Google uses artificial neural networks to power voice search. Microsoft claims to have developed a speech-recognition system that can transcribe conversation slightly more accurately than humans.

II — Virtual Personal Assistants

Now that voice-to-text technology is accurate enough to rely on for basic conversation, it has become the control interface for a new generation of smart personal assistants. Siri, Google Now, and Cortana are all intelligent digital personal assistants on various platforms (iOS, Android, and Windows Mobile) which could perform internet searches, set reminders, and integrate with your calendar. In short, they help find useful information when you ask for it using your voice; you can say “Where’s the nearest Tibetan restaurant?”, “What’s on my schedule today?”, “Remind me to call John at eight o’clock,” and the assistant will respond by finding information, relaying information from your phone, or sending commands to other apps.

Amazon expanded upon this model with the announcement of complimentary hardware and software components:

· Alexa, an AI-powered personal assistant that accepts voice commands to create to-do lists, order items online, set reminders, and answer questions (via internet searches)

· Echo (and later, Dot) smart speakers that allow you to integrate Alexa into your living room and use voice commands to ask natural language questions, play music, order pizza, hail an Uber, and integrate with smart home devices.

AI is important in these apps, as they collect information on your requests and use that information to better recognize your speech and serve you results that are tailored to your preferences. Microsoft says that Cortana “continually learns about its user” and that it will eventually develop the ability to anticipate users’ needs. Virtual personal assistants process a huge amount of data from a variety of sources to learn about users and be more effective in helping them organize and track their information.

9) Video Games

One of the instances of AI that most people are probably familiar with, video game AI has been used for a very long time — since the very first video games, in fact. But the complexity and effectiveness of that AI has increased exponentially over the past several decades, resulting in video game characters that learn your behaviors, respond to stimuli, and react in unpredictable ways.

10) Purchase Prediction

Large retailers like Target and Amazon stand to make a lot of money if they can anticipate your needs. Amazon’s anticipatory shipping project hopes to send you items before you need them, completely obviating the need for a last-minute trip to the online store. While that technology isn’t yet in place, brick-and-mortar retailers are using the same ideas with coupons; when you go to the store, you’re often given a number of coupons that have been selected by a predictive analytics algorithm.

This can be used in a wide variety of ways, whether it’s sending you coupons, offering you discounts, targeting advertisements, or stocking warehouses that are close to your home with products that you’re likely to buy.

11) Online Customer Support

Many websites now offer customers the opportunity to chat with a customer support representative while they’re browsing — but not every site actually has a live person on the other end of the line. In many cases, you’re talking to a rudimentary AI. Many of these chat support bots amount to little more than automated responders, but some of them are actually able to extract knowledge from the website and present it to customers when they ask for it.

Perhaps most interestingly, these chat bots need to be adept at understanding natural language, which is a rather difficult proposition; the way in which customers talk and the way in which computers talk is very different, and teaching a machine to translate between the two isn’t easy. But with rapid advances in natural language processing (NLP), these bots are getting better all the time.

12) Smart Home Devices

Anyone who’s used a device like Amazon’s Echo or Google’s Home smart speakers — the physical embodiment of the Alexa and Assistant software — knows that the experience is compelling. Asking for a specific song over dinner, adjusting the settings on your thermostat, or setting a timer while cooking all make life just that little bit more pleasant.

Lighting is another place where you might see basic artificial intelligence; by setting defaults and preferences, the lights around your house (both inside and outside) might adjust based on where you are and what you’re doing; dimmer for watching TV, brighter for cooking, and somewhere in the middle for eating, for example. The uses of AI in smart homes are limited only by our imagination.

13) Music and Movie Recommendation Services

There are many different kinds of information associated with music that could aid recommendation: tags, artist and album information, lyrics, text mined from the web (reviews, interviews,..), and the audio signal itself.

By monitoring the choices you make and inserting them into a learning algorithm, these apps make recommendations that you’re likely to be interested in. Much of this functionality is dependent on human-assigned factors. For example, a song might have “driving bass,” “dynamic vocals,” and “guitar riffs” listed as characteristics; if you like that song, you’ll probably like other songs that include the same characteristics.

14) News Content Generation

AI isn’t writing in-depth investigative articles, but it has no problem with very simple articles that don’t require a lot of synthesis.

When Jeff Bezos bought the Post back in 2013, AI-powered journalism was in its infancy. A handful of companies with automated content-generating systems, like Narrative Science and Automated Insights, were capable of producing the bare-bones, data-heavy news items familiar to sports fans and stock analysts. But strategists at the Post saw the potential for an AI system that could generate explanatory, insightful articles.

15) Security Surveillance

While some traditional security measures in place today do have a significant impact in terms of decreasing crime or preventing theft, today video analytics gives security officers a technological edge that no surveillance camera alone can provide.

Surveillance systems that include video analytics analyze video footage in real-time and detect abnormal activities that could pose a threat to an organization’s security. Essentially, video analytics technology helps security software “learn” what is normal so it can identify unusual, and potentially harmful, behavior that a human alone may miss.

Wrap Up

I’ve only scratched the surface of examples of AI and ML in day-to-day life. Specific industries and hobbies have habitual interaction with AI far beyond what’s explored.

For example, casual chess players regularly use AI powered chess engines to analyze their games and practice tactics, and bloggers often use mailing-list services that use ML to optimize reader engagement and open-rates.

ABOUT THE AUTHOR

Vartul Mittal is an Independent Director — Technology & Innovation and a Global Business Transformation & Automation leader. He has 11+ years of strong Global Business Transformation experience in Management Consulting and with GICs with a remit to drive understanding and deliver Business & Operations Strategy solutions globally. He is always looking for new ideas and ways that can make things simpler.

A Mechanical Engineer and MBA by education, a Digital Business Transformation & Automation Consultant by profession, he is essentially a Technology Evangelist by passion. He lives his life around technology and is particularly keen to explore the intersection of technology and human behavior. The ease with he can explain the most complex stuff impresses people around him.

Vartul is a notable keynote speaker on Digital Automation and Innovation among Top Universities and International Conferences.