Top 8 real-life examples of Machine Learning
Machine learning is one modern innovation that has helped man enhance not only many industrial and professional processes but also advances everyday living. But what is machine learning? It is a subset of artificial intelligence, which focuses on using statistical techniques to build intelligent computer systems in order to learn from databases available to it. Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc.
The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data.
Machine learning applications provide results on the basis of past experience. In this article, we will discuss 10 real-life examples of how machine learning is helping in creating better technology to power today’s ideas.
Source: bigdata-madesimple.com
Image Recognition
Image recognition is one of the most common uses of machine learning. There are many situations where you can classify the object as a digital image . For example, in the case of a black and white image, the intensity of each pixel is served as one of the measurements. In colored images, each pixel provides 3 measurements of intensities in three different colors — red, green and blue (RGB).
Machine learning can be used for face detection in an image as well. There is a separate category for each person in a database of several people. Machine learning is also used for character recognition to discern handwritten as well as printed letters. We can segment a piece of writing into smaller images, each containing a single character.
Medical diagnosis
Machine learning can be used in the techniques and tools that can help in the diagnosis of diseases . It is used for the analysis of the clinical parameters and their combination for the prognosis example prediction of disease progression for the extraction of medical knowledge for the outcome research, for therapy planning and patient monitoring. These are the successful implementations of the machine learning methods. It can help in the integration of computer-based systems in the healthcare sector.
Statistical Arbitrage
In finance, arbitrage refers to the automated trading strategies that are of a short-term and involve a large number of securities. In these strategies, the user focuses on implementing the trading algorithm for a set of securities on the basis of quantities like historical correlations and the general economic variables. Machine learning methods are applied to obtain an index arbitrage strategy. We apply linear regression and the Support Vector Machine to the prices of a stream of stocks.
Learning associations
Learning associations is the process of developing insights into the various associations between the products. A good example is how the unrelated products can be associated with one another. One of the applications of machine learning is studying the associations between the products that people buy. If a person buys a product, he will be shown similar products because there is a relation between the two products. When any new products are launched in the market, they are associated with the old ones to increase their sales.
Classification
A classification is a process of placing each individual under study in many classes. Classification helps to analyze the measurements of an object to identify the category to which that object belongs. To establish an efficient relation, analysts use data. For example, before a bank decides to distribute loans, it assesses the customers on their ability to pay loans. By considering the factors like customer’s earnings, savings, and financial history, we can do it. This information is taken from the past data on the loan.
Prediction
Machine learning can also be used in the prediction systems. Considering the loan example, to compute the probability of a fault, the system will need to classify the available data in groups. It is defined by a set of rules prescribed by the analysts. Once the classification is done, we can calculate the probability of the fault. These computations can compute across all the sectors for varied purposes. Making predictions is one of the best machine learning applications.
Extraction
Machine learning can extract structured information from unstructured data. Organisations amass huge volumes of data from customers. A machine learning algorithm automates the process of annotating datasets for predictive analytics tools.
Real-world examples of extraction:
Generate a model to predict vocal cord disorders
Develop methods to prevent, diagnose, and treat the disorders
Help physicians diagnose and treat problems quickly
Typically, these processes are tedious. But machine learning can track and extract information to obtain billions of data samples.
Financial services
Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud.
Financial services
Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud.
1. Yelp — Image Curation at Scale
Few things compare to trying out a new restaurant then going online to complain about it afterwards. This is among the many reasons why Yelp is so popular (and useful).
While Yelp might not seem to be a tech company at first glance, Yelp is leveraging machine learning to improve users’ experience Since images are almost as vital to Yelp as user reviews themselves, it should come as little surprise that Yelp is always trying to improve how it handles image processing.
This is why Yelp turned to machine learning a couple of years ago when it first implemented its picture classification technology.
Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently — no small feat when you’re dealing with tens of millions of photos.
Source: wordstream.com
2. Pinterest — Improved Content Discovery
Whether you’re a hardcore pinner or have never used the site before, Pinterest occupies a curious place in the social media ecosystem. Since Pinterest’s primary function is to curate existing content, it makes sense that investing in technologies that can make this process more effective would be a priority — and that’s definitely the case at Pinterest.
In 2015, Pinterest acquired Kosei , a machine learning company that specialized in the commercial applications of machine learning tech (specifically, content discovery and recommendation algorithms).
Today, machine learning touches virtually every aspect of Pinterest’s business operations, from spam moderation and content discovery to advertising monetization and reducing churn of email newsletter subscribers . Pretty cool.
Source: wordstream.com
3. Facebook — Chatbot Army
Although Facebook’s Messenger service is still a little…contentious (people have very strong feelings about messaging apps, it seems), it’s one of the most exciting aspects of the world’s largest social media platform.
That’s because Messenger has become something of an experimental testing laboratory for chatbots
Some chatbots are virtually indistinguishable from humans when conversing via text Any developer can create and submit a chatbot for inclusion in Facebook Messenger. This means that companies with a strong emphasis on customer service and retention can leverage chatbots , even if they’re a tiny startup with limited engineering resources.
Of course, that’s not the only application of machine learning that Facebook is interested in.
AI applications are being used at Facebook to filter out spam and poor-quality content , and the company is also researching computer vision algorithms that can “read” images to visually impaired people
Source: wordstream.com
4. Twitter — Curated Timelines
Twitter has been at the center of numerous controversies of late (not least of which were the much-derided decisions to round out everyone’s avatars and changes to the way people are tagged in @ replies), but one of the more contentious changes we’ve seen on Twitter was the move toward an algorithmic feed Rob Lowe was particularly upset by the introduction of algorithmically curated Twitter timelines Whether you prefer to have Twitter show you “the best tweets first” (whatever that means) or as a reasonably chronological timeline, these changes are being driven by Twitter’s machine learning technology.
Twitter’s AI evaluates each tweet in real time and “scores” them according to various metrics
Ultimately, Twitter’s algorithms then display tweets that are likely to drive the most engagement. This is determined on an individual basis;
Twitter’s machine learning tech makes those decisions based on your individual preferences , resulting in the algorithmically curated feeds, which kinda suck if we’re being completely honest. (Does anybody actually prefer the algorithmic feed? Tell me why in the comments, you lovely weirdos.)
Source: wordstream.com
5. Google — Neural Networks and ‘Machines That Dream’
These days, it’s probably easier to list areas of scientific R&D that Google — or, rather, parent company Alphabet — isn’t working on, rather than trying to summarize Google’s technological ambition.
Needless to say, Google has been very busy in recent years, having diversified into such fields as anti-aging technology, medical devices, and — perhaps most exciting for tech nerds — neural networks The most visible developments in Google’s neural network research has been the DeepMind network, the “machine that dreams.” It’s the same network that produced those psychedelic images everybody was talking about a while back.
According to Google, the company is researching “ virtually all aspects of machine learning ,” which will lead to exciting developments in what Google calls “classical algorithms” as well as other applications including natural language processing, speech translation, and search ranking and prediction systems.
Source: wordstream.com
6. Edgecase — Improving Ecommerce Conversion Rates
For years, retailers have struggled to overcome the mighty disconnect between shopping in stores and shopping online. For all the talk of how online retail will be the death-knell of traditional shopping, many ecommerce sites still suck.
Edgecase, formerly known as Compare Metrics, hopes to change that.
Edgecase hopes its machine learning technology can help ecommerce retailers improve the experience for users . In addition to streamlining the ecommerce experience in order to improve conversion rates, Edgecase plans to leverage its tech to provide a better experience for shoppers who may only have a vague idea of what they’re looking for, by analyzing certain behaviors and actions that signify commercial intent — an attempt to make casual browsing online more rewarding and closer to the traditional retail experience.
Source: wordstream.com
7. Baidu — The Future of Voice Search
Google isn’t the only search giant that’s branching out into machine learning. Chinese search engine Baidu is also investing heavily in the applications of AI.
A simplified five-step diagram illustrating the key stages of a natural language processing system One of the most interesting (and disconcerting) developments at Baidu’s R&D lab is what the company calls Deep Voice a deep neural network that can generate entirely synthetic human voices that are very difficult to distinguish from genuine human speech . The network can “learn” the unique subtleties in the cadence, accent, pronunciation and pitch to create eerily accurate recreations of speakers’ voices.
Far from an idle experiment, Deep Voice 2 — the latest iteration of the Deep Voice technology — promises to have a lasting impact on natural language processing, the underlying technology behind voice search and voice pattern recognition systems. This could have major implications for voice search applications, as well as dozens of other potential uses, such as real-time translation and biometric security.
Source: wordstream.com
8. HubSpot — Smarter Sales
Anyone who is familiar with HubSpot probably already knows that the company has long been an early adopter of emerging technologies, and the company proved this again earlier this month when it announced the acquisition of machine learning firm Kemvi.
Predictive lead scoring is just one of the many potential applications of AI and machine learning HubSpot plans to use Kemvi’s technology in a range of applications — most notably, integrating Kemvi’s DeepGraph machine learning and natural language processing tech in its internal content management system.
This, according to HubSpot’s Chief Strategy Officer Bradford Coffey will allow HubSpot to better identify “trigger events” — changes to a company’s structure, management, or anything else that affects day-to-day operations — to allow HubSpot to more effectively pitch prospective clients and serve existing customers.
Source: wordstream.com