Who will be liable for harmful speech generated by large language models? As advanced AIs such as OpenAI's GPT-3 are being cheered for impressive breakthroughs in natural language processing and generation -- and all sorts of (productive) applications for the tech are envisaged from slicker copywriting to more capable customer service chatbots -- the risks of such powerful text-generating tools inadvertently automating abuse and spreading smears can't be ignored. Nor can the risk of bad actors intentionally weaponizing the tech to spread chaos, scale harm and watch the world burn. Indeed, OpenAI is concerned enough about the risks of its models going "totally off the rails", as its documentation puts it at one point (in reference to a response example in which an abusive customer input is met with a very troll-esque AI reply), to offer a free content filter that "aims to detect generated text that could be sensitive or unsafe coming from the API" -- and to recommend that users don't return any generated text that the filter deems "unsafe". But, given the novel nature of the technology, there are no clear legal requirements that content filters must be applied.

This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information on the political ideology of around 91,000 users as well as records of their comment frequency and the comments' text corpus in over 190,000 different subforums of interest. Applying a set of statistical learning approaches, we show that information about activity in non-political discussion forums alone, can very accurately predict a user's political ideology. Depending on the model, we are able to predict the economic dimension of ideology with an accuracy of up to 90.63% and the social dimension with and accuracy of up to 82.02%. In comparison, using the textual features from actual comments does not improve predictive accuracy. Our paper highlights the importance of revealed digital behaviour to complement stated preferences from digital communication when analysing human preferences and behaviour using online data.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program .

The advent of artificial intelligence and machine learning has introduced a new set of challenges to the world. As algorithms seem to have a bias problem in the used data training, big tech isn't doing enough to fix it. This has led to an increased need for general governance policies that protect the people and the planet while also ensuring that cultural differences are taken into consideration. So, why it is important to talk about AI governance, and what can be done so AI stays on track with social responsibility as technology advances and changes in time? This article will dive deep into the answers and will establish 5 key dimensions that need to be addressed by organizations to make sure AI governance is safely and fairly established and what are its limitations.AI is a major factor in the future of our society, but who decides? AI is one of the most captivating segment of technology.

Law firms throughout Europe, like their counterparts around the world, are seeing a flood of new work advising clients on doing business in the metaverse--from blockchain and cryptocurrency to artificial intelligence and non-fungible tokens. But the firms' approaches, like the projects they are called on to advise, vary widely--reflecting the newness of the market, its fast growth, and the experimental nature of much of the work, lawyers in Europe told Law.com "We are discussing it with our whole client base--finance, luxury, fashion, sports, video games--every industry," said Franck Guiader, who heads an innovation and Web 3.0 team at the elite French firm Gide Loyrette Nouel. "This is just the beginning," said Boriana Guimberteau, an intellectual property partner and head of the metaverse practice at Stephenson Harwood in Paris. "There is a lot of speculation now, but in time this will be a part of the landscape. Everyone wants to know more."

Much has changed in the AI/ML world but the concept of'garbage in; garbage out' remains stoic. Any algorithm is only as good as its training data. And, no training data is without bias, not even the ones generated through automation. In the past, many machine learning algorithms have been unfair to certain religions, races, genders, ethnicities, and economical statuses, among others. The Watson supercomputer from IBM that gave suggestions to doctors using a dataset of medical research papers was found to favor reputable studies only.

By raising awareness about the different pathways into AI and making it more accessible, we want to inspire participation from historically underrepresented groups so that together we can build a more equitable and ethical tech future. AI Ethics and Policy Researcher, Favour Borokini takes us through her career journey and shares what inspired her to join this space and how she landed her current role at Pollicy. She also talks about some of the most common barriers and challenges she tackles on a daily basis and how she deals with them as someone who comes from a non-technical background. She also shares her thoughts on diversity and the most practical tips to get started in this space especially if you're someone who comes from a non-technical background. You can listen to the podcast or read through their conversation below.

Various sector such as #engineering, #environmentalscience, #forestry, #agriculture, #publichealth, #management and #informationtechnology can take advantage of both #geospatial and artificial technologies (GeoAI).Geospatial technology associates any problem with coordinates and gives us a better picture of how things are located, related, and changing. Thus, an application can be broad and can be utilized to answer any research gap that you feel in our society and supports the UN's SDGs (https://doi.org/10.18494/SAM.2019.2706). The concept can be small and viable considering one's situation. The field is not limited and the project can be object detection such as land cover and features, supporting sustainable development goals in the land, water, forest, crop, and energy dynamics, linking people's perception, activities, government action/policy to climate change, etc. A project can be an individual or a group (3 max.).

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.

Simply put, it is the ability of machines to learn and work like humans. AI is making it possible for machines to not just understand our language, but also our thoughts and intentions. This is making it possible for them to not just carry out specific commands, but also to learn on their own and get better over time. Since the early days of computing, scientists have been working on ways to create machines that can think and learn like humans. This area of research is known as artificial intelligence, or AI. Although AI has been around for a long time, it has only recently become a hot topic due to the advancement of machine learning techniques.