Artificial Intelligence (AI) is on the verge of transforming the commercial world. Process automation and business logic codification are no longer the only responsibilities of information technology.
Instead, insight has replaced money as the new currency. The speed with which we can scale that insight and the knowledge it generates is the foundation for value creation and the key to competitive advantage. According to Gartner, AI will be one of the top five investment priorities for more than 30 per cent of CIOs globally by 2020. Many firms are still beginning their data science journey, attempting to figure out how AI can help them grow.
Artificial intelligence (AI) is the foundation for simulating human intelligence processes by developing and deploying algorithms in a dynamic computing environment. Defined, artificial intelligence (AI) aims to make computers think and act like humans.
Three critical components are required to achieve this goal:
- Computer-aided design
- Information and information management
- AI algorithms that are cutting-edge (code)
The more human-like the intended result, the more data, and processing power are needed.
AI encompasses a wide range of approaches and technologies that are constantly changing, as well as the following essential subfields:
- Machine Learning (ML) use neural networks and statistical analysis to uncover hidden patterns in data without being explicitly taught to look for or draw conclusions. It automates the process of creating analytical models.
- Natural Language Processing (NLP) refers to a computer’s ability to analyze, understand, and synthesize human language, such as speech.
- Deep Learning (DL) is a type of machine learning that involves employing vast neural networks with many layers of processing units to enable machines to acquire self-learning skills from large volumes of data. Image and speech recognition are two common uses.
Why is artificial intelligence important?
The amount of data generated today, by humans and machines alike, significantly outpaces humans’ ability to consume, comprehend, and make complicated decisions based on it. Artificial intelligence is the foundation for all computer learning and represents the future of all complex decision-making. Most humans, for example, can figure out how to win in tic-tac-toe (noughts and crosses) even though there are 255,168 possible movements, 46,080 of which result in a tie. With more than 500 x 1018, or 500 quintillions, possible moves, far fewer people would be declared great checkers champions. Computers are pretty good at calculating these combinations and permutations and coming up with the best decision. Artificial intelligence (AI) and deep learning (the logical progression of machine learning) are the core future of commercial decision-making.
Artificial intelligence can be used in businesses to:
i. Improve competitive advantage and efficiency
ii. Advance automated interactions with customers, partners, and workers
iii. Multiply productivity gains by automating processes
iv. Power smarter machinery, vehicles, and structures
v. Improve customer intimacy and thus increase consumer demand
vi. Improve real-time analysis of video and audio
Applications of AI & ML
There are numerous uses of artificial intelligence and machine learning throughout industries, ranging from picture and speech recognition to medical diagnostics and self-driving vehicles to personalized healthcare:
- Fraud detection: The ability to examine millions of transactions and accurately discriminate between legal and fraudulent ones.
- Data Security: Recognize patterns in how cloud data is accessed and identify anomalies that could indicate security breaches.
- Marketing: Increases the likelihood of a user clicking on an ad by programming targeted adverts and determining the best product mix to display.
- Recommendations: Ability to predict what you’ll want to buy or binge watch next based on millions of other users’ preferences.
- Security screening: To ensure safer events, eliminate false alarms and discover anything human screeners might miss during security checks.
- Online search: Google and its competitors are continuously working to improve what the search engine understands to provide better results in the future.
- Language mining: Language processing can take the place of customer care personnel, allowing customers to get information more rapidly.
Let’s look at some machine learning applications in three main industries:
Online retailers employ machine learning algorithms in the following ways.
- An online recommendation engine enables retailers to provide customized promotions or user experiences based on previous purchases or activities by customers.
- Smart machines can improve customer service and delivery systems by reducing response time and providing support to limited personnel.
- E-commerce organizations can track patterns in price swings and set pricing according to demand by monitoring price changes over time.
Machine learning’s transformational promise is prompting the financial services industry to embrace it wholeheartedly. In a variety of ways, machine learning can assist banks, insurers, and investors in making better decisions:
- Client satisfaction management
- Recognizing and responding to market trends
- Risk prediction
- Stay competitive by innovating.
Artificial intelligence is being used in the Banking and Finance (BFSI) sector to manage the massive amounts of data created and spot irregularities in transactions or frauds. Financial organizations increasingly use machine learning for portfolio management tasks such as forecasting trade volatility and managing wealth and assets. These algorithms are capable of detecting patterns faster than humans and reacting in real-time.
To enable more innovative healthcare solutions, healthcare organizations may take advantage of the confluence of the Internet of Things (IoT) and data analytics.
- The use of robots in surgery is becoming more common. They are more exact, but they also have a higher success rate and a faster recovery time and more minor surgical scars.
- Health telemetry has become a reality thanks to personalized health monitoring via smartwatches and other wearable devices.