Artificial Intelligence: Key challenges and opportunities

With humans and machines joining forces more than ever before, AI is no longer restricted to innovation labs and is being hailed for its immense transformational possibilities. Though, organizations need to overcome particular challenges before they can realize the real potential of this evolving technology. The key lies in leveraging the right prospects in AI.

Provability

Companies involved in AI cannot validate clearly why it does and what it does. No matter AI is a "black box." People are uncertain about it, as they fail to comprehend how it makes decisions. Provability – the level of mathematical cert behind AI predictions – remains a grey area for enterprises. There’s no way they can guarantee that the intellect behind the AI system’s decision-making is clear. The solution lies in making AI explicable, provable, and transparent. Businesses must embrace Explainable AI as a best practice.

Data privacy and security

Most AI applications depend on massive volumes of data to learn and make smart decisions. Machine Learning systems feast on data – often personal and sensitive in nature – to learn from them and improve themselves. This makes it exposed to severe problems such as data breach and identity theft. Here is some good news; the growing awareness among consumers about the rising number of machine-made decisions using their own personal data has stimulated the European Union (EU) to device the General Data Protection Regulation (GDPR), intended to ensure the protection of personal data. Besides, an evolving method – ‘Federated Learning’ – is all set to interrupt the AI paradigm. It will allow data scientists to develop AI without compromising users’ data security.

Algorithm bias

An intrinsic problem with AI systems is that they are only as good or as bad – as the data, they are trained on. Bad data is often mixed with gender, racial, communal or ethnic prejudices. Proprietary algorithms are used to govern who are granted bail, who’s called for a job interview, or whose loan is sanctioned. If the partiality lying in the algorithms that make crucial decisions goes unrecognized, it could lead to immoral and unfair consequences.

In the future, such biases will perhaps be more stressed, as many AI systems will last to be trained using bad data. Therefore, the need of the hour is to train these systems with impartial data and develop algorithms that can be effortlessly explained. Microsoft is developing a tool that can automatically detect bias in a series of AI algorithms. It’s a substantial step towards automating the finding of prejudice that may find their way into Machine Learning. It's an excellent opportunity for organizations to leverage AI without inadvertently perceptive against a particular group of people. You can also use approaches such as "Path-specific Counterfactual fairness" by DeepMind researchers Silvia Chiappa and Thomas Gillam to remove biases.

Data scarcity

It is true that organizations have access to more data today than ever before. Nevertheless, datasets that are relevant for AI applications to learn are definitely rare. The most potent AI machines are the ones that are trained on supervised learning. This training needs labeled data – data that is ordered to make it ingestible for machines to learn. Labeled data is limited. In the nearfuture, the automated development of increasingly complex algorithms, mostly driven by deep learning, will only worsen the problem. There’s a ray of hope though. As a trend that’s fast catching up, companies are investing in design practices, trying to figure out how to make AI models learn despite the dearth of labeled data.

The way ahead

Collecting data is just the first step for organizations towards building effective marketing campaigns. However, they must be able to infer the numbers and recognize relationships within them. This calls for distinguishing between correlation and causality. The future belongs to organizationsthat can blend the predicting capabilities of AI-driven machines with the ability of human instinct and judgment.

Artificial intelligence program from a top quality institution can help you get useful insights into the subject-matter. Top institutions have the right resources and faculty to ensure quality learning for students.