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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

FROM CHAOS TO ORDER Thrilled to share my latest FXStreet article, "From Chaos to Order," highlighting AI's role in transforming finance from complexity to clarity. I discuss how AI, through machine learning, deep learning, and more, deciphers vast data to predict trends and improve decision-making, emphasizing the need for data reliability and clear goals. The piece covers AI's impact on financial strategies, including market trend analysis, risk management, and portfolio optimization, stressing the importance of precise objectives and data integrity. Collaboration among professionals is key to leveraging AI for better trading and investment outcomes. Discover how AI is revolutionizing finance, leading to smarter strategies and enhanced returns. #ArtificialIntelligence #Finance #Trading #Investment #TechnologyInFinance #FutureOfFinance https://lnkd.in/dFiSHffK

AI technology is transforming the financial services sector, with numerous opportunities for investing. Forbes provides a great primer on AI in financial services, discussing the potential for AI across various domains. Check out the article and stay ahead of the curve in this rapidly evolving industry. #AI #financialservices #investing

#TalentServe Topic:AI in Finance AI has revolutionized the financial sector by automating processes and enhancing decision-making capabilities. In recent years, financial institutions have increasingly turned to AI technologies such as machine learning, natural language processing, and predictive analytics to streamline operations and gain a competitive edge. One of the primary applications of AI in finance is automating repetitive tasks, such as data entry, document processing, and customer service inquiries. By leveraging AI-powered algorithms, organizations can significantly reduce the time and resources required to perform these tasks, thereby improving operational efficiency and cutting costs. Furthermore, AI enables financial institutions to make more informed decisions by analyzing vast amounts of data in real-time. Machine learning algorithms can identify patterns, trends, and anomalies in financial data, helping organizations detect fraud, manage risks, and optimize investment strategies. Additionally, natural language processing technology allows firms to extract valuable insights from unstructured data sources, such as news articles, social media posts, and research reports, to inform their decision-making processes. Moreover, AI-powered predictive analytics tools enable financial institutions to forecast market trends, customer behavior, and financial performance with greater accuracy. By leveraging historical data and advanced statistical models, organizations can anticipate future outcomes and adjust their strategies accordingly, minimizing risks and maximizing returns. Overall, AI has become an indispensable tool for the financial sector, empowering organizations to automate processes, enhance decision-making capabilities, and stay ahead in an increasingly competitive landscape. As AI technologies continue to evolve, we can expect further innovations that will reshape the way financial services are delivered and consumed.

How does AI work in capital markets? AI-driven algorithms employ machine learning, natural language processing, and computer vision to analyse historical and real-time market data. They identify trends, predict market fluctuations, and determine the most favourable execution venues to maximise profits. AI has significantly changed the traditional operating models of financial institutions, Entrepreneur Media’s latest article explores how financial services firms can better embrace the technology. Some of the key themes identified are: -Investing: empowering retail investors through democratisation of data. -Operating: accelerating automated reporting and data-driven investor relations. -Market Dynamics: changes to company valuation models and assumptions.   Without a doubt, we are on the threshold of a new era. #GenerativeAI #CapitalMarket #StockMarket https://lnkd.in/ggnUgvrU

Exploring AI Operating Systems: The Final Frontier Salik Shah's latest LinkedIn article, "AI Operating Systems: The Final Frontier," explores the rapidly evolving landscape of artificial intelligence (AI) operating systems and their pivotal role in shaping the future of technology. Shah emphasizes the transformative potential of AI, highlighting its widespread adoption across various industries and its ability to revolutionize how we interact with technology. He delves into the concept of AI operating systems, likening them to the foundational frameworks that underpin traditional operating systems like Windows or Linux. However, he emphasizes that AI operating systems operate on a fundamentally different level, enabling intelligent decision-making, automation, and adaptation based on vast data sets and complex algorithms. Drawing parallels to science fiction narratives, Shah portrays AI operating systems as the "final frontier" in technology, representing a convergence of human ingenuity and machine intelligence. He discusses the key components of AI operating systems, including machine learning algorithms, natural language processing, and neural networks, which empower these systems to perceive, reason, and act autonomously. Moreover, Shah explores the implications of AI operating systems across various domains, including healthcare, finance, transportation, and beyond. He underscores their potential to enhance efficiency, optimize resource allocation, and drive innovation across industries, ultimately leading to unprecedented levels of productivity and progress. However, Shah also acknowledges the ethical and societal challenges posed by AI operating systems, such as data privacy concerns, algorithmic bias, and the displacement of human labor. He stresses the importance of responsible AI development and governance to mitigate these risks and ensure that AI technologies serve the collective good. In conclusion, Shah envisions AI operating systems as the vanguard of technological advancement, heralding a new era of intelligent automation and decision-making. By embracing the opportunities and addressing the challenges they present, he argues that society can harness the full potential of AI to create a more prosperous and equitable future.

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! 

The world of wealth management is undergoing a significant transformation, and Generative Artificial Intelligence (Gen AI) is leading the way. Gen AI, when combined with natural language processing and machine learning, is creating unprecedented opportunities for firms in the industry. BlackRock and Morgan Stanley are already integrating these innovations, paving the way for a new era in financial services. PwC reports that Gen AI serves as a 'massive accelerator of productivity and revenue growth' in the industry. Its unique ability to generate new insights from data is revolutionizing how firms operate and interact with clients. As AI becomes more integral in finance, the pressure mounts for firms to adopt or risk falling behind. The emerging generation of digitally-native investors demands more digitization, personalized solutions, and efficiency - something AI is perfectly equipped to deliver. The ideal scenario is an AI co-pilot for every employee, blending human insight with AI efficiency. Are you ready for the AI revolution in wealth management? #WealthManagement #AIInFinance #TechEvolution

[Worth reading] Foundation models: What they are and why they matter Foundation models are AI models trained on a vast quantity of usually unlabeled data. Some of these models are open source, while others are proprietary. If released, the latter are usually accessible via application programming interfaces. Foundation models can be trained on a single medium, such as unstructured textual data, or on multiple mediums, for example text and images, or text and code. The models "learn" the relationship between data points. The breadth of the training data means that the models are versatile and can address many use cases, including use cases on which they haven't been specifically trained. Foundation models can be trained in three stages: Pre-training: High quantities of training data to inform large, general-purpose models. Fine-tuning: Addition of context-specific data to align outputs more closely with use cases. Inference: Incorporation of user feedback for further tuning and addition of more data as users interact with the models via plug-ins. Foundation models power generative AI and have emerged as a new category of deep learning (see Figure 3). Like deep learning algorithms, foundation models are AI neural networks, but they are more complex and larger than older models, both in terms of the number of embedded parameters and the volume of data needed to train them.

🚀 **AI Advancement in Financial Analysis: GPT-4's Breakthrough** The **University of Chicago** study reveals that **GPT-4** outperforms human analysts in predicting earnings. Both, Accuracy and F1 scores were higher than human analysts during this experiment.📈 🌐 As AI evolves, it could revolutionize financial analysis since here we are talking about a structured data set unlike Social media. 🤖💼 #AI #Machinelearning #DataAnalytics