Deep Learning is changing the way we look at technologies. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech.
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.
So, here are the 10 Amazing ways Deep Learning will rule the world in 2018 and beyond,
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 having already made significant investments in this area, voice-activated assistants can be found on nearly every smartphone. Apple’s Siri is in 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. 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.
Advertising is another key area that has been transformed by deep learning. It has been used by both publishers and advertisers to increase 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.
6. 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 life.
7. Neural Networks for Brain Cancer Detection
A team of French researchers note 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 matches advanced image recognition and classification with various types of cancer and screening apparatus–more in the short list below.
8. Automatic Colorization
Deep learning can be used to use the objects and their context within the photograph to color the image, much like a human operator might approach the problem. A visual and highly impressive feat.
This capability leverages of the high quality and very large convolutional neural networks trained for ImageNet and co-opted for the problem of image colorization. Generally the approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of color.
9. Neural Networks in Finance
Futures markets have seen a 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.
10. Energy Market Price Forecasting
Researchers in Spain and Portugal have applied artificial neural networks to the energy grid in 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. More on how this model was put together and deployed here.