Companionship bots, including those created on Replika, are designed to foster humanlike connections, using artificial intelligence software to make people feel seen and needed. A host of users report developing intimate relationships with chatbots -- connections verging on human love -- and turning to the bots for emotional support, companionship and even sexual gratification. As the pandemic isolated Americans, interest in Replika surged. Amid spiking rates of loneliness that some public health officials call an epidemic, many say their bonds with the bots ushered profound changes into their lives, helping them to overcome alcoholism, depression and anxiety.

Chatbots are taking away a key line of defence against fraudulent phishing emails by removing glaring grammatical and spelling errors, according to experts. The warning comes as policing organisation Europol issues an international advisory about the potential criminal use of ChatGPT and other "large language models". Phishing emails are a well-known weapon of cybercriminals and fool recipients into clicking on a link that downloads malicious software or tricks them into handing over personal details such as passwords or pin numbers. Half of all adults in England and Wales reported receiving a phishing email last year, according to the Office for National Statistics, while UK businesses have identified phishing attempts as the most common form of cyber-threat. However, a basic flaw in some phishing attempts – poor spelling and grammar – is being rectified by artificial intelligence (AI) chatbots, which can correct the errors that trip spam filters or alert human readers.

Probabilistic machine learning methods are becoming increasingly powerful tools in data analysis, informing a range of critical decisions across disciplines and applications, from forecasting election results to predicting the impact of microloans on addressing poverty. This class of methods uses sophisticated concepts from probability theory to handle uncertainty in decision-making. But the math is only one piece of the puzzle in determining their accuracy and effectiveness. In a typical data analysis, researchers make many subjective choices, or potentially introduce human error, that must also be assessed in order to cultivate users' trust in the quality of decisions based on these methods. To address this issue, MIT computer scientist Tamara Broderick, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems (LIDS), and a team of researchers have developed a classification system--a "taxonomy of trust"--that defines where trust might break down in a data analysis and identifies strategies to strengthen trust at each step.

For years now, "artificial intelligence" has been a hot buzzword in the cybersecurity industry, promising tools that spot suspicious behavior on a network, quickly figure out what's going on, and guide incident response if there's an intrusion. The most credible and useful of services, though, have actually been machine learning algorithms trained to spot characteristics of malware and other dubious network activity. Now, as generative AI tools proliferate, Microsoft says it has finally built a service for defenders that's worthy of all the hype. Two weeks ago, the company launched Microsoft 365 Copilot, which builds on a partnership with OpenAI along with Microsoft's own work on large language models. The company is rolling out Security Copilot, a sort of security field notebook that integrates system data and network monitoring from security tools like Microsoft Sentinel and Defender and even third-party services.

But although the ServCity trial is clearly impressive, Mr Beiker thinks it will be years before use of self-driving cars in cities becomes widespread, because of the need to prepare infrastructure and develop high-definition 3D maps in every area they are to be used.

Let's start at the beginning. The term "artificial intelligence" gets tossed around a lot to describe robots, self-driving cars, facial recognition technology and almost anything else that seems vaguely futuristic. A group of academics coined the term in the late 1950s as they set out to build a machine that could do anything the human brain could do -- skills like reasoning, problem-solving, learning new tasks and communicating using natural language. Progress was relatively slow until around 2012, when a single idea shifted the entire field. It was called a neural network.

Google typically gets the blame for the lack of competition in web search. The US government is even suing to block the company from using allegedly monopolistic tactics, like making itself the default search engine in widely used software such as Android, Chrome, and Safari. But some upstart search engines trying to woo users with privacy protections or ad-free searches say their latest challenge doesn't come from Google. Search startups have long relied on licensing search results from Bing, tapping a web indexing operation larger than a small company could easily afford and adding their own features and ways of parsing queries. But Microsoft's rollout of a Bing search chatbot based on technology underlying OpenAI's ChatGPT has prompted concerns that Microsoft is unfairly squeezing out its search data customers as it launches a renewed attempt to bite off more market share from Google.

But while companies and executives see a clear chance to cash in, the likely impact of the technology on workers and the economy on the whole is far less obvious. Despite their limitations--chief among of them their propensity for making stuff up--ChatGPT and other recently released generative AI models hold the promise of automating all sorts of tasks that were previously thought to be solely in the realm of human creativity and reasoning, from writing to creating graphics to summarizing and analyzing data. That has left economists unsure how jobs and overall productivity might be affected. For all the amazing advances in AI and other digital tools over the last decade, their record in improving prosperity and spurring widespread economic growth is discouraging. Although a few investors and entrepreneurs have become very rich, most people haven't benefited.

The "jaws" of a Venus flytrap attached to a robotic arm Venus flytraps can be tricked into snapping shut on command, researchers have shown, effectively turning them into biological robots that can be controlled wirelessly. The Venus flytrap (Dionaea muscipula) is a carnivorous plant that catches its prey, such as flies, by snapping its circular leaves shut around it. The leaves' edges are studded with thin hairs that generate electrical impulses when an insect touches them – this burst of electricity causes the trap to close in as little as 0.1 seconds.

Early computer vision studies aimed at developing computerized driver intelligence appeared in the mid-1980s when scientists first demonstrated a road-following robot.36 Studies performed from the mid-1980s until 2000 established the fundamentals for automated driver intelligence in related tasks, including detection of pedestrians,39 lanes,3 and road signs.9 However, the vast majority of initial computer vision algorithms aimed at detecting objects required developers to manually program dedicated features. The increase in computational power available in recent years changed the way AI models are created: Features are automatically extracted by training various neural network architectures on raw data. Automatic feature extraction outperformed and replaced the traditional approach of manually programming an object's features.