What Are The Top 5 Challenges Faced By The AI-Startups?

While artificial intelligence is managing to penetrate diverse industries and sectors by attracting AI investment, including financial services, education, healthcare, e-commerce and many more, this advanced field is facing quite a few serious challenges. Artificial intelligence is suddenly just about everywhere and there is hardly any industry that has not felt the effects of hype created by it.

However, with all the excitement about artificial intelligence and new ventures that are pushing their ideas around it, there is an apparent difficulty in evaluating and comprehending the prospects, dearth of entrepreneurial know-how, shortage of solid talent, and even in some cases there’s criminal deficiency of substance.

There is no doubt about this fact that some AI startups are doing incredibly well and successfully transforming their ideas into reality. However, the focus of this post is to identify those challenges that are impeding the growth and success of budding AI startups.

1. Computing Isn’t Adequately Advanced

Deep learning and machine learning are some of the top and most effective artificial intelligence techniques. They need a consistent series of complicated calculations that are made rather swiftly (in nanoseconds or microseconds). This shows that these techniques of artificial intelligence make use of a tremendous amount of processing power.

Artificial intelligence has been a hot topic of discussion amongst experts for quite a few years now. One thing that keeps on coming out of these highly technical talks is the absence of adequate amount of power that is required to optimally execute these artificial intelligence techniques.

Plus, if at some place such advanced technologies are available which can effectively accommodate deep learning and machine learning, they happen to be extremely expensive. Startups find it difficult to draw artificial intelligence investment of that level.

In addition to it, systems that allow parallel processing and cloud computing have created some hope to execute these AI techniques for short periods of time. However, once the volumes of data go up, cloud computing no longer works as a dependable solution.

2. Low Actual Implementation of AI

Artificial intelligence is apparently getting a lot attention, but the problem is there are mot many AI based solid use-cases that are available in the marketplace. It does not really matter how popular the idea of artificial intelligence is becoming. Without proper application of artificial intelligence that can truly serve businesses in different ways, AI startups will keep on struggling to convince corporations to invest a considerable amount of money in their projects.

In addition to it, there aren’t so many experts who have the skill to make corporations comprehend how AI can play an integral role in radically increasing their productivity. To resolve this problem, it is crucial for the AI startups to resolve apparent problems of businesses with unprecedented levels of precision and accuracy.

3. Lack of Provability

AI startups that are working on diverse range of products and solutions fail to coherently demonstrate their vision as well as exactly what they aim to accomplish by making the most out of their artificial intelligence techniques. AI is still a pretty new concept, and the potential users of this technology have their doubts regarding how it takes various decisions, plus whether the decisions made by it are reliable or not.

In order to clear this fog of confusion, AI startups have to come up with those algorithms that are transparent, provable and explicable. The main goal of AI startups should be to convince business corporations that their artificial intelligence solutions are consistent and reliable. That is the only way to get AI investment.

4. Algorithm Biases

A substantial issue with artificial intelligence systems is that the efficiency and dependability of these solutions is based on the data which they process. Usually bad data is associated with racial, gender, communal or ethnic biases. Some proprietary AI algorithms are utilized to find different things, such as loan defaulters, granted bails etc.

In case, an AI startup develops and deploys an algorithm in which some sort of bias is hidden and this algorithm is utilized by organizations to makes critical decisions. Under these circumstances, it highly likely that such algorithm will generate outcomes which are unfair and unethical. It is the responsibility of AI startups to make sure that their algorithms and solutions are bias-free.

5. Data Security and Privacy

Most of the AI based applications make use of huge data volumes in order to make smart decisions and constantly keep on learning. Machine learning programs rely on data that is usually sensitive. Because of the colossal quantities of data sometimes it becomes hard for AI startups to ensure its protection and integrity.

Loopholes in data security and privacy can make organizations as well as individual customers feel uncomfortable about the AI solutions offered to them. It can lead to demonstration of reluctancy by business organizations to integrate the AI technology into their current systems, due to which AI startups can lose artificial intelligence investment.

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