ARTIFICIAL INTELLIGENCE: It's Not as Difficult as You Think

 

The ability of machines to mimic or improve human intellect, such as reasoning and experience-based learning, is known as artificial intelligence (AI). Although artificial intelligence has long been included in computer programs, it is currently utilized in a wide range of other goods and services. For instance, certain digital cameras use artificial intelligence software to identify the objects in a picture. Experts also anticipate a plethora of other cutting-edge applications for AI in the future, such as intelligent electrical grids.

What is Artificial Intelligence?

Artificially made images, robots, ChatGPT or other AI chatbots, and self-driving automobiles are some examples of what comes to mind when you hear the word artificial intelligence (AI). It's crucial to examine AI's processes and effects on current and upcoming generations in addition to its results.

In the 1950s, artificial intelligence (AI) was first characterized as a machine's capacity to carry out a task that would have previously required human intelligence. With decades of research and technical developments, this term has been refined to be fairly broad.

It is reasonable to begin by defining the term "intelligence" before considering the assignment of intelligence to a machine, such as a computer, particularly when attempting to conclude.

How can I use AI?

AI is now widely available in several forms for use in daily life. Artificial intelligence is exemplified by the smart speakers on your mantle that come equipped with Google or Alexa speech assistants. New Bing Chat, ChatGPT, and Google Bard are a few more well-known AI chatbots. 

AI systems are used to generate the answers you receive when you ask ChatGPT for a country's capital or ask Alexa to provide you with a weather update.

Types of Artificial Intelligence:

1. Artificial Narrow AI

The only kind of artificial intelligence that is now in use is artificial narrow intelligence, or weak AI as we refer to it. All other types of AI are purely theoretical. It can be trained to do a particular, specific activity far faster and more accurately than the human mind is capable of. It cannot, however, function outside of its designated role. Rather, it focuses on a particular subset of cognitive capacities and progresses along that range. Narrow AI includes Siri, IBM Watson, and Alexa from Amazon. Because ChatGPT is restricted to text-based communication, even Open AI's offering is regarded as a type of narrow artificial intelligence.

2. General AI

Strong AI, or artificial general intelligence, is now only a theoretical idea. Without the requirement for human training of the underlying models, AGI may do new tasks in a different context by utilizing prior knowledge and abilities. AGI can learn and carry out any intellectual task that a human being is capable of thanks to this aptitude.

3. Super AI

Like AGI, super AI is entirely theoretical and is sometimes known as artificial superintelligence. If Super AI were ever developed, it would be able to reason, understand, learn, make decisions, and be cognitively superior to humans. Applications with Super AI capabilities will have advanced past the stage of comprehending human emotions and experiences to the point where they can feel emotions and wants, and have their own opinions and desires.

The four types of AI based on functionalities

1. Reactive Machine AI

AI systems known as reactive machines are memoryless and created with a single, highly specialized purpose in mind. They are limited to using the data that is currently accessible since they are unable to recall past results or choices. Reactive AI is based on statistical mathematics and can generate seemingly intelligent outputs by analyzing large volumes of data.

Reactive Machine AI Examples

• IBM Deep Blue: By examining the pieces on the board and projecting the likely results of each move, IBM's chess-playing supercomputer AI defeated chess grandmaster Garry Kasparov in the late 1990s.

• The recommendation engine of Netflix: Netflix uses models to process large amounts of data from users' watching histories and recommend content based on what they are most likely to enjoy.

2. Limited Memory AI

Limited Memory AI can use past data for a specific amount of time, but it cannot retain that data in a library of past experiences to use over an extended period. As it is trained on more data over time, Limited Memory AI can improve in performance.

 Examples of Limited Memory AI

• Generative AI: Tools like ChatGPT, Bard, and DeepAI rely on limited memory AI capabilities to predict the next word, phrase, or visual element within a given text. In contrast to Reactive Machine AI: This type of AI can recall past events and outcomes and monitor specific objects or situations over time.

3. Theory of Mind AI

Mental Theory In general AI is a functional class of AI called artificial intelligence. Even if it's not yet possible, AI with Theory of Mind capabilities could comprehend other beings' feelings and thoughts. The way the AI interacts with those around them may be influenced by this understanding. Theoretically, this would enable the AI to model interactions with humans. Theory of Mind AI would tailor its interactions with people according to their particular emotional needs and goals since it could deduce human motives and reasoning. Additionally, Theory of Mind AI would be capable of contextualizing and understanding writings and artwork, something that current generative AI tools are not. A theory of mind AI that is presently being developed is emotion AI. Researchers studying AI hope

4. Self-Aware AI

A type of functional AI class for applications with super AI capabilities is called self-aware AI. Self-aware AI, like the theory of mind AI, is purely theoretical. If it were ever realized, it would be able to comprehend human emotions and ideas in addition to its own internal conditions and characteristics. It would also have feelings, needs, and beliefs all of its own.

Presently under development is a Theory of Mind AI called Emotion AI. Researchers anticipate that it will be able to evaluate sounds, pictures, and other types of data to identify, mimic, observe, and react to people emotionally in a suitable manner. Emotion AI is yet unable to comprehend and react to human emotions.

Some examples of AI

• ChatGPT (and the GPTs)

ChatGPT is an AI chatbot that can translate, create natural language, and respond to queries. Despite being perhaps the most widely used AI tool, OpenAI created the GPTs 1, 2, and 3 and caused quite a stir in the AI community because of its easy accessibility. 

• Self-driving cars

Even while potential customers are primarily concerned about the safety of self-driving cars, the technology is still developing and getting better thanks to advances in AI. These cars sense their environment and choose the optimal course of action by combining data from sensors and cameras with machine-learning algorithms. 

While most people generally associate self-driving cars with Tesla's autopilot feature for its electric vehicles, Alphabet, the parent company of Google, also operates Waymo, which offers autonomous trips in San Francisco, CA, and Phoenix, AZ. Think of it as a taxi without a driver.

Automakers such as Apple, Audi, GM, and Ford are likely developing self-driving car technology; Cruise is another robotaxi provider. 

Robotics

In the fields of robotics and AI, Boston Dynamics' accomplishments are noteworthy. While we're still a long way from developing artificial intelligence (AI) on the scale of the movie Terminator, it's impressive to observe how Boston Dyanmics' robots employ AI to navigate and react to various terrains. 

DeepMind

DeepMind, a Google subsidiary, is a leader in artificial intelligence (AI) and is moving closer to the ultimate goal of artificial general intelligence (AGI). The business first gained notoriety in 2016 when its AlphaGo system defeated a professional human Go player, albeit it is still far from there. 

Since then, DeepMind has built systems that can identify eye illnesses as accurately as the world's best doctors, and it has constructed a protein-folding prediction system that can anticipate the intricate 3D forms of proteins.

Conclusion

In the last five years, artificial intelligence has advanced remarkably and is now influencing individuals, organizations, and society in the real world. Since the field's inception in the 1950s, its fundamental issues of language and image processing have been greatly advanced by computer programs' capacity to carry out complex jobs in both domains. Research and development teams are making use of these advancements and applying AI to applications that will directly affect society, even if the field's original goal of fully replicating human intellect in machines is still far off. The application of AI methods in healthcare, for instance, is starting to happen, and the brain sciences are both a beneficiary and a contributor to these advancements.