Written by

Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

View Latest Book

Follow Me

Bernard Marr ist ein weltbekannter Futurist, Influencer und Vordenker in den Bereichen Wirtschaft und Technologie mit einer Leidenschaft für den Einsatz von Technologie zum Wohle der Menschheit. Er ist Bestsellerautor von 20 Büchern, schreibt eine regelmäßige Kolumne für Forbes und berät und coacht viele der weltweit bekanntesten Organisationen. Er hat über 2 Millionen Social-Media-Follower, 1 Million Newsletter-Abonnenten und wurde von LinkedIn als einer der Top-5-Business-Influencer der Welt und von Xing als Top Mind 2021 ausgezeichnet.

Bernards neueste Bücher sind ‘Künstliche Intelligenz im Unternehmen: Innovative Anwendungen in 50 Erfolgreichen Unternehmen’

View Latest Book

Follow Me

Artificial Intelligence: What’s The Difference Between Deep Learning And Reinforcement Learning?

2 July 2021

The various cutting-edge technologies that are under the umbrella of artificial intelligence are getting a lot of attention lately. As the amount of data we generate continues to grow to mind-boggling levels, our AI maturity and the potential problems AI can help solve grows right along with it. This data and the amazing computing power that’s now available for a reasonable cost is what fuels the tremendous growth in AI technologies and makes deep learning and reinforcement learning possible. With the rapid changes in the AI industry, it can be challenging to keep up with the latest cutting-edge technologies. In this post, I want to provide easy-to-understand definitions of deep learning and reinforcement learning so that you can understand the difference. 

Both deep learning and reinforcement learning are machine learning functions, which in turn are part of a wider set of artificial intelligence tools. What makes deep learning and reinforcement learning functions interesting is they enable a computer to develop rules on its own to solve problems. This ability to learn is nothing new for computers – but until recently we didn’t have the data or computing power to make it an everyday tool.

What is deep learning?

Deep learning is essentially an autonomous, self-teaching system in which you use existing data to train algorithms to find patterns and then use that to make predictions about new data. For example, you might train a deep learning algorithm to recognise cats on a photograph. You would do that by feeding it millions of images that either contains cats or not. The programme will then establish patterns by classifying and clustering the image data (e.g. Edges, shapes, colours, distances between the shapes, etc.). Those patterns will then inform a predictive model that is able to look at a new set of images and predict whether they contain cats or not, based on the model it has created using the training data.

Deep learning algorithms do this via various layers of artificial neural networks which mimic the network of neurons in our brain. This allows the algorithm to perform various cycles to narrow down patterns and improve the predictions with each cycle. A great example of deep learning in practise is Apple’s Face ID. When setting up your phone you train the algorithm by scanning your face. Each time you log on using e.g. Face ID, the TrueDepth camera captures thousands of data points which create a depth map of your face and the phone’s inbuilt neural engine will perform the analysis to predict whether it is you or not.

What is reinforcement learning?

Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximising rewards, or in other words, it is learning by doing in order to achieve the best outcomes. This is similar to how we learn things like riding a bike where in the beginning we fall off a lot and make too heavy and often erratic moves, but over time we use the feedback of what worked and what didn’t to fine-tune our actions and learn how to ride a bike. The same is true when computers use reinforcement learning, they try different actions, learn from the feedback whether that action delivered a better result, and then reinforce the actions that worked, i.e. Reworking and modifying its algorithms autonomously over many iterations until it makes decisions that deliver the best result.

A good example of using reinforcement learning is a robot learning how to walk. The robot first tries a large step forward and falls. The outcome of a fall with that big step is a data point the reinforcement learning system responds to. Since the feedback was negative, a fall, the system adjusts the action to try a smaller step. The robot is able to move forward. This is an example of reinforcement learning in action.

One of the most fascinating examples of reinforcement learning in action I have seen was when Google’s Deep Mind applied the tool to classic Atari computer games such as Break Out. The goal (or reward) was to maximise the score and the actions were to move the bar at the bottom of the screen to bounce the playing ball back up to break the bricks at the top of the screen. You can watch the video here which shows how, in the beginning, the algorithm is making lots of mistakes but quickly improves to a stage where it would beat even the best human players.

Difference between deep learning and reinforcement learning

Deep learning and reinforcement learning are both systems that learn autonomously. The difference between them is that deep learning is learning from a training set and then applying that learning to a new data set, while reinforcement learning is dynamically learning by adjusting actions based in continuous feedback to maximise a reward.

Deep learning and reinforcement learning aren’t mutually exclusive. In fact, you might use deep learning in a reinforcement learning system, which is referred to as deep reinforcement learning and will be a topic I cover in another post.     

Business Trends In Practice | Bernard Marr
Business Trends In Practice | Bernard Marr

Related Articles

The Top 10 Tech Trends In 2023 Everyone Must Be Ready For

As a futurist, it’s my job to look ahead — so every year, I cover the emerging tech trends that will be shaping our digital world in the next 12 months.[...]

The Top Five Cybersecurity Trends In 2023

Here, we look at the most important trends to watch out for in 2023, including the increased threats from connected IoT devices, hybrid working, and state-sponsored attacks.[...]

The Disruptive Economic Impact Of Artificial Intelligence

I firmly believe that artificial intelligence (AI) has the potential to be among the most disruptive technologies we will ever develop.[...]

Artificial Intelligence | Bernard Marr

The 5 Biggest Artificial Intelligence (AI) Trends In 2023

Over the last decade, Artificial intelligence (AI) has become embedded in every aspect of our society and lives.[...]

The Problem With Biased AIs (and How To Make AI Better)

AI has the potential to deliver enormous business value for organizations, and its adoption has been sped up by the data-related challenges of the pandemic.[...]

Is AI Really A Job Killer? These Experts Say No

If you believe all the doom and gloom in the news today, you might think automation and the deployment of AI-enabled systems at work will replace scores of jobs worldwide.[...]

Stay up-to-date

  • Get updates straight to your inbox
  • Join my 1 million newsletter subscribers
  • Never miss any new content

Social Media

0
Followers
0
Followers
0
Followers
0
Subscribers
0
Followers
0
Subscribers
0
Yearly Views
0
Readers

Podcasts

View Podcasts