Deep Learning is changing the way we look at technologies. There is a lot of excitement around Artificial Intelligence (AI) along with its branches namely Machine Learning (ML) and Deep Learning at the moment.
With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart.
It’s predicted that many deep learning applications will affect your life in the near future. Actually, they are already making an impact. Within the next five to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit.
1. Self-driving cars
Companies building these types of driver-assistance services, as well as full-blown self-driving cars like Google’s, need to teach a computer how to take over key parts (or all) of driving using digital sensor systems instead of a human’s senses. To do that companies generally start out by training algorithms using a large amount of data.
You can think of it how a child learns through constant experiences and replication. These new services could provide unexpected business models for companies.
2. Deep Learning in Healthcare
Breast or Skin-Cancer diagnostics? Mobile and Monitoring Apps? or prediction and personalised medicine on the basis of Biobank-data? AI is completely reshaping life sciences, medicine, and healthcare as an industry. Innovations in AI are advancing the future of precision medicine and population health management in unbelievable ways. Computer-aided detection, quantitative imaging, decision support tools and computer-aided diagnosis will play a big role in years to come.
3. Voice Search & Voice-Activated Assistants
One of the most popular usage areas of deep learning is voice search & voice-activated intelligent assistants. With the big tech giants have already made significant investments in this area, voice-activated assistants can be found on nearly every smartphone. Apple’s Siri is on the market since October 2011. Google Now, the voice-activated assistant for Android, was launched less than a year after Siri. The newest of the voice-activated intelligent assistants is Microsoft Cortana.
4. Automatically Adding Sounds To Silent Movies
In this task, the system must synthesize sounds to match a silent video. The system is trained using 1000 examples of video with sound of a drumstick striking different surfaces and creating different sounds. A deep learning model associates the video frames with a database of pre-rerecorded sounds in order to select a sound to play that best matches what is happening in the scene.
The system was then evaluated using a turing-test like a setup where humans had to determine which video had the real or the fake (synthesized) sounds.
This uses application of both convolutional neural networks and Long short-term memory (LSTM) recurrent neural networks (RNN).
5. Automatic Machine Translation
This is a task where given words, phrase or sentence in one language, automatically translate it into another language.
Automatic machine translation has been around for a long time, but deep learning is achieving top results in two specific areas:
- Automatic Translation of Text
- Automatic Translation of Images
Text translation can be performed without any pre-processing of the sequence, allowing the algorithm to learn the dependencies between words and their mapping to a new language.
6. Automatic Text Generation
This is an interesting task, where a corpus of text is learned and from this model new text is generated, word-by-word or character-by-character.
The model is capable of learning how to spell, punctuate, form sentences and even capture the style of the text in the corpus. Large recurrent neural networks are used to learn the relationship between items in the sequences of input strings and then generate text.
7. Automatic Handwriting Generation
This is a task where given a corpus of handwriting examples, generate new handwriting for a given word or phrase.
The handwriting is provided as a sequence of coordinates used by a pen when the handwriting samples were created. From this corpus, the relationship between the pen movement and the letters is learned and new examples can be generated ad hoc.
8. Image Recognition
Another popular area regarding deep learning is image recognition. It aims to recognize and identify people and objects in images as well as to understand the content and context. Image recognition is already being used in several sectors like gaming, social media, retail, tourism, etc.
This task requires the classification of objects within a photograph as one of a set of previously known objects. A more complex variation of this task called object detection involves specifically identifying one or more objects within the scene of the photograph and drawing a box around them.
9. Automatic Image Caption Generation
Automatic image captioning is the task where given an image the system must generate a caption that describes the contents of the image.
In 2014, there was an explosion of deep learning algorithms achieving very impressive results on this problem, leveraging the work from top models for object classification and object detection in photographs.
Once you can detect objects in photographs and generate labels for those objects, you can see that the next step is to turn those labels into a coherent sentence description.
Generally, the systems involve the use of very large convolutional neural networks for the object detection in the photographs and then a recurrent neural network (RNN) like a Long short-term memory (LSTM) to turn the labels into a coherent sentence.
10. Automatic Colorization
Image colourization is the problem of adding colour to black and white photographs. Deep learning can be used to use the objects and their context within the photograph to colour the image, much like a human operator might approach the problem. This capability leverage the high quality and very large convolutional neural networks trained for ImageNet and co-opted for the problem of image colourization. Generally, the approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of colour.
Advertising is another key area that has been transformed by deep learning. It has been used by both publishers and advertisers to increase the relevancy of their ads and boost the return on investment of their advertising campaigns. For instance, deep learning makes it possible for ad networks and publishers to leverage their content in order to create data-driven predictive advertising, real-time bidding (RTB) for their ads, precisely targeted display advertising and more.
12. Predicting Earthquakes
Harvard scientists used Deep Learning to teach a computer to perform viscoelastic computations, these are the computations used in predictions of earthquakes. Until their paper, such computations were very computer-intensive, but this application of Deep Learning improved calculation time by 50,000%. When it comes to earthquake calculation, timing is important and this improvement can be vital in saving a life.
13. Neural Networks for Brain Cancer Detection
A team of French researchers noted that spotting invasive brain cancer cells during surgery is difficult, in part because of the effects of lighting in operating rooms. They found that using neural networks in conjunction with Raman spectroscopy during operations allows them to detect the cancerous cells easier and reduce residual cancer post-operation. In fact, this piece is one of many over the last few weeks that match advanced image recognition and classification with various types of cancer and screening apparatus–more in the short list below.
14. Neural Networks in Finance
Futures markets have seen phenomenal success since their inception both in developed and developing countries during the last four decades. This success is attributable to the tremendous leverage the futures provide to market participants. This study analyzes a trading strategy which benefits from this leverage by using the Capital Asset Pricing Model (CAPM) and cost-of-carry relationship. The team applies the technical trading rules developed from spot market prices, on futures market prices using a CAPM based hedge ratio. Historical daily prices of twenty stocks from each of the ten markets (five developed markets and five emerging markets) are used for the analysis.
15. Energy Market Price Forecasting
Researchers in Spain and Portugal have applied artificial neural networks to the energy grid in an effort to predict price and usage fluctuations. The daily and intraday markets for the region are organized in a daily session where next-day sale and electricity purchase transactions are carried out and in six intraday sessions that consider energy offer and demand, which may arise in the hours following the daily viability schedule fixed after the daily session. In short, being able to make adequate predictions based on the patterns of consumption and availability yields to far higher efficiency and cost savings.
We have discussed TOP 15 applications of deep learning that are intended to rule the world in 2018 and beyond. This show rather than tell approach is expected to give you a clearer idea of the current and future capabilities of deep learning technology.