Artificial intelligence systems will have to identify a legal person to be held responsible for any problems under proposals for regulating AI unveiled by the government today. The proposed'pro innovation' regime will be operated by existing regulators rather than a dedicated central body along the lines of that being created by the EU, the government said. The proposals were published as the Data Protection and Digital Information Bill, which sets out an independent data protection regime, is introduced to parliament. Regulators - such as Ofcom, the Competition and Markets Authority, the Information Commissioner's Office, the Financial Conduct Authority and the Medicine and Healthcare Products Regulatory Agency - will be asked to interpret and implement the principles. They will be encouraged to consider lighter touch options which could include guidance and voluntary measures or creating sandboxes - such as a trial environment where businesses can check the safety and reliability of AI tech before introducing it to market.

Imagine you are working on your novel on your home computer. It's nearly finished; you have already written approximately one million words. All of a sudden, the online word processing software tells you that you can no longer open the draft because it contains illegal information. Within an instant, all your words are lost. This is what happened in June to a Chinese novelist writing under the alias Mitu.

The UK government will reveal its plans for regulating artificial intelligence (AI) today, and says it wants to hand more powers to existing regulators to deal with algorithms and automated systems, rather than setting up a dedicated body to look at issues around AI. Plans outlined in a new AI paper, being published this morning, would involve regulators such as the Information Commissioner's Office (ICO) and the Competition and Markets Authority being asked to monitor the impact of AI on their sectors, based on a set of guiding principles. The government says the regulators will be encouraged to take a "light touch" approach to enforcing these principles. The paper will be published this morning when the Data Protection and Digital Information Bill, previously referred to as the Data Reform Bill, which sets the UK's post-Brexit data regime, is introduced in parliament. Full details of the UK AI regulations have yet to be revealed, but the government says its plans will "allow different regulators to take a tailored approach to the use of AI in a range of settings." It claims this "better reflects the growing use of AI in a range of sectors".

Acknowledging the use of AI is still in early stages, the UK Intellectual Property Office said, "[W]e will keep the law under review and could amend, replace or remove protection in future if the evidence supports [the change]." The direction to which these changes may occur remain in obscurity: the few currently offering protection may step back, more may join the group, or we may see a new system in compromise. Companies will need to keep an eye on these changes to adopt appropriate legal strategies and protection for their global portfolio of AI technology. Acknowledging the use of AI is still in early stages, the UK Intellectual Property Office said, "[W]e will keep the law under review and could amend, replace or remove protection in future if the evidence supports [the change]." The direction to which these changes may occur remain in obscurity: the few currently offering protection may step back, more may join the group, or we may see a new system in compromise.

The good news however is that since Artificial Intelligence technology is a simulation, it can be controlled and modified to even be better than human beings. Astro Teller, the famous British computer scientist once said – "Building intelligent machines can teach us about our minds – about who we are – and those lessons will make our world a better place. To win that knowledge, though, our species will have to trade in another piece of its vanity." Take for example, the case of sexbots. They are often made to be/look better than the average man/woman, by amplifying certain aspects about them.

Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification suitable for federated learning. FLAIR has 429,078 images from 51,414 Flickr users and captures many of the intricacies typically encountered in federated learning, such as heterogeneous user data and a long-tailed label distribution. We implement multiple baselines in different learning setups for different tasks on this dataset. We believe FLAIR can serve as a challenging benchmark for advancing the state-of-the art in federated learning. Dataset access and the code for the benchmark are available at \url{https://github.com/apple/ml-flair}.

Identifying the diverse and often competing values of citizens, and resolving the consequent public value conflicts, are of significant importance for inclusive and integrated urban development. Scholars have highlighted that relational, value-laden urban space gives rise to many diverse conflicts that vary both spatially and temporally. Although notions of public value conflicts have been conceived in theory, there are very few empirical studies that identify such values and their conflicts in urban space. Building on public value theory and using a case-study mixed-methods approach, this paper proposes a new approach to empirically investigate public value conflicts in urban space. Using unstructured participatory data of 4,528 citizen contributions from a Public Participation Geographic Information Systems in Hamburg, Germany, natural language processing and spatial clustering techniques are used to identify areas of potential value conflicts. Four expert workshops assess and interpret these quantitative findings. Integrating both quantitative and qualitative results, 19 general public values and a total of 9 archetypical conflicts are identified. On the basis of these results, this paper proposes a new conceptual tool of Public Value Spheres that extends the theoretical notion of public-value conflicts and helps to further account for the value-laden nature of urban space.

The system, called iBorderCtrl, analyzed facial movements to attempt to spot signs a person was lying to a border agent. The trial was propelled by nearly $5 million in European Union research funding, and almost 20 years of research at Manchester Metropolitan University, in the UK. This content can also be viewed on the site it originates from. Polygraphs and other technologies built to detect lies from physical attributes have been widely declared unreliable by psychologists. Soon, errors were reported from iBorderCtrl, too.

It's been almost one year since the European Commission unveiled the draft for what may well be one of the most influential legal frameworks in the world: the EU AI Act. According to the Mozilla Foundation, the framework is still work in progress, and now is the time to actively engage in the effort to shape its direction. Mozilla Foundation's stated mission is to work to ensure the internet remains a public resource that is open and accessible to everyone. Since 2019, Mozilla Foundation has focused a significant portion of its internet health movement-building programs on AI. We met with Mozilla Foundation's Executive Director Mark Surman and Senior Policy Researcher Maximilian Gahntz to discuss Mozilla's focus and stance on AI, key facts about the EU AI Act and how it will work in practice, as well as Mozilla's recommendations for improving it, and ways for everyone be involved in the process.

Artificial intelligence--AI--is an information technology. And it is already deeply embedded into our social fabric, both in ways we understand and in ways we don't. It will hack our society to a degree and effect unlike anything that's come before. I mean this in two very different ways. One, AI systems will be used to hack us. And two, AI systems will themselves become hackers: finding vulnerabilities in all sorts of social, economic, and political systems, and then exploiting them at an unprecedented speed, scale, and scope. We risk a future of AI systems hacking other AI systems, with humans being little more than collateral damage. Okay, maybe it's a bit of hyperbole, but none of this requires far-future science-fiction technology. I'm not postulating any "singularity," where the AI-learning feedback loop becomes so fast that it outstrips human understanding. My scenarios don't require evil intent on the part of anyone. We don't need malicious AI systems like Skynet (Terminator) or the Agents (Matrix). Some of the hacks I will discuss don't even require major research breakthroughs. They'll improve as AI techniques get more sophisticated, but we can see hints of them in operation today. This hacking will come naturally, as AIs become more advanced at learning, understanding, and problem-solving. In this essay, I will talk about the implications of AI hackers. First, I will generalize "hacking" to include economic, social, and political systems--and also our brains. Next, I will describe how AI systems will be used to hack us. Then, I will explain how AIs will hack the economic, social, and political systems that comprise society. Finally, I will discuss the implications of a world of AI hackers, and point towards possible defenses. It's not all as bleak as it might sound. Caper movies are filled with hacks. Hacks are clever, but not the same as innovations. Systems tend to be optimized for specific outcomes. Hacking is the pursuit of another outcome, often at the expense of the original optimization Systems tend be rigid. Systems limit what we can do and invariably, some of us want to do something else. But enough of us are. Hacking is normally thought of something you can do to computers. But hacks can be perpetrated on any system of rules--including the tax code. But you can still think of it as "code" in the computer sense of the term. It's a series of algorithms that takes an input--financial information for the year--and produces an output: the amount of tax owed. It's deterministic, or at least it's supposed to be.