AI was once a thing of science fiction movies, but that is no longer the case. The artificial intelligence that we know today, specifically deep learning, first appeared in 2012, and now it is everywhere. The difference between back then and now is staggering. With such fast growth, many people wonder, “what’s next?” There are no signs of AI growth slowing down, though how that growth gets achieved is likely to look very different in the next few years. What people consider cutting edge now will become obsolete. There are a few different directions artificial intelligence learning can go. Luckily, possible AI advancement fits into three different sectors. However, before getting into the details of each, it is important to understand how an AI can learn things.

Educating a Machine

If Artificial Intelligence was not so prevalent, educating a machine would sound crazy. Up until now, machine learning under supervision was the primary method for teaching an AI. The below details other more advanced methods, though some of the information crosses between them. To learn, AI typically needs datasets, features, and algorithms.

Datasets

Datasets are, as the name suggests, sets, or samples of data. Images, text, numbers, words, and a wide variety of other data forms can all be used. A human would put together a dataset for the AI. This can be time-consuming, and the content should match the intended process. However, that is changing.

Features

Features are specific pieces of data that help Artificial Intelligence reach the correct answer. Again, these pieces of data are decided by a human. As an example, consider an AI that can provide a property evaluation. A human would tell the AI to focus on specific parameters such as the area of the property. The AI would then ignore other information in favor of the one it is told will give the correct result. However, again, like datasets, AI is becoming less reliant on features.

Algorithm

The algorithm is the foundation of AI. Each AI can use different algorithms, with some being faster or more efficient. It is these algorithms that give an AI its independence. The code tells the Artificial Intelligence how to handle the datasets. The more advance these algorithms become, the more independent the AI will be. With the right advancements, the need for a human will be eliminated. In turn, that will lead to unsupervised learning, which is almost here.

Learning Without Supervision

Right now, AI systems use provided datasets to learn. These datasets usually fit into certain categories and are fed to the platform. What each AI system needs to learn will depend on its purpose. Humans decide this and then feed it the relevant information. This process is called supervised learning. Supervised learning in Artificial Intelligence has led to many systems that we now couldn’t imagine life without. Home assistants and autonomous vehicles are just some examples. But, it is reaching its end. Every time an AI needs improving, a human must put together thousands, if not millions of data points. The data must then get distributed correctly. Otherwise, the AI will fail to learn the intended function. As you can imagine, this is time-consuming and expensive. It also limits what an AI system can learn. In short, supervised learning just isn’t cutting it anymore. 

The solution then is to make the leaning unsupervised. An AI can use algorithms to learn from data without human input.

Remove the Human

Many market leaders see unsupervised learning as the next logical step in Artificial Intelligence growth. Removing dataset labels will cut down on costs and leave the system managers to work on new innovations. However, leaving a machine to learn on its own can sound like a recipe for disaster. For that reason, it can help to understand what unsupervised learning is. In truth, it’s not so different from how a human learns. The AI system observes patterns from text, video, and other forms of data. It then uses these patterns to make predictions for its operating environment. It can explore as it pleases, to become better than it would with the restrictions of supervision. Again, labeling data for an Artificial Intelligence to learn from takes a long time, and it’s also limited. No labels mean no restrictions.

It’s Already Here

Leaving an Artificial Intelligence to learn at its own convenience may sound like it’s years away. However, it is actually here already, in a limited capacity. Natural Language Processing is the act of feeding a system text or audio, with it then using the information for future interactions. Home assistants are the best example of this. You tell Alexa to play a song, and then acknowledge you like the song. Alexa takes this information and holds it for future reference. The next time you play a song, you receive something similar because Alexa has processed your language to learn. There are other areas where unsupervised learning is being used, though it is not as far along. However, it won’t take long to catch up. Experts feel that unsupervised learning is the way for machines to achieve human-level intelligence. That doesn’t mean that it won’t be a challenge.

Collaborative Learning

When dealing with any form of data, privacy issues come into play. AI learning is no exception. In fact, security is paramount because so much data is being used. AI systems need to learn without compromising the security of the datasets they use. One way this can be achieved is through collaborative learning. Google was the first to come up with federated learning, another term for collaborative learning. That was in 2017, and it took some time for the idea to really take off. However, now in 2020, more than one thousand research papers on the subject were published. What exactly is this form of learning that captured interest?

A New Way of Data Storage

Machine learning typically uses data from a single location. This training data gets stored in a cloud. The system then accesses it to learn. That works for machine learning, but an Artificial Intelligence system needs much more data. For security reasons, the amount of data we are talking about cannot be stored in a single location. That’s where federated learning steps in. Instead of putting all the necessary learning data in one location, federated learning leaves it where it is. The AI system then sends multiple versions of its self out to the stored datasets. They will train themselves locally before returning to the main AI system. They get pieced back together, and you have an AI system that is trained as well as it would have been on a single dataset stored in one location.

Federated Learning in Use

Federated learning was initially used to train AI systems with personal data found on mobile devices. The data stored in these mobile devices were sensitive, as it was personal data, and it took up a lot of space collectively. Instead of pulling all of that data to a single data center, where it would be at risk, the AI system would go straight to the source.  Now, there is interest in using federated learning in other fields.

The health industry, in particular, has gained a lot of attention. AI can bring a lot to the healthcare industry, but the nature of the data is personally identifiable. It needs to be handled with care to avoid breaching the rights of consumers. Federated learning would allow AI systems to access that data without putting it at risk. The chance to have lifesaving Artificial Intelligence systems is too good to pass up. The training of these systems just needs handling the right way. Several startups are already exploring this possibility.

Federated Learning in Other Areas

The healthcare industry has received the most interest from federated learning, though it’s not the only area that can benefit. Really, any area that deals with data that needs security and has learning possibilities for Artificial Intelligence can be of use. Financial services, government, and more are all possibilities. Privacy is at the forefront of all things data, with the likes of the GDPR and the CCPA setting the rules businesses must play by. Federated learning could be a way to ensure artificial intelligence can grow without compromising security.

Transformers – A New Machine Learning Model

Discussing transformers for AI learning often leads back to natural language processing. The summer of 2020 led to a significant peak in interest for AI learning enthusiasts. That’s because Open AI released GPT-3, a very powerful language model. There are similar systems already available, but GPT-3 took things to new heights. However, it would not have been possible without the use of transformers.

Before transformers, language learning happened one word at a time, in the order the words were delivered. This utilized RNNs or recurrent neural networks. There is nothing wrong with the sequence way of learning, though AI is about improving systems. Transformers do away with learning each word at a time and instead uses a parallel approach. All the tokens in a body of text get analyzed all at once. This parallelization allows for Artificial Intelligence systems to learn from much larger datasets. In part, this is possible due to another AI learning method called attention. Attention allows an AI to find a connection between words, no matter how far apart they are, relationship-wise. The AI will pay attention to the most important terms, hence the name.

Not a One Trick Pony

Transformers have always been paired with natural language processing. The success of GPT-3 only solidifies this relationship. Its primary use was with translation and summarization of text. Google uses transformers for its autocomplete suggestions. If an Artificial Intelligence needed to learn language processing, transformers were the way to go. That was the case until a research paper was released that applied transformers to computer vision. Computer vision deals with how computers learn from digital media, such as images or videos. If transformers can process data from these digital mediums as they do with text, it will open up a lot of learning potential. They can streamline image detection for a start. 

The Future Is Bright for Transformers

Many market leaders of the technology world are investing in transformers. Some have got as far as production, while others are still in the concept stages. OpenAI plan to take their own system and put it into API form. This will make distribution and implementation much more straightforward. From there, expect to see many startups utilizing the software for their own applications. Transformers are a foundation for great Artificial Intelligence advancements. Natural language processing is just the beginning. 

What Does This Mean for Artificial Intelligence?

The above may seem like developing new learning methods without an end goal to those with not much knowledge of AI systems. However, the possibilities are endless, and all will benefit, from a homeowner to a multinational business. One key point of interest for developers is AI for communications. With the right skills, AI will be able to analyze voice and video content in real-time. The cues picked up can then assist businesses with handling client calls, whether judging the results from an inquiry or managing a complaint. Transformers will be vital in such advancements. 

As touched on above, AI can have a massive effect on enhancing the healthcare industry. Training Artificial Intelligence to carry our admin duties is only the beginning. Automated diagnosis using video and still images, robot surgeons, and AI monitoring are not as far away as it might seem. With the use of AI, it will get here sooner. It’s not a case of replacing a human, but assisting them in making the healthcare service better than ever. Finally, AI will be able to interact with the Internet of Things better. Real-time data is already becoming more accessible, and AI systems can benefit. Data that is more effectively accessed will not only help AI systems learn more efficiently. It can also help the same systems maintain IoT devices. In short, as Artificial Intelligence learning becomes more advanced, the world will benefit.