Google just launched LUMIERE, a new artificial intelligence platform that aims to revolutionize the way we interact with the web. LUMIERE stands for Learning, Understanding, and Manipulating Information with Enhanced Reasoning and Empathy. It is a powerful system that can understand natural language queries, generate relevant and personalized content, and provide feedback and guidance to users.
LUMIERE is based on the latest advances in deep learning, natural language processing, computer vision, and knowledge graph. It can handle complex tasks such as summarizing articles, answering questions, creating presentations, designing websites, and more. LUMIERE can also learn from user behavior and preferences, and adapt its responses accordingly. LUMIERE is not just a tool, but a companion that can help users achieve their goals and enhance their creativity.
LUMIERE is available as a cloud service that can be integrated with various applications and platforms. Users can access LUMIERE through a web browser, a mobile app, or a voice assistant. LUMIERE can also interact with other devices and services, such as smart speakers, smart TVs, smartwatches, and social media platforms. LUMIERE is designed to be user-friendly and intuitive and to respect user privacy and security.
LUMIERE is the result of years of research and development by Google’s AI team, in collaboration with academic institutions and industry partners. LUMIERE is part of Google’s vision to make the web more accessible, useful, and enjoyable for everyone. LUMIERE is not only a technological breakthrough but also a social innovation that can empower users and foster collaboration and communication.
LUMIERE is now open for beta testing, and users can sign up for a free trial on the official website. Google invites users to explore the possibilities of LUMIERE and to share their feedback and suggestions. Google hopes that LUMIERE will inspire users to discover new things, learn new skills, and create new experiences.
Microsoft announced today that it will lay off 1,900 employees as part of a restructuring plan to streamline its operations and focus on its core businesses. The layoffs will affect employees across various divisions, including engineering, sales, marketing, and finance. Microsoft said that the majority of the affected employees will be notified by the end of March and will receive severance packages and transition assistance.
The company said that the layoffs are necessary to adapt to the changing market conditions and customer demands, as well as to invest in new areas of growth and innovation. Microsoft CEO Satya Nadella said in a statement: “We are taking these steps to ensure that Microsoft remains a leader in the technology industry and a trusted partner for our customers. We are grateful for the contributions of our employees and we are committed to supporting them through this transition.”
Microsoft also said that it will continue to hire in strategic areas, such as cloud computing, artificial intelligence, gaming, and cybersecurity. The company said that it expects to create more than 2,000 new jobs in these fields by the end of the year. Microsoft said that it aims to become a more agile and efficient organization that can deliver value to its customers and shareholders.
Google Circle to Search is a new feature that allows users to search for information on any image or screenshot on their devices. With Google Circle to Search, users can simply draw a circle around any object or text on an image and get relevant results from Google. For example, if you see a product you like on an online store, you can circle it and get more details, reviews, or similar products. Or if you see a word you don’t know on a document, you can circle it and get its definition, pronunciation, or translation.
Google Circle to Search is powered by Google Lens, a technology that uses artificial intelligence to understand the content of images and provide useful information. Google Lens can recognize objects, landmarks, animals, plants, text, and more. It can also perform actions such as scanning QR codes, copying text or calling phone numbers.
To use Google Circle to Search, you need to have the latest version of the Google app installed on your device. You also need to enable the feature in the app settings. Once you do that, you can access Google Circle to Search from any app that supports sharing images or screenshots. Just tap on the share icon and select Google Circle to Search from the list of options. Then you can draw a circle around anything you want to search for and see the results in a pop-up window.
Google Circle to Search is a convenient and fast way to get more information from any image or screenshot on your device. It can help you learn new things, discover new products, or solve problems. You can try it out today and see what you can find with Google Circle to Search.
How do you enable Circle to Search
Circle to Search is a feature that allows you to search for anything on the web by drawing a circle around it on your screen. It is available on Android devices that support Google Lens, such as Pixel phones and some Samsung models. Here is how you can enable and use this feature:
Open the Google app on your device and tap on the Lens icon at the bottom right corner.
Grant the app permission to access your camera and photos if prompted.
Point your camera at something you want to search for, such as a product, a landmark, or a text.
Draw a circle around the object or text with your finger. You will see a blue circle with a magnifying glass icon appear on the screen.
Tap on the icon to see the search results for the circled item. You can also swipe up to see more options, such as copy text, translate text, or shop for similar products.
You can also use Circle to Search from other apps that support Google Lens, such as Google Photos, Chrome, or Camera. Just look for the Lens icon and follow the same steps as above.
Circle to Search is a convenient and fast way to find information about anything you see around you. You can use it to identify plants and animals, learn about historical places, compare prices, and much more. Try it out today and see what you can discover with Circle to Search!
The term “AI” has been used in computer science since the 1950s, but most people outside the industry didn’t start talking about it until the end of 2022. That’s because recent advances in machine learning led to big breakthroughs that are beginning to have a profound impact on nearly every aspect of our lives. We’re here to help break down some of the buzzwords so you can better understand AI terms and be part of the global conversation.
Artificial intelligence Artificial intelligence is basically a super-smart computer system that can imitate humans in some ways, like comprehending what people say, making decisions, translating between languages, analyzing if something is negative or positive, and even learning from experience. It’s artificial in that its intellect was created by humans using technology. Sometimes people say AI systems have digital brains, but they’re not physical machines or robots — they’re programs that run on computers. They work by putting a vast collection of data through algorithms, which are sets of instructions, to create models that can automate tasks that typically require human intelligence and time. Sometimes people specifically engage with an AI system — like asking Bing Chat for help with something — but more often the AI is happening in the background all around us, suggesting words as we type, recommending songs in playlists and providing more relevant information based on our preferences.
Machine learning If artificial intelligence is the goal, machine learning is how we get there. It’s a field of computer science, under the umbrella of AI, where people teach a computer system how to do something by training it to identify patterns and make predictions based on them. Data is run through algorithms over and over, with different input and feedback each time to help the system learn and improve during the training process — like practicing piano scales 10 million times in order to sight-read music going forward. It’s especially helpful with problems that would otherwise be difficult or impossible to solve using traditional programming techniques, such as recognizing images and translating languages. It takes a huge amount of data, and that’s something we’ve only been able to harness in recent years as more information has been digitized and as computer hardware has become faster, smaller, more powerful and better able to process all that information. That’s why large language models that use machine learning — such as Bing Chat and ChatGPT — have suddenly arrived on the scene.
Large language models Large language models, or LLMs, use machine learning techniques to help them process language so they can mimic the way humans communicate. They’re based on neural networks, or NNs, which are computing systems inspired by the human brain — sort of like a bunch of nodes and connections that simulate neurons and synapses. They are trained on a massive amount of text to learn patterns and relationships in language that help them use human words. Their problem-solving capabilities can be used to translate languages, answer questions in the form of a chatbot, summarize text and even write stories, poems and computer code. They don’t have thoughts or feelings, but sometimes they sound like they do, because they’ve learned patterns that help them respond the way a human might. They’re
often fine-tuned by developers using a process called reinforcement learning from human feedback (RLHF) to help them sound more conversational.
Generative AI Generative AI leverages the power of large language models to make new things, not just regurgitate or provide information about existing things. It learns patterns and structures and then generates something that’s similar but new. It can make things like pictures, music, text, videos and code. It can be used to create art, write stories, design products and even help doctors with administrative tasks. But it can also be used by bad actors to create fake news or pictures that look like photographs but aren’t real, so tech companies are working on ways to clearly identify AI-generated content.
Hallucinations Generative AI systems can create stories, poems and songs, but sometimes we want results to be based in truth. Since these systems can’t tell the difference between what’s real and fake, they can give inaccurate responses that developers refer to as hallucinations or confabulations — much like if someone saw what looked like the outlines of a face on the moon and began saying there was an actual man in the moon. Developers try to resolve these issues through “grounding,” which is when they provide an AI system with additional information from a trusted source to improve accuracy about a specific topic. Sometimes a system’s predictions are wrong, too, if a model doesn’t have current l doesn’t have current information after it’s trained.
Responsible AI Responsible AI guides people as they try to design systems that are safe and fair — at every level, including the machine learning model, the software, the user interface and the rules and restrictions put in place to access an application. It’s a crucial element because these systems are often tasked with helping make important decisions about people, such as in education and healthcare, but since they’re created by humans and trained on data from an imperfect world, they can reflect any inherent biases. A big part of responsible AI involves understanding the data that was used to train the systems and finding ways to mitigate any shortcomings to help better reflect society at large, not just certain groups of people.
Multimodal models A multimodal model can work with different types, or modes, of data simultaneously. It can look at pictures, listen to sounds and read words. It’s the ultimate multitasker! It can combine all of this information to do things like answer questions about images.
Prompts A prompt is an instruction entered into a system in language, images or code that tells the AI what task to perform. Engineers — and really all of us who interact with AI systems — must carefully design prompts to get the desired outcome from the large language models. It’s like placing your order at a deli counter: You don’t just ask for a sandwich, but you specify which bread you want and the type and amounts of condiments, vegetables, cheese and meat to get a lunch that you’ll find delicious and nutritious.
Copilots A copilot is like a personal assistant that works alongside you in all sorts of digital applications, helping with things like writing, coding, summarizing and searching. It can also help you make decisions and understand lots of data. The recent development of large language models made copilots possible, allowing them to comprehend natural human language and provide answers, create content or take action as you work within different computer programs. Copilots are built with Responsible AI guardrails to make sure they’re safe and secure and are used in a good way. Just like a copilot in an airplane, it’s not in charge — you are — but it’s a tool that can help you be more productive and efficient.
Plugins Plugins are like relief pitchers in baseball — they step in to fill specific needs that might pop up as the game develops, such as putting in a left-handed pitcher when a left-handed hitter steps up to the plate for a crucial at-bat. Plugins enable AI applications to do more things without having to modify the underlying model. They are what allow copilots to interact with other software and services, for example. They can help AI systems access new information, do complicated math or talk to other programs. They make AI systems more powerful by connecting them to the rest of the digital world.