If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
One of the exciting things about Artificial Intelligence is the steady stream of new accomplishments that we see in the news. Every week, some research institution or company accomplishes something amazing with AI, whether it is translating a long lost language, or building a massive model, the scale of which has never been done before. But what does it all mean? If I am a business CEO, what impact if any does this have on my business? Is there any way I can leverage it?
When it was released by Google just a few years ago, a deep-learning model called BERT demonstrated a major step forward in natural language processing (NLP). BERT's core structure, based on a type of neural network known as a Transformer, has become the underpinning for a range of NLP applications, from completing search queries and user-written sentences to language translation. The models even score well on benchmarks intended to test understanding at a high school level, such as Large-scale ReAding Comprehension (RACE) developed at Carnegie Mellon University. In doing so, they have become marketing tools in the artificial intelligence (AI) gold rush. At Nvidia's annual technology conference, president and CEO Jen-Hsun Huang used RACE to claim high performance for his company's implementation of BERT.
The coronavirus has unleashed a medical, social and economic crisis of unimaginable intensity. And in response, the world is discovering a state of new normal. Prime Minister Narendra Modi has highlighted how the COVID-19 pandemic has resulted in an ever-increasing adoption of technology at a scale unknown till recently. Where does Artificial Intelligence (AI) feature in this new normal? In the medical world, AI has been applied to COVID-19 in four areas: diagnosis, public health, clinical decision-making, and therapeutics.
Human interaction with machines has experienced a great leap forward in recent years, largely driven by artificial intelligence (AI). From smart homes to self-driving cars, AI has become a seamless part of our daily lives. Voice interactions play a key role in many of these technological advances, most notably in language translation. Here, AI enables instant translation across a number of mediums: text, voice, images and even street signs. The technology works by recognizing individual words, then leveraging similarities in how various languages express the relationships between those words.
Before IBM's Deep Blue computer program defeated world champion Garry Kasparov in chess in 1997, ... [ ] many AI pundits believed that machines would never possess the creativity required to rival humans at the game. Years ago, Marvin Minsky coined the phrase "suitcase words" to refer to terms that have a multitude of different meanings packed into them. He gave as examples words like consciousness, morality and creativity. "Artificial intelligence" is a suitcase word. Commentators today use the phrase to mean many different things in many different contexts. As AI becomes more important technologically, economically and geopolitically, the phrase's use--and misuse--will only grow.
More than 70 years ago, researchers at the forefront of artificial intelligence research introduced neural networks as a revolutionary way to think about how the brain works. In the human brain, networks of billions of connected neurons make sense of sensory data, allowing us to learn from experience. Artificial neural networks can also filter huge amounts of data through connected layers to make predictions and recognize patterns, following rules they taught themselves. By now, people treat neural networks as a kind of AI panacea, capable of solving tech challenges that can be restated as a problem of pattern recognition. They provide natural-sounding language translation.
Driven by advanced techniques in machine learning, commercial systems for automated language translation now nearly match the performance of human linguists, and far more efficiently. Google Translate supports 105 languages, from Afrikaans to Zulu, and in addition to printed text it can translate speech, handwriting, and the text found on websites and in images. The methods for doing those things are clever, but the key enabler lies in the huge annotated databases of writings in the various language pairs. A translation from French to English succeeds because the algorithms were trained on millions of actual translation examples. The expectation is that every word or phrase that comes into the system, with its associated rules and patterns of language structure, will have been seen and translated before.
In the 1960s, the Star Trek television series brought the vision of artificial intelligence into the living rooms of millions of people. AI was everywhere in the show, in the form of machines that had all the intelligence of humans -- and a lot more. Take, for example, the universal translator on the USS Enterprise. It could translate alien languages into English or any other language instantaneously. That, of course, was all science fiction back in the days when Lyndon B. Johnson was the U.S. president, as were a lot of the other AI applications in use on the starship.
Before IBM's Deep Blue computer program defeated world champion Garry Kasparov in chess in 1997, ... [ ] many AI pundits believed that machines would never possess the creativity required to rival humans at the game. Years ago, Marvin Minsky coined the phrase "suitcase words" to refer to terms that have a multitude of different meanings packed into them. He gave as examples words like consciousness, morality and creativity. "Artificial intelligence" is a suitcase word. Commentators today use the phrase to mean many different things in many different contexts.
The process includes several activities such as pre-processing, tokenisation, normalisation, correction of typographical errors, Named Entity Reorganization (NER), and dependency parsing. To attain high-quality models, NLP performs an in-depth analysis of user inputs like lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis, etc. The main challenge is information overload, which poses a big problem to access a specific, important piece of information from vast datasets. Semantic and context understanding is essential as well as challenging for summarisation systems due to quality and usability issues. Also, identifying the context of interaction among entities and objects is a crucial task, especially with high dimensional, heterogeneous, complex and poor-quality data.