The future of artificial intelligence: Trends to Watch

brain with computer text scrolling artificial intelligence
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Artificial intelligence is not going to put humanity in junk soon. Nor are we a single Google DeepMind release out of superintelligence. But make no mistake about it: artificial intelligence is a huge step forward.

Like Artificial Intelligence Index Report 2021, last year the number of magazine articles in the field increased by 34.5%. That’s a much higher percentage than the 19.6% seen a year earlier. Artificial intelligence changes everything from medicine to transportation, and few claim otherwise.

Here in 2021 we are well involved deep learning a revolution that loaded artificial intelligence in the 21st century. But “deep learning” is a broad term that most people now know well. Where are the great advances in artificial intelligence? Where should you look to see the future evolve in front of you? Here are some techniques to keep an eye on.

Transformers: More than you can see

“Disguised robots // Autobots are fighting // To destroy the forces of destruction // From the Decepticons.” Wait, it’s something else!

In fact, Transformers – an artificial intelligence model – far from the booming franchise of the last century is one of the most promising advances in the industry today, especially in the field of natural language processing research.

Understanding language has been a key interest in artificial intelligence since it was even called artificial intelligence, until then. A test proposed by Alan Turing machine intelligence. Transformer models, Google researchers first filmed in 2017, has been shown to be significantly better than previous language models. One reason is the almost incomprehensibly large data sets to which they can be trained. They can be used for machine translation, summarizing documents, answering questions, understanding video content, and more. Although large language models certainly cause problems, their success should not be denied.

transformer (machine learning model)

Transformers input led to the development of GPT-3 (Generative Pre-training Transformer), with 175 billion parameters, was trained in 45 TB of text data and cost more than $ 12 million to build. Earlier this year, Google took back its crown by introducing a new language model about 1.6 trillion parameters, which makes it nine times the size of GPT-3. The Transformer Revolution is just beginning.

Generative adversarial networks

Conflict does not usually make the world a better place. But it certainly makes artificial intelligence better.

Considerable progress has been made in the creation of images in recent years: reference is made to the use of artificial intelligence to dream images that appear inseparable from real images in the real world. It’s not just the conspiracy theories used by social media that trick people into thinking that President Biden is also caught partying with the Illuminati. Imaging can be used for everything from improving search capabilities to helping designers create variations on a theme produces works of art that are sold to millions at auction.

So where does the conflict occur? One of the most important image generation techniques is called the generative adversarial network (GAN). This class of machine learning frameworks uses a combative rope pulling method to transmit images and feedback between the “generator” and the “separator” algorithm, leading to gradual improvements until the separator is unable to tell what is true and false. GANs have also been used produces a falsified genetic code which researchers could use.

Look for many more innovative applications in the near future.

Neuro-symbolic artificial intelligence

Inside something December 2020 releaseResearchers Artur d’Avila Garcez and Luis Lamb described neurosymbolic artificial intelligence as the “third wave” of artificial intelligence. Strictly speaking, neurosymbolic artificial intelligence is not entirely new. It’s more than getting the two biggest rock stars in the world, who once fought at the top of the list, to create a supergroup. In this case, the supergroup consists of self-learning neural networks and rule-based symbolic artificial intelligence

“Neural networks and symbolic ideas complement each other really wonderfully,” said David Cox, director of MIT-IBM Watson AI Lab in Cambridge, Massachusetts. previously told Digital Trends. “Because neural networks give you the answers to get from the clutter of the real world to the symbolic representation of the world, find all the correlations within the images. Once you’ve got the symbolic representation, you can do pretty magical things about reasoning.”

The results could give us artificial intelligence that is better at carrying out this reasoning process, as well as more explanatory artificial intelligence that may well explain why it made the decision. Find this to be a promising tool for artificial intelligence research in the years to come.

Machine learning encounters molecular synthesis

Together with GPT-3 the most significant step forward in artificial intelligence last year was DeepMind’s amazing AlphaFold, which applied in-depth learning to the decades-old biology challenge of protein folding. The answer to this problem will lead to healing of diseases, the discovery of new drugs, a better understanding of life at the cellular level, and more. This last entry in the list is a less specific example of artificial intelligence technology and more of an example of how artificial intelligence makes a big difference in one domain.

Machine learning techniques in this field are proving transformative in the fields of healthcare and biology, such as molecular synthesis, where ML can help researchers identify potential drugs they should evaluate and then how to synthesize them most effectively in the laboratory. Perhaps there is no other life-changing area where artificial intelligence will be used over the next decade and beyond.

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