Amazon and iRobot, maker of the Roomba vacuum line, just announced that they would be dropping their proposed merger. The potential acquisition was announced back in August of 2022 and was immediately the target of antitrust watchdogs, particularly in the EU. The European Commission (the EU's executive branch) officially announced it was looking into the 1.4 billion dollar deal last July and it raised formal concerns over the potential impact on competition in November. The company says it is laying off about 350 employees, which represents 31 percent of iRobot's workforce. Colin Angle, founder, chairman of the iRobot board of directors and CEO is also stepping down as chairman and CEO, effective today. While the companies didn't mention the pressure from the EU specifically, Bloomberg notes that a veto looked likely.

With the rapid development of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) has become a predominant method in the field of professional knowledge-based question answering. Presently, major foundation model companies have opened up Embedding and Chat API interfaces, and frameworks like LangChain have already integrated the RAG process. It appears that the key models and steps in RAG have been resolved, leading to the question: are professional knowledge QA systems now approaching perfection? This article discovers that current primary methods depend on the premise of accessing high-quality text corpora. However, since professional documents are mainly stored in PDFs, the low accuracy of PDF parsing significantly impacts the effectiveness of professional knowledge-based QA. We conducted an empirical RAG experiment across hundreds of questions from the corresponding real-world professional documents. The results show that, ChatDOC, a RAG system equipped with a panoptic and pinpoint PDF parser, retrieves more accurate and complete segments, and thus better answers. Empirical experiments show that ChatDOC is superior to baseline on nearly 47% of questions, ties for 38% of cases, and falls short on only 15% of cases. It shows that we may revolutionize RAG with enhanced PDF structure recognition.

This paper fundamentally reformulates economic and financial theory to include electronic currencies. The valuation of the electronic currencies will be based on macroeconomic theory and the fundamental equation of monetary policy, not the microeconomic theory of discounted cash flows. The view of electronic currency as a transactional equity associated with tangible assets of a sub-economy will be developed, in contrast to the view of stock as an equity associated mostly with intangible assets of a sub-economy. The view will be developed of the electronic currency management firm as an entity responsible for coordinated monetary (electronic currency supply and value stabilization) and fiscal (investment and operational) policies of a substantial (for liquidity of the electronic currency) sub-economy. The risk model used in the valuations and the decision-making will not be the ubiquitous, yet inappropriate, exponential risk model that leads to discount rates, but will be multi time scale models that capture the true risk. The decision-making will be approached from the perspective of true systems control based on a system response function given by the multi scale risk model and system controllers that utilize the Deep Reinforcement Learning, Generative Pretrained Transformers, and other methods of Artificial Intelligence (DRL/GPT/AI). Finally, the sub-economy will be viewed as a nonlinear complex physical system with both stable equilibriums that are associated with short-term exploitation, and unstable equilibriums that need to be stabilized with active nonlinear control based on the multi scale system response functions and DRL/GPT/AI.

The application of Machine learning to finance has become a familiar approach, even more so in stock market forecasting. The stock market is highly volatile, and huge amounts of data are generated every minute globally. The extraction of effective intelligence from this data is of critical importance. However, a collaboration of numerical stock data with qualitative text data can be a challenging task. In this work, we accomplish this by providing an unprecedented, publicly available dataset with technical and fundamental data and sentiment that we gathered from news archives, TV news captions, radio transcripts, tweets, daily financial newspapers, etc. The text data entries used for sentiment extraction total more than 1.4 Million. The dataset consists of daily entries from January 2018 to December 2022 for eight companies representing diverse industrial sectors and the Dow Jones Industrial Average (DJIA) as a whole. Holistic Fundamental and Technical data is provided training ready for Model learning and deployment. Most importantly, the data generated could be used for incremental online learning with real-time data points retrieved daily since no stagnant data was utilized. All the data was retired from APIs or self-designed robust information retrieval technologies with extremely low latency and zero monetary cost. These adaptable technologies facilitate data extraction for any stock. Moreover, the utilization of Spearman's rank correlation over real-time data, linking stock returns with sentiment analysis has produced noteworthy results for the DJIA and the eight other stocks, achieving accuracy levels surpassing 60%. The dataset is made available at https://github.com/batking24/Huge-Stock-Dataset.

Elon Musk's artificial intelligence startup, xAI, is seeking to raise $1bn (£0.8bn) as the world's richest man tries to keep pace with rivals including OpenAI, Microsoft and Google in the race to dominate the field. The company has already raised $135m (£107m) from investors and is seeking a total of $1bn in equity financing, according to a filing with the US Securities and Exchange Commission. The race to develop generative AI – products that generate convincing text, image and audio from simple prompts – has intensified as Silicon Valley's biggest companies battle for supremacy after the release of OpenAI's ChatGPT in November last year. After the sensational impact of that chatbot, Microsoft announced a deepening of its partnership with OpenAI in January backed by a $10bn investment. Musk, the chief executive of Tesla and SpaceX and the owner of the X platform formerly known as Twitter, was one of OpenAI's co-founders in 2015 but left three years later. In July, Musk launched xAI and last month the company released its first AI model, a chatbot with a "rebellious streak" called Grok.

GM is massively slashing spending on its self-driving vehicle subsidiary Cruise after a string of debilitating setbacks, according to a conference call by company executives transcribed by TechCrunch . GM Chair and CEO Mary Barra said that operations would resume in some capacity, but that any plans for Cruise moving forward would be more "deliberate." To that end, the cuts will amount to hundreds of millions of dollars in the next year. This is expected to result in widespread layoffs at the San Francisco-based company that currently employees nearly 4,000 people. Earlier this month, Cruise CEO Kyle Vogt told staffers at an all-hands meeting that he'd have information regarding layoffs in the coming weeks, but he resigned shortly thereafter along with co-founder Dan Kan.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

Firm disclosures about future prospects are crucial for corporate valuation and compliance with global regulations, such as the EU's MAR and the US's SEC Rule 10b-5 and RegFD. To comply with disclosure obligations, issuers must identify nonpublic information with potential material impact on security prices as only new, relevant and unexpected information materially affects prices in efficient markets. Financial analysts, assumed to represent public knowledge on firms' earnings prospects, face limitations in offering comprehensive coverage and unbiased estimates. This study develops a neural network to forecast future firm earnings, using four decades of financial data, addressing analysts' coverage gaps and potentially revealing hidden insights. The model avoids selectivity and survivorship biases as it allows for missing data. Furthermore, the model is able to produce both fiscal-year-end and quarterly earnings predictions. Its performance surpasses benchmark models from the academic literature by a wide margin and outperforms analysts' forecasts for fiscal-year-end earnings predictions.

SoundThinking, the company behind the gunshot-detection system ShotSpotter, is quietly acquiring staff, patents, and customers of the firm that created the notorious predictive policing software PredPol, WIRED has learned. In an August earnings call, SoundThinking CEO Ralph Clark announced to investors that the company was negotiating an agreement to acquire parts of Geolitica--formerly called PredPol--and transition its customers to SoundThinking's own "patrol management" solution. "We have already hired their engineering team," Clark said during the call, a transcript of which is public. He added that the acquisition of patents and staff would "facilitate our application of AI and machine learning technology to public safety." SoundThinking's absorption of Geolitica marks its latest step in becoming the Google of crime fighting--a one-stop shop for policing tools.

As part of the deal, Amazon, the world's largest provider of cloud infrastructure services through its AWS unit, will become the primary provider of computational processing power, also called compute, for Anthropic. The process of training and running state-of-the-art AI models requires vast amounts of compute, and many analysts expect future AI models to require increasing amounts of compute. In return, Amazon will acquire a minority ownership position in Anthropic, and Amazon's engineers will be able to incorporate Anthropic's AI models into their products and services such as Amazon's personal assistant, Alexa. Anthropic has also committed to offering its models via Bedrock, Amazon's online platform on which it hosts foundation models--broadly capable AI models that can be adapted for different tasks. Anthropic was founded in 2021, after a group of OpenAI employees left over differences in their approach to AI safety.