Deep learning is a subset of artificial intelligence, in particular, the field of machine learning.
Deep learning differs from traditional machine learning systems in that it is capable of self-learning and improving as it analyses large data sets.
A highly flexible system it has a number of applications in business.
In this article, we explain exactly what deep learning is and explore the ways that it is already transforming businesses.
What is Deep Learning?
Deep learning is a function of artificial intelligence. It is designed to replicate the way that the human brain processes data. It also re-creates the patterns found in the brain’s decision-making process. Sometimes called deep neural networking or neural learning, it is part of the wider field of machine learning.
It is powered by networks that can carry out unsupervised learning.
This process uses algorithms to analyse raw data, extracting information and presenting it in a structured, useful model.
Often it is also used to process unstructured or unlabeled data.
The Growth in Popularity of Neural Networks
During the 1980s neural networks, while not a new thing, became increasingly popular.
As researchers worked and refined the process their potential developed.
However practical adoption was slow.
This was primarily because a lot of data and time was required to get a good result.
In recent decades, computers have become more powerful.
This means that they are capable of performing complex tasks both accurately and quickly.
This has made machine learning a more practical operation.
Today machine learning is used in many different fields.
Deep learning allows computers to solve complex problems.
These systems are even capable of handling diverse masses of unstructured data set.
The more information these algorithms are fed, and allowed to work through, the better they perform.
How Deep Learning Works
Traditionally analytics has used presented data to engineer new features and derive new variables.
Once the information is processed an analytic model is selected.
This is placed inside unknowns, or parameters, to create a model.
While this approach can create a reliable, predictive system it doesn’t generalise well.
This means the model may not be complete enough, or correct enough, to handle variables such as new information.
Instead, you must begin the process all over again.
Deep learning improves this process.
It replaces the formulation and the specification of the model with layers, or hierarchical characteristics.
These layers are able to learn.
This means they can recognise various features of the imputed data set that separate them from the set regularities.
Humans can take hours, even years, to sort through unstructured data and extract the relevant information.
Deep learning systems are able to deal with unstructured data, analysing and sorting it, incredibly quickly.
These systems can then present this information in a useful way.
Machine Driven Systems are Able to Sort and Structure Large Amounts of Data
Deep learning allows us to create predictive systems that are able to both generalise and adapt.
This means that they improve every time they are presented with new information.
The most advanced applications are more dynamic than conventional predictive systems that rely on hard business rules.
It also allows us to map inputs to outputs, finding correlations in large data sets.
For this reason, it is known as the universal approximator.
Deep learning systems affect how we think about representing problems solved with analytics.
In short, we no longer need to process presented information and try to fit it into a workable model.
We can simply train the computer to solve the problem or carry out the task, itself.
Deep Learning and Machine Learning
The most common way of processing large amounts of data is with machine learning.
This is a self-adaptive, or self-learning, algorithm.
The more information presented, and more analysis and patterns the system creates the better it becomes.
Deep learning builds on the process of machine learning by using a hierarchical level of artificial neural networks.
These networks function in a similar manner to the human brain.
While other systems analyse data in a linear manner, deep learnings hierarchical functioning allows data to be processed in a fluid, nonlinear approach.
What is Deep Learning Used for?
This process has a number of different, useful applications.
Classification tasks require labeled datasets.
A labeled data set requires the human user to transfer their own knowledge to the data set.
This allows the system, or neural network, to make connections between the data and the labels.
This process is supervised learning.
In this way, deep learning can recognise gestures in the video, detect voices and identify who is speaking.
It can also transcribe speech to text, infer the sentiment in speech, identify images such as road signs and faces.
Clustering or grouping is the process of detecting similarities in datasets.
Deep learning can do this on unstructured or unlabeled data, in the process called unsupervised learning.
The more data given to an algorithm, the more accurate it becomes.
Allowing systems to operate unsupervised learning can, potentially, create incredibly accurate models.
Clustering has a number of different uses.
Firstly it is used to search through data, information or documents.
It can also search through sounds and images, looking for similarities.
Clustering can also do the opposite.
It can sift through masses of data looking for anomalies or behaviour that doesn’t fit the established pattern.
This is useful in identifying and preventing fraud, for example.
Deep learning allows us to make accurate predictions.
This means we are better able to prevent or pre-empt undesirable scenarios.
It also allows analytics to become more efficient.
Finally, the information generated here can be applied quickly and usefully to ever-changing scenarios, in a reactive manner.
Deep Learning Transforming the Retail Industry
People are increasingly choosing to do their shopping online, with giants such as Amazon.
Traditional brands especially established high street names, often struggle in this new climate.
Many have sought to embrace new solutions, seeking to adapt and innovate.
One such brand is Burberry.
Luxury fashion retailers Burberry have used deep learning and big data applications to reinvent their entire business model.
The application of deep learning models is allowing the company to forge deeper connections with their customers.
Customer data gathered by traditional loyalty schemes are allowing the company to offer personalised recommendations, both online and in-store.
As well as recommending products, RFID tags fitted to products can send further information to customer’s devices.
This information can include suggestions on how to wear the product or information about how and where it was made.
Burberry’s CEO, Angela Ahrendts, said: “Walking through our doors is just like walking into our website.”
This statement sums up Burberry’s innovative approach to machine learning applications.
The company has also noticed a benefit, in 2015 they reported a 50% increase in repeat custom.
Burberry has also used these systems to identify which products sell better in-store than online.
This information led to the company creating new product images for poorly performing items, which in turn boosted sales.
Running a Stellar Social Media Campaign
Burberry also promotes itself well with social media.
It was the first major brand to use Snapchat’s Snapcode feature.
This allows customers to access extra information about a product by simply scanning the barcode.
In 2016, Burberry began using Facebook chatbots to deliver product updates and report on London Fashion Week.
Now the chatbots are also able to help customers browse new collections and even book an Uber to their nearest store.
Burberry may be an old name in business but their approach is refreshingly new.
By seeking to embrace and adopt deep learning and machine-driven applications the brand has managed to stay relevant.
Consequently, Burberry has remained a world leader, not just in fashion but also in technology.
Providing Better Customer Service
Deep learning applications are allowing customer services to improve and evolve.
For example, Disney is using these applications to improve its already famed customer service.
After much testing in 2013 Disney World launched the MyMagicPlus system.
This innovative approach to customer service integrates many aspects of the visitor experience.
Upon arrival, each visitor to Disney World is given their own MagicBand wristband.
This serves not only as a means of identification but also as the visitor’s hotel room key, tickets, and FastPasses to the park.
It can also be used as an on-site credit card.
Instead of having to carry keys, wallets, cash, and other items, guests simply have to remember their wristband.
To gain entry or pay for something the visitor simply swipes the wristband across one of the many sensors located around the park.
Not only is this application of smart technology convenient it also provides Disney with a wealth of useful information.
Disney can use this data to see the location of all its visitors and what they are doing.
From this Disney can anticipate anything that the visitor may need.
This translation of information allows Disney to deliver a smooth, personalised experience.
This information also allows the park to be run more efficiently.
Disney can see where queues are forming and encourage people to other areas or add more staff.
Improving the performance of Disney World in this way also helps to improve the visitor experience.
Disney is Continually Looking to Improve
As deep learning and the associated techniques continue to be developed and enhanced Disney are also continuously looking to improve.
The company’s Next Generation Experience is developing robots equipped with cameras that will track visitors around the park.
This system is made possible by deep learning and neural network applications.
Disney hopes to use this information to understand which areas and routes are heavily used.
This can inform marketing and operational decisions and help to further increase the productivity of the site.
Disney isn’t just interested in using machine learning and neural networks to enhance the visitor experience.
They are also, reportedly, looking at introducing robotic versions of their most famous characters.
Deep learning to Improve the Customer Experience
Microsoft has long used deep and machine learning, as well as neural networks, to enhance and develop their systems.
Their virtual assistant Cortana and Skype-compatible chatbots are only made possible by deep learning-driven systems.
Both applications are capable of quickly and accurately answering queries on the weather, traffic or any other topic.
Similarly, Amazon, with its Alexa system, and Google are also making the most of deep learning possibilities.
Google has been using these systems to improve YouTube video recommendations for a number of years now.
Deep and machine learning and artificial neural networks are also helping Google to improve its search engine and optimize Android.
Google, in particular, are looking to extend these applications.
Google is using machine learning and deep learning, geo-mapping, satellite data and cloud computing to identify and prevent illegal fishing.
Global Fishing Watch monitors over 22 million data points, tracking shipping activity in the world’s waterways.
Applied deep and machine learning systems allow engineers to work out why any chosen vessel has set sail.
This information can then identify suspicious or illegal activity, such as fishing in restricted waters.
Revitalising the Energy Industry
Established, global leader BP is seeking to implement deep learning solutions throughout their business.
This has seen BP become a driving force, encouraging others to adopt deep learning, big data, and artificial intelligence technologies.
This approach was summed up by Morag Watson, BP’s chief digital innovation officer.
Watson announced that AI is “one of the most critical digital technologies to drive new levels of performance” in the industry.
This belief has led to BP investing heavily in deep learning and big data technology.
This investment has driven an improvement in safety and resource management.
BP has also used this investment to improve the reliability of its gas and oil extraction and refinement processes.
Almost all of BP’s oil and gas wells are fitted with smart sensors.
These sensors constantly produce and relay data.
This lets BP’s technicians quickly and reliably assess numerous factors, including onsite conditions, production levels, and equipment performance.
This information can be accessed remotely from anywhere in the world.
The reliability and ease of access mean that this information is incredibly useful.
BP can, for example, monitor equipment performance, performing maintenance before a costly failure of machinery occurs.
This can save the company time and money, as well as preventing prolonged production downtime.
Further Adoptions of Deep Learning in Energy Production
BP isn’t the only energy giants seeking to adopt smart solutions.
For example, Shell’s rigs in the Gulf of Mexico are fitted with similar smart sensors.
Here automation enables safe extraction while saving the company time and money.
Similarly, GE Power is also using deep learning, big data and advanced analytics to modernize its operations.
All of these technologies are being developed with the end goal of delivering a digital power plant.
GE Power is keen to modernize the energy production process. To this end, the company uses big data and machine learning and deep learning alongside Internet of Things technology.
By using a combination of smart solutions GE Power is aiming to develop a totally “digital power plant.”
Deep Learning is Making Manufacturing Safer
Deep learning and smart solutions are increasingly being used to conduct manufacturing tasks.
This is especially useful when conducting repetitive, time-consuming tasks.
While a human can easily lose concentration, and possibly make a mistake, a robot won’t.
In some industries, a mistake can be easily rectified but in heavy industries, or large scale operations, it is more difficult.
People can become injured or even lose their lives.
Data produced by Cortexica reveals that businesses operating in high-risk environments in 2018 had to deal, on average, with 27 non-fatal injuries.
Almost half of these accidents, 47%, were caused by human error.
Deep learning-powered systems are making manufacturing processes safer.
These systems can not only scan workers on arrival for Personal Protection Equipment compliance but sensors can also monitor every worker on the site.
If the equipment is removed, or another form of non-compliance detected then site managers can be alerted or systems can be shut down.
This application of smart systems aims to protect workers before an accident can happen.
Automation can also help to make manufacturing a safer process.
The automotive industry has particularly adopted this application of deep learning.
BMW, for example, use KUKA’s LBR iiwa robots alongside humans in their factories.
Here robots take on monotonous or physically demanding tasks.
Deep learning applications allow robots to work safely alongside human workers who are carrying out more skilled assembly tasks.
Improving Quality Control
Having to dispose of or replace inferior goods is a major expense for many companies.
For example this aspect of manufacturing accounts of up to 30% of costs for semiconductor manufacturers.
Deep learning and machine driven solutions, such as image recognition tools, allow for the automating of the quality control process.
It can also increase defects detection while still in the factory by up to 90%.
Machine learning powered systems are capable of constantly evolving.
This means that the applications can adapt to new product specifications or requirements.
For example, Fujitsu currently uses a system that integrates the assembly line.
Not only are their systems able to detect defects in products but they also prepare the product for the next phase of automated assembly.
Predictive Maintenance cuts System Downtime
Hardware failure can lead to significant periods of production downtime.
Costly repairs can seriously hamper the viability of operations and companies.
Predictive maintenance, made possible by deep learning applications, is a smart solution to this issue.
Machines can be fitted with smart sensors.
As well as monitoring operational flow, these sensors can monitor the performance levels of the machine.
This information can be easily accessed and interpreted by skilled technicians who can identify potential problems in machinery.
These minor problems can be quickly resolved before they turn into major complications.
Predictive maintenance is being adopted by numerous companies who see the potential for improving productivity this provides.
For example, Shell uses a non-invasive listening solution devised by OneWatt
OneWatts devices listen to the sound of a machine.
If a tonal change is detected, engineers are alerted and the machine can be overhauled before a major breakdown occurs.
Transforming the way Media is Produced
Machine learning powered systems are also transforming the way we consume and produce media.
UK based, world-renowned media outlet the BBC is using deep learning applications in its ongoing Talking with Machines project.
This is an audio drama with a difference.
Listeners are able to join in, conversing with the characters through their smart speakers.
Meanwhile, the Press Association is developing machine learning and artificial intelligence driven applications to report on local news stories.
Once the area where many journalists learnt their trade, in recent years local news has been struggling to survive.
The Press Association hopes that robotics and deep learning applications can save this sector.
Partnerings with Urbs Media, a specialist news automation company, the PA have launched RADAR (Reporters and Data and Robots).
Once fully realised this project will use robots to produce 30,000 local news stories a month.
Deep learning systems will process data from local authorities, public services and government press releases as well as other sources.
This information will be turned, via natural language generation applications, to produce local news stories.
In this way, automation, and smart solutions can fill a gap in the market.
READ MORE – Reuters Gambles on AI-Powered Journalism
Deep Learning is Reducing Financial Fraud
Deep learning can also be used to detect system vulnerabilities and suspicious behaviour in customer accounts.
Traditional nonlinear methods of identifying fraud were limited, often to large and obvious transactions.
However deep learning and neural networks offer companies a more adaptable, comprehensive system.
Deep learning-powered systems can highlight even the slightest change in a customer’s established behaviour pattern.
As well as the transaction they register the time, location, type of retailer, IP addresses and many other pieces of information.
It also means that connections can be drawn between suspicious transactions.
This allows for a more secure, complete provision to be made.
Deep learning-powered systems have allowed Visa to cut credit card fraud by two thirds.
READ MORE – 10 Applications of Machine Learning in Finance
READ MORE – AI Revolution Disrupts Investment Banking
Increasingly Sophisticated Fraud Detection with deep learning
Increasingly financial transactions are carried out online, via smartphone apps and wifi connections.
This means that the possibility of fraud and identity theft has increased.
Online security giant McAfee estimated, in a recent report, that cybercrime costs the global economy 0.8% of its gross domestic product.
While the customer has to take some responsibility for their actions, increasingly the onus is on banks and financial providers.
In this increasingly digital world, financial service providers are striving to create reliable ways for financial transfers to be securely made.
They are also constantly reviewing and developing fraud identification methods.
Today’s sophisticated systems are capable of reliably highlighting any suspicious behaviour.
How Banks are Identifying Fraud
Danske Bank is just one of the major banks using deep learning systems to detect fraud and improve customer safety.
Realising that their old systems were returning 1200 false positives every day, Danske Bank turned to technology to improve their systems.
With the help of Think Big Analytics, the Danish bank has developed a sophisticated fraud detection system.
This system can alert the bank to numerous examples of unusual behaviour.
This includes a customer logging in on a new computer or a customer filling in forms suspiciously faster than average.
By applying deep learning Danske Bank has made its systems more secure and comprehensive, improving the user’s experience and safety.
Similarly, Crowe, a public accounting and consulting firm, have developed Crowe Data Anomaly Detection.
This application of deep learning allows Crowe’s forensic investigators to identify possible fraud and suspicious activity.
Some banks, as well as constantly improving systems, are also seeking to educate their customers.
To this end, the Bank of America has launched Erica, a chatbot.
Erica doesn’t just help users make financial transactions.
The chatbot also identifies better investments or accounts, answers queries and educates users about financial safety.
READ MORE – 10 Applications of Machine Learning in Finance
The Transformation of Consumer Products
Deep learning, machine learning, natural language processing, advanced analytics and other smart solutions are even transforming children’s toys.
One example is Hello Barbie.
This is a Barbie doll that listens and responds to the child.
A microphone concealed on Barbies necklace records what the child is saying and sends it to ToyTalk servers.
This conversation is analysed by a deep learning powered system, selecting an appropriate response from 8,000 recorded possibilities.
The selected response is then sent back to the doll, so that Barbie can respond within a matter of seconds.
However, what some people may see as a charming childrens toy others see as a security threat.
This is partly because every conversation is stored. The information, such as a child’s favourite colour, can then be reused in later conversations.
Coca-Cola is also using deep learning to make the most of the data that it creates.
Unsurprisingly for a company that sells over 500 different brands in over 200 countries, Coca-Cola generates a lot of data.
Machine and deep learning allow this data to be sorted and transformed into useful information.
Coca-Cola uses the information for everything from developing new products to testing augmented reality its bottling plants.
Allowing Data to be Used More Efficiently
Many companies produce large amounts of data.
This data includes product information, transport and manufacturing details, sales and stock inventory and customer purchasing habits.
Analysing this data can be slow and time-consuming
Making connections between these pieces of information is all but impossible to do manually.
Deep learning applications can process unstructured sets of data quickly and efficiently.
These applications can also present the data in a useful way, and highlight links and connections.
This allows the user to have a complete overview of the entire business model, and assess its efficiency.
Developing Personalised Marketing Campaigns
One useful application of this information is to analyse customer buying habits.
Deep learning allows businesses to identify customers that share a similar trait, such as vinyl record buyers.
This information can then be used to devise personalised marketing campaigns.
For example, Starbucks are applying neural networks and deep learning to targeted marketing schemes.
Starbucks has integrated their established customer reward system with purchase history, location, order preferences and other pieces of information.
Integrating this information with their app encourages continued customer engagement and also allows the company to offer local, personal discounts and customisation options.
Starbucks credits this personalised service with helping to increase their revenue by $2.56 billion.
Starbucks is not the only company making use of deep learning and neural networks.
Other companies, such as cosmetics brand Sephora, are using the flexibility offered by deep learning data analysis to deliver a highly personalised email marketing campaign.
Every subscriber receives a slightly different email, highlighting products based on their purchase and search history.
This flexibility has campaign interaction, maintaining click rates and reducing email fatigue.
Consequently the Sephora business model is succeeding in an increasingly competitive market.
Deep Learning Applications are Driving Innovations in Business
Deep learning algorithms are already impacting greatly in a number of different fields
Unsupervised learning, driven by deep learning, can be used to improve services and increase safety and security.
As these applications are developed and become more complex, they will continue to improve and mature.
This will impact on businesses, allowing them to further refine and enhance all aspects of their model.
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