More than a year on from a child safety content moderation scandal on YouTube and it takes just a few clicks for the platform's recommendation algorithms to redirect a search for "bikini haul" videos of adult women towards clips of scantily clad minors engaged in body contorting gymnastics or taking an ice bath or ice lolly sucking "challenge." A YouTube creator called Matt Watson flagged the issue in a critical Reddit post, saying he found scores of videos of kids where YouTube users are trading inappropriate comments and timestamps below the fold, denouncing the company for failing to prevent what he describes as a "soft-core pedophilia ring" from operating in plain sight on its platform. He has also posted a YouTube video demonstrating how the platform's recommendation algorithm pushes users into what he dubs a pedophilia "wormhole," accusing the company of facilitating and monetizing the sexual exploitation of children. We were easily able to replicate the YouTube algorithm's behavior that Watson describes in a history-cleared private browser session which, after clicking on two videos of adult women in bikinis, suggested we watch a video called "sweet sixteen pool party." Clicking on that led YouTube's side-bar to serve up multiple videos of prepubescent girls in its "up next" section where the algorithm tees-up related content to encourage users to keep clicking.

A scientific coalition is urging a ban on the development of weapons governed by artificial intelligence. A scientific coalition is urging a ban on the development of weapons governed by artificial intelligence (AI), warning they may malfunction unpredictably and kill innocent people. The coalition has established the Campaign to Stop Killer Robots to lobby for an international accord. Said Human Rights Watch's Mary Wareham, autonomous weapons "are beginning to creep in. Drones are the obvious example, but there are also military aircraft that take off, fly, and land on their own; robotic sentries that can identify movement."

As the use of artificial intelligence applications – and machine learning – grows within businesses, government, educational institutions, and other organizations, so does the likelihood of bias. Researchers have studied and found significant racial bias in facial recognition technology, for example, and in particular in the underlying algorithms. That alone is a massive problem. When you more broadly consider the role AI and ML will play in societal and business contexts, the problem of AI bias becomes seemingly limitless – one that IT leaders and others need to pay close attention to as they ramp up AI and ML implementations. AI bias often begins with people, which runs counter to the popular narrative that we'll all soon be controlled by AI robot overlords.

The government has awarded grants totalling over £6.4m to 18 legal artificial intelligence (AI) and data analytics projects. The projects span the whole range of legal services, from City law firm DLA Piper and private client specialists Withers to consumer forum Legal Beagles and Islington Citizens Advice Bureau. The biggest grant of £1.53m from the Next Generation Services Industrial Strategy Challenge Fund went to a project focusing on the acquisition of confidential data. The project partners include Withers, Imperial College in London, Oxford University and Genie AI. The second biggest, £1.36m, went to help develop AI software that "detects and interprets emotion and linguistics from voice" to combat insurance fraud through "credibility/vulnerability assessment".

The race for Artificial Intelligence is launched. Like any technology, AI is not a risk in itself; it is its use that will determine the level and nature of the threats it is likely to cause. AI opens fabulous perspectives in the medical field, in the transport sector, even the protection of the environment. Nevertheless, these latest developments also draw a new spectrum of threats that we will attempt to outline, focusing on three specific types: military ethics, socio-political and geopolitical. In the military field, the use of AI is considered mainly from two angles, that of collaborative combat (between man and the "machine") and that of autonomous weapons.

Folks that don't do much of it are often astounded about how quickly costs escalate and how much the process can cost. While the trial itself can cost upwards of $50K, just getting to trial with all the back and forth between the attorneys can cost several times that. A general rule of thumb is that unless the judgment is reasonably likely to be over $100K and include attorney's fees, you'll probably end up in the hole even if you win. Litigation was one of the initial target industries for IBM's advanced artificial intelligence (AI) platform Watson because litigation was so well defined and well documented. The promise was a significant reduction in costs for those bringing or defending against lawsuits and a far better way of determining if it was economically viable to bring or defend against the action to begin with.

Convolutional Neural Networks (CNNs) are the state-of-the-art algorithms used in computer vision. However, these models often suffer from the lack of interpretability of their information transformation process. To address this problem, we introduce a novel model called Sparse Deep Predictive Coding (SDPC). In a biologically realistic manner, SDPC mimics how the brain is efficiently representing visual information. This model complements the hierarchical convolutional layers found in CNNs with the feed-forward and feed-back update scheme described in the Predictive Coding (PC) theory and found in the architecture of the mammalian visual system. We experimentally demonstrate on two databases that the SDPC model extracts qualitatively meaningful features. These features, besides being similar to some of the biological Receptive Fields of the visual cortex, also represent hierarchically independent components of the image that are crucial to describe it in a generic manner. For the first time, the SDPC model demonstrates a meaningful representation of features within the hierarchical generative model and of the decision-making process leading to a specific prediction. A quantitative analysis reveals that the features extracted by the SDPC model encode the input image into a representation that is both easily classifiable and robust to noise.

Talking about Intel, one immediately thinks of computer science, since this American company manufactures world-renowned microprocessors for computers. It now aims to use artificial intelligence to protect elephants, the largest terrestrial mammals threatened with extinction due to poaching. These pachyderms are killed for their ivory tusks. It is a software that works with "intelligent" cameras. The project is supported by the National Geographic Society and the Leonardo DiCaprio Foundation.

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In a new essay for The Conversation, University of Minnesota law professor Francis Shen grapples with the difficult legal and social issues raised by sexbots -- including the specter of ones designed to look like children. "Childlike sex robots are robots, not humans," Shen wrote. "Like virtual child pornography, the development of a childlike sex robot does not require interaction with any children. Yet it might also be argued that childlike sex robots would have serious detrimental effects that compel state action." There's already been federal legislation to outlaw childlike sex robots, Shen pointed out.