Artificial Intelligence Stocks List

Recent Signals

Date Stock Signal Type
2019-10-15 AAP Crossed Above 20 DMA Bullish
2019-10-15 AAP Spinning Top Other
2019-10-15 AAP Crossed Above 200 DMA Bullish
2019-10-15 AIEQ Stochastic Reached Overbought Strength
2019-10-15 AIEQ Cup with Handle Other
2019-10-15 AIQ Cup with Handle Other
2019-10-15 AIQ Narrow Range Bar Range Contraction
2019-10-15 AIQ Stochastic Reached Overbought Strength
2019-10-15 BOTZ Bollinger Band Squeeze Range Contraction
2019-10-15 BOTZ Crossed Above 20 DMA Bullish
2019-10-15 CNRG NR7 Range Contraction
2019-10-15 CNRG Volume Surge Other
2019-10-15 CNRG Narrow Range Bar Range Contraction
2019-10-15 CNRG Cup with Handle Other
2019-10-15 DOGS Bollinger Band Squeeze Range Contraction
2019-10-15 DOGS Narrow Range Bar Range Contraction
2019-10-15 DOGS Crossed Above 20 DMA Bullish
2019-10-15 DT 180 Bearish Setup Bearish Swing Setup
2019-10-15 IDEX MACD Bearish Signal Line Cross Bearish
2019-10-15 IQ MACD Bullish Signal Line Cross Bullish
2019-10-15 IQ Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2019-10-15 IRBO Cup with Handle Other
2019-10-15 IRBO MACD Bullish Signal Line Cross Bullish
2019-10-15 LAIX New 52 Week Closing Low Bearish
2019-10-15 LAIX New 52 Week Low Weakness
2019-10-15 LAIX Expansion Breakdown Bearish Swing Setup
2019-10-15 LK MACD Bullish Signal Line Cross Bullish
2019-10-15 LK Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2019-10-15 LK 20 DMA Resistance Bearish
2019-10-15 LK Narrow Range Bar Range Contraction
2019-10-15 LX Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2019-10-15 LX Crossed Above 20 DMA Bullish
2019-10-15 LX New Uptrend Bullish
2019-10-15 NIO 1,2,3 Retracement Bearish Bearish Swing Setup
2019-10-15 NIO Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2019-10-15 NIO MACD Bullish Signal Line Cross Bullish
2019-10-15 NUAN 1,2,3 Retracement Bearish Bearish Swing Setup
2019-10-15 NUAN Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2019-10-15 OPRA Bollinger Band Squeeze Range Contraction
2019-10-15 PING 180 Bullish Setup Bullish Swing Setup
2019-10-15 POAI 1,2,3 Retracement Bearish Bearish Swing Setup
2019-10-15 POAI Calm After Storm Range Contraction
2019-10-15 POAI NR7 Range Contraction
2019-10-15 POAI NR7-2 Range Contraction
2019-10-15 POAI Narrow Range Bar Range Contraction
2019-10-15 POAI Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2019-10-15 QVM Cup with Handle Other
2019-10-15 QVM Narrow Range Bar Range Contraction
2019-10-15 ROBT Stochastic Reached Overbought Strength
2019-10-15 ROBT Crossed Above 50 DMA Bullish
2019-10-15 SONG Narrow Range Bar Range Contraction
2019-10-15 UBOT Bollinger Band Squeeze Range Contraction
2019-10-15 UBOT Crossed Above 20 DMA Bullish
2019-10-15 UBOT MACD Bullish Centerline Cross Bullish
2019-10-15 VERI Non-ADX 1,2,3,4 Bearish Bearish Swing Setup
2019-10-15 VERI 1,2,3 Retracement Bearish 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 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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