Top 5 Key Artificial Intelligent Problems will be Serious in 2019

Top 5 Surprising Challenges Comes to Artificial Intelligence In 2019

 

Artificial Intelligence (AI) brings both skepticism and enthusiasm in the mind of the visionaries and pioneers of the field.

For these Artificial Intelligent problems, the reason is very simple.

The consequences of the AI developed by many leading brands have shown a lot of promises and hope along with a small slideshow of what an AI can do against us.

The lack of ethics in this aspect can be devastating whereas the outcome of a benevolent project can be remarkable.

This is where the challenges lie in the world of AI.

By the end of 2030, the AI will add a contribution of $15.7 Trillion, almost equal to the output of India and China combined.

The future of artificial intelligence is not confined anymore within the boundaries of a laboratory experiment. It has crossed such boundaries and has found its functionality in various industries via automation and other aspects.

The innovations in this field have opened a gateway to immense possibilities. This supreme power can be used in excellent ways to make our lives better.

The key to the use of this boon is choosing the right opportunities.

Artificial Intelligence Challenge

Key Artificial Intelligent Problems and Challenges to Face in 2019

From countering terrorism to space exploration, AI can be used in almost every industry where there is the slightest scope.

AI is reshaping Digital marketing and it is also entering in the manufacturing, production, marketing, and management segments of a business.

The true potential of AI can be realized only when the key challenges are scouted.

  1. Data scarcity

AI will become stronger every day due to the innovations made by the Artificial Intelligence Solution Provider.

The emerging platform will also need a huge pile of data feed to give a constructed result based on which various decisions will be made or a process will be executed.

The biggest challenge in this aspect is the lack of data sets that an AI needs to learn more and self-train. The supervised learning has a big limit to overcome in terms of data sets.

Labeled data is limited in this aspect. The AI platform will create complex algorithms depending on the labeled data it has been fed with.

A new trend is aggressively catching up among the leading AI firms is ‘Transfer Learning’.

In this case, the design methodologies will be concentrating on making AI models despite the scarcity of the data sets.

  1. Algorithm Bias

An AI platform needs proper training from the data it has been fed.

The training an AI receives is often biased due to the lack of ethical understanding of the platform.

For an instance, the AI systems used these days are biased ethically, communally, and racially.

The proprietary algorithms are specifically used to find out who has called in for an interview, who has got a bail, whose loan has been sanctioned, etc.

If the undetected bias is still in the system, it can lead to an unethical consequence while making a vital decision. An example will be perfect to describe the limitation.

Google Photos uses AI to recognize or identify people, scenes or objects. The same platform cannot be used to make a prediction regarding the future criminals as it might show a biased result for the African American race.

  1. Data privacy and data security

The use of AI platforms will let us take excellent decisions in the long run. We will find out how to take the best decision depending on the particular factors of the event.

Using an AI platform will make it easier to decide as it will use the data sets to learn and make an intelligent decision.

This feature will help us to use this platform for various purposes.

The automation will become more versatile. There are so many chatbot AI services offering automation for customers support.

In this part of the development, the information used in the form of data sets can be breached by cybercriminals. Identity thefts and other antisocial activities can lead to a big breach in the system.

The consumer awareness programs will become more stringent and a must-add feature to the applications used. The machine-made decisions will require personal data from the user’s account.

The European Union has designed the General Data Protection Regulation (GDPR) to implement and protect the personal data of the users.

This new implementation will empower all the data scientists of the Artificial Intelligence services for the development of AI without compromising the confidentiality and security of data.

Good Read: 3 Top Internet of Things Privacy Issues You Should Avoid

  1. Provability

AI technology is very complex to understand and execute. It will become even more complex as the time will pass by.

The definition of AI shows that it becomes impossible for a creator regarding vesting some light on how the process is executed and why the platform has chosen this path.

This is the reason why people show skepticism due to the black box nature of the platforms.

The scientists fail to find out why the program took a decision so complex and cannot provide a proper reason behind the decision made.

In this aspect, ‘provability’ is a brilliant segment in the subject of mathematical certainty that determines why an AI takes such decisions giving solid proof.

The more we advance towards a new era; the black box feature needs to be explored more so that the AI scientists can provide transparency in the process chosen by an AI platform.

AI needs to be more explainable, transparent and provable. Explainable AI is one of the biggest challenges this technology will face in this decade.

  1. Teaching what is right and wrong

An AI platform cannot determine what is right and wrong for us. It is needed to be taught using data sets and other feeds.

The wide adoption of AI will affect the economy in various ways.

The machine learning and AI services will provide excellent platforms for the leading brands of various industries where the decisions will be made.

The intentional or accidental harm imparted by an AI cannot be passed for a trial. Safety, ethical boundaries, emotions, etc will be a part of the teaching program that an AI platform has to master.

Conclusion

Over the years, the Artificial Intelligence Services Company will become more prominent for the industries seeking smarter solutions for their production and management processes.

These are the key Artificial Intelligent problems and challenges that the developers have to face while working with AI technology for future applications.

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