AI holds a promising future for economies of G20 countries. Source: Accenture & Frontier Economics Applied AI is the use of artificial intelligence to enhance the efficiency of solving real-world problems. It can involve using AI to automate repetitive tasks and save human resources, using AI to detect patterns that might be missed by human judgement, or using AI to project and anticipate the behaviour of the stock market – the possibilities are limitless.

Over the past few years, AI has transformed a lot. Deep learning, a branch of machine learning that relies on artificial neural networks, has seen tremendous progress in recent years. The availability of large amounts of data, both labelled and unlabeled, the affordability of computing and storage resources, and the improved quantizations technique in Deep Learning Model – which makes it affordable – are some of the main reasons for the feasible development in Deep Learning

The major point of inflection came in 2017, when Google released their famous "Attention is All You Need" research paper. The paper introduced a new network architecture called the Transformer. Transformers were revolutionary in the fields of Natural Language Processing (NLP), because this new network architecture was based solely on attention mechanisms – which allowed the model to focus on different parts of the input sequence when producing an output. Moreover, Transformers did not use recurrence or convolutions – which are two different types of neural networks used in deep learning, which allows these Transformer models superior in quality, as well as really fast to train.

After the introduction to Transformers, innovations under AI skyrocketed. Companies and enterprises belonging to various industries started adopting AI in their workflow and enterprise – with each of them showing a significant boost in revenue.

In the domain of security too, AI can be helpful. AI can analyse large amounts of data, network logs, user behaviour, and past malware records, to identify potential malicious behaviour by any system. VentureBeat predicts that AI will boost cybersecurity in 2023 and beyond in many ways, from behavioural analytics to endpoint and patch management. A CNBC report suggests that organisations can leverage the latest AI-based tools to better detect threats and protect their systems and data resources.

The trend of relying and using AI in modern enterprise is expected to grow continuously, even intensify in the future. According to an article by McKinsey, Artificial intelligence will be involved in more than 90% of the interactions between brands and customers – either directly or indirectly.

Even though the revolutionary field of Artificial Intelligence is presently promising - and it will continue to be promising in the future as well – it has its inherent flaws that shouldn’t be overlooked.

Two of the most challenging issues that these superintelligent machines face are hallucination and algorithmic bias.

Hallucination is when AI apps, such as ChatGPT, create their own facts that are not based on reality. This can lead to misinformation, confusion, and even deception, especially when the AI-generated content is not clearly labelled or verified.

Algorithmic bias, on the other hand, refers to the unfair or discriminatory outcomes that an AI system generated due to existing human biases present in the training data. For example, facial recognition systems that fail to recognize people of colour, or hiring algorithms that favour male candidates over female ones.

AI is not truly intelligent, but rather imitates human intelligence. This is because AI learns from a specific data set, and its knowledge is bounded by the data that it is exposed to during its training. They do not possess any intelligence of their own.

Therefore, AI does not have reasoning abilities; it cannot distinguish between right and wrong, true and false, or biased and unbiased facts. For a machine, all information – regardless of its quality – is simply ‘data’ from the viewpoint of AI.

This leads to another problem of AI – it can make biassed conclusions. This depends largely on the quality of the data that is used to train the AI system. If the data is skewed or incomplete, or if the algorithm that runs the AI system has some errors, it could lead to unwanted outcomes.

These 'undesirable' outputs can have serious implications for enterprises that rely on large-scale data. For example, CNET, a well-known news outlet, experimented with AI to produce web articles for its website. However, to their disappointment, the news-outlet found errors in more thanhalf of its AI-written stories.

This undesirable outcome is the result of bad AI deployment and insufficient consideration towards the risks associated with AI

Therefore, it is essential for organisations to exercise caution when integrating AI into one's organisation or workflow.

As artificial intelligence (AI) becomes more prevalent and influential in the modern world, it is crucial to address its limitations and challenges. To leverage the full potential of AI, one needs to comprehend how the AI model operates and plan the optimal way to integrate it into the organisation, resulting in enhanced efficiency.

This is whereAiProffcan assist you. AiProff is a leading company in the AI domain that enables you to discover the endless opportunities that AI provides. We are a group of skilled experts with a wealth of knowledge and experience in machine learning, artificial intelligence, and data science.

We not only know how to build machine learning models, but also how to detect and prevent their vulnerabilities and biases that can lead to erroneous or harmful outcomes.

We excel at creating state-of-the-art solutions as Minimum Viable Products for Enterprises and Academic Institutions to lower the entry barrier – using cutting edge AI/ML solutions – and expedite time to market.