AI News, The State of Artificial Intelligence Infographic

The State of Artificial Intelligence Infographic

While those are indeed examples of artificial intelligence, examples of AI in the real world of today are a bit more mundane and a whole lot less sinister.

The personal assistant on your smartphone that helps you locate information, the facial recognition software on Facebook photos, and even the gesture control on your favourite video game are all examples of practical AI applications.

Rather than being a part of a dystopian world view in which the machines take over, current AI makes our lives a whole lot more convenient by carrying out simple tasks for us.

10 Powerful Examples Of Artificial Intelligence In Use Today

From voice-powered personal assistants like Siri and Alexa, tomore underlying and fundamental technologies such as behavioral algorithms, suggestivesearches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today.

While companies like Apple, Facebook and Tesla rollout ground-breaking updates and revolutionary changes to how we interact with machine-learning technology, many of us are still clueless on just how A.I.is being used today by businesses both big and small.

And although the past 100 years have seen the most dramatictechnological upheavalsto life than in all of human history, the next 100 years is set to pave the way for a multi-generational leap forward.

Quantum computers will not only solve all of life's most complex problems and mysteries regarding the environment, aging, disease, war, poverty, famine, the origins of the universe and deep-space exploration, just to name a few, it'll soon power all of our A.I.

Considering that the world lacks any formidable quantum resistant cryptography (QRC), how will a country like the United States or Russia protect its assets from rogue nations or bad actors that are hellbent on using quantum computers to hack the world's most secretive and lucrativeinformation?

The internet will not be secure,as we rely on algorithms which are broken by quantum computersto secure our connections to web sites, download emails andeverything else.

Even updates to phones, and downloadingapplications from App stores will be broken and unreliable.Banking transactions via chip-and-PIN could [also] be rendered insecure(depending on exactly how the system is implemented ineach country).'

However, it's usefulness and its uncanny ability to decipher speech from anywhere in the room has made it a revolutionary product that can help us scour the web for information, shop, schedule appointments, set alarms and a million other things, but also help power our smart homes and be a conduit for those that might have limited mobility.

#4 -- Cogito Originally co-founded byCEO, Joshua Feast and, Dr. Sandy Pentland,Cogito is quite possibly one of the most powerful examples of behavioral adaptation to improve the emotional intelligence of customer support representatives that exists on the market today.

#5 -- Boxever Boxever, co-founded by CEO, Dave O’Flanagan, is a company that leans heavily on machine learning to improve the customer's experience in the travel industry and deliver 'micro-moments,' or experiences that delight the customers along the way.

Based on 400 musical characteristics, each song is first manually analyzed by a team of professional musicians based on this criteria, and the system has an incredible track record for recommending songs that would otherwise go unnoticed but that people inherently love.

The Nest learning thermostat, which, by the way, can now be voice-controlled by Alexa, uses behavioral algorithms to predictively learn from your heating and cooling needs, thus anticipating and adjusting the temperature in your home or office based on your own personal needs, and also now includes a suite of other products such as the Nest cameras.

Everyday Examples of Artificial Intelligence and Machine Learning

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

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.

According to a 2015 report by the Texas Transportation Institute 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.

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;

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.

Glimpse into the future In the future, AI will shorten your commute even further via self-driving cars that result in up to 90% fewer accidents, more efficient ride sharing to  reduce the number of cars on the road by up to 75%, and smart traffic lights that reduce wait times by 40% and overall travel time by 26% in a pilot study.

“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.

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.

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?

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.

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.

– 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.

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.

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).

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.

– 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.

– 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”).

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.

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.

–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.

While Amazon doesn’t reveal what proportion of its sales come from recommendations, research has shown that recommenders increase sales (in this linked study, by 5.9%, but in other studies recommenders have shown up to 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).

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.

however, a month later Amazon’s press release boasted a 9x increase in Echo family sales over the previous year’s holiday sales, suggesting that 5 million sold is a significant underestimate.

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.

8 ways artificial intelligence is going to change the way you live, work and play in 2018

'Because of the popularity of voice-based personal assistants, we're starting to see the technology embedded across a wide range of devices, from lamps, to TVs, to cars and beyond.

Combine that with the fact that in 2018 we'll likely start seeing vendors enabling people to customize the trigger word for these various assistants to start listening and, well, it's a quick recipe for disaster.

The future of Artificial Intelligence: 6 ways it will impact everyday life

Technology moves at breakneck speed, and we now have more power in our pockets than we had in our homes in the 1990s.

Artificial intelligence (AI) has been a fascinating concept of science fiction for decades, but many researchers think we’re finally getting close to making AI a reality.

is a type of “deep learning” that allows machines to process information for themselves on a very sophisticated level, allowing them to perform complex functions like facial recognition.

Google began testing a self-driving car in 2012, and since then, the U.S. Department of Transportation has released definitions of different levels of automation, with Google’s car classified as the first level down from full automation.

Yoky Matsuka of Nest believes that AI will become useful for people with amputated limbs, as the brain will be able to communicate with a robotic limb to give the patient more control.

Robot Worx explains that robotic welding cells are already in use, and have safety features in place to help prevent human workers from fumes and other bodily harm. 4.

climate change might seem like a tall order from a robot, but as Stuart Russell explains, machines have more access to data than one person ever could—storing a mind-boggling number of statistics.

These interactions will clearly help our society evolve, particularly in regards to automated transportation, cyborgs, handling dangerous duties, solving climate change, friendships and improving the care of our elders.

Beyond these six impacts, there are even more ways that AI technology can influence our future, and this very fact has professionals across multiple industries extremely excited for the ever-burgeoning future of artificial intelligence.

10 Real-World Examples of Machine Learning and AI [2018]

Smart machines and applications are steadily becoming a daily phenomenon, helping us make faster, more accurate decisions.

As of 2017, a quarter of organisations are spending 15 percent or more of their IT budget on machine learning capabilities, and we expect the number of machine learning examples to rise in the near future.

With cloud computing offering organisations an unprecedented level of scalability and power, we’re finally at a point where machine learning can hit the mainstream and drive innovation in every sector.

Before we introduce you to 10 real-world applications of machine learning, let’s take a look at some of the more transformative machine learning examples in a three key industries: The transformative potential of machine learning is the driving force behind its popularity in the financial services industry (see graph below) and is the reason why the insurance sector is slowly moving into the digital age.

Machine learning can help banks, insurers, and investors make smarter decisions in a number of different areas: Examples of machine learning can also be found in the health and social care industry.

Having studied a facial dataset of 4 million Facebook users, DeepFace has become adept at recognising the nuances in human countenance across 4000 separate identities.

These deep learning algorithms help the app extract street names and house numbers from photos taken by Street View cars and increase the accuracy of search results.

Machine learning frees up more time for Google engineers, automatically extracting information from geo-located images and achieving an accuracy rate of 84.2 percent for some of France’s most convoluted street signs.

RankBrain now handles around 15 percent of Google’s daily queries, working out the intent behind never before seen searches much faster than the previous old rules-based system.

In 2015, they introduced asmart reply functionto Gmail to help users tackle their inbox, with 10 percent ofmobileusers' emails sent using this tool the following year.

But with machine learning and neural networks, PayPal is able to draw upon financial, machine, and network information to provide a deeper understanding of a customer’s activity and motives.

Machine learning is integral to this process, as the platform caters to more than 100 million subscribers While the finer details of Netflix’s machine learning algorithms are kept behind closed doors, Tod Yellin, the company’s VP of product innovation states there are two things that feed the neural network: user behaviour and programme content.

To do this the machine learning model must handle three distinct requirements: You know that one cheesy pop song you listened to that triggered numerous other cheesy pop recommendations?

If we look at the five current biggest companies in the global market, we see that every single one of them has embraced digital transformation and used technology such as machine learning to change the game for everyone else.

Applications of Artificial Intelligence

The uses in daily life and medicine.

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