Artificial Intelligence and Machine Learning have recently become popular topics in the technology industry. But, perhaps more than in our daily lives, Artificial Intelligence (AI) impacts the business world. In 2014, about $300 million in venture capital was invested in AI startups, a 300 per cent increase over the previous year.
Artificial Intelligence is present everywhere, from gaming stations to managing complex data at work. Engineers and scientists are working hard to say intelligent beheviour in machines, allowing them to think and respond in real-time situations. As a result, AI moves from a research topic to the early stages of enterprise adoption. Google and Facebook have made significant investments in Artificial Intelligence and Machine Learning and are already incorporating them into their products. However, this is only the beginning. Over the next few years, we may see AI creep into one product after another.
AI SERVICES ARE CLASSIFIED AS EITHER VERTICAL OR HORIZONTAL AI.
So what exactly is Vertical AI?
What exactly is Horizontal AI?
WHAT IS MACHINE LEARNING?
THE 3 MAJOR AREAS OF MACHINE LEARNING
1. Supervised Learning – In supervised learning, the system is given training datasets. Data is analysed by supervised learning algorithms, which produce an inferred function. The resulting correct solution can be used to map new examples. One example of a Supervised Learning algorithm is credit card fraud detection.
2. Unsupervised Learning – Unsupervised Learning algorithms are much more difficult to implement because the data to be fed is unclustered rather than in the form of datasets. The machine’s goal is to learn independently, without any supervision. There is no correct solution to any problem. Instead, the algorithm discovers patterns in the data. Recommendation engines, which are present on all e-commerce sites, are examples of supervised learning, as are Facebook friend request suggestion mechanisms.
3. Reinforcement Learning – This type of Machine Learning algorithm enables software agents and machines to automatically determine the best behaviour in a given context to maximise their performance. Reinforcement learning is defined by defining a learning problem rather than learning methods. We consider the reinforcement learning method to be any method that is well suited to solving the problem. Reinforcement learning presumes that a software agent, such as a robot, computer programme, or bot interacts with a dynamic environment to achieve a specific goal. This technique chooses the action that will produce the desired result in the shortest amount of time.
ARTIFICIAL INTELLIGENCE IN ITS EARLY DAYS
THE RISE OF MACHINE LEARNING
HOW ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING WORK TOGETHER
When determining the distinction between artificial intelligence and machine learning, it is useful to consider how they interact due to their close relationship. This is how Artificial intelligence and machine learning interact:
Step 1: Machine learning and other techniques build an Artificial intelligence system.
Step 2: Machine learning models are created by analysing data patterns.
Step 3: Data scientists optimise machine learning models based on data patterns.
Step 4: The process is repeated and refined until the models’ accuracy is sufficient for the tasks at hand.
CAPABILITIES OF AI AND MACHINE LEARNING
Almost every industry is discovering new opportunities due to the connection between Artificial intelligence and machine learning. These are just a few of the capabilities that have proven useful in assisting businesses in transforming their processes and products:
- Analytics Predictive – This capability assists businesses in predicting trends and behavioural patterns by identifying cause-and-effect relationships in data.
- Engines of Recommendation – Companies use recommendation engines to recommend products that someone might be interested in based on data analysis.
- Natural Language Understanding and Speech Recognition – Natural language understanding recognises meaning in written or spoken language, whereas speech recognition enables a computer system to identify words in spoken language.
- Processing of Images and Videos – These capabilities enable the recognition of faces, objects, and actions in images and videos and the implementation of functionalities such as visual search.
- Analysis of Sentiment – A computer system uses sentiment analysis to identify and categorise positive, neutral, and negative attitudes expressed in text.
BENEFITS OF AI AND MACHINE LEARNING
The connection between artificial intelligence and machine learning provides significant benefits to businesses in almost every industry, with new possibilities emerging regularly. These are just a few of the many advantages that businesses have already realised:
- Additional Data Input Sources – AI and machine learning enable businesses to gain valuable insights from a broader range of structured and unstructured data sources.
- Improved and Faster Decision-Making – Companies use machine learning to improve data integrity and artificial intelligence (AI) to reduce human error, resulting in better decisions based on better data.
- Enhanced Operational Effectiveness – Companies become more efficient through process automation with AI and machine learning, which reduces costs and frees up time and resources for other priorities.
APPLICATIONS OF AI AND MACHINE LEARNING
Companies from various industries are developing applications that take advantage of the relationship between artificial intelligence and machine learning. These are just a few examples of how AI and machine learning are assisting businesses in transforming their processes and products:
- Retail – Retailers use artificial intelligence and machine learning to optimise their inventories, build recommendation engines, and improve the customer experience with visual search.
- Healthcare – Health organisations use AI and machine learning in applications such as image processing for improved cancer detection and predictive analytics for genomics research.
- Finance and Banking – AI and machine learning are useful tools in financial contexts for detecting fraud, predicting risk, and providing more proactive financial advice.
- Sales and Marketing – Sales and marketing teams, use AI and machine learning for personalised offers, campaign optimisation, sales forecasting, sentiment analysis, and customer churn prediction.
- Cybersecurity – AI and machine learning are potent cybersecurity weapons, assisting organisations in protecting themselves and their customers by detecting anomalies.
- Customer Care – Chatbots and cognitive search are used by businesses across industries to answer questions, gauge customer intent, and provide virtual assistance.
- Transportation – AI and machine learning are useful in transportation applications because they help companies improve the efficiency of their routes and use predictive analytics for things like traffic forecasting.
Manufacturing – Manufacturing firms use AI and machine learning to predict maintenance and make their operations more efficient than ever before.