Most datasets for sentiment analysis lack context in which an opinion was expressed, often crucial for emotion understanding, and are mainly limited by a few emotion categories. Foundation large language models (LLMs) like GPT-4 suffer from over-predicting emotions and are too resource-intensive. We design an LLM-based data synthesis pipeline and leverage a large model, Mistral-7b, for the generation of training examples for more accessible, lightweight BERT-type encoder models. We focus on enlarging the semantic diversity of examples and propose grounding the generation into a corpus of narratives to produce non-repetitive story-character-centered utterances with unique contexts over 28 emotion classes. By running 700K inferences in 450 GPU hours, we contribute with the dataset of 100K contextual and also 300K context-less examples to cover both scenarios. We use it for fine-tuning pre-trained encoders, which results in several Emo Pillars models. We show that Emo Pillars models are highly adaptive to new domains when tuned to specific tasks such as GoEmotions, ISEAR, IEMOCAP, and EmoContext, reaching the SOTA performance on the first three. We also validate our dataset, conducting statistical analysis and human evaluation, and confirm the success of our measures in utterance diversification (although less for the neutral class) and context personalization, while pointing out the need for improved handling of out-of-taxonomy labels within the pipeline.

A tech expert with a track record of predicting sea changes in the industry has made several eye-popping new forecasts in a new book. Google's Ray Kurzweil famously predicted the iPhone era and the fact that a computer would beat someone at chess by 1998. In his new book, 'The Singularity is Nearer', Kurzweil predicts that humans fully merge with AI, becoming immortal cyborgs, by 2045. He also predicts that advancements in AI will make it possible to resurrect loved ones and connect our brains to cloud technology, in what he calls the'fifth epoch' of human intelligence. Google's Ray Kurzweil believes immortality is around the corner (Getty) The singularity is the idea that artificial intelligence (AI) will eventually surpass human intelligence, fundamentally changing human existence.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. Ridley Scott, director of sci-fi classics like "Alien" and "Blade Runner," is terrified about AI technology running away with society. In an interview with Rolling Stone promoting his film "Napoleon," Scott was asked if artificial intelligence worried him, and the answer was an emphatic yes. "We have to lock down AI. And I don't know how you're gonna lock it down," he told the outlet.

Stories about artificial intelligence have been with us for decades, even centuries. In some, the robots serve humanity as cheerful helpers or soulful lovers. In others, the machines eclipse their human makers and try to wipe us out. "The Creator," a sci-fi film that hits theaters Friday, turns that narrative around: The United States is intent on wiping out a society of androids in Asia, afraid the artificially intelligent beings threaten human survival. Do any of these stories reflect our real-life future?

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

A new survey conducted by Replicant found that nearly 80% of consumers indicated they would prefer to speak with a virtual agent or machine to avoid long hold times. Moreover, 57% of consumers would speak with conversational AI even if the hold time was only five minutes. Beyond consumers' willingness to speak with AI, the survey found pervasive customer service problems, with 91% of consumers reporting they have experienced poor customer service in the past six months. Consumers say auto and home insurance companies have had the best customer service since the pandemic began, while cell phone and internet providers ranked the worst. Airlines, in the news for long hold times, came in second place for worst customer service overall since the pandemic began.

Rivers of ink have been spilled over the centuries warning scientist and engineer types not to create technology that might turn against its human creators. The story of Frankenstein would arguably be the prototype, although stories of murderous golem and living statues predate Mary Shelly's 1818 horror novel. Karel Čapek brought the morality play into its modern form by inventing the term "robot" in 1920 to describe his artificially-created humanoid workers. Stronger and more resilient than humans, two of his robots free themselves from bondage and hint that they may be capable of self-replication. Like slaveholders spooked by rumors of revolt, scientists are urged to double-down on stronger methods to keep artificial minds in chains.

The decade isn't over yet, but we've seen some remarkable advancements in the field of artificial intelligence. We've marveled at the invention of the first self-driving car in 1995. We've witnessed Deep Blue beat Garry Kasparov in 1997. Lastly and more recently we've had the chance to enjoy the company of Apple's Siri, Google's Assistant, Microsoft's Cortana, and Amazon's Alexa. While much advancement in artificial intelligence came about relatively recently, the idea of a machine-based artificial intelligence actually existed even before the computer. Its theoretical basis came about in the 1950s, introduced by British mathematician Alan Turing.

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.