Important Differences Between Different Types Of Artificial Intelligence
Important Differences Between Different Types Of Artificial Intelligence
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Imagine a world where your morning coffee is prepared by a smart machine that exactly knows just how you like to taste it. Now that’s not too far off, because the future of Artificial Intelligence (AI) promises to redefine our daily lives in light and profound ways.
This has all happened very quickly as for most the first half of the 20th century the concept of AI was mainly for science fiction. But now, AI has enabled us to do things faster and better and has significantly advanced technology in the 21st century.
AI technology has created opportunities to progress on real-world problems concerning health, education, and the environment and in some cases, AI can achieve things more effectively and efficiently, or methodically, than human intelligence.
In the future, intelligent machines will replace or enhance human capabilities in many areas.
AI is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial Intelligence is becoming a popular field in computer science as it has enhanced the human life in many areas. AI has in the last two decades greatly improved performance of the manufacturing and service systems.
Study in the area of AI has given rise to the rapidly growing technology known as Expert System. AI is the process of building intelligent machines from vast volumes of data. Systems learn from past learning and experiences and perform human-like tasks. It enhances the speed, precision, and effectiveness of human efforts. AI uses complex algorithms and methods to build machines that can make decisions on their own.
Branches Of Artificial Intelligence
AI research has successfully developed effective techniques for solving a wide range of problems, from game playing to medical diagnosis.There are many branches of AI, each with its focus and set of techniques. Some of the essential branches of AI include:
Machine learning: It deals with developing algorithms that can learn from data. ML algorithms are used in various applications, including image recognition, spam filtering, and natural language processing.
Deep learning: It is a branch of machine learning that harnesses artificial neural networks to acquire knowledge from data. Deep learning algorithms effectively solve various problems, including NLP, image recognition and speech recognition.
Natural Language Processing: It deals with the interaction between computers and human language. NLP techniques are used to understand and process human language and in various applications, including machine translation, speech recognition, and text analysis.
Robotics: It is a field of engineering that deals with robot design, construction, and operation. Robots can perform tasks automatically in various industries, including manufacturing, healthcare, and transportation.
Expert Systems: They are computer programs designed to mimic human experts' reasoning and decision-making abilities. Expert systems are used in various applications, including medical diagnosis, financial planning, and customer service.
Current Developments In AI
There are a lot of ongoing AI discoveries and developments, most of which are divided into different types.
These classifications reveal more of a storyline than a taxonomy, one that can tell us how far AI has come, where it’s going and what the future holds. These are the seven types of AI to know, and what we can expect from the technology.
Based on how they learn and how far they can apply their knowledge, all AI can be broken down into three capability types:- Narrow AI, General AI and Super AI. Here is an overview about each.
Narrow AI
Narrow AI, also known as Artificial Narrow Intelligence (ANI) or weak AI, describes AI tools designed to carry out very specific actions or commands. ANI technologies are built to serve and excel in one cognitive capability, and cannot independently learn skills beyond its design. They often use machine learning and neural network algorithms to complete these specified tasks. For example, natural language processing is a type of narrow AI because it can recognise and respond to voice commands, but cannot perform other tasks beyond that.
Some examples of narrow AI include image recognition software, self-driving cars and AI virtual assistants.
Artificial General Intelligence (AGI)
Artificial General Intelligence (AGI) also called general AI or strong AI, describes AI that can learn, think and perform a wide range of actions similarly to humans. The goal of designing artificial general intelligence is to be able to create machines that are capable of performing multifunctional tasks and act as lifelike, equally-intelligent assistants to humans in everyday life. Though still a work in progress, the groundwork of artificial general intelligence could be built from technologies such as supercomputers, quantum hardware and Generative AI models like ChatGPT.
Artificial Super-Intelligence
Artificial Super-Intelligence (ASI), or super AI, is the stuff of science fiction. It’s theorised that once AI has reached the general intelligence level, it will soon learn at such a fast rate that its knowledge and capabilities will become stronger than that even of humankind. ASI would act as the backbone technology of completely self-aware AI and other individualistic robots. Its concept is also what fuels the popular media trope of “AI takeovers.” But at this point, it’s all speculation.
Functionality-Based Types of Artificial Intelligence
Functionality concerns how an AI applies its learning capabilities to process data, respond to stimuli and interact with its environment. As such, AI can be sorted by four functionality types.
Reactive Machines AI
Reactive machines are just that, reactionary. They can respond to immediate requests and tasks, but they aren’t capable of storing memory, learning from past experiences or improving their functionality through experiences. Additionally, reactive machines can only respond to a limited combination of inputs. Reactive machines are the most fundamental type of AI. In practice, reactive machines are useful for performing basic autonomous functions, such as filtering spam from your email inbox or recommending items based on your shopping history.
But beyond that, reactive AI can’t build upon previous knowledge or perform more complex tasks. Reactive Machine AI Examples
- IBM Deep Blue: IBM’s reactive AI machine Deep Blue was able to read real-time cues in order to beat Russian chess grandmaster Garry Kasparov in a 1997 chess match.
- Netflix Recommendation Engine: Media platforms like Netflix often utilise AI-powered recommendation engines, which process data from a user’s watch history to determine and suggest what they would be most likely to watch next.
Limited Memory AI
Limited memory AI can store past data and use that data to make predictions. This means it actively builds its own limited, short-term knowledge base and performs tasks based on that knowledge.
The core of limited memory AI is Deep Learning, which imitates the function of neurons in the human brain. This allows a machine to absorb data from experiences and “learn” from them, helping it improve the accuracy of its actions over time.
Today, the limited memory model represents the majority of AI applications. It can be applied in a broad range of scenarios, from smaller scale applications, such as chatbots, to self-driving cars and other advanced use cases.
Examples Limited Memory AI include:-
- Chatbots & Virtual Assistants: Chatbots and virtual assistants are forms of limited memory AI that use deep learning to mimic human conversation. As users interact more with these systems, they learn from this data and remember details about the user, allowing them to provide relevant and personalised responses.
- Self-Driving Cars: Self-driving cars continually observe and process environmental data around them as they travel on the road. This helps them predict when they need to turn, stop or avoid an obstacle.
Theory of Mind
Theory of mind refers to the concept of AI that can perceive and pick up on the emotion of others. The term is borrowed from psychology, describing humans’ ability to read the emotions of others and predict future actions based on that information. Theory of mind hasn’t been fully realised yet, and stands as the next substantial milestone in AI’s development.
Theory of mind could bring plenty of positive changes to the tech world, but it also poses its own risks. Since emotional cues are so nuanced, it would take a long time for AI machines to perfect reading them, and could potentially make big errors while in the learning stage. Some people also fear that once technologies are able to respond to emotional signals as well as situational ones, the result could mean automation of some jobs. An examples of Theory of Mind AI has been provided by a senior AI researcher at insurance company Acrisure,Rafael Tena, to illustrate how a successful theory of mind application would revolutionise AI technology:-
- A self-driving car may perform better than a human driver the majority of the time because it won’t make the same human errors. But if you, as a driver, know that your neighbor’s kid tends to play close to the street after school, you’ll know instinctively to slow down while passing that neighbor’s driveway, something an AI vehicle equipped with basic limited memory wouldn’t be able to do.
Self-Aware AI
Self-aware AI describes artificial intelligence that possesses self-awareness. Referred to as the AI point of singularity, self-aware future includes greater innovation, life-changing applications, and advances in AI creativity.
As AI research expands, and AI development continues to enhance AI algorithms, machine intelligence and thought processes will continue to grow, working towards the goals of general intelligence, and even super intelligence. The most obvious change that many people will feel across society is an increase in the tempo of engagements with large institutions.
Any organisation that engages regularly with large numbers of users, businesses, government units, non-profits, will be compelled to implement AI in the decision-making processes and in their public- and consumer-facing activities. AI will allow these organisations to make most of the decisions much more quickly.
Industries On Which Will AI Have The Greatest Impact
AI will transform the way people relate to technology, but also it will change business processes and industries.
The following Industries will be affected most by AI:
Education: At all levels of education, AI will likely be transformative. Students will receive educational content and trainings tailored to their specific needs. AI will also determine optimal educational strategies based on students' individual learning styles. By 2028, the education system could be barely recognisable.
Healthcare: AI will likely become a standard tool for doctors and physician assistants tasked with diagnostic work. Society should expect the rate of accurate medical diagnosis to increase.
AI’s adoption in the healthcare sector promises to bring a lot of benefits to adopters. Primarily, the healthcare sector as a whole has been geared towards collecting accurate and relevant data about patients and those who come into care.
This makes AI a good fit for the data-rich world of healthcare. Secondarily, AI can find a variety of use-cases in the healthcare sector. But the sensitivity of patient data and complexity of navigating the laws that protect them are also likely to lead to an even more complicated medical-legal environment and increased costs of doing business.
Finance: Natural Language Processing combined with machine learning will allow banks and financial advisors as well as sophisticated chatbots to efficiently engage with clients across a range of typical interactions: credit score monitoring, fraud detection, financial planning, insurance policy matters and customer service.
AI systems will also be used to develop more complex and rapidly executed investment strategies for large investors. Banking is a sector where paperwork and documentation are ever-present. AI can also automate processes that were previously done manually, such as paperwork and documentation.
This will not only decrease the time required to solve issues but also enable banks to serve customers better.
Law: We can expect to see the number of small and medium-sized firms to fall over the next five years, as small teams of one to three humans working with AI systems do the work that would have required 10-20 lawyers in the past and do it more quickly and more cost effectively.
Transport: The near-term future will see more autonomous vehicles in private and commercial use.
Autonomous driving is considered as one of the most revolutionary uses of AI in the real world. Self-driving cars have already made their way into the mainstream due to companies like Tesla, and even Uber is looking into deploying autonomous vehicles. Giants like Google are also creating self-driving technology.
From the cars many of us drive to work, to the trucks carrying goods along the highway, to the space craft ferrying humans and cargo to the moon, transport by autonomous vehicles will probably be the most dramatic instance of our having arrived in the age of AI.
References:
Image: Ideogram
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