Artificial intelligence

Artificial intelligence | AI artificial intelligence

What is ai artificial intelligence?

In simple terms, artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve based on the information they collect. AI manifests itself in various ways. Some examples are:

 

Chatbots use AI artificial intelligence to understand customer issues faster and provide more efficient responses

Intelligent assistants use AI artificial inteligence to analyze critical information from large free-text data sets to improve programming Recommendation engines can provide automated recommendations for TV shows based on users' viewing habits.

 

Definition of artificial intelligence.

Artificial intelligence is the ability of a machine to present the same capacities as human beings, such as reasoning, learning, creativity and the ability to plan.

AI artificial intelligence allows technological systems to perceive their environment, relate to it, solve problems and act with a specific purpose. The machine receives data (already prepared or collected through its own sensors, for example a camera), processes it and responds to it.

AI artificial intelligence systems are able to adapt their behavior to some extent, analyze the effects of previous actions, and work autonomously.

What are the advantages and disadvantages of artificial intelligence?

Advantages

• Good at detail-oriented jobs;
• Reduced time for data-heavy tasks;
• Delivers consistent results; and
• AI-powered virtual agents are always available.

Disadvantages

• Expensive;
• Requires deep technical expertise;
• Limited supply of qualified workers to build AI tools;
• Only knows what it's been shown; and
• Lack of ability to generalize from one task to another.

What are the 4 types of artificial intelligence?

AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows:

1: Reactive machines.

 These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.

2: Limited memory. 

These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.

3: Theory of mind. 

Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.

4: Self-awareness.

 In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.

 

History of AI Artificial intelligence?

The concept of inanimate objects endowed with intelligence has been around since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold. Engineers in ancient Egypt built statues of gods animated by priests. Throughout the centuries, thinkers from Aristotle to the 13th century Spanish theologian Ramon Llull to René Descartes and Thomas Bayes used the tools and logic of their times to describe human thought processes as symbols, laying the foundation for AI concepts such as general knowledge representation.

1950s. 

With the advent of modern computers, scientists could test their ideas about machine intelligence. One method for determining whether a computer has intelligence was devised by the British mathematician and World War II code-breaker Alan Turing. The Turing Test focused on a computer's ability to fool interrogators into believing its responses to their questions were made by a human being.

 

1956.

 The modern field of artificial intelligence is widely cited as starting this year during a summer conference at Dartmouth College. Sponsored by the Defense Advanced Research Projects Agency (DARPA), the conference was attended by 10 luminaries in the field, including AI pioneers Marvin Minsky, Oliver Selfridge and John McCarthy, who is credited with coining the term artificial intelligence. Also in attendance were Allen Newell, a computer scientist, and Herbert A. Simon, an economist, political scientist and cognitive psychologist, who presented their groundbreaking Logic Theorist, a computer program capable of proving certain mathematical theorems and referred to as the first AI program.

 

1950s and 1960s. 

In the wake of the Dartmouth College conference, leaders in the fledgling field of AI predicted that a man-made intelligence equivalent to the human brain was around the corner, attracting major government and industry support. Indeed, nearly 20 years of well-funded basic research generated significant advances in AI: For example, in the late 1950s, Newell and Simon published the General Problem Solver (GPS) algorithm, which fell short of solving complex problems but laid the foundations for developing more sophisticated cognitive architectures; McCarthy developed Lisp, a language for AI programming that is still used today. In the mid-1960s MIT Professor Joseph Weizenbaum developed ELIZA, an early natural language processing program that laid the foundation for today's chatbots.

 

1970s and 1980s.

 But the achievement of artificial general intelligence proved elusive, not imminent, hampered by limitations in computer processing and memory and by the complexity of the problem. Government and corporations backed away from their support of AI research, leading to a fallow period lasting from 1974 to 1980 and known as the first "AI Winter." In the 1980s, research on deep learning techniques and industry's adoption of Edward Feigenbaum's expert systems sparked a new wave of AI enthusiasm, only to be followed by another collapse of government funding and industry support. The second AI winter lasted until the mid-1990s.

 

1990s through today. 

Increases in computational power and an explosion of data sparked an AI renaissance in the late 1990s that has continued to present times. The latest focus on AI has given rise to breakthroughs in natural language processing, computer vision, robotics, machine learning, deep learning and more. Moreover, AI is becoming ever more tangible, powering cars, diagnosing disease and cementing its role in popular culture. In 1997, IBM's Deep Blue defeated Russian chess grandmaster Garry Kasparov, becoming the first computer program to beat a world chess champion. Fourteen years later, IBM's Watson captivated the public when it defeated two former champions on the game show Jeopardy!. More recently, the historic defeat of 18-time World Go champion Lee Sedol by Google DeepMind's AlphaGo stunned the Go community and marked a major milestone in the development of intelligent machines.

Why is AI artificial intelligence important?

 

Some smart technologies have been around for more than 50 years, but advances in computing power, the availability of huge amounts of data, and new algorithms have enabled huge AI advances in recent years.

 

Artificial intelligence has a central role in the digital transformation of society and has become an EU priority.

 

Its future applications are expected to bring about big changes, but AI is already present in our lives.

 

Find out more about the opportunities and challenges of artificial intelligence and how Parliament wants to regulate it.

 

AI artificial inteligence is much more about the process and ability of superpowered thinking and data analysis than it is about any particular format or function. Although AI shows images of high-functioning human-like robots taking over the world, AI artificial inteligence is not intended to replace humans. Its goal is to significantly enhance human capabilities and contributions. That makes it a very valuable business asset.

 

Uses of AI artificial inteligence in our daily life.

 

Software: virtual assistants, image analysis software, search engines, voice and face recognition systems Integrated artificial intelligence: robots, drones, autonomous vehicles, Internet of Things

 

Artificial intelligence in everyday life.

Below are some AI apps you may not have known used this ability.

 

Internet shopping and advertising.

Artificial intelligence is widely used to create personalized recommendations for consumers, based on, for example, their previous searches and purchases or other online behavior. AI is very important in commerce, to optimize products, plan inventory, logistics processes, etc.

 

Web searches.

Search engines learn from the vast amount of data that their users provide to deliver relevant search results.

 

Personal digital assistants.

Smartphone mobile phones use AI for a product that is as relevant and personalized as possible. The use of virtual assistants that answer questions, give recommendations and help organize their owners' routines has become widespread.

 

Automatic translations.

Language translation software, either based on written or spoken text, relies on artificial intelligence to provide and improve translations. This also applies to functions such as automated subtitling.

 

Smart houses, cities and infrastructure.

Smart thermostats learn from our behavior to save energy, while smart city developers hope to regulate traffic to improve connectivity and reduce congestion.

 

Vehicles.

Although self-driving vehicles are not yet widespread, cars already use AI-powered safety features. For example, the EU helped finance the vision-based driver assistance system VI-DAS, which detects potentially dangerous situations and accidents.

 

Navigation is heavily based on AI.

 

Artificial intelligence IN Cybersecurity.

Artificial intelligence systems can help recognize and fight cyber-attacks and other online threats based on the data they continuously receive, recognizing patterns and preventing attacks.

 

Artificial intelligence to fight against Covid-19.

AI has been used in thermal imaging cameras installed at airports and elsewhere. In medicine, it can help recognize an infection of the lungs from a test called a CT scan. It has also been used to provide data to track the spread of the disease.

 

Fight against disinformation.

Some applications of artificial intelligence can detect fake news and disinformation by extracting information from social networks, looking for sensational or alarming words, and identifying which online sources are considered authoritative.

 

Find out more about how MEPs want to regulate data legislation to promote innovation and ensure security.

 

Other examples of AI applications.

AI artificial intelligence is intended to transform almost every aspect of life and the economy. These are other examples:

 

Artificial intelligence in Health.

Researchers are studying how to use AI to analyze vast amounts of health data to find patterns that could lead to new discoveries in medicine and other ways to improve individual diagnoses.

 

For example, researchers developed an AI program that responded to emergency calls and said it detected cardiac arrest faster than a doctor could.

 

Separately, EU co-funded KConnect is developing multilingual text and search services that help people find the most relevant medical information available

 

Artificial intelligence in  Transport.

Artificial intelligence could improve the safety, speed and efficiency of rail traffic by minimizing wheel friction, maximizing speed and enabling autonomous driving.

 

Artificial intelligence in manufactures.

Artificial intelligence can help make European producers more efficient and empower factories in Europe again by using robots, optimizing sales journeys, or with timely predictions of needed maintenance or breakdowns in "smart factories."

 

The EU co-funded research project SatisFactory uses collaborative augmented reality systems to increase job satisfaction in "smart factories".

 

Artificial intelligence in  food and agriculture.

AI can be used to build a sustainable food system: it could ensure healthier food by minimizing the use of fertilizers, pesticides and irrigation; improve productivity and reduce environmental impact. In addition, the robots could remove weeds and reduce the use of herbicides.

 

In the EU, many farmers are already using AI to monitor the movement, temperature and feed consumption of their herds.

 

Artificial intelligence in Public administration and services.

By using vast amounts of data and recognizing patterns, AI could anticipate natural disasters, enable proper preparation, and reduce their consequences.

 

 What is Artificial intelligence terminology.

AI artificial intelligence has become an umbrella term for applications that perform complex tasks that previously required human input, such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning and deep learning. However, there are certain differences. For example, machine learning focuses on creating systems that learn or improve their performance based on the data they consume. It is important to note that while all machine learning is AI, artificial intelligence not all AI artificial intelligence is machine learning.

 

To get the full value of AI, artificial intelligence many companies are making significant investments in data science teams. An interdisciplinary field that uses scientific and other methods to extract value from data, data science combines skills from fields such as statistics and computer science with business knowledge to analyze data collected from multiple sources.

 

How AI artificial inteligence  can help organizations.

The fundamental principle of AI is to replicate, and then exceed, the way humans perceive and react to the world. It is fast becoming the cornerstone of innovation. AI, powered by various forms of machine learning that recognize patterns in data to enable predictions, can add value to your business by:

 

Provide a more complete understanding of the wealth of data available

Relying on predictions to automate overly complex or mundane tasks.

 

AI artificial inteligence in the companies.

Currently, AI technology improves the performance and productivity of the company by automating processes or tasks that previously required human effort. AI can also make sense of data on a scale no human ever could. This capability can lead to significant business benefits. For example, Netflix uses machine learning to provide a level of personalization that helped the company grow its customer base by more than 25% in 2017.

 

Most companies have made data science a priority and are investing heavily in it. In Gartner's latest survey of more than 3,000 CIOs and CIOs, respondents ranked analytics and business intelligence as the most important differentiating technologies for their organizations. CTOs and CIOs surveyed consider these technologies to be the most strategic for their companies and therefore are attracting new investment.

 

AI has value to almost every function, business, and industry. Includes general and industry-specific applications such as:

 

Using transactional and demographic data to predict how much certain customers will spend over the course of their relationship with a business (or customer lifetime value)

Price optimization based on customer behavior and preferences

Using image recognition to analyze X-ray images for cancer symptoms.

 

How companies use AI.

According to the Harvard Business Review, companies use AI primarily to:

 

Detect and deter security intrusions (44%)

Solve user technology problems (41%)

Reduce the work of production management (34%)

Measure internal compliance in the use of approved vendors (34%)

 

 

What is driving AI artificial intelligence adoption?

Three factors that are driving the development of AI across industries:

 

Affordable, high-performance computing power is now available. The abundance of commodity computing power in the cloud allows easy access to affordable, high-performance computing power. Prior to this development, the only computing environments available for AI were non-cloud based and cost prohibitive.

Large volumes of data are available for training. AI must be trained on a lot of data to make the right predictions. The emergence of different tools for labeling data, along with the ease and affordability with which organizations can store and process structured and unstructured data, enables more organizations to design and train AI algorithms.

Applied AI provides a competitive advantage. Increasingly, companies are recognizing the competitive advantage of applying AI insights to business goals and are making it a company-wide priority. For example, the specific recommendations provided by AI can help companies make better decisions faster. Many of the features and capabilities of AI can reduce costs and risks, speed time to market, and much more.

 

The benefits and challenges of putting AI into practice.

There are numerous success stories that demonstrate the value of AI. Organizations that embed machine learning and cognitive interactions into traditional business processes and applications further improve user experience and productivity.

 

However, the foundation is not strong enough. Few companies have implemented AI in a balanced way for various reasons. For example, if they don't use cloud computing, AI projects are often computationally expensive. They are also complex to design and require expertise that is in high demand but in short supply. Knowing when and where to incorporate AI, as well as when to turn to a third party, will help minimize these pitfalls.

 

AI artificial intelligence  success stories

AI is the driving factor behind some significant success stories:

 

According to the Harvard Business Review, the Associated Press produced 12 times more stories by training AI software to automatically write low-profit news stories. This effort freed up their journalists to write more detailed accounts.

Deep Patient, an AI-powered tool designed by the Icahn School of Medicine at Mount Sinai, enables clinicians to identify high-risk patients even before illnesses are diagnosed. The tool analyzes a patient's medical history to predict nearly 80 diseases up to a year before onset, according to insideBIGDATA.

 

 

artificial inteligence

ai artificial inteligence

artificial general inteligence