Artificial Intelligence (AI) Technologies
Artificial intelligence is the ability of machines to perform specific tasks, which need the intelligence showcased by humans and animals in some of their behavior. This definition is often ascribed to Marvin Minsky and John McCarthy from the 1950s, who were also known as the fathers of the field.
Artificial intelligence allows machines to understand and achieve specific goals. AI includes machine learning via deep learning. The former refers to machines automatically learning from existing data without human assistance. Deep knowledge lets the device absorb vast amounts of unstructured data such as text, images, and audio.
Any AI system must be able to have some of the following characteristics: Observation, analytical ability, problem-solving, learning, Etc.
AI was a term first coined at Dartmouth College in 1956. Cognitive scientist Marvin Minsky was optimistic about the technology's future. 1974-1980 saw government funding in the field drop, a period known as "AI winter" when several criticized progress.
However, the fervor was revived in the 1980s when the British government started funding the technology again, mainly because they were worried about competition with the Japanese.
Some technology companies have recently managed to double their market value and get many profits. This is due to its investments in developing AI departments and adapting artificial intelligence techniques to its innovative applications and devices.
Apple:
Apple is integrating as much AI into its products and operations as possible.
In 2019, Apple, the world's largest technology company, made several AI-based technologies that served the following purposes:
1) Bring greater personalization to web search and Siri results.
2) Shore up their competitive disadvantage in the self-driving car space.
3) Make images "shoppable" by allowing users to search using photos rather than keywords.
4) Improve iPhone photography with AI-powered photo enhancement.
Siri, of course, is another example of how Apple runs on AI. The voice-powered assistant is designed for continual, at-the-edge improvement, which means it uses customer communications to further train itself without transmitting those private communications to Apple servers.
Ford:
Ford is at the forefront of the transportation transformation. It was one of the first companies on earth to deploy a neural net at scale and has since brought artificial intelligence to its assembly lines and the operation of the vehicles they sell.
Ford Edge's all-wheel-drive system, for example, uses artificial intelligence to automatically determine if all-wheel drive is needed — more quickly and accurately than a human driver. In the Ford factory, AI can detect wrinkles in seat fabric.
In 2019, the organization made a $ 1 billion investment into Argo.ai to better compete in the race for full vehicle autonomy. Ford expects to roll out a Geo-fenced self-driving car fleet within three years.
Alphabet (Google):
Last year, Google released TensorFlow, its open-sourced platform for machine learning, giving everyone access to one of the most advanced machine learning platforms ever created. More than 50 Google products have adopted the platform to put deep knowledge to work.
Internally, Google has hundreds of employees working in the AI field. Their ultimate goal is to transform their panoply of AI-related services into a cohesive digital assistant that can proactively manage and automate your entire life.
By releasing Tensorflow to the Open Source community, Google conveys that artificial intelligence is for everyone. The platform makes available all sorts of pre-trained models and machine learning algorithms.
Together, they represent millions of hours of computer training, meaning everyone can access the most powerful AI tools.
Today, machine learning is driven, in part, by major technology companies that train models using their massive data sets. These out-of-the-box tools are potent, but we can make them even more powerful by layering additional functionality customized to your needs.
By applying pre-trained machine learning models to new datasets or information, we can efficiently use complex rules and learning to a new problem without reinventing the wheel.
Bank of America:
Bank of America is turning to artificial intelligence to help reduce its labor force and drive more customers to receive help via automated systems and chatbots.
In 2018, the company rolled out Erica, an in-app customer service agent. By October 2019, the digital assistant had handled about 75 million in-app customer service interactions.
Unsurprisingly, 2019 also saw a steep decline in new hires for customer service positions. One study on Bank of America's hiring practices found that the conglomerate had reduced branch-related job openings by half. At that same time, the number of Artificial Intelligence and Machine Learning (AI and ML) related Bank of America job openings had doubled.
As artificial intelligence becomes increasingly adept at automating repetitive tasks and interacting with humans, we can expect customer service as we know it to be handled by machines increasingly.
AI integrated chips:
Tesla aims to create AI-integrated chips enabling cars to navigate freeways and even traffic. Approximately 6 billion transistors constitute the circuit of each Tesla chip.
These Tesla chips are 21 times faster than the original Nvidia chips and 20% cheaper. The chip has 32 megabytes of high-speed SRAM memory, which fetches data quicker and easier than DRAM.
For better performance, Tesla automobile systems have two AI chips. Both the chips make separate assessments of the traffic and danger situation around the cars.
The assessments are matched, and the car is guided accordingly if the outputs are identical. If there is ambiguity in the outcomes obtained from the chips, revaluation is done until a safe and suitable decision is taken. Thus, dual chips will enable better control over the navigation in self-driving Tesla cars.
The specialty of AI includes many areas and applications, and among these areas:
Machine learning
This specialty involves designing data-driven machine learning models, which can use this data to predict future outcomes and make decisions. These models include neural networks, deep learning, image classification, and talking to machines.
Natural language processing
This major focuses on understanding and analyzing human language using computing and statistical analysis. These areas include translating languages, talking to robots, and personal assistants like Siri and Alexa.
Computer vision
This specialty relates to developing techniques for understanding and analyzing digital images and videos. This specialty includes image classification, face detection, object and person recognition, and motion recognition.
AI in industry
This major focuses on the applications of AI in various industries, such as automobile manufacturing, maintenance, health, finance, e-commerce, and marketing.
Robotics and automation
This major is concerned with developing robots and intelligent automation to replace human labor in various tasks, such as industry, maintenance, cleaning, and logistics.
In the modern world, we are surrounded by artificial intelligence. From assistants such as Amazon's Alexa to the Internet, they predict what we may like to buy next. Artificial intelligence (AI) is everywhere. Self-driving cars are also an example of the application of artificial intelligence (AI).
Broadly, AI can be divided into two categories: Narrow AI and General AI.
Narrow AI is the kind we use everywhere -- from flagging content online and detecting faces in pictures to simple customer care inquiries.
General artificial intelligence (AI) to date remains just a concept. The idea behind General AI is to make it as adaptable and flexible as human intelligence. When scientists can develop general AI remains a hotly contested debate, with some saying it'll arrive as soon as 2040 to others saying it's centuries away, given the lack of understanding of the human brain.
What is the concept of artificial intelligence? What is artificial intelligence and its fields?
Artificial intelligence is a branch of computer science that aims to design systems and programs capable of performing tasks that require human intelligence. The fields of AI include machine learning, natural language processing, computer vision, robotics, and general intelligence. It is used in many areas, such as medicine, commerce, marketing, security, manufacturing, Etc.
What are the most important applications of artificial intelligence?
The main applications of artificial intelligence include machine learning, data analysis, intelligent control of industrial processes, thoughtful conversations, and recognition of sound, images, robots, and autonomous vehicles.
What is the goal of artificial intelligence?
Artificial intelligence aims to develop technologies and systems that can perform tasks independently in a way that resembles the way humans do them. It also seeks to improve people's daily lives by improving the services provided, increasing productivity, and reducing costs.
Does artificial intelligence affect the future of humanity?
Yes, artificial intelligence has a significant impact on the future of humanity in terms of changing many industries and jobs, technological development, and improving the quality of life. However, some challenges and risks need to be dealt with, such as the loss of some jobs and threats to privacy and cybersecurity.
Who is the founder of artificial intelligence?
The founder of artificial intelligence is John McCarthy, one of the prominent scientists who contributed to the establishment of the field in the 1950s and provided processors for fake intelligence problems such as chess and natural language learning.
What will artificial intelligence be like in the future?
Artificial intelligence is expected to become more advanced and complex, with an improved ability to self-learn, understand natural languages, and analyze data better. It will include new fields such as advanced robotics, intelligent sensors, and the Internet of Things.
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