Artificial Intelligence

ARTIFICIAL INTELLIGENCE

       

What is Artificial Intelligence?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

A Brief History of Artificial Intelligence.

The history of Artificial Intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

The field of AI research was founded at a workshop held on the campus of Dartmouth College during the summer of 1956.[1] Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true.

Eventually, it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism from James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence, and the difficult years that followed would later be known as an "AI winter". Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again.

Investment and interest in AI boomed in the first decades of the 21st century when machine learning was successfully applied to many problems in academia and industry due to new methods, the application of powerful computer hardware, and the collection of immense data sets.

Types of artificial intelligence.

There are four (4) types of artificial intelligence

       

 

                      1.Reactive Machines

                    2.limited Memory

                      3.Theory of Mind

                      4.Self Aware

1) Reactive Machines

These are the oldest forms of AI systems that have extremely limited capability. They emulate the human mind’s ability to respond to different kinds of stimuli. These machines do not have memory-based functionality. This means such machines cannot use previously gained experiences to inform their present actions, i.e., these machines do not have the ability to “learn.” These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same. A popular example of a reactive AI machine is IBM’s Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesn’t have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

2) limited Memory

Limited memory machines are machines that, in addition to having the capabilities of purely reactive machines, are also capable of learning from historical data to make decisions. Nearly all existing applications that we know of come under this category of AI. All present-day AI systems, such as those using deep learning, are trained by large volumes of training data that they store in their memory to form a reference model for solving future problems. For instance, an image recognition AI is trained using thousands of pictures and their labels to teach it to name objects it scans. When an image is scanned by such an AI, it uses the training images as references to understand the contents of the image presented to it, and based on its “learning experience” it labels new images with increasing accuracy.

Almost all present-day AI applications, from chatbots and virtual assistants to self-driving vehicles are all driven by limited memory AI

So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this. My own research into methods inspired by Darwinian evolution can start to make up for human shortcomings by letting the machines build their own representations.

3) Theory of mind

While the previous two types of AI have been and are found in abundance, the next two types of AI exist, for now, either as a concept or a work in progress. Theory of mind AI is the next level of AI systems that researchers are currently engaged in innovating. A theory of mind level AI will be able to better understand the entities it is interacting with by discerning their needs, emotions, beliefs, and thought processes. While artificial emotional intelligence is already a budding industry and an area of interest for leading AI researchers, achieving Theory of mind level of AI will require development in other branches of AI as well. This is because to truly understand human needs, AI machines will have to perceive humans as individuals whose minds can be shaped by multiple factors, essentially “understanding” humans.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.

4) Self  Aware

This is the final stage of AI development which currently exists only hypothetically. Self-aware AI, which, self explanatorily, is an AI that has evolved to be so akin to the human brain that it has developed self-awareness. Creating this type of Ai, which is decades, if not centuries away from materializing, is and will always be the ultimate objective of all AI research. This type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, beliefs, and potentially desires of its own. And this is the type of AI that doomsayers of the technology are wary of. Although the development of self-aware can potentially boost our progress as a civilization by leaps and bounds, it can also potentially lead to catastrophe. This is because once self-aware, the AI would be capable of having ideas like self-preservation which may directly or indirectly spell the end for humanity, as such an entity could easily outmaneuver the intellect of any human being and plot elaborate schemes to take over humanity.

The alternate system of classification that is more generally used in tech parlance is the classification of the technology into Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

Branches Of Artificial Intelligence.

There are six (6) major branches in artificial intelligence.

                     

                      1.  Machine learning

                       2. Neural Network

                       3. Robotics

                        4. Expert Systems

                         5.Fuzzy Logic

                         6.Natural Language Processing  

 1) Machine learning

In terms of advanced technology, one of the most demanded fields is Machine Learning, it is making buzz every day whenever a new product is introduced by any company that deploys ML techniques and algorithms for delivering the consumer in a highly creative manner. 

2) Neural Network

Incorporating cognitive science and machines to perform tasks, the neural network is a branch of artificial intelligence that makes use of neurology ( a part of biology that concerns the nerve and nervous system of the human brain). 

3) Robotics

This has emerged as a very sizzling field of artificial intelligence. An interesting field of research and development mainly focuses on designing and constructing robots

4) Expert Systems

Expert systems were considered amid the first successful model of AI software. For the first time, they were designed in the 1970s and after that escalated in the 1980s. 

5) Fuzzy logic

In the real world, sometimes we face a condition where it is difficult to recognize whether the condition is true or not, their fuzzy logic gives relevant flexibility for reasoning that leads to inaccuracies and uncertainties of any condition

6)Natural Language Processing  

NLP is the processing of the human language by computer programs, examples include; spam detection by looking at the subject of a line or text of an email and checking if it is junk.

 Advantages and dis advantages of artificial intelligence

Advantages of artificial intelligence.

The advantages of Artificial intelligence applications are enormous and can revolutionize any professional sector. Let’s see some of them

1) Reduction in huma error:

The phrase “human error” was born because humans make mistakes from time to time. Computers, however, do not make these mistakes if they are programmed properly. With Artificial intelligence, the decisions are taken from the previously gathered information applying a certain set of algorithms. So errors are reduced and the chance of reaching accuracy with a greater degree of precision is a possibility.

Example: In Weather Forecasting using AI they have reduced the majority of human error.

2) Takes risk instead of humans:

This is one of the biggest advantages of Artificial intelligence. We can overcome many risky limitations of humans by developing an AI Robot which in turn can do the risky things for us. Let it be going to mars, defuse a bomb, explore the deepest parts of oceans, mining for coal and oil, it can be used effectively in any kind of natural or man-made disasters.

Example: Have you heard about the Chernobyl nuclear power plant explosion in Ukraine? At that time there were no AI-powered robots that can help us to minimize the effect of radiation by controlling the fire in early stages, as any human went close to the core was dead in a matter of minutes. They eventually poured sand and boron from helicopters from a mere distance.

AI Robots can be used in such situations where intervention can be hazardous.

3) Available 24×7:

An Average human will work for 4–6 hours a day excluding the breaks. Humans are built in such a way to get some time out for refreshing themselves and get ready for a new day of work and they even have weekly offed to stay intact with their work-life and personal life. But using AI we can make machines work 24x7 without any breaks and they don’t even get bored, unlike humans.

Example: Educational Institutes and Helpline centers are getting many queries and issues which can be handled effectively using AI.

4) Helping in repetitive jobs:

In our day-to-day work, we will be performing many repetitive works like sending a thanking mail, verifying certain documents for errors and many more things. Using artificial intelligence we can productively automate these mundane tasks and can even remove “boring” tasks for humans and free them up to be increasingly creative.

Example: In banks, we often see many verifications of documents to get a loan which is a repetitive task for the owner of the bank. Using AI Cognitive Automation the owner can speed up the process of verifying the documents by which both the customers and the owner will be benefited.

5) Digital assistance

Some of the highly advanced organizations use digital assistants to interact with users which saves the need for human resources. The digital assistants also used in many websites to provide things that users want. We can chat with them about what we are looking for. Some chatbots are designed in such a way that it’s become hard to determine that we’re chatting with a chatbot or a human being.

Example: We all know that organizations have a customer support team that needs to clarify the doubts and queries of the customers. Using AI the organizations can set up a Voice bot or Chatbot which can help customers with all their queries. We can see many organizations already started using them on their websites and mobile applications.

6) Faster decisions:

Using AI alongside other technologies we can make machines take decisions faster than a human and carry out actions quicker. While taking a decision human will analyze many factors both emotionally and practically but AI-powered machine works on what it is programmed and delivers the results in a faster way.

Example: We all have played Chess games in Windows. It is nearly impossible to beat CPU in the hard mode because of the AI behind that game. It will take the best possible step in a very short time according to the algorithms used behind it.

7) Daily applications:

Daily applications such as Apple’s Siri, Window’s Cortana, Google’s OK Google are frequently used in our daily routine whether it is for searching a location, taking a selfie, making a phone call, replying to a mail and many more.

Example: Around 20 years ago, when we are planning to go somewhere we used to ask a person who already went there for the directions. But now all we have to do is say “OK Google where is Visakhapatnam”. It will show you Visakhapatnam’s location on google map and the best path between you and Visakhapatnam.

8) New Inventions:

AI is powering many inventions in almost every domain which will help humans solve the majority of complex problems.

Example: Recently doctors can predict breast cancer in the woman at earlier stages using advanced AI-based technologies

  Disadvantages of artificial intelligence::

As every bright side has a darker version in it. Artificial Intelligence also has some disadvantages. Let’s see some of them

1) High costs of creations:

As AI is updating every day the hardware and software need to get updated with time to meet the latest requirements. Machines need repairing and maintenance which need plenty of costs. It’ s creation requires huge costs as they are very complex machines.

2) Making humans lazy:

AI is making humans lazy with its applications automating the majority of the work. Humans tend to get addicted to these inventions which can cause a problem to future generations.

3) Unemployment

As AI is replacing the majority of the repetitive tasks and other works with robots,human interference is becoming less which will cause a major problem in the employment standards. Every organization is looking to replace the minimum qualified individuals with AI robots which can do similar work with more efficiency.

4) No emotions:

There is no doubt that machines are much better when it comes to working efficiently but they cannot replace the human connection that makes the team. Machines cannot develop a bond with humans which is an essential attribute when comes to Team Management.

5) lacking out of box thinking:

Machines can perform only those tasks which they are designed or programmed to do, anything out of that they tend to crash or give irrelevant outputs which could be a major backdʀᴏᴘ.

 

                        

  Conclusion:

AI is at the centre of a new enterprise to build computational models of intelligence. The main assumption is that intelligence (human or otherwise) can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer. There is much debate as to whether such an appropriately programmed computer would be a mind, or would merely simulate one, but AI researchers need not wait for the conclusion to that debate, nor for the hypothetical computer that could model all of human intelligence. Aspects of intelligent behaviour, such as solving problems, making inferences, learning, and understanding language, have already been coded as computer programs, and within very limited domains, such as identifying diseases of soybean plants, AI programs can outperform human experts. Now the great challenge of AI is to find ways of representing the commonsense knowledge and experience that enable people to carry out everyday activities such as holding a wide-ranging conversation, or finding their way along a busy street. Conventional digital computers may be capable of running such programs, or we may need to develop new machines that can support the complexity of human thought.

                  * THE END*

                                                    ~written by ,

                                                       Yaswanth