Artificial intelligence is shaping in different directions every day: spanning into platforms, tools and applications. AI is the new bride for major tech giants attracting huge spending in the form of investment.
At the same time, this technology is becoming more affordable by the day and growing in its use. AI is now a special interest in trading, asset management, healthcare, manufacturing, automobile, sales, marketing and many more.
Considering AI’s opportunities that lie ahead of us, here are some of the valued expectations:
AI for Video and Voice Calls
With the use of AI for business communications, human interaction between clients and customer service or sales representatives will experience new capabilities.
AI-enabled communications will positively reflect on call center performance, customer satisfaction and generate revenue.
Voice and video contents can be analysed in real time allowing enterprise users to get interpretations to even non-verbal cues.
For this, image and speech recognition and analysis algorithms will be on the front row. Data queries will be faster to reply, there will be increased efficiency and productivity, business and communication will be more sophisticated.
Artificial Intelligence for Healthcare
Healthcare solutions are becoming more effective with the use of AI. This is made possible by training machines to learn and carry out clinical and administrative duties. With human supervision, AI will help to achieve precision medical services.
Diagnostics by using images, robot-surgeons, digital consultations, personal medical experience, health predictions and monitoring with the use of wearable devices, etc. are possible industrial applications of AI in healthcare.
People will trust AI more in health matters, and the technology will help human professionals to make the best decisions. Medical data reports analysed by AI and intelligent tools will be the instruments for more progress in healthcare.
The Union of AI and IoT
The use of Machine learning and big data will be a smooth facilitator for the Internet of Things. The connections and communications between billions of devices will make real-time data more available to enterprise systems for analysis by AI.
Streaming data collected by IoT will become more effective. It will also be easier to class algorithms based on the data types they can accept, their similarities in structure and the fraction of processed data with time data records. Metadata and logic execution by IoT devices will be useful in machine learning and the training of other forms of AI.
AI will also help in outlier detection to run root cause analysis and predictive maintenance of IoT devices. Advanced ML models built on neural networks will enhance running on Edge.
These ML models will be more compatible with structured data such as video frames, speech synthesis, time-series generated by microphones, cameras, and sensors.
Automation of DevOps through AIOps
The automation of DevOps through AIOps will become mainstream and change the management of IT infrastructure. It will make the root cause analysis faster hence solving a task with taking a shorter time interval.
IT operations will move from reactive to being more predictive. This is a piece of great news for cloud vendors as they are benefactors.
There are tons of data sets harvested from operating systems, hardware, application and server software. These generated data sets are essential for searching, indexing, and analytics. They can be summed and compared for correlation and similar patterns by application of machine learning models.
Open Source AI and Neural Network Interoperability
The ease of using AI is on the rise with the introduction of open source AI solutions by Tech giants such as Google, Apple, Tesla, and NVIDIA.
Open source in AI is becoming even more promising because more companies are interested in sharing knowledge and make AI stacks available. This will be the next step of AI evolution and will create empowerment for a better AI community with wider support.
Selecting the best frameworks for neural networks is a big challenge.
This is due to the fact that once you select and train a specific AI model, it is difficult to change the framework of that AI to use another tool.
AI adoption rate will boost with enhanced interoperability among neural networks. Open source in AI is becoming even more promising because more companies are interested in sharing knowledge and making AI stacks available. This will be the next step of AI evolution and will create empowerment for a better AI community with wider support.
Selecting the best frameworks for neural networks is a big challenge. This is due to the fact that once you select and train a specific AI model, it is difficult to change the framework of that AI to use another tool.
AWS, Facebook and Microsoft have already built an open ecosystem for interchangeable AI models called ONNX – Open Neural Network Exchange.
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