When Jensen Huang, the chief executive of the chipmaker Nvidia, met with Donald Trump in the White House last week, he had reason to be cheerful. Most of Nvidia's chips, which are widely used to train generative artificial-intelligence models, are manufactured in Asia. Earlier this year, it pledged to increase production in the United States, and on Wednesday Trump announced that chip companies that promise to build products in the United States would be exempt from some hefty new tariffs on semiconductors that his Administration is preparing to impose. The next day, Nvidia's stock hit a new all-time high, and its market capitalization reached 4.4 trillion, making it the world's most valuable company, ahead of Microsoft, which is also heavily involved in A.I. Welcome to the A.I. boom, or should I say the A.I. bubble? It has been more than a quarter of a century since the bursting of the great dot-com bubble, during which hundreds of unprofitable internet startups issued stock on the Nasdaq, and the share prices of many tech companies rose into the stratosphere.

Investors sold technology stocks across the globe on Monday as they worried that the emergence of a low-cost Chinese artificial intelligence model would threaten the dominance of current AI leaders like Nvidia, shaving 592.7 billion off the chipmaker's market value. Last week, Chinese startup DeepSeek launched a free AI assistant that it says uses less data at a fraction of the cost of incumbent services. By Monday, the assistant had overtaken U.S. rival ChatGPT in downloads from Apple's app store. This led the tech-heavy Nasdaq to fall 3.1% on Monday. Nvidia was the Nasdaq's biggest drag, with its shares tumbling just under 17% and marking a record one-day loss in market capitalization for a Wall Street stock, according to LSEG data.

As investor enthusiasm for artificial intelligence, and lately for a Trump Presidency, has been driving the stock market to record highs this year, Jeremy Grantham has been having flashbacks. At the end of the nineteen-nineties, the veteran value investor--one that looks for undervalued stocks--shied away from soaring Internet and technology stocks, believing that their prices had departed from financial reality, and that the market was heading for a crash. Far from thanking him for sounding the alarm, many clients of G.M.O., a Boston-based investment-management firm that Grantham had co-founded, held it responsible for making them miss out on a vertiginous rise in the Nasdaq, which went up by about a hundred and sixty per cent between 1998 and 1999. Some withdrew their money from the company. "We started off in a good position, and in two years we lost almost half of our business," Grantham recalled.

CryptoGPT: a 7B model competing with GPT-4 in a specific task -- The Impact of Automatic Annotation and Strategic Fine-Tuning via QLoRAIn this article, we present a method aimed at refining a dedicated LLM of reasonable quality with limited resources in an industrial setting via CryptoGPT. It is an LLM designed for financial news analysis for the cryptocurrency market in real-time. This project was launched in an industrial context. This model allows not only for the classification of financial information but also for providing comprehensive analysis. We refined different LLMs of the same size such as Mistral-7B and LLama-7B using semi-automatic annotation and compared them with various LLMs such as GPT-3.5 and GPT-4. Our goal is to find a balance among several needs: 1. Protecting data (by avoiding their transfer to external servers), 2. Limiting annotation cost and time, 3. Controlling the model's size (to manage deployment costs), and 4. Maintaining better analysis quality.

Lexicon-based sentiment analysis (SA) in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts. Although lexicon-based methods are simple to implement and fast to operate on textual data, they require considerable manual annotation efforts to create, maintain, and update the lexicons. These methods are also considered inferior to the deep learning-based approaches, such as transformer models, which have become dominant in various NLP tasks due to their remarkable performance. However, transformers require extensive data and computational resources for both training and testing. Additionally, they involve significant prediction times, making them unsuitable for real-time production environments or systems with limited processing capabilities. In this paper, we introduce a novel methodology named eXplainable Lexicons (XLex) that combines the advantages of both lexicon-based methods and transformer models. We propose an approach that utilizes transformers and SHapley Additive exPlanations (SHAP) for explainability to learn financial lexicons. Our study presents four main contributions. Firstly, we demonstrate that transformer-aided explainable lexicons can enhance the vocabulary coverage of the benchmark Loughran-McDonald (LM) lexicon, reducing the human involvement in annotating, maintaining, and updating the lexicons. Secondly, we show that the resulting lexicon outperforms the standard LM lexicon in SA of financial datasets. Thirdly, we illustrate that the lexicon-based approach is significantly more efficient in terms of model speed and size compared to transformers. Lastly, the XLex approach is inherently more interpretable than transformer models as lexicon models rely on predefined rules, allowing for better insights into the results of SA and making the XLex approach a viable tool for financial decision-making.

Angie Wisdom and Dr. Chirag Shah discuss how artificial intelligence could play a role in online and professional relationships. Humans may remain in vital roles as artificial intelligence begins to reshape many industries, but one expert argued that the global supply chain and shipping jobs may realize full automation within the next 20 years. "Right now, there's documented success in utilizing autonomous driving, but when we talk on when and how long [to fully automate], well, it's here now," Dr. Larry D. Parker Jr., department chair, supply chain & logistics, at American Public University System, told Fox News Digital. "Every industry that we've mentioned, the trucking, the air and all the other modes of cargo … right now, there's documented success in utilizing autonomous driving. But when we say fully [automated], I would say it will probably within the next 20 years."

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Zscaler (NASDAQ: ZS) accelerates digital transformation so that customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange is the company's cloud-native platform that protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. With more than 10 years of experience developing, operating, and scaling the cloud, Zscaler serves thousands of enterprise customers around the world, including 450 of the Forbes Global 2000 organizations. In addition to protecting customers from damaging threats, such as ransomware and data exfiltration, it helps them slash costs, reduce complexity, and improve the user experience by eliminating stacks of latency-creating gateway appliances. Zscaler was founded in 2007 with a mission to make the cloud a safe place to do business and a more enjoyable experience for enterprise users.

Flagship Labs 97, Inc. (FL97) is privately held, early-stage biotechnology pioneering the application of Autonomous Science to biology. At FL97 we recognize the potential for artificial intelligence to transform all aspects of the scientific method, from hypothesis generation to experimental execution. Our platform provides intelligent agents the autonomy to execute programmable experiments in closed-loop toward valuable biological and therapeutic products. FL97 is backed by Flagship Pioneering, which brings the courage, long-term vision, and resources needed to realize unreasonable results. FL97 is seeking an experienced, creative, and talented Machine Learning Scientist to join our team.

Artificial Intelligence (AI) has become ubiquitous in the past several years. There is not a part of our businesses, cultures, governments and consumer markets. The continuous research and innovation directed by tech giants are driving the adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing, staffing and education. Technology has always been an essential element for these industries, but artificial intelligence has brought technology to the center of organizations. For instance, from self-driving vehicles to crucial life-saving medical gear, AI is being infused virtually into every apparatus and program.