The rapid development of technology has brought us products that were long thought to only be imaginations of science fiction writers. The connection of the globe using this technology has opened up new doors and new avenues for broadening human interaction and knowledge sharing. What used to seem so distant is much closer to being realized. Among some of the intelligent technology being offered are self-driving and parking cars, instant language translations, video chatting across time zones and even having personalized groceries delivered to your door. But with the advancements come multiple ways to classify the technology.
There is an array of differences between artificial intelligence and machine learning. On a surface level, they might almost appear identical as both rely on heavy use of machinations and computers in order to complete tasks. Consider that artificial intelligence is the broader concept and machine learning is a more concentrated approach. Still, the topic is a little more complex. For example, the increasingly-popular voice assistants available on computers, mobile devices and even as standalone speakers utilize both artificial intelligence and machine learning.
Machine learning can be defined as evolving algorithms and programs that record user data and make appropriate adjustments to improve the user experience. This is in contrast to artificial intelligence. Artificial intelligence (AI) refers to the ability for machines to process information using human-like sensory perceptions (of light, sound, etc.) to do things like recognize voices, make decisions and mimic human behavior.
Whether used together or separately, both aspects have helped propel technology forward. How we make use of these machines to better our lives depends largely on what we need the technology for and how we can use it to solve problems. Consider the following as some of the best applications for utilizing the perks of artificial intelligence and machine learning.
Email is still one of the more convenient ways to communicate with others. It can be used for personal correspondence, like signaling safe arrival in a place where phone usage isn’t optimal or for more professional purposes like scheduling interviews. But it can still be difficult to keep up with responding to all the emails that come into an inbox. It can be even trickier if you have multiple inboxes, each with their own function. And because of the sometimes sensitive and confidential nature of the messages, it might not be the best option to have someone else answer them for you.
To combat this, Google is developing a new email interface where your inbox can decipher the message and respond for you. All that’s required from you is a simple click for the response you want to send. This is known as smart reply. Smart reply seeks to optimize email communication by having three readymade responses available at the end of the email. These are usually generic responses that can either confirm information, reject it or make an inquiry. If you don’t like the generic options, they can be customized to suit your needs. Google is also proposing an app called Allo that would also perform these tasks but in instant messaging.
Even with the advancement of drive-up tellers and ATMs, one thing that has not really changed is the hours that bank branches are open. Typically following a very strict workday schedule, getting to the bank can be a hassle. What’s more is that even though most offer online services, speaking to an actual bank teller can help resolve financial matters faster. There are also some bigger financial decisions, typically dealing with loans that require customers to find a branch. One simple task that many people need to do is deposit their checks. When direct deposit is not an option, having to drive to a bank to deposit a check and then wait 48 hours or more to process it can be a major inconvenience.
Banks like Chase now offer online mobile check deposit. Most banks use the technology developed by Mitek that combines artificial intelligence and machine learning to convert handwriting, bank routing numbers into data that can be uploaded to a user’s account. All that’s required from the user is access to their bank account via an app and the ability to take a photo of the check. The AI and ML are able to recognize acceptable signatures and can expedite the deposit.
For the most part, school curriculums have remained faithful to the same reading lists for many years. This means that there is a wealth of information available online that can be used to help students understand the material. However, this also means that there is readily accessible information that students could potentially pull and try to pass as their own. Plagiarism is one of the more consistent problems in education. Some students will opt to change a few words while others will boldly copy essays and presentations verbatim. Unfortunately, because teachers cannot research the source of every single paper they have to read and grade, some are able to get away with it.
There are programs like Turnitin; however, that can help put a dent in the percentage of dishonest submissions. Generally, services like these are dependent on using a search function to compare a student’s work against the wealth of knowledge stored in a database. But this means that anything outside of the database’s stored data could potentially pass. Machine learning has the potential to improve the accuracy rate of detecting plagiarism. Rather than relying on a single database, its sensors can be triggered by the words or phrases the machines learn over time.
Credit scores are ratings that can either help or hinder someone from making traditionally big purchases. When trying to buy a house or a car, most consumers need to enlist the help of a bank in order to secure a loan to cover the costs. A credit score can determine the interest rate, length and other factors of the loan terms. It can also help a bank in justifying refusal of a loan. Getting all of your personal information verified can take time and affect the customized terms than the financial institutions will offer. With the development of machine learning, your credit score will not be the only way your risk is assessed. The purpose of credit scores is to determine whether or not you should be given a loan based on the likelihood of your ability to make timely payments back to the lending party. This good-faith agreement is used before approving credit cards, home loans, and even education loans. Machine learning could not only make this process faster by gaining and retaining knowledge about customers, but it could also help determine how high or low the actual risk assessment would be. Banks and other lenders who make use of this technology could see a drastic decrease in the amount of delinquent accounts they are held responsible for.
Facebook incorporates so many aspects of social interaction that it might be considered unnecessary to use machine learning or artificial intelligence. On the contrary, it is the presence of both of these concepts that continue to keep Facebook not only relevant but also easy to use. For example, machine learning is what is mostly responsible for the Facebook facial tracking that allows people to be tagged in photos, even if their faces are not completely in the front-facing position. They’ve been successful in this by acquiring previously independent facial recognition companies like Masquerade and Face.com. But their AI is used for more than tracking faces.
The content seen on the newsfeed is customized to your preferences. There is a choice between most recent stories, which are chronological posts by your friends or family, and top stories, which are the most popular posts in your network. The aim is to show more posts that fit your interests. There is also a development that uses AI to decode text messages for intent. For example, typing words related to making an appointment might open a calendar app while writing about going somewhere might inquire about calling Uber or Lyft.
Online shopping has been revolutionized with the rise of technology and largely, in part, thanks to machine learning and artificial intelligence. The algorithms can categorize and group the things it predicts that you want to see. Amazon, for instance, following a browsing session will compile a list of specific products that have been tailored to your preferences. These might be completely outside the scope of what you were looking for but could still be appealing. While it has not been made public how the machines or AI are capable of making these groups, it is certainly helpful in expediting the online shopping process.
By combining aspects of products by relevance to the search parameters, the AI is not only making online shopping more convenient but also more efficient. Someone searching for wall decals would not want to scroll through tens of pages related to things other than the decals. This learned behavior from the AI is also a way for businesses to improve their sales percentages. Neural networks can capitalize on presenting the most relevant products first. This way potential customers have a lowered chance of being frustrated, bored or distracted and not completing the sale.
On the internet, the term bot is short for robot and is used to differentiate between a human user and a computer program. Bots have been used for many years as a way to pass the time. Instant messaging programs as far back as AOL Instant Messenger had bots that could be used for communication. Some bots were triggered to respond by a user’s away message. The bot would then present an instant response explaining that the intended recipient was not available. Though primitive, this is an example of the foundational machine learning that is being developed today. Chatbots often need to use machine learning elements and artificial intelligence sequences to create responses that are natural to the specified language.
Language processing software like Wit.ai lets developers create chatbots that cannot only respond in real time but respond in ways that mimic human speech. Chatbots are actually quite versatile in how they can be used. Some are for entertainment while others have more of a function. Slack, a communication service specializing in uniting coworkers and business teams, lets third-party bots capable of demonstrating artificial intelligence be integrated into their system. The purpose behind it is to allow a user to always get a real-time response.
It’s a subtle feature, but the key to the success and widespread popularization of online shopping has been customer personalization. By creating unique shopping experiences, online shopping has become easier and more enjoyable. Contrast that to shopping offline, where customers can be overwhelmed by product choices or frustrated by a lack of customer service. Using machine learning and artificial intelligence to instead offer products that are tailored to your interest also means there’s a higher chance a customer will go forward with the purchase rather than simply browsing. However, it isn’t only the type of products being shown that make the shopping experience personal. This knowledge lets the machines adapt to how information is displayed.
The startup company LiftIgniter aims to capitalize on online personalization. They offer the feature as a service to various online businesses. Research has shown that consumers are more likely to be repeat customers and also recommend a business if they feel like a personal relationship or connection has been established. Using machine learning can help create these relationships by making the shopping experience less generic and more customer-focused.
Credit and debit cards make online shopping possible and easy. There are built-in security features, like chip data encryption and the three digit code usually on the back of cards that are meant to make it safer to use the cards. What many consumers are unaware of is that for each card transaction, businesses must pay a small fee. This fee is used to offset the cost of the service. Fraudulent situations contribute to the increased fees that the businesses must pay. These fees also cover the cost of operating or renting the card terminal which customers will use. These terminals are no longer restricted to actual buildings, however.
There is a credit card processing company called Square that rents a small card reader that can be attached to phones for small business owners or mobile transactions. They charge more to their customers for transactions completed without the presence of a credit card. The artificial intelligence used by these types of companies do more than just track possible fraudulent transactions. They also lower the amount of wrongfully declined card transactions. This is vital because even if a card is declined, the business is still responsible for paying the associated fee.
What began as a simple way to send quick and non-permanent visual or video messages has blossomed into a social media favorite. Using Snapchat was previously just a way to document a short event. The messages could not be saved, and they would disappear within a day’s time. Since the original release, there have been some major changes to Snapchat’s model. For instance, the snaps (videos or photos) can now be saved by either the poster or the viewer. There is also the addition of a stories feed that is generated based on the data collected regarding a user’s age group, location, and entertainment interests.
These stories are a result of machine learning. However, one of the major points that separate Snapchat from other social media is the use of facial filters. Unlike Instagram or Facebook filters which are applied to the entire image, these filters are specifically made to alter a user’s face and sometimes voice. These facial filters, called Lenses, put an overlay of digital masks including makeup, glasses or flowers over a person’s face. They are not restricted to static images, and some also have the potential for animated changes. Machine learning allows the digital overlays to remain on the user, even when they move and sometimes requiring motion to be activated.
From elementary school to the highest levels of university, there are tons of assignments that need to be graded and reviewed by professors. Some are simple in that they only need to be turned in while others require more scrutiny to check for grammar or computational error. Other times, as with standardized testing, there is a need for the actual grades themselves to be checked before being submitted. The development of robo-readers, automated grading systems, is one of the machine learning applications that can be used to make this process more efficient. Robo-readers are used as one side of a pair of graders, the other being a human. They each score the material independently; both trained to spot correct answers.
For subjects like math or science, this can be pretty straightforward. But more open-ended subjects that touch on creativity or opinions, like English, require more advanced learning techniques for the machines. If the human reader and the robo-reader submit scores that are too great in variance, another human grader is brought in to be the deciding factor. There are some concerns that students could potentially learn the trigger phrases of the robo-reader and manipulate their grades that way but as the machines become more intelligent, this is less likely to happen.
There is one major fear that many consumers have when using online services, including shopping, paying bills or even just sending messages. The fear is identity theft and credit card fraud. There are many ways potential scammers can get this information. These include devices that clone credit card numbers digitally by scanning digital frequencies to even something as basic as hacking into a social media account. Most digital users try to be careful but cyber-attacks still happen. Generally, most of these users are not aware of the attacks until after they have happened and after suspicious charges have been made. This can be a costly situation. Luckily, there are artificial intelligence programs being developed that can spot these transactions before they happen.
FICO, the leading company in credit scores and ratings, has employed the use of neural networks. These networks forecast the probability of fraudulent transactions and deny them. The denials are made on a combination of information that is pulled from a user’s regular transaction history. So depending on what type of seller is requesting the transaction, where it’s being requested (including zip code and country) and the frequency of purchases, the networks can determine the validity of account use.
Getting the correct mail, whether in paper form or in a digital inbox can sometimes be a hassle. Whether it’s the mail being delivered to the wrong address or being a stack of unwanted catalog ads, mail sorting can be cumbersome. Electronic mail is also susceptible to these issues. Spam emails can come from many different places. Background programs or harmful links might expose your email address to unwanted solicitors. You might see emails congratulating you for winning a prize, offering large sums of money or offering you bogus job prospects. Sorting through them to keep your inbox clear could potentially take at least an hour.
With the use of machine learning, spam can be filtered and rejected before it makes it to your main inbox. It works by grouping emails that match a predefined string of words (i.e., Nigerian prince, etc.) and also certain suspicious email handles. This is based more on machine learning than artificial intelligence. The inbox script must learn at a nearly constant rate in order to accurately filter out unwanted messages. It must learn to decode messages based on their metadata signals which are typically who sent it, where it came from, the subject of the message and also if there are potentially harmful attachments like viruses or malware.
Undoubtedly, as people become adults and become responsible for their own finances, they will have questions about which route to take regarding not only the protection but also spending of their money. Asking friends or family might be difficult or unwise for a number of reasons. Generally, no two financial situations are alike and what worked for one party might be disastrous for another. In that same way, what methods worked for the previous generation might not be a viable option for the current working class.
As such, many people hire fund managers and accountants who can use their experience to help make better decisions. But even those options leave room for human or clerical errors. That’s why startup companies like Betterment are trying to automate this process through the use of machine learning and artificial intelligence. The company uses the data of highly-experienced financial advisors and investors to guide their consumers. In addition to being able to access this knowledge, using machines instead of people means the customers will pay a lower cost.
Another company, Wealthfront, has publicized its mission to push AI-driven financial advice giving under the claims that it will be more personalize and thus more efficient.
The concept behind Pinterest is based on the previous practice of creating look-books or vision boards that displayed a person’s interest in a specific topic. This could range from anything from fashion to cooking to interior design. The general idea is to collect inspiration clippings and save them for future use on projects. The program behind the app is known as computer vision. Computer vision uses an artificial intelligence script that teaches the computers how to see. This ‘seeing’ is a means for the AI to translate the visual images into data for pinning based on the specific elements of the image.
For example, if a user wants to create a board based on the topic blue hair, the AI will pull from the database all the images that it has seen to match the topic. The AI will even send a user alerts when new pins are available that match their previous boards. Pinterest’s AI is also capable of more than just collecting the images and creating similar alerts. It allows for search optimization where users search the app’s records based on their topic of choice. There is also a background element where the machine learning tracks search histories, prioritizes certain ads for monetization, can be used for enhanced email marketing and also blocks spam from reaching its users.
Imagine that you go to a restaurant and the dish that you want is sold out. Rather than let you leave empty-handed, a worker suggests an alternative that is similar to the one you wanted. It could be comparable in price or taste, but the important thing is that you have the option of buying it right now. Online recommendations work under a very similar principle, though the products do not always need to be out of stock for them to be offered to you as backups. When browsing the internet, data is stored as cookies. These cookies are just one element of machine learning.
During your browsing, the machine is learning about what you like and would likely buy. In response, it might present you ads related to the products or services. Similarly, your data is compressed and categorized with other users with similar interests. This allows the artificial intelligence to show products grouped according to what other users, who match your data type, bought. It has been shown in multiple studies that customers are more likely to buy something if others have bought it before them.
Commuting is one of the most disliked aspects plaguing the workforce. Over the years, in multiple cities, traffic conditions have gotten worse as the previous urban planning models have been unable to keep pace with growing populations and larger vehicles. There have been a few attempts like the HOV lane or promotions of ridesharing between coworkers to help alleviate long commute times.
Commuting is not only about lost time but lost money. On average, over a $100 billion is lost in productivity costs because of commuting factors. Machine learning apps like Waze can help commuters by presenting better routes based on AI decisions in response to the current traffic conditions, even if there is a sudden change like an accident.
These days, talking to our machines is standard. Saying triggers like “Alexa” or “Okay, Google” will alert the corresponding artificial assistant to respond and offer help. The audio can also be converted to text. What we consider typical and somewhat expected machine learned responses are actually the results of many years of data processing and audio transcription. Being able to process the different accents and stresses have been difficult for even the most advanced computers. These days, Google voice searches are powered by neural networks driven by artificial intelligent computing. Microsoft also has their own networks capable transcribe audio conversation, thus making responding to text messages or emails simply by speaking a possibility.
Traveling by air has been a field of travel marred by obstacles including flight delays and overbooking. There have also been some problems with pilot performances. Conditions like being intoxicated or fatigued can contribute to making flying dangerous. Autopilot, in its most basic form, has been used for flights since the inception of air travel. There are several instruments being operated simultaneously during flights. Some monitor cabin pressure, ensuring the proper amounts of oxygen is available to the passengers, while others check for atmospheric readings of temperature and wind direction.
Having two human pilots capable of reading the measurements has been a way to ensure safety and real-time responses to sudden changes. However, by integrating advanced AI into the autopilot software, more accurate reactions can be made. Boeing, one of the leading companies in both private and commercial flight, estimates that pilot engagement is actually less than ten minutes per flight. While they are there to supervise and monitor the computer pilots, the human pilots are used primarily for taking off and landing.
Personal voice assistants are only a small piece of the advancement of smart applications. They are used mostly for entertainment purposes, like playing music or light business applications like making calls or scheduling appointments. They can be connected to other devices via Bluetooth, but even that has limitations. Facebook CEO Mark Zuckerberg modeled an advanced simulation of an in-home artificially intelligent personal assistant. While it was capable of performing the requested tasks, it was limited by the availability of devices available that could handle and accurately respond to the requests.
Smart homes work on this same principle of a centralized AI that controls multiple machines throughout the home. This can include a range of activities, from cooking meals to setting alarms and opening windows. As AI and machine learning become more developed, it will be possible for these homes to exist.
The factors behind how rideshare companies like Lyft or Uber charge their customers are largely based on artificial intelligence and machine learning. The compilation of this data is used to determine when and where the rider-driver supply and demand relationship might change. For example, a sporting event might see surge pricing (a period of increased rates based on high demand) in an area, as would a holiday like New Year’s Eve. But a Wednesday morning after standard commute times might be more of a dead zone in that same place.
Uber, in particular, uses machine learning to calculate fares based on not only the numerical distance but also previously recorded hazards and conditions along the route. The subsection app, UberEats, will also use algorithms to determine a ‘busy area’ and make price adjustments as well as changing the expected time of delivery. Their AI is also capable of detecting potentially fraudulent or dangerous areas.
Meeting the needs to ensure that all students learn effectively is difficult because there are multiple ways that students learn. The learning styles range from auditory, those who can learn by simply hearing the material, to sensory, those students who need to touch or otherwise interact with something to truly grasp a concept. Unfortunately, large class sizes and limited resources make it hard to tailor education to each individual child while in a group setting.
By addressing the one-size format of classrooms, machine learning can help created targeted approaches to stimulate student comprehension. Additionally, machine learning has the capacity to use its collected data to determine which students could be at risk for failure or potential learning disabilities. This kind of early detection can help students by putting them back on paths to success rather than having them struggle to keep up with their peers. This is known as adaptive learning, and the goal is to find the balance between student strengths and weaknesses for their success.
Most email users will be familiar with the categorization of their emails. This menu, usually featured on the left side of the inbox has a number of section (and sometimes subsections) for the email to be classified. And though this is a ubiquitous feature, users are unaware that it happens mostly automatically. While it is true that users can sift through the emails manually, it’s the machine learning behind the inboxes that make such grouping possible.
Out of the hundreds of emails generated daily, the algorithms read or look for specific phrasing so that the email can be placed in the appropriate folder. Emails offering discounts can be found in shopping while messages related to airline tickets would be sorted into travel. It is a time-saving feature that saves users from getting lost in the messages.
Personal assistants have existed for as long as anyone has needed help completing a task, running a business or organizing their daily lives. The costs associated with this role has kept it fairly limited to business uses. But the development of artificial intelligence-driven smart assistants has made the service more accessible to the general public. Depending on what brand of phone a customer has, they have access to Siri (iPhone), Alexa (Android) or Google Now (Google). Microsoft also offers their own smart personal assistant, Cortana, on both its phones and computers.
The key function behind each of these assistants is to perform tasks that make a user’s day easier. This can be anything within the scope of the machine’s understanding. Tasks like ordering groceries, making phone calls, answering questions, looking up recipes or even playing music can all be done with simple natural voice commands. The prevalence of these different assistants is an example of how advanced and accurate machine learning and artificial intelligence have become.
Another social media giant, Instagram has become a go-to for sharing images and videos. Unlike other social media platforms, there are more socializing options available. One of the avenues being explored is using machine learning and artificial intelligence to detect and identify emoji use and determine its meaning based purely on the context. In theory, this can be accomplished by the AI recognizing a key phrase that could be represented with an emoji but also retain its meaning. Using these elements to identify emoji connotative meanings, Instagram is poised to create an auto-fill option strictly for emojis or emoji hashtags.
On Instagram, hashtags are a way that machine learning can group similar posts and allow users to browse just the subsection. Applying the advanced technology of machine learning to an emoji might seem useless, but it has greater consequence. Many people use emojis to express a quick emotion. The demographics of those who use emojis are spread out across both age and location. Some emojis are used true to their original meaning while others create a situational connotation to the emoji. By tapping into the latter, Instagram will be able to further develop how the app is used for communication.
Create, edit, customize, and share visual sitemaps integrated with Google Analytics for easy discovery, planning, and collaboration.