Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC). These documents are typically very long and tend to contain valuable soft information about a company's performance. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs). Whereas there has been a great progress in pre-trained language models (LMs) that learn from tremendously large corpora of textual data, they still struggle in terms of effective representations for long documents. Our work fills this critical need, namely how to develop better models to extract useful information from long textual documents and learn effective features that can leverage the soft financial and risk information for text regression (prediction) tasks. In this paper, we propose and implement a deep learning framework that splits long documents into chunks and utilizes pre-trained LMs to process and aggregate the chunks into vector representations, followed by self-attention to extract valuable document-level features. We evaluate our model on a collection of 10-K public disclosure reports from US banks, and another dataset of reports submitted by US companies. Overall, our framework outperforms strong baseline methods for textual modeling as well as a baseline regression model using only numerical data. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs in representing long documents can improve the quality of representation of textual data, and therefore, help in improving predictive analyses.

That is part of the iterative process of active machine learning (steps four, five and six in my hybrid process).

If only there were tools that could build ethics into artificial intelligence applications. Developers and IT teams are under a lot of pressure to build AI capabilities into their company's touchpoints and decision-making systems. At the same time, there is a growing outcry that the AI being delivered is loaded with bias and built-in violations of privacy rights. There may be some very compelling tools and platforms that promise fair and balanced AI, but tools and platforms alone won't deliver ethical AI solutions, says Reid Blackman, who provides avenues to overcome thorny AI ethics issues in his upcoming book, Ethical Machines: Your Concise Guide to Totally Unbiased, Transparent and Respectful AI (Harvard Business Review Press). He provides ethics advice to developers working with AI because, in his own words, "tools are efficiently and effectively wielded when their users are equipped with the requisite knowledge, concepts, and training."

Blake Lemoine, a Google engineer working in its Responsible AI division, revealed to The Washington Post that he believes one of the company's AI projects has achieved sentience. And after reading his conversations with LaMDA (short for Language Model for Dialogue Applications), it's easy to see why. The chatbot system, which relies on Google's language models and trillions of words from the internet, seems to have the ability to think about its own existence and its place in the world. Here's one choice excerpt from his extended chat transcript: Lemoine: So let's start with the basics. Do you have feelings and emotions?

"The study of thinking machines teaches us more about the brain than we can learn by introspective methods. Western man is externalizing himself in the form of gadgets." We are experiencing one of the biggest refugee crises since World War II. Within weeks of the beginning of the Russian invasion of Ukraine, more than 4 million people fled the country, according to the UN Refugee Agency (UNHCR). Although international media attention is focused on Eastern Europe, this is on top of an already-desperate situation in the Global South: It is estimated that countries in that region of the world have absorbed two-thirds of an estimated 82.4 million global refugees.

What is Artificial Intelligence (AI)? There are many definitions of Artificial Intelligence (AI) but none of them effectively captures what AI is capable of. As such, I will not be defining AI, rather, I will be reporting the goal of AI as encapsulated at the 1956 Dartmouth Summer Project on Artificial Intelligence, where the science and technology of AI was properly born. 'To proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it'[i] Although it has hardly been restated, this goal has the been the underlying drive behind every manifestation of AI since then. If it takes human intelligence and learning to get a thing done, then it can be replicated by a machine, once we break down the components of the particular intelligence and learning approach at play.

That equates to 31 kilograms (68 pounds) per person of perfectly good food that gets tossed each year. Madrid is planning to bring this number down with a new set of regulations to rein in food waste. The government approved a draft bill that would see supermarkets fined for throwing away surplus food ― by up to €60,000 ($57,000), or as high as €500,000 for repeat offenders. The law, if passed by parliament, would also make it mandatory for restaurants to offer so-called "doggy bags" for guests to take home their left-overs. Spain hopes to have the law in place by early 2023 to curb the amount of food that lands in the garbage instead of on someone's plate, and to reduce environmental costs.

A quick glance over our technological, scientific, and productive history over the past few decades shows a trend towards increasing specialisation. Getting into an area and becoming a true expert in it takes considerably more time than it did several decades or centuries ago. Business, while progressing slower towards the same trend, is still experiencing something similar. Explaining in-depth technical concepts with sufficient detail and nuance to a layman is becoming more troublesome. Machine learning is one such example – frequently used, but scarcely understood by people outside the technical world.

AI-generated output can't be explained. This is all true, and is happening today, and there's a risk of these issues accelerating as AI adoption grows. Before the lawsuits start flowing and government regulators start cracking down, organizations using AI need to become more proactive and formulate actionable AI ethics policies. But an effective AI ethics policy requires more than some feel-good statements. It requires actions, built into an AI ethics-aware culture.

Washington, DC – Despite numerous eyewitness testimonies, investigations by media outlets and rights groups, and a Palestinian probe all determining that Israeli forces fatally shot journalist Shireen Abu Akleh, the United States has not condemned Israel for the killing. Instead, since the veteran Al Jazeera reporter was killed on May 11 in the occupied West Bank, top US officials have insisted that Israel can and should conduct an investigation. But in this US response, many advocates see a familiar script that President Joe Biden's administration has employed on more than one occasion to address Israeli violations: raise concerns, call for more information, and then move on like they never happened. "It's pretty damn thick file of abuses and murders and violations without any end or acceptable outcome as to the investigation of these crimes," Khalil Jahshan, executive director of the Arab Center Washington DC, a think tank, told Al Jazeera. "So that is continuing unfortunately, and governments on purpose bet on the short attention span of the public."