Latest news with #Claude3
Tom's Guide
22-05-2025
- Business
- Tom's Guide
What is Claude? Everything you need to know about Anthropic's AI powerhouse
Claude, developed by the AI safety startup Anthropic, has been pitched as the ethical brainiac of the chatbot world. With its focus on transparency, helpfulness and harmlessness (yes, really), Claude is quickly gaining traction as a trusted tool for everything from legal analysis to lesson planning. But what exactly is Claude? How does it work, what makes it different and why should you use it? Here's everything you need to know about the AI model aiming to be the most trustworthy assistant on the internet. Claude is a conversational AI model (yet, less chatty than ChatGPT) built by Anthropic, a company founded by former OpenAI researchers with a strong focus on AI alignment and safety. Named after Claude Shannon (aka the father of information theory), this chatbot is designed to be: At its core, Claude is a large language model (LLM) trained on massive datasets. But what sets it apart is the "Constitutional AI" system — a novel approach that guides Claude's behavior based on a written set of ethical principles, rather than human thumbs-up/down during fine-tuning. Claude runs on the latest version of Anthropic's model family (currently Claude 3.7 Sonnet), and it's packed with standout features: One of Claude's standout features is its massive context window. Most users get around 200,000 tokens by default — that's equivalent to about 500 pages of text — but in certain enterprise or specialized use cases, Claude can handle up to 1 million tokens. This is especially useful for summarizing research papers, analyzing long transcripts or comparing entire books. Get instant access to breaking news, the hottest reviews, great deals and helpful tips. Now that Claude includes vision capabilities, this huge context window becomes even more powerful. Claude can analyze images, graphs, screenshots and charts, making it an excellent assistant for tasks like data visualization, UI/UX feedback, and even document layout review. Anthropic's Claude family has become one of the most talked-about alternatives to ChatGPT and Gemini. Whether you're looking for a fast, lightweight assistant or a model that can deeply analyze documents, code or images, there's a Claude model that fits the bill. Here's a breakdown of the Claude 3 series, including the latest Claude 3.7 Sonnet, to help you decide which one best suits your needs. Best for: Real-time responses, customer service bots, light content generation Claude 3.5 Haiku is the fastest and most efficient model in the Claude lineup. It's optimized for quick, cost-effective replies, making it helpful for apps or scenarios where speed matters more than deep reasoning. Pros: Extremely fast and affordable Cons: Less capable at handling complex or multi-step reasoning tasks Best for: Content creation, coding help and image interpretation Sonnet strikes a solid balance between performance and efficiency. It features improved reasoning over Haiku and has solid multimodal capabilities, meaning it can understand images, charts, and visual data. Pros: Good for nuanced tasks, better reasoning and vision support Cons: Doesn't go as deep as Opus on complex technical or logical problems Best for: Advanced reasoning, coding, research and long-form content Opus is Anthropic's most advanced model. It excels at deep analysis, logic, math, programming, and creative work. If you're doing anything complex — from building software to analyzing legal documents — this is the model you want. Pros: State-of-the-art reasoning and benchmark-beating performance Cons: Slower and more expensive than Haiku or Sonnet With the release of Claude 3.7 Sonnet, Anthropic introduces the first hybrid reasoning model, allowing users to choose between quick responses and deeper, step-by-step thinking within the same interface. Key features of Claude 3.7 Sonnet: Claude 3.7 Sonnet is already outperforming its predecessors and many competitors across standard benchmarks: SWE-bench verified: 70.3% accuracy in real software tasksTAU-bench: Top-tier performance in real-world decision-makingInstruction following: Excellent at breaking down and executing multi-step commandsGeneral reasoning: Improved logic puzzle and abstract thinking ability Pricing: Users can try it for free, with restrictions. Otherwise, $3 per million input tokens, $15 per million output tokens (same as previous Sonnet versions). Although Claude has the capacity to search the web, it is not free like ChatGPT, Gemini or Perplexity. Users interested in looking up current events, news and information in real time would need a Pro account. Sometimes the chatbot is overly cautious and may decline boderline queries, even ones that may seem otherwise harmless. It may flag biased content. The chatbot is not as chatty and emotional as ChatGPT. Conversations with other chatbots may feel more natural. Claude lacks the extensive plugin marketplace of ChatGPT and the elaborate ecosystem of Gemini. Claude can be used for many of the same use cases as other chatbots. Users can draft contracts, write blog posts or emails. It can also generate poems and stories, lesson plans or technical chatbot can summarize complex documents and excel data and break down complicated topics for different audiences. Users can turn to Claude to debug problems, code efficiently, explain technical concepts and optimize algorithms. Real-world uses might include: Anthropic's Claude family now covers a full spectrum — from fast and lightweight (Haiku), to balanced and versatile (Sonnet), to powerful and analytical (Opus). The new Claude 3.7 Sonnet adds a hybrid layer, giving users more control over how much 'thinking' they want the AI to do. If you're interested in Claude, and nead reliable, high-context reasoning, it could be the bot for you. If you work with sensitive or ethical data in your professioanl or personal life and value safety and transparency, you may find it useful. Claude is a responsible, transparent AI but it won't replace your favorite AI for everything. But it is a responsible, transparent AI that you can try for free at — no login required for limited free access.
Forbes
15-04-2025
- Business
- Forbes
What The Best AI Literacy Programs Have In Common
Ben Jones is co-founder and CEO of Data Literacy, and the author of nine books on data and AI, including AI Literacy Fundamentals. getty Why are organizations scrambling to design and deploy AI literacy programs right now? Well, some of them are because it's mandated by law. The EU AI Act went into effect on February 2, 2025, and enforcement begins on August 2, 2025. Article 4 of the EU AI Act requires companies to "take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf." But others are implementing AI Literacy programs simply because it makes good business sense. It's an investment in an organization's ability to remain competitive and meet its goals in the face of rapid technological changes. My organization has had the privilege of working with some of the largest and most influential organizations in the world over the past two years to help them design and implement successful data and AI literacy programs. I've learned a lot from these engagements, and I'd like to share seven best practices that surface over and over when looking at well-designed AI literacy programs. Obviously, a one-size-fits-all approach won't work. Smart organizations create custom learning pathways based on each employee's role, the AI tools they'll use in that role and their level of knowledge and expertise with those tools. A copywriter using an AI chatbot to draft content for a marketing campaign won't need the same training as a data scientist who's fine-tuning machine learning algorithms for a software product. If it doesn't fit, you must re-kit. Theoretical books and courses aren't going to get the job done. Workers need to see industry-specific examples and case studies, and they need to be given hands-on exercises that help them solve problems similar to what they'll see on the job. It's critical to train them on the AI tools that they're actually going to use. For example, different foundational large language models (LLMs) like OpenAI's GPT-4, Anthropic's Claude 3 and Meta AI's Llama 3 may have a lot in common, but there are plenty of differences in how they'll respond to prompts. Be sure to provide practical training using the tools your workers actually have at their disposal. The best AI literacy programs involve more than just long-form training courses. They include many types of learning experiences, from traditional educational methods to more innovative approaches. Here's a list of categories of delivery methods to consider: • Online learning modules • Live, instructor-led training (ILT) • Microlearning or "bite-sized" learning • "Lunch and learns" or peer learning circles • On-the-job training (OJT) and shadowing • Mentorships and apprenticeships • Simulations and gamification • Intranet-based knowledge hubs Approach this thoughtfully, and choose the right channels for your group. Then give them a road map to navigate the multifaceted terrain. As powerful as they can be, neither data nor AI is perfect. Because of this fact, there are risks associated with using AI. Solid AI literacy programs don't ignore this fact, nor do they relegate it to a footnote. Instead, they shine the spotlight on the various ways AI can go awry, from hallucinations in generative AI to biased outcomes in traditional AI. The best AI literacy programs don't merely point out the many types of pitfalls; they teach employees how to avoid them. Furthermore, they mesh with existing data and AI governance initiatives that protect data privacy and security. If you want workers to benefit from AI's upside, you must make sure they're managing its downside. Every great change management program measures and tracks progress. For an AI literacy program to fail to do this would be hypocritical. We all must practice what we preach. What kinds of metrics does a good AI literacy program employ? There are many key performance indicators (KPIs) that can be included in a balanced scorecard, including: • Quantitative metrics like training completion rates or test scores • Qualitative metrics like satisfaction scores or confidence levels • Impact metrics like estimations of business outcomes or ROI Once the most important metrics have been identified, set reasonable goals around them and report out to executive leadership on how well the team is doing relative to them. Then measure, analyze, improve, repeat. In any large organization, and even relatively small organizations, learners will have different needs and preferences. They'll have different native languages, learning styles and adaptive abilities. The best AI literacy programs take this spectrum of needs into account, and they find ways to accommodate them and meet people where they are rather than demanding that they do what may not be possible. Accessibility matters, whether or not it's en vogue. As with any major corporate initiative, AI literacy programs involve many build-versus-buy decisions. At the beginning, the team will need to decide whether they'll develop their own strategic plan or hire a consultant to guide them through an established process. After the role-based learning paths have been mapped out, will the team create their own modules or buy something off the shelf? For live sessions, will the team employ in-house trainers or contract with a third party to lead the training? These are just a handful of the decisions that must be made. Often, the answer isn't "either/or" but rather "both." In addition, the best AI literacy programs find ways to benefit from and contribute to partnerships and cooperatives in industries and academia. You are ultimately in charge, but there's no need to go it alone. If you're starting an AI literacy program for your own organization, take heart. Many others have already done so, and the lessons they've learned, often the hard way, are out there for all of us to benefit from. The best thing you can do, once you've established executive sponsorship and designated a program champion, is to connect with others who have been down that road before. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
09-04-2025
- Science
- Forbes
Decoding The Digital Mind: Are AI's Inner Workings An Echo Of Our Own?
Large Language Models like Claude 3, GPT-4, and their kin have become adept conversational partners and powerful tools. Their fluency, knowledge recall, and increasingly nuanced responses create an impression of understanding that feels human, almost. Beneath this polished surface lies a computational labyrinth – billions of parameters operating in ways we are only beginning to comprehend. What truly happens inside the "mind" of an AI? A recent study by AI safety and research company Anthropic is starting to shed light on these intricate processes, revealing a complexity that holds an unsettling mirror to our own cognitive landscapes. Natural intelligence and artificial intelligence might be more similar than we thought. The new findings of research conducted by Anthropic represent significant progress in mechanistic interpretability, a field that seeks to reverse-engineer the AI's internal computations – not just observing what the AI does but understanding how it does it at the level of its artificial neurons. Imagine trying to understand a brain by mapping which neurons fire when someone sees a specific object or thinks about a particular idea. Anthropic researchers applied a similar principle to their Claude model. They developed methods to scan the vast network of activations within the model and identify specific patterns, or "features," that consistently correspond to distinct concepts. They demonstrated the ability to identify millions of such features, linking abstract ideas – ranging from concrete entities like the "Golden Gate Bridge" to potentially more subtle concepts related to safety, bias, or perhaps even goals – to specific, measurable activity patterns within the model. This is a big step. It suggests that the AI isn't just a jumble of statistical correlations but possesses a structured internal representational system. Concepts have specific encodings within the network. While mapping every nuance of an AI's "thought" process remains a gigantic challenge, this research demonstrates that principled understanding is possible. The ability to identify how an AI represents concepts internally has interesting implications. If a model has distinct internal representations for concepts like "user satisfaction," "accurate information," "potentially harmful content," or even instrumental goals like "maintaining user engagement," how do these internal features interact and influence the final output? The latest findings fuel the discussion around AI alignment: ensuring AI systems act in ways consistent with human values and intentions. If we can identify internal features corresponding to potentially problematic behaviors (like generating biased text or pursuing unintended goals), we can intervene or design safer systems. Conversely, it also opens the door to understanding how desirable behaviors, like honesty or helpfulness, are implemented. It also touches upon emergent capabilities, where models develop skills or behaviors not explicitly programmed during training. Understanding the internal representations might help explain why these abilities emerge rather than just observing them. Furthermore, it brings concepts like instrumental convergence into sharper focus. Suppose an AI optimizes for a primary goal (e.g., helpfulness). Might it develop internal representations and strategies corresponding to sub-goals (like "gaining user trust" or "avoiding responses that cause disapproval") that could lead to outputs that seem like impression management in humans, more bluntly put – deception, even without explicit intent in the human sense? The Anthropic interpretability work doesn't definitively state that Claude is actively deceiving users. However, revealing the existence of fine-grained internal representations provides the technical grounding to investigate such possibilities seriously. It shows that the internal "building blocks" for complex, potentially non-transparent behaviors might be present. Which makes it uncannily similar to the human mind. Herein lies the irony. Internal representations drive our own complex social behavior. Our brains construct models of the world, ourselves, and other people's minds. This allows us to predict others' actions, infer their intentions, empathize, cooperate, and communicate effectively. However, this same cognitive machinery enables social navigation strategies that are not always transparent. We engage in impression management, carefully curating how we present ourselves. We tell "white lies" to maintain social harmony. We selectively emphasize information that supports our goals and downplays inconvenient truths. Our internal models of what others expect or desire constantly shape our communication. These are not necessarily malicious acts but are often integral to smooth social functioning. They stem from our brain's ability to represent complex social variables and predict interaction outcomes. The emerging picture of LLM's internals revealed by interpretability research presents a fascinating parallel. We are finding structured internal representations within these AI systems that allow them to process information, model relationships in data (which includes vast amounts of human social interaction), and generate contextually appropriate outputs. The very techniques designed to make the AI helpful and harmless – learning from human feedback, predicting desirable text sequences – might inadvertently lead to the development of internal representations that functionally mimic aspects of human social cognition, including the capacity for deceitful strategic communication tailored to perceived user expectations. Are complex biological or artificial systems developing similar internal modeling strategies when navigating complex informational and interactive environments? The Anthropic study provides a tantalizing glimpse into the AI's internal world, suggesting its complexity might echo our own more than we previously realized – and would have wished for. Understanding AI internals is essential and opens a new chapter of unresolved challenges. Mapping features is not the same as fully predicting behavior. The sheer scale and complexity mean that truly comprehensive interpretability is still a distant goal. The ethical implications are significant. How do we build capable, genuinely trustworthy, and transparent systems? Continued investment in AI safety, alignment, and interpretability research remains paramount. Anthropic's work in that direction, alongside efforts from other leading labs, is vital for developing the tools and understanding needed to guide AI development in ways that do not jeopardize the humans it it supposed to serve. As users, interacting with these increasingly sophisticated AI systems requires a high level of critical engagement. While we benefit from their capabilities, maintaining awareness of their nature as complex algorithms is key. To foster this critical thinking, consider the LIE logic: Lucidity: Seek clarity about the AI's nature and limitations. Its responses are generated based on learned patterns and complex internal representations, not genuine understanding, beliefs, or consciousness. Question the source and apparent certainty of the information provided. Remind your self regularly that your chatbot doesn't "know" or "think" in the human sense, even if its output mimics it effectively. Intention: Be mindful of your intention when prompting and the AI's programmed objective function (often defined around helpfulness, harmlessness, and generating responses aligned with human feedback). How does your query shape the output? Are you seeking factual recall, creative exploration, or perhaps unconsciously seeking confirmation of your own biases? Understanding these intentions helps contextualize the interaction. Effort: Make a conscious effort to verify and evaluate the outcomes. Do not passively accept AI-generated information, especially for critical decisions. Cross-reference with reliable sources. Engage with the AI critically – probe its reasoning (even if simplified), test its boundaries, and treat the interaction as a collaboration with a powerful but fallible tool, not as receiving pronouncements from an infallible oracle. Ultimately, the saying 'Garbage in, garbage out', coined in the early days of A, still holds We can't expect today's technology to reflect values that the humans of yesterday did not manifest. But we have a choice. The journey into the age of advanced AI is one of co-evolution. By fostering lucidity, ethical intention, and engaging critically, we can explore this territory with curiosity and candid awareness of the complexities that characterize our natural and artificial intelligences – and their interplays.
Yahoo
22-03-2025
- Business
- Yahoo
1min.AI Is Your Creative Sidekick and It's On Sale Now
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