Ten Trending Applications of Artificial Intelligence

In 2018, we all experienced a dramatic emergence of the tools, platforms and applications based on Artificial Intelligence and Machine Learning. These technology tools not only transformed the internet and software industry, but it also had a massive impact on a wide range of verticals, including manufacturing, health, agriculture and automobile. 

AI-related and ML technologies will continue to grow in 2019 and the coming years. Organizations such as IBM, Facebook and Google are investing a lot of money and time in development and research of AI techniques to offer benefits to the users. 

Most of us wonder what transformation will AI bring in 2019. From the Gmail email response prompter to transcription of voicemails, automatic appointment calls, Alexa for smart homes and self-driving cars on the road, we had witnessed a lot in 2018. But the question is what changes are we going to experience in 2019 and 2020.. 

We shall discuss some of the fantastic Artificial Intelligence trends to watch. Following are the artificial intelligence trends to watch out in 2019:

Artificial intelligence depends on specialized processors, complementing the CPU. The advanced CPU models can also not enhance the speed of the AI training model. 

The AI model requires additional hardware to solve complex mathematical problems to increase the speed of tasks, like facial recognition and object detection. 

Chip manufacturers, including NVIDIA, ARM, Intel and Qualcomm will deliver specialized chips to enhance the speed of AI-based applications. 

AI-enabled chips will be designed for particular use cases and scenarios related to natural language processing, speech recognition and computer vision. Industry-grade applications will soon depend on these chips to provide intelligence to consumers or end-users. 

Recently, facial recognition has been widespread for a lot of negative press releases, whether it is China's SenseTime or Google winning the lawsuit. 

But, this technology will continue to grow in 2019. Facial recognition is an AI-based technique that is introduced to identify an individual using patterns of their facial features and their digital image. 

2019 would witness an increased usage of facial recognition technology with high reliability and accuracy. For example, Facebook's Deepface program is used for tagging friends and family in photos. Also, almost every smartphone now come up with a face lock. 

From advertising to shipping experience, facial recognition will continue to be used for biometric identification. Due to non-invasive identification and ease of deployment, this AI technology trend will continue to rise. 

Other use cases of Facial recognition include payment processing via security checks and law enforcement. The upcoming facial recognition techniques can also be used in the healthcare industry for clinical trials and medical diagnostics. Openwater, one of the portable medical imaging technologies, is breaking the boundaries that can read images from our brain.

AI will meet IoT at the edge computing layer in 2019. We will witness more use cases of convergence of AI with IoT. 

For instance, the concept of self-driving cars would not have become practical without the combination of AI and IoT. IoT-enabled sensors collect real-time data and AI models power decision-making programs.

Deep learning algorithms help in taking actions and making decisions based on the data gathered by IoT sensors. Some of the actions include eye-tracking to enhance driver monitoring, route planning, self-direct move to a gas station when the car runs low on fuel or gas and natural language processing for voice commands.  

IoT is ready to become the significant driver of artificial intelligence in the enterprise. Edge devices will be equipped with AI-enabled chips based on ASICs and FPGAs. 

As artificial intelligence is gaining a lot of traction with each passing day, almost everyone comes up with a common question, i.e., "Would AI take away jobs soon?" The answer is, "it depends." 

While artificial intelligence will take away jobs where resources will be scarce, it will also bring newer jobs with multiple skillsets. 

No matter what is the answer, the topic is under discussion by various governments and the World Economic Forum. It is because the rise of AI applications will have a risk of widening skills gap and can create polarized societies. 

Though automation may eliminate the need for jobs, there will always be a demand for jobs like teachers, caregivers, customer service executives and more. 

Redistributive programs will be the focus of attention for lawmakers in 2019. 

One of the biggest challenges in building neural network models depends on selecting the right framework. Developers and data scientists have to choose the right platform from a plethora of options, including TensorFlow, Caffe2, Apache MXNet, Microsoft Cognitive Toolkit and PyTorch.

After a model is trained and assessed in a particular framework, it is difficult to port the trained model to another framework. It happens due to the lack of interoperability among neural network toolkits. To overcome this challenge, Facebook, Microsoft and AWS have partnered to develop Open Neural Network Exchange, that allows reusing trained neural network models across various frameworks.  It will become a crucial technology for the industry in 2019. 

Modern infrastructure and applications generate log data which is captured for searching, indexing and analytics. The huge data sets obtained from the operating systems, application software, server software and hardware can be correlated to search patterns and insights. 

After machine learning models are applied to such data sets, IT operations can transform from being reactive to predictive. When the potential of Artificial Intelligence is applied to operations, it will reconstruct the way infrastructure is handled. The application of AI and ML in DevOps and IT operations will offer intelligence to companies. It will help the ops team conduct an accurate and precise root cause analysis. 

It is the reason why AIOps will become a focus in 2019. The convergence of AI and DevOps will benefit both enterprises and public cloud vendors. 

The AI trend that will change the ML-based models is AutoML. It will allow developers and business analysts to develop machine learning models that can solve complicated scenarios without undergoing the process of training ML models. 

When using AutoML platform, business analysts can stay emphasized on the business problem rather than getting lost in the workflow and progress. 

The platform can fit between custom ML platforms and cognitive APIs and deliver the right level of personalization without requiring developers to go through the complete workflow. 

Machine learning becomes complicated when the number of dimensions of data gets increased. Imagine you try to transcribe your voice into the text. The problem is aggravated several times. 

However, deep learning is a technology behind self-driving cars, image recognition and voice control. With the emergence of both Google Home and Amazon's Alexa, you may find a wide range of voice-based applications using natural language processing, an application of Deep Learning. 

Therefore, we can witness increased interest in the next-generation of deep learning algorithms which can overcome complex problems, for example, interpretation of technology infrastructure issues. 

As we all know that the blockchain deals with challenges like scalability while AI has trust and privacy issues, these two technologies can be combined to resolve these challenges.  

Powering decentralized marketplaces, blockchain can help AI algorithms to become more trustworthy and transparent. For instance, Enigma is a startup that provides a secure data marketplace that users can subscribe and access via smart contracts. 

The introduction of GDPR was the most hyped topic in 2018. We are expected to see more conversations related to policy and privacy in 2019 and 2020.. 

Most of us do not know how our digital information is being used over the internet. Facebook's crisis over privacy has led to consciousness regarding the privacy of digital data. 

It is the reason why legislators and countries will continue to view privacy policy as a crucial concern in 2019. Issues of the consent of usage of the digital ecosystem around Artificial Intelligence will be given immense importance and the laws developed around AI requires further understanding. Countries around the world will continue to work on initiatives to develop artificial intelligence regulations. 

From the AI trends mentioned above, it can be concluded that AI is not going to witness a decline any time soon. We will continue to see new AI trends with each passing year.