AI IN 2020

Development of AI In 2020 It is fascinating to witness how AI is influencing so many sectors of various industries. The extent of its popularity is such that it’s assumed that the global AI market will be worth almost $118.6 billion by This exponential growth of AI has led many of us to wonder, what’s next for AI in 2020?

7 Predictions on the progress of AI in Prominence of reinforcement learning Reinforcement learning is a framework that employs experience- driven sequential decision-making. This method interacts with the environment to move towards an objective that rewards the actions taken.

1. Many game playing algorithms use reinforcement learning, which helps determine the moves the computer should make to win the game. 2. The algorithm learns from playing the game and exploring various options without the need to specify all the rules of the game. 3. The use of reinforcement learning presently hasn’t been explored extensively but many industries are experimenting with it. It is a remunerative field of future.

2. Rise of Quantum Computing Quantum computers employ the quantum physics to make calculations faster than any supercomputer today. It uses qubits (Quantum bits) to store important data. In 2020, the focus would be on developing strategies to minimise the errors and make meaningful computations possible. Morever, managing terabytes of data would be easier

3. Confluence of AI and other technologies There would be more instances of the convergence between AI with IoT and AI with Blockchain. The self-driving cars wouldn’t be a practical possibility without the implementation of AI with IoT. The sensors used by a car to gather real-time data is supported by the Internet of Things (IoT) and the programs employed for decision-making is powered by AI models.

4. Stringent processes to deal with biased data Machine learning models are now being adopted for decision- making like mortgage loans, hiring, prisoners released from parole or the type of social service advantages. Amazon had reportedly scraped an internal hiring tool that resorted to bias against hiring women. The responsibility lies with the businesses to take proactive steps to adopt principles of non-discriminatory data.

5. Implementation of ethics in AI Are the intelligent systems being developed in a manner that includes individual, community and society’s ethical values? What are the measures taken to ensure the ethical design of AI applications? As per the IEEE global initiative on ethics of intelligent systems, there are certain aspects to focus on while developing policies.

Transparency: This isn’t only to implement the policy of data usage but also for reasonable access to the rules embedded in these systems along with audit trails. Legal accountability: Whether it’s property laws or legal responsibility for inconvenience caused by the AI systems. Governance frameworks: Keeping processes and standards in place to not infringe upon basic human rights.

6. More emphasis on deep learning Deep learning is also the technology that goes into voice control, self-driving cars, and image recognition. With the popularity of both Amazon’s Alexa and Google home, there is a vast array of voice-enabled applications that use natural language processing (NLP) algorithms, which is an example of deep learning.

7. The integration of neural networks Neural networks or artificial neural networks are known to imitate the human brain. There is quite a demand of neural networks in robotics, to enhance order fulfilment, prediction of the stock market, and diagnosis of health conditions or even to compose music. The present neural network technology would allow for this type of AI to become more efficient as more improved training methods and network architectures are desgned.

Artificial Intelligence is here to stay and in fact it’s just the beginning of what could be a widespread phenomenon. Now the focus wouldn’t only be on new technologies in the industry but also how it intertwines with the society and promotes technological advancements.

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