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If you’ve ever seen at least one sci-fi movie, you probably know that in the future machines will take over the world. You know that we’ll live in a dystopian society and won’t see sunlight ever again.
Those movies are to blame for building this stereotype about artificial intelligence. In reality, machine learning is not about that at all. This type of AI is spreading across the world quicker than a virus and on the picture below you can see why.
What is machine learning
As the infographic above shows, the ML is much more diverse than those robots from sci-fi movies. And much safer too.
Machine Learning is specific AI-based method that not only solve the assigned task itself but is also learning in the process. This technology is extremely useful when it comes to analytics and forecasting, but it can be used elsewhere. In this article, we’ll talk about machine learning technology from the perspective of the well-known companies that are using it to enhance their performance.
How implementation of machine learning technology is spreading across the market
Statista predicts that AI software market growth will reach $22.6 billion by the end of 2020. ML, among others AI technologies, will probably spread on the market the quickest. Its quantum leap will be as important to the world as smartphone revolution. On the graph below you can see the ML impact on different industries.
Potential aggregate economic impact of artificial intelligence worldwide in the future.
As you can see, this technology will most significantly impact the retail, transport, and logistics industries. The high importance of data analyzing and forecasting in those industries will probably be the reason. In the transport and logistics industry, machine learning applications are able to provide more data for insights analysis on the company's performance to improve inventory planning, eliminate the possibility of malfunction and enhance resource planning. In the retail industry, ML is often used to improve customer service, gather clients’ experience within an application, and make the bridge between virtual and physical sales channels.
This technology may also be used for getting customers insights and cost-reducing, which pays off in 37% - 36% of all companies using machine learning.
As you can see, the artificial intelligence won’t take over the world the way you expected. But it will definitely change our lives and we hope that it is for the better. This is why we’re giving you 6 examples of how well-known companies use ML in their applications.
6 well-known companies that use machine learning in their applications
You’ve probably heard of Pinterest before. This is a platform where users can place and promote their content. In Pinterest’s case, the content is images, or arts. It’s one of a kind platform for being an artist on the internet - popular pictures from Pinterest tend to go viral. An improved feed could help users quickly find what interests them. Maybe that’s why this company decided to invest in machine learning for their web and mobile apps.
In 2015, they acquired a machine learning company Kosei, that uses ML technology for commercial applications. It’s specifically focused on algorithms of content search and recommendations. Machine learning algorithms provide users with accurate recommendations. Then based on their reaction ML technology predicts whether the content will be pinned by the user. Kosei also predicts the effectiveness and relevance of personalized ads using machine learning algorithms in collecting customer experience. This process takes place through data analysis, as well as the collection and discussion of customers' experience. Machine learning is trained with Spark, which improves its efficiency in real time. Thus, it improves the search for content in the application, giving the user the most accurate and suitable result. So now Pinterest is using machine learning for almost everything in their application - from advertising to moderation. They reduce spam and inappropriate content in the app. Isn’t this what made them so popular all over the world?
As you can see in this graphic, the international popularity of Pinterest has experienced significant growth since 2016 to the first quarter of 2020. Today Pinterest has up to 367 million users worldwide. You can easily connect one thing to another, and notice the results. Commercial algorithms for content discovery, based on machine learning, made Pinterest one of top-5 applications among US users. With the smart algorithmic feed Pinterest is now able to improve the control over user’s activity and helps everyone find exactly what they need right now.
Oh, Twitter, a place of every argument on the internet. This sweet world of something funny and revolutionary at the same time; it is Twitter where you can find everything from a dumb joke to political speech. And not so long ago Twitter had changed its feed to make it algorithmic.
Machine learning algorithms find the “most interesting tweets for you” and show them at the top of the feed. They analyze tweets in real-time and score them from “the best” to “the worst”. Maybe you don’t like such a change and you’d like to see your feed in chronological order, but don’t be pissed off just yet. Give Twitter a chance!
These algorithms make a decision based on your personal interests and preferences, and whether you like it or not let’s face the truth - you won’t quit your Twitter account anyway.
But there are good and less frustrating examples of ML technology in Twitter application. For example, it moderates the racist and other inappropriate content in your feed. Along with that, machine learning helps Twitter fight terrorism! In the past, ML technology helped to take down more than 300,000 terrorist accounts.
In the case of Twitter, Machine Learning technology might not be used in the best way possible and had driven application users to give negative feedback about it. So, the one thing you must remember - if you want to implement machine learning into your application and don’t yet know what positive changes it’ll bring to you, you’d better be as popular as Twitter. If not - think twice (or get help from your technical partner).
Facebook using AI for a number of features that help them to stay popular for so many years. We can say without a doubt that social media is inclusive in a good way.
The first thing they use machine learning for in their application is chatbots. This means that every business account on Facebook, with the help of ML technologies, can create itself a chatbot for better customer service. It isn’t, of course, a complicated software, but it can be extremely helpful for pre-seed/seed startups with low funding. A chatbot has to be created by the startup's developers themselves. However, they can add it to Facebook Messenger and program it for any necessary activity - whether it is statistical data for forecasting using ML algorithms or a unique adaptation to each user with the use of AI technology.
The second thing is the algorithms for reading the text for visually impaired people. This helps people who can’t see the text or a picture to hear it.
And the last is something similar to Pinterest ML - Facebook uses this technology for spam moderation and content filtering.
Facebook’s ML workflow is based on users’ data analysis to provide clients with the best results. For example, if its goal is to analyze user experience, it will collect data about the customers’ activity in the application, and then display an indicator of the customer satisfaction level.
As you can see on the chart above, Facebook keeps on growing and gets more users every year. In 4th quarter of 2019 this social media gained 2.5 billions users (most of which are located in Asia), and who knows what comes next.
We’re not saying that all the credit for this goes to machine learning algorithms in their app, but it definitely earned a place in Facebook success. This is achieved in the same way as in the example above. Facebook ML algorithms analyze content by key parameters and learn based on sorted publications.
Google's ambitions are so endless that it is easier to name something that Google isn’t working on than write a list of everything they do. But here we want to know only stuff related to AI and machine learning, so let’s say a few words the way about how Google is using this technology.
The most exciting thing Google did in this industry is the neural network. Maybe they’ve done a step towards the dystopia that Matrix, Terminator, and others were warning us about when they’ve built the machine that dreams. Even though DeepMind is more like of a machine that memes.
This has driven Google to a new wave of popularity because a neural network that uses other pictures to draw made a lot of masterpieces like the one above. By the way, this is not everything Google can do. Remember when you search for something, Google suggests the ending of your sentence? It is also possible thanks to machine learning. As well as those incredible things that Google does when fixes your misspelled words.
Also, Google Translator can learn and adapt to your writing style if you’re using it long enough. Thus, Google’s ML adapts to the most frequent requests of the user and to the unique pattern of the user’s actions. How is the ML process at Google? Depends on what this process is aimed at. If this aims to create a picture, as in the example above, then the technology will collect data and format it in the correct order to create the image. If the goal is to find the right route for your trip, Google Maps uses data about your location, analyzes it with machine learning algorithms, and provides you with information about vehicles or route that suits you best. All this is done in order to improve the customer's experience and provide search results that are most relevant for a particular user.
Baidu is a popular Chinese search engine that took an example from Google. They’re investing in AI applications as well, but they are aiming elsewhere. If Google’s neural net creates pieces of art, Baidu’s technology Deep Voice can even create a human voice.
Well, not quite human but at least it sounds very much alike.
In this case, an example of machine learning technology looks something like this: there is a collection of samples and technology performs their qualitative analysis. Hence, it finds info on the accents and specific pronunciation of the human speech depending on different countries and regions.
This neural net can imitate intonations, accents, and pronunciations to create sounds that exceptionally accurate. It can have a major impact on voice search since technology is learning by analyzing human speech. Machine learning applications of this kind also can be used for live translations or for transforming voice conversations into text. So, as you can see ML technology does have a potential.
This company’s AI Watson shows how fast an old company can adapt the rules of the new world. Watson is famous for giving well-formed recommendations on how to cure cancer, and hence this technology is slowly but effectively changing the world. This machine learning-based technology can predict scenarios of every individual patient curing process and find the most efficient treatment for each one.
IBM also has a mobile application for people with diabetes, which shows great results in the healthcare industry. For example, in 2019 the app predicted hypoglycemic events with 90% accuracy. By the way, if you’re thinking about implementing an ML solution from IBM into your business - we can help you! MassMedia Group is using IBM Watson's speech to text technology for high precision speech analysis. This can help you optimize your company’s work with text. In this case, different speakers are recognized in the audio materials with the addition of these key points in real time with high accuracy and confidence.
The companies we’ve mentioned above are well-known and had their peak of popularity long before today. But machine learning is popular not only among industry giants. There are successful ML using startups and now we’ll talk more about their cases.
4 successful 2020 startups that use machine learning in their applications
All the startups below are completely ML-based. Using this technology is at the core of the services they provide.
It is a healthcare machine learning-based startup that recently (in March 2020) raised their series E funding. Their main goal is to help healthcare organization cut costs by avoiding administrative malfunctions and hence improve the performance in patient care and treatment effectiveness. So as you can see, this is achieved using machine learning technology. The AI ââsolution for this startup acts as a bridge between systems and organizations, automating large-scale tasks and simplifying their implementation. Thus, Olive optimizes the routine work of medical staff, allowing them to transfer human resources to more vital needs.
They’re creating a software for the cancer treatment improvement. Machine learning solutions from Paige are not applications, but are still extremely helpful. They’re aiming to provide the experts’ data on cancer care and recently raised their series B funding. Their success is achieved thanks to an extensive database and its analysis capabilities. Thus, the startup provides more information to medical professionals about the patient care improvement, and diagnosis accuracy based on previous industry experience.
It is a marketplace that uses AI technologies for buying and selling mostly electronic devices. They use machine learning algorithms to get rid of online scammers and improve the online trading itself. It is achieved through users verification and automated lists, which are powered by ML which increases the level of security in platforms for electronic sales. They just started, and in 2020 have already received Pre-Seed funding.
.4 Jus Mundi
An international law search engine that is comfortable for user and based on machine learning. It is used to help increase efficiency of law researches. Database of this startup keeps more than 4000 international contracts. They collect and analyze important for their customers documents using machine learning algorithms in order to save them from spending extra time when searching for important legal information. This startup raised its Seed funding in 2020.
But not all of those startups used a custom machine learning solutions. Some of 2020 AI startups were based on existing off-the-shelf solutions that help them increase their popularity and efficiency. Some startups created their own solution so other businesses can implement it in their workflow and make the whole better process of work.
So, if you decided to use machine learning technology to give your application a competitive advantage - think wisely. This technology is incredible, and, as the examples above show, it can be very useful not only for giant companies, but for everyone who wants to implement innovations in their business. But you need to know what exactly is necessary for your application and how to implement it.
So, if you decided when and in what configuration you want to implement this technology into your application - get an aid from a reliable IT partner. And if you haven’t decide on it yet or you’re having any doubts - you need a development company with an IT-consulting service.
We’re online whenever you need us so don’t hesitate. With the help of right people you can do even more than knock the world flat.