The unstoppable march of artificial intelligence carries on. In mere weeks, AI has oozed into nearly everything we interact with on the internet, from conversations, to journalism, to how we look stuff up online. It's even got Google scrambling to reclaim its spot on the search throne after Microsoft implemented its own AI tools to miraculously make Bing feel relevant again. This week, we talk with WIRED senior writer Will Knight about how generative AI is changing how we search for information and create content online, and whether we should actually be freaking out about our new robot overlords. Follow all of WIRED's ChatGPT and AI coverage.

For the past 300,000 years we've been unique in our ability to make art, cuisine, manifestos, societies: to envision and craft something new where there was nothing before. While you're reading this sentence, artificial intelligence (AI) programs are painting cosmic portraits, responding to emails, preparing tax returns, and recording metal songs. Artificial intelligence has already had a pervasive impact on our lives. AIs are used to price medicine and houses, assemble cars, determine what ads we see on social media. But generative AI, a category of system that can be prompted to create wholly novel content, is much newer.

In the weeks following the release of OpenAI's viral chatbot ChatGPT late last year, Google AI chief Jeff Dean expressed concern that deploying a conversational search engine too quickly might pose a reputational risk for Alphabet. But last week Google announced its own chatbot, Bard, which in its first demo made a factual error about the James Webb Space Telescope. Also last week, Microsoft integrated ChatGPT-based technology into Bing search results. Sarah Bird, Microsoft's head of responsible AI, acknowledged that the bot could still "hallucinate" untrue information but said the technology had been made more reliable. In the days that followed, Bing claimed that running was invented in the 1700s and tried to convince one user that the year is 2022.

By Nidhi Bhardwaj: Artificial intelligence (AI)-related cryptocurrency tokens saw an increase in prices after OpenAI's ChatGPT was initially made available on 30 November last year, revealed a recent report from CoinGecko, a crypto analysis platform. After OpenAI debuted the premium version of ChatGPT on 11 January, the move rekindled the investor's interest in AI chatbots and led to a positive shift in the prices of AI tokens once more after ChatGPT's launch. Recent data from CoinGecko also showed that the AI tokens like SingularityDAO (SDAO) and Artificial Liquid Intelligence (ALI), have also experienced consistent rises over the past month. While comparing the prices of AI tokens from 13 February 2022 to 30 November 2023, an increase of value by 16 times was recorded. This surge was directly related to growing investors' interest in the AI narrative, the report highlighted.

Artificial intelligence (AI) is on everyone’s mind: autopilot for Teslas, deepfakes for fun or fake news, DALL·E 2 for the untalented artist, big data and machine learning for data analysis. And CERN is no exception. While all those AI applications come with pros and cons, with benefits and risks, AI systems deserve their own consideration when it comes to computer security. So we took the opportunity to ask a renowned expert in AI, ChatGPT, how best to secure AI systems. Here’s the answer we got: “Artificial intelligence (AI) is a rapidly advancing technology that has the potential to greatly benefit society in a variety of ways. However, it also poses new security risks that must be addressed. Here are some best practices for ensuring the security of AI systems: Secure data inputs and outputs. AI systems rely on large amounts of data to learn and make decisions. This data must be protected from unauthorised access and tampering to ensure the integrity and accuracy of the system. Use secure algorithms and protocols. AI systems use complex algorithms and protocols to process data and make decisions. It is important to use secure algorithms that have been thoroughly vetted and tested, and to ensure that all communication between the AI system and other systems is encrypted. Monitor and audit AI systems. Regular monitoring and auditing of AI systems can help detect and prevent security breaches. This includes monitoring for unusual or suspicious behavior, as well as auditing the system’s log files for any signs of unauthorised access or tampering. Implement access controls. Access controls can help prevent unauthorised access to AI systems and the data they process. This includes setting up user accounts and assigning roles and permissions, as well as implementing authentication mechanisms such as biometrics or multi-factor authentication. Stay informed and educated. As AI technology continues to evolve, it is important to stay informed about the latest security risks and best practices. This includes attending relevant conferences and workshops, reading industry publications and staying up-to-date with software updates and patches. Be prepared for the worst. AI systems can be vulnerable to cyberattacks, and it is important to have a plan in place to respond to a security incident. This includes having a trained incident response team, and regularly testing incident response plans to ensure they are effective. By following these best practices, organisations can help ensure the security of their AI systems and protect themselves from potential cyber threats. As AI is permeating all industries, it is crucial for everyone to be aware of the security risks and best practices to mitigate them.” Thanks a lot, ChatGPT bot. So far, nothing special here for AI – it’s a matter of applying the same general security paradigms that every computer user and every service manager should apply to any well-managed computing service (like those run by CERN IT) and individual computing device (like your smartphone or laptop): Make sure that you only input valid and trusted data, URLs, etc. Use encryption to protect your privacy Have intrusion detection and/or an anti-malware program running in the background Deploy access control and opt into 2FA security Train yourself and have auto-update enabled Have an incident response plan. Six simple steps to make your computing service more secure, and to protect your personal assets stored on your laptop and smartphone. Six simple steps, common sense for security-aware humans. Six simple steps, apparently also rather logical for an AI bot. So, tell us: how human is AI; how AI are you? ______ Do you want to learn more about computer security incidents and issues at CERN? Follow our Monthly Report. For further information, questions or help, check our website or contact us at Computer.Security@cern.ch.

Ever since OpenAI has launched Chat Generative Pre-Trained Transformer (GPT), it has emerged as a trending discussion among the companies across the industries. ChatGPT, which can be useful for a variety of applications such as chatbots, virtual assistants, customer service, and content creation, may offer a competitive advantage in industries where language understanding is critical, says GlobalData. Misa Singh, Business Fundamentals Analyst at GlobalData, comments: "ChatGPT, which is trained using AI and machine learning, is built to provide information and respond to queries through a conversational interface. Businesses are discussing about using this platform to improve their capabilities and provide a user-friendly experience. The platform is anticipated to increase competitiveness in both business and technology." JD.com talks about the Snapshop function, which enables customers to locate products using provided images.

Speaking to German newspaper Welt am Sonntag, Google vice president Prabhakar Raghavan warned that users may be delivered complete nonsense by chatbots, despite answers seeming coherent. Google is set to launch its own rival to OpenAI's ChatGPT, a language model that can answer your questions and queries. Named Bard, the chatbot will roll out to the public in the coming weeks according to Google CEO Sundar Pichai. Ahead of the launch, Google demonstrated the powers of Bard in a promo video. Unfortunately, people noticed that the chatbot – a scaled-down version of their Language Model for Dialogue Applications (LaMDA) which convinced one engineer it was sentient – came up with incorrect statements about the JWST.

MORE OFTEN than I like, after scanning the endless carousels on streaming apps, I find myself re-watching "Seinfeld." I attribute this to a combo of laziness and mediocre recommendation engines, which rarely highlight anything I actually want to watch. It's a problem that seemed custom-designed for ChatGPT, the bot made by Microsoft-backed artificial intelligence research firm, OpenAI. Over 100 million people have tried ChatGPT since its launch in November, posing it tasks as disparate as writing English essays and negotiating down internet bills. By comparison, "What movie should I watch?" seemed simple.

Microsoft's new and improved Bing, powered by a custom version of OpenAI's ChatGPT, has experienced a dizzyingly quick reversal: from "next big thing" to "brand-sinking albatross" in under a week. ChatGPT is a really interesting demonstration of a new and unfamiliar technology that's also fun to use. So it's not surprising that, like every other AI-adjacent construct that comes down the line, this novelty would cause its capabilities to be overestimated by everyone from high-powered tech types to people normally uninterested in the space. It's at the right "tech readiness level" for discussion over tea or a beer: what are the merits and risks of generative AI's take on art, literature, or philosophy? How can we be sure what it is original, imitative, hallucinated?

Each year, expert-level performance is attained in increasingly-complex multiagent domains, where notable examples include Go, Poker, and StarCraft II. This rapid progression is accompanied by a commensurate need to better understand how such agents attain this performance, to enable their safe deployment, identify limitations, and reveal potential means of improving them. In this paper we take a step back from performance-focused multiagent learning, and instead turn our attention towards agent behavior analysis. We introduce a model-agnostic method for discovery of behavior clusters in multiagent domains, using variational inference to learn a hierarchy of behaviors at the joint and local agent levels. Our framework makes no assumption about agents' underlying learning algorithms, does not require access to their latent states or policies, and is trained using only offline observational data. We illustrate the effectiveness of our method for enabling the coupled understanding of behaviors at the joint and local agent level, detection of behavior changepoints throughout training, discovery of core behavioral concepts, demonstrate the approach's scalability to a high-dimensional multiagent MuJoCo control domain, and also illustrate that the approach can disentangle previously-trained policies in OpenAI's hide-and-seek domain.