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5 trends of artificial intelligence in 2019

With the burst in AI in recent years, we will witness the existence of AI everywhere in near future, from Business to Information Technology. Some giants such as Google, Facebook, IBM, Amazon, Apple and Microsoft are putting their efforts in AI research and development, this will help customers approach to AI closer and closer.

Here are some AI trends in 2019:

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1. The increment in AI SoC (System on a chip):

– Unlike other software, AI is dependent on dedicated CPU, even the up-to-date CPU can’t upgrade the AI model training time. In reasoning, the model needs extra hardware with high-rate performance to accelerate tasks such as object detection and face recognition.

– In 2019, processors manufacturers such as Intel, NVIDIA, AMD and Qualcomm will produce dedicated processors that will improve the runtime of AI applications. They also will be optimized for specific use cases and situations involving computer vision (CV), natural language processing (NLP) and voice recognition. Next-generation applications from the healthcare industry and automotive will also rely on these processors.

– 2019 will also be the year that giant technical companies such as Amazon, Microsoft, Google and Facebook will pay their attention to custom chips based on Field-Programmable Gate Array (FPGAs) and Application-Specific Integrated Circuit (ASIC). These chips will be strongly optimized to run huge workloads of AI and High-Performance Computing (HPC). Some of these chips will also support next-generation databases to improve query performance and predictive analysis.

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2. The combination of IoT (Internet of Things) and AI in the Cloud:

– In 2019, AI and IoT will convert in the cloud, almost AI model will be trained and built in the Cloud.

– IoT industry is the leading use case for Artificial Intelligence that can perform exception detection, root cause analysis (RCA) and predictive maintenance (PdM) of equipment.

– Advanced ML models based on deep neural networks (DNNs) will be optimized to run in the Cloud. They will be able to handle video frames, synthesize speech, time series data and unstructured data created by devices such as cameras, microphones and other sensors.

– IoT is ready to become the biggest motivation of AI of technical enterprises. Edge devices will be equipped with special AI chips based on GPUs and ASICs.

3. The interaction between AI framework:

– One of the key challenges of developing neural networks(NN) models is choosing suitable framework. Scientists and data developers must choose the right tool from a variety of options including Caffe2, PyTorch, Apache MXNet, Microsoft and TensorFlow Cognitive Toolkit. When a model is trained and evaluated in a specific framework, it is difficult to convert the trained model to another framework.

– The lack of interaction between neural networks tools is limiting the power of AI. To address this challenge, AWS, Facebook and Microsoft have teamed up to build Open Neural Network Exchange (ONNX), allowing re-use of neural network models trained on multiple frameworks.

– In 2019, ONNX will become an essential technology for AI industry. From researchers to edge device manufacturers, all of the ecosystem will rely on ONNX as the standard runtime.

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4. Automatic ML will achieve prominence:

– A trend that will change the figure of ML-based solutions is basically AutoML. It will empower analysts and business developers to develop machine learning models that can solve complex issues without typical ML mohdel.

– AutoML perfectly matches the cognitive APIs and the custom ML platform. It provides the right level of customization without forcing developers to go through a complex workflow. Unlike cognitive APIs that are often considered as black boxes, AutoML represents the same level of flexibility but with the combination between custom data and portability.

5. AI will automate DevOps through AIOps:

– Modern applications and infrastructure are creating log data for indexing, searching, and analyzing. Huge datasets collected from hardware, operating systems, server software and application software can be synthesized to learn deeply.

– When AI is applied, it will redefine the infrastructure management. The application of ML and AI in IT operations and DevOps will provide data for enterprises. It will help Operation groups perform RCA accurately.

– AIOps will become the trend in 2019. Public cloud providers and businesses will benefit from the combination of AI and DevOps.

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