While the farm machinery industry is busy trumpeting news of all the latest digital gadgetry it is bringing to market, the EU is equally busy sowing confusion over the legislation surrounding these developments. In its latest position paper on the planned directives, CEMA – the association representing the European agricultural machinery industry – has pinpointed several problems with the proposals which it feels will be detrimental to the industry. The first point raised is the absence of any strict definition of artificial intelligence (AI) and the term'safety function', both of which are critical components of the directives. There are two directives in the pipeline which cover the implementation of AI installed in farm machinery. The first is the general regulatory framework for AI, while the second is the machinery directive itself. It should be noted that both cover all industries and not just agriculture, and that machinery is considered to be powered equipment fitted with a tool to carry out specific tasks.

Rules governing the use of artificial intelligence across the EU will likely take over a year to be agreed upon. Last year, the European Commission drafted AI laws. While the US and China are set to dominate AI development with their vast resources, economic might, and light-touch regulation, European rivals – including the UK and EU members – believe they can lead in ethical standards. In the draft of the EU regulations, companies that are found guilty of AI misuse face a fine of €30 million or six percent of their global turnover (whichever is greater). The risk of such fines has been criticised as driving investments away from Europe.

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Apple has secured another patent related to autonomous vehicles even as it remains tight-lipped about its AV plans. Patent number 11,243,532 from the U.S. Patent and Trademark Office relates to machine learning systems and algorithms for reasoning, decision-making and motion-planning for controlling the motion of autonomous or partially autonomous vehicles. First unearthed by Patently Apple, the patent, titled "evaluating varying-sized action spaces using reinforcement learning," details a system that evaluates actions using a reinforcement learning model to help direct the movements of a vehicle. "A set of actions corresponding to a particular state of the environment of a vehicle is identified. A respective encoding is generated for different actions of the set, using elements such as distinct colors to distinguish attributes such as target lane segments," the abstract reads.

In statistics, hypothesis testing is a form of inference using data to draw certain conclusions about the population. First, we make an assumption about the population which is known as the Null Hypothesis. It is denoted by H₀. Then we define the Alternate Hypothesis which is the opposite of what is stated in the Null Hypothesis, denoted by Hₐ. After defining both the Null Hypothesis and Alternate Hypothesis we perform what is known as a hypothesis test to either accept or reject the Null Hypothesis.

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That's according to a recent World Health Organization policy brief explaining that data used by A.I. in healthcare can be unrepresentative of older people. A.I. is a product of its algorithms, the brief explains, and can draw ageist conclusions if the data that feeds the algorithms is skewed toward younger individuals. This could affect, for example, telehealth tools used to predict illness or major health events in a patient. It could also provide inaccurate data for drug development. Ultimately, not including older adults in the development process for A.I. can make it harder to get them to adopt new A.I. applications in the future.

Un magazine cybersécurité, IA, crypto-monnaies, metaverse. You can’t copyright AI-created art

Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has been made. Such an ability is especially important at a time when machine learning is being used to automate decision processes which concern sensitive factors and legal outcomes. Indeed, it is even a requirement according to EU law. Furthermore, researchers concerned with imposing overly restrictive functional form (e.g., as would be the case in a linear regression) may be motivated to use machine learning algorithms in conjunction with explainability techniques, as part of exploratory research, with the goal of identifying important variables which are associated with an outcome of interest. For example, epidemiologists might be interested in identifying `risk factors' - i.e. factors which affect recovery from disease - by using random forests and assessing variable relevance using importance measures. However, and as we demonstrate, machine learning algorithms are not as flexible as they might seem, and are instead incredibly sensitive to the underling causal structure in the data. The consequences of this are that predictors which are, in fact, critical to a causal system and highly correlated with the outcome, may nonetheless be deemed by explainability techniques to be unrelated/unimportant/unpredictive of the outcome. Rather than this being a limitation of explainability techniques per se, we show that it is rather a consequence of the mathematical implications of regression, and the interaction of these implications with the associated conditional independencies of the underlying causal structure. We provide some alternative recommendations for researchers wanting to explore the data for important variables.

Artificial intelligence is revolutionary, but it's not without its controversies. Some believe it can take us down a dangerous path, potentially arming governments with dangerous Orwellian surveillance and mass control capabilities. We have to remember that any technology is only as'good' or'bad' as the people who use it. Consider the EU's hailed'blueprint for AI regulation' and China's proposed crackdown on AI development; these instances seek to regulate AI as if it were already an autonomous, conscious technology. The U.S. must think wisely before following in their footsteps and consider addressing the actions of the user behind the AI.