Recent developments in artificial intelligence (AI) have been characterized by an explosion of new techniques, algorithms, and applications. Some refer to this as The AI Winter, because many predicted that after years of progress, we would see stagnation or even a decline in performance.
Fortunately, that has not happened yet! If anything, things seem to be getting more interesting every year.
In fact, there are some who argue that we’re at the dawn of a second AI winter, where interest withers and people start thinking about giving up on AI altogether. Fortunately, I don’t think this is very likely.
I believe we will continue to see steady improvements in AI for the foreseeable future. But what kind of advances we can expect depends a lot on one question: how much data do you have?
Data plays a key role in almost all areas of AI today — from neural networks to reinforcement learning systems to computer vision. And it seems increasingly clear that we need lots and lots of data if we want to make the most out of AI.
In this article, I’ll talk a little bit more about why having enough data is so important for advanced AI, and then I’ll describe two strategies for gathering such data.
If you’re looking to dive into deep learning, these are must-reads. They’ll give you a strong foundation and help you pick your next steps.
The significance of data
We now have very powerful software that can perform advanced computer vision, natural language processing, and other tasks usually done by humans. These systems are referred to as artificial intelligence (AI).
The success of AI depends heavily on the availability of large amounts of structured and unstructured datasets that contain examples or instances of what you want the system to learn.
Structural datasets like images and videos provide necessary information for learning how to identify objects, understand context, and determine relationships. Unstructured datasets such as documents, voice recordings, and conversations give computers the ability to process content beyond simple image searches.
By having enough diverse examples in these areas, machines get trained about what information is contained within them and how to organize this information into meaningful patterns. This helps make AI more intelligent than those without vast quantities of data.
There are several reasons why we need lots of data to train AI algorithms. Let’s take a closer look at some of them.
How to gain more data
There are two main ways to gather new data for your deep learning algorithm. You can either use existing datasets that have already been curated, or you can take it upon yourself to collect new data yourself.
The first way is definitely the easier one! Using pre-existing datasets allows you to test out your algorithms on material that has been organized into categories and defined features. These organized databases make it easy to find almost any type of dataset related to natural language processing, computer vision, or other areas using neural networks as an underlying structure.
By collecting new data yourself, you get to choose what types of content you want to include in your database. This gives you much greater flexibility than if you had to search through someone else’s collection!
Gaining data by creating your own takes longer, but it also offers much higher quality information. By adding some of your own materials to the mix, you will be able to learn more about the field and potentially create your own tool or technique.
How to improve your data quality
Recent developments in deep learning require very large amounts of training data, so how you organize and manage that data is important!
Data quality can make a big difference in the effectiveness of AI algorithms. If there are not enough high-quality examples of any given concept, the algorithm will struggle to learn it properly.
It’s like trying to teach someone who has never before seen anyone walk what it means when they start walking with a limp. You may be able to get some limited insights, but nothing more than “I don’t know why she isn’t walking straight.”
Similarly, if you’re teaching an algorithm about dogs, you’ll probably have a harder time getting it to recognize shapes of ears and nose as those features rather than things like white tails or black noses.
In both cases, chances are good that people will develop their own theories about what was going on, which won’t help the learner understand the concepts well.
Quality control at the source is always the best way to go, but in the case of AI, it’s even more important because these systems are potentially life-changing for individuals who depend on them.
Tips for data science
A lot of people get stuck when it comes to learning how to use neural networks because they do not have enough training data.
Data is one of the most important things you will need as an aspiring machine learner. An easy way to gain this knowledge is by doing practical applications using pre-built, well-tested models instead of creating your own network architecture from scratch.
There are many free and paid resources available online that offer such trained models. By practicing with these already designed networks, you can learn some fundamental concepts about what goes into designing good AI systems.
In this article, we will discuss three different types of datasets that anyone can download and start experimenting with immediately. These sets range in size from a few thousand examples to millions!
What are experimental data sets?
An experimental dataset is any collection of information that has been organized, cleaned, and made searchable. The reason these are called experimentally generated is because someone had to create them for the purposes of testing or learning something new.
Some companies make their experimental datasets publicly available so other individuals can add to and improve upon the original set.
Relate to the topic
Recent developments in artificial intelligence (AI) depend heavily upon large datasets that the algorithms are trained with. Neural networks, one type of AI algorithm, work by using very sophisticated software components called neurons.
Neurons connect to other neurons via small fiber bundles or “axons” and messages can be passed from neuron to neuron through repeated exchanges of chemical messengers known as neurotransmitters.
The more connections there are, the better the network learns and the more intelligent the system becomes. Technically speaking, the more neurons you have, the smarter the system gets!
But how do you get lots of neurons? You need to use your brain! Simply put, the more experiences you have- the more neurons you have! That is why it is important to read, learn about and study things – the more neurons you have!
Another way to gain new neurons is by interacting with and giving opinions to different people and concepts. By sharing thoughts and ideas, your mind creates new ones, which grow and strengthen neural pathways. This is what makes humans smart; we constantly expose ourselves to various information and concepts.
Given this, wouldn’t it make sense to start creating online products and services now? If you already have an audience, then why not leverage those relationships to promote yourself and your product/service?
This article will talk about some ways to gather data for training your neural net and also discuss alternatives to traditional deep learning.
Try to use deep learning
Recent developments in artificial intelligence (AI) have been referred to as AI or machine learning. Artificial neural networks, commonly called “neural nets” after their inspiration from neurons in our brains, are one specific type of algorithm used for this purpose.
A key component of most neural net algorithms is what we call an input layer, which receives external information from your computer or device being trained. The more data you put into the network, the better it learns!
By putting in lots of examples of different sounds, images, text and other materials, the system gets smarter at identifying those components. Technology like Siri, Amazon Alexa and Google Assistant all use advanced neural net models that depend heavily on large amounts of training data.
Machine learning has become very popular recently due to its success. Developers can now create applications that utilize these techniques to perform complex tasks automatically.
Challenge and opportunity
Recent developments in deep learning have been nothing short of spectacular. Machines can now learn complex functions directly from data, without any examples that know how to perform those functions!
This is an impressive feat as it requires very little human guidance for knowledge of the function. Take a look at this, for example:
Google’s patented system uses what they call “deep neural networks” to process images. These NNs are inspired by the way neurons work in our brains- only connected to other neurons directly or via connections called synapses.
A key ingredient in creating these NN systems is lots and lots of data. The more data there is, the better the network will be!
That’s not to say that there isn’t one right answer to many questions, but instead of guessing with no real proof, machines can test different answers until they find one that works well. This is why we need vast amounts of data and research into AI has exploded recently!
We’re living in an era where every product or service needs some kind of artificial intelligence (AI) software. Even something as simple as looking up information online or finding pictures and putting them together takes computers using algorithms to do.
Future of deep learning
Recent developments in neural network architectures have led to what some call “deep learning” or, more accurately, “neural networks with lots of layers.” These advanced models use many interconnected nodes (layers) to learn complex patterns between inputs and outputs.
The key difference between these newer models and older ones is that they can be much better at generalizing how to solve problems than previous state-of-the-art algorithms. This means they can work well even if there are significant changes to the input data!
Generalization refers to a model being able to apply its learned knowledge to new examples. For example, given an image of a dog, a good natural language processing algorithm will be able to identify features such as dogs have fur and mouths.
This ability to recognize similar patterns across different types of objects is one of the reasons why AI has been getting so popular recently. People are leveraging computer technology to accomplish increasingly difficult tasks for money.
