Cloud and IoT trends ruling the technology trend, tech leaders across the globe are looking to hop on the fast-paced development of Artificial Intelligence (AI) and Machine Learning initiatives. The usage of new innovative technology will provide the enterprises with the upper hand of providing solutions solving the complicated and cumbersome task in the development, deployment, and security. The business leaders are viewing artificial intelligence as the next big bet for business growth. According to the EY report, almost one-quarter of the professional business leaders actually believe in investing in AI to have a positive impact on the progress in the next five years. The report also added that close to 23 percent of the leaders think that business leaders will significantly impact business growth. The EY report had surveyed close to five hundred CXO from the US, they were questioned about their company strategies based on new innovative technology, future technology opportunities and struggle with innovative technology. More than half of the C-level executive said that their enterprise is spending more than 5 percent of their budget towards the innovative new technology and 42 percent agreed that they are facing the budget constraint towards the deployment of new technology. The other obstacle many of the enterprises are currently facing includes the market volatility, and the competitors can outpace the development are some of the external obstacles. Despite all the concerns regarding the development of the innovative technology, 75 percent of the CXOs added their organization innovative exceeded more than the expectations.
The decision makers of the enterprise have to actually invest in innovative technology to improve the growth of the enterprise, so the initiative has to be from their side. Every organization should invest in a team that can actually focus on developing such innovative technology. Almost 44 percent of the enterprises are providing the incentive towards innovative accomplishments. 43 percent of them supported the external training, 42 percent investing in the hosting focused innovation. Around 12 percent of the respondents added that the mentorship program will be a key driver for the innovation in the organization.
Michael J. Inserra, EY Americas Senior Vice-chairman, and Deputy Managing partner said in a press release that “Becoming the partner for the different mentorship programs or adopting them will actually improve the senior leader’s position to unlock the full potential of innovation within the organization.”An effective program that induces creative thinking can forge the connection between all the employees depending on the talent and create effective space for everyone to create innovation.
Machine learning is a subtype of Artificial Intelligence. AI includes various subtypes such as rule-based engines, evolutionary algorithms, and Bayesian statistics. Many of the early AI programs where the rule-based that depending on the human-based program. Machine learning is a tool through which the system can actually learn and train itself on large data sets.
Here are Different Kinds of Machine Learning Technology
1. Supervised learning: The trainer or programmer will implement a set of rules beyond which the object should function within the specified limit.
2. Unsupervised learning: The system is fed with data and left alone to learn from the data. It should be able to identify different patterns among the data.
3. Reinforced learning: Sets of devices continuously provides with data, and the system is set to take actions. The output or actions will depend on the set of input data.
When it comes to machine learning a massive set of data is required to train the algorithm before it can actually be deployed. Initially, the training data is labeled and then it’s classified. The algorithm works in sync towards when the features of the object in question are labeled and put in the system with a defined set of rules that actually lead towards the prediction. The learning algorithm can be left alone to create its own rule that is applied when provided with a large set of objects. Many cases of machine learning actually involve the deep learning that uses the algorithm that is layered and forms a network to process information and reach predictions.
There have been numerous examples for the industry leaders using machine learning techniques to improve the data set. For enterprises, the major is toward data usage, and that can provide insights. There are various examples of how business is improving the customer experience with the implementation of machine learning technology.
Below are Some of the Examples of Machine Learning From Various Businesses.
1. Security firm chronicle is using machine learning technology to identify cyber threats and minimizes the damage they can cause.
2. The global Fishing watch is implementing the GPS technology to keep a tap on fishing vessels to stop overfishing.
3. In air defense and to improve the imaging technology Airbus defense and space is using ML-based image processing technology.
4. Insurance firm AXA has raised the accident prediction accuracy by 78 percent by using the machine learning technology for building the driver profiles accurately.
Conclusion
Machine Learning and Artificial Intelligence (AI) have also raised ethical questions about machine predictions and also varies on situational decision-making capabilities. If a self-driving car is said to make a choice between a pedestrian and people using the vehicle then what decision will it take? AI is one of the biggest technology shifts in the world. The current manual or repetitive process can soon be replaced with machine learning technology. It will also be an exciting time for the enterprises to make prediction depending on the future technology and investment that they put in.
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