Financial news plays a critical role in the information diffusion process in financial markets and is a known driver of stock prices. However, the information in each news article is not necessarily self-contained, often requiring a broader understanding of the historical news coverage for accurate interpretation. Further, identifying and incorporating the most relevant contextual information presents significant challenges. In this work, we explore the value of historical context in the ability of large language models to understand the market impact of financial news. We find that historical context provides a consistent and significant improvement in performance across methods and time horizons. To this end, we propose an efficient and effective contextualization method that uses a large LM to process the main article, while a small LM encodes the historical context into concise summary embeddings that are then aligned with the large model's representation space. We explore the behavior of the model through multiple qualitative and quantitative interpretability tests and reveal insights into the value of contextualization. Finally, we demonstrate that the value of historical context in model predictions has real-world applications, translating to substantial improvements in simulated investment performance.

Will you be content if you hire and pay four people but only three show up to actually do the work? And yet, many employers do just that. According to McKinsey, an average worker spends 1.8 hours daily gathering and aggregating data, a task that is redundant and doesn't have a direct impact on business success, and, above all, can be easily automated. Is your insurance company looking for ways to relieve your employees from this routine burden while cutting costs and minimizing errors? If so, you can consult a robotic process automation company to build or customize an RPA solution specific to your needs.

As companies seek ways to use artificial intelligence they will find AI is best applied to tasks that humans can't do as well or don't want to do at all. The technological dreams of humans when it comes to artificial intelligence typically start with the self-centered "what does it mean to me?" As individuals, we want to know how and when technology will change things for us, our life, and our workday. That was the case with the automobile, the television, the computer, and more. The auto was little more than a toy before companies like Ford created new manufacturing and marketing models. A tire company called Michelin then developed guidebooks to show drivers where to go.

A.I is here to stay, and is growing at a tremendous pace. As are the amount of startups in the field. In 2020, VCs poured in $27.8B in A.I startups, and in 2021 so far, VC funding has ballooned to $29.5B, all into A.I startups. Tons of new projects out there that deserve getting coverage! So here is a list of the Top 107 A.I journalists in 2021.

IDC estimates that global budgets for Artificial Intelligence will double over the next four years, to $110 billion in 2024, per its recent Worldwide Artificial Intelligence Spending Guide. "Companies will adopt AI -- not just because they can, but because they must," IDC's AI program vice president Ritu Jyoti noted. "AI is the technology that will help businesses to be agile, innovate, and scale." The arrival of AI capabilities in the enterprise is no longer theoretical. "The last year has demonstrated a rapid acceleration that has changed the question from'Where do artificial intelligence technologies fit within our organization?'

A recent article in the Financial Times argued -- fairly -- that despite the billions of dollars poured into "AI" companies, investors have, on the whole, not seen returns consistent with the hype. There are exceptions of course, but, by and large, the promise(s) appear to have not been met, as of yet. The argument was not simply a lamentation, however, with the author suggesting that the next wave of focused AI solutions might indeed generate better results and returns. Such a sentiment is not uncommon in technology. In order to garner investment, entrepreneurs employ hyperbolic language to excite potential investors and the business press follows this lead in order to ensure that they don't miss out on the appearance of prescience. So, out of the gates, there is much promise and little delivered and when this gap is revealed, negativity enters the scene.

Companies need to align the AI strategy with the business strategy. First the company needs a business strategy. Can the AI help with that? AI is being applied to the model decision-making of governments and corporations. A recent article in Forbes described the Real Time Strategy (RTS) technology involved in Google DeepMind's gaming software that works with "imperfect information."

Now machine learning is seen as the new way forward in software development. It is seen as potentially able to take the programming out of programming. Google research engineer Peter Warden said, "In 10 years, I predict most software jobs won't involve programming," in a recent article in TopBots. Waterfall software development, also called the Systems Development Life Cycle (SDLC), entailed a programmer using a language such as Python or C to write code to deliver on the application requirements. These came from a requirements definition stage, often involving a software business analyst sitting with a business executive.

Israel is a country full of history, which is why they have more museums per capita than any other country. They also have the oldest continuously used cemetery in the world and the oldest continuously inhabited city in the world. Hearing that, you'd think that not a whole lot has changed over the years, but one thing that has constantly been evolving is their ability to innovate and be productive. Next to the U.S. and Canada, Israel has the largest number of publicly traded companies, which shows that they can also build successful businesses. Our recent article on "The Top-10 Biggest Startups in Israel by Funding" proved to be quite popular so we decided to do another article on the top 10 Israeli artificial intelligence (AI) startups.

In our recent article on Indonesia's Big "Big Data" Problem, we looked at how technologies like blockchain and artificial intelligence (AI) are being used to uncover new sets of data that corporations can use to better understand the world's fourth biggest country by population. A consistent theme throughout the time we spent talking to local tech firms was that great potential was simply waiting to be unlocked, and that probably holds true at a smaller scale for many developed market corporations. For example, think about something like "data entry." The mere fact that you require a human to take data from FORM A and then manually input that data into FORM B means that your archaic business processes need changing. This is the equivalent of those backwards companies that ask you to fax them a form in order to request a change of address for your account.