Artificial intelligence can be simply described as the simulation of the processes of human intelligence by machines, most importantly, computer systems. Important applications of AI are speech recognition, expert systems, machine vision, and natural language processing. This term is also applicable to machines exhibiting traits that are associated with the mind of humans like problem-solving and learning.
The main feature of artificial intelligence is its ability of rationalizing as well as taking actions which have a very good possibility of achieving a goal. Machine learning, denoted as ML, forms a part of artificial intelligence. This explains that computer programs have the ability of learning from and adapting to new data form automatically without human assistance. The automatic learning is enabled by deep learning techniques via the absorption of large unstructured data amounts like video, images, or text.
How Does Artificial Intelligence (AI) Work?
Since the acceleration of the hype surrounding AI, vendors have been seeking ways of promoting the way their services and products make use of AI. Most times, what they call AI is actually just one of the components of AI like machine learning.
In addition, AI needs specialized software and hardware for the training and writing of the algorithms of machine learning. No programming language is similar to AI. However, a few like Java, R, and Python are well-known.
Generally, AI functions through the ingestion of large training data, and then analyzes the data for patterns and correlations. Finally, it makes use of these patterns in making predictions regarding future states. This way, chatbots can produce some lifelike discussions with people. Also, a tool for image recognition could learn how to identify, as well as describe objects present in images through the review of many examples.
Furthermore, AI programming usually focuses on three main cognitive skills. These include self-correction, reasoning, and learning.
Self correction processes: the design of this AI programming aspect is to fine-tune the algorithms continually and also make sure that they offer very accurate results.
Reasoning Processes: This AI programming aspect is focused on selecting the appropriate algorithm to achieve a specific outcome.
Learning Processes: Also, this AI programming aspect is focused on data acquisition and rules creation for ways of transforming data into actionable information. These rules, referred to as algorithms, help in providing these computing devices with instructions on how a task should be completed.
The Significance of Artificial Intelligence (AI)
AI is significant because it has the ability to give insights to enterprises into how they should operate, which they had no idea about. Also, at times, Artificial intelligence can handle tasks much better than human beings.
Importantly, when we talk of detail-oriented and repetitive tasks such as analyzing many legal documents to make sure relevant fields are properly filled in, AI tools usually finish jobs faster and with few errors.
With this, there has been a huge rise in efficiency, which has created ways to new opportunities for business for some huge enterprises. Before this present AI wave, it could have been difficult to think that making use of computer softwares in connecting riders to taxis is possible. However, as of today, Uber is now one of the biggest companies globally doing just this.
Furthermore, it makes use of complex algorithms of machine learning in predicting the time at which people will need to order a ride in some areas. This helps in getting drivers proactively on the road even before their services are needed.
In addition, another example is Google, which is now a great player for different online services by making use of machine learning in understanding the way people make use of their services and also improving them. Sundar Pichai, the CEO of the company, announced in 2017 that the company will be operating as “AI first” company. The most successful and largest enterprises of today have utilized AI in improving their operations. They have also been able to get better than their competitors.
What are the Benefits and Pitfalls of Artificial Intelligence?
Deep learning AI technologies and artificial neural networks are evolving very quickly. This is due to the fact that Artificial Intelligence can process large data very quickly, and also make some predictions much more appropriately then it is possible when humans handle the task.
While the creation of large data volume every day could bury human researchers, the AI applications using machine learning could take this data and transform it quickly to actionable information. Presently, the main disadvantage of making use of Artificial Intelligence is that processing large data amounts which is required by AI programming is expensive.
- Handles detail-oriented jobs perfectly
- Delivers results that are consistently
- Reduces time for any data-heavy task
- Virtual agents that are AI powered are usually available
- It is expensive
- Great technical expertise required
- The qualified workers required to build different AI tools are in short supply
- It can only known what it is shown
- Lacks the ability to generalize between different tasks
Categories of Artificial Intelligence: Strong AI vs Weak AI
You can categorize artificial intelligence as either strong or weak
Weak artificial intelligence, which is also called narrow AI, can be described as an AI system, which is trained and designed to finish a task. Virtual assistants and industrial robots like Siri of Apple make use of weak AI.
Strong AI, which is called AGI (Artificial general intelligence), describes programming, which has the ability to replicate the human brain’s cognitive abilities. When a strange task is presented, the strong artificial intelligence system has the ability to use fuzzy logic in applying knowledge between different domains and then get a solution. Theoretically speaking, strong AI programs must pass both a Chinese room test and Turing test.
Types of Artificial Intelligence
In 2016, Arend Hintze, who is an assistant professor at the Michigan University, explained that artificial intelligence can be grouped into four main types. Below are the categories.
Type 1: The Reactive Machines
These are task specific AI systems, with no memory. Deep Blue is an example, which is an IBM chess program. This Deep Blue has the ability to identify some pieces present on the chessboard, and then make some predictions. However, because there is no memory present, it can’t make use of past experiences in a bit to inform the future ones.
Type 2: The Limited Memory
These Artificial Intelligence systems have a memory. Therefore they make use of past experiences in informing future decisions. Consider some of the self-driving cars’ decision making functions to be designed like this
Type 3: The theory of mind
Theory of mind is a well-known psychology term. Whenever you apply it to artificial intelligence, it means the system will have the required social intelligence to comprehend emotions. Furthermore, this AI type can predict human behavior and infer the intentions of humans. This skill is necessary for the AI systems to be transformed into integral human team members.
Type 4: Self-awareness
For this category, the artificial intelligence system has this unique sense of self. This is what gives them their consciousness. In addition, self-awareness machines comprehend their present state. However, this AI type is yet to exist.
Examples of Artificial Intelligence (AI) Technology and How to Use them Today
Artificial intelligence can be integrated in different technologies. Below are six major examples where they can be useful.
Anytime it is combined with AI technologies, the automation tools have the ability to expand the types and volumes of tasks that have been performed. One example is the RPA – Robotic process automation. This is a software, which automates rules-based and repetitive data processing jobs, which were initially handled by human beings.
Furthermore, when you combine it with emerging AI tools and machine learning, RPA has the ability to automate larger enterprise job portions. This enables the tactical bots of the RPA to pass intelligence from the AI and then respond to the process changes.
Machine learning is the science involved in getting your computer to function without any programming. In addition, deep learning forms a part of machine learning which can be considered as predictive analysis automation. Three algorithms of machine learning exist here.
- Unsupervised learning: Here, the data sets are not labeled. They are grouped with respect to their differences and similarities.
- Reinforcement learning: Here data sets are not labeled. However, after performing several actions or just one action, the AI system gets feedback.
- Supervised learning: For supervised learning, the data sets will be labeled. This ensures the detection of patterns and then used in labeling the newly created data sets
Machine vision gives the machine the seeing ability. Also, machine vision has the ability to capture and analyze any visual information by making use of a camera, digital signal processing, and analog to digital conversion. Usually, this is compared to the eyesight of humans, however, machine vision is not biology bound. This is why you can program it to see the inside of walls, for instance.
It is useful in different applications. These include analysis of medical images to signature identification. Furthermore, computer vision, which is usually focused on image processing (machine-based), is usually combined with machine vision.
Natural Language Processing
This involves human language processing with the help of a computer program. Spam detection is one of the best-known and older examples of the NLP. This considers an email’s text and the subject line in deciding if it is junk. Present approaches to natural language processing have to do with machine learning. Also, some NLP tasks are speech recognition, sentiment analysis, and text translation.
Also, autonomous vehicles make use of a blend of deep learning, image recognition, and computer vision in building automated skill in piloting the vehicle when it stays in a specific lane and also avoid some unexpected obstructions like pedestrians.
This engineering field deals with the manufacturing and designing of robots. Usually, robots are useful for performing tasks which are very difficult for human beings to handle or handle consistently. Robots are useful for car production assembly lines or NASA in moving large objects present in space. In addition, researchers are making use of machine learning in building robots which can have interactions in different social settings.
Applications of Artificial Intelligence (AI)
AI has been able to get into different markets. Here are some applications of artificial intelligence.
One of the biggest bets is to reduce costs as well as improve patient outcomes. Today, companies are now making use of machine learning in making faster and better diagnosis compared to humans. IBM Watson is one of the well known healthcare technologies. It comprehends natural language and can give answers to any questions it is asked.
Furthermore, this system is useful in the mining of patient data as well as other available sources of data to form hypotheses. This eventually presents a scoring schema. In addition, other applications of Artificial intelligence are making use of virtual health chatbots and assistants in assisting healthcare customers and patients in finding some medical information, understanding billing processes, scheduling appointments and then finalizing administrative processes. A combination of AI technologies is also useful in predicting, fighting, and understanding pandemics like COVID-19.
Algorithms for machine learning are being incorporated into customer relationship management and analytics platforms. This helps in uncovering information on ways of serving customers better. In addition, these Chatbots have become integrated in websites to offer services immediately to customers.
Automating job positions have now become an important point of discussion among IT analysts and academics.
Artificial intelligence has the ability to automate grading, which will give the educators some more time. Furthermore, it has the ability to assess students as well as adapt to students’ needs. This is just to ensure they are on track. In addition, this could change how and where students learn; could even replace some teachers
Artificial intelligence in personal finance applications like TurboTax or Intuit Mint is disrupting different financial institutions. Also, applications like these help in collecting personal data coupled with offering financial advice. Programs like IBM Watson are now applied to home buying processes. As of today, software for artificial intelligence helps in performing most trading conducted on Wall Street.
The process of discovery, sifting into documents in law could be overwhelming. Making use of artificial intelligence in automating the labor-intensive processes of the legal industry is improving the client service and saving time. Furthermore, law firms are now making use of machine learning in describing data as well as predicting outcomes. They also make use of computer vision in classifying and extracting information from different documents.
When it comes to integrating robots in the workflow, manufacturing takes the forefront. For instance, industrial robots, which were initially programmed to help handle single tasks as well as separated from the human workers, function increasingly as cobots. Cobots are multitasking and smaller robots which partner with humans and handle the responsibility for most parts of the task in workspaces, factory floors, and warehouses.
As of today, banks now successfully employ chatbots in letting their customers know about their offerings and services in handling transactions, which doesn’t need human intervention. Artificial Intelligence virtual assistants are also useful in improving and cutting the cost of complying with the banking regulations.
Furthermore, banking organizations also make use of AI in improving their decision making to give out loans. They also identify possible investment opportunities and set credit limits using AI.
Aside from the fundamental role of artificial intelligence in the operation of autonomous vehicles, they are also useful in transportation in order to manage the traffic, ensure ocean shipping is more efficient and safer, and predict possible flight delays.
Machine learning and AI are among the top buzzword lists which security vendors make use of today in differentiating their offerings. Furthermore, these terms also stand for viable technologies. Also, organizations make use of machine learning for event management and security information software as well as other related areas in detecting anomalies, as well as identifying any suspicious activities which could indicate threats.
Through data analysis, as well as making use of logic in identifying similarities that are called malicious code, artificial intelligence could give alerts to emerging and new attacks earlier than earlier technology iterations and human employees. This maturing technology helps in playing a major role in assisting organizations to fight against any cyber attacks.
Comparing Augmented Intelligence and Artificial Intelligence: What’s the Difference?
There are some experts in the industry that believe that there is a close link between artificial intelligence and popular culture. This has now led the public to have some expectations regarding ways in which artificial intelligence will alter life generally and the workplace.
There are marketers and researchers who hope that the augmented intelligence that features extra neutral connotation, will assist people in comprehending the best AI implementation and will simply improve services and products.
It is a fact that artificial general intelligence or True AI is closely linked with the technological singularity concept. This is a future that has been ruled by artificial superintelligence, which surpasses the ability of the human brain to comprehend it or the way it shapes our reality. This stays within the science fiction realm, although there are developers working on this issue
Ethical Uses of AI (Artificial Intelligence)
Although AI tools can present different functionalities for businesses, using artificial intelligence raises ethical questions. This is because, either worse or better, the AI system usually reinforces what it has learned already.
This could become a problem because the algorithms of machine learning that underpins the majority of AI tools that are most advanced, are just as smart like data given to them during training. Due to the fact that humans choose the data used in training an AI program, which is a potential for the bias of machine learning must be closely monitored and is inherent.
Anyone seeking to make use of machine learning in the real world, there is a need for an in-production system to be able to factor the ethics in the processes of AI training and to prevent bias. Also, this holds true when making use of inherently unexplainable AI algorithms in generative adversarial network and deep learning applications.
Another possible stumbling block to the use of artificial intelligence in industries operating under strict compliance requirements is explainability. For instance, the financial institutions present in the U.S. function under regulations requiring that they explain their decisions for credit issuance.
Also, when AI programming makes a decision to refuse the credit, it could become difficult to clarify how they got to this decision. This is because AI tools that help in making these decisions function by teasing the subtle correlations out between so many variables. When it becomes difficult to explain the decision making process, this program can be called black box AI.
Will Crafting Laws for the Regulation of Artificial Intelligence Work?
Crafting laws for the regulation of artificial intelligence won’t be easy. This is partly because AI is made up of different technologies, which companies make use of for many different ends. Another part is due to the fact that regulations can affect the development and progress of artificial intelligence.
Another issue regarding the formation of a meaningful regulation of artificial intelligence is the quick evolution of the AI technologies. Also, technology as well as novel applications could make already existing laws obsolete.
For instance, the existing laws that regulate the privacy of recorded conversations and conversations don’t cover challenges posed by some voice assistants such as Siri of Apple and Alexa of Amazon which gather conversation, but doesn’t distribute it.
They only distribute it to the technology teams of the companies that utilize it in improving the algorithms of machine learning. Sure, crafted laws by the government in regulating AI don’t stop criminals from utilizing a technology with malicious intent.
Artificial Intelligence is very useful in our world today. In summary, it involves the simulation of the processes of human intelligence by machines, most importantly, computer systems. It is also useful in different applications.