This article is about Artificial Intelligence
Artificial Intelligence Trends That Are Breaking New Ground
By NIIT Editorial
Published on 15/12/2020
Unless you’ve been living under a rock, the growth in AI is evident to see for all. Aided by the unprecedented interest in machine learning and algorithmic sciences, the AI-marketplace will show unfettered growth as interest in the field continues to aim for historic highs. In light of the same and thanks to insights from expert opinion, curated insights in this article shed light on what to expect when it comes to the current trends in Artificial Intelligence that are most expected to shape the future of AI.
There’s No Dodging the AI-Bullet
Business use cases for AI-powered business development are profound in both number and variety. From conversational interfaces (chatbots) to the most intuitive search engines, and business management software, AI has found an application in all. If new to the topic, you can read about it in detail with this in-depth guide on an introduction to Artificial Intelligence that paints a broad picture of the uses of Artificial Intelligence.
Search query patterns from Google validate the same where a collective increase in public curiosity to explore Artificial Intelligence also made it a trending technology of 2020.
The arrival of 5G technology adds fuel to the proverbial AI-fire in accelerating industrial adoption. Driverless cars, which for the time being are semi-autonomous, will certainly become self-monitoring in a decade. As per Mckinsey by 2030, 15% of the vehicles sold will be driven by algorithms and guided by supercharged internet. As research in the field continues to mature, let us look at the most recent trends in Artificial Intelligence and its associated technologies.
Trends Impacting Artificial Intelligence
The following areas are expected to have the most far-reaching effect on how the field of Artificial Intelligence formalizes. Let us go through such latest innovations in Artificial Intelligence.
One of the most integral functions of AI, deep learning is the ability of the code to replicate human actions such as speech and language recognition, autonomous decision making, and object identification. For the code to reach such conclusions it needs to process a tremendous amount of data. Although the fastest CPUs of today do not offer a one-stop-shop solution for high-level processing tasks, yet they can be optimized with AI-chips. The global AI-chip market is firing all cylinders to reach 91,185 million by 2025. The Big Tech (Facebook, Google, Amazon) is also investing heavily in procuring such chipsets.
GPUs are a type of AI-chipsets. They specialize in processing thousands of images simultaneously thus reducing the total computing power required to perform the same task. Of late their functional area has been expanded from image rendering to also include cryptocurrency mining, statistical analysis, and machine learning.
Quantum computers are the new thing in AI. Conventional computers are designed to work with binary states, 0 and 1. however, quantum computers work with binary states as well as a parallel third state with the help of quantum mechanics. They use qubits that permit the usage of 0, 1, and a third state where both 0 and 1 can co-exist. This defining characteristic allows quantum computers to consider possibilities that vastly outnumber conventional computers. Google has stated that its quantum computer is 100 million times faster than a typical computer in its lab.
The algorithms running the show for AI-powered operations will undergo massive upliftment on multiple fronts. One of these centers around Explainable AI. A frequent association of AI is with code complexity. That is true. While an AI-run software could display results, it gives little insight to users about how it got there. Explainable-AI would offer an improvement in that it offers easy-to-grasp explanations for both developers and users about its methodology. To give an example, medical practitioners could better understand how AI arrived at a particular diagnosis. Similarly, engineers could know the blind spots in their code when AI scans it and the technique deployed to do so by the AI.
It refers to the practice of training a brand new AI model leveraging the results of a relatively similar, but already trained version of the AI. It is a time-consuming process to train an AI model not to mention the massive amounts of data feeds required to stay put. Occasionally, businesses could find themselves in a sticky spot with a dearth of data. Transfer learning can step in to fill the gap. It can integrate the results of a previously trained model to give a relatable estimation than paint an accurate picture. In cases of insufficient data, results could be priceless. For instance, a model trained on identifying wolves.
Reinforcement Learning (RL)
Artificial Intelligence and machine learning are closely linked. In fact, the latter is a subset of AI. Usually, AI and machine learning algorithms learn by analysing historic trends but with reinforcement learning, the pattern reverses. RL-enabled code adjusts and sequences program events in order to maximize the success rate, whatever metrics that involves. In due process, the code then learns through the results to improve its next move. The most glaring use case of RL would be Googles’ DeepMind AlphaGo. Reinforcement learning will be key in the coming years for industrial automation.
Hitherto, Artificial Intelligence engineering required developers to label and differentiate (or define, whichever applies) data and mitigate subliminal AI bias. With self-supervised learning all that will be a thing of the past. AI will be able to label the data cutting out its human dependence. This pattern of AI learning is expected to reveal insights into how humans act decisively therefore emboldening AI to become self-sufficient.
Drawing it to a Close
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