The future of artificial intelligence: 3 major breakthroughs we will soon see.
The future of artificial intelligence is already upon us. These are the three fields where we will see the best use of this technology, and they promise to change the way we live.
Artificial intelligence is probably the most powerful tool that mankind has created in recent times. Although it is still at an early stage -if we take into account its true potential-, there are already thousands of proposals in which it has proven to be really useful. For example, there is an AI capable of deciphering ancient writings ruined by time; while others can predict if you are about to have a heart attack just by listening to your voice. Their usefulness is invaluable, and we are just beginning to understand it.
That’s why today we want to show you the three fields in which artificial intelligence will have the greatest breakthrough in the years to come. After all, this type of technology is becoming increasingly prominent, and it has been reported that journalistic interest in it has grown by as much as 34.5%. A much higher amount than the 19.6% reported in 2020.
Do you want to know in which fields artificial intelligence will be advancing? Here we leave you with some technologies and sciences on which you should focus on.
Researchers Artur d’Avila Garcez and Luis Lamb have described Neuro-symbolic Artificial Intelligence as the third wave of AI. With it, we are expected to see a major breakthrough in the recognition patterns that systems use. After all, so far AI is nothing more than a compendium of knowledge that, together with prior training, is capable of delivering a result.
However, this new IBM research plans to make artificial intelligence capable of recognizing symbols and, at the same time, giving them semantic and logical meaning. In this way, it is hoped to generate an artificial intelligence system capable of performing more complex tasks, with higher accuracy, while requiring fewer data and training.
Thus, it would be possible to create artificial intelligence capable of responding to reasoning and a process, with the ability to explain why it has made certain decisions.
“Neural networks and symbolic ideas complement each other beautifully. Because neural networks give you the answers to go from the messiness of the real world to a symbolic representation of the world, finding all the correlations within the images. Once you have that symbolic representation, you can do very magical things in terms of reasoning.”
David Cox, director of the MIT-IBM Watson A.I. Laboratory in Cambridge, Massachusetts.
Generative adversarial networks
How generative adversarial networks work
Remember when they used to say that conflict is not good? Well, it is, but this is a rule that doesn’t hold true for artificial intelligence.
If you’ve been keeping up with the internet, you’ve probably seen certain AIs capable of creating images from the text; or recreating things that don’t exist with incredible realism. This, of course, maybe horrifying to millions of artists around the world, but it is at the same time one of the most important demonstrations of the power of technology.
But what does conflict have to do with it? Simple. With Generative Adversarial Networks (GAN), this artificial intelligence-based image generation is expected to get better and better. The reason is quite simple, and that is that with the use of “generating” entities, and other “discriminating” entities, the AI is able to create feedback. Thus, a result can be achieved in which this discriminating algorithm is not able to differentiate the artificially created image from those that are real.
Some researchers have gone even further and are using Generative Adversarial Networks to create totally fake genetic codes. This is undoubtedly one of the most interesting things to come.
Machine learning and molecular synthesis and Artificial intelligence molecular synthesis
In 2020, DeepMind’s AlphaFold succeeded in applying deep learning to biology tasks. Specifically, it was used on the protein folding problem. This field has been studied for decades, and a possible resolution with the use of AI could lead to the discovery of cures for diseases, new drugs, and a deeper understanding of the behavior of cellular life.
This time we do not find an AI with a specific and revolutionary function. However, it is a clear example of how applying the use of artificial intelligence can be beneficial, regardless of the field.
In fact, already the use of artificial intelligence and machine learning is proving beneficial to fields such as biology and healthcare. With them, scientists can determine which drugs are potentially better, and which ones to evaluate. They can also reach different conclusions about the most effective ways to synthesize them.
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