Half of adults in the UK are concerned about the impact of artificial intelligence on their job, according to a poll, as union leaders call for a "step change" in the country's approach to new technologies. Job losses or changes to terms and conditions were the biggest worries for the 51% of 2,600 adults surveyed for the Trades Union Congress who said they were concerned about the technology. AI is a particular concern for workers aged between 25 and 34, with nearly two-thirds (62%) of those surveyed reporting such worries. The TUC poll was released as a string of large employers – including BT, Amazon, and Microsoft – have said in recent months that advances in AI could lead them to cut jobs. Britain's job market is slowing amid a cooling economy, with the UK's official jobless rate at a four-year high of 4.7%, although most economists do not believe this is linked to an acceleration in investment in AI.

The conventional wisdom of manifold learning is based on nonlinear dimensionality reduction techniques such as IsoMAP and locally linear embedding (LLE). We challenge this paradigm by exploiting the blessing of dimensionality. Our intuition is simple: it is easier to untangle a low-dimensional manifold in a higher-dimensional space due to its vastness, as guaranteed by Whitney embedding theorem. A new insight brought by this work is to introduce class labels as the context variables in the lifted higher-dimensional space (so supervised learning becomes unsupervised learning). We rigorously show that manifold untangling leads to linearly separable classifiers in the lifted space. To correct the inevitable overfitting, we consider the dual process of manifold untangling -- tangling or aliasing -- which is important for generalization. Using context as the bonding element, we construct a pair of manifold untangling and tangling operators, known as tangling-untangling cycle (TUC). Untangling operator maps context-independent representations (CIR) in low-dimensional space to context-dependent representations (CDR) in high-dimensional space by inducing context as hidden variables. The tangling operator maps CDR back to CIR by a simple integral transformation for invariance and generalization. We also present the hierarchical extensions of TUC based on the Cartesian product and the fractal geometry. Despite the conceptual simplicity, TUC admits a biologically plausible and energy-efficient implementation based on the time-locking behavior of polychronization neural groups (PNG) and sleep-wake cycle (SWC). The TUC-based theory applies to the computational modeling of various cognitive functions by hippocampal-neocortical systems.

With a low electrical hum, a small team of boxy, wheeled robots called "ants" criss-cross the top of a giant 3D grid of grey storage crates – 60,000 of them - ceaselessly arranging and rearranging them to order. Just one man, jokingly known as the robot whisperer, walks among them with a laptop. It would be hard to conceive of a more vivid example of robots taking on human jobs. "As robot technology advances, we can use them more and more, together with humans, to do useful work, and I think this is the future," says Jeroen Dekker, co-founder of Active Ants, the Dutch firm behind this newly opened e-commerce warehouse outside Northampton. "Yes, some jobs are disappearing, but that's the nasty jobs, for which we cannot find enough people."

Almost 60% of people would like to see the UK government regulate the use of generative AI technologies such as ChatGPT in the workplace to help safeguard jobs, according to a survey. As leading figures in the tech industry call for restrictions on the rapid development of AI, research by the Prospect trade union suggests strong public support for regulation. In a survey of more than 1,000 people last month, 58% agreed that "the government should set rules around the use of generative AI to protect workers' jobs". Just 12% said the government should not interfere because "the benefits are likely to outweigh any costs". Employers have used various forms of AI for some time – including in target-setting, and hiring and firing decisions – but the salience of the technologies has increased dramatically since the release of ChatGPT, which hit 100 million users within two months of launch.

Later in this article we include an extensive discussion about best practices for this IML workflow to flesh out the taxonomy and deliver rigorously tested diagnostics to consumers. Ultimately, there could be an increasingly complete taxonomy that allows consumers (C) to find suitable IML methods for their use cases and helps researchers (R) to ground their technical work in real applications (as seen on the right side of Figure 2). For instance, the accompanying table highlights concrete examples of how three different potential diagnostics, each corresponding to different types of IML methods (local feature attribution, local counterfactual, and global counterfactual, respectively), may provide useful insights for three use cases. In particular, the computer vision use case from the table is expanded upon as a running example. An increasingly diverse set of methods has been recently proposed and broadly classified as part of IML. Multiple concerns have been expressed, however, in light of this rapid development, focused on IML's underlying foundations and the gap between research and practice.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Legal experts and the Trades Union Congress (TUC) have warned that gaps in employment law will lead to staff "hired and fired by algorithm". A report, commissioned by the TUC and carried out by leading employment rights lawyers Robin Allen QC and Dee Masters from the AI Law Consultancy, claims there are "huge gaps" in British law. "The TUC is right to call for urgent legislative changes to ensure that workers and companies can both enjoy the benefits of AI," the lawyers say. "[Employment law gaps] must be plugged quickly to stop workers from being discriminated against and mistreated." The report warns that employment law is failing to keep pace with the rapid adoption of AI in the workplace and workers will become powerless to challenge "inhuman" forms of AI performance management.

Deep learning algorithms take millions of data points as inputs, correlating specific features to produce an output. While humans are involved in the initial management of data, such as data labeling, once fed into a system the process is largely self-directed. Even for the data scientists and programs involved in the model's development, it can be difficult to interpret and subsequently explain how a process has led to a specific output. This is a complex issue earning the label'black box AI' -- and it is becoming a greater problem as artificial intelligence (AI) and machine learning plays a bigger role in our day-to-day and working lives. When the workings of software used for important operations and processes within a business cannot be easily viewed or understood, errors and bias can go unnoticed and snowball into much bigger, potentially irreparable, problems.

The actions of intelligent agents, such as chatbots, recommender systems, and virtual assistants are typically not fully transparent to the user. Consequently, using such an agent involves the user exposing themselves to the risk that the agent may act in a way opposed to the user's goals. It is often argued that people use trust as a cognitive shortcut to reduce the complexity of such interactions. Here we formalise this by using the methods of evolutionary game theory to study the viability of trust-based strategies in repeated games. These are reciprocal strategies that cooperate as long as the other player is observed to be cooperating. Unlike classic reciprocal strategies, once mutual cooperation has been observed for a threshold number of rounds they stop checking their co-player's behaviour every round, and instead only check with some probability. By doing so, they reduce the opportunity cost of verifying whether the action of their co-player was actually cooperative. We demonstrate that these trust-based strategies can outcompete strategies that are always conditional, such as Tit-for-Tat, when the opportunity cost is non-negligible. We argue that this cost is likely to be greater when the interaction is between people and intelligent agents, because of the reduced transparency of the agent. Consequently, we expect people to use trust-based strategies more frequently in interactions with intelligent agents. Our results provide new, important insights into the design of mechanisms for facilitating interactions between humans and intelligent agents, where trust is an essential factor.

The Trade Union Congress (TUC) has called on the UK government to make sure workers are not left out by technology-driven productivity gains. According to TUC general secretary Frances O'Grady, it is essential the UK makes the most of "the most of the economic opportunities that new technologies are offering", especially with the UK failing to make productivity gains in the past decade. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.