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Unleashing the Potential of AI in the Financial Markets By Amir Shayan In recent years, the financial markets have witnessed a rapid transformation with the integration of artificial intelligence (AI) and machine learning (ML) technologies. These groundbreaking advancements have given rise to a new era of trading, where algorithms, data analytics, and automation play a pivotal role. #AIinfinance #algorithmictrading #dataanalysis #financialmarkets #InvestmentTechnology #MachineLearning #markettrends #PredictiveAnalytics #TradingAutomation #tradingstrategies https://lnkd.in/gHZqb5db

Gain valuable insights into how Artificial Intelligence (AI) is transforming investment decisions in the latest article from the Forbes Technology Council. The article features Atal Bansal, CEO of Chetu, Inc. and an official member of the Forbes Technology Council, who discusses how traditional methods are being replaced by machine learning algorithms that offer automated trading. Click here to read the full Forbes article: https://lnkd.in/exW8WW9v. #ForbesTechnologCouncil #ArtificialIntelligence #FinancialServices #MachineLearning #CustomSoftwareSolutions

💡 Preventing cognitive bias in the era of machine learning 💡 A few years ago, I began delving into the mechanisms of cognitive biases to understand their influence on decision-making processes. This exploration led me to the groundbreaking research of Daniel Kahneman, a Nobel laureate in Economics (2002), and his book "Thinking, Fast and Slow." Kahneman's work illustrates how minds work through two distinct systems: System 1, characterized by fast, intuitive, and unconscious thinking, and System 2, known for its slow, deliberate, and analytical approach. The ascendancy of Machine Learning (ML) over traditional rule-based systems in recent years suggests a shift toward a dominance of System 1 in AI. ML algorithms, with their ability to learn patterns and associations from data, resemble the automatic, intuitive processes of System 1. In contrast, historical rule-based systems, while more deliberate and analytical like System 2, have taken a back seat in many applications. This shift raises intriguing and important questions about the nature of decision-making in the age of AI. Are we increasingly relying on rapid, heuristic-driven judgments at the expense of slower, more thoughtful analysis? How do cognitive biases inherent in System 1 thinking influence the outcomes of ML algorithms, and what are the implications for fairness, transparency, and accountability? While Machine Learning (ML) offers unparalleled efficiency and scalability, we must remain vigilant against its pitfalls. These include hallucinations and biases stemming from incomplete data sets and poorly distributed learning data leading to erroneous decisions, emphasizing the need for careful data curation and robust validation processes in ML endeavors. Striking a balance between the rapid intuition of System 1 and the thorough analysis of System 2 is paramount. By harnessing the strengths of both systems and integrating principles of cognitive science into ML development and deployment, we can harness the power of AI while mitigating its inherent biases. Ultimately, the active involvement of human judgment in monitoring AI outcomes serves as a critical guardrail to prevent bias and ensure ethical, equitable, and effective decision-making in the digital age. I'm curious to hear your thoughts! 💭

Gain valuable insights into how Artificial Intelligence (AI) is transforming investment decisions! Atal Bansal, CEO of Chetu, Inc. and an official member of the Forbes Technology Council, discusses how traditional methods are being replaced by machine learning algorithms that offer automated trading. The latest Forbes Technology Council article highlights how AI is revolutionizing the financial services industry. Check out the full article to learn more about the benefits of machine learning and custom software solutions in investment decision-making. #ForbesTechnologyCouncil #ArtificialIntelligence #FinancialServices #MachineLearning #CustomSoftwareSolutions https://lnkd.in/exW8WW9v

Artificial intelligence (AI) is transforming the way that investment decisions are made. Rather than relying primarily on intuition and research, traditional methods are being replaced by machine learning algorithms that offer automated trading and improved data-driven decisions.

AI and Machine Learning (ML) can play a significant role in enhancing data quality controls and processes. However, it's important to first establish a robust foundation of data quality measures. Go to article https://lnkd.in/guj9SjkQ 

My reply to Herve is too long ... republishing it allows me to skip this constraint ! The intersection of AI & Cognitive Science is an endless endeavor and passion! Herbert Simon made a great career on it and, so does Demis Hassabis today! Whereas there is (was?) an historical opposition in AI between the Symbolic school (rule-based) and connectionist school (ML but mostly Deep Learning), I do not see such opposition between these schools along the axis of Kahneman System 1/ System 2. To start, ML models are probabilistic, and probability remains an analytical tool that we apply under uncertainty. Whereas these models exhibit inductive mechanism, they are (fortunately) unconscious but not intuitive as they have their own mathematical optimization processes. The fact they sometime exhibit bias, errors and (for Gen AI) Hallucinations cannot be compared to “poor reasoning” but to various problems such as poor or biased data at any stage in the process (that lead to biased outcomes), model underfitting & model overfitting. I would not call these last two “intuitive” but especially for overfitting there is some room for a philosophical discussion! This is a possibility among others ... earlier this week, I met a Medicine Professor and AI expert who told me that a relevant hypothesis is that Bias and hallucinations could be due to some lack of super-ego in AI! His argument is also valuable ... we should "ask" chatGPT to read Freud ! When it come to Rule-based models, there is no a priori reason to classify these as analytical as you a “rule of thumb” or a “heuristic” are intuition-based rules … Expert models encapsulate more sophisticated and analytical rules but these are not immune to contradiction (some form of deductive overfitting may be …) Today, and even more tomorrow, decision-making is more and more transferred to AI /ML systems. Kahneman himself has written (among other on “Noise” his following book) that he believes there AI (the ML kind of) will reach superior abilities to human brain in decision-making. He believes he might also exhibit superior judgement capabilities (the topic of “Noise”). Last but not least, the performance level provides a different winer in System 1 / System 2 vs Data/ML-Driven / Rule-Based systems as you concede … Regarding the development of the super-ego (I love the analogy) there is a whole industry dedicated to bias detection, Model explainability, observability and causality (up until recently a philosophical language that moved to science thanks to the great Judea Pearl). Difficult to predict what will be the outcome of these endeavors but I would hope something positive out of it ! Herve, I do not take the Thalys as I did in my previous life but happy to continue this discussion with you! 

I took a stab at drawing the distinction between artificial intelligence and machine learning. If you want to be rigorous about it, AI is about what a system can do - whether it can solve a general set of problems and communicate like a human - and ML is about how it was developed - trained from data rather than explicitly programmed. However, people mean different things when drawing distinctions between these ideas. You'll need to read the context to discuss them most effectively. I hope you’ll give my article a read and let me know what you think!

Top 10 Artificial Intelligence Companies for Investing in AI Stocks Source: assets.techrepublic.com Introduction Artificial Intelligence (AI) has become one of the most talked-about technologies in recent years, with advancements in machine learning, natural language processing, and computer vision. The potential of AI to revolutionize various industries, including finance, healthcare, and transportation, has caught the attention of investors looking to capitalize on this growing trend. In this blog post, we will provide an overview of AI and its impact on the stock market. We will also discuss how investing in AI stocks can be profitable for investors. Overview of Artificial Intelligence (AI) and its impact on the stock market Artificial Intelligence […] Read More.. https://lnkd.in/dV3pZJ7Q

Top 10 Artificial Intelligence Companies for Investing in AI Stocks Source: assets.techrepublic.com Introduction Artificial Intelligence (AI) has become one of the most talked-about technologies in recent years, with advancements in machine learning, natural language processing, and computer vision. The potential of AI to revolutionize various industries, including finance, healthcare, and transportation, has caught the attention of investors looking to capitalize on this growing trend. In this blog post, we will provide an overview of AI and its impact on the stock market. We will also discuss how investing in AI stocks can be profitable for investors. Overview of Artificial Intelligence (AI) and its impact on the stock market Artificial Intelligence […] Read More.. https://lnkd.in/dsj87ZeU