Artificial Intelligence (AI) is a major topic of every technology driven company. AI Integration provides a business with a massive amount of transformation opportunities to leverage the value chain. Adopting and integrating AI technologies is a grear risk tegardless of how business-friendly it may seem.
A Deloitte report says , around 94% of enterprises face potential Artificial Intelligence problems while implementing it.
There is a great need to know about the merits and the challenges associated with the adoption of AI. To allow the user/developer to mitigate the risks linked to the technology as well as to take the full advantage of it.
AI technologies must be accepted as a friend not as a foe. It is extremely important for a developer to know how they should address/tackle the AI problems in the real world.
The top 10 potential Artificial Intelligence problems that need to be addressed.
1. The price problem
Small and mid-sized organization struggle a lot when it comes to adopting AI technologies as it is very expensive. Even big firms like Facebook, Apple, Microsoft, Google, Amazon (FAMGA) allocate a separate budget for adopting and implementing AI technologies.
To integrate, deploy and implement AI applications in the enterprise, the organization needs the knowledge of the latest AI advancement and technologies as well as its weaknesses. This lack of technical knowledge is slowing the adoption of this niche department of the organization. Only 10 % of enterprises, currently, are having a smooth passage in adopting AI technologies. The company needs a specialist to identify the roadblocks in the deployment process.
3. Expensive experts
The adoption and deployment of AI technologies require experts including data scientists, data engineers and other specialists. These experts are expensive and dificult to find currently. Therefore small and medium-sized enterprises can not afford to bring in the manpower to match the project requirements.
4. Data acquisition and storage
One of the biggest Artificial Intelligence problems is data acquisition and storage. Business AI systems depend on sensor data as its input. For the validation of AI, a mountain of sensor data is collected. Irrelevant and noisy datasets may cause obstruction as they are hard to store and analyze.
AI works best when it has good quality data available to it. The algorithm then becomes stronger and performs better as the relevant data grows. The AI system fails when enough quality data isn’t given to it.
With such small input variations in data quality having profound results on outcomes and predictions, there is a great need to ensure greater stability and accuracy in Artificial Intelligence. Furthermore, in some industries, such as industrial applications, sufficient data might not be available, limiting AI adoption.
5. Lack of computation speed
AI, Machine learning and deep learning solutions need a lot of computation speeds that are offered only by high-end processors. Larger infrastructure requirements and the pricing associated with these processors has become a major hindrance in their general adoption of the AI technology. Therefore cloud computing and multiple processors running in parallel offer a powerful alternative to for these computational requirements. As the volume of data available for processing grows exponentially, the computation speed requirements will grow with it. It is therefore imperative to continually develop new computational infrastructure solutions.
The implementation of AI application comes with great responsibility. Inviduals must bear the burden of any sort of hardware malfunctions. Previously it was relatively easy to determine whether an incident was the result of the actions of a user, developer or manufacturer.
7. Ethical challenges
One of the major AI problems that are yet be tackled are the morality and ethical issues.The way the developers are technically design the AI bots to perfection where they can flawlessly imitate human conversations, making it increasingly difficult to spot the difference between a machine and a real customer service person.
How an artificial intelligence algorithm carries out predictions is based on the training given to it. The algorithm will label things according to the assumption of data on which it is trained. Therfore it will ignore the correctness of data, for example if the algorithm is trained on data that reflects racism or sexism, the result of prediction will reflect it instead of correcting it..
8. Legal Challenges
An AI application with a faulty algorithm and data governance can cause legal challenges for the company. This is one of the biggest Artificial Intelligence problems that a developer faces rralistically. A flawed algorithm made from a faulty set of data can cause a major loss in a companies profit. An erroneous algorithm will always make incorrect and unfavorable predictions. Problems like data breach can be a consequence of weak and poor data governance. Because to an algorithm, a user’s PII (personal identifiable information) acts as a feed stock which may slip into the hands of hackers. Consequently, the organization will fall suffer legal challenges.
9. AI Myths & Expectation:
There is a large discrepancy between the actual potential of the AI system and the expectations of people.the media says that Artificial Intelligence, with its cognitive capabilities, will replace human jobs.
However, the IT industry has the challenge to address these expectations by conveying that AI is just a tool that operates only with the assistance of humans AI can definitely can help to replace human roles like the automation of routine or common work, optimizations of every industrial work and , data-driven predictions, etc.
However, in most of the expectations particularly in highly specialized roles- AI cannot substitute the qualities of human brain.
10. Difficulty of assessing vendors
High tech procurement is quite challenging and AI is particularly vulnerable. Businesses face problems in finding out how they can use AI effectively.
Many non-AI companies engage in AI washing etc.
AI technology is a challenge as you cannot oversee the major changes that it brings in to a company. To implement AI an organization needs experts who are difficult to find. AI needs a high-degree of computation processing. However companies should think of how they can responsibly mitigate these Artificial Intelligence problems - instead of holding back and ignoring this exciting technology.
The key lies in minimizing the Artificial Intelligence risks and maximizing the benefits - by producing a detailed AI technology adoption - Strategy and Road Map that is built to use the core capabilities of artificial intelligence.
There is a serious need to understand advanced AI, machine learning, deep learning, neural networks, advanced analyistics solutions..
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