Artificial Intelligence is a subdivision of Data Science that centers on the development of smart machines that can execute a vast range of tasks that most often demand human cognition and intelligence. Artificial Intelligence is an interdisciplinary science that utilizes tools and concepts from several fields such as mathematics, neuroscience, psychology, linguistics, cognitive science, and computer science.
How Artificial intelligence Works?
Artificial Intelligence works by integrating huge amounts of data with swift intelligent algorithms and iterative processing to automatically learn from features or patterns in the data.
The following processes and technologies are involved in the risk of how artificial intelligence works:
- Machine learning
- A neural network
- Deep learning
- Cognitive computing
- Computer vision
- Graphical processing units
- The Internet of Things
- Advanced algorithms
- APIs
- Natural language processing
Why artificial intelligence is important?
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AI automates repetitive learning and discovery through data
We are currently living in a period of data. When you consider the amount of data your company utilizes and generates, you would agree with this. If you are unaware of the data you have to work with, then it’s about time you understood it.
A little long ago the Big Data was another trend. It was a significant hype for valid reasons because it involved the infrastructure needed to collect, store, and process large amounts of data. Although the hype is no longer huge, technology still operates silently behind several organizations running all over the world.
You need to consider the value your data can provide you with. There can be priceless information in the contents of your stored data. However, searching for these treasures is going to be quite tedious for a human; in fact, more like looking for a needle in a haystack.
With Artificial Intelligence, you can parse your data and discover patterns and valuable and useful insights regarding, for instance, your security challenges, the cause of sales fluctuations, and even your customer behavior. AI will achieve this one day at a time, without being burnt-out or bored.
The most effective way to reduce the number of buttons is by making your product carry out the task for the customer and determining what must be done in varying scenarios. For instance, by utilizing a collection of smart algorithms that carry out several scanning and data analysis, ImmuniWeb® Discovery decreases costs of cybersecurity and complexity, and adherence with non-intrusive security and monitoring testing, and continuous discovery of the whole external attack surface area.
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AI analyzes more and deeper data
Since Artificial Intelligence is usually linked with machine learning nowadays, then ML is usually executed in the form of deep learning. By utilizing neural networks with several hidden layers, AI is capable of analyzing additional and deeper data.
A few years ago, it would have been almost impossible to build a fraud detection system using 5 hidden layers. Due to big data and remarkable computer power, so many things are now possible. A huge amount of data is needed to teach deep learning models since they are trained directly from the data. The higher the data you input to them, the better their accuracy becomes.
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AI adds intelligence
It is quite possible to develop a superb product that has spent years on the market, and still lose your customers to another competitor who will implement a couple of smart characteristics if yours doesn’t become smart.
It’s not any surprise that customers are after a product featuring only a single button (or better still, features no buttons at all). Just as Siri was integrated as a component of the most recent generations of Apple products in order to streamline many capabilities of a mobile system.
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AI achieves incredible accuracy
AI can now achieve remarkable accuracy via deep neutral networks – which was unattainable before. For instance, your interactions with Google Photos, Google Search, and Alexa are all founded on deep learning – and their accuracy increases as we keep on using them.
In the healthcare sector, AI strategies from deep learning object recognition, and image classification can now be employed to detect cancer on MRI scans with the same level of accuracy as professionally trained radiologists.
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AI adapts through progressive learning algorithms
AI has evolved through various forms: starting with machine translation and simple neural nets in the 1950s, to the heuristic search which became popular in the 60s, then the emergence of the scope of multi-agent systems, and even till the area of automated planning.
Moving on to the aspect of machine learning. Thanks to the soaring computer power availability and the swift rise of neural network experiments, deep learning had contributed significantly in accuracy. Although we can not explicitly call AI the same as machine learning, they are however interlinked disciplines with significant convergence.
Machine learning describes a collection of algorithms that assists a machine to identify patterns in data. The algorithm becomes a predictor or classifier. Thus, in the same way, the algorithm can learn how to play chess by itself, it can also teach itself what items to subsequently recommend online. And when provided with new data, the models adapt.
Importantly, machine learning is used to analyze the large amount of metadata that has been collected about clients’ sites. An innovative AI system masters patterns that can be associated with security issues that have the potential to disrupt clients’ businesses.
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AI gets the most out of data
Data in itself is an intellectual asset when algorithms become self-learning. The solution is buried in the data: you only need to implement AI to get it out. Considering the fact that the function of the data is now more crucial than ever before, this can lead to a competitive edge. If a business has the best data in a competitive market, even if other competitors are using similar strategies, the best data will win.
History of artificial intelligence: Key dates and names
The coinage of the term “Artificial Intelligence” was in 1956 by John McCarthy when he hosted the first academic meeting on the subject matter. Five years later, Alan Turing published a paper on the concept of machines having the ability to mimic humans and engage in intelligent activities like playing chess.
From the 1950s to the 1970s, the pioneering work for neutral networks birthed the notion of “thinking machines”, and from the 1980s to 2010s, the concept of Machine Learning has become widespread in the history of artificial intelligence. In this contemporary moment, deep learning developments have driven the AI boom.
What Are the Different Types Of AI?
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Narrow (or “weak”) AI
This is the AI that exists in our present contemporary world. It is AI that has been designed to execute a single task – whether it is to create journalistic content, analyze raw data, play chess, or check the weather.
A few examples of Narrow AI include:
- Google search
- Image recognition software
- Siri, Alexa, and other personal assistants
- Self-driving cars
- IBM’s Watson
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General (or “strong”) AI
This AI describes machines that demonstrate human intelligence. This means that it can effectively perform any intellectual task that can be done by a human. This kind of AI can be seen in Sci-fi movies.
Artificial intelligence applications
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AI in Marketing
In the early 2000s, it was nearly impossible to search for an online store to locate a product without knowing its name. However, when we search on any e-commerce platform today for a specific item, we are presented with every possible result similar to the product. We can almost say these search engines are reading our minds! An excellent example is searching for the best movies on Netflix. Netflix is able to provide extremely accurate predictive technology based on the user’s reactions to movies.
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AI in Banking
The application of AI in banking is skyrocketing at an incredible rate! Several banking institutions have already implemented AI-based models to offer customer support, detect credit card frauds and other anomalies. HDFC Bank is an example of one such financial organization.
The bank has developed an AI-based Chatbot known as EVA (Electronic Virtual Assistant). Eva, since its launch, has attended to more than 3 million queries, maintained more than 1 million conversations, and interacted with more than 500,000 special users.
Organizations such as RBS WorldPay and MasterCard have utilized deep learning and AI to identify fraudulent transaction patterns and alleviate card fraud for many years already. This has consequently saved them millions of dollars.
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AI in Finance
In this era of ultra-high-frequency trading, AI is being utilized by financial organizations to maximize profit and increase their stock trading performance.
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AI in Agriculture
With AI, farmers can get higher yields from their land and at the same time utilize more resources sustainably. Several organizations are making use of robotics and automation to assist farmers to discover better efficient techniques to protect their crops.
In fact – PEAT, an Agric-tech start-up based in Berlin, has created an application called Plantix. This application helps farmers to identify potential nutrient deficiencies and defects in the soil via images.
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AI in HealthCare
Truly, a plethora of medical care centers and organizations depend on Artificial Intelligence to be able to save lives effectively. For example, Cambio Health Care is an organization that created a clinical decision support system for the prevention of stroke. The application can notify the physician when any patient is at the risk of suffering a heat stroke.
Another good example is Coala life. The company has developed a digitalized device that has the capability to detect cardiac diseases. There is a range of organizations looking to use AI to combat COVID before and in light of the vaccine.
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AI in Gaming
AI has grown to be an indispensable part of the gaming sector over the last few years. As a matter of fact, the gaming industry houses one of the best accomplishments of Artificial Intelligence. DeepMind has developed an AI-based AlphaGo software which defeated Lee Sedol, the number one champion in the GO game.
Not long after the victory, the company again launched an advanced version of the software known as AlphaGo Zero which emerged victorious in an AI-AI face-off with the predecessor.
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AI in Space Exploration
Space explorations always involve processing large amounts of data. In this context, Machine Learning and Artificial Intelligence are the ideal way to deal with and analyze data. Following extensive research, astronomers utilized AI to maneuver through years of data collected by the Kepler telescope so as to observe a distant 8-planet solar system.
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AI in Autonomous Vehicles
For a very long time now, auto-driving cars have been a hot topic in the AI industry. The transportation system will surely be revolutionized through the development of autonomous vehicles.
Companies like Tesla and Waymo are famous examples of self-driving cars. AI implements deep learning, image detection, and computer vision to manufacture vehicles that can automatically drive around and detect objects without human intervention.
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AI in Chatbots
Chatbots have become very rampant technology these days. An increasing number of households are getting a virtual assistant that manages the appliances at home. Good examples of these artificial intelligence applications are Cortana and Siri, which are becoming more popular as a result of the user experience they offer.
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AI in Artificial Creativity
What do you think would happen if AI machines try to form art and music? MuseNet is an AI-based system that has been developed to compose classical music that reflects the classical kings, Mozart and Bach.
The Benefits of AI
AI offers so many benefits, some of which are:
- End-to-end efficiency
- Improved accuracy and decision-making
- Intelligent offerings
- Empowered employees
- Superior customer service
Artificial Intelligence Challenges
- It suffers bias (ethnic, gender or racial) that could result in unfair and unethical consequences.
- Acquiring and funding computing power
- Complex transitioning process to AI
- Gathering and using useful data
- Insufficient manpower
- Legal issues
Artificial Intelligence Examples
Below is a list of modern AI examples:
- Smart assistants (like Siri and Alexa)
- Disease mapping and prediction tools
- Manufacturing and drone robots
- Optimized, personalized healthcare treatment recommendations
- Conversational bots for marketing and customer service
- Robo-advisors for stock trading
- Spam filters on email
- Social media monitoring tools for dangerous content or false news
- Song or TV show recommendations from Spotify and Netflix
Differences Between Artificial Intelligence and Human Intelligence
| Artificial Intelligence | Human Intelligence |
| The aim of AI is to build machines that can imitate human behavior and execute human-like actions.
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While the aim of Human Intelligence is the adaptation to new environments through the use of several cognitive processes.
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| AI-based systems depend on specific instructions and data inputted into the machine.
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Human intelligence on the other hand utilizes the brain’s memory, computing power, and ability to think.
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| Unlike Human Intelligence, Artificial Intelligence machines, unfortunately, can not think or achieve the unique thought process of man.
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Human Intelligence is based on intelligent thoughts and behaviors. It focuses on learning from different past experiences and mistakes.
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Future of Artificial Intelligence
As of today, AI is still in its development and progressive stage. AI systems consume a relatively high amount of time to train, and this cannot be achieved without human intervention. However, a 2018 study conducted by the WEF, predicts that AI will not only displace 75 million jobs all over the world by 2022 but also create 133 million new jobs.
Although the full potentials of AI are yet to be attained, it is feasible that the future of AI will be characterized by advanced autonomous robots and vehicles, in addition to highly sophisticated technologies such as image processing and natural language processing, all of which would feature minimal human interference.
However, considering that scientists still don’t fully understand how the human thought process works, it is unlikely that we will have AI machines thinking like humans anytime in the nearest future. Overall, the future of AI will largely be governed by human abilities and complemented by human cognizance and intelligence.
Final Thoughts
In all, Artificial Intelligence is a priceless tool that is being used to shape the industry and intelligent workflow. In addition to automation, AI will be adopted by all businesses in the near future. Take advantage of the limitless potentials of AI today by utilizing machine learning and AI in your business processes today.
