DISQO is the CX intelligence platform for understanding every customer experience

DISQO is the CX platform for understanding every customer experience. Businesses trust DISQO to power better decisions for every customer, touchpoint, and outcome. DISQO’s insights, agile testing and advertising measurement products are powered by millions of consumers on the industry’s largest opt-in consumer data platform. DISQO is recognized in Deloitte’s Fast 500 and Ad Age’s Best Places to Work, and is a Top 100 Next Gen Workplace.

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Real-world cases and expert opinions about the present and future of audio deepfakes and AI-powered fraud in particular and how to use AI to build a strong defense against malicious AI. John Dow, the CEO of an unnamed UK-based energy firm, once from his boss that he wishes he'd never answered. Being confident he was talking to the CEO of the firm's German parent company, he followed the instruction to immediately transfer €220,000 (app. $243,000) to a Hungarian supplier's bank account. got a call Having completed a transaction, John got another call from the boss confirming the reimbursement. However, he noticed that the purported reimbursement hadn't gone through, and the call had been made from an Austrian phone number. At this point, John got the third call seeking a follow-up payment. That's where he went sceptical and sounded the alarm. The investigation found that the voice in the call belonged to a hacker using AI voice technology to spoof John's boss. In fact, the fake voice was so close to the real one that John could recognize the subtle German accent and the man's voice "melody". Audio deepfakes are gaining traction and that's creepy According to , audio deepfakes are extremely hard to catch red-handed. Victims rarely have any evidence as they don't typically record inbound calls, and the technology detecting synthetic speech in real-time is still in its infancy. As a result, the actual number of cyber fraud cases based on audio deepfakes remains very low. According to antivirus provider Symantec, in 2020, there were only three registered cases where audio deepfakes were used against their clients. Forbes "Applying machine learning technology to spoof voices makes cybercrime easier," says Irakli Beridze, head of the Center on AI and Robotics at the UN Interregional Crime and Justice Research Institute. I've reached out to , co-founder and CPO at , an identity verification and compliance company, for a comment on the future of deepfakes. Jacob Sever Sumsub " It still takes plenty of effort to make a truly realistic deepfake. But I believe that, in the future, creating them will take just a few clicks. Indeed, the deepfakes of the future will be so high-quality that you won't be able to distinguish them from a real individual. Once deepfakes can be made easily, we'll be faced with a game of cat-and-mouse. That's because the only way to fight malicious deepfakes would be through the same neural networks that create them. So, as deepfake and neural network technology become more sophisticated, we'll have to upgrade our defences constantly. " AI will eventually learn to recognize any deepfake through analyzing specific, invisible features. Nevertheless, we'll still need to fortify identity verification going forward. This includes implementing technology based on state-of-the-art cryptography, including chips that could be embedded in humans for identification. Another plausible scenario of malicious AI usage that can be detrimental for business is . robocalling If you get a call, answer it and hear a recorded message instead of a live person, it's a robocall. If you get many robocalls trying to sell you something, such calls are most likely illegal and are scams. According to the , robocalling is one of the biggest consumer complaints they receive daily. Federal Communications Commission How does robocalling work? Scammers use AI and ML to fully automate their robocalling systems and discover types of victims that are likely to respond to their fraud or better understand which logic and arguments would lead to more successful shenanigans. With an abundance of data points, cybercriminals refine their scamming schemes and wage more advanced and sophisticated attacks. I've talked to Nelson Sherwin, General Manager of that provide HR and regulation compliance services to businesses. The company fell victim to a flurry of autodialer calls early in 2020. PEO Companies " Our old telephony system was one we somewhat inherited with our space, and it was working fine for us until this. There was no real way for our IT department to manage the influx of calls adequately. It very quickly began to impact my employees' capability to do business with our clients. Employees essentially had to disconnect their phones for several hours over a few days while working out a solution. We hastily but successfully switched to a software VoIP solution, which allowed our IT team to use a much better call management environment while providing the granularity needed to block the incoming calls much quicker. ." Eventually, after what I assume were hundreds of repeated attempts that failed, they gave up on trying to bombard our phones again How organizations use AI and ML to fight and prevent AI-driven cyber fraud and deepfakes Ragavan Thurairatnam, co-founder and Chief of Machine Learning for , a startup currently working on , believes using a traditional software-based approach would make it extremely difficult to figure out the rules to write in order to catch deepfakes. Dessa developing AI for detecting audio deepfakes "On top of this, deepfake technology will constantly change, and traditional software would have to be rewritten by hand every time," Thurairatnam. "AI, on the other hand, can learn to detect deepfakes on its own as long as you have enough data. In addition, it can adapt to new deepfake techniques as they surface even when detection is difficult to human eyes." explains To build public awareness about the risks of AI-powered synthetic speech, a few months ago, Dessa shared their own example of the technology with the broader audience. Using a proprietary speech synthesis model, they built RealTalk, a recreation of the popular podcaster Joe Rogan's voice. How deepfake detector works To distinguish between real and fake audio, the visual representations of spectrograms, which are also used to train speech synthesis models. While sounding almost identical to the unsuspecting ear, spectrograms of fake versus real audio differ. detector technology uses Engineers at Dessa trained their detector on Google's 2019 containing over 25,000 audio clips featuring fake and real voices of a great variety of male and female speakers. AVSSpoof dataset The company built a deep neural network that uses Temporal convolution. Raw audio is pre-processed and then converted into a mel-frequency spectrogram, which serves as the input for the model. The model performs convolutions and uses masked pooling to prevent overfitting. The output is finally passed into a dense layer and a sigmoid activation function, which ultimately outputs a predicted probability between 0 (fake) and 1 (real). Image source: Dessa As a result, the detector model can currently predict over 90% of the audio deepfakes, which puts it front and center in the effective combat of AI fraud. Building predictive flags for AI fraud potential When an enterprise creates insights for decision-making on fraudulent data, the consequences can be dire — product design misses the mark, while faulty competitive intelligence or bad brand experiences damage reputation and user loyalty. This inspires companies having the firsthand experience with AI-driven fraud to develop defensive technologies. One example is , an audience insights platform that has modernized the panel-based approach to consumer surveys. In particular, the company employed Cloud-based technology allowing users to easily participate in research anywhere on any device. DISQO asks people if they would be willing to share their opinions and online behaviors, and rewards them for this. DISQO "This is the antithesis of prevalent ad tech solutions that use technology to deduce consumer identity and follow them online without their full understanding or consent," says , DISQO's Chief Customer Officer. Bonnie Breslauer DISQO's approach has skyrocketed in popularity, with more than 10M users to date and more than $25M in cash rewards paid out. But wherever money is to be made, thieves are sure to follow. Driven by the allure of the compensation for survey participation and likely spurred by the pandemic, fraudsters generated a huge spike in fraudulent activity in 2020. This inspired DISQO to lead in fighting fraud by developing defensive technologies that keep cheaters out of their panel. DISQO's data scientists built predictive flags for fraud potential by studying good and bad interactions with their platform. As they needed more computational power, they moved to Spark Engine, which gave the team the needed "horsepower" for next-level machine learning. DISQO created a scoring system to rank panelists' behaviors that might indicate fraudulent activity. Using Bayesian optimization, the simple initial grading system was evolved into a model for maximum fraud detection, which continuously improves through ML. In less than six months, DISQO's data science team achieved its best results by solving the optimal fraud coverage segment and training the company's machine learning models on a hand-picked number of classifiers. The team constructed a fraud vector of four dimensions that consisted of the probabilities retrieved from the hand-picked classifiers. One of the fraud vector's dimensions contained the CNN probability; the other two were XGBoost and Logistic based probabilities. The system learned the separation in the data between "good" and "bad" panelists. DISQO now compares fraud vector magnitudes on every user to assess overall fraud risk. This allows DISQO to detect and remove "bad actors" while preserving a high-quality experience for the rest of the community. Final Thoughts , VP of AI/ML at and the former Principal at Microsoft AI and Research Group,  believes there's nothing inherently malicious about AI. Shane Lewin GSK " Facebook currently runs an AI-powered feed that is constantly learning how to keep you engaged on the platform. It exploits parts of your brain that reinforce community and social structure with the intent for more screen time. Those AI models are quite effective but not without cost — a convincing and overwhelming body of evidence suggests it's quite harmful to your productivity, relationships, happiness, and even health. I've not personally been subjected to deepfakes, but I've seen the impact firsthand — a targeted and deliberate deepfake campaign leads to real reputation and health impacts to an innocent victim. Racial and gender bias is rampant in the search algorithms on career pages, notably Linkedin. This is actually not malicious in intent, but the AI has learned the behavior of the bias in users, based on their clicks; or the biases of the people who trained it. There's nothing inherently malicious about AI — it's just software that gets iteratively better at some defined function. The analogy is a human businessperson trying to make more money. They might try a wide variety of strategies and increase usage and investment in what works, but our value system constrains most humans. There're lines we won't cross – things that are too odious to profit from. These rules — we call them values — limit our profit to a degree, but society is better for having them. Alas, not all humans are self constrained. There's a class of us that we have names for, ranging from selfish to narcissistic to psychopaths, but that all describes individuals that are too lax with our values. Society has learned to reject these people, to a degree at least. " AI is the same. If it's not constrained with some value system, it will adopt whatever strategy is optimal without any sense of values or ethics. The Facebook algorithm says maximize screen time when it should say maximize screen time within constraints above, which are dangerous to mental health (or better yet, just turn the personalized feed off). Linkedin should maximize relevance without any signals that might convey bias (such as user click data or racial identifiers). It's quite possible to build ethical AI if you're willing to put values above profit. I can't agree more! There are many malicious companies, led by malicious executives, deliberately maximizing profit in an evil way. And to date, there's no better way to fight AI-based fraud than using AI. Fight fire with fire! Sharing and liking this article is appreciated! If you have an interesting tech story that you believe deserves a good shoutout – just drop me a line on LinkedIn and I'll be happy to cover it in my next article on Hacker Noon.

Fight Fire with Fire: Using Good AI to Combat Bad AI

Automated solutions have been around for over a decade in the cybersecurity space. However, their use has been confined to very surface-level areas—it’s time this changed. With the recent advancements in generative AI, the technology has proven to have a higher ceiling for application, including time-consuming yet vital processes like cyber discovery. AI In fact, organizations that automate cyber defense mechanisms will experience a in security breaches by 2026. So, it’s evident that automation will become the norm for many cybersecurity processes. This is especially important given the more targeted and aggressive attacks, such as advanced persistent threats (APTs), that have been launched in the past year. 66% reduction APTs make cybersecurity much murkier. If a company experiences an attack of this nature, they’re as good as gone; the sophistication of these threats can penetrate the most robust systems when mildly overlooked. One of many examples from last year is the highly secure USBs, gaining access to government data via APTs by subtly infecting and taking over every device they connected them to. malicious actors that targeted Here’s where more advanced automation comes in to keep APTs at bay. Let’s see how adopting these new practices can highly benefit your security team and business. Faster and more controlled threat intelligence turnarounds The APT protection market is set to grow by a staggering annually until 2032 when it will be worth $51.5 billion. The increasing severity of these attacks is what’s driving CISOs to spend more of their budget protecting their company’s attack surfaces — starting with proper threat intelligence and asset discovery methods. 18% Automation can speed up asset discovery, and time is of the essence when it comes to data security. A typical discovery process for a company of around 10,000 employees would take a security team a couple of days to finish. By automating this process, the timeline plummets to just one day. Instead of outsourcing this process to cybersecurity companies, in-house security teams can cut the middleman and use threat intelligence tools to automate the discovery process for them. This is possible with SaaS programs that use open source and proprietary codes to create a workflow, allowing companies to run tailored discovery as often as they see fit. The time difference between manual and automated can be a costly APT attack or a safe system. More precise and accurate discovery to bridge the skills gap You wouldn’t begin securing your systems without knowing exactly what needs to be done. It would be a waste of both time and money. This is why discovery is so crucial to threat intelligence and successful cybersecurity practices — it’s the foundation for protecting attack surfaces. And for discovery to be worthwhile, it must be done right. However, the cybersecurity talent shortage is making it harder for companies to properly protect their systems, especially when are being affected by the lack of professionals globally. Automation is helping bridge this gap and giving companies peace of mind for their foundational cybersecurity strategies. 71% of organizations By using threat intelligence SaaS tools that automate discovery, security teams can forego a discovery expert while knowing this crucial piece of the puzzle is still being handled with precision and accuracy by an AI workflow. APTs don’t let up, and minimal human error could open the gates for malicious actors to break in and set up shop. Increased human power in other specialized activities CISOs that human error is their organization’s biggest cyber vulnerability. Discovery can be a very time-consuming task, and human processes can become faulty at any point in the job. Now that threat intelligence automation is a more accessible and developed practice, security leaders shouldn’t have to place their human power in these intensive and often flawed activities. highly agree After adopting SaaS tools for discovery, security teams will rest assured their discovery needs will be handled by software that takes less time and significantly reduces errors in threat detection. What will this translate to? More time applying threat intelligence data into more robust security protocols like vulnerability prioritization and remediation, meeting data security compliance requirements, and other activities that require human input. Keeping APTs away from your company encompasses several steps in your cybersecurity practices. Automation is making it easier for security teams to spend their time on valuable activities while others run on AI workflows with less margin for errors. In this new age of AI and automation, these tools are becoming more affordable and user-friendly to address skills shortages and the increase in complex cyber attacks. Ultimately, automating certain tasks is a win-win for security teams, budgeting, and cybersecurity measures — bringing in convenience, precision, and affordability. , co-founder, Trickest Nenad Zaric

Ensuring Cyber Resilience: The Imperative of Automating the APT Discovery Process

The world of work is changing––rapidly. Thanks to the advent and rapid adoption of generative AI, tech stocks had a bumper 2023, with the share price of the Big Five ( , , , , and ) leaping by 60% from the start of that year. 2023 was also a , which saw its shares triple in price. Alphabet Amazon Apple Meta Microsoft smash success for chipmaker, Nvidia And while adoption isn’t even across the board yet, corporate America is sitting up and taking notice. At JPMorgan Chase, for example, the company said it had 600 machine-learning engineers, with AI working on more than 300 different internal applications. A recent found that 71% of C-suite leaders said that advancing their use of AI is either a high or medium priority for their organization, with 62% of executives noting that they are worried they’re not moving fast enough with AI. study from UKG That has led to some companies doing “AI washing”, aka exaggerating the amount of AI technology they use in their products or services. While some companies might not be entirely truthful about their motivations and use of artificial intelligence or machine learning technologies, what is increasingly true is that in an AI future, tech workers will need to upskill. The World Economic Forum (WEF) says that “the 2020s are becoming the decade of development for employees and employers in terms of skills and career progression”. Its most recent identified that 44% of workers’ skills will be disrupted in the next five years, and six in 10 staff members will require training before 2027. Future of Jobs Report Upskilling for free Key skills to acquire will include new technology capabilities, but the WEF has also identified key human skills such as analytical and creative thinking, as well as resilience, flexibility and agility, motivation, and self-awareness. AKA, the things that artificial intelligence (so far) can’t do. If you’re thinking about some upskilling, then one great place to begin is (OCW) platform. MIT’s OpenCourseWare A free and open space to learn, OCW contains material from thousands of MIT courses across every MIT department and degree program, ranging from the introductory to the most advanced graduate level. Courses include a syllabus, instructional material (such as lecture notes or a reading list), and learning activities like assignments or exams. You can use all the courses at your own pace, and there is no enrollment fee or date to complete by––useful for those working in busy, pressurized roles. A huge range of computer science, engineering, and technology courses are available, across topics such as machine learning, generative AI, Python, deep learning, cognitive robotics, data science, and much more, all for free. If upskilling to get a new job is on your 2024 agenda, then the courses above are a great start. And your job hunt is made far easier too thanks to the . It contains thousands of interesting tech roles, like the three below. HackerNoon Job Board DataAnnotation is seeking a to train its AI chatbots to code. This flexible, part-time or full-time role allows you to choose the projects you want to work on, involves coming up with diverse problems and solutions for a coding chatbot, writing high-quality answers and code snippets, and evaluating the code quality produced by the AI model. You will need to be proficient in at least one programming language and able to solve coding problems (LeetCode, HackerRank). Software Developer Adobe is looking for a in San Jose. The salary range for this position is $135,200 - $250,900, and you will have either research or industry experience in building scalable data and ML platforms. In this role, you will have the opportunity to build a new GenAI foundation to change the creative landscape and will collaborate with data platform engineers and architects to seamlessly integrate low latency data pipelines into the ML platform for model training, as well as writing high quality, product level code that is easy to maintain and test following standard methodologies. Other tasks include designing and implementing reusable and scalable data-loading framework to support video and audio foundational model training in large-scale and distributed environments. Machine Learning Engineer Python programmers looking for a new role can consider this role at SAIC in Chantilly. You’ll be experienced, results-oriented, and mission-driven, with a specialized focus on data engineering in order to perform data model design, data formatting, and ETL development optimized for efficient storage, access, and computation in support of national security objectives. A solid understanding and experience with developing, automating, and enhancing all parts of the data pipeline to include ingestion, processing, storage, and exposing data for consumption is required, along with a focus on improving inefficient tooling and adopting new transformative technologies while maintaining operational continuity. Python Developer Want a great new tech role? Discover it now on the HackerNoon Job Board right By Kirstie McDermott

MIT Has Released a Bunch of Free Courses: Discover 5 To Boost Your Tech Career

এমআইটি একগুচ্ছ বিনামূল্যের কোর্স প্রকাশ করেছে: আপনার কারিগরি ক্যারিয়ারকে উন্নত করতে 5 আবিষ্কার করুন

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Le MIT a publié un certain nombre de cours gratuits : découvrez-en 5 pour booster votre carrière technologique

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Finding the right AI model to build a workflow around is hard. With so many models available across different platforms, it’s impossible to know where to start or how to find the one that best fits your specific needs. That’s the problem I set out to solve with AIModels.fyi, the only search engine for discovering and comparing AI models from across the web. The site has a simple premise: describe your problem in natural language, and get back a list of AI models that can potentially solve it. For example, you might search for “I need a model that can help upscale my images without losing clarity” or “make my cat look like it’s singing”. The goal is to , not just ML engineers, to find useful AI tools for their projects. make it easy for anyone Under the hood, the search works by taking the user’s query and comparing it to a database of over 240,000 AI models from 7,500+ creators across major platforms like Hugging Face, Replicate, Cerebrium, and DeepInfra. When a user types in a search, the query gets embedded into a vector using OpenAI’s text-embedding-ada-002 model. This query vector is then compared to pre-computed vector representations of the model names, descriptions, and use cases in the database. The most similar models by cosine distance are returned as the search results. Here’s a simplified version of the core search logic I’ve been using for about six months: const fetchData = async (query) => {
  // Embed the search query
  const embeddingResponse = await openAi.createEmbedding({
    model: "text-embedding-ada-002",
    input: query,
  });
  
  const embedding = embeddingResponse.data.data[0].embedding;

// Find the most similar models in the vector database 
  const { data: modelsData, error } = await supabase.rpc("search_models", {
    query_embedding: embedding,
    similarity_threshold: 0.75,
    match_count: 10,
  });
  return modelsData;
}; This vector search approach works reasonably well for surfacing relevant models when there’s a direct overlap between the query terms and the model descriptions. However, it struggles with more abstract, complex, or niche queries where the user’s language doesn’t exactly match the technical terminology in the database. For example, I recently wanted to find a model that could animate a portrait image using an audio voice clip to create a talking head effect. I knew there were some good models out there for this, but my searches for things like “talking portrait from image and audio” weren’t turning up the results I expected. The problem was that my query terms didn’t have enough literal matches with the specific words used in the model descriptions, even though the semantic intent was similar. This got me thinking about ways to improve the search experience to better handle those kinds of abstract or complex queries. How could I make the system smarter at understanding the user’s true intent and mapping it to relevant models in the database? That’s when I came across the idea of Hypothetical Document Embeddings (HyDE) from after reading about it in . this research paper this Medium post Meet Mr. HyDE — The query expander! The key insight behind HyDE is to use a large language model (LLM) to expand the user’s short query into a more detailed hypothetical “ideal” document before embedding it for search. So, instead of directly matching the query embedding to the database embeddings, it first translates the query into a richer representation in the same semantic space as the real documents. Here’s how the process works: The user enters a natural language query expressing their needs, like “I want to convert an image into a 3D model.” This generated pseudo-document is then embedded into a dense vector using a model like OpenAI’s text-embedding-ada-002. The resulting embedding is used to query the vector database and find the most similar real model documents. The query is fed into an instruction-tuned LLM like GPT-3 to generate a hypothetical document. The prompt asks the model to imagine an ideal document for answering the query. For example: Query: I want to convert an image into a 3D model Hypothetical Document: There are several AI models available that can convert a 2D image into a 3D model. The process is known as single-view reconstruction or 3D estimation. It typically involves using deep learning to predict the depth map and 3D shape from the 2D input image. Some popular models for this task include Pix2Vox, Pixel2Mesh, AtlasNet, and OccNet. These models are trained on large datasets of 3D objects and can handle complex shapes. To use them, you provide an input image and the model will output a 3D mesh or voxel representation that you can render or edit further in 3D modeling tools… The LLM step helps bridge the gap between the user’s simple query and the more technical language used in the model descriptions. It expands on the initial query to infer additional relevant terms, concepts, and context that can help retrieve better matches. At the same time, using real document embeddings as the final search index keeps the results grounded and prevents hallucination. I implemented a basic version of HyDE on top of the existing AIModels search, and the initial results are promising. In some sample queries I tested, HyDE surfaced relevant models that the original keyword-matching approach missed. For the talking head portrait query, it was able to generate a hypothetical document with key terms like “lip sync”, “facial animation”, and “audio-driven animation”, which helped retrieve some strong model matches that the plain search had overlooked. two to three times more How to implement HyDE in your own search system Let me show you how HyDE can be added in on top of an existing semantic search system to dramatically improve its quality. Let’s start with a simplified code sketch based on my current search flow. Here’s the full file — you can glance through it in totality before we look at the individual parts: const fetchData = async (query) => {
  // Generate a hypothetical document embedding for the query 
  const hypotheticalDocument = await generateHypotheticalDocument(query);
  
  const embeddingResponse = await openAi.createEmbedding({
    model: "text-embedding-ada-002",
    input: hypotheticalDocument,
  });
  
  const embedding = embeddingResponse.data.data[0].embedding;

// Search the real model embeddings using the hypothetical document embedding
  const { data: modelsData, error } = await supabase.rpc("search_models", {
    query_embedding: embedding,
    similarity_threshold: 0.75,
    match_count: 10,
  });
  return modelsData;
};
const generateHypotheticalDocument = async (query) => {
  // Use GPT-3 to expand the query into a hypothetical ideal document
  const prompt = `The user entered the following search query: 
  "${query}"
  
  Please generate a detailed hypothetical document that would be highly relevant to answering this query. The document should use more technical language and expand on the key aspects of the query.
  Hypothetical Document:`;
  const response = await openAi.createCompletion({
    model: "text-davinci-002",
    prompt: prompt,
    max_tokens: 200,
    n: 1,
    stop: null,
    temperature: 0.5,
  });
  const hypotheticalDocument = response.data.choices[0].text.trim();
  return hypotheticalDocument;
} Let’s see how it works. We’ll start with the function, which is the main entry point for the search process. First up: fetchData const hypotheticalDocument = await generateHypotheticalDocument(query); This line calls the function (which we'll explain in more detail later) to generate a hypothetical ideal document based on the user's search query. generateHypotheticalDocument The hypothetical document is a key part of the HyDE approach, as it aims to capture the user’s search intent in a more detailed and technical way than the original query. const embeddingResponse = await openAi.createEmbedding({
  model: "text-embedding-ada-002",
  input: hypotheticalDocument,
});
const embedding = embeddingResponse.data.data[0].embedding; These lines use OpenAI’s model to generate an embedding vector for the hypothetical document. I think it's the best embedding option out there so far. text-embedding-ada-002 Embeddings are a way to represent text as a dense vector of numbers, where similar texts have similar vectors. This allows us to perform semantic similarity searches. By embedding the hypothetical document instead of the original query, we aim to get a vector representation that better captures the expanded search intent. Next up: const { data: modelsData, error } = await supabase.rpc("search_models", {
  query_embedding: embedding,
  similarity_threshold: 0.75,
  match_count: 10,
}); This part performs the actual similarity search in the model database. It is called a remote procedure ( ) on the Supabase database, passing in the embedding of the hypothetical document as the . search_models query_embedding The and parameters control how closely the results need to match and how many to return. similarity_threshold match_count The search looks for model embeddings that are most similar to the query embedding based on cosine similarity. Now let’s look at the function: generateHypotheticalDocument const prompt = `The user entered the following search query: 
"${query}"

Please generate a detailed hypothetical document that would be highly relevant to answering this query. The document should use more technical language and expand on the key aspects of the query.
Hypothetical Document:`; This part constructs the prompt that will be sent to the language model to generate the hypothetical document. The prompt includes the user’s original query and instructions for the model to generate a detailed, technical document that expands on the query’s key aspects. Crafting an effective prompt is crucial for guiding the language model to generate useful hypothetical documents. Now for the next part: const response = await openAi.createCompletion({
  model: "text-davinci-002",
  prompt: prompt,
  max_tokens: 200,
  n: 1,
  stop: null,
  temperature: 0.5,
}); These lines use OpenAI’s model to generate the hypothetical document based on the prompt. text-davinci-002 The parameter limits the length of the generated document while controls the randomness (higher values make the output more diverse but potentially less focused). Adjusting these parameters can help tune the quality and variety of the generated documents. max_tokens temperature Next, we have: const hypotheticalDocument = response.data.choices[0].text.trim(); This line extracts the generated hypothetical document text from the GPT-3 API response. The generated text is then returned to be used in the embedding step. Then we can just retreive the data as before, this time using the hypothetical document as the query, but getting a lot more relevant results due to the expanded search term and all it’s extra, juicy context! Recap — what did we just build? I hope the code and my explanation demonstrate how the HyDE approach can be implemented on top of an existing semantic search system. It’s not that hard! The key idea is this: By inserting the hypothetical document generation and embedding steps into the search pipeline, we aim to improve the search results by better capturing the user’s intent. To recap, the key parts were: Using a language model to expand the user’s query into a more detailed, technical hypothetical document Embedding the hypothetical document to get a vector representation that aligns with the model database Performing a similarity search between the hypothetical document embedding and the model embeddings to find the most relevant results Of course, this is just a simplified implementation (simplementation?), and there are many details and optimizations to consider in a real implementation. But it illustrates the core flow and components of the HyDE approach. Taking it further Since this is just an initial prototype there’s still a lot of room for experimentation and refinement. Some of the key open questions I’m thinking about: What’s the optimal prompt structure and output length for generating useful hypothetical documents? I’d like to try different prompt templates and output sizes to see how they impact result quality. How can the hypothetical documents be made more interpretable and transparent to users? It could be confusing if users don’t understand why certain terms are being added. I’m considering showing parts of the generated text as a “query interpretation” to provide more insight into the process. To what extent does the result quality depend on the specific LLM and embedding models used? I’m curious to benchmark different model combinations (e.g. GPT-4 vs GPT-3, vs OpenAI embeddings, etc.) to find the best recipe. Contriever How can this approach be efficiently scaled to work over millions of database entries? Inference on large LLMs like GPT-3 is expensive and slow. I’ll need to look into caching, optimizing, or distilling the models to make this viable at scale. I’m excited about the potential for language models to help make search interfaces like AIModels more intuitive and powerful for a wider audience. Bridging the semantic gap between how users naturally express their needs and how AI models are technically described feels like an important piece of the puzzle. Techniques like HyDE point a way toward search experiences that deeply understand the intent and meet users where they are. My long-term goal is to keep refining this interface until anyone can easily find relevant AI models for their needs, no matter their level of technical expertise. I’ll be sharing more details as I continue experimenting with HyDE and other approaches. If you’re curious to learn more about the technical side, I recommend checking out the original paper. And if you have ideas or feedback on the AIModels search experience, I’d love to hear from you! Let me know what you think in the comments. Also published . here

Improve Semantic Search and Enhance AI Model Discovery with Hypothetical Document Embeddings (HyDE)

I worked in research all my life, so I know a stereotype that researchers write ugly code (e.g. see , , or ). But I thought: we can fix it, right? So multiple times I tried to design nice research frameworks. I tried to bring in interfaces and create nice abstractions using software engineering books and blogs I liked reading. here here here But over and over again all those efforts went in vain. The majority of research software I worked on never went to production (although some did). It would have been great for my mental health if someone told me a simple truth: . Researchers should not spend much time engineering it in the first place. dying research code is actually what is supposed to happen Professional software engineers always look down on researchers who are not using the best software practices. There are several posts trying to raise the bar of research code (e.g. this and a ). But this post goes other way around: it argues how to not overdo the best software practices and instead invest only in fast exploration. It is targeted for research-oriented companies where your goal is to try out many ideas fast. great post research code handbook 1. Take on some strategic tech debt A successful research project at a company has two phases: exploration and exploitation. In “exploration” you want to try out as many diverse solutions as you can. During “exploitation” you have to robustify the best solution and turn it into a useful product. Optimal software practices are quite different between the two. That's why companies often have separate research and product divisions. All the books you might typically read on software design are mainly about the second "exploitation" phase. In this phase you are building foundations for a scalable product. This is where all the design patterns come in: nice APIs, logging, error handling and so on. But in the first "exploration" phase you are building foundations that will live forever. In fact, if the majority of your efforts survive, then you (by definition) did not explore enough. not Many practices in this post are examples of what would normally become “tech debt”. It’s what you get by not writing clean reusable well-abstracted code. Is debt always bad? We prefer never getting a loan or a mortgage, but borrowing money is often a good strategy in life. It's ok to get into debt to move fast and profit later. Similarly, by not taking technical debt you might be slowing down your research. The good news is that the majority of the time you don’t have to pay it back. Most of your research code is likely to die anyway. So on average, you will not be suffering from the whole tech debt you've taken. The case against code reuse Many software architectures and refactoring techniques are specifically oriented to improve code reusability. There are generic to code reuse. But in production they are outweighed by the well-known benefits (for example, see ). In research projects, the majority of the code is destined to sink to oblivion. Striving for code reuse could actually slow you down. downsides this typical post Limiting code reuse is the type of technical debt that is ok to take in research. There are several patterns of code reuse I want to discuss: adding an unneeded dependency, copypasting code, maintaining a lot of shared research code, premature design investments. Think twice before importing something new If you know a well-maintained versioned library that is going to speed you up - go for it! But before taking in a new dependency, try to make a judgment call whether it’s worth it. Every additional one brings you closer to dependency hell. It makes you invest time into learning and troubleshooting it. See more pitfalls of dependencies in this . concise post It’s probably fine to depend on something if: you used it already, there is not much to learn, it has a large community, good docs and tests it is versioned, easy to install and finally, there is no way you can implement it yourself. But be wary about a dependency if: you can’t figure out how to use it quickly, it is very new (or very old) or no one seems to know about it; there are no docs or tests it is from your monorepo and is constantly being changed by other teams it pulls in many other dependencies and tools; or it's just hard to install and finally, you feel that you (or some LLM) can write this code in a few hours. Instead of an explicit dependency, you can follow a nice Go proverb: “ ", which is our next topic. a little copying is better than a little dependency Copypaste gives you freedom of experimentation Some say that “ ”. But to my surprise I found myself arguing in favor of it quite often. Copypaste could be the optimal choice during the exploration phase. copy-paste should be illegal If you depend on a heavily-used function from another part of the codebase you can forget about easily changing it. You’re likely to break something for someone and have to spend precious time in code reviews and fixes. But if you copypaste the necessary code into your folder, you are free to do anything you want with it. This is a big deal in research projects where experimentation is a norm rather than an exception. Especially if you are not sure if changes are going to be useful for everyone. I find that deep learning codebases are suitable for copypasting the most. Typically, the amount of code needed to describe a model and its training is not so huge. But at the same it could be very nuanced and hard to generalize. Shareable training scripts tend to grow to an unmanageable size: e.g. Hugging Face has +4k lines. Interestingly enough, transformers opted for copypaste on the model level. Please check out their with the reasoning behind their "single file model" policy. See more resources about the beauty of copypaste at the end. transformers Trainer post An alternative to copypaste is staying on a branch. But I feel like it brings too much overhead in teamwork. Also, I found several more posts about the beauty of copypaste - see more posts in the conclusion. Maintaining shared research code is hard Maintenance of heavily used shared code requires a lot of work. Take a look at the number of file lines plotted against the version. You can see that even the most advanced research teams struggle to keep the complexity in check. torch.nn.Module Pytorch Don’t underestimate time and resources needed to maintain a large shared research code. The more a research library is used the more complicated it becomes. It happens faster than for a typical library because every research direction has a slightly different usecase. Establish very strict rules of what could be contributed back. Otherwise, the shared code becomes fragile and overgrown with a slew of options, buggy optimizations and edgecases. Since the majority of research code dies out, all this extra complexity will never be used again. Dropping some of your shared code will free up some time to do actual research. Design for exploration, not for code reuse It is somewhat true that you don’t want too much even in production. Try to implement the simplest possible solution that meets the requirements. But in production code there are always aspects of maintainability to consider. For example, error handling, speed, logging, modularization is what you typically need to think about. to future-proof your code In research code, none of that matters. You just want to quickly prove that an idea is good or bad in the fastest possible manner and move on. So the dirty simplicity that achieves it without any modules or APIs is totally ok! Don’t waste valuable time on premature software investments such as: creating component interfaces too early in the project. You'll spend too much time fitting into self-made artificial constraints optimizing deep learning training infrastructure before committing to deep learning solution using production config/factories/serialization systems or base classes. Quite often you don't need their functionality during prototyping overly-strict linting and type checking systems. No reason to slow down fast-changing throw-away research code. 2. Invest into fast exploration The goal of a research project is to find a novel solution. No one knows (by definition) what it looks like. It is akin to an optimization process in a complicated research landscape with limited information. To find a good minimum, you need to try many paths, recognize good and bad paths and not get stuck in local minima. To do all of it fast, you sometimes need to make software investments instead of taking tech debt. Speed up common paths There are several different research paths you want to try. Is there a design, a library or an optimization that would shave off time from the majority of paths? You should be careful to not over-engineer anything because you don’t always know all the ideas you’re about to try. This is very custom to every project, but here are some examples: if you train deep networks, invest into training infrastructure. Figure out hyperparameters allowing you to converge during training quickly and reliably if every experiment requires you to use a different model, figure out how you can quickly swap them (e.g. by using a simple factory system or just copypaste) if every experiment has too many parameters and is hard to manage, invest into a nice configuration library. Branch out quickly Researchers should be able to initiate new diverse ideas quickly. It seems easy at the start of the project. But then it gradually becomes harder and harder as people get entrenched in their favourite research paths. To address this, cultural and organizational changes are essential. There should be a process stopping non-promising research before sinking too much money and emotion into it. Regular demo days and technical peer reviews can serve as effective strategies for this purpose. It's also important to find a balance between people jumping on a new shiny idea and properly closing current projects. But this is a software post, so here are some practices to make branching out new projects easy: keep evaluation code disentangled from algorithms. Evaluations are typically more stable than research directions embrace starting a new project from a blank slate, but then watch out which components are reused. Modularizing and cleaning them up is a good investment in a new research project, implement the most innovative and risky component first. Doing so identifies the majority of bottlenecks guiding future software design. Increase signal to noise ratio Noisy and buggy code makes results so ambiguous and inconclusive that the whole project is going to be a waste of time. While you shouldn't over engineer things, you can easily follow these simple rules of thumb to avoid messy code: avoid code with side-effects default to functions rather than classes; and with classes, prefer encapsulation vs. inheritance minimize length of functions/classes/modules; minimize number of if-statements know python well, but use simple techniques. Resist the temptation going into intellectual weeds of metaclasses, decorators and functional programming. Software that produces different results during different runs is tricky to work with. If you made an important but wrong decision based on an unlucky seed, you are going to waste a lot of time recovering. Here are some tips when dealing with non-deterministic software: understand whether noisy is coming from the algorithm or it's evaluation. Noise sources compound and you should strive for completely deterministic evaluation. don’t stop finding sources of randomness until you really get a reproducible script. Remember that after finding all random seeds, noise might come from data or from generic functions with side-effects. vary seeds and determine baseline variance of your results. Don’t take decisions on non-statistically significant results. Conclusion The punchline comes from about research code: this post . “You don't bother with [good software design] because the code is not the point. The code is a tool that gets you the answer that you need” It is extremely important to have great coding foundations. But at the end of the day, the exploration and actually useful product is what matters. If you use too much production software in research, you waste time needed to discover something new. Instead, find what slows down your exploration process. Speed up research paths by investing in fast branching, time to results and clean noiseless code. It would be crazy to argue completely against code reuse. I just want to point out that code reuse should be a well-balanced activity. In research, the ratio of throw-away code is larger than in production. The balance is tilted further against reuse. Here are few more great posts with pitfalls of code reuse: Reusable Code: The Good, The Bad, and The Ugly When It Makes Sense to Repeat Yourself Balanced StackExchange with pros and cons of reuse And here are few more posts arguing for copypasting practices: Is copypaste really a problem? When Should You Copy-Paste Code? The Good and the Bad of Copy-Paste Programming "As a senior with 5 YOE, I feel like part of my pay is knowing what code to copy lol" Thank you for reading! I feel some bits are a bit controversial, please let me know in the comments! Also appears . here

Software for Exploration: Achieving Balance in Discovery

অনুসন্ধানের জন্য সফ্টওয়্যার: আবিষ্কারে ভারসাম্য অর্জন করা

Software für die Erkundung: Ausgewogenheit bei der Entdeckung erreichen

Software para la exploración: lograr el equilibrio en el descubrimiento

Logiciels d'exploration : parvenir à un équilibre dans la découverte

अन्वेषण के लिए सॉफ्टवेयर: खोज में संतुलन हासिल करना

探索用ソフトウェア: 発見のバランスを実現する

탐험을 위한 소프트웨어: 발견의 균형 달성

Software para Exploração: Alcançando Equilíbrio na Descoberta

Программное обеспечение для геологоразведки: достижение баланса в Discovery

Keşif Yazılımı: Keşifte Dengeyi Sağlamak

Phần mềm khám phá: Đạt được sự cân bằng trong khám phá

用于探索的软件：在发现中实现平衡

The TechBeat: Product Discovery in B2B and B2B2C Environments: A Guide for Product Managers (2/18/2024)

How Dramatron Empowers Co-Creative Scriptwriting with AI Assistance 

The Easy Button

সহজ বোতাম

Der Easy-Button

El botón fácil

Le bouton facile

आसान बटन

簡単ボタン

쉬운 버튼

O botão fácil

Простая кнопка

Kolay Düğme

Nút dễ dàng

简易按钮

I entered the programming world without a formal degree, relying on a Udemy course and some personal projects. Four months later, I landed an internship as an FE developer at BMW. For almost two years, I worked as a Front-End (FE) developer. Deep down, I’ve always wanted to dive into Back-End (BE) development, but my team’s leadership believed it wasn’t the right time yet. The beginning was tough. I had no real understanding of programming; I just knew I was passionate about it. My initial interest wasn’t in creating websites or dealing with the visual aspects users see. Instead, I was drawn to the underlying technology and processes. Starting with FE was a practical choice, as friends in the field advised me it was a more accessible entry point for beginners. In retrospect, they were right, at least for that time. Don’t get me wrong — FE development isn’t easier than BE. They each come with their own complexities. It took me a long time to get my bearings, to program with fluency, and to become self-sufficient. Once I had established myself in my FE role, I began to express my interest in BE more assertively. I started attending BE refinements and sought out study advice relevant to our project’s BE context. My initiative was welcomed, and within a couple of sprints, I had my first BE task. First Impressions Have you ever walked into a supermarket for the first time and felt completely lost, trying to take everything in but seeing nothing clearly? That’s exactly how I felt when I first saw the BE repository. I couldn’t make sense of how everything interacted or what communicated with what. I tried reading the documentation, but it only added to my confusion. It felt like I was back to day one of programming. This time, however, I had a general understanding of the business context, which made my frustration even greater. As an intern, I didn’t know what I was supposed to do or how to do it. This time, I knew what needed to be done but not how to do it, and I was no longer a novice. I had high expectations for myself. I had to step back, ask for help, and study over the weekends to wrap my head around things. Slowly but surely, with a lot of assistance, I began to understand how things worked (at least within the context of my story) and managed to deliver everything on time. The Complexity Difference It’s not that one role is more or less complex than the other, but my experience suggests that BE developers need to be more aware of all the relationships and interconnections within an application. While FE developers focus on how to present data, BE developers are concerned with ensuring all the necessary data is available, which requires a broader understanding of the business. Important Points to Consider If you’re thinking about making this career shift, here are a few things to keep in mind: Ask Questions: Lots of them. It’s normal to feel lost at first, but with the right guidance, you’ll find your way.


Embrace Discomfort: Don’t cling to your previous position, expecting nothing to change. You’ve stepped out of your comfort zone, so learn to be comfortable with discomfort. Remember, just because you were proficient in FE doesn’t mean you’ll start as proficient in BE. You might begin as a junior again, but your established background might help you become proficient more quickly than the first time.


Enjoy the Journey: Programming is all about challenges, learning, problem-solving, and creativity. Believe in yourself and enjoy the process. Embrace the journey with enthusiasm. Programming is about facing challenges, continuous learning, solving problems, and being creative. Believe in yourself, and good luck on your journey! I entered the programming world without a formal degree, relying on a Udemy course and some personal projects. Four months later, I landed an internship as an FE developer at BMW. For almost two years, I worked as a Front-End (FE) developer. Deep down, I’ve always wanted to dive into Back-End (BE) development, but my team’s leadership believed it wasn’t the right time yet. The beginning was tough. I had no real understanding of programming; I just knew I was passionate about it. My initial interest wasn’t in creating websites or dealing with the visual aspects users see. Instead, I was drawn to the underlying technology and processes. Starting with FE was a practical choice, as friends in the field advised me it was a more accessible entry point for beginners. In retrospect, they were right, at least for that time. Don’t get me wrong — FE development isn’t easier than BE. They each come with their own complexities. It took me a long time to get my bearings, to program with fluency, and to become self-sufficient. Once I had established myself in my FE role, I began to express my interest in BE more assertively. I started attending BE refinements and sought out study advice relevant to our project’s BE context. My initiative was welcomed, and within a couple of sprints, I had my first BE task. First Impressions Have you ever walked into a supermarket for the first time and felt completely lost, trying to take everything in but seeing nothing clearly? That’s exactly how I felt when I first saw the BE repository. I couldn’t make sense of how everything interacted or what communicated with what. I tried reading the documentation, but it only added to my confusion. It felt like I was back to day one of programming. This time, however, I had a general understanding of the business context, which made my frustration even greater. As an intern, I didn’t know what I was supposed to do or how to do it. This time, I knew what needed to be done but not how to do it, and I was no longer a novice. I had high expectations for myself. I had to step back, ask for help, and study over the weekends to wrap my head around things. Slowly but surely, with a lot of assistance, I began to understand how things worked (at least within the context of my story) and managed to deliver everything on time. The Complexity Difference It’s not that one role is more or less complex than the other, but my experience suggests that BE developers need to be more aware of all the relationships and interconnections within an application. While FE developers focus on how to present data, BE developers are concerned with ensuring all the necessary data is available, which requires a broader understanding of the business. Important Points to Consider If you’re thinking about making this career shift, here are a few things to keep in mind: Ask Questions: Lots of them. It’s normal to feel lost at first, but with the right guidance, you’ll find your way. Embrace Discomfort: Don’t cling to your previous position, expecting nothing to change. You’ve stepped out of your comfort zone, so learn to be comfortable with discomfort. Remember, just because you were proficient in FE doesn’t mean you’ll start as proficient in BE. You might begin as a junior again, but your established background might help you become proficient more quickly than the first time. Enjoy the Journey: Programming is all about challenges, learning, problem-solving, and creativity. Believe in yourself and enjoy the process. Ask Questions: Lots of them. It’s normal to feel lost at first, but with the right guidance, you’ll find your way. Ask Questions: Lots of them. It’s normal to feel lost at first, but with the right guidance, you’ll find your way. Embrace Discomfort: Don’t cling to your previous position, expecting nothing to change. You’ve stepped out of your comfort zone, so learn to be comfortable with discomfort. Remember, just because you were proficient in FE doesn’t mean you’ll start as proficient in BE. You might begin as a junior again, but your established background might help you become proficient more quickly than the first time. Embrace Discomfort: Don’t cling to your previous position, expecting nothing to change. You’ve stepped out of your comfort zone, so learn to be comfortable with discomfort. Remember, just because you were proficient in FE doesn’t mean you’ll start as proficient in BE. You might begin as a junior again, but your established background might help you become proficient more quickly than the first time. Enjoy the Journey: Programming is all about challenges, learning, problem-solving, and creativity. Believe in yourself and enjoy the process. Enjoy the Journey: Programming is all about challenges, learning, problem-solving, and creativity. Believe in yourself and enjoy the process. Embrace the journey with enthusiasm. Programming is about facing challenges, continuous learning, solving problems, and being creative. Believe in yourself, and good luck on your journey!

From Front-End to Back-End: A Personal Journey Through the Programming Field

Debugging Kubernetes: Part 1 - Introduction and Core Concepts

The dream of homeownership is fading for many. A global housing crisis is making it a struggle for millions to find affordable places to live. The burden is heavy, with people across the world forced to spend a crippling amount of their income just for rent. In the United States alone, Barron's estimates a staggering 11 million people are spending over half their income to keep a roof over their heads. There is a reason why this is called a global housing crunch: UN Habitat estimates we need to build an incredible 96,000 new affordable homes every single day to house the 3 billion people who will need proper housing by 2030. Solving the global housing crisis also requires innovative solutions such as blockchain technology. In this interview, Adrian Gaffor, the CEO and founder of The Real Lifestyle Company(TRL)shared valuable insights into the role of blockchain technology in the search for a lasting solution to the global housing problem. Mr. Gaffor, can you briefly tell us about yourself and your route to web3? Sure! I Co-founded and currently hold an active role as the Board Member and Group Chief Executive Officer at Esports Business Network (EBN), one of the leading esports marketing groups in Asia. Our focus over the years was to develop the esports industry by bringing brands into the World of gaming and esports across multiple regions to enable brands and communities to thrive through our ecosystem. We have activated across 19 markets with the most recent expansions being in the Middle East and Africa, and worked with over 60+ brands, publishers and developers throughout our ecosystem including; Mountain Dew, Red Bull, Valve, Riot Games and Tencent Games to name a few. We started working with web3 brands in early 2020 which sparked a new area of interest for me personally. With successful case studies built for our clients in the web3 gaming and esports space, this developed into a passion of discovery where I sought to find the segment of the industry that excited me the most, and finally landed on the Real World Asset (RWA) space. Creating businesses and frameworks that not only solve problems in the world today but are defining in creating the future is something of a passion, and that’s basically our starting point. We started with a simple question: “how do we enable people to live borderless, live seamlessly and live accessible across the globe?”. According to the World Bank, 1.6 billion people will be affected by the global housing shortage by 2025. How worse can it get? The forecast you have credited to the World Bank highlights a critical and escalating issue staring us in the face. As urban populations grow, the strain on housing increases, leading to not just higher costs but also to compromised living conditions and this is present in most countries around the world. If this problem is not adequately addressed, this housing shortage could drastically increase urban slums, elevate homelessness rates, and widen the gap in socio-economic conditions globally. Adequate measures must be taken by the United Nations and all stakeholders to prevent it. We came onboard because we are deeply concerned about these developments and are dedicated to innovating real solutions that make housing more accessible and affordable across the globe. What does democratizing access to the global housing markets entail? In the real sense, democratizing access to global housing markets simply means enabling people from all walks of life to participate in and benefit from real estate opportunities—regardless of their geographic or economic constraints. This largely involves lowering entry barriers through innovative financing models and leveraging technology such as blockchain to simplify and secure transactions, thus opening up the global real estate market to investors and homeowners who were previously sidelined or shut out by the status quo. How do you explain the accessibility and affordability of homeownership? Accessibility simply means removing traditional hurdles such as hefty down payments and strict mortgage requirements. We focus on affordability so that homeownership costs remain within the economic reach of people across various income levels. I strongly believe that these can be achieved through fractional ownership and rent-to-own models(which has practical demonstrations on the blockchain), enabling customers to gradually invest in or own homes in a financially manageable way, aligning with their budget and lifestyle needs. Can you take us through the founding of The Real Lifestyle Company(TRL)? The Real Lifestyle (TRL) was born from a vision to transform the traditional real estate landscape. Recognizing the significant barriers that prevent many from entering the housing market, we sought to create solutions that not only simplify the investment and ownership process but also make it inclusive. It is our cardinal philosophy that the challenge of securing affordable housing undermines the foundational needs of Maslow’s Hierarchy. Essential needs like shelter should be easily within everyone’s reach, yet today’s youth are confronted with a grim reality. By harnessing blockchain technology, we have developed platforms that offer flexible, transparent, and efficient pathways to property investment and ownership, with the sole purpose of making real estate accessible to a broader, more diverse audience across the world. How do you leverage blockchain technology as a RWA company? The use of blockchain technology is pivotal in our approach to handling the tokenization of real world assets to the maximum advantage of the investors. It basically empowers us to tokenize properties, breaking down the value into digital shares that can be easily and transparently traded on-chain and in real time. This technology enhances the flow of more liquidity into the market and lowers the barriers to entry for real estate investments, creating opportunities for millions of people across the world. Furthermore, blockchain's inherent security and transparency instill greater confidence among investors, ensuring that every transaction is recorded immutably. We set out to build an ecosystem that fluidly integrates the tangibility of the real world with blockchain technology. What are the some of the home affordability problems that must be addressed? The prohibitive costs associated with entering the housing market, the rigidity of traditional homeownership, and the geographical limitations tied to property investments are some of the affordable housing challenges that we believe must be tackled once and for all. By introducing models like fractional ownership and home subscription services, we seek to create solutions that lower financial entry points, provide flexibility in living arrangements, and present opportunities for global property investments, thereby enabling a lifestyle unbound by traditional constraints. Our belief is that choice equals freedom. By giving people the freedom to make choices that best align with their life’s vision, we’re enabling them to live their best lives. Is there any success story you would like to share with our readers? Instead of a story, I will share a mindset that has helped me throughout my journey; “you must know what rock bottom feels like to understand how to build a solid foundation for yourself”. What does it take to run  a digital asset company? There is really nothing magical about it. My modus operandi is simple. I call this the TRL octagon of strength because they are eight in number, what it takes to run a digital asset company is": clear vision, great people, value driven ecosystem, dynamic partners, secure technology, robust legal framework, diversified revenue model, and evolving investment strategy. Any parting words? It’s time to Live For Real.  The future that we envision is one where assets like Real Estate should be transacted seamlessly, transparently, without borders, enabling the lifestyle of the future. Watch this space! You can also read my previous HackerNoon articles on how blockchain is a viable tool to combat global affordable housing crisis: https://hackernoon.com/re-housing-11-million-americans-using-dao The dream of homeownership is fading for many. A global housing crisis is making it a struggle for millions to find affordable places to live. The burden is heavy, with people across the world forced to spend a crippling amount of their income just for rent. In the United States alone, Barron's estimates a staggering 11 million people are spending over half their income to keep a roof over their heads. Barron's There is a reason why this is called a global housing crunch: UN Habitat estimates we need to build an incredible 96,000 new affordable homes every single day to house the 3 billion people who will need proper housing by 2030. Habitat Solving the global housing crisis also requires innovative solutions such as blockchain technology. In this interview, Adrian Gaffor , the CEO and founder of The Real Lifestyle Company(TRL)shared valuable insights into the role of blockchain technology in the search for a lasting solution to the global housing problem. global housing crisis Adrian Gaffor Mr. Gaffor, can you briefly tell us about yourself and your route to web3? Sure! I Co-founded and currently hold an active role as the Board Member and Group Chief Executive Officer at Esports Business Network (EBN), one of the leading esports marketing groups in Asia. Our focus over the years was to develop the esports industry by bringing brands into the World of gaming and esports across multiple regions to enable brands and communities to thrive through our ecosystem. We have activated across 19 markets with the most recent expansions being in the Middle East and Africa, and worked with over 60+ brands, publishers and developers throughout our ecosystem including; Mountain Dew, Red Bull, Valve, Riot Games and Tencent Games to name a few. We started working with web3 brands in early 2020 which sparked a new area of interest for me personally. With successful case studies built for our clients in the web3 gaming and esports space, this developed into a passion of discovery where I sought to find the segment of the industry that excited me the most, and finally landed on the Real World Asset (RWA) space. Creating businesses and frameworks that not only solve problems in the world today but are defining in creating the future is something of a passion, and that’s basically our starting point. We started with a simple question: “how do we enable people to live borderless, live seamlessly and live accessible across the globe?”. “how do we enable people to live borderless, live seamlessly and live accessible across the globe?”. According to the World Bank, 1.6 billion people will be affected by the global housing shortage by 2025. How worse can it get? The forecast you have credited to the World Bank highlights a critical and escalating issue staring us in the face. As urban populations grow, the strain on housing increases, leading to not just higher costs but also to compromised living conditions and this is present in most countries around the world. If this problem is not adequately addressed, this housing shortage could drastically increase urban slums, elevate homelessness rates, and widen the gap in socio-economic conditions globally. Adequate measures must be taken by the United Nations and all stakeholders to prevent it. We came onboard because we are deeply concerned about these developments and are dedicated to innovating real solutions that make housing more accessible and affordable across the globe. What does democratizing access to the global housing markets entail? In the real sense, democratizing access to global housing markets simply means enabling people from all walks of life to participate in and benefit from real estate opportunities—regardless of their geographic or economic constraints. This largely involves lowering entry barriers through innovative financing models and leveraging technology such as blockchain to simplify and secure transactions, thus opening up the global real estate market to investors and homeowners who were previously sidelined or shut out by the status quo. How do you explain the accessibility and affordability of homeownership? Accessibility simply means removing traditional hurdles such as hefty down payments and strict mortgage requirements. We focus on affordability so that homeownership costs remain within the economic reach of people across various income levels. I strongly believe that these can be achieved through fractional ownership and rent-to-own models(which has practical demonstrations on the blockchain), enabling customers to gradually invest in or own homes in a financially manageable way, aligning with their budget and lifestyle needs. Can you take us through the founding of The Real Lifestyle Company(TRL)? The Real Lifestyle (TRL) was born from a vision to transform the traditional real estate landscape. Recognizing the significant barriers that prevent many from entering the housing market, we sought to create solutions that not only simplify the investment and ownership process but also make it inclusive. It is our cardinal philosophy that the challenge of securing affordable housing undermines the foundational needs of Maslow’s Hierarchy. Essential needs like shelter should be easily within everyone’s reach, yet today’s youth are confronted with a grim reality. By harnessing blockchain technology, we have developed platforms that offer flexible, transparent, and efficient pathways to property investment and ownership, with the sole purpose of making real estate accessible to a broader, more diverse audience across the world. How do you leverage blockchain technology as a RWA company? How do you leverage blockchain technology as a RWA company? The use of blockchain technology is pivotal in our approach to handling the tokenization of real world assets to the maximum advantage of the investors. It basically empowers us to tokenize properties, breaking down the value into digital shares that can be easily and transparently traded on-chain and in real time. This technology enhances the flow of more liquidity into the market and lowers the barriers to entry for real estate investments, creating opportunities for millions of people across the world. Furthermore, blockchain's inherent security and transparency instill greater confidence among investors, ensuring that every transaction is recorded immutably. We set out to build an ecosystem that fluidly integrates the tangibility of the real world with blockchain technology. What are the some of the home affordability problems that must be addressed? The prohibitive costs associated with entering the housing market, the rigidity of traditional homeownership, and the geographical limitations tied to property investments are some of the affordable housing challenges that we believe must be tackled once and for all. By introducing models like fractional ownership and home subscription services, we seek to create solutions that lower financial entry points, provide flexibility in living arrangements, and present opportunities for global property investments, thereby enabling a lifestyle unbound by traditional constraints. Our belief is that choice equals freedom. By giving people the freedom to make choices that best align with their life’s vision, we’re enabling them to live their best lives. Is there any success story you would like to share with our readers? Instead of a story, I will share a mindset that has helped me throughout my journey; “you must know what rock bottom feels like to understand how to build a solid foundation for yourself”. What does it take to run  a digital asset company? There is really nothing magical about it. My modus operandi is simple. I call this the TRL octagon of strength because they are eight in number, what it takes to run a digital asset company is": clear vision, great people, value driven ecosystem, dynamic partners, secure technology, robust legal framework, diversified revenue model, and evolving investment strategy. clear vision, great people, value driven ecosystem, dynamic partners, secure technology, robust legal framework, diversified revenue model, and evolving investment strategy. Any parting words? It’s time to Live For Real.  The future that we envision is one where assets like Real Estate should be transacted seamlessly, transparently, without borders, enabling the lifestyle of the future. Watch this space! You can also read my previous HackerNoon articles on how blockchain is a viable tool to combat global affordable housing crisis: https://hackernoon.com/re-housing-11-million-americans-using-dao You can also read my previous HackerNoon articles on how blockchain is a viable tool to combat global affordable housing crisis: https://hackernoon.com/re-housing-11-million-americans-using-dao https://hackernoon.com/re-housing-11-million-americans-using-dao

Can Blockchain Solve The Affordable Housing Crisis? Exclusive Interview With TRL CEO Adrian Gaffor

Authors:
(1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research;
(2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind;
(3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Participant Interviews Participant Surveys Discussion and Future Work Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS 6 PARTICIPANT SURVEYS 6.1 Qualitative and Quantitative Results on Participant Surveys Of the total 15 study participants, 13 provided responses on our post-session feedback form. The form gave participants the following instruction: “When answering these questions, please reflect on the interactive co-authorship session as well as considering the use of an interactive AI system like Dramatron in the future”, and asked nine questions. Each question could be answered using a Likert-type scale ranging from Strongly Disagree 1 to 5 Strongly Agree. These questions are slightly adapted from Yuan et al. [122] and Stevenson et al. [104]: (1) I found the AI system helpful, (2) I felt like I was collaborating with the AI system, (3) I found it easy to write with the AI system, (4) I enjoyed writing with the AI system, (5) I was able to express my creative goals while writing with the AI system, (6) The script(s) written with the AI system feel unique, (7) I feel I have ownership over the created script(s), (8) I was surprised by the responses from the AI system, and (9) I’m proud of the final outputs. We also asked five free-form questions. Two questions aimed at assessing the participants’ exposure to AI writing tools (In a few words: what is your experience in using AI tools for writing for theatre of film or during performance on stage?) and their industry experience (In a few words: what is your experience in theatre or film/TV?). We used these questions to manually define a binary indicator variable (Has experience of AI writing tools) and a three-class category for their primary domain of expertise (Improvisation, Scripted Theatre and Film/TV). Three more questions gave participants an opportunity to provide developmental feedback about the system: What is one thing that the AI system did well?, What is one improvement for the AI system? and Please provide any comments, reflections, or open questions that came up for you during the co-authorship session or when answering this survey. Aggregated responses are plotted on Figure 5 (Left), and (Right) factors responses to the question on collaboration by industry background and experience (see Figure 7 for more). The results are summarized below. 6.1.1 AI-generated scripts can be surprising, enjoyable, and unique. The statements that received the strongest positive response were I was surprised by the responses from the AI system (92% participants agreed, 46% strongly agreed) followed by I enjoyed writing with the AI system (77% agreed, 69% strongly agreed) and The script(s) written with the AI system feel unique (69% agreed, 46% strongly agreed), referring to lines of dialogue or connections between characters. 6.1.2 Dramatron can be helpful for expressing creative goals for screenwriting. Positive responses to statements I felt like I was collaborating with the AI system, (77% agreed, 54% strongly agreed), I found the AI system helpful (84% agreed, 38% strongly agreed) and I was able to express my creative goals with the AI system (77% agreed, 23% strongly agreed) suggest a fruitful interaction between a writer and an interactive AI writing tool, in particular for ideation, through multiple choices, twists to the narrative, or specific details. 6.1.3 Dramatron outputs were preferred for screenplays over theatre scripts. We noticed that the subgroup of respondents with a film or TV background gave the highest scores for the surprise, enjoyment, collaboration, helpful, expression, ease and ownership questions. Respondents with theatre background judged enjoyment, collaboration, helpful, creative, expression, ease, proud and ownership the least. This reinforces our observations during interviews (see the criticism of log line-based conditioning for generating theatre scripts in Section 5.6). 6.1.4 Dramatron’s hierarchical prompting interface is moderately easy to use. Participants were more split about the ease of use of the tool (61% agreed) and whether they felt proud about the output of the system (46% agreed), with one participant strongly disagreeing. We note that these two answers were highly correlated (𝑟 = 0.9), and that several questions had a relatively strong correlation (𝑟 ≥ 0.66): helpful, collaboration, ease, enjoyment, expression and ease. As observed during interviews, recurrent criticism was about Dramatron needing detailed log lines to start the generation. 6.1.5 Participants felt a low level of ownership for the co-written scripts. More than half of respondents felt they did not own the result of the AI system. One participant commented that the generated script was only a starting point provided by the AI and that the writer still needed to create their own script. One interpretation is that Dramatron is seen by the industry experts not as much as a full script writing tool but rather as a learning tool to practice writing and a source of inspiration that generates “not necessarily completed works so much as provocations” (p7). 6.2 Quantitative Observations We observed that participants would skim the text and remark on specific inspiring lines, but were less focused on the overall coherence of the discourse. That said, participants did notice if the dialogue of a scene was unrelated to that scene’s beat or log line, and most noticed when the generation had excessive repetition. Thus, we pair our qualitative results with a small set of descriptive statistics measuring these features and relevant to our participants’ feedback. Lemma-based Jaccard similarity is a metric measuring the overlap between two sets of lemmatised vocabularies. We found that when participants made multiple generations for a scene description, they generally did not choose outputs with greatest Jaccard similarity. We performed a one-sided Wilcox signed-rank test on Jaccard similarity score differences, testing whether chosen output seeds were more similar to the log line provided by participants. We found that the beats for chosen generations are not more similar to the log line when compared to the beats for generations not chosen (W = 28, p = 0.926). In fact, there is a strong trend in the opposite direction. This suggests that word overlap is not predictive of which output generation is selected. Using an open-source repetition scorer [123], we examined the repetition scores for texts where participants generated multiple dialogue outputs but chose only one to incorporate into the script (𝑛 = 3). After computing a one-sided Wilcox signed-rank test, we found no significant difference between the repetition scores for chosen Dramatron output against alternative seed outputs (W = 57, p = 0.467). This finding aligns with our observations in the interactive sessions where participants tended to delete degenerate repetitions, or as one participant (p13) put it: "[Dramatron] puts meat on the bones... And then you trim the fat by going back and forth." We interpret our distance measures as indicators of engagement with Dramatron. Figure 6 (right) displays mean relative Levenshtein distances for each participant, along with Jaccard similarity. We also report document length differences, to show the directionality of editing (i.e. whether participants are adding or deleting material to or from Dramatron output. Figure 6 (left) shows the mean document length difference by participant. Figure 6 (middle) shows these measures normalised and grouped by type of generation. We notice that four participants tended to add to generated outputs (three of them, p6, p13, and p15 expressed interest in staging the resulting scripts) while eight others tended to remove from and curate the outputs (including p1 who staged productions). We also notice that the plot outline was the least edited part of the script. During co-authorship we observed that participants tended to delete longer location descriptions and parts of dialogue (especially loops), and rewrite the character descriptions. This paper is available on arxiv under CC 4.0 license. Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Authors: Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Abstract and Intro Storytelling, The Shape of Stories, and Log Lines Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Evaluating Text Generated by Large Language Models Participant Interviews Participant Interviews Participant Surveys Participant Surveys Discussion and Future Work Discussion and Future Work Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS G. CO-WRITTEN SCRIPTS 6 PARTICIPANT SURVEYS 6.1 Qualitative and Quantitative Results on Participant Surveys Of the total 15 study participants, 13 provided responses on our post-session feedback form. The form gave participants the following instruction: “When answering these questions, please reflect on the interactive co-authorship session as well as considering the use of an interactive AI system like Dramatron in the future”, and asked nine questions. Each question could be answered using a Likert-type scale ranging from Strongly Disagree 1 to 5 Strongly Agree. These questions are slightly adapted from Yuan et al. [122] and Stevenson et al. [104]: (1) I found the AI system helpful, (2) I felt like I was collaborating with the AI system, (3) I found it easy to write with the AI system, (4) I enjoyed writing with the AI system, (5) I was able to express my creative goals while writing with the AI system, (6) The script(s) written with the AI system feel unique, (7) I feel I have ownership over the created script(s), (8) I was surprised by the responses from the AI system, and (9) I’m proud of the final outputs. We also asked five free-form questions. Two questions aimed at assessing the participants’ exposure to AI writing tools (In a few words: what is your experience in using AI tools for writing for theatre of film or during performance on stage?) and their industry experience (In a few words: what is your experience in theatre or film/TV?). We used these questions to manually define a binary indicator variable (Has experience of AI writing tools) and a three-class category for their primary domain of expertise (Improvisation, Scripted Theatre and Film/TV). Three more questions gave participants an opportunity to provide developmental feedback about the system: What is one thing that the AI system did well?, What is one improvement for the AI system? and Please provide any comments, reflections, or open questions that came up for you during the co-authorship session or when answering this survey. Aggregated responses are plotted on Figure 5 (Left), and (Right) factors responses to the question on collaboration by industry background and experience (see Figure 7 for more). The results are summarized below. collaboration 6.1.1 AI-generated scripts can be surprising, enjoyable, and unique. The statements that received the strongest positive response were I was surprised by the responses from the AI system (92% participants agreed, 46% strongly agreed) followed by I enjoyed writing with the AI system (77% agreed, 69% strongly agreed) and The script(s) written with the AI system feel unique (69% agreed, 46% strongly agreed), referring to lines of dialogue or connections between characters. 6.1.1 AI-generated scripts can be surprising, enjoyable, and unique. surprised enjoyed unique 6.1.2 Dramatron can be helpful for expressing creative goals for screenwriting. Positive responses to statements I felt like I was collaborating with the AI system, (77% agreed, 54% strongly agreed), I found the AI system helpful (84% agreed, 38% strongly agreed) and I was able to express my creative goals with the AI system (77% agreed, 23% strongly agreed) suggest a fruitful interaction between a writer and an interactive AI writing tool, in particular for ideation, through multiple choices, twists to the narrative, or specific details. collaborating express 6.1.3 Dramatron outputs were preferred for screenplays over theatre scripts. We noticed that the subgroup of respondents with a film or TV background gave the highest scores for the surprise, enjoyment, collaboration, helpful, expression, ease and ownership questions. Respondents with theatre background judged enjoyment, collaboration, helpful, creative, expression, ease, proud and ownership the least. This reinforces our observations during interviews (see the criticism of log line-based conditioning for generating theatre scripts in Section 5.6). 6.1.3 Dramatron outputs were preferred for screenplays over theatre scripts. 6.1.4 Dramatron’s hierarchical prompting interface is moderately easy to use. Participants were more split about the ease of use of the tool (61% agreed) and whether they felt proud about the output of the system (46% agreed), with one participant strongly disagreeing. We note that these two answers were highly correlated (𝑟 = 0.9), and that several questions had a relatively strong correlation (𝑟 ≥ 0.66): helpful, collaboration, ease, enjoyment, expression and ease. As observed during interviews, recurrent criticism was about Dramatron needing detailed log lines to start the generation. 6.1.4 Dramatron’s hierarchical prompting interface is moderately easy to use. 6.1.5 Participants felt a low level of ownership for the co-written scripts. More than half of respondents felt they did not own the result of the AI system. One participant commented that the generated script was only a starting point provided by the AI and that the writer still needed to create their own script. One interpretation is that Dramatron is seen by the industry experts not as much as a full script writing tool but rather as a learning tool to practice writing and a source of inspiration that generates “not necessarily completed works so much as provocations” (p7). 6.1.5 Participants felt a low level of ownership for the co-written scripts. 6.2 Quantitative Observations We observed that participants would skim the text and remark on specific inspiring lines, but were less focused on the overall coherence of the discourse. That said, participants did notice if the dialogue of a scene was unrelated to that scene’s beat or log line, and most noticed when the generation had excessive repetition. Thus, we pair our qualitative results with a small set of descriptive statistics measuring these features and relevant to our participants’ feedback. Lemma-based Jaccard similarity is a metric measuring the overlap between two sets of lemmatised vocabularies. We found that when participants made multiple generations for a scene description, they generally did not choose outputs with greatest Jaccard similarity. We performed a one-sided Wilcox signed-rank test on Jaccard similarity score differences, testing whether chosen output seeds were more similar to the log line provided by participants. We found that the beats for chosen generations are not more similar to the log line when compared to the beats for generations not chosen (W = 28, p = 0.926). In fact, there is a strong trend in the opposite direction. This suggests that word overlap is not predictive of which output generation is selected. Using an open-source repetition scorer [123], we examined the repetition scores for texts where participants generated multiple dialogue outputs but chose only one to incorporate into the script (𝑛 = 3). After computing a one-sided Wilcox signed-rank test, we found no significant difference between the repetition scores for chosen Dramatron output against alternative seed outputs (W = 57, p = 0.467). This finding aligns with our observations in the interactive sessions where participants tended to delete degenerate repetitions, or as one participant (p13) put it: "[Dramatron] puts meat on the bones... And then you trim the fat by going back and forth." We interpret our distance measures as indicators of engagement with Dramatron. Figure 6 (right) displays mean relative Levenshtein distances for each participant, along with Jaccard similarity. We also report document length differences, to show the directionality of editing (i.e. whether participants are adding or deleting material to or from Dramatron output. Figure 6 (left) shows the mean document length difference by participant. Figure 6 (middle) shows these measures normalised and grouped by type of generation. We notice that four participants tended to add to generated outputs (three of them, p6, p13, and p15 expressed interest in staging the resulting scripts) while eight others tended to remove from and curate the outputs (including p1 who staged productions). We also notice that the plot outline was the least edited part of the script. During co-authorship we observed that participants tended to delete longer location descriptions and parts of dialogue (especially loops), and rewrite the character descriptions. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

What Users Say About Dramaton’s AI Writing Tool

Professional Screenwriters Share Pros and Cons of Co-Writing with AI

পেশাদার চিত্রনাট্যকাররা AI এর সাথে সহ-লেখার সুবিধা এবং অসুবিধাগুলি ভাগ করে নেন৷

Professionelle Drehbuchautoren teilen die Vor- und Nachteile des Co-Writings mit KI

Los guionistas profesionales comparten los pros y los contras de la coescritura con IA

Des scénaristes professionnels partagent les avantages et les inconvénients de la co-écriture avec l'IA

पेशेवर पटकथा लेखक एआई के साथ सह-लेखन के फायदे और नुकसान साझा करते हैं

プロの脚本家がAIとの共同執筆の長所と短所を語る

전문 시나리오 작가들이 공유하는 AI 공동 집필의 장단점

Roteiristas profissionais compartilham prós e contras da co-escrita com IA

Профессиональные сценаристы делятся плюсами и минусами совместного написания сценариев с искусственным интеллектом

Profesyonel Senaristler Yapay Zeka ile Birlikte Yazmanın Artılarını ve Eksilerini Paylaşıyor

Các nhà biên kịch chuyên nghiệp chia sẻ ưu và nhược điểm của việc đồng sáng tác với AI

专业编剧分享与人工智能合作写作的利弊

Authors:
(1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research;
(2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind;
(3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Participant Interviews Participant Surveys Discussion and Future Work Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS 2 STORYTELLING, THE SHAPE OF STORIES, AND LOG LINES Archaeological evidence including artists’ depictions of narratives on jars from as far back as 2400 BCE, show how ancient societies used the technologies of their time to help tell their stories [43]. In Poetics [5], Aristotle identified plot (or mythos) as the most important part of a tragic story—the other elements being characters, their thoughts, language, melody and spectacle. The plot is the sequence of actions that shape the story. Each plot point is coherent with, and a consequence of, the previous point(s). Aristotle introduced the well-known simple 3-point plot: beginning, middle, and end. There are many dramatic structures extending the work of Aristotle [82]. For example, one of the many adaptations of Freytag’s pyramid [40], popular in Western storytelling is illustrated in Fig. 2. It includes this sequence of plot points: Exposition, Inciting Incident, a Rising Action composed of a series of Conflicts, Complications, and Dilemmas, Climax, Falling Action, Resolution, and Dénouement. Seminal work in narrative analysis [58, 83, 93] suggests general but mutable structures present in storytelling processes across many linguistic and cultural traditions (e.g. abstract, orientation, complicating action, and coda narrative clauses). The finding that narratives in many societies and in various languages make use of large, modular elements has inspired the use of models of narrative in automated storytelling, as can be seen in fields such as computational narratology [53, 63]. The general structures found in the Structuralist narratology of Propp [83] and the Personal Experience Narratives of Labov and Waletzky [58] are aligned with Freytag’s pyramid, which we choose because it is more in line with the specific discourse genre of dramatic scripts, and arguably more familiar to the playwrights we engaged with over the course of our study. However, we note that our choice of narrative structure is not universal in scope, and is, in fact, "characteristically Western" [26, 53]. Alternative story shapes, or "story grammars" [94] are possible [6, 17]. Fig. 2 also shows an alternative story structure: the Hero’s Journey or Monomyth [15, 110]. For narrative coherence, we leverage plot beats in our hierarchical generation. Oftentimes the seed of narrative inspiration for a screenplay or theatre script is a log line [98]. The log line summarizes the story in a few sentences and is often adapted over the course of production due to creative team preferences and cultural references. Log lines typically contain the setting, protagonist, antagonist, a conflict or goal, and sometimes the inciting incident [8]. In this work, we use log lines to start the hierarchical story generation process because it contains plot elements in the answers to questions: Who? What? When and Where? How? Why? [101] This paper is available on arxiv under CC 4.0 license. Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Authors: Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Abstract and Intro Storytelling, The Shape of Stories, and Log Lines Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Evaluating Text Generated by Large Language Models Participant Interviews Participant Interviews Participant Surveys Participant Surveys Discussion and Future Work Discussion and Future Work Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS G. CO-WRITTEN SCRIPTS 2 STORYTELLING, THE SHAPE OF STORIES, AND LOG LINES Archaeological evidence including artists’ depictions of narratives on jars from as far back as 2400 BCE, show how ancient societies used the technologies of their time to help tell their stories [43]. In Poetics [5], Aristotle identified plot (or mythos) as the most important part of a tragic story—the other elements being characters, their thoughts, language, melody and spectacle. The plot is the sequence of actions that shape the story. Each plot point is coherent with, and a consequence of, the previous point(s). Aristotle introduced the well-known simple 3-point plot: beginning, middle, and end. There are many dramatic structures extending the work of Aristotle [82]. For example, one of the many adaptations of Freytag’s pyramid [40], popular in Western storytelling is illustrated in Fig. 2. It includes this sequence of plot points: Exposition, Inciting Incident, a Rising Action composed of a series of Conflicts, Complications, and Dilemmas, Climax, Falling Action, Resolution, and Dénouement. Seminal work in narrative analysis [58, 83, 93] suggests general but mutable structures present in storytelling processes across many linguistic and cultural traditions (e.g. abstract, orientation, complicating action, and coda narrative clauses). The finding that narratives in many societies and in various languages make use of large, modular elements has inspired the use of models of narrative in automated storytelling, as can be seen in fields such as computational narratology [53, 63]. The general structures found in the Structuralist narratology of Propp [83] and the Personal Experience Narratives of Labov and Waletzky [58] are aligned with Freytag’s pyramid, which we choose because it is more in line with the specific discourse genre of dramatic scripts, and arguably more familiar to the playwrights we engaged with over the course of our study. However, we note that our choice of narrative structure is not universal in scope, and is, in fact, "characteristically Western" [26, 53]. Alternative story shapes, or "story grammars" [94] are possible [6, 17]. Fig. 2 also shows an alternative story structure: the Hero’s Journey or Monomyth [15, 110]. For narrative coherence, we leverage plot beats in our hierarchical generation. Oftentimes the seed of narrative inspiration for a screenplay or theatre script is a log line [98]. The log line summarizes the story in a few sentences and is often adapted over the course of production due to creative team preferences and cultural references. Log lines typically contain the setting, protagonist, antagonist, a conflict or goal, and sometimes the inciting incident [8]. In this work, we use log lines to start the hierarchical story generation process because it contains plot elements in the answers to questions: Who? What? When and Where? How? Why? [101] This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Storytelling, The Shape of Stories, and Log Lines

Many people fail to grasp the wider meaning of the cryptocurrency movement and why it’s important to invest in projects that you find to be personally meaningful. When you use a currency, you are effectively supporting the country that the currency is designed to represent. This includes the legal, financial, and societal infrastructure that is in place, which is currently under gigantic strain in Western economies. This infrastructure has led to huge inequality, unaffordable rent, and an overall cost of living crisis - and it needs to be replaced. The most efficient method of replacement is a decentralized economy using cryptocurrencies as a payment mechanism. The Origins Of Cryptocurrency While many are aware of cryptocurrency now, few understand its prime purpose as a disruptive mechanism. The Bitcoin Genesis Block reads “The Times 03/Jan/2009 Chancellor on the brink of second bailout for banks,” a reference to the fact that banks were being paid off with taxpayer money. Meanwhile, taxpayers were often evicted from their own homes as a result of a bank-induced crisis. The mainstream media would spend more than 10 years in an outright assault against Bitcoin, calling it a Ponzi scheme and damaging to the environment. This is ironic, given that the banking system relies nearly exclusively on money printing to maintain its dominance. It has recently changed its tune somewhat. While initially trading for practically nothing, Bitcoin is now valued at ~$65,000 and could soar higher in 2025 and beyond. Ethereum, meanwhile, also has a strong price forecast according to industry and AI analysis, potentially hitting $3,500 in the near future. Such impressive valuations are no longer representative of arbitrary price increases but signal a changing lifestyle and investor sentiment. Bitcoin is increasingly accepted in smaller regions and is even an official legal tender for countries like El Salvador. BTC is further being used in many smaller regions in South Africa. At present, there are a plethora of cryptocurrencies to choose from, though the majority have next to zero utility value or long-term relevance. Choosing Sustainable Crypto Projects When selecting your cryptocurrency, you can approach it from a purely financial perspective, aiming to capitalize on technical analysis. But you can also approach it from a more humanitarian standpoint. It must be kept in mind that financial greed and the failure to hold politicians accountable are what has led to such a societal collapse - your investment makes a difference, so please make sure you allocate your funds to projects that make the best use of it for wider society. For instance, there are decentralized options involved in the renewable energy sector, such as PowerLedger. This ecosystem aspires to create a P2P energy economy without third parties overcharging for an efficient energy model. Clearly, it’s more efficient to send energy to a nearby household than to transfer it to a large national hub, where it is potentially sold and transferred to other countries. The media industry has come under fire from its financial sources and biases, which encourage outlets to represent large conglomerates and uphold certain narratives while failing to genuinely represent the views of ordinary people. Multiple projects are involved in the field of honest reporting and further provide alternative means of expression. This is what the meme industry is at its core. In spite of many degen coins and a bad reputation, they offer a means of expression for subcultural communities as an alternative form of expression. Veme offers a platform for decentralized community expression through video memes, which could be the next evolution of memes. Another sector is privacy, and there are plenty of privacy coins to choose from that are hard to trace. In particular, Monero which tends to have a stronger reputation than other coins. The Brave browser enables online privacy, so outside of investment, you can use these tools as a form of support. The list of projects and industries is too large to expand upon. But some projects are much more holistic than others and have better long-term potential. You can find those that match your ideological preferences while also having strong technical and financial architectures. Don’t invest in a well-meaning project if it has poor management or sloppy fundamentals - such an endeavor won’t help anyone at all. Crypto Integrity  - The Golden Investment Path Many people make the mistake of failing to invest in projects that are personally meaningful to them. This means that your money could be going towards an area that you don’t really believe in. Supporting an industry that you are against is actually a form of dishonesty against yourself! Do technical and personal research to align as many areas as possible for integrated, sustainable investment. This concept is also gaining traction in the Web2 industry with the rise of ESG investment. Align your personal values with your financial aspirations, and you might find it helpful not only to your bank balance - but to your overall well-being and peace of mind. Many people fail to grasp the wider meaning of the cryptocurrency movement and why it’s important to invest in projects that you find to be personally meaningful. Many people fail to grasp the wider meaning of the cryptocurrency movement and why it’s important to invest in projects that you find to be personally meaningful. When you use a currency, you are effectively supporting the country that the currency is designed to represent. This includes the legal, financial, and societal infrastructure that is in place, which is currently under gigantic strain in Western economies. This infrastructure has led to huge inequality, unaffordable rent, and an overall cost of living crisis - and it needs to be replaced. The most efficient method of replacement is a decentralized economy using cryptocurrencies as a payment mechanism. cost of living crisis The Origins Of Cryptocurrency While many are aware of cryptocurrency now, few understand its prime purpose as a disruptive mechanism. The Bitcoin Genesis Block reads “The Times 03/Jan/2009 Chancellor on the brink of second bailout for banks,” a reference to the fact that banks were being paid off with taxpayer money. Meanwhile, taxpayers were often evicted from their own homes as a result of a bank-induced crisis. The Bitcoin Genesis Block The Bitcoin Genesis Block “The Times 03/Jan/2009 Chancellor on the brink of second bailout for banks,” The mainstream media would spend more than 10 years in an outright assault against Bitcoin, calling it a Ponzi scheme and damaging to the environment. This is ironic, given that the banking system relies nearly exclusively on money printing to maintain its dominance. It has recently changed its tune somewhat. While initially trading for practically nothing, Bitcoin is now valued at ~ $65,000 and could soar higher in 2025 and beyond. Ethereum, meanwhile, also has a strong price forecast according to industry and AI analysis, potentially hitting $3,500 in the near future. S uch impressive valuations are no longer representative of arbitrary price increases but signal a changing lifestyle and investor sentiment. $65,000 strong price forecast strong price forecast $3,500 S Bitcoin is increasingly accepted in smaller regions and is even an official legal tender for countries like El Salvador. BTC is further being used in many smaller regions in South Africa . At present, there are a plethora of cryptocurrencies to choose from, though the majority have next to zero utility value or long-term relevance. smaller regions in South Africa Choosing Sustainable Crypto Projects When selecting your cryptocurrency, you can approach it from a purely financial perspective, aiming to capitalize on technical analysis. But you can also approach it from a more humanitarian standpoint. It must be kept in mind that financial greed and the failure to hold politicians accountable are what has led to such a societal collapse - your investment makes a difference, so please make sure you allocate your funds to projects that make the best use of it for wider society. For instance, there are decentralized options involved in the renewable energy sector, such as PowerLedger. This ecosystem aspires to create a P2P energy economy without third parties overcharging for an efficient energy model. Clearly, it’s more efficient to send energy to a nearby household than to transfer it to a large national hub, where it is potentially sold and transferred to other countries. The media industry has come under fire from its financial sources and biases, which encourage outlets to represent large conglomerates and uphold certain narratives while failing to genuinely represent the views of ordinary people. Multiple projects are involved in the field of honest reporting and further provide alternative means of expression. This is what the meme industry is at its core. In spite of many degen coins and a bad reputation, they offer a means of expression for subcultural communities as an alternative form of expression. Veme offers a platform for decentralized community expression through video memes, which could be the next evolution of memes. Another sector is privacy, and there are plenty of privacy coins to choose from that are hard to trace. In particular, Monero which tends to have a stronger reputation than other coins. The Brave browser enables online privacy, so outside of investment, you can use these tools as a form of support. The list of projects and industries is too large to expand upon. But some projects are much more holistic than others and have better long-term potential. You can find those that match your ideological preferences while also having strong technical and financial architectures. Don’t invest in a well-meaning project if it has poor management or sloppy fundamentals - such an endeavor won’t help anyone at all. Crypto Integrity  - The Golden Investment Path Many people make the mistake of failing to invest in projects that are personally meaningful to them. This means that your money could be going towards an area that you don’t really believe in. Supporting an industry that you are against is actually a form of dishonesty against yourself! Do technical and personal research to align as many areas as possible for integrated, sustainable investment. This concept is also gaining traction in the Web2 industry with the rise of ESG investment. Align your personal values with your financial aspirations, and you might find it helpful not only to your bank balance - but to your overall well-being and peace of mind. rise of ESG investment.

Integrity And Sustainability In Crypto Investment - Why It Matters More Than You Think

In this article, I'm going to discuss something really important. If you're a data analyst or you want to learn data analysis, please watch this video till the end because it's really important. The topic of this video is: Can data analysts survive because ChatGPT-4o is changing the game? If you want to know why, please read this article till the end, and you will know everything; plus, you'll see an example of how you can analyze anything within a couple of minutes. Era of ChatGPT-4o As you may know, on May 16th, OpenAI announced the new ChatGPT-4o. It has a lot of improvements, and one of them is related to data analysis. In this article, I will highlight a few things that may help you decide on your data analysis career path or learning path. I will also show you the improved version of GPT-4o and how it can analyze data. My Thoughts Let me share my thoughts regarding the data analysis learning path. If you ask me whether you should learn it or not, I can't give you a direct answer, but I will explain my thinking process, and maybe you can make a decision based on that. I took a data analysis course about two years ago. It was the Google Data Analytics certification, and it took me around six months to finish everything. Now, when I think about my learning journey and up-to-date things, I understand that many things I learned might be useless because of ChatGPT. Of course, I need to understand the main concepts, foundations, and certain things, but I don't have to do a lot of manual work related to cleaning, visualizing, etc., manually anymore. Most things can be done with ChatGPT. So, if you are on your learning path right now or considering taking a data analysis course, you can go ahead, but keep in mind that you need to stay updated with new technology and trends, especially with AI. Many courses don't have up-to-date information, and things are changing rapidly, so in your learning, you definitely should include AI and ChatGPT. What will happen with data analysis in general? I think within two to three years, this profession will not be the same anymore. If people don't utilize AI, they will fall behind, and this will be a big problem. Many people will probably lose their jobs, and this is really scary. Disclaimer: These are just my thoughts. I don't want to discourage you from your learning path or scare you, just sharing my thoughts. Improvements of ChatGPT-4o for Data Analysis Now, let's go back to the improved version of ChatGPT. What has improved? There are four things I would like to highlight: File Integration: You don't have to upload files from your computer anymore. You can upload them from Google Drive or Microsoft OneDrive. **   ** Interactive Tables: You can edit tables inside ChatGPT. For example, if you ask ChatGPT to bring your data in a table format, you'll be able to make changes inside those tables without changing your data set. Everything can be changed directly inside ChatGPT. **   ** Customizable Charts: You can customize charts, download them, and edit them, all directly within ChatGPT. **   ** Security and Privacy: If you are using ChatGPT Plus, Team, or Enterprise, you will be able to turn off training on your data. This means ChatGPT won't use your data for training purposes. ChatGPT-4o Data Analysis Demo Now, the important part. Let's see how we can analyze a dataset with ChatGPT-4o. In this one-minute video, ChatGPT will analyze my dataset without detailed direction. This is my simple prompt, and look at what I get: https://youtu.be/w9DETYpYMS0?si=KEGL8GM2s0Sauzif&t=246&embedable=true As you can see, it's amazing and mind-blowing. Of course, we can improve our analysis by writing better and more detailed prompts. At the end of the analysis, you can ask ChatGPT to create a presentation or PDF file with all the findings and visualizations. It's amazing. Don't expect perfection, but you can add beauty yourself. As you can see, for a regular data analyst, this work would take around a week or two, but ChatGPT did it within a minute. It's wow! That's it! I hope you enjoyed this article. Thank you so much for supporting me with your likes and comments! In this article, I'm going to discuss something really important. If you're a data analyst or you want to learn data analysis, please watch this video till the end because it's really important. The topic of this video is: Can data analysts survive because ChatGPT-4o is changing the game? If you want to know why, please read this article till the end, and you will know everything; plus, you'll see an example of how you can analyze anything within a couple of minutes. Era of ChatGPT-4o Era of ChatGPT-4o As you may know, on May 16th, OpenAI announced the new ChatGPT-4o. It has a lot of improvements, and one of them is related to data analysis. In this article, I will highlight a few things that may help you decide on your data analysis career path or learning path. I will also show you the improved version of GPT-4o and how it can analyze data. My Thoughts My Thoughts Let me share my thoughts regarding the data analysis learning path. If you ask me whether you should learn it or not, I can't give you a direct answer, but I will explain my thinking process, and maybe you can make a decision based on that. I took a data analysis course about two years ago. It was the Google Data Analytics certification, and it took me around six months to finish everything. Now, when I think about my learning journey and up-to-date things, I understand that many things I learned might be useless because of ChatGPT. Of course, I need to understand the main concepts, foundations, and certain things, but I don't have to do a lot of manual work related to cleaning, visualizing, etc., manually anymore. Most things can be done with ChatGPT. So, if you are on your learning path right now or considering taking a data analysis course, you can go ahead, but keep in mind that you need to stay updated with new technology and trends, especially with AI. Many courses don't have up-to-date information, and things are changing rapidly, so in your learning, you definitely should include AI and ChatGPT. What will happen with data analysis in general? What will happen with data analysis in general? I think within two to three years, this profession will not be the same anymore. If people don't utilize AI, they will fall behind, and this will be a big problem. Many people will probably lose their jobs, and this is really scary. Disclaimer: These are just my thoughts. I don't want to discourage you from your learning path or scare you, just sharing my thoughts. Disclaimer: These are just my thoughts. I don't want to discourage you from your learning path or scare you, just sharing my thoughts. Disclaimer: These are just my thoughts. I don't want to discourage you from your learning path or scare you, just sharing my thoughts. Improvements of ChatGPT-4o for Data Analysis Improvements of ChatGPT-4o for Data Analysis Now, let's go back to the improved version of ChatGPT. What has improved? There are four things I would like to highlight: File Integration: You don't have to upload files from your computer anymore. You can upload them from Google Drive or Microsoft OneDrive. File Integration: You don't have to upload files from your computer anymore. You can upload them from Google Drive or Microsoft OneDrive. ** ** Interactive Tables: You can edit tables inside ChatGPT. For example, if you ask ChatGPT to bring your data in a table format, you'll be able to make changes inside those tables without changing your data set. Everything can be changed directly inside ChatGPT. Interactive Tables: You can edit tables inside ChatGPT. For example, if you ask ChatGPT to bring your data in a table format, you'll be able to make changes inside those tables without changing your data set. Everything can be changed directly inside ChatGPT. ** ** Customizable Charts: You can customize charts, download them, and edit them, all directly within ChatGPT. Customizable Charts: You can customize charts, download them, and edit them, all directly within ChatGPT. ** ** Security and Privacy: If you are using ChatGPT Plus, Team, or Enterprise, you will be able to turn off training on your data. This means ChatGPT won't use your data for training purposes. Security and Privacy: If you are using ChatGPT Plus, Team, or Enterprise, you will be able to turn off training on your data. This means ChatGPT won't use your data for training purposes. ChatGPT-4o Data Analysis Demo ChatGPT-4o Data Analysis Demo Now, the important part. Let's see how we can analyze a dataset with ChatGPT-4o. In this one-minute video, ChatGPT will analyze my dataset without detailed direction. This is my simple prompt, and look at what I get: https://youtu.be/w9DETYpYMS0?si=KEGL8GM2s0Sauzif&t=246&embedable=true https://youtu.be/w9DETYpYMS0?si=KEGL8GM2s0Sauzif&t=246&embedable=true As you can see, it's amazing and mind-blowing. Of course, we can improve our analysis by writing better and more detailed prompts. At the end of the analysis, you can ask ChatGPT to create a presentation or PDF file with all the findings and visualizations. It's amazing. Don't expect perfection, but you can add beauty yourself. As you can see, for a regular data analyst, this work would take around a week or two, but ChatGPT did it within a minute. It's wow! That's it! I hope you enjoyed this article. Thank you so much for supporting me with your likes and comments!

New ChatGPT-4o: A Game-Changer That Could Replace Data Analysts

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