Benefits which MNCs are getting from Artificial Intelligence(AI) and Machine Learning(ML)
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⭕ All machine learning is AI, but not all AI is machine learning
Hello Everyone🌟….. Today I came up with a new article which will help you know how MNCs are using Machine Learning / Artificial Intelligence??
With all the excitement and hype about AI that’s “just around the corner” — Self-Driving Cars, Instant Machine Translation, Voice Assistant (Alexa , Siri, Jarvis, 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?
- Google’s AI-Powered Predictions
Using anonymized location data from smartphones, Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its 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.
2. Ridesharing Apps Like UBER and OLA
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.
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.
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.
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.
The timeline for some of these changes is unclear, as predictions vary about when self-driving cars will become a reality: BI Intelligence predicts fully-autonomous vehicles will debut in 2019; Uber CEO Travis Kalanick says the timeline for self-driving cars is “a years thing, not a decades thing”.
4. E-MAIL
(i). Spam Filters
Your email inbox seems like an unlikely place for AI, but the technology is largely powering one of its most important features: the spam filter. Simple rules-based filters aren’t effective against spam, because spammers can quickly update their messages to work around them. Instead, spam filters must continuously learn from a variety of signals, such as the words in the message, message metadata (where it’s sent from, who sent it, etc.).
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.
(ii). 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.
5. 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.
Examples of Artificial Intelligence:-
Social Networking
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).
Facebook also uses AI to personalize your newsfeed and ensure you’re seeing posts that interest you and, of particular business interest to Facebook is showing ads that are relevant to your interests. Better targeted ads mean you’re more likely to click them and buy something from the advertisers — and when you do, Facebook gets paid.
Facebook also 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.
Online Shopping: AMAZON
- 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.
3. 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. 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.
Mobile Use
- 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 uses artificial neural networks to power voice search. Microsoft claims to have developed a speech-recognition system that can transcribe conversation slightly more accurately than humans.
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 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.
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.
Machine Learning
Imagine that you were in charge of building a machine learning prediction system to try and identify images between dogs and cats. For Example, the first step would be to gather a large number of labelled images with “dog” for dogs and “cat” for cats. Second, we would train the computer to look for patterns on the images to identify dogs and cats, respectively.
Trained machine learning system capable of identifying cats or dogs.
Once the machine learning model has been trained, we can throw at it (input) different images to see if it can correctly identify dogs and cats. As seen in the image, a trained machine learning model can (most of the time) correctly identify such queries.
for example, the image search and translation tools use sophisticated machine learning. This allows the computer to see, listen and speak in much the same way as humans do. Much wow!
Google uses machine learning algorithms to provide its customers with a valuable and personalized experience. Gmail, Google Search and Google Maps already have machine learning embedded in services. Google is the master of all. It takes advantage of machine learning algorithms and provides customers with a valuable and personalized experience. Machine learning is already embedded in its services like Gmail, Google Search and Google Maps.
Conclusion:
AI becomes more deeply integrated in our lives, it will become the new infrastructure powering a second industrial revolution. These changes brought an evolution in the overall operating scenario of companies by providing them insights to improve their product and service offerings. It wouldn’t be wrong to say that AI made lives easier through chatbots, algorithms, recommendation engines, hardware infrastructure, language processing and much more. Now, the industry is expected to experience some strategic shifts from enterprises. Emphasizing the enhancement of AI provided to their products and make them the top-notch companies of this generation.
Artificial intelligence and its applications have made a significant impact on nearly every industry. Defined as a technique enabling machines to mimic human behaviour, brands are using AI to automate processes at an increasing rate. AI isn’t a new phenomenon. It has been around for almost 50 years, learning constantly, almost on a daily basis. As we evolve and become more efficient, and artificial intelligence learns to better emulate human intelligence, businesses benefit from the increased process and operational efficiencies.
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