Back in the 1950s, the fathers of the field, Minsky and McCarthy, described artificial intelligence as any task performed by a machine that would have previously been considered to require human intelligence.

That's a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not.

Modern definitions of what it means to create intelligence are more specific. Francois Chollet, an AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalize its knowledge, and apply it to unfamiliar scenarios.

"Intelligence is the efficiency with which you acquire new skills at tasks you didn't previously prepare for," he said.

"Intelligence does not skill itself; it's not what you can do; it's how well and how efficiently you can learn new things."

It's a definition under which modern AI-powered systems, such as virtual assistants, would be characterized as having demonstrated 'narrow AI', the ability to generalize their training when carrying out a limited set of tasks, such as speech recognition or computer vision.

Typically, AI systems demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation, and, to a lesser extent, social intelligence and creativity.

AI is important because it can give enterprises insights into their operations that they may not have been aware of previously and because, in some cases, AI can perform tasks better than humans. Particularly when it comes to repetitive, detail-oriented tasks like analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors.

This has helped fuel an explosion of inefficiency and opened the door to entirely new business opportunities for some larger enterprises. Before the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but today Uber has become one of the largest companies in the world by doing just that. It utilizes sophisticated machine learning algorithms to predict when people are likely to need rides in certain areas, which helps proactively get drivers on the road before they're needed. As another example, Google has become one of the largest players for a range of online services by using machine learning to understand how people use their services and then improve them. In 2017, the company's CEO, Sundar Pichai, pronounced that Google would operate as an "AI-first" company.

Today's largest and most successful enterprises have used AI to improve their operations and gain an advantage over their competitors.

Artificial Intelligence can be used to automate anything ranging from tasks that involve extreme labor to the process of recruitment. That’s right!

There is n number of AI-based applications that can be used to automate the recruitment process. Such tools help to free the employees from tedious manual tasks and allow them to focus on complex tasks like strategizing and decision making.

An example of this is the conversational AI recruiter MYA. This application focuses on automating tedious parts of the recruitment process such as scheduling screening and sourcing.

Mya is trained by using advanced Machine Learning algorithms and it also uses Natural Language Processing (NLP) to pick up on details that come up in a conversation. Mya is also responsible for creating candidate profiles, performing analytics, and finally shortlisting applicants.

Artificial Intelligence has become a necessity in the business world. It is being used to manage highly computational tasks that require maximum effort and time.

An example of such an application is the Legal Robot. I call it the Harvey Spectre of the virtual world.

This bot uses Machine Learning techniques like Deep Learning and Natural Language Processing to understand and analyze legal documents, find and fix costly legal errors, collaborate with experienced legal professionals, clarify legal terms by implementing an AI-based scoring system, and so on. It also allows you to compare your contract with those in the same industry to ensure yours is standard.

One of the most important goals of Artificial Intelligence is to help in making smarter business decisions. Salesforce Einstein which is a comprehensive AI for CRM (Customer Relationship Management), has managed to do that quite effectively.

Throughout the years, AI has progressed from simple Machine Learning algorithms to advanced machine learning concepts such as Deep Learning. This growth in AI has helped companies solve complex issues such as fraud detection, medical diagnosis, weather forecasting, and so on.

Performing repetitive tasks can become very monotonous and time-consuming. Using AI for tiresome and routine tasks can help us focus on the most important tasks on our to-do list.

An example of such an AI is the Virtual Financial assistant used by the Bank Of America, called Erica.

Erica implements AI and ML techniques to cater to the bank’s customer service requirements. It does this by creating credit report updates, facilitating bill payments, and helping customers with simple transactions.

Research from McKinsey found that brands that excel at personalization deliver five to eight times the marketing ROI and boost their sales by more than 10% over companies that don’t personalize. Personalization can be an overwhelming and time-consuming task, but it can be simplified through artificial intelligence. It’s never been easier to target customers with the right product.

An example of this is the UK-based fashion company ‘Thread’ which uses AI to provide personalized clothing recommendations for each customer.

Most customers would love a personal stylist, especially one that comes at no charge. But staffing enough stylists for 650,000 customers would be expensive. Instead, UK-based fashion company Thread uses AI to provide personalized clothing recommendations for each of its customers. Customers take style quizzes to provide data about their style.

Each week, customers receive personalized recommendations that they can vote up or down. Threads uses a Machine Learning algorithm called Thimble that uses customer data to find patterns and understand the likes of the buyer. It then suggests clothes based on the customer’s taste.

AI can be categorized as either weak or strong.

Weak AI, also known as narrow AI, is an AI system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI.

Strong AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously. In theory, a strong AI program should be able to pass both a Turing Test and the Chinese room test.

Throughout this blog post, we learn everything we need to know about artificial intelligence, if it's important to us, the benefits of artificial intelligence, as well as how strong and weak AI is.

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