Current trends in Artificial Intelligence, Machine Learning and NLP
Looking at the current chatbot trends in artificial intelligence, machine learning and natural language processing we can be sure that an automated future is just around the corner. An increase in the amount of attention, education and awareness associated with these fields is a clear indicator of this. The fields of AI, ML and NLP are all closely associated with one another. So, their collective development is imperative to achieve complete and true automation.
Future of Machine Learning at a glance
To set a background about why it is essential to follow the trends in AI, ML and NLP, let us look at some key statistics.
- By the end of 2022, the global ML industry will be worth around $9 billion taking into account the Current Aggregate Growth Rate(CAGR) of ~42%
- In the next 5 years, the neural networks market is estimated to be worth around $23 billion.
- The Deep Learning applications market is estimated to make a 9-fold leap from $100 million in 2018 to $935 million in 2025 in the US alone!
- By the end of 2021, a total investment of $58 billion can be expected towards Machine Learning. Being recognized as a key driver for digital transformation, ML is set to deliver higher standards in real-world industry applications.
With the boom of ML and AI based applications in 2018, many scientists and researchers started to make claims on behalf of the AI-based technologies. However, large-scale market application had to wait for the allied technologies to catch up.
AI and ML integration among more chatbot trends
With chatbots and digital agents in CRM, AI powered HRIS, Robotic bosses and automated assembly lines, VR powered shop-floor demos etc. AI promises something for every industrial sector. According to a paper published by McKinsey and Company, Notes from the Frontier: Modeling the Impact of AI on the World Economy, an estimated 70% of businesses will be using some form of AI by 2030. AI adoption rates will rise as more global businesses will open up to AI and ML integration, making it a market of about $13 Trillion by 2030.
Mckinsey and Company speculates that this might further widen the gap between those markets that successfully integrate AL and ML into their businesses as opposed to those that do not. In other words, the divide between developed and undeveloped nations can develop further with the growth in AI integration. On the other hand, the study also points out that there can be a steep decline in the jobs held by the upper tier society.
Chatbot Trends and Industry Implications
Increase in the usage of AI-ready chips
The speed with which AI models can be trained is heavily hardware reliant. Unlike software, specialized hardware is a necessity for AI and compliments CPU function. Complex tasks such as Facial Recognition and Object Detection can be accomplished at a faster rate with the increased processing power provided by additional hardware.
Specialized chips for speeding up AI- based applications will gain more appreciation and application by the end of 2019. Computer Vision, Natural Language Processing and Speech Recognition are some specific use cases that will stand to benefit most from the introduction of AI-ready chips. End-users can expect a significant leap in the intelligence offered in the next generation AI based applications offered by the Automobile and Healthcare industry.
Hyperscale infrastructure companies are taking a lead in investing towards research on manufacturing custom chips. Field Programmable Gate Arrays(FPGA) and Application Specific Integrated Circuits(ASIC) are some fields that are set to reap the benefits. Heavy optimisation for running modern workloads based on AI and High Performance Computing(HPC) is also expected from these chips. Some of them will also help next-generation databases in speeding up query processing and predictive analytics.
IoT meets AI at the edge
With the beginning of 2019, a lot of hype has surrounded the topic of edge computing. Most models trained on the public cloud will be deployed on the edge as IoT meets AI at the edge computing layer.
Outlier detection, predictive maintenance of equipment and root cause analysis can be expected to be performed by Industrial IoT integration, one of the top use-cases of AI. US- Army is one of the first to use ML to understand and predict when military combat vehicles might need repairs. They are able to achieve this, they are embedding a few sensors in the engines of a few dozen infantry transports.
The sensors will record RPM and temperature data and transmit it to the software, which will then compare it with past data to look for patterns indicating engine failure in similar vehicles. With military applications starting, civilians can expect this technology soon to be integrated into all vehicles by the automobile industry.
With special AI chips based on FPGA and ASIC built into edge computing devices, IoT is all set to become a major driver for AI in Enterprises.
Neural Network Interoperability
When it comes to building neural network models, choice of frameworks defines the scope of training for the model built. This becomes an interference when you consider the fact that models trained and evaluated in one particular framework are hard to port to another framework.
Widespread AI adoption is being hampered by lack of interoperability among neural network toolkits. So, to tackle this the industry leaders have collaborated to build Open Neural Network Exchange, ONNX. The ONNX essentially enables reuse of trained neural network models across multiple frameworksAs ONNX becomes an essential industrial technology in 2019, all key players will start relying on it heavily. From researchers to edge device manufacturers, the whole ecosystem will reply on ONNX as the standard runtime for inferencing.
Automated Machine Learning
AutoML can change the fundamental comprehension of ML-based solutions. Business analysts and developers will be enabled to develop machine learning models that will be able to address complex scenarios bypassing the typical ML model training process.
When dealing with AutoML platforms, business analysts remain focused on the business problem as opposed to getting lost in the process and workflow. AutoML perfectly places in the middle of the spectrum. With cognitive API on one end and custom ML platforms on the other. It helps the developers dodge the tedious workflows by providing the right level of customisation. In contrast to cognitive APIs, AutoML combines probability and custom data to provide the same level of flexibility. API in itself is a blackbox anyway.
AI, ML and NLP are increasingly gaining traction and attention in the automation industry. As we approach the second half of 2019, AI and ML have already proven to be key in technology trends. With increasing business applications and IT support integration, AI aided by ML and NLP is showing significant impact on the industry.
AI can be safely called a more mature technology now. Even in its relatively young life, we are using AI more extensively to automate a lot of enterprise applications. We are doing this with help of ML and NLP together.
In conclusion, the future of AI is here and it is looking brighter going ahead. A great deal of resources and research is being done across all fields. Industries are trying to understand and address the scope for automation.
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