The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

It's surprisingly easy to stumble into a relationship with an AI chatbot Looking for help with her art project, she strikes up a conversation with her assistant. One thing leads to another, and suddenly she has a boyfriend she's introducing to her friends and family. Her new companion is an AI chatbot. The first large-scale computational analysis of the Reddit community r/MyBoyfriendIsAI, an adults-only group with more than 27,000 members, has found that this type of scenario is now surprisingly common. In fact, many of the people in the subreddit, which is dedicated to discussing AI relationships, formed those relationships unintentionally while using AI for other purposes. Researchers from MIT found that members of this community are more likely to be in a relationship with general-purpose chatbots like ChatGPT than companionship-specific chatbots such as Replika.

While it may take many by surprise, that's the fresh call to action among analysts paying close attention to how companies are – or aren't – factoring artificial intelligence (AI) and machine learning (ML) into their data management plans and playbooks. After years of reading sensational stories about the limitless potential of intelligent machines, stakeholders and C-suite, executives in particular appear to be confused about the best course of action to take. Commercial missteps and the total failure of some products have resulted. Experts say it doesn't have to be this way. "AI and ML has become crucial and necessary for nearly all businesses in every sector," says Elliott Young, CTO, Dell Technologies UK. "In the same way that businesses have had to transform digitally and become digital-first, companies are going to need AI and ML to remain competitive. Those on the path towards this are already reaping the benefits of being able to make decisions driven by predictive analytics."

AI systems are becoming increasingly popular and central in many industries. They decide who might get a loan from the bank, whether an individual should be convicted, and we may even entrust them with our lives when using systems such as autonomous vehicles in the near future. Thus, there is a growing need for mechanisms to harness and control these systems so that we may ensure that they behave as desired. One important issue that has been gaining popularity in the last few years is fairness. While usually ML models are evaluated based on metrics such as accuracy, the idea of fairness is that we must ensure that our models are unbiased with regard to attributes such as gender, race and other selected attributes.

Most artificial intelligence systems strive for 95% accuracy of results when benchmarked against the traditional methods of determining outcomes. But how can organizations safeguard against systems so the AI doesn't inadvertently inject bias that affects the accuracy of results? Bias can be injected into AI by faulty algorithms, by lack of complete data on which the algorithms operate or even by machine learning that operates on certain biased assumptions. One example is an Amazon recruiting tool that began with an AI project in 2014. The intent of the AI application was to save recruiters time going through resumes.

With the advent of APIs that offer state-of-the-art services a click away, setting up a machine learning shop has become more accessible. But with rapid democratisation, there is a risk of non-ML players who have jumped the gun, finding themselves in a flurry of privacy attacks, never been heard of before. In a first of its kind survey carried out on ML privacy by a team from Czech Technical University, the researchers address the different ways an ML application can be vulnerable. In privacy-related attacks, wrote the researchers, an adversary's goal is related to gaining knowledge, not intended to be shared, such as knowledge about the training data or information about the model, or even extracting information about properties of the data. Black-box attacks are those attacks where the adversary does not know the model parameters, architecture or training data.

AI has been rapidly embraced by organizations, which has given rise to the dialogue on how the platform needs to be further developed and deployed. It has played a major role in helping firms tackle cybercrimes and also helped the healthcare industry to provide a faster response during the pandemic. However, IT leaders say that an organization must be well aware of AI's shortcomings before implementing it in the system. IT leaders acknowledge that AI implementation is largely done without a set of ethics guidelines. A structure is imperative to ensure effective implementation along with governance policies and ethical principles.

If your organization is implementing or thinking of implementing a contact-tracing app, it's wise to consider more than just workforce safety. Failing to do so could expose your company other risks such as employment-related lawsuits and compliance issues. More fundamentally, companies should be thinking about the ethical implications of their AI use. Contact-tracing apps are raising a lot of questions. For example, should employers be able to use them? If so, must employees opt-in or can employers make them mandatory?

Jamie Heinemeier Hansson had a better credit score than her husband, tech entrepreneur David. They have equal shares in their property and file joint tax returns. Yet David was given permission to borrow 20 times the amount on his Apple Card than his wife was granted. The situation was far from unique. Even Apple's co-founder Steve Wozniak tweeted that the same thing happened to him and his wife despite having no separate bank accounts or separate assets.

Violent video games are often blamed for people behaving aggressively in real life, but a new study claims that there is no clear link between the two. They found that, while people who played video games as a child were more likely to get into fights as an adult, gaming could not be pinpointed as the cause. Other factors such as gender and environment may have just as important role to play in people becoming violent as adults, the researchers claim. 'While the data show that fighting later in life is related to playing video games as an adolescent, most of this is because, relative to females, males both play games more often and fight more often,' said Dr Michael Ward from the University of Texas Arlington, who authored the study. 'Estimates that better establish causality find no effect, or a small negative effect.'