Panel data models are a standard empirical tool in statistics, economics, marketing, and financial research. The conventional modeling approach is to assume that all individual heterogeneity can be summarized by an individual specific intercept, often known as the fixed effects, while assuming all covariates have a common effect among all the individuals, such that information can be pooled across individuals to gain efficiency of these common parameters. However, heterogeneous responses towards observed control variables are often better supported by empirical evidence, especially as detailed individual level data becomes more available. An increasingly popular approach to model unobserved heterogeneity in the effects of covariates on individual responses is to assume the existence of a finite number of homogeneous groups.

We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by the inferred model with the empirical network, according to a set of network descriptors. We observe that the SBM is capable of providing an accurate description for the majority of networks considered, but falls short of saturating all modeling requirements. In particular, networks possessing a large diameter and slow-mixing random walks tend to be badly described by the SBM. However, contrary to what is often assumed, networks with a high abundance of triangles can be well described by the SBM in many cases. We demonstrate that simple network descriptors can be used to evaluate whether or not the SBM can provide a sufficiently accurate representation, potentially pointing to possible model extensions that can systematically improve the expressiveness of this class of models.

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

In today's digital age, artificial intelligence (AI) and machine learning (ML) are emerging everywhere: facial recognition algorithms, pandemic outbreak detection and mitigation, access to credit, and healthcare are just a few examples. But, do these technologies that mirror human intelligence and predict real-life outcomes build a consensus with human ethics? Can we create regulatory practices and new norms when it comes to AI? Beyond everything, how can we take out the best of AI and mitigate the potential ill effects? We are in hot pursuit of the answers. AI/ML technologies come with their share of challenges.

In public proceedings, the Legal Board of Appeal of the EPO confirmed that under the European Patent Convention (EPC), an inventor designated in a patent application must be a human being. This was the judgement in combined cases J 8/20 and J 9/20, where the board just dismissed the applicant's appeal. Here, both the applications were made by a Missouri physicist Stephen Thaler, whose AI-system DABUS had made the inventions. Device for the Autonomous Bootstrapping of Unified Sentience, or DABUS, is a computer system programmed to invent by itself. It is, basically, a swarm of disconnected neutral nets that can continuously generate thought processes and even memories that can, over time, generate new and inventive outputs independently.

We'd all like to think our technology has a positive impact on people's lives (or at least a neutral one). But too many examples of the negative effects of AI algorithms keep surfacing in the headlines. As global corporate investments in AI set a pace to exceed a staggering $120 billion by 2025, it's more critical than ever for companies to embrace responsible AI. Responsible AI is a framework for organizations to figure out how to address both the legal and ethical challenges surrounding AI. While many of the largest developers of AI have established centers or guidelines around responsible AI, even the smallest companies must create best practices around the responsible use of AI in their products and services.

One day after work last month, Tom Otieno* went to a shopping centre in Nairobi to pick up groceries before heading home. He got a call from someone he had been chatting to for a week on Grindr, a social networking app for gay, bi, trans and queer people. The man had already tried ringing several times during the day while Otieno was with colleagues and was keen to meet. Otieno, 29, mentioned where he was but said that he did not want to see the man. Then, as he was heading to his car, he got another call.

China's love for artificial intelligence (AI) and machine learning has now resulted in a smart prosecutor bot that can press charges on criminal behavior with up to 97-percent accuracy. The AI prosecutor was developed by a team from the Chinese Academy of Sciences' big data and knowledge management lab headed by Professor Shi Yong – who claims that the machine can determine a crime and file a charge solely based on a verbal description of what happened. The AI program currently runs on a desktop computer, and was developed to its current state after the team trained it between 2015 and 2020 using over 17,000 different criminal cases. Currently, the bot can charge suspects using 1,000 different "traits" derived from human-described case documentations, and can be used to charge some of Shanghai's most common felonies – including fraud, credit card fraud, theft, intentional harm, dangerous driving, obstructions of justice, running illegal gambling operations, and provoking trouble. While this sounds pretty revolutionary overall, it isn't the first time China has used AI in its justice system.

There's a lot of misinformation in this comment section, and it probably doesn't help that the linked article isn't particularly well-written. I'll try to correct a few misconceptions as I understand them. I am a machine learning engineer although I don't work in CV so my knowledge isn't highly specific there. Fundamentally, facial recognition (FR) is a function which takes as input an image of a face (or faces) and outputs an identity. A closely related technology is used for authentication, like on newer phones.

With their rich history of multistakeholder collaboration, the EU is poised to "set the standard" of AI regulation for all of us. It's a phenomenon that came to pass with the General Data Protection Act (GDPR), in which countries and U.S. states seeking similar protections simply copied most provisions of the EU law into their own jurisdictions. The GDPR (and the AI Act) have serious implications for U.S. companies, given that these rules apply to any technologies their citizens use, even if the company operates elsewhere. The AI Act also leaves room for further complication, given that some portions of the law will be up to member states for enforcement and clarifying guidance. This "regulatory divergence" problem is already a huge drain on compliance departments, and is about to get a whole lot worse.