AI Tools for Content Creation

MAJOR AI LEGAL NEWS. The revised EU Product Liability Directive came into force yesterday, 8 December 2024. It represents a fundamental shift in how liability for AI systems and software is addressed. The Directive could directly impact organisations using and developing AI, and they may wish to consider if they need to reassess their contracts, policies, and operational approaches to liability management. Under the new framework, AI system providers (treated as manufacturers in the legislation) are liable for defects in AI systems and software that cause harm, potentially including defects that emerge after deployment. This potentially includes harm linked to updates, upgrades, or the evolving behaviour of machine-learning systems. Organisations should also consider the liability implications for failing to have sufficient AI literacy among their staff which is a requirement under the AI Act from 2 February. AI training may now be a business imperative for some organisations. The Directive’s approach to defectiveness considers not only when a product is placed on the market but also whether the manufacturer retains control over it post-market, such as through updates or connected services. This means manufacturers may be held liable for defects that arise after deployment if they could reasonably foresee and mitigate risks but fail to act. Organisations, particularly those providing software or AI systems, should look at ongoing compliance and risk management to meet evolving safety expectations. The Directive's coverage of potential liability for post-market defects could have big implications for contracts. Organisations should consider whether their agreements with suppliers, integrators, and distributors include clear terms governing responsibility for defects. The focus is on whether the product provides the safety consumers are entitled to expect. A proactive approach to risk management, extending beyond initial product deployment to encompass ongoing updates and system monitoring may be prudent. Software providers should take note that they potentially could be held liable even if their product operates as a component of a larger system. This liability regime incentivises stronger warranties, indemnities, and cooperation agreements to allocate risk effectively across supply chains. Companies should review existing contracts to confirm they reflect the Directive's requirements and renegotiate where necessary to close gaps in accountability. The Directive also works in tandem with EU regulations like the AI Act. Businesses that fail to meet mandatory product safety requirements under the likes of the AI Act risk facing presumptions of defectiveness under the Product Liability Directive. With the AI Liability Directive in progress, organisations should also prepare for further changes that will make it easier for claimants to bring AI-related liability claims.

A nice review article "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation" covers the scope of tools and approaches for how AI can support science. Some of areas the paper covers: (link in comments) 🔎 Literature search and summarization. Traditional academic search engines rely on keyword-based retrieval, but AI-powered tools such as Elicit and SciSpace enhance search efficiency with semantic analysis, summarization, and citation graph-based recommendations. These tools help researchers sift through vast scientific literature quickly and extract key insights, reducing the time required to identify relevant studies. 💡 Hypothesis generation and idea formation. AI models are being used to analyze scientific literature, extract key themes, and generate novel research hypotheses. Some approaches integrate structured knowledge graphs to ground hypotheses in existing scientific knowledge, reducing the risk of hallucinations. AI-generated hypotheses are evaluated for novelty, relevance, significance, and verifiability, with mixed results depending on domain expertise. 🧪 Scientific experimentation. AI systems are increasingly used to design experiments, execute simulations, and analyze results. Multi-agent frameworks, tree search algorithms, and iterative refinement methods help automate complex workflows. Some AI tools assist in hyperparameter tuning, experiment planning, and even code execution, accelerating the research process. 📊 Data analysis and hypothesis validation. AI-driven tools process vast datasets, identify patterns, and validate hypotheses across disciplines. Benchmarks like SciMON (NLP), TOMATO-Chem (chemistry), and LLM4BioHypoGen (medicine) provide structured datasets for AI-assisted discovery. However, issues like data biases, incomplete records, and privacy concerns remain key challenges. ✍️ Scientific content generation. LLMs help draft papers, generate abstracts, suggest citations, and create scientific figures. Tools like AutomaTikZ convert equations into LaTeX, while AI writing assistants improve clarity. Despite these benefits, risks of AI-generated misinformation, plagiarism, and loss of human creativity raise ethical concerns. 📝 Peer review process. Automated review tools analyze papers, flag inconsistencies, and verify claims. AI-based meta-review generators assist in assessing manuscript quality, potentially reducing bias and improving efficiency. However, AI struggles with nuanced judgment and may reinforce biases in training data. ⚖️ Ethical concerns. AI-assisted scientific workflows pose risks, such as bias in hypothesis generation, lack of transparency in automated experiments, and potential reinforcement of dominant research paradigms while neglecting novel ideas. There are also concerns about the overreliance on AI for critical scientific tasks, potentially compromising research integrity and human oversight.

🚨 BREAKING: The NEW Product Liability Directive has just been PUBLISHED in the Official Journal of the European Union, and it will enter into force in 20 days. 👉HINT: IT APPLIES TO AI. Here's what EVERYONE in AI should know: 1️⃣ The new directive expressly acknowledges that AI - and the need to compensate victims of AI-related harm - was one of the factors that made it necessary to update the old product liability directive. It also applies to AI. 👉 According to Recital 3: "Directive 85/374/EEC [the old product liability directive] has been an effective and important instrument, but it would need to be revised in light of developments related to new technologies, including artificial intelligence (AI), new circular economy business models and new global supply chains, which have led to inconsistencies and legal uncertainty, in particular as regards the meaning of the term ‘product’. Experience gained from applying that Directive has also shown that injured persons face difficulties obtaining compensation due to restrictions on making compensation claims and due to challenges in gathering evidence to prove liability, especially in light of increasing technical and scientific complexity. That includes compensation claims in respect of damage related to new technologies. The revision of that Directive would therefore encourage the roll-out and uptake of such new technologies, including AI, while ensuring that claimants enjoy the same level of protection irrespective of the technology involved and that all businesses benefit from more legal certainty and a level playing field." 2️⃣ AI providers - defined as such according to the EU AI Act - should be treated as manufacturers (Recital 13). 3️⃣ Substatial modifications of AI systems through continuous learning should be considered made available at the time that modification is actually made. (Recital 40) 4️⃣ The "black box" paradox is also considered, and 🚨might lead to a presumption of defectiveness🚨. 👉 Recital 48 establishes that when: a) the defectiveness of an AI product; b) the causal link between the damage and the defectiveness or c) both are difficult to prove, national courts can presume the defectiveness of a product. This assessment should be made by national courts on a case-by-case basis. 5️⃣ We still need the AI Liability Directive, which has not been approved yet. 6️⃣ Link to the Directive below. 7️⃣ STAY INFORMED: join 38,800+ people who subscribe to my weekly newsletter covering the latest developments in AI policy, compliance & regulation, and NEVER MISS my updates (link below) 8️⃣ To learn more about AI Regulation & Governance, join me for an intensive 3-week AI Governance Training in December, already in its 15th cohort (8 live sessions / 12 hours total). 1,000+ people have benefited from our training programs; learn more here & register below. 9️⃣ Share this post and spread the news. #AI #AIRegulation #AILiability #AILiabilityDirective #AIGovernance

🎥 Embracing AI in Video Editing: A Personal Shift in Workflow Until recently, video editing was one of the most time-consuming parts of my content creation process. Tasks like trimming footage, cleaning up audio, and adding subtitles required significant manual effort and often slowed down production. That changed when I began incorporating AI-powered tools like Descript, Runway, and Munch into my workflow. The impact has been remarkable: 🔹 Automated editing that preserves narrative flow 🔹 Instant, accurate subtitles 🔹 Smart audio enhancement 🔹 Relevant B-roll suggestions generated from context What once took several hours can now be completed in a fraction of the time — without compromising on quality. Beyond saving time, these tools have opened up space for more creativity and strategic thinking. AI hasn’t just made video editing easier — it’s redefined how I approach storytelling. I'm always exploring new tools and methods. If you're using AI in your creative process, I’d love to hear what’s working for you. #AI #Video via @winchumbo #Editing #Productivity #Content #Innovation #Digital #Tools

🚨 ATTENTION CONTENT CREATORS 🚨 AI is not going to replace you. But creators who use AI will replace those who don’t. After building one of India’s largest tech YouTube networks over the past 9 years, here’s what I’ve learned: When I started TrakinTech in 2016, editing one video took 8–10 hours. Today, with AI tools, we manage content across 15+ channels without compromising quality. But we didn’t let AI make us lazy — we used it to boost our creativity. Many believe AI creates soulless content. Not true. It’s a powerful assistant. It handles the repetitive work, so we can focus on storytelling, emotion, and audience connection. At Trakin Tech and Armoks Media, we’ve tested nearly every major AI tool: ✅ Great for research, scripting, and editing  ❌ Weak at emotional depth, cultural nuances, and human insight Our most viral videos? They still come from lived experience and cultural relevance, things AI can’t replicate. What excites me most: AI is a great equaliser. Even small creators can now produce at a level that once needed large teams. The key is balance: use AI for efficiency, double down on originality and authenticity. By 2027, the most successful creators won’t avoid or rely blindly on AI, they’ll collaborate with it. To aspiring creators: Start now. Use AI for ideas, research, and speed — but never lose your unique voice. That’s your biggest advantage. The future belongs to creators who think like humans and build with AI. Are you experimenting with AI in your process? I’d love to hear your experience.

Travelers ask many questions before booking, and most of them are already answered somewhere in a property’s listing. However, surfacing the right information at the right moment is far from simple, especially when listings are inconsistent or lengthy. In a recent blog, Agoda’s engineering team shares how they tackled this with a conversational AI assistant called the Property AMA Bot. Instead of hardcoding answers or relying solely on generative models, they built a retrieval-augmented system that combines relevance scoring with language generation to provide accurate, grounded responses. Here’s how it works: First, they break down each property’s content into structured “facts” using heuristics and keyword filtering. When a user asks a question, the system retrieves relevant facts using a hybrid of sparse and dense retrieval techniques. Then, these facts are passed into a fine-tuned LLM, which generates a concise answer grounded in the retrieved content. To keep answers factual and safe, they also include fallback rules, so that the bot will refrain from answering if confidence is low or the topic falls outside the known scope. This setup strikes a good balance between traditional Information Retrieval methods and generative models, making the bot both responsive and reliable. This approach is a great example of retrieval-augmented generation in practice, blending engineering pragmatism with the strengths of LLMs to improve real-world user experience. #DataScience #MachineLearning #Analytics #LLMs #ConversationalAI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gVUT97F7

⚖️Generative AI in EU law 🔍 This paper serves as a critical analysis of the AI Act, identifying gaps and challenges in addressing the rapidly advancing applications of Generative AI. It provides recommendations to ensure the safe and compliant deployment of LLMs. 🚀 Regarding liability: 🎯 Benefits The Product Liability Directive and AILD provide valuable structures for addressing liability in GenAI applications by recognizing the potential liability from post-deployment learning. This scope supports claims for damages, including rights violations, and addresses AI opacity and information asymmetry between providers and users. Both directives shift the burden of proof, requiring providers to disclose relevant information if harm is suspected. 🎯Gaps Both directives rely on the AI Act, which has limitations when applied to General-Purpose AI (GPAI) models. Initially, the AI Act classified GPAI as high-risk by default, but it has since adopted a 'systemic risk' approach. Yet, it lacks clear criteria for defining societal risks specific to GPAI, creating ambiguity around liability and making it challenging to determine the conditions under which GenAI falls within AILD’s scope. 🎯 Recommendations for a Tailored Code of Practice (CoP) The authors recommend establishing a CoP for GPAI models presenting systemic risks. This CoP would clarify the model’s compliance with the AI Act and provide a framework for risk management specific to GenAI. Extending the disclosure mechanism and rebuttable presumption of causation to all GPAI models would also enhance accountability, as GenAI developers typically possess incident-relevant information and should be obligated to share it. 🎯Clarifying Model Development and Data Intent The lack of a singular purpose in GenAI models complicates risk prediction and compliance assessments as required by the AI Act. To manage risks more effectively, the authors propose emphasizing criteria such as model scalability, input diversity, and transparent data usage objectives. For models trained on restricted datasets that rely on few/zero-shot learning capabilities, developers may need to disclose auxiliary information, thereby clarifying links between observed and unobserved object classes and aligning with transparency goals. 🎯Incorporating Ethical and Technical Safeguards The paper suggests combining conventional fault criteria with additional ethical and technical safeguards within the CoP. These would guide GenAI developers to: 🔸 Enhance Data Transparency: Document data intent and collection methods. 🔸 Ensure Data Quality: Construct representative datasets of sufficient quality, reducing risks of overfitting and increasing generalizability. 🔸 Implement (Pro)Active Monitoring: Includes reporting potential harm incidents and forming alliances with credible third-party organizations for validation and evidence access. 🔗 https://lnkd.in/dERy5n9u #AI #AIAct

A lot of people think TikTok is the *only* place that matters for creators. It’s not. TikTok is where creators often pop fast. But presence on other platforms like Instagram and Snapchat isn’t optional, it’s a strategy. Instagram helps lock in brand aesthetic and trust. Snapchat has a different discovery engine, not to mention monetization tools that pay out weekly for consistent content. Each platform hits a different audience. That means smarter targeting for brands and better leverage for creators. For example, a TikTok viral moment boosts short-term views, but reposting that content on Instagram can extend its shelf life and help seal longer-term brand deals. Creators who win long-term don’t just go wide. They go deep on multiple platforms, on purpose. If you’re a brand working with creators, look beyond TikTok. Ask how they’re building across the board. That’s where the real influence lives. #BrookeMonk #Advice #ContentCreator #Brand

AI stocks may be overpriced, but the real edge belongs to organizations that embed AI in daily workflows—speeding decisions, reducing repetition, and freeing time for creativity and strategy. I’m always energized when a team member surfaces a practical AI use case in our daily work at Wright Research. To build on that momentum, I organized an AI competition with a clear challenge: demonstrate how an AI tool can meaningfully improve the way you work each day. The responses were eye-opening. Teams explored: → Cursor for coding and debugging with speed and accuracy. To be honest I myself am addicted to the platform and use it extensively. → ChatGPT for sales scripting, content generation, and research → CRM integrations that enhance CRM productivity and customer journeys → Canva AI for instant marketing visuals → Figma AI for user interface prototyping → Fascinating workflow automation with agents and n8n that can enhance research process. → ChatGPT-driven summarization and market research collation → Experiments with emerging tools like Google Veo for creative applications. (I am fascinated by creators using AI to generate youtube videos 😛) What stood out was not the novelty of the tools, but how quickly employees translated them into meaningful use cases. The exercise highlighted that the value of AI lies in augmentation, not replacement—equipping professionals to operate at a higher level of efficiency, creativity, and analytical depth. The long-term winners in this wave will be the firms that embed AI deeply into their processes, moving beyond experimentation to systemic adoption. That is where organizational edge will be built. Which AI tools have you found transformative in your own workflow?

I don’t recommend using AI to create content. But if you have to, do it right. Here’s how I’d stack the tools: 1. For short-form text and outlines - Use ChatGPT: Great for quick ideas, cold emails, social posts, and rough drafts. Fast, flexible, and good enough if you know how to prompt. 2. For longer-form content - Use Claude: Better at structure, tone, and flow. Ideal if you need to generate blog posts or long-form copy with minimal editing. 3. For research: - Use Perplexity It cites sources, which matters when you want truth over fluff. Especially useful for technical or SEO content. 4. For marketing-specific content - Use Writesonic It automates internal linking, adds SEO metadata, and even generates FAQS. Good for scaling SEO or building email workflows fast. 5. For creative writing and storytelling - Use Sudowrite It’s built for fiction. Need help with dialogue, world-building, or plot development? This is your AI writing buddy. One last thing: AI is a tool. Not a strategy. It’s only as good as the person using it. If you can’t brief it well, you’ll just get average output at scale.