Top Artificial Intelligence Trends to Watch Out For in 2019

Cache Merrill -

Businesses are leveraging Artificial Intelligence (AI) to optimize their performance and functionalities. AI is opening the door for new business opportunities and creating entirely new business models. The adoption of AI by industries is ushering in this digital evolved era.

In 2018, AI made a huge impact on how companies run their operations and engage with customers. It had a profound effect on almost every sector such as IT, healthcare, banking, automobiles, and manufacturing.

The year 2019 is also going to witness the advancement of AI. Considering its potential, companies are investing in AI research and development. It will transform the way humans interact with computers.

Here are the top Artificial Intelligence trends in 2019-

1. Use of Specialized Chatbots in DevOps: A revolutionary upholder in enterprise

Current industry trends show huge popularity for chatbots. Industries are paving the way to embrace this technology. They are adopting chatbot solutions to reduce operational cost and time-consuming tasks.

Generic bots like Cortana, Siri, and Alexa are capable of handling basic end-user queries. But they lack domain expertise and are unable to grasp the context. This makes them ill-suited for enterprise industries. To evolve the automation, organizations are now focusing on developing specialized chatbots. These chatbots possess self-learning capabilities to understand the contextual information.

Specialized chatbots used in DevOps are the top use case for AI. They understand the requirement of regular human language. It makes them a great tool for the whole support system. These chatbots help in:

  • Doing all back-end jobs and fetching appropriate files
  • Invoking automated test cases
  • Providing status reports from data-center, etc.

Microsoft Azure Bot service is making headway in this technological advancement. It is helping organizations in creating, building, deploying and managing intelligent bots. It will speed up the development process and meet their business requirements.

2. AI- Cognitive computing and IoT at the Edge: Making businesses more responsive to customers

Mammoth amounts of data are getting generated across organizations. So, the businesses are looking for flexible solutions to deploy AI and IoT services in a wide range of environments. The edge computing is receiving interest at the global level. It processes the data at the source instead of a central data-storage warehouse. To gain more control over data and to extract insight the data has to offer, organizations are adopting intelligent Edge.

In 2019, Microsoft announced Azure Cognitive Services Container. It lets developers seamlessly add cognitive features—such as natural language processing, face detection in images or videos, speech translator—into their applications, with no need of having thorough domain expertise in AI or Data Science skills.

NVIDIA Jetson TX2 is a powerful platform, empowering AI computing at the edge. It is unlocking the door of high-powered intelligent robots, drones, and smart cameras.

Rapidly advancing AI technologies are integrating with IoT networks. It enables smart machines to simulate intelligent behavior and make accurate decisions, with little or no human intervention.

3. Specialized AI chips: Empowering businesses in the era of smart machines

AI chips have lessened the cost of accelerating AI applications in cloud/edge computing and enterprise data centers. They are also meant to increase the performance of both training and inference phases in Machine Learning.

The market for AI accelerators (GPUs, FPGA, ASICs) is expected to grow from $15 billion in 2019 to $25.5 billion in 2022. Companies like NVIDIA, IBM, and Intel are planning to develop next-gen AI chips. These will optimize machine learning skills and reduce data analytics workloads in data centers. The performance of AI-enabled edge applications will also get enhanced.

Traditional von Neumann chip architecture consumes precious energy and time due to the data shuttle between memory and processor. To overcome this challenge, companies like IBM have designed Neuromorphic AI enabled chips. It models more closely to the biological brain, hence, extracting the accurate conclusions from little information.

In 2019, Intel is all set for the shipment of two Nervana Neural Network Processors–

  • Intel NNP-L 1000 for Training
  • Intel NNP-i 1000 for Inference.

These NNPs put the trained model to work for deeper insights and drawing conclusions. At the same time, they support general as well as Neural network acceleration.

Amazon has announced AWS Inferentia– a machine learning inference chip that will use trained machine models and make predictions accordingly. It will support the deep learning frameworks such as:

  • TensorFlow
  • Apache MXNet
  • PyTorch
  • And, models that use the ONNX format.

4. Automated machine learning: Training your own custom ML model

Automated machine learning (AutoML) is renovating Machine Learning (ML) based solutions. It is assisting businesses by making the machine learning available as a service that they can use to easily train predictive ML models.

It lets business analysts and software developers stay focused on their projects while addressing the complex issues at the same time, without distracting them with the process of training ML models.

AutoML will find the middle ground between cognitive APIs and custom machine learning platforms. It offers software developers with the customization options, with which they can train, assess, improve and deploy models based on their company’s own data.

Yet, some AutoML approaches take too long to train the models and operate for only a handful of parameters. To address these issues, Microsoft’s research center is creating AutoML techniques. These techniques are expecting to outperform existing approaches.

5. Enabling interoperability among neural network: Get great ideas into production faster

In 2019, Interoperability has become a cornerstone for AI platforms. It will standardize the Neural Network models.

Over the past few years, the lack of interoperability has complicated the development of neural network models. Data scientists and developers are required to select the right machine learning tools (Caffe2, PyTorch, TensorFlow). Once the model is trained and evaluated in a specific framework, its deployment to another platform complicates the issue.

To address this challenge, tech giants, including AWS, Facebook, and Microsoft, have united and released Open Neural Network Exchange (ONNX). ONNX will become a crucial technology for the industry. Here are some key points about ONNX:

  • It enables interoperability between machine learning frameworks, thus making cross-platform deployment easy.
  • It will make the hardware optimization easier.
  • The ONNX project has spanned more than 27 companies.
  • ONNX Runtime is an open source, high-performance inference engine for machine learning models in the ONNX format.