June 3, 2021 - In The AI-First Company: How to Compete and Win with Artificial Intelligence, prominent venture capitalist Ash Fontana asserts that we are in the second half of a century-long cycle in the development of artificial intelligence (AI). Pointing to Google, Apple, Amazon, and other tech giants, Fontana contends that businesses in all industries will be dominated by companies that prioritize and rely upon AI in the next 50 years. That is, the world will be dominated by "AI-First Companies" – companies that focus on "collecting important data and then using that data to train predictive models that automate core functions" within their, or their customers, businesses. In Fontana's vision, AI empowers the predictive models to process the collected data to generate information, information which both provides value to the business and permits the business to generate proprietary insights. This self-reinforcing process is a "loop," which Fontana asserts is a competitive advantage, akin to a moat but more powerful because it is dynamic, capable of both widening and deepening on its own.

The 89 people who work at Buffer, a company that makes social-media management tools, are used to having an unconventional employer. Everyone's salary, including the CEO's, is public. All employees work remotely; their only office closed down six years ago. And as a perk, Buffer pays for any books employees want to buy for themselves. So perhaps it is unsurprising that last year, when the pandemic obliterated countless workers' work-life balance and mental health, Buffer responded in a way that few other companies did: It gave employees an extra day off each week, without reducing pay--an experiment that's still running a year later. "It has been such a godsend," Essence Muhammad, a customer-support agent at Buffer, told me. Miraculously--or predictably, if you ask proponents of the four-day workweek--the company seemed to be getting the same amount of work done in less time. It had scaled back on meetings and social events, and employees increased the pace of their day. Nicole Miller, who works in human resources at Buffer, also cited "the principle of work expanding to the time you give it": When we have 40 hours of work a week, we find ways to work for 40 hours.

In July 2020, OpenAI launched GPT-3, an artificial intelligence language model that quickly stoked excitement about computers writing poetry, news articles, and programming code. Just as quickly, it was shown to sometimes be foulmouthed and toxic. OpenAI said it was working on fixes, but the company recently discovered GPT-3 was being used to generate child porn. Now OpenAI researchers say they've found a way to curtail GPT-3's toxic text by feeding the program roughly 100 encyclopedia-like samples of writing by human professionals on topics like history and technology but also abuse, violence, and injustice. OpenAI's project shows how the tech industry is scrambling to constrain the dark side of a technology that's shown enormous potential but also can spread disinformation and perpetuate biases.

The use of AI within the life sciences industry is a hot topic with many excited about its potential uses, but how will its use be regulated? The EU Commission recently adopted a proposal for the first legal framework on AI which will impose obligations on businesses across multiple sectors, including life sciences (the Regulation). Given its potential impact, it's definitely one to watch as it goes through the legislative process. The Regulation currently defines AI systems as"software that is developed with one or more of [certain] approaches and techniques . . . Annex I of the Regulation (which the EC can update periodically) lists techniques and software that would be caught by the definition, including machine learning, logic and knowledge based approaches, and statistical approaches.

The European Commission's (EC) proposed Artificial Intelligence (AI) regulation – a much-awaited piece of legislation – is out. While this text must still go through consultations within the EU before its adoption, the proposal already provides a good sense of how the EU considers the development of AI within the years to come: by following a risk-based approach to regulation. Other use-cases such as FRT for authentication processes are not part of the list of high-level risks and thus should require a lighter level of regulation. While technology providers have to maintain the highest level of performance and accuracy of their systems, this necessary step isn't the most critical to prevent harm. The EC doesn't detail any threshold of accuracy to meet, but rather requires a robust and documented risk-mitigation process designed to prevent harm.

Artificial intelligence (AI) is one of the fastest-growing technological industries today, but what effects will it have on legal practices? In addition to the growing number of legal questions that arise as the explosive growth of AI creeps into our everyday lives, artificial intelligence is already enabling some software to carry out legal functions. Let's discuss the future of AI in law. Artificial intelligence, simply put, is teaching computers to "think" the way humans would, using the given data and desired output requested. There are many different types of systems that utilize AI, from advertising and marketing to shopping, to scheduling.

We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds. Our dataset provides 24198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7836 heldout instances over 9 additional games in the test set. We further provide baseline models using rules-based, question-answering, and sequence learning approaches in addition to an analysis of the data and corresponding learning tasks.

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods as they leverage human-understandable symbols (i.e. concepts) to predict class memberships. However, most of these approaches focus on the identification of the most relevant concepts but do not provide concise, formal explanations of how such concepts are leveraged by the classifier to make predictions. In this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from neural networks using the formalism of First-Order Logic. The method relies on an entropy-based criterion which automatically identifies the most relevant concepts. We consider four different case studies to demonstrate that: (i) this entropy-based criterion enables the distillation of concise logic explanations in safety-critical domains from clinical data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy.

The bulk of the algorithmic fairness literature deals with group fairness along the lines of demographic parity [9] or equal opportunity We study a novel problem of fairness in ranking aimed at minimizing [16]: this is typically expressed by means of some fairness the amount of individual unfairness introduced when enforcing constraint requiring that the top-positions (for any) in the ranking group-fairness constraints. Our proposal is rooted in the contain enough elements from some groups that are protected distributional maxmin fairness theory, which uses randomization from discrimination based on sex, race, age, etc. In fact, [6] shows to maximize the expected satisfaction of the worst-off individuals.

This is the 12th "Future of the Internet" canvassing Pew Research Center and Elon University's Imagining the Internet Center have conducted together to get expert views about important digital issues. In this case, the questions focused on the prospects for ethical artificial intelligence (AI) by the year 2030. This is a nonscientific canvassing based on a nonrandom sample; this broad array of opinions about where current trends may lead in the next decade represents only the points of view of the individuals who responded to the queries. Pew Research and Elon's Imagining the Internet Center built a database of experts to canvass from a wide range of fields, choosing to invite people from several sectors, including professionals and policy people based in government bodies, nonprofits and foundations, technology businesses, think tanks and in networks of interested academics and technology innovators. The predictions reported here came in response to a set of questions in an online canvassing conducted between June 30 and July 27, 2020. In all, 602 technology innovators and developers, business and policy leaders, researchers and activists responded to at least one of the questions covered in this report. More on the methodology underlying this canvassing and the participants can be found in the final section. Artificial intelligence systems "understand" and shape a lot of what happens in people's lives. AI applications "speak" to people and answer questions when the name of a digital voice assistant is called out. They run the chatbots that handle customer-service issues people have with companies. They help diagnose cancer and other medical conditions. They scour the use of credit cards for signs of fraud, and they determine who could be a credit risk. They help people drive from point A to point B and update traffic information to shorten travel times. They are the operating system of driverless vehicles. They sift applications to make recommendations about job candidates. They determine the material that is offered up in people's newsfeeds and video choices. They recognize people's faces, translate languages and suggest how to complete people's sentences or search queries. They can "read" people's emotions. They beat them at sophisticated games.