Author:- Careers of Tomorrow
This year, we have witnessed an intense increase in Machine Learning and AI applications, platforms and tools. It has influenced not only the software industry but also the internet, healthcare, automobile, and other verticals.
We will certainly continue to see progress in ML and AI-based technologies the next year and beyond. Large companies such as Amazon, IBM, Google are investing in AI for further research and development.
Here’s what to watch out for:
1) AI-enabled chips
Artificial Intelligence highly depends on specialized processors. Sometimes, even a highly advanced CPU may not be able to increase the speed of training an AI model. While concluding, the model requires additional hardware to complete complex mathematical calculations to accelerate tasks like facial recognition and detection of objects. We can expect to see Intel, Qualcomm and other chip manufactures offer chips that can increase the speed of AI-enabled applications. The next-gen applications will depend on these chips for providing intelligence from healthcare to automobile industries.
Industry giants like Amazon, Facebook, Google, Microsoft are looking to grow their investments in custom chips that will be optimized for current workloads built on AI and high-performance computing. Several AI-enabled chips will also help the next-generation databases to increase the speed in data processing and predictive analytics.
2) Union of IoT and AI
Artificial Intelligence converges IoT at the edge; most models training in the public cloud will deploy at the edge.
One of the top use cases of AI is industrial IoT that can execute irregularity detection, analyze root cause and analytical maintenance of the equipment.
Advanced Machine Learning models will be able to deal with time-series data, speech synthesis as well as video frames. It is based on deep neural networks make it capable of dealing with unstructured data from sensors, cameras, and microphones.
In 2018, we will see IoT setting to be the biggest driver of enterprise AI. Special AI chips will be embedded on edge devices. Edge devices will be equipped with the special AI chips based on Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC).
3) Emphasis on the interaction between neural networks
Selecting the right framework when developing neural network models can pose as one of the acute challenges. Having to choose the right tool from a vast number of options can put data scientists and developers into a dilemma. From Caffe2, MS Cognitive Toolkit, TensorFlow, Apache, one must select the right tool. When a model is trained in a specific framework, porting the model to another framework is difficult.
The lack of the ability to exchange and make use of information among neural network toolkits is hindering the use of AI.
To confront this difficulty, companies like Facebook, Microsoft, Amazon have collaborated to build Open Neural Network Exchange (ONNX), which makes it possible to reuse trained neural network models across multiple frameworks. We will see ONYX becoming an important technology for researchers, manufactures to rely on the ONYX format as the standard runtime for reasoning.
4) Increased recognition of automated machine learning
One trend that will change the face of solutions based on Machine Learning is AutoML. It will engage developers and analysts to advance machine learning models that can address difficult situations without experiencing the typical procedure of having to train ML models.
When managing an AutoML platform, an analyst must concentrate on the problem as opposed to losing all sense of direction and work process.
AutoML helps deliver the right customization as it comes right between APIs and custom ML platforms. It delivers the correct level of customization without driving the developers to experience the detailed workflow. Contrasting to cognitive APIs that are frequently considered as black boxes, automated machine learning provides a similar level of flexibility, however, with custom data joined with portability.
5) Automated DevOps through AIOps
Applications and infrastructure are creating log information to index, search and analyze. The enormous data sets acquired from the hardware, server and application software; operating systems can be accumulated and corresponded to discover insights and patterns. When machine learning models are applied to these data sets, IT operations change from being receptive to prescient.
When the power of Artificial Intelligence is applied to activities, it will transform the way we manage infrastructure. The use of ML and AI in technological activities and DevOps will bring insight and intelligence to organizations. It will help the operation teams perform exact and precise underlying cause analysis.
We will see AIOps in the mainstream this year. The union of AI and DevOps will profit public cloud vendors and enterprise.
Machine learning and AI will be the technology trend of 2019. From business applications to customer support, AI will affect businesses significantly.
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