Guide

Top 6 Trends That Will Shape the Future of AI

AI systems already predict migraines and help to choose the best coffee, what else can they do in the future?

Artificial intelligence is the latest buzzword in the technology industry. He excited mobile developers about the possibilities. You can’t imagine a future without artificial intelligence systems, and mobile artificial intelligence applications offer luxurious experiences to users in many different industries.

Gartner expects that by 2021, 70% of companies will have integrated AI to help their employees achieve the highest level of productivity.

Let’s take a look at the main trends that are expected to take first place in the AI world in 2020.

NLP and Machine Learning

Natural language processing (NLP) and machine learning are the two areas where the probability of AI growth is highest.

Natural language processing is the technology that helps computers understand human language. To do this, NLP breaks down the human language into small pieces, called tokens (points or words), and analyses the relationships between these tokens. NLP uses the following concepts:

  1. Categorization of content
  2. Sentiment analysis
  3. Compression of documents
  4. Machine translation
  5. Conversion of a text into a speech and translation of a speech into a text
  6. Contextual extraction
  7. Modeling and discovery of the subject

The rise of voice assistants such as Siri and Google assistant has occurred due to the advancement of NLP technology.

NLP now crosses the boundaries of human-machine communication and helps to bridge the gap between what we say and what the computer understands. Many application developers use advanced NLP techniques in their applications and develop innovative applications.

Hello, Barbie is a doll who listens and responds to the child, helping to improve the child’s engagement with the baby. When a child speaks something, the data is sent to the company’s servers, where NLP algorithms understand the message and select the most appropriate response from a collection of 8,000 pre-recorded responses. Answers to common specific questions such as: What is your favorite Barbie food? Are stored in the doll to shorten the response time.

It is expected that NLP-compatible applications will soon increase human engagement levels with their smartphones. The size of the speech and voice recognition market is estimated to reach $31.82 billion by 2025, so NLP can be expected to play an essential role in the mobile application industry in the future.

More Focus on Machine Learning

Machine learning is a branch of artificial intelligence that consists in equipping computers with a “learning ability.” It allows machines to learn by themselves and reduces the need for explicit programming.

Machine learning gives computers the elusive ability to “think,” and application developers are awakening to the possibilities of technology. Machine learning helps to create personalized user experiences because the machine is taught like a human brain about user preferences. If a person does not like the recommendations provided by the application, then the machine will record the user’s response and remember to display that particular recommendation no longer.

Many applications have emerged, using the capabilities of machine learning to make the user experience more enjoyable.

Airplay is an application that aims to help visually impaired people see the world. Using machine learning algorithms, airplay can identify three objects at a time, including objects such as plants, animals, food, colors, and more than 1000 common objects.

The Uber application uses machine learning to analyze the usage pattern of a particular runner and also providing the estimated time of arrival and cost to the runners.

Migraine Buddy is an AI application that predicts the probability that a patient will suffer from migraines and recommends ways to prevent migraines.

The machine learning market is expected to reach $12.3 billion by 2026, with a compound annual growth rate of 22.4%. It is, therefore, safe to say that the importance of machine learning as a component of AI will increase in the future.

Combining Blockchain with AI

Blockchain has emerged as the technology behind bitcoin, but application developers realize that the potential of this technology is much higher than just developing cryptocurrency.

Artificial intelligence can be used in conjunction with Blockchain to create applications that will significantly improve people’s lives.

Blockchain provides an unprecedented level of transparency, security, and ease in a system that has the potential to stimulate growth in unexplored territory for applications.

Many innovative applications have emerged, which exploit the potential of both blockchains as well as AI in the construction of innovative solutions. Take the case of bext360, which focuses on digitizing the coffee supply chain. Many cases have been reported where coffee plantations across Africa use child labor and abusive business practices to make the most of it without regard for their workers or the environment. To put power in the hands of coffee buyers, Bext360 uses Blockchain technology to track a coffee bean from the seed to the store. This enhances transparency in supply chain management, and when used in combination with AI, this technology can accurately determine the quality of coffee beans and help buyers by predicting coffee-growing patterns.

The blockchain market is expected to grow at a compound annual growth rate of 69% between 2091 and 2025 and will reach a staggering $59 billion by 2025.

Reinforcement Learning

Reinforcement learning is a new area of machine learning, which is essentially a self-directed learning system that learns through trial and error. A constant feedback loop is established, and the computer learns by analyzing what works and what doesn’t. It’s similar to the functioning of a human brain, which is a fascinating thing with reinforcement systems.

Scientists are exploring the exciting possibilities of learning reinforcement, and they have even designed an algorithm that can be used to train a robot using images as inputs.

The reinforcement learning algorithms have proven their worth by training the computer to learn a popular atari game called a breakout. The goal was to move a sliding bar down and hit the bricks on top with a bouncing ball. In less than 30 minutes, the computer was able to train to fight even the best human players.

Ai-Based Assistants – Chatbots

With the help of NLP and AI, chatbots have now evolved to a level where they can provide the user with a personalized recommendation. The Chatbots reduced dependence on customer service managers and helped them focus on high income generating tasks such as customer acquisition rather than on solving customer problems.

Chatbots are used in various sectors, such as retail and online banking, to solve customers’ most common questions.

By using chat rooms powered by artificial intelligence, companies acquire knowledge that was previously unknown. The artificial intelligence chatbot learns to know the customer’s preferences in order to offer a more personalized experience.

Applications like Duolingo use the power of artificial intelligence chat rooms to teach people a new language. The Duolingo application will offer the right words, and the user can choose to chat with the bot for as long as they want.

Another good example of a chatbot in action would be a robot called U-Report, which is a chatbot developed by UNICEF. The bot sends surveys on a wide range of social issues, particularly children, then analyses public reactions and helps UNICEF formulate a strategy to curb social ills. U-Report sent a survey to 13,000 children in Liberia asking if they were forced to have sex with their teacher in exchange for better grades when 86% of them answered yes, UNICEF mobilized with Liberia’s Minister of Education and put an end to this social evil.

The chatbots market is expected to reach $7.5 billion by 2024; from these statistics, it is clear that the future of chatbots is bright.

Better Ways to Deal with Biased Data

Recent research has highlighted a fascinating aspect of IT. They can also be biased. The study examined the probability of a prisoner’s recidivism in the United States of America. When an AI system was used to analyze a data set on the recidivism rate of black and white inmates, the system miscalculated and resulted in black inmates were more likely to re-offend than white inmates.

There are questions about the degree of bias in AI systems. The problem is that an AI system produces its interference based on the data we provide it; if the data is biased, so will the intervention. The old IT adage “Garbage in, garbage out” also applies to AI systems.

Efforts are already underway to reduce this bias. IBM is conducting research to reduce bias in AI systems by creating automated bias detection algorithms, which will be trained to model anti-human bias thought processes.

We anticipate that in the future, AI systems would have evolved to mitigate this problem.

Wrapping Things Up

We are entering an exciting phase in the history of humanity, where we are trying to solve our problems with the help of intelligent machines. Technologies such as machine learning, reinforcement learning, and NLP are evolving and will take AI to the next level.

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