Challenges for Successful Implementation for AI

Implementation for AI

Technological changes redefine the way businesses operations are performed today. What parameters would decide the future of work and in what way can we navigate to the next horizon are the questions being asked by leaders.

Businesses are on the verge of exploiting smart technologies like AI, ML and this will decide the future of work as we know it. Of course, it is easy enough to make use of this data-driven trend. To do so, you’ll need to master concepts like machine learning algorithms, deep neural networks, deep learning, supervised learning, etc., which you can do through an Artificial Intelligence course.

Roles that were earlier considered to require a high-level human intellect are now being automated with the help of AI. While there is no doubt that AI uplifts efficiency, decision-makers should be concerned about how this may impact brand identity and user experience.

A human perspective is an essential factor to consider. Which fields will still need human involvement? Do we have tools and techniques to make the most of technological advancements? What would be the consequences of AI if used carelessly?  You certainly need to get answers to these questions to reap the desired benefits when utilizing artificial intelligence.

Though AI helps detect anomalies, predict an outcome, and optimize a procedure or practice, companies are hostile toward AI because of the challenges they face when implementing it.

This post highlights those challenges and maps out a path for its successful implementation.

TA threat of Autonomous Weapons

People are often wary of implementing AI due to their fear of autonomous weapons. Studies reveal that a super-intelligence AI is not developed enough to understand human emotions like love or hate and therefore cannot become intentionally malevolent or benevolent. The most likely condition where it can become a threat to the world is through autonomous weapons. In this scenario, autonomous weapon refer to  AI systems that can kill due to malicious programming. When used the wrong way, these weapons could quickly lead to casualties. Moreover, this could cause an AI war that would destroy the human race.

Leaders and experts in science and technology like Stephen Hawking, Elon Musk, Bill Gates, have recently warned about the risks posed by AI.  As AI has enough potential to become extra intelligent than a human, there is no sure shot way of predicting its impact.

Data Access

Data is essential for the digital economy. For companies looking to implement AI to different fields, access to information is a significant challenge. According to George Zarkadakis, digital lead at global advisory firm Willis Towers Watson, organizing data is the most significant challenge most companies face.

“To use machine learning algorithms a massive and clean data sets is a major requirement,” he told AI Business. “Moreover, data privacy issues are important to consider when it comes to using personal data, according to the General Data Protection Regulation that is coming into effect in 2018.”

Today, most companies are aware of how being data-driven can benefit them. This is because of the ad market, companies now know the potential of first-party data, particularly in light of the high cost of accessing third-party data.

Therefore, most companies have been investing in building the infrastructure to mine and store the data they develop and in recruiting talent who can uncover insights from it. Those who already understand this area well are likely to get a competitive advantage in integrating AI into their businesses. 

What benefits can AI and machine learning bring to pathology? 

Since pathology has gone digital, a tremendous amount of data flows into technologists along with the accessibility to a large number of computing resources at low cost. By implementing AI technology in the lab through intelligent algorithms to evaluate specific patterns on a whole slide image while translating features apparent in the tissue into prediction, for example, metastasis and recurrence and classification which includes staging, grading, and differential diagnosis.

Depending upon precise measurements of histological patterns, pathologists are able to create predictive biomarkers which allows them to answer questions is concerned with a patient’s disease. Its primary use is in the cases where molecular tests fall short of image-based assays which are generally part of tests in a pathologist’s precision medicine portfolio.

Training deep neural networks through scanned slides can reveal how the cellular morphology impacts genetic and epigenetic changes in the tissue. Though the ideas have to view lately, most cutting-edge laboratories have started exploring for and implementing in diagnosing fatal diseases. 

Will there still be a need for pathologists? 

A common notion prevails in regard to “artificial intelligence in healthcare” is when a machine works faster and derive same conclusions as a human does, then machines surpass the conclusions of humans in the diagnostic process. It is wrong as AI cannot be a substitute for expertise and experience of pathologists. Moreover, through the software and deep learning applications, pathologists are armed with data and insights which are not possible to achieve through traditional microscopy.

Now, companies own software designed to automate pathologists’ tasks like checking the interface cycle of mitotic cells or finding a benign tissue and diagnosing the growth of tumour cells. With the automation of such tasks, pathologists can save a lot of their time and use it in prescribing the right diagnosis by using the insights he/she would generate from digital pathology software.

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