Plans focused around artificial intelligence (AI) are everywhere and as we shuffle into 2018 that trend will continue.
It would be impossible to list all the promising AI trends to watch out for in 2018.
So, instead we’ve thrown together 10 that we feel are going to make the most difference:
1. Hybrid learning models
This type of model uses the deep learning techniques combined with a Bayesian/probabilistic approach in which to model uncertainty. An example of a hybrid model is Bayesian deep learning.
Hybrid learning models are important as they make it possible to include a level of uncertainty in deep learning solutions.
2. Explainable AI
While there are many useful AI applications around, many of the algorithms used to program them are considered black boxes.
This is because they give very little away as to how they arrived at the answer.
Explainable AI is about developing machine learning techniques to be more transparent yet still achieve the same level of results.
This is critical for establishing trust in AI and will also encourage other enterprises to adopt it too.
3. Deep learning theory
Even though we’ve known about deep learning for quite a while now, there’s still so much we’ve yet to learn about it, including how and why neural networks learn so well.
However, thanks to a new theory, that may be about to change.
It suggests that deep learning involves an information bottleneck.
This occurs when a large amount of data is taken in at once. Initially, some of the less meaningful data is forgotten to allow for more meaningful data to be processed.
By gaining a better understanding as to how deep learning works scientists can put it to better use across various applications.
4. Probabilistic programming
This type of programming will enable developers to design probability models reusing model libraries in which to support interactive modelling.
It allows experts to work around incomplete or uncertain information.
5. Capsule networks
These networks are a new kind of deep neural network that processes visual data in a very similar way to that of the brain.
This means that hierarchical relationships can be maintained, resulting in a much lower error rate than with convolutional neural networks.
Capsule networks are an important leap forward as they don’t require much data for training.
6. Digital twin
This is a virtual model that’s used largely to analyze and monitor systems among the industrial world.
Digital twins are also being used to predict customer behavior.
They help them to accelerate wider adoption of the internet of things (IoT) as well as help to maintain those systems already in place.
In the future, we’ll see much more of these in areas such as consumer choice modeling as well as in physical systems.
7. Deep reinforcement learning
A neural network that learns via a system of action and reward and observations with its surroundings is called Deep reinforcement learning (DRL).
It’s the same strategy that was used to teach the famous AlphaGo program.
DRL covers such a wide variety of applications and is the most general of all learning techniques.
It doesn’t need much data to train its models and can be trained completely via simulation.
8. Lean and augmented data learning
One of the biggest challenges for scientists in the world of deep learning is having access to sorted and labelled data in which to train these AI systems.
However, there are two general methods that can help with this.
The first is to synthesize new data and the second is to transfer a model that’s been trained on one task to do another.
Through these methods, scientists are able to deal with a wider variety of problems, especially those that lack historical data.
9. Automated machine learning (AutoML)
The development of machine learning models is no quick task. It takes time to prepare data, then trains and tune the system.
AutoML uses various deep learning techniques to automate much of this process, cutting down the time it takes to develop deep learning models immensely.
Through the use of different AI tools, business owners are now able to develop their own deep learning models even if they have very little or no prior programming knowledge.
10. Generative adversarial networks (GANs)
This is a type of deep learning system that uses the power of two competing neural networks to run.
The first network acts as a kind of generator. It creates lots of data that mimics the real thing.
The second network behaves like a kind of discriminator, soaking up both the real and fake data.
Between them, over time, they learn the whole data set and gradually improve.
Using GANs reduces the load needed for a deep neural network as the is shared between the two networks.