We stay with the deep learning subject. This technology returns in our blog posts so frequently because it’s developing rapidly and gaining great interest among data scientists, machine learning specialists, and entrepreneurs. The fact is, deep learning has everything it takes to revolutionize our world, even though it’s a slow and silent revolution. To bring deep learning a bit closer, we decided to gather some of the most popular and promising deep learning applications in one place. Interested?
If you read our blog, you know that deep learning is one of the most sophisticated and advanced AI-related technologies. It’s build to imitate the human cortex, and, therefore, deal with complex issues and projects all on its own, with no human assistance.
That’s the theory, but what does it look like in real life? Can deep learning be found anywhere outside labs and R&D departments in large corporations? The answer is yes! And today, we are going to show you some of the most exciting real-life applications of this tremendous technology.
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Autonomous vehicles fueled by deep learning
AVs, also known as self-driving cars, are built based on deep learning technology. According to NVIDIA’s Deep Learning Institute, the automotive AI market will be valued at 11 billion USD by 2025. The installation rate of AI-based systems in new vehicles will rise by 109%. When it comes to AVs, the camera-based machine vision systems and radars are essential. They are at the core of every self-driving car. The thing is, one of the biggest challenges is pedestrian and obstacle detection and lane navigation. If autonomous vehicles are to be broadly used, they have to be above all safe.
Thanks to deep learning, all of that is possible. As you know, deep learning and machine learning are technologies based on examples and experience. The regular cycle of testing and implementation is typical to deep learning algorithms. The exposure to millions of scenarios that could happen on the road helps scientists build safe AVs. The deep learning apps have to comprise a variety of autonomous driving scenarios, including traffic navigation, obstacle avoidance, and robotic ridesharing.
In 2015, UBER announced the launch of its own AI lab, built in order to improve self-driving cars. They have also acquired a start-up company called Geometric Intelligence with the same goal in mind. Their prototype cars are packed with many sensors, including cameras, radars, and lidars—radar-like scanners that use laser light instead of radio waves.
Granted, we are still before the wide application of this technology, but it grows every year, and who knows, maybe in ten years from now AVs will be a much more frequent sight on the streets of big cities?
Let’s discover more deep learning applications!
Chatbots and virtual assistants
We talked about that in our last article covering the topic of NLP Algorithms. Virtual assistants like Google Assistant, Amazon Alexa, and Siri are based on sophisticated deep learning NLP algorithms. The idea is to teach machines to understand human language. And it’s the deep learning technology that allows machines to catch linguistic nuances (idioms, sarcasm, various expressions, tonal differences), process them, and come up with an appropriate answer.
Today, virtual assistants and chatbots are commonly used in:
- Motor industry (voice navigation is available in almost every modern car)
- Hotlines and helpline
- Mobile devices (Siri and Alexa as mentioned earlier)
These virtual assistants are designed to understand and process written and spoken language alike. Let’s take an example of Talkie.ai. It’s a Polish start-up company working on voice bots. Their technology is based on AI and allows them to answer frequently asked questions, transfer more complex queries directly to the human consultant, conduct phone surveys, book, change or cancel appointments or reservations, or inform about the delivery status.
Talkie.ai had gained a lot of media coverage and the industry’s attention when they announced their Coronabot. It was designed to support the NFZ (Polish National Health Fund) in answering incoming calls about the COVID-19 disease. They trained their coronabot with the usage of the NFZ knowledge base and the current disease stats. As a result, Talkie.ai has built a fully-fledged voice bot, fully capable of answering questions about the disease. It works 24/7 and can serve many patients simultaneously.
Discover different deep learning applications below.
Deep learning is a technology that learns your preferences and requirements. As a result, you can get very accurate, personalized recommendations. It’s also an application widely used in the e-commerce sector. Today, every decent store has a developed recommendations engine that proposes additional products or other products that a given customer might be interested in.
The result? Growth of sales, of course! When these recommendations are really personalized and accurate, people are willing to follow them, and, as a result, buy more or more expensive products. Machine/deep learning algorithm study the way you behave on the website, the products you buy, and, based on that, learns your needs and preferences. Although it sounds simple, there is a lot of computing power involved to make that possible.
Let’s take an example–Netflix. Today, it’s one of the most popular Internet television networks, with over 160 million members worldwide. Naturally, movies and shows recommendations cannot be done by hand. An advanced AI algorithm was indispensable. According to Netflix, they “invest heavily in machine learning to continually improve member experience and optimize the Netflix service end-to-end.”
Now, what do they use machine learning for?
- Shows and movies recommendations
- Learning characteristics that make content successful (in other words, it’s the AI algorithm that decides what’s the next Netflix Original Series is going to be about!)
- Optimizing the production of their movies and TV shows
- Optimizing video and audio encoding
What seems like a pretty straightforward enterprise (they’re just making TV series and streaming movies) is actually a complex AI-based endeavor that grows and adjusts every week.
Let’s move to other successful deep learning applications.
Deep learning in healthcare
Possibly, it’s one of the most important deep learning applications in the modern world. It’s true; deep learning helps to save human lives! How is that possible? This subject also repeatedly comes back in our articles. You can read on our blog about various AI applications in the modern healthcare industry:
- Drug development
- Analysis of medical images, what accelerates the diagnosis stage and compensates the lack of trained radiologists
- Treatment recommendation based on individual patient historical data
- Robot-assisted surgery
And many more. In this article, we are going to focus on just one application–genomics. As we can read in the “A guide to deep learning in healthcare” article published in Nature Medicine in 2019, “Modern genomic technologies collect a wide variety of measurements, from an individual’s DNA sequence to the quantity of various proteins in their blood. There are many opportunities for deep learning to improve the methods used to analyze these measurements, which will ultimately help clinicians provide more accurate treatments and diagnoses. […]Understanding the genetics of disease allows clinicians to recommend treatments and provide more accurate diagnoses.[…] Given their greater power and ability to effectively integrate disparate data types, deep-learning techniques are likely to provide more accurate pathogenicity predictions than are possible today […]”
All in all–deep learning is hugely promising when it comes to improving treatments and diagnoses. For instance, Huimei Technology, a China-based start-up, currently develops and designs medical artificial intelligence solutions that focus on improving clinical quality. Their products include the clinical decision support system, disease process quality management system, and medical knowledge base. Huimei is using natural language processing, deep learning, and other AI techniques to process clinical big data. As a result, they expect to improve and accelerate the entire clinical process, from diagnosis up to treatment.
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Advertising and marketing
Clive Humby once said that “Data is the new oil”. And that’s true, especially in marketing. You see, modern advertising has gone through a massive shift in recent years. Even 15-20 years ago, advertising was mostly based on offline methods (flyers, radio & TV ads, billboards, etc.), often difficult to measure. Today, marketing is data-based. Every marketing campaign, every activity, every post can be measured and assessed with amazing precision.
Deep learning has a lot to say here, as well! Deep learning has allowed brands to gain much more knowledge about their customers and a complete understanding of how they think, react, and purchase products. This knowledge is priceless, chiefly because it enables the design of much more accurate marketing campaigns that resonate with customers’ preferences and needs. Moreover, companies frequently use other DL-related technologies like image recognition.
Facebook is without a shadow of a doubt one of the most impressive examples. Their Facebook Ads engine is based almost entirely on AI algorithms. Facebook’s deep neural networks analyze each user’s age, gender, location, page likes, interests, and even mobile data to profile them into chosen categories, and then show them ads specifically targeted towards them. And you know what? It’s immensely effective. That’s because people are so happy to share their personal data and interests. Facebook algorithms just utilize that data!
However, there’s also the other side of the coin. In 2019, Karen Hao, a reporter for MIT Technology Review, said that Facebook’s algorithms “started to discriminate [users by] showing different people different types of housing or employment opportunities.” Although it may seem unfair for our human point of view, these ads were simply effective. But this means that even such an advanced technology as deep learning has to be verified from time to time by human managers. That’s the recipe for success.
Are you, just like us, amazed by deep learning? Thankfully, your company can utilize this technology as well! Drop us a line and tell us something more about your company, your products, and your work. We will gladly show you all the DL-related possibilities waiting for you. With deep learning on your side, you can make more accurate decisions and predictions, improve your campaigns, and automate the vast majority of tasks taking place in your enterprise.
 Nvidia. Deep Learning for Autonomous Vehicles—Perception. URL: https://www.nvidia.com/content/dam/en-zz/Solutions/deep-learning/deep-learning-education/Deep_Learning_for_Autonomous_Vehicles_Perception.pdf. Accessed Jul 16, 2020.
 Darrell Etherington. Uber acquires Geometric Intelligence to create an AI lab. Dec 5, 2016. URL: https://techcrunch.com/2016/12/05/uber-acquires-geometric-intelligence-to-create-an-ai-lab/. Accessed Jul 16, 2020.
 Grzegorz Marynowicz. Masz pytanie o koronawirusa? Polacy stworzyli bota, który odpowie na kluczowe kwestie. Mar 19, 2020. URL: https://mambiznes.pl/wlasny-biznes/masz-pytanie-o-koronawirusa-polacy-stworzyli-bota-ktory-odpowie-na-kluczowe-kwestie-98694. Accessed Jul 16, 2020.
 Netflix. Machine Learning. URL: https://research.netflix.com/research-area/machine-learning. Accessed Jul 16, 2020.
 Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov. A guide to deep learning in healthcare. Jan 2019. URL: https://www.researchgate.net/publication/330203264_A_guide_to_deep_learning_in_healthcare. Accessed Jul 16, 2020.
 MedicalStartups. Huimei Healthcare. URL: https://www.medicalstartups.org/startup/huimei/. Accessed Jul 16, 2020.
 Jeremy Fain. How Deep Learning Is Transforming Marketing. Mar 2, 2020. URL: https://www.forbes.com/sites/forbesagencycouncil/2020/03/02/how-deep-learning-is-transforming-marketing/?sh=5c15e7f54a1f. Accessed Jul 16, 2020.