Artificial Intelligence Stocks List

Recent Signals

Date Stock Signal Type
2021-05-06 AIAG Stochastic Reached Oversold Weakness
2021-05-06 AIAG Lower Bollinger Band Walk Weakness
2021-05-06 AIAI Lower Bollinger Band Walk Weakness
2021-05-06 AIAI Stochastic Reached Oversold Weakness
2021-05-06 AIAI 180 Bearish Setup Bearish Swing Setup
2021-05-06 DTEC Lower Bollinger Band Walk Weakness
2021-05-06 DTEC Stochastic Reached Oversold Weakness
2021-05-06 DVRG Pocket Pivot Bullish Swing Setup
2021-05-06 DVRG Crossed Above 20 DMA Bullish
2021-05-06 DVRG Crossed Above 50 DMA Bullish
2021-05-06 IBM Stochastic Reached Overbought Strength
2021-05-06 INTL Lower Bollinger Band Walk Weakness
2021-05-06 IQAI Expansion Breakdown Bearish Swing Setup
2021-05-06 PRSM Lower Bollinger Band Walk Weakness
2021-05-06 RENX NR7 Range Contraction
2021-05-06 RENX Non-ADX 1,2,3,4 Bullish Bullish Swing Setup
2021-05-06 ROAI Lower Bollinger Band Walk Weakness
2021-05-06 ROAI 180 Bearish Setup Bearish Swing Setup
2021-05-06 TRMR Stochastic Sell Signal Bearish
2021-05-06 TRMR Bollinger Band Squeeze Range Contraction
2021-05-06 TRMR Upper Bollinger Band Walk Strength
2021-05-06 TRMR Slingshot Bullish Bullish Swing Setup
2021-05-06 TRMR MACD Bearish Signal Line Cross Bearish
2021-05-06 TRMR Bearish Engulfing Bearish
2021-05-06 WTAI Lower Bollinger Band Walk Weakness
2021-05-06 WTAI 180 Bearish Setup Bearish Swing Setup

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations.
Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence. Analytical AI has only characteristics consistent with cognitive intelligence generating cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive as well as emotional intelligence, understanding, in addition to cognitive elements, human emotions and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), able to be self-conscious and self-aware in interactions with others.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.
The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.

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