In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, bravely suggested that "any aspect of learning or any other characteristic of intelligence, in principle, can be described as accurately as a machine can be made to simulate it. "
McCarthy called this new field of research" artificial intelligence "and suggested that a two-month effort by a group of 10 scientists could make significant progress in the development of machines that" Using language ". Form abstractions and concepts, solve problems that are now reserved for humans, and improve themselves. "
At that time, scientists optimistically believed that we would soon have thinking machines that would do all the work a human could do. more than six decades later, advances in computer science and robotics have helped us to automate many of the tasks that previously required human physical and cognitive work.
But the true artificial intelligence McCarthy invented escapes
What exactly is AI?
A major challenge for artificial intelligence is that it is a broad concept, and there is no clear consensus about its definition.
As previously mentioned McCarthy suggested that AI would solve the problems the way people do: "The ultimate effort is in it "Computers make programs that can solve problems and achieve goals in the world as well as in people," said McCarthy .
. Andrew Moore Dean of Computer Science at Carnegie Mellon University, offered a more modern definition of the term in an interview with Forbes from 201
But our understanding of "human intelligence" and our expectations Technology is constantly evolving. Zachary Lipton, editor of Approx approx Correct calls AI the "aspiring goal, a moving target based on the abilities people possess, but not the machines." In other words, the things that we ask of the AI change over time.
In the 1950s, scientists regarded chess and checkers as a major challenge to artificial intelligence. But today only a few chess players would be considered AI. Computers are already solving many more complicated problems, including cancer detection, driving, and voice command processing.
Narrow AI vs. General AI
The first generation of AI scientists and visionaries believed that ultimately we could create people's intelligence at the highest level.
However, several decades of AI research have shown that mimicking the complex problem-solving and abstract thinking of the human brain is extremely difficult. For one, we humans are very good at generalizing knowledge and applying concepts that we learn in one area to another. We can also make relatively reliable decisions based on intuition and with little information. Over the years, AI has become known on a human scale as Artificial Common Intelligence (AGI) or as a strong AI.
The initial hype and excitement surrounding the AI found great interest in government agencies. However, it soon turned out that, contrary to early perception, human intelligence was not just around the corner, and scientists could hardly reproduce the basic functions of the human mind. In the 1970s, unfulfilled promises and expectations eventually led to "AI Winter," a long period of dampening public interest and AI funding.
It took many years of innovation and a revolution in deep learning technology to revive interest in AI. But despite huge advances in artificial intelligence, none of the current approaches to AI can solve problems in the same way as the human mind, and most experts believe that AGI is at least decades away.
The flip side, narrow or faint KI is not aimed at to reproduce the functionality of the human brain. and focuses instead on optimizing a single task. Narrow AI has already found many real-world applications, such as recognizing faces, converting audio to text, recommending videos on YouTube, and displaying personalized content in the Facebook Newsfeed.
Many scientists believe that someday we will create an AGI have a dystopian vision of the age of thinking machines . In 2014, renowned English physicist Stephen Hawking described AI as an existential threat to humanity warning that "full artificial intelligence could mean the end of humanity".
In 2015, Y Combinator President Sam Altman and Elon Musk, CEO of Tesla, two other AGI believers, founded the OpenAI, a nonprofit research lab that aims to create artificial general intelligence in a way that benefits all humanity comes. (Musk has since gone.)
Others believe that artificial general intelligence is a pointless goal. "We do not have to duplicate people, so I focus on tools that help us instead of copying what we already know, we want people and machines to team up and do something they can not do on their own." says Peter Norvig Research Leader at Google.
Scientists like Norvig believe that a close AI can help automate repetitive and tedious tasks and increase people's productivity. For example, physicians can use AI algorithms to examine x-ray scans at high speeds so that they can see more patients. Another example of a close AI is the fight against cyberthreats: security analysts can use AI to locate signals that detect data breaches in the gigabytes of data being transmitted through their corporate networks.
Rule-based AI against Machine Learning
Early AI The creation efforts focused on the transformation of human knowledge and intelligence into static rules. Programmers had to write code (if-then statements) meticulously for each rule that defined the behavior of the AI. The benefit of rule-based AI, later known as "Good Old Fashioned Artificial Intelligence" (GOFAI), is that man has complete control over the design and behavior of the system he has developed.
The rule-based AI is still very popular in areas where the rules are unique. An example is video games where developers want AI to provide a predictable user experience.
The problem with GOFAI is that unlike McCarthy's original premise, we can not accurately describe every aspect of learning and each aspect of behavior in a way that can be transformed into computer rules. For example, defining logical rules for recognizing voices and images – a complex accomplishment that man instinctively accomplishes – is an area in which classical AI has struggled in the past. AI ” border=”0″ class=”center” src=”https://assets.pcmag.com/media/images/624713-ai-future.jpg?thumb=y&width=980&height=495″/>
An alternative approach to the generation of artificial intelligence is machine learning. Instead of manually developing rules for AI, machine engineers "train" their models by providing them with a huge number of samples. The machine learning algorithm analyzes and finds patterns in the training data and then develops its own behavior. For example, a machine learning model can train large volumes of historical sales data for a business and then generate revenue forecasts.
Deep Learning A subset of machine learning, has become very popular in recent years. It is especially good for processing unstructured data such as images, video, audio and text documents. For example, you can create a deep-learning image classifier and train it with millions of available photos with captions. For example, the ImageNet dataset . The trained AI model can recognize objects in images with an accuracy that often surpasses humans. Advances in deep learning have driven AI into many complicated and critical areas such as medicine, self-driving cars and education.
One of the challenges of deep learning models is that they develop their own behavior based on training data. That makes them complex and opaque . Even deep learning experts often find it difficult to explain the decisions and inner workings of the AI models they create.
What are examples of artificial intelligence
Here are some of the possibilities of AI bringing with it tremendous changes in various fields.
Self-propelled cars: Advances in artificial intelligence have brought us very close to realizing the decades-long dream of autonomous driving. AI algorithms are one of the key components that enable self-driving cars to understand their environment by capturing the cameras installed around the vehicle and recognizing objects such as roads, traffic signs, other cars, and people.
Digital Assistants and Smart Speakers: Siri, Alexa, Cortana, and Google Assistant use Artificial Intelligence to turn spoken words into text and assign the text to specific commands. AI helps digital assistants understand different nuances in spoken language and synthesize human-like voices.
Translation: For many decades, translating text between different languages has been a pain point for computers. However, deep learning has helped create a revolution in services such as Google Translate. To be clear, AI still has a long way to go before mastering human language, but progress so far has been spectacular.
Face detection: Face detection is one of the most popular applications of artificial intelligence. It has many uses, such. For example, unlocking your phone, paying with your face, and finding intruders in your home. However, the increasing availability of face recognition technologies has also raised concerns about privacy, security and civil liberties.
Medicine: From the detection of skin cancer to the analysis of X-rays and MRI scans to the provision of personalized health tips and recommendations Artificial intelligence, which manages entire health systems, is becoming a key element of health and medicine. KI does not replace your doctor, but it could contribute to better health services, especially in deprived areas where AI-assisted healthcare professionals may lose some of the health care of the few general practitioners who serve large populations
The Future of AI
In our quest to crack the code of AI and create thinking machines, we've learned a lot about the importance of intelligence and reasoning. And thanks to the advances in AI, in addition to our computers, we are also completing tasks that were once considered the exclusive domain of the human brain.
Among the emerging areas in which the KI is moving include Music and Art . where AI algorithms manifest their very own kind of creativity. There is also hope that AI will help combat climate change care for the elderly and eventually create a utopian future in which people do not work at all have to .
There is also the fear that AI will cause mass unemployment, disrupt economic equilibrium, trigger another World War and eventually drive people into slavery.
We are still here I do not know in which direction the AI will go. But as the science and technology of artificial intelligence continues to improve, our expectations and definitions of AI will change, and what we now call AI might become the mundane functions of tomorrow's computers.