5 Artificial Intelligence Trends to Watch Out for in 2017

Artificial Intelligence Trends

Artificial intelligence has been the focus of technology research for quite a while now. Recent years have seen considerable development in machine learning, narrow AI and deep learning. What does 2017 promise us in the realm of AI? How closer could we come to the glorious liberating visions of AI that have been the subject of fantasy and disbelief in previous decades? In this article, we talk about the five most significant developments that might be realized in 2017. With easy commercialization, greater investment in research and applicability across sectors, this could be the year when AI finally takes off as a commercially viable part of the technology industry.

1. Diverse Applications across Industries

Artificial intelligence is expected to become more widely applicable in a variety of industries in 2017. For the past few years, industry has been watching the progress of AI from the sidelines, with mixed feelings of skepticism and excitement. This is due to the recent successes in narrow AI, data mining and machine learning that have proven the applicability of AI in the corporate world.

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At the same time, the AI industry has matured with watered-down expectations from all-encompassing general AI systems. Current trends indicate that organizations will expand their R&D budgets in the present year, with AI receiving a significant chunk. Over the next few months, we might be seeing greater investment in developing industrial robots, self-driving automobiles, applications for ecommerce, and more industry-specific projects.

2. Advancements in Neural Networks

Neural networks are expected to become the focus of the AI industry in 2017. Neural networking is the latest development in artificial intelligence that aims to replicate human mental processes in computer systems. Neural networks closely resemble human thinking processes because they are able to replicate the processes we use to identify patterns and rules for interpreting information.

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Recent technology has made it possible for programmers to program computers to self-learn based on responses received from previous actions. Scientists have been working to develop computer systems that are capable of self-learning and can learn to recognize handwritten numbers, for example.

3. Natural Language Processing

Closely related to neural networks is the concept of natural language processing. Artificial intelligence researchers are excited about developing techniques that can enable computers to recognize voice, image and other stimuli through training. In addition, the purpose of such systems is to develop systems that can communicate with humans through natural speech and dialogue.

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Given the complexity of human language, this is a formidable undertaking. However, it appears that researchers have thrown the gauntlet and are ready to embrace the challenges and rewards of research in this field in 2017. This year promises to witness significant developments in language learning.

4. Potential for Misuse and Systems Breach

As we step into the New Year, it is useful to remind ourselves to be cautious as far as the development of artificial intelligence is concerned. Almost all major developments in computer technology have brought about a tide of criminal and unethical practices. With its vast potential for impacting social and economic activity, AI is no different. Thus, 2017 could also witness a rise in crimes related to AI applications and technology.

Fraud is a major area of concern that could target commoners as well as corporations. There is a heightened risk of hackers getting into corporate artificial intelligence systems and manipulating the algorithms to make the systems malfunction. More research will be required to make such systems more secure and difficult to penetrate.

5. Growth in Deep Learning

2017 could be the year when AI takes off commercially. The technology relating to deep learning has been in the making for a long time and is ready for widespread application. Deep learning promises to be the growth area for artificial intelligence. Some of the fields that represent new areas in which deep learning systems can penetrate include smartphones, power generation and transmission, and even agriculture.

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Companies are more likely to invest in deep learning applications with more are more opportunities for commercialization of deep learning systems emerging. Medicine, commerce, production, and ecommerce stand to gain immensely from commercially available deep learning systems and seem to be ready to make the financial commitment. The industry is expected to respond favorably to such gains, by investing more in developing deep learning algorithms and supporting hardware.

A Final Word

We see positive trends as far as the growth of artificial intelligence in the year ahead is concerned. Clearly, there will be new challenges as scientists and users push the boundaries of their systems to accommodate AI applications and tools.

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Therefore, it is necessary for the industry to be ready to invest in researching solutions to those problems, particularly the risk of criminal and unethical activity related to AI-based applications. As long as the industry remains open to returning to the drawing board with a focus on commercialization, the future of AI looks bright.

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