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.

Financial performance management is at the core of business management and has historically relied on financial ratio analysis using Balance Sheet and Income Statement data to assess company performance as compared with competitors. Little progress has been made in predicting how a company will perform or in assessing the risks (probabilities) of financial underperformance. In this study I introduce a new set of financial and macroeconomic ratios that supplement standard ratios of Balance Sheet and Income Statement. I also provide a set of supervised learning models (ML Regressors and Neural Networks) and Bayesian models to predict company performance. I conclude that the new proposed variables improve model accuracy when used in tandem with standard industry ratios. I also conclude that Feedforward Neural Networks (FNN) are simpler to implement and perform best across 6 predictive tasks (ROA, ROE, Net Margin, Op Margin, Cash Ratio and Op Cash Generation); although Bayesian Networks (BN) can outperform FNN under very specific conditions. BNs have the additional benefit of providing a probability density function in addition to the predicted (expected) value. The study findings have significant potential helping CFOs and CEOs assess risks of financial underperformance to steer companies in more profitable directions; supporting lenders in better assessing the condition of a company and providing investors with tools to dissect financial statements of public companies more accurately.

Large language models (LLM), based on generative pre-trained transformers (GPT), such as ChatGPT show high efficiency in the analysis of complex texts. These days, we can observe the emerging of many new smaller open source LLMs, e.g. Llama, Falcon, GPT4All, GPT-J, etc. Open source LLMs can be fine-tuned for specific custom problems and deployed on custom servers, e.g. in cloud computing services such as AWS, GCP. LLMs have some new features as compared to conventional language models based on transformers. One of them is zero-shot and few-shot learning, which consists in good performance of the model when we show it only few training examples or even no examples at all, but only the instructions describing what should be done. Another important feature is the reasoning when a model can generate new patterns and conclusions which are based on an input prompt and facts known by the model and which were not included into it directly during a training process. So, the model can generate analytical texts with unexpected but useful chains of thoughts. One of the approaches of using LLMs is based on retrieval augmented generation (RAG), which uses the results from other services e.g.

The stock market darling on everyone's lips is Nvidia, which makes the processing chips that power everything from home computers to industrial machinery to cutting-edge artificial intelligence technology. On Thursday, the company stunned Wall Street with results that blew the roof off analysts' expectations, reporting $13.5bn in quarterly profits, $2bn higher than pundits had predicted. Its performance is being driven in particular by the AI boom, which has tripled the value of its shares this year and given it a market value of more than $1tn. California-based Nvidia is one of just five companies to have reached that milestone – along with Apple, Amazon, Microsoft and Google's owner, Alphabet – and the only one that isn't a household name. So why are its chips so hot, and what does the future hold?

Chip designer Nvidia has forecast third-quarter revenue above Wall Street targets and said it will buy back $25bn more of its shares as sales benefit from soaring demand for its chips that power nearly all the world's major artificial intelligence apps. Shares of the Santa Clara, California-based company rose 8 percent in trading after the bell, hitting an all-time high. Nvidia's forecast on Wednesday beat expectations by billions of dollars, demonstrating that a boom in generative AI technologies that can read and write in human-like ways – and powered almost exclusively by Nvidia's chips – shows no signs of slowing down. Nvidia's additional $25bn in share repurchases come as shares have already tripled this year, making the company the first-ever trillion-dollar chip business as investors bet Nvidia will be the key beneficiary of the AI boom. Analysts have estimated that demand for Nvidia's prized AI chips is exceeding supply by at least 50 percent, adding that the imbalance will stay in place for the next several quarters.