What AI Development Will the Market Need in 2020

Article in News
artificial intelligence

The advancement and innovation of artificial intelligence technologies are becoming a promising significant value for businesses and other industries. In the era of digitalization, we’re already seeing how artificial intelligence technologies are affecting the daily lives of many individuals in their homes, workplaces, and all around them.

The benefits of artificial intelligence technology significantly influenced other industries such as agriculture, automobile, healthcare, legal, and manufacturing. Since the development of artificial intelligence is still continuing, there’s still plenty of room for developers to enhance their software to impact the market efficiently and effectively.

There still plenty of room for new features to be introduced in the future of the digital world. As more and more artificial intelligence developments are emerging, the capabilities of artificial intelligence will increase to keep up with the exponential growth of data. Thus, here are the few AI developments the market will need in 2020.

Reinforcement Learning

Reinforcement learning is the third common category of machine learning algorithms and is a framework that uses experience sequential decision-making similar to trial-and-error. This method of machine learning algorithm moves towards a goal that gains a reward after taking appropriate action by interacting with the environment to learn.

The reinforcement learning algorithm is completely different from the supervised and unsupervised learning algorithm. Supervised learning is responsible for learning the labeled datasets and to build a system capable of predicting the potential of new sets of data. For instance, finding the price of a new car given the car prices of a specific location.

On the other hand, the unsupervised learning algorithm is in charge of finding the similarities and connections between unlabeled data as well as even clustering them. For instance, the unsupervised learning algorithm can provide the colors, sizes, dimensions, and many more parameters of a set of unlabeled images.

What makes the reinforcement learning algorithm unique from the other two common forms of machine learning algorithms is that it doesn’t use data recognition techniques despite being a framework. Thus, video game developers are slowly utilizing it to their computers to determine the moves it needs to take to beat the game.

Since the reinforcement learning algorithm is fairly new in machine learning, there are only a few gaming machines and robots that incorporate the algorithm. However, various industries are already putting their attention to the reinforcement learning algorithm to discover its uses and benefits as well as continuing to experiment with it.

Potential Uses of Reinforcement Learning

There has been a lot of consideration as to how the market can use reinforcement learning technologies. However, a few industries have provided their ideas as to how they can incorporate reinforcement learning technologies to aid their workplace and workforce.

In the healthcare industry, reinforcement learning machines can help determine the different treatment policies for chronic illnesses such as asthma, diabetes, schizophrenia, and more. In higher education levels, reinforcement learning can be used for personalized learning systems and teaching through data-driven intelligence tutoring systems.

Quantum Computing

The market will need a new way of computing to go against massive and complex sets of data that traditional way computing doesn’t stand a chance on. Quantum computing as an artificial intelligence technology that the market will definitely need because it could influence new breakthroughs in many fields.

Some of these breakthroughs can significantly improve facilities to benefit everyone such as machine learning methods to diagnose illnesses sooner. Another breakthrough made possible by quantum computing are algorithms to quickly direct resources, medications to save lives, and even build new materials to make efficient structures and devices.

The innovation of technology allowed quantum computers to exponentially process more data by performing calculates based on the probability of the state of an object. Further, quantum computing supremacy is a term used for quantum computers outperforming classical computers in managing any given tasks.

Quantum computers enabled developers to compute calculations faster than ever, outperforming any supercomputer with high-end and expensive components. However, the unit of stored information used by quantum computers is quantum bits or qubits.

There are still a lot of loopholes such as having no coherence or producing unnecessary computers. Researchers and developers are finding a way to maintain the coherence of qubits to reduce the error rates of essential computation.

The Convergence of Artificial Intelligence and New Technologies

One vital factor of the development of artificial intelligence is its union with other emerging technologies. The convergence of artificial intelligence and the Internet of Things is something that the market will need because the rise of cryptocurrency is dramatically increasing.

Another innovation produced by the convergence of artificial intelligence and the internet of things is self-driving cars. The self-driving cars have been made possible using sensors all around the car to obtain real-time data enabled only by the Internet of Things. Along with other programs and software to conduct judgment-based decision powering the AI models.

The market will need the converge of artificial intelligence and the Internet of Things is because smart actions can be taken by making decisions based on the collected data. However, all these actions can only happen if technologies contain artificial intelligent algorithms found in deep learning, a subset of machine learning.

Since artificial intelligence still isn’t perfect, the integration of another disruptive technology can fill in the loopholes of artificial intelligence to make it better. Integrating Blockchain and AI can help fix each other’s weaknesses to benefit the market. The problem with AI is privacy and trust issues while Blockchain is security and scalability issues.

Combining the two disruptive technologies together can allow them to address their own issues. The benefit of integrating them is so that Blockchain can power decentralized data marketplaces to assist the transparency and trustworthiness of artificial intelligence algorithms.

Takeaway

Artificial intelligence still has a long way with their use and benefits in the market and are still being improved by developers to prevent errors from being made. It is clear for many industries as to how artificial intelligence technologies are making their workplace better and helping their workforce to become more efficient and productive.

The AI development that the market will need for the following year is only a few of the technological advancements and innovations in line. Sooner or later, new trends for technologies will be released to make the market better as well as to help consumers’ lives easier.

Catherrine Garcia is a web developer and master in developing WordPress theme. She is also an enthusiast blogger who loves to share her knowledge with other bloggers.
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