Artificial Intelligence (AI) is arguably the most revolutionary technology in several decades that would completely turn the world upside down and then shape it along with new contours. AI will reinvent everything from the nature of work to our modes of communication and transportation. The ‘creative destruction’ unleashed by AI would make a large number of current skills and jobs redundant while opening avenues for new skills.
The preeminence of AI and its far-reaching influence can be gauged from the fact that the nascent AI rivalry between USA and China has been dubbed as ‘The New Space Race’.
In 2018, increase in AI-based applications, anxiety about a ‘war of the worlds’ esque robotic workforce and China outpacing USA in the number of AI startups and AI related patents were the most highlighted AI trends. This year, both the hopes and the skepticism about AI continue to soar. Let’s have a look at the top AI trends for 2019.
AI depends wholly on specialized processors that work in tandem with the CPU. However, one main drawback is that even the fastest and most technologically advanced CPU would prove to be incapable of training an AI model. The model would require extra hardware to perform mathematical calculations for complex tasks like detecting objects and facial recognition.
This year, leading chip manufacturers like Intel, NVidia, AMD, ARM, Qualcomm will make chips that will rapidly enhance the speed of execution of AI-based apps. These chips will be used in multiple customized uses in language processing and speech recognition. More R&D into these chips would lead to the development of applications for healthcare and automotive sectors.
Confluence of AI and IoT
AI and IoT will increasingly converge at edge computing. Most Cloud-trained models will be put at the edge layer.
AI’s utility in industrial IoT applications are also expected to increase manifold as AI would give it a cutting edge precision and increase its functional ability in root cause analysis and predictive maintenance. Advanced Machine Learning models predicated on neural networks will also be optimized with AI.
For enterprises, IoT is all set to emerge as the principal driver of AI. Most Edge devices will be embedded with specially designed AI chips.
Rise of Automated Machine Learning
Machine Learning would undergo a radical change with the arrival of AutoML( automated Machine Learning) algorithms. AutoML will allow developers and programmers to solve complex problems without creating specific models. The advantage of AutoML is that it would enable analysts and developers to focus only on the problem concerned and not on the entire process and workflow.
AutoML seamlessly aligns with cognitive APIs and custom ML platforms. It saves a lot of time and efforts by directly addressing the issues instead of going through the entire workflow. AutoML uniquely blends flexibility with portability.
Cybersecurity and AI
Owing to the huge gap in the demand and supply of cybersecurity experts, the traditional shortcomings of cybersecurity and the mounting risks of security breach that call for innovative approaches, the use of AI and Machine Learning in cybersecurity will increase. This will transform the way organizations look at cybersecurity. The incorporation of AI in cybersecurity wouldn’t mean that there would be no requirement of experts, but AI will empower the experts and make the system more robust.
With the expansion in the size of the systems and the need to vigilantly monitor threats, carrying on cybersecurity without AI would make processes vulnerable and lead to less efficiency.
In 2018, it was reported that AI would be among the highest paying jobs and big companies would opt for AI reskilling. The same trends would continue this year as well, however, there is a big gap that companies are finding it hard to overcome: the AI skills gap. Another alternative that companies are looking at is designing AI-powered tools that won’t require supervision. Though skillsets would remain in demand due to the high requirement and also because different organizations would need different skills.
Automation of DevOps through AI
Applications these days generate reams of data that has to be filtered for analytics. The datasets can be collated to find correlations and new patterns that would then cater to hardware and other application softwares. Applying Machine Learning models on these datasets makes them predictive. With AI being added to it, the way we manage IT infrastructure would be reoriented. Deploying AI in IT operations will help them perform tasks in shorter time and get at the root of the problem quickly. AI-based DevOps would become operational in 2019. Cloud vendors would greatly benefit from them.
Neural Network Interoperability
In neural networks, the main issue is selecting the most suitable framework. Developers face a difficult choice of selecting from an array of tools: Apache MXNet, Microsoft Cognitive Toolkit, TensorFlow. Also, once a particular model is selected and trained, it becomes very difficult to work on another framework using another tool. Interoperability in neural networks is an impediment in the path of AI adoption.
Open Source AI
Most of the Cloud-based technologies that we use today have their genesis in open source projects. AI is expected to follow the same trajectory as more and more companies are looking at collaboration and knowledge sharing. Open Source AI would be the next phase in the evolution of AI. Multiple companies would start open sourcing their AI stacks for building a wider support network of AI communities. This would lead to the development of an authoritative AI open source stack.