Artificial Intelligence Stocks List

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Recent Signals

Date Stock Signal Type
2020-05-28 AAP Fell Below 200 DMA Bearish
2020-05-28 AAP Upper Bollinger Band Walk Strength
2020-05-28 AIO Upper Bollinger Band Walk Strength
2020-05-28 BILL Volume Surge Other
2020-05-28 BILL 20 DMA Resistance Bearish
2020-05-28 BOTZ New 52 Week Closing High Bullish
2020-05-28 BOTZ Shooting Star Candlestick Bearish
2020-05-28 BOTZ Lizard Bearish Bearish Day Trade Setup
2020-05-28 BOTZ Pocket Pivot Bullish Swing Setup
2020-05-28 BOTZ New 52 Week High Strength
2020-05-28 BTAI Fell Below 20 DMA Bearish
2020-05-28 DT Pocket Pivot Bullish Swing Setup
2020-05-28 DT New 52 Week Closing High Bullish
2020-05-28 DUOT Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2020-05-28 DUOT 50 DMA Resistance Bearish
2020-05-28 DUOT NR7 Range Contraction
2020-05-28 DUOT Narrow Range Bar Range Contraction
2020-05-28 IDEX Narrow Range Bar Range Contraction
2020-05-28 IDEX MACD Bullish Signal Line Cross Bullish
2020-05-28 IDEX Stochastic Reached Oversold Weakness
2020-05-28 IQ NR7 Range Contraction
2020-05-28 IQ Narrow Range Bar Range Contraction
2020-05-28 KOMP Bearish Engulfing Bearish
2020-05-28 KOMP Upper Bollinger Band Walk Strength
2020-05-28 KOMP Stochastic Sell Signal Bearish
2020-05-28 LAIX Jack-in-the-Box Bearish Bearish Swing Setup
2020-05-28 LAIX NR7 Range Contraction
2020-05-28 LAIX Narrow Range Bar Range Contraction
2020-05-28 LK Narrow Range Bar Range Contraction
2020-05-28 LX MACD Bearish Signal Line Cross Bearish
2020-05-28 LX 20 DMA Resistance Bearish
2020-05-28 NIO Slingshot Bullish Bullish Swing Setup
2020-05-28 NIO Stochastic Reached Overbought Strength
2020-05-28 NIO Upper Bollinger Band Walk Strength
2020-05-28 NUAN Upper Bollinger Band Walk Strength
2020-05-28 NUAN Stochastic Sell Signal Bearish
2020-05-28 OCFT 20 DMA Resistance Bearish
2020-05-28 OCFT 1,2,3 Pullback Bullish Bullish Swing Setup
2020-05-28 OCFT NR7 Range Contraction
2020-05-28 OCFT Non-ADX 1,2,3,4 Bullish Bullish Swing Setup
2020-05-28 OPRA New Uptrend Bullish
2020-05-28 POAI Boomer Buy Setup Bullish Swing Setup
2020-05-28 QVM Narrow Range Bar Range Contraction
2020-05-28 ROKT Upper Bollinger Band Walk Strength
2020-05-28 ROKT Stochastic Sell Signal Bearish
2020-05-28 ROKT Pocket Pivot Bullish Swing Setup
2020-05-28 UBOT New 52 Week High Strength
2020-05-28 UBOT Pocket Pivot Bullish Swing Setup
2020-05-28 UBOT Upper Bollinger Band Walk Strength
2020-05-28 UBOT Lizard Bearish Bearish Day Trade Setup
2020-05-28 UBOT Shooting Star Candlestick Bearish
2020-05-28 UBOT New 52 Week Closing High Bullish
2020-05-28 VVNT MACD Bullish Centerline Cross Bullish
2020-05-28 VVNT Narrow Range Bar Range Contraction

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 in detail, 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, also human emotions 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 others.
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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