Chinese tech company Baidu is blocking prompts with political content for its image AI. ERNIE-ViLG is the name of the Chinese counterpart to DALL-E 2, Midjourney and Stable Diffusion. Unlike the Western AI models, ERNIE-ViLG specifically handles Chinese characters and is better with anime images. The model was trained with 145 million text-image pairs and manages ten billion parameters. By comparison, Stable Diffusion has 890 million parameters, while DALL-E 2 has a total of about 3.5 billion parameters.

A novel New York City law that penalizes employers for bias in artificial intelligence hiring tools is leaving companies scrambling to audit their AI programs before the law takes effect in January. The law, which requires employers to conduct an independent audit of the automated tools they use, marks the first time employers in the US will face heightened legal requirements if they wish to use those any automated decision-making tools. Such tools--which can range from algorithms built to find ideal candidates to software that assesses body language--have faced scrutiny in recent years for their potential to perpetuate bias against protected groups. But without guidance from the city, employers aren't clear what, exactly, is expected of them and how to prepare. "Notably, the law does not define who or what is meant by an'independent auditor,'" said Danielle J. Moss, a partner at Gibson Dunn & Crutcher LLP.

Now is the time to talk, to put in place standards and regulations to mitigate the risk of a society ... [ ] based on surveillance and other nightmarish scenarios. Artificial Intelligence has become commonplace in the lives of billions of people globally. Research shows that 56% of companies have adopted AI in at least one function, especially in emerging nations. AI is used in everything from optimizing service operations through to recruiting talent. It can capture biometric data and it already helps in medical applications, judicial systems, and finance, thus making key decisions in people's lives. But one huge challenge remains to regulate its use.

Will countries need soldiers and fighter pilots in future conflicts, or will drones do the job? Private companies and governments already own huge databases of our DNA and other biometrics, making it easy for drones equipped with facial recognition to target individuals anywhere. In 2018, Venezuelan President Nicolas Maduro said he survived a drone assassination attempt. And in 2020, Israel assassinated Iran's nuclear programme chief, Mohsen Fakhrizadeh, with an autonomous satellite-operated machine gun. Futurist and security consultant Marc Goodman tells host Steve Clemons about the scientific advances that are benefitting humanity – and those that are making the world a darker place.

With the rapid development of artificial intelligence (AI), there is no doubt that this technology is starting to have a profound impact on our lives. One of the most controversial topics surrounding AI is whether or not it will eventually lead to large-scale job losses. There are certainly some jobs that AI is already starting to take over. For example, many retail stores are now using self-checkout kiosks instead of human cashiers. And there are many other examples of tasks that can now be performed by AI-powered software, such as customer service, data entry, and even legal research.

A teenage student in Ireland has won a national science competition for developing technology that can more easily detect "deepfake" videos online. Greg Tarr, from County Cork, was declared the winner of the 2021 BT Young Scientist & Technologist of the Year award last week for his project, "Towards Deepfake Detection". The picture or audio of deepfake videos is altered by artificial intelligence (AI) to make it appear as though someone has said or done something they have not. The viral spread of deepfake videos has caused international concern, in an age of digital news consumption, and social media companies have come under renewed scrutiny on how to tackle the spread of this misinformation. An altered video, claiming to show US President-elect Joe Biden falling asleep during a television interview, was widely shared before November's election.

In June, the Canadian government proposed new legislation to regulate artificial intelligence (AI). The proposed Artificial Intelligence and Data Act (AIDA) is part of Bill C-27, which also proposes a new privacy framework, the Consumer Privacy Protection Act (For more on federal privacy reform, see our recent blog). If passed, AIDA would be the first comprehensive law in Canada regulating AI. AIDA intends to promote the responsible use of AI. It aims to ensure high-impact AI systems are developed in a way that mitigates risk of harm and bias.

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The highest level in the Endsley situation awareness model is called projection when the status of elements in the environment in the near future is predicted. In cybersecurity situation awareness, the projection for an Advanced Persistent Threat (APT) requires predicting the next step of the APT. The threats are constantly changing and becoming more complex. As supervised and unsupervised learning methods require APT datasets for projecting the next step of APTs, they are unable to identify unknown APT threats. In reinforcement learning methods, the agent interacts with the environment, and so it might project the next step of known and unknown APTs. So far, reinforcement learning has not been used to project the next step for APTs. In reinforcement learning, the agent uses the previous states and actions to approximate the best action of the current state. When the number of states and actions is abundant, the agent employs a neural network which is called deep learning to approximate the best action of each state. In this paper, we present a deep reinforcement learning system to project the next step of APTs. As there exists some relation between attack steps, we employ the Long- Short-Term Memory (LSTM) method to approximate the best action of each state. In our proposed system, based on the current situation, we project the next steps of APT threats.

It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. Traditional algorithmic fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. In this paper, we conversely focus on unsupervised learning using probabilistic graphical models with discrete latent variables. We develop a fair stochastic variational inference technique for the discrete latent variables, which is accomplished by including a fairness penalty on the variational distribution that aims to respect the principles of intersectionality, a critical lens on fairness from the legal, social science, and humanities literature, and then optimizing the variational parameters under this penalty. We first show the utility of our method in improving equity and fairness for clustering using na\"ive Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a special-purpose graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.