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Google search results using machine learning

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Google search results using machine learning

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Carter.I

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The Google RankBrain algorithm went online in April 2015 but wasn't introduced to the world until October 2015 via a Bloomberg news story. Here's how RankBrain was described at the time in the article:

"RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities - called vectors - that the computer can understand. If RankBrain sees a word or phrase it isn't familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries."

RankBrain is the only live Artificial Intelligence (AI) that Google uses in its search results. While Google uses machine learning to teach the algorithms, AI isn't being used in the wild - and for good reason. If search broke, Google's engineers would have no clue how to fix it.

RankBrain, however, is used to sort live search results to help give users a best fit to their search query.

RankBrain as a Ranking Signal

RankBrain has been called Google's third most important ranking signal (behind content and links).

But is RankBrain really a "ranking signal"?

Not really. At least not in the way we think of traditional ranking signals.

RankBrain is a method of processing search queries in a way that infers a "best fit" for queries that are unknown to Google.

About 15 percent of the queries Google processes every day are new - in other words, nobody has ever searched using these exact terms before.

How can there be so many unknown queries? It's a hard concept to wrap your brain around.

But if you think about all the different ways we talk about a person, place, or thing you can quickly see how there could be millions of way to ask even one simple question. This will likely even expand exponentially as we move more to voice search as smartphones get better at Voice to Text and devices move into the home that take only voice.

So, in the simplest terms, RankBrain is a processing algorithm that uses machine learning to bring back the best match to your query when it isn't sure what that query "means."

At first, RankBrain was only present in a small number of Google queries (about 15 percent). However, over time, it has expanded and is involved in almost all queries entered into Google.

That being said, if Google is sure of the query meaning RankBrain has very little influence. RankBrain is only there to help when Google is unsure of the queries meaning.

What Does it Mean for Google to 'Know' a Query Set?

When Google rolled out Hummingbird and moved from "strings to things", it moved from inferring a match to your search query by using on-page and off-page factors to understanding relationships of people, places, and things to each other by seeding the algorithm with known relationships.

This was in part determined from a database called Freebase at first. Then Google used WikiData. Now they use data fed machine learning for the most part.

How does this work?

This means that instead of determining your article about "red apples" was about red apples from optimization signals such as inbound link anchor text and H1 tags, it already knew that a red apple was a round edible fruit that came in the color known as red.

The database told Google that this string was actually a thing called "red apple". Then Google can pull back all the best match results for the term "red apple."

However, maybe you meant "red apple" as in a "red apple computer." If Google isn't sure you meant "apple the fruit" or "apple the computer", it might throw a few alternate results into your query set.

So instead of 10 fruit related results, you might get 8 fruit related and 2 computer related, or vice versa.

This is how Google RankBrain works in the most basic of ways.

When Is RankBrain Heavily Influencing a Query Result?

RankBrain impacts queries in all languages and all countries.

When RankBrain is most in "play" is when the query is unique and unknown.

For instance, before RankBrain was announced, I had written an article about something I was observing in my own Google searches.

It started when I was searching for information on water rights in Nevada during the California drought. (We share a river with them). When I looked up Clark County or Las Vegas water rights, there was a lot of information on Google related to the topic. However, when I searched Mesquite NV water rights (a town 90 miles north) I got back the water authority and nothing related to water rights. Instead, I got pages on mesquite trees, mesquite wood, mesquite barbecue chips, etc.

At the time I didn't know what it was called, just that this existed. However, this is what we now know as a result where RankBrain was in full effect.

Why? Because Google did not know what the relationship between the "thing or place" mesquite and the "thing" water rights was, it sent back a "kitchen sink" of results.

The idea of the "best guess" kitchen sink is that over time Google will learn what would be the best match to that query.

If you've been in search long enough you might remember when you would do a search and Google would show you what words it actually used in that search (despite what you typed in). This was the precursor to RankBrain.

What Google RankBrain Is Not

Up until now, we've discussed RankBrain in general, non-specific, layman's terms.

So what really happens behind the scenes?

RankBrain is not a Natural Language Processor, otherwise known as NLP.

NLP is the holy grail of search, where a computer can break down full sentences and understand the intent of the user from the users sentence structure and linguistics.

Words infer meaning on other words and the NLP can understand language similar to the way a human does, albeit through a different process.

While RankBrain is a step closer toward that ultimate goal, RankBrain can't infer meaning from your searches based on language alone.

RankBrain requires a database of relationships, and vectors of known relationships between similar queries, to pull back a best guess. Inference occurs when the queries are not understood, but the results returned are still based on that data.

So How Does RankBrain Actually Work?

RankBrain used a series of databases based on people, places, and things (otherwise known as entities) to seed the algorithm and its machine learning processes.

These words (queries) are then broken down into word vectors using a mathematical formula to give those words an "address". Similar words share similar "addresses."

When Google processes an unknown query, it uses these mathematically mapped out relationships to assume a best fit to the query and returns multiple related results.

Over time Google refines the results based on user interaction and machine learning to improve the match between users search intent and the search results that Google returns.

It's important to note that words that search engines used to throw away like "and" or "the" are not in RankBrain's analysis. RankBrain is also meant to help better understand queries to deliver the best search results, particularly for negative-oriented queries, such as queries using words like "without" or "not."

Also, as explained on The Next Web:

"RankBrain converts the textual contents of search queries into 'word vectors,' also known as 'distributed representations,' each of which has a unique coordinate address in mathematical space. Vectors close to each other in this space correspond to linguistic similarity."

The process at a mathematical level is much more involved, but at a process summary level, it's not overly complex.

Words go in. Words get assigned a mathematical address. Words are retrieved based on your query and the words it locates in the "best fit" vector.

These word "interpretations" are used to return results.

Behind the scenes, data is continually fed into the machine learning process, so as to make those results more relevant the next time.

Simple on the surface, but incredibly complicated and difficult at the microlevel.

Can You Optimize for RankBrain?

Google's Gary Illyes tells us we can optimize for RankBrain by just writing naturally:

"Optimizing for RankBrain is actually super easy, and it is something we've probably been saying for 15 years now, is - and the recommendation is - to write in natural language. Try to write content that sounds human. If you try to write like a machine then RankBrain will just get confused and probably just pushes you back.

But if you have a content site, try to read out some of your articles or whatever you wrote, and ask people whether it sounds natural. If it sounds conversational, if it sounds like natural language that we would use in your day to day life, then sure, you are optimized for RankBrain. If it doesn't, then you are 'un-optimized.'"

However, if you were always writing good content, you are probably asking what else can you do though? What can give you that "edge"? How can you optimize for this "ranking signal".

This answer to this question isn't an answer, but another question:

Why would you want to try?

RankBrain might be beneficial to some unique use cases. However, for most sites, the time and energy you would use to try to rank for a query that is unknown to Google (meaning no one is using it) would be far better spent on other things.

Because not only are you trying to optimize for a query that few people are using, but it is constantly changing.

RankBrain results are designed to change and bring back better results. So optimizing for it would be like trying to hit a moving target - all the time.

The best advice? Follow Illyes's advice.

Write good content.

Make sure it sounds natural.

Move on.

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A Complete Guide to the Google RankBrain Algorithm

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The Google RankBrain algorithm went online in April 2015 but wasn't introduced to the world until October 2015 via a Bloomberg news story. Here's how RankBrain was described at the time in the article: "RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities - called vectors - that the computer can understand.

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OpenAI Bot learns to be the best at the video game Dota 2

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OpenAI Bot learns to be the best at the video game Dota 2

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Nolan

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You might not have noticed, but over the weekend a little coup took place. On Friday night, in front of a crowd of thousands, an AI bot beat a professional human player at Dota 2 - one of the world's most popular video games. The human champ, the affable Danil "Dendi" Ishutin, threw in the towel after being killed three times, saying he couldn't beat the unstoppable bot. "It feels a little bit like human," said Dendi. "But at the same time, it's something else."

The bot's patron was none other than tech billionaire Elon Musk, who helped found and fund the institution that designed it, OpenAI. Musk wasn't present, but made his feelings known on Twitter, saying: "OpenAI first ever to defeat world's best players in competitive eSports. Vastly more complex than traditional board games like chess & Go." Even more exciting, said OpenAI, was that the AI had taught itself everything it knew. It learned purely by playing successive versions of itself, amassing "lifetimes" of in-game experience over the course of just two weeks.

But how big a deal is all this? Was Friday night's showdown really more impressive than Google's AI victories at the board game Go? The short answer is probably not, but it still represents a significant step forward - both for the world of e-sports and the world of artificial intelligence.

Yes, video games are more complex than chess

First, we need to look at Musk's claim that Dota is "vastly more complex than traditional board games like chess & Go." This is completely true. Real-time battle and strategy games like Dota and Starcraft II pose major challenges that computers just can't handle yet. Not only do these games demand long-term strategic thinking, but - unlike board games - they keep vital information hidden from players. You can see everything that's happening on a chess board, but you can't in a video game. This means you have to predict and preempt what your opponent will do. It takes imagination and intuition.

In Dota, this complexity is increased as human players are asked to work together in teams of five, coordinating strategies that will change on the fly based on which characters players choose. To make things even more complex, there are more than 100 different characters in-game, each with their own unique skill set; and characters can be equipped with a number of unique items, each of which can be game-winning if deployed at the right moment. All this means it's basically impossible to comprehensively program winning strategies into a Dota bot.

But, the game that OpenAI's bot played was nowhere near as complex as all this. Instead of 5v5, it took on humans at 1v1; and instead of choosing a character, both human and computer were limited to the same "hero" - a fellow named the Shadow Fiend, who has a pretty straightforward set of attacks. My colleague Vlad Savov, a confirmed Dota addict who also wrote up his thoughts on Friday's match, said the 1v1 match represents "only a fraction of the complexity of the full team contest." So: probably not as complex as Go.

You can't beat a calculator at calculation

The second major caveat is knowing what advantages OpenAI's agent had over its human opponents. One of the major points of discussion in the AI community was whether or not the bot had access to Dota's bot API - which would let it tap directly into streams of information from the game, like the distances between players. OpenAI's Greg Brockman confirmed to The Verge that the AI did indeed use the API, and that certain techniques were hardcoded in the agent, including the items it should use in the game. It was also taught certain strategies (like one called "creep block") using a trial-and-error technique known as reinforcement learning. Basically, it did get a little coaching.

Andreas Theodorou, a games AI researcher at the University of Bath and an experienced Dota player, explains why this makes a difference. "One of the main things in Dota is that you need to calculate distances to know how far some [attacks] travel," he says. "The API allows bots to have specific indications of range. So you can say, 'If someone is in 500 meters range, do that,' but the human player has to calculate it themselves, learning through trial and error. It really gives them an advantage if they have access to information that a human player does not." This is particularly true in a 1v1 setting with a hero like Shadow Fiend; where players have to focus on timing their attacks correctly, rather than overall strategy.

Brockman's response is that this sort of skill is trivial for an AI to learn, and was never the focus of OpenAI's research. He says the institute's bot could have done without information from the API, but "you'd just be spending a lot more of your time learning to do vision, which we already know works, so what's the benefit?"

Some skills aren't taught - they're learned

So, knowing all this, should we dismiss OpenAI's victory? Not at all, says Brockman. He points out that, perhaps more important than the bot's victory, was how it taught itself in the first place. While previous AI champions like AlphaGo have learned how to play games by soaking up past matches by human champions, OpenAI's bot taught itself (nearly) everything it knows.

"You have this system that has just played against itself, and it has learned robust enough strategies to beat the top pros. That's not something you should take for granted," says Brockman. "And it's a big question for any machine learning system: how does complexity get into the model? Where does it come from?"

As OpenAI's Dota bot shows, he says, we don't have to teach computers complexity: they can learn it themselves. And although some of the bot's behavior was preprogrammed, it did develop some strategies by itself. For example, it learned how to fake out its opponents by pretending to trigger an attack, only to cancel at the last second, leaving the human player to dodge an attack that never comes - exactly like a feint in boxing.

Others, though, are still a little skeptical. AI researcher Denny Britz, who wrote a popular blog post that put the victory in context, tells The Verge that it's difficult to judge the scale of this achievement without knowing more technical details. (Brockman says these are forthcoming, but couldn't give an exact time frame.) "It's not clear what the technical contribution is at this point before the paper comes out," says Britz.

Theodorou points out that although OpenAI's bot beat Dendi onstage, once players got a good look at its tactics, they were able to outwit it. "If you look at the strategies they used, they played outside the box a bit and they won," he says. The players used offbeat strategies - the sort that wouldn't faze a human opponent, but which the AI had never seen before. "It didn't look like the bot was flexible enough," says Theodorou. (Brockman counters that once the bot learned these strategies, it wouldn't fall for them twice.)

All the experts agree that this was a major achievement, but that the real challenge is yet to come. That will be a 5v5 match, where OpenAI's agents have to manage not just a duel in the middle of the map, but a sprawling, chaotic battlefield, with multiple heroes, dozens of support units, and unexpected twists. Brockman says that OpenAI is currently targeting next year's grand Dota tournament in 12 months' time to pull this off. Between now and then, there's much more training to be done.

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Did Elon Musk's AI champ destroy humans at video games? It's complicated

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You might not have noticed, but over the weekend a little coup took place. On Friday night, in front of a crowd of thousands, an AI bot beat a professional human player at Dota 2 - one of the world's most popular video games.

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Corey

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Artificial Intelligence and Creativity

Artificial intelligence (AI) is set to become the most groundbreaking and defining technology of the 21 st century. But what exactly comes to mind when people think about AI? Programs that can beat you at poker? Robots that can perform complex tasks with perfection? Or perhaps autonomous beings that are on the verge of replacing your job?

When we think about AI, we often look at those areas where humans can easily be replaced: high-level computation, manual labour, or data-driven optimization. Yet, there is now a new wave of emerging potential for AI in creative industries - one of which happens to be musical composition.

Aiva Technologies is one of the leading startups in the field of AI music composition. It was founded just last year in Luxembourg and London by Pierre Barreau, Denis Shtefan, Arnaud Decker, and Vincent Barreau.

They have created an AI called "Aiva" (Artificial Intelligence Virtual Artist) and taught it how to compose classical music - an emotional art which is usually considered to be a uniquely human quality. Aiva's musical pieces are used as soundtracks for film directors, advertising agencies, and even game studios.

This February, they were invited to participate in the highly-acclaimed European Film Market in Berlin, as well as the Artificial Intelligence in Business & Entrepreneurship (AIBE) Summit in London. In the past, they were backed by Luxinnovation's incubator program, and have even received praise from Xavier Bettel, the Prime Minister of Luxembourg himself.

Having already released its first album called Genesis, as well as many single tracks, Aiva recently became the first AI ever to officially acquire the worldwide status of Composer. It was registered under the France and Luxembourg authors' right society (SACEM), where all of its works reside with a copyright to its own name.

Give Aiva's music a listen and see what you think:

Music With a Purpose

The technology behind Aiva is based on deep learning algorithms which use reinforcement learning techniques. Deep learning is a particular type of machine learning whereby multiple layers of "neural networks" are programmed to process information between various input and output points. Although only loosely based on the human brain's neural structure, it helps to think of it that way. This allows the AI to understand and model high-level abstractions in data, such as the patterns in a melody or the features in a person's face.

Reinforcement learning, on the other hand, is a machine learning technique which teaches a software agent (AI) to decide what action to take next in order to reach certain objectives by maximizing its "cumulative reward." Unlike supervised learning, reinforcement learning does not require labelled inputs and outputs of data. This allows the AI to "find its own way" around the data and improve its performance without being given any explicit instructions, which makes it easier to capture the diversity and variation found in creative arts like music.

According to the team: "We have taught a deep neural network to understand the art of music composition by reading through a large database of classical partitions written by the most famous composers (Bach, Beethoven, Mozart, etc). Aiva is capable of capturing concepts of music theory just by doing this acquisition of existing musical works."

After having listened to a large amount of music and learned its own models of music theory, Aiva composes its very own sheet music. These partitions are then played by professional artists on real instruments in a recording studio, achieving the best sound quality possible.

Although Aiva is able to compose classical melodies in a matter of minutes, its clients are usually looking for music which "supports the storytelling of their visual content." To achieve that, it can sometimes take them several iterations before the right sound is generated.

When asked why they chose to focus on classical music, the founders of Aiva Technologies explain: "(1) it is the predominant style used in movies, games, commercials, and trailer soundtracks, and (2) all of the partitions we use to train Aiva are copyright-expired". Although the music Aiva listens to and learns from is indeed copyright-free, its own compositions are not in the public domain as they are registered under SACEM.

A Musical Turing Test

In the future, the team plans to teach its AI how to learn any style of music. The interesting challenge presented by modern music is not with the composition itself, but the instrumentation and sound design. For example, the most iconic bands have a distinct sound that makes them so unique. Trying to create those unique sounds that makes their music stand out with an AI will be the next problem to crack in order to reach human-level performance.

Will AI-composed music ever be indistinguishable from the work of human musicians? Well, according to the team, they have already conducted several Turing tests by asking professionals to listen to Aiva's pieces - and so far none of them were able to tell that they were composed by an AI.

However, there is no need to worry just yet. Aiva's compositions still require human input with regards to orchestration and musical production. In fact, Aiva's creators envisage a future where man and machine will collaborate to fulfill their creative potential, rather than replace one another.

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A New AI Can Write Music as Well as a Human Composer

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Artificial intelligence (AI) is set to become the most groundbreaking and defining technology of the 21 st century. But what exactly comes to mind when people think about AI? Programs that can beat you at poker? Robots that can perform complex tasks with perfection?

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Michael

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All it takes is a simple sentence: "Show me action movies starring Tom Cruise." That's all that's needed to automatically start browsing through the selection on the TV. This intuitive, hands-free way of accessing visual entertainment is a reminder of how far we have come in getting things done around the home just by speaking to an electronic device.

Take a look at Bixby, Samsung's intelligent assistant. Voice recognition technology has drawn big crowds at recent home appliance and electronics exhibitions such as CES and IFA, but Bixby stands out in its ability to control appliances and TVs in optimized ways that fit users' lifestyle. It's natively embedded into some of the most important electronics or home appliances such as the TV and fridge, and it uses AI to be context-aware. In addition, an entire array of smart home gadgets, not just those made by Samsung, can be connected to the same connected network with just one click of an app. It has created a new way to experience and interact with the home.

No More Hassle in Front of the TV

The TV is one area that Bixby innovates and enhances entertainment. The boredom of channel surfing is banished once and for all. Many will already be familiar with searching for channels or shows with their voice. Bixby goes further by using the power of AI to recommend shows based on previous viewing habits and searching based on additional parameters beyond just the title or channel.

Bixby draws on information from set-top boxes and real-time TV channels Samsung Electronics has accumulated, thus recommending content that exactly meets users' demand. It analyzes users' voice commands to hear whether the commands include genre, cast, or title, thereby drawing accurate results. When a user plugs a set-top box into a TV, the TV automatically detects the brand of the set-top box and provides the user with customized content and channel information. So the user doesn't have to command specifically by saying, "Find Suits." Instead, if the user says, "Play TV series starring Meghan Markle," Bixby analyzes the content and channels to deliver the desired result.

Bixby also gives information associated with the content while watching TV. When watching television, users are often curious about background music (BGM) or program details. If the user says "What is this song," he or she can know right away. Searching information on BGM is possible in Samsung smart TVs, thanks to the collaboration with Shazam.

The viewer's voice can also control a wide range of TV menu options, not just changing channels, but making sure everything is just right when sitting down to watch a movie.

Users can not only search content but also adjust TV settings using only their voices. Without having to fumble with the remote control several times over, users can change settings to fit their needs through voice commands such as "Make screen sharper," and "Turn on Game Mode." Samsung TVs went through the most rigorous tests to make sure TV use is made easy through Bixby.

Viewers in the mood for a movie, or a particular show, may also want their TV to help them set the mood for a perfect night in. A simple command is enough to control the entire smart home, with the TV as the interface. For example, a viewer watching TV can see the contents of the fridge right on the TV screen through a simple voice command, and if it's empty, can also order food on the spot, all without leaving the couch.

Bixby can also bring up weather information, favorite photos and play music on-screen without the need to open a standalone app. It's an integrated way to use voice with the TV as the heart of the smart home. Now, commands such as "Show me photos from yesterday," and "What's the weather forecast in NY this week," can become commonplace in the living room thanks to Bixby. These new features all come together in the 2018 premium smart TV. Through its most recent TV line, Samsung Electronics has focused on delivering the most optimal experience to TV viewers via Bixby.

Bixby Becomes Expert Home Assistant in Your Fridge

Taking into account that the kitchen is the 'heart' of the home where families and friends share warmth and social interactions, as opposed to just being a place for food and drink, Samsung installed Bixby in the 2018 Family Hub, the company's smart refrigerator.

While other voice recognition platforms merely control refrigerator functions, Bixby in Family Hub learns and interacts with multiple users of the fridge and helps to manage the food inside. Bixby is an excellent home assistant that can even lend a hand when users' hands are full with food or kitchen utensils.

Organizing the fridge inventory and preparing meals is easy and practical with Bixby. For example, after coming home with groceries, users can simply put everything away in the fridge and tell Bixby, "Move all items on Shopping List to the fridge." With the list saved in the interface, Bixby can also help users find optimized recipes that work with the ingredients on hand while taking into consideration users' dietary preferences. Users will then be guided on how to prepare the dish.

Another noteworthy function of the 2018 Family Hub is that it recognizes individual voices, so everyone gets the information they need. When a user asks, "Hi Bixby, what's new today?" it will provide a readout of the news, weather and calendar updates specifically tailored to the user. Bixby's role as a home assistant will continue to grow as it provides better, more personalized solutions in the future.

Bixby Connects the Home More Tightly than Ever

Bixby communicates with various appliances in the home. In fact, thanks to Samsung's SmartThings IoT platform, all Samsung devices can be connected easily. Bixby handles simple voice commands to control devices and provide solutions based on what users need. It's like having a normal conversation.

Samsung Electronics' plan to upgrade Bixby is based on the company's vision to maximize the usability of home appliances and is rooted in a deep understanding of consumer needs. Rather than simply executing the user's commands on behalf of the remote control, Bixby will constantly evolve based on user experience so that it can understand and perform with more precision.

What's more, as Bixby continues to evolve, users will be able to access and control various home appliances at once, all by simply talking to Bixby. For example, by telling Bixby "I'm going to bed," users can automatically turn off the TV, dim the lights, and switch the air conditioner to sleep mode-all thanks to the fact that home appliances are becoming easily customizable through a connected learning system. Samsung Electronics plans to continue expanding Bixby to various home appliances in the near future.

*Specific features and availability may vary by region and market

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'All in' on AI, Part 5: Bixby Creates a Customized Home Experience for Everyday Life

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All it takes is a simple sentence: "Show me action movies starring Tom Cruise." That's all that's needed to automatically start browsing through the selection on the TV. This intuitive, hands-free way of accessing visual entertainment is a reminder of how far we have come in getting things done around the home just by speaking to an electronic device.

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Mark

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Artificial Intelligence (AI), once a notion confined to Sci-Fi novels, movies and research papers, is now making a tremendous impact on society.

Today, there are numerous applications of artificial intelligence in the consumer and business spaces, from Apple's Siri to Google's DeepMind. Siri, for example, uses natural language processing (NLP) to interpret voice commands and respond accordingly. Google's DeepMind, on the other hand, uses deep learning. It is capable of making connections and reaching meanings without relying on predefined behavioural algorithms, instead learning from experience and using raw data as its inputs. In fact, by applying findings from DeepMind, Google was able to improve the efficiency of its own power centres, reducing the energy used for cooling by 40%.

In the business world, artificial intelligence is enabling businesses to work smarter and faster, doing more with significantly less. As technology and society continue to advance, more organisations are looking for powerful, sophisticated solutions that will improve and streamline operations.

But it's important to appreciate that artificial intelligence is an umbrella under which a number of different technologies reside. Machine learning, deep learning, robotics, computer vision, cognitive computing, artificial general intelligence, natural language processing and knowledge reasoning are just some of the main branches of artificial intelligence.

The current state of artificial intelligence

However, many of the applications of artificial intelligence we see today are considered to be 'weak AI' because we have yet to release their true potential. Weak AI, also known as 'narrow AI', is non-sentient artificial intelligence, which focuses on one task alone. The applications of artificial
intelligence that are currently available need to be taught or directed in order to provide the insight a user needs.

Strong AI, on the other hand, refers to artificial intelligence applications that can readily formulate their own decisions without human input, apply intelligence to multiple problems, and function and behave more like a human. We are quite some way off strong AI.

Yet despite current AI solutions not being 'true' artificial intelligence, the benefits and capabilities they provide are extraordinary - and many industries have already incorporated some form of artificial intelligence into their day-to-day processes.

Current industries applying artificial intelligence to day-to-day operations

In some industries, AI is capable of automating business intelligence and analytics processes, providing a holistic end-to-end solution. In others, computer vision is being deployed to map and navigate terrain, contributing to the development of smart, self-driving cars that are learning to drive as humans do. Below are just a few examples of how AI is being used to improve efficiency:

Banking and Finance - fraud detection
Many banks use the various applications of artificial intelligence to detect fraudulent activity. The AI software is given a very large sample of data that includes fraudulent and non-fraudulent purchases and is trained to determine whether a transaction is valid based on data. Over time, the software becomes incredibly adept at spotting fraudulent transactions based on what it has learned previously.

Retail - online customer support
Many websites now offer some form of 'chat' functionality where you can talk to a customer support representative or sales representative. In most instances, it is some form of automated AI that begins these conversations. As these AI chat bots are capable of understanding natural language, i.e. human conversation, they can readily assist customers in finding out what they need to know, extracting information from the website, and directing them to the appropriate web page or person for further support.

Security
As cyber-attacks increase in frequency and more sophisticated tools are used to breach cyber defences, human operators are no longer enough. Top firms across the world are investing heavily in cybersecurity to ensure their data is protected. Real-time threat detection, mitigation, and ideally, prevention, are what's needed for businesses - and AI can deliver. Usingmachine learning algorithms and feeding those algorithms great quantities of data, IT and security experts can teach the AI solution to monitor behaviour, detect anomalies, adapt and respond to threats and issue alerts. AI has quickly become a key component in a business' cybersecurity infrastructure, providing a multi-layered security strategy that is robust and sophisticated.

Preparing for the integration of AI-based solutions across departments

Organisations that respond rapidly to opportunities in artificial intelligence application will have the advantage in the landscape of the future. But, because AI is evolving rapidly, the challenge is to ensure that the business has the necessary strategies and plans to support AI capabilities as they become available, and the right technical infrastructure to support AI implementation. For many businesses, it's not a question of if but rather when to adopt AI. On that basis, monitoring the development of AI technology and planning far in advance is necessary to adopt AI successfully.

The optimum strategy is to observe, learn and experiment with current AI. Investing too much into AI which turns out to be ineffective will be damaging for the business' adoption and utilisation of future AI-based solutions. Instead, try to determine how your business can benefit from AI - and how it can be built into core processes to drive efficiency. Start with the outcomes you want to achieve to modernise your IT environment. Remember, AI won't necessarily replace human operators any time soon, but it will empower organisations to do much, much more.

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What are the business applications of artificial intelligence?

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Artificial Intelligence (AI), once a notion confined to Sci-Fi novels, movies and research papers, is now making a tremendous impact on society. Today, there are numerous applications of artificial intelligence in the consumer and business spaces, from Apple's Siri to Google's DeepMind. Siri, for example, uses natural language processing (NLP) to interpret voice commands and respond accordingly.

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By

Watson is an IBM supercomputer that combines artificial intelligence ( AI) and sophisticated analytical software for optimal performance as a "question answering" machine. The supercomputer is named for IBM's founder, Thomas J. Watson.

The Watson supercomputer processes at a rate of 80 teraflops (trillion floating point operations per second). To replicate (or surpass) a high-functioning human's ability to answer questions, Watson accesses 90 servers with a combined data store of over 200 million pages of information, which it processes against six million logic rules. The system and its data are self-contained in a space that could accommodate 10 refrigerators.

Watson's key components include:

Apache Unstructured Information Management Architecture (UIMA) frameworks, infrastructure and other elements required for the analysis of unstructured data.

Apache's Hadoop, a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.

SUSE Enterprise Linux Server 11, the fastest available Power7 processor operating system.

2,880 processor cores.

15 terabytes (TB) of RAM.

500 gigabytes (GB) of preprocessed information.

IBM's DeepQA software, which is designed for information retrieval that incorporates natural language processing (NLP) and machine learning.

Applications for Watson's underlying cognitive computing technology are almost endless. Because the device can perform text mining and complex analytics on huge volumes of unstructured data, it can support a search engine or an expert system with capabilities far superior to any previously existing.

In May 2016, BakerHostetler, an Ohio-based law firm, signed a contract for a legal expert system based on Watson to work with its 50-person bankruptcy team. That system, called Ross, can mine data from about a billion text documents, analyze the information and provide precise responses to complicated questions in less than three seconds. Natural language processing allows the system to translate legalese to respond to the lawyers' questions.

As Ross' creators add more legal modules, similar expert systems are transforming medical research.

Watson in healthcare

Healthcare was one of the first industries to which Watson technology was applied. The first commercial implementation of Watson came in 2013 when the Memorial Sloan Kettering Cancer Center began using the system to recommend treatment options for lung cancer patients to ensure they received the right treatment while reducing costs. Since that time, providers such as Cleveland Clinic, Maine Center for Cancer Medicine and Westmed Medical Group have also implemented Watson tools.

However, not every implementation has gone smoothly. The MD Anderson Cancer Center in Houston launched a project in 2013 to build a decision support system powered by Watson technology to help doctors determine the best treatment options. But after spending more than $62 million on the project over the course of four years, hospital administrators canceled the project, saying it had failed to meet its goals.

Healthcare remains a primary focal point for IBM as it tries to prove Watson technology, and the company continues to forge partnerships with healthcare organizations. In May 2018, for example, India's largest specialty healthcare systems, Apollo, agreed to adopt Watson for Oncology and Watson for Genomics. The two IBM cognitive computing platforms will help doctors make decisions for personalized cancer care.

IBM's use of Watson to solve some of the biggest problems around patient care and using data-driven insights to recommend treatment options would prove the value of Watson technologies.

Watson Analytics

Watson Analytics is one of the primary implementations of Watson technology. It is a platform for exploring, visualizing and presenting data that utilizes Watson's cognitive capabilities to automatically surface data-driven insights and recommend ways of presenting the data.

The platform is made up of an exploration component, which allows users to upload their data, automatically recommends potentially correlated variables and builds comparisons; a prediction tool that allows users to get answers to complex questions based on their data; and a reporting tool that supports dashboard and report development.

Each component is accessed using a graphical user interface (GUI), which minimizes the need for advanced data science training. The platform is intended to make advanced analytics accessible to workers with limited technical knowledge. The cost of Watson Analytics depends on the version; there is a free version which includes the ability to upload spreadsheets, get visualizations, get insights and build dashboards. The "Plus" edition includes the capabilities in the free version along with 2 GB of storage and data sources, including databases, starts at $30 per user, per month. A "Professional" edition with all of the above features, as well as a multiuser tenant to collaborate, 100 GB of storage and more data, costs $80 or more per user, per month. (2018 pricing sourced from IBM Watson Analytics website).

Watson APIs let businesses build AI applications

IBM has published a range of application program interfaces ( APIs) on its cloud that allow users to build their own AI applications that utilize Watson's core technology on the back end. There are APIs that support popular development frameworks like Java, Python and others.

IBM also has API connectors to pretrained deep learning algorithms that allow users to build applications for things like natural language processing, image recognition and tone analysis. One API supports the development of smart assistants using Watson technology on the back end.

IBM Watson's history

In a fall 2010 AI Magazine article, IBM researchers reported on their three-year journey to build a computer system that could compete with humans in answering questions correctly in real time on the TV show Jeopardy! This project led to the design of IBM's DeepQA architecture and Watson.

In 2011, Watson challenged two top-ranked players on Jeopardy! -- champions Ken Jennings and Brad Rutter -- and famously beat them. The Watson avatar sat between the two contestants, as a human competitor would, while its considerable bulk sat on a different floor of the building. Like the other contestants, Watson didn't have internet access.

IBM Watson on 'Jeopardy!' in 2011

In the practice round, Watson demonstrated a human-like ability for complex wordplay, correctly responding, for example, to the answer clue, "Classic candy bar that's a female Supreme Court justice," with, "What is Baby Ruth Ginsburg?" Rutter noted that although the retrieval of information is "trivial" for Watson and difficult for a human, the human is still better at the complex task of comprehension. Nevertheless, machine learning allows Watson to examine its mistakes against the correct answers to see where it erred and inform future responses.

IBM researchers concluded that DeepQA proved to be an effective and extensible architecture which could be used to combine, deploy, evaluate and advance a wide range of algorithmic techniques in the field of question answering.

Continue Reading About IBM Watson supercomputer

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What is IBM Watson supercomputer? - Definition from WhatIs.com

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By Watson is an IBM supercomputer that combines artificial intelligence ( AI) and sophisticated analytical software for optimal performance as a "question answering" machine. The supercomputer is named for IBM's founder, Thomas J. Watson. The Watson supercomputer processes at a rate of 80 teraflops (trillion floating point operations per second).

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See more articles like this one in the following categories

With all the excitement and hype about AI that's "just around the corner"-self-driving cars, instant machine translation, etc.-it can be difficult to see how AI is affecting the lives of regular people from moment to moment What are examples of artificial intelligence that you're already using-right now?.

In the process of navigating to these words on your screen, you almost certainly used AI. You've also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases.

We distinguish between AI and machine learning (ML) throughout this article when appropriate. At TechEmergence, we've developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI. All machine learning is AI, but not all AI is machine learning.

Our enumerated examples of AI are divided into Work & School and Home applications, though there's plenty of room for overlap. Each example is accompanied with a "glimpse into the future" that illustrates how AI will continue to transform our daily lives in the near future.

Examples of Artificial Intelligence: Work & School

Commuting

by the According to a 2015 report Texas Transportation Institut e at Texas A&M University, commute times in the US have been steadily climbing year-over-year, resulting in 42 hours of rush-hour traffic delay per commuter in 2014-more than a full work week per year, with an estimated $160 billion in lost productivity. Clearly, there's massive opportunity here for AI to create a tangible, visible impact in every person's life.

Reducing commute times is no simple problem to solve. A single trip may involve multiple modes of transportation (i.e. driving to a train station, riding the train to the optimal stop, and then walking or using a ride-share service from that stop to the final destination), not to mention the expected and the unexpected: construction; accidents; road or track maintenance; and weather conditions can constrict traffic flow with little to no notice. Furthermore, long-term trends may not match historical data, depending on the changes in population count and demographics, local economics, and zoning policies. Here's how AI is already helping to tackle the complexities of transportation.
1 - Google's AI-Powered Predictions
Using anonymized , Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its location data from smartphones acquisition of crowdsourced traffic app Waze in 2013, Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to and from work.
Image: Dijkstra's algorithm (Motherboard)2 - 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.

Engineering Lead for Uber ATC Jeff Schneider discussed in an NPR interview how the company 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. Uber's Head of Machine Learning Danny Lange confirmed Uber's use of machine learning for ETAs for rides, estimated meal delivery times on UberEATS, computing optimal pickup locations, as well as for fraud detection.
Image: Uber heat map (Wired)3 - Commercial Flights Use an AI Autopilot
AI autopilots 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 New York Times reports that the average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for takeoff and landing.
Glimpse into the future
In the future, AI will shorten your commute even further via self-driving cars that result in and smart up to 90% fewer accidents , more efficient ride sharing to reduce the number of cars on the road by up to 75%, traffic lights that reduce wait times by 40% and overall travel time by 26% in a pilot study.

The timeline for some of these changes is unclear, as predictions vary about when self-driving cars will become a reality: the that who say fully BI Intelligence predicts fully-autonomous vehicles will debut in 2019; Uber CEO Travis Kalanick says timeline for self-driving cars is "a years thing, not a decades thing"; Andrew Ng, Chief Scientist at Baidu and Stanford faculty member, predicted in early 2016 self-driving cars will be mass produced by 2021. On the other hand, The Wall Street Journal interviewed several experts autonomous vehicles are decades away. TechEmergence also discussed the timeline for a self-driving car with Eran Shir, CEO of AI-powered dashcam app Nexar, who believes virtual chauffeurs are closer than we think.

Email
1 - Spam Filters
Your email inbox seems like an unlikely place for AI, but the technology is largely powering one of its most i mportant features: the spam filter. Simple rules-based filters (i.e. "filter out messages with the words 'online pharmacy' and 'Nigerian prince' that come from unknown addresses") 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.).

It must further personalize its results based on your own definition of what constitutes spam-perhaps that daily deals email that you consider spam is a welcome sight in the inboxes of others. Through the use of machine learning algorithms, Gmail successfully filters 99.9% of spam .
2 - Smart Email Categorization
Gmail uses a similar approach to categorize your emails into primary, social, and promotion inboxes, as well as labeling emails as important. In a research paper titled, "The Learning Behind Gmail Priority Inbox", Google outlines its machine learning approach and notes " a huge variation between user preferences for volume of important mail...Thus, we need some manual intervention from users to tune their threshold. When a user marks messages in a consistent direction, we perform a real-time increment to their threshold. " 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."
Glimpse into the future
Can your inbox reply to emails for you? Google thinks so, which is why it introduced smart reply to Inbox in 2015, a next-generation email interface. Smart reply uses machine learning to automatically suggest three different brief (but customized) responses to answer the email. As of early 2016 , 10% of mobile Inbox users' emails were sent via smart reply. In the near future, smart reply will be able to provide increasingly complex responses. Google has already demonstrated its intentions in this area with Allo , a new instant messaging app which can use smart reply to provide both text and emoji responses.

Grading and Assessment
1 -Plagiarism Checkers
Many high school and college students are familiar with services like Turnitin, a popular tool used by instructors to analyze students' writing for plagiarism. While Turnitin doesn't reveal precisely how it detects plagiarism, research demonstrates how ML can be used to develop a plagiarism detector.

Historically, plagiarism detection for regular text (essays, books, etc.) relies on a having a massive database of reference materials to compare to the student text; however, ML can help detect the plagiarizing of sources that are not located within the database, such as sources in foreign languages or older sources that have not been digitized. For instance, two researchers used ML to predict, with 87% accuracy, when source code had been plagiarized. They looked at a variety of stylistic factors that could be unique to each programmer, such as average length of line of code, how much each line was indented, how frequent code comments were, and so on.

The algorithmic key to plagiarism is the similarity function, which outputs a numeric estimate of how similar two documents are. An optimal similarity function not only is accurate in determining whether two documents are similar, but also efficient in doing so. A brute force search comparing every string of text to every other string of text in a document database will have a high accuracy, but be far too computationally expensive to use in practice. One MIT paper highlights the possibility of using machine learning to optimize this algorithm. The optimal approach will most likely involve a combination of man and machine. Instead of reviewing every single paper for plagiarism or blindly trusting an AI-powered plagiarism detector, an instructor can manually review any papers flagged by the algorithm while ignoring the rest.
2 -Robo-readers
Essay grading is very labor intensive, which has encouraged researchers and companies to build essay-grading AIs. While their adoption varies among classes and educational institutions, it's likely that you (or a student you know) has interacted with these "robo-readers' in some way. The Graduate Record Exam (GRE), the primary test used for graduate school, grades essays using one human reader and one robo-reader called e-Rater. If the scores differ substantially, a second human reader is brought in to settle the discrepancy. This addresses the primary concern with robo-readers: if students can deduce the heuristics e-Rater's use for determining their grade, they could easily exploit them to write nonsensical essays that would still score highly. This hybrid approach contrasts with how the ETS handles the SAT, where two human graders evaluate essays and a third is brought in if the scores differ substantially between the two humans. The synergistic approach in the former shows that by pairing human intelligence with artificial intelligence, the overall grading system costs less and accomplishes more.
Glimpse into the future
There are many promising avenues for AI to improve education in the future. One-size-fits-all classes may be replaced by personalized, adaptive learning that is tailored to each student's individual strength and weaknesses. ML may also be used to identify at-risk students early on so that schools can focus extra resources on those students and decrease dropout rates.

Banking/Personal Finance

One of TechEmergence's most popular guides is on machine learning in finance . While the guide discusses machine learning in an industry context, your regular, everyday financial transactions are also heavily reliant on machine learning.
1 - 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.
Image: Mobile deposit (The New York Times)2 - Fraud Prevention
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. FICO, the company that creates the well-known credit ratings used to determine creditworthiness, uses neural networks to predict fraudulent transactions . Factors that may affect the neural network's final output include recent frequency of transactions, transaction size, and the kind of retailer involved.
3 - 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. FICO uses ML both in developing your FICO score, which most banks use to make credit decisions, and in determining the specific risk assessment for individual customers. MIT researchers found that machine learning could be used to reduce a bank's losses on delinquent customers by up to 25%.
Glimpse into the future
Can a robot give you sound investing advice? That's the premise behind upstarts like Wealthfront and Betterment , which attempt to automate the best practices of seasoned investors and offer them to customers at a much lower cost than traditional fund managers. In early 2016, Wealthfront announced it was taking an AI-first approach , promising "an advice engine rooted in artificial intelligence and modern APIs, an engine that we believe will deliver more relevant and personalized advice than ever before." While there is no data on the long-term performance of robo-advisors (Betterment was founded in 2008, Wealthfront in 2011), they will become the norm for regular people looking to invest their savings. This is already happening with younger people-in the above announcement, Wealthfront notes that 60% of its customers are under the age of 35.

Examples of Artificial Intelligence: Home

Social Networking
1 - 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. In a short video highlighting their AI research (below), Facebook discusses the use of artificial neural networks-ML algorithms that mimic the structure of the human brain-to power facial recognition software. The company has invested heavily in this area not only within Facebook, but also through the acquisitions of facial-recognition startups like Face.com, which Facebook acquired in 2012 for a rumored $60M, Masquerade (2016, undisclosed sum), and Faciometrics (2016, undisclosed sum).
Image: Facebook's facial recognition (Huffington Post)
Facebook also uses AI to personalize your newsfeed and ensure you're seeing posts that interest you, as discussed in a TechEmergence interview with Facebook's Hussein Mehanna . 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 the first quarter of 2016, Facebook and Google secured a total of 85% of the online ad market -precisely because of deeply-targeted advertisements.

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." DeepText is also used for automating the removal of spam, helping popular public figures sort through the millions of comments on their posts to see those most relevant, identify for sale posts automatically and extract relevant information, and identify and surface content in which you might be interested.
2 - 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.
3 - 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.
4 - 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. This technology is powered by the 2015 acquisition of Looksery (for a rumored $150 million), a Ukranian company with patents on using machine learning to track movements in video.
Glimpse into the future
Facebook is betting that the future of messaging will involve conversing with AI chatbots. In early 2015, it acquired Wit.ai , an engine that allows developers to create bots that easily integrate natural language processing into their software. A few months later, it opened its messenger platform to developers , allowing anyone to build a chatbot and integrate Wit.ai's bot training capability to more easily create conversational bots. Slack, a social messaging tool typically used in the workplace, also allows third parties to incorporate AI-powered chatbots and has even invested in companies that make them. Soon, your shopping, errands, and day-to-day tasks may be completed within a conversation with an AI chatbot on your favorite social network.
GIF: Facebook-hosted chatbot (VentureBeat)
Online Shopping
1 -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."
2 -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.

While Amazon doesn't reveal what proportion of its sales come from recommendations, research has show n that in other studies a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale). recommenders increase sales (in this linked study, by 5.9%, but recommenders have shown up to
3 - (More) Fraud Protection
Machine learning is used for fraud prevention in online credit card transactions. Fraud is the primary reason for online payment processing being more costly for merchants than in-person transactions. Square, a credit card processor popular among small businesses, charges 2.75% for card-present transactions, compared to 3.5% + 15 cents for card-absent transactions. AI is deployed to not only prevent fraudulent transactions, but also minimize the number of legitimate transactions declined due to being falsely identified as fraudulent.

In a press release announcing the rollout of its AI technology , MasterCard noted that 13 times more revenue is lost to false declines than to fraud. By utilizing AI that can learn your purchasing habits, credit card processors minimize the probability of falsely declining your card while maximizing the probability of preventing somebody else from fraudulently charging it.
Glimpse into the future
The key to online shopping has been personalization; online retailers increase revenue by helping you find and buy the products you're interested in. We may soon see retailers take it one step further and design your entire experience individually for you. Google already does this with search , even with users who are logged out, so this is well within the realm of possibility for retailers. Startups like LiftIgniter offer "personalization as a service" to online businesses. Others, like Optimizely , allow businesses to run extensive "A/B tests", where businesses can run multiple versions of their sites simultaneously to determine which results in the most engaged users.

Mobile Use
1 -Voice-to-Text
A standard feature on smartphones 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 claims to have developed uses artificial neural networks to power voice search . Microsoft that can transcribe conversation slightly more accurately than humans. a speech-recognition system
2 - Smart 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. The first iteration were simpler phone assistants like Siri and Google Now (now succeeded by the more sophisticated Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar.

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.

Microsoft has followed suit with Cortana , its own AI assistant that comes pre-loaded on Windows computers and Microsoft smartphones.
Glimpse into the future
Smart assistants will be the key to bridging the gap between humans and "smart" homes. In October 2016, Google announced According to market research firm , boasted a 9x Google Home-its competitor to Amazon Echo that features deep integration with other Google products, like YouTube, Google Play Music, Nest, and Google Assistant. Through voice commands, users can play music; ask natural language questions; receive sports, news, and finance updates; call an Uber; and make appointments and reminders. Consumer Intelligence Research Partners Amazon's press release Amazon has sold over 5 million Echo devices as of November 2016; however, a month later increase in Echo family sales over the previous year's holiday sales, suggesting that 5 million sold is a significant underestimate. AI-assistants, while still not used by the majority of Americans, are rapidly spilling over into the mainstream.

Facebook CEO Mark Zuckerberg showed what's currently possible by spending a year building Jarvis, an imitation of the super-intelligent AI assistant in Robert Downey Jr.'s Iron Man films. In a Facebook post, he outlines connecting the myriad of home devices to one network; teaching Jarvis his preferences so it could play music and recognize friends at the door and let them in; building a Facebook messenger bot for Jarvis to issue text commands; and creating an iOS speech recognition app to issue voice commands.

The primary limitation for Zuckerberg, a billionaire with daily access to the world's best engineers, was not technology, but rather having devices that could easily communicate with each other and Jarvis in a central, unified system. This suggests that if Google or Amazon is successful in integrating their smart speakers with many other home devices (or proprietary versions), that Jarvis-like home AI would be available to anyone in the next five years.
Image: Mark Zuckerberg's Jarvis
Wrap Up

We'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 in this article. 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.

How will AI affect daily life on a grand scale in the near future? Futurist and that, as AI becomes more deeply integrated in our lives, it will become the Wired magazine co-founder Kevin Kelly predicts new infrastructure powering a second industrial revolution.

Further reading for current and future uses of AI:
Image credit: Geek Photography

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Everyday Examples of Artificial Intelligence and Machine Learning

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See more articles like this one in the following categories With all the excitement and hype about AI that's "just around the corner"-self-driving cars, instant machine translation, etc.-it can be difficult to see how AI is affecting the lives of regular people from moment to moment What are examples of artificial intelligence that you're already using-right now?.

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Mr. Castellarin

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What the AI Behind AlphaGo Teaches Us About Humanity

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When Google's AI beat the world's Go champion 4-1, it stirred a certain sadness in many people. But the reality is the technologies at the heart of AlphaGo are the future. So it's a time to be excited not scared.