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Artificial intelligence (AI) needs data and a lot of it. Gathering the necessary information is not always a challenge in todays environment, with many public datasets available and so much data generated every day. Securing it, however, is another matter. The vast size of AI training datasets and the impact of the AI models invite attention from cybercriminals.
Few settings would seem worse suited for submitting AI-generated text than a court of law, where everything you say, write, and do, is subjected to maximum scrutiny. And yet lawyers keep getting caught relying on crappy, hallucination-prone AI models anyway , usually to the judge's and the client's chagrin. After all the public shaming, you'd think they'd know better by now.
The landscape of AI-powered research just became even more competitive with the launch of Perplexitys Deep Research. Previously, OpenAI and Google Gemini were leading the way in this space, and now Perplexity has joined the ranks. What does this mean for users? Those who leverage these technologies effectively will find their work becoming faster, more […] The post Perplexity Deep Research is HERE to Compete Against OpenAI and Gemini appeared first on Analytics Vidhya.
Quantization is a crucial technique in deep learning for reducing computational costs and improving model efficiency. Large-scale language models demand significant processing power, which makes quantization essential for minimizing memory usage and enhancing inference speed. By converting high-precision weights to lower-bit formats such as int8, int4, or int2, quantization reduces storage requirements.
Start building the AI workforce of the future with our comprehensive guide to creating an AI-first contact center. Learn how Conversational and Generative AI can transform traditional operations into scalable, efficient, and customer-centric experiences. What is AI-First? Transition from outdated, human-first strategies to an AI-driven approach that enhances customer engagement and operational efficiency.
Suchir Balaji, a former OpenAI employee, was found dead in his San Francisco apartment on Nov. 26; on Friday, the citys medical examiner ruled his death a suicide, countering suspicions by his family that had fueled widespread speculation online.
Large Language Models (LLMs) have advanced significantly in natural language processing, yet reasoning remains a persistent challenge. While tasks such as mathematical problem-solving and code generation benefit from structured training data, broader reasoning taskslike logical deduction, scientific inference, and symbolic reasoningsuffer from sparse and fragmented data.
Large Language Models (LLMs) have advanced significantly in natural language processing, yet reasoning remains a persistent challenge. While tasks such as mathematical problem-solving and code generation benefit from structured training data, broader reasoning taskslike logical deduction, scientific inference, and symbolic reasoningsuffer from sparse and fragmented data.
Language models have become increasingly expensive to train and deploy. This has led researchers to explore techniques such as model distillation, where a smaller student model is trained to replicate the performance of a larger teacher model. The idea is to enable efficient deployment without compromising performance. Understanding the principles behind distillation and how computational resources can be optimally allocated between student and teacher models is crucial to improving efficiency.
OpenAI's 'deep research' is the latest artificial intelligence (AI) tool making waves and promising to do in minutes what would take hours for a human expert to complete.
AI chatbots create the illusion of having emotions, morals, or consciousness by generating natural conversations that seem human-like. Many users engage with AI for chat and companionship, reinforcing the false belief that it truly understands. This leads to serious risks. Users can over-rely on AI , provide sensitive data, or rely on it for advice beyond its capabilities.
Today’s buyers expect more than generic outreach–they want relevant, personalized interactions that address their specific needs. For sales teams managing hundreds or thousands of prospects, however, delivering this level of personalization without automation is nearly impossible. The key is integrating AI in a way that enhances customer engagement rather than making it feel robotic.
AI has witnessed rapid advancements in NLP in recent years, yet many existing models still struggle to balance intuitive responses with deep, structured reasoning. While proficient in conversational fluency, traditional AI chat models often fail to meet when faced with complex logical queries requiring step-by-step analysis. On the other hand, models optimized for reasoning tend to lose the ability to engage in smooth, natural interactions.
Last Updated on February 17, 2025 by Editorial Team Author(s): Lalit Kumar Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Blackbox nature of DL models Deep learning systems are a kind of black box when it comes to analysing how they give a particular output, and as the size of the model increase this complexity is further increased.
The guide for revolutionizing the customer experience and operational efficiency This eBook serves as your comprehensive guide to: AI Agents for your Business: Discover how AI Agents can handle high-volume, low-complexity tasks, reducing the workload on human agents while providing 24/7 multilingual support. Enhanced Customer Interaction: Learn how the combination of Conversational AI and Generative AI enables AI Agents to offer natural, contextually relevant interactions to improve customer exp
When many of us think about artificial intelligence, we have a sort of nebulous idea about digital entities trying to trick us into believing that theyre human. But in some ways, its a lot more complex than that.
Large language models (LLMs) have demonstrated exceptional problem-solving abilities, yet complex reasoning taskssuch as competition-level mathematics or intricate code generationremain challenging. These tasks demand precise navigation through vast solution spaces and meticulous step-by-step deliberation. Existing methods, while improving accuracy, often suffer from high computational costs, rigid search strategies, and difficulty generalizing across diverse problems.
Large Language Models (LLMs) have gained significant importance as productivity tools, with open-source models increasingly matching the performance of their closed-source counterparts. These models operate through Next Token Prediction, where tokens are predicted in sequence when computing attention is between each token and its predecessors. Key-value (KV) pairs are cached to prevent redundant calculations and optimize this process.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Have you ever felt like managing your social media is a full-time job? After all, the average person spends nearly 2 hours and 21 minutes per day on social media. But for businesses and creators, it's safe to assume that number increases when factoring in content creation, scheduling, engagement, and analytics. I recently came across socialchamp , a powerful yet affordable social media management tool that takes the stress out of scheduling , publishing, and analyzing your content.
This marks the first in a series by Unite.AI exploring the growing connections between international government bodies and AI surveillance. Across the globe, state-driven surveillance programs are rapidly evolving, often underpinned by partnerships with powerful technology exporters such as China, Israel, and Russia. Uganda serves as a compelling case study, revealing how AI surveillance has been deployed, expanded, and justified in the name of national security.
The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.
Here, we'll look at how AI has harmed schools and educational programs around the globe, from increasing the possibility of student cheating to tech dependency and more. Just as importantly, though, we'll dive into the reason why the tech's most flagrant drawbacks might also belie its greatest benefit: how institutions are being forced to consider how to make education increasingly human in the face of unprecedented technological change.
Speaker: Alexa Acosta, Director of Growth Marketing & B2B Marketing Leader
Marketing is evolving at breakneck speed—new tools, AI-driven automation, and changing buyer behaviors are rewriting the playbook. With so many trends competing for attention, how do you cut through the noise and focus on what truly moves the needle? In this webinar, industry expert Alexa Acosta will break down the most impactful marketing trends shaping the industry today and how to turn them into real, revenue-generating strategies.
Dealing with an evil cyber-intelligence probably sounds more like a movie than real life. It may be closer to reality than we realize some researchers even think we should begin preparing for artificial intelligence (AI) going rogue.
We are moving from the hybrid workplace, with the flexibility to work where and when you want, to the hybrid workforce, where humans and AI agents work together.
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