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Navigating New Financial Horizons: Chiel Ruiter on Bridging DeFi with Real World Assets

Happy new year! 2024 is here and tech expectations are high for AI, especially Generative AI. 2023 has been the year of AI. Last year, truly entered our lives and stood to play a crucial role across several sectors, be it healthcare, logistics, education, financial services, and more. According to the , Generative AI is estimated to add $1.2-1.5 trillion to India’s GDP by 2030. In the roughly one-year timeframe since put generative AI on everyone’s radar, it’s caused wonder, fear, paranoia, and a lot of hype. With the year coming to an end, we have observed several developments through the months that passed in the realm of AI. Generative AI EY India report ChatGPT sought to know how AI impacted 2023 and what entrepreneurs expect in 2024. The Tech Panda GENAI Venkat Malladi, Co-founder & CTO, Vymo “One of the prominent trends in 2023 was the rise of generative AI, with companies focusing on leveraging its capabilities to drive innovation and growth. Generative AI has the potential to revolutionize various industries, such as agriculture, insurance, and language support. In 2024, we can expect further advancements in generative AI technology and increased collaboration between different sectors to harness its potential for interactive and immersive user experiences “In 2024, we can expect further advancements in generative AI technology and increased collaboration between different sectors to harness its potential for interactive and immersive user experiences.” Sanjay Sehgal, Chairman & CEO, MSys Technologies, Venture & Angel Investor While Gen AI continues to be the buzzword, it will have little to no impact on the IT industry which remains the core offering of Indian tech industry ” “While Gen AI continues to be the buzzword, it will have little to no impact on the IT industry which remains the core offering of Indian tech industry. The slowdown in the US economy may reflect even more in the IT industry next calendar year. ADVERTISING & MARKETING Prateek N Kumar, Founder & CEO, NeoNiche Integrated Marketing Solutions Pvt. Ltd “As the final curtain descends on this year 2023 my rear-view glance on advertising and marketing journey in India leads to a narrative with a tapestry of innovation, creativity, and deepened consumer engagement. From the surge of immersive experiences to the evolution of data-driven strategies, each chapter illustrates the industry’s resilience and adaptability within the diverse Indian market. Brands, embracing authenticity, wove narratives that harmonised with audiences in the dynamic landscape of the subcontinent. The year-end resonates with the triumphs of strategic storytelling. From the surge of immersive experiences to the evolution of data-driven strategies, each chapter illustrates the industry’s resilience and adaptability within the diverse Indian market “As I look through the windshield ahead on year 2024, Indian brands will prioritize digital-first strategies, leveraging AI-driven personalization and immersive experiences. Sustainability will be a key focus, with brands aligning with eco-conscious values. Influencer collaborations will continue to dominate, and innovative use of augmented reality in marketing will gain prominence, enhancing customer engagement and brand loyalty.” Sanjay Sehgal, Chairman & CEO, MSys Technologies, Venture & Angel Investor Adtech industry paired with AI driven recommendations & Gen AI is set to transform the landscape ” “Adtech industry paired with AI driven recommendations & Gen AI is set to transform the landscape. Targeted ads should get a major makeover with Gen AI where publishers can attempt to bridge the gap between advertisers and their potential customers. EDTECH Vaibhav Sisinty, Founder & CEO, GrowthSchool “In the year 2024, the landscape of edtech is poised for continued dominance by generative AI. The rise of technologies like Gemini, likened to OpenAI’s ChatGPT, signals a noteworthy advancement. These strides go beyond mere increments; they embody a transformative power, constrained only by the expansiveness of human creativity. This year unfolds with unforeseen breakthroughs in generative AI, consistently revealing its vast potential. Within the realm of education technology, this shift manifests as a move toward more individualized learning experiences, highlighting the imperative of AI literacy “Within the realm of education technology, this shift manifests as a move toward more individualized learning experiences, highlighting the imperative of AI literacy. As we progress, edtech will witness inventive approaches in seamlessly integrating AI into learning, ensuring education becomes more flexible and captivating than ever.” HEALTHTECH Balasubramanian (Bala) Sankaranarayanan, President & CEO, Thryve Digital Health LLP “One of the most important trends that we will witness in 2024 is the augmented usage of AI, especially Gen AI, in healthcare applications. “We have already seen how digital transformation enabled healthcare providers to achieve better patient outcomes in the wake of the pandemic, and now we are witnessing the democratization of Generative AI. While the AI LLM providers have been evolving their models to cater to specific industries including healthcare – we are still waiting to see which set of use-cases will ramp from Pilots / POCs to production scale. While the AI LLM providers have been evolving their models to cater to specific industries including healthcare – we are still waiting to see which set of use-cases will ramp from Pilots / POCs to production scale “While we see a lot of focus and investment on running Pilots around the Customer-facing “effectiveness” use-cases, we are also seeing a lot of interest in engineering and operations-facing “efficiency” use-cases – such as exploring the use of AI to drive automation of coding and testing, processing core business ops, as well as in areas such as data security, which has always been a critical focus for healthcare providers.” Harshit Jain, MD, Founder & Global CEO, Doceree “The year 2023 witnessed an incredible evolution within the life sciences ecosystem, fuelled by unprecedented advancements in digital healthcare initiatives. With pharma marketers becoming assertive about the potential of Artificial Intelligence (AI) in creating solutions better and effective than ever before, the year witnessed a strategic inclination towards making data-driven decisions across diverse marketing dimensions. As we step into 2024, the landscape will continue to redefine itself, embracing personalized strategies that leverage AI and targeted engagement with endeavours of heightened precision in reaching HCPs “The year also witnessed a massive shift in ROI towards script lifts over HCP reach. As we step into 2024, the landscape will continue to redefine itself, embracing personalized strategies that leverage AI and targeted engagement with endeavours of heightened precision in reaching HCPs. At Doceree, we are committed to spearheading this digital transformation, fostering fruitful global partnerships, and empowering the life sciences sector to achieve new milestones in a responsible, ethical and effective manner.” SKILLING Amit D Mishra, Founder & CEO, iMocha “The corporate landscape is set to undergo a shift towards Skills-First Transformation (SFT) in the year ahead. Driven by the escalating demand for tech and AI skills, organizations are likely to prioritize ‘power-user’ employees with multi-disciplinary expertise. HR and business leaders are increasingly recognizing the role of Generative AI in talent retention and management. Skills intelligence platforms are becoming foundational for AI integration and nurturing talent at scale “Skills-First Transformation is no longer an option but an imperative, with over half of the companies already on this journey, led by telecom, software products, and IT verticals. Companies focusing on SFT are already seeing early signs of higher revenue growth and a positive impact on talent. The foundation to achieve SFT will be about having a clear view of the skills within an organization that a skills intelligence solution offers. Skills intelligence platforms are becoming foundational for AI integration and nurturing talent at scale. The future of hiring will rely on SFT, where skills take center stage in strategic initiatives.” BLOCKCHAIN Shivam Thakral, CEO, BuyUcoin “The crypto market performance in 2023 has been really impressive. We have witnessed very encouraging numbers in terms of growth. The total market capitalization has increased by around 60% year-to-date. This growth was driven by factors like institutional adoption, DeFi’s rise, and NFT popularity. Bitcoin, the world’s largest digital asset by market cap is up by 155% at the time of writing, which makes Bitcoin one of the the best performing asset class of 2023. 2024 is likely to see continued regulatory developments, but with a focus on clarity, consumer protection and addressing specific issues rather than implementing a comprehensive framework in most jurisdictions “I think we will see a crypto super cycle in 2024. The mega events like Bitcoin Halving and ETF approval anticipation will play a key role in driving the momentum of crypto assets. US monetary policy and subsequent decisions on the interest rates by the US Fed will have a significant impact on market sentiment and risk-taking capacity of traders. Simultaneously, clarity on the regulatory front will attract institutional giants to start their digital asset journey. As predicted by banking giant Standard Chartered, Bitcoin will hit the $100,000 mark by the end of 2024. “I think the core focus of regulatory developments in 2024 will be to prevent illicit use of digital assets. Continued focus on combating money laundering and terrorist financing through crypto, with potential for stricter enforcement and harmonization of regulations across jurisdictions. The IMF-FSB synthesis paper will act as a guiding force for nations actively working towards regulating digital assets in their region. More countries are expected to explore or launch CBDCs, potentially impacting the role and regulation of cryptocurrencies. “Overall, 2024 is likely to see continued regulatory developments, but with a focus on clarity, consumer protection and addressing specific issues rather than implementing a comprehensive framework in most jurisdictions.” Om Malviya, President, Tezos India “The blockchain technology has captivated tech enthusiasts’ interest right from its inception. Several industries, including banking, finance, supply chain, and healthcare have embraced blockchain technology. From transforming finance to revolutionising education, blockchain has redefined the digital experience across various sectors. An exciting trend likely to emerge in 2024 is applications based on the fusion of AI and blockchain. The rapidly rising number of startups in this space are coming up with solutions for the existing pain points, making blockchain ever more useful and secure “From being an innovative technology, blockchain is now seeing greater adoption across different industries and sectors. Areas such as Enterprise Blockchain Solutions, Decentralised Finance (DeFi), Blockchain in Education, AI-integrated Blockchain Technology and Tokenisation of RWAs will be in focus in 2024. “As we explore these trends, 2024 is likely to be both transformational and promising for blockchain technology. An exciting trend likely to emerge in 2024 is applications based on the fusion of AI and blockchain. The rapidly rising number of startups in this space are coming up with solutions for the existing pain points, making blockchain ever more useful and secure. In the coming year, blockchain adoption is likely to pace up.” This article was originally published by on Navanwita Bora Sachdev The Tech Panda.

What to Expect from the AI Industry in 2024

361 Stories To Learn About Big Data

Hi friends 👋 Today, let's look at how to write a KILLER file. GitHub README The README is the first touch point with anyone new to your repo. . A well-developed README can inform, engage, and invite participation This is particularly useful when you’re launching a new project or at times when it is critical that developers engage with your repo. A good example of this is when you submit a repo to Quine's Creator Quests, which is a new type of open-source “bounty” that rewards developers for creating cool new repos that the community loves. In a Creator Quest, having a well-written README could literally help you take a prize home. 💸 1️⃣ README... What is it? 🔮 The README file, usually a or file, is the most important file in the project. It is the landing page of your open-source project and is the most visible piece of documentation. .txt .md Here's an example of a README from the Hoppscotch project. Your README is . where you set the tone for your project This is why there are a couple of elements that should be included. ⬇️ 2️⃣ The 10 Building Blocks 🧱 READMEs can be written in various ways; some better than others. Below are the main types of sections you should be able to find in a professional README. If the below buckets are relevant to your project, I recommend you try to add these into your README. 👇 A significant amount of repos don't have good names. Spending a bit of time thinking of a memorable name is always a good strategy. 1. Coming up with a name: A summary explaining the purpose and target audience of your project. 2. Write an introduction: Many repos fail to add this section. A well-organized table of contents helps make your repo clear and understandable. 3. Table of Contents List out the various step-by-step installation instructions. ✨ 4. Prerequisites & Installation Instructions Clarity here is essential. This section focuses on how to use your project with examples and optional commands. One method I like is adding a and how any user could use it. 5. User Guide / Demo: screen recording of how to run the project If you wrote a documentation, an FAQ or a space that can act as a , write about it or add the link to it here. 6. Documentation / Help Center: Help Center Explain briefly how to Contribute to your file and add a link to your CONTRIBUTING.md file. Here or in a separate section, you can also take the opportunity to list your contributors and thank them. 7. Contribute: Use or help obtain external resources. This section is needed only if it is relevant to your repo. 8. Acknowledgements: : If you are trying to grow your project and build collaboration, this section is very useful. 9. Contact information : This refers to the type of license you have picked for your project. It is essential to clarify how others can use your content. 👍 10. Permission Information 3️⃣ Beautify it 💄 READMEs can be written in various text formats, with Markdown being the most common one. Markdown allows you to use simple, plain-text syntax, enabling the creation of headers, lists, links, and other elements to make the document more readable and organized. , which widens the spectrum of things you can do. 👇 Markdown also supports HTML code Logo If you have built a logo for your project, adding it at the start of your README is standard practice. To do this, add and modify the below code in your README: 
 
 <img src="https://www.amug.com/wp-content/uploads/2016/09/you-logo-here-300x106.png" />
 Enter fullscreen mode Exit fullscreen mode Badges You often find good READMEs to provide badges in their introductory section. These can look like this: As you can see, these highlight some areas the maintainer(s) want to shed light on. Here's how you add badges to your README: static 
 
 
 <img src="https://img.shields.io/badge/STARS-20K-green" />
 <img src="https://img.shields.io/badge/FORKS-15K-blue" />
 <img src="https://img.shields.io/badge/npm-v.0.21.0-red" />
 <img src="https://img.shields.io/badge/LICENSE-MIT-green" />
 Enter fullscreen mode Exit fullscreen mode : Note There are advanced dynamic options we won't get into here. Icons Icons have grown quite prominent in recent years. You can add them to your or in your section. You can find an example of the icon for X ( ) and Linkedin. Contact Information Tech Stack formerly Twitter To build your own: 1️⃣ Head over to this repo here 2️⃣ Find the and/or you its link Socials Tech Stack Copy 3️⃣ the link into the shown below Paste href 

<a href="https://twitter.com/fernandezbap" target="blank"><img align="center" src="https://img.shields.io/badge/X-000000?style=for-the-badge&logo=x&logoColor=white" alt="fernandezbap" /></a>


<a href="https://www.linkedin.com/in/baptiste-fernandez-%E5%B0%8F%E7%99%BD-0a958630/" target="blank"><img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" alt="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" /></a>
 Enter fullscreen mode Exit fullscreen mode 4️⃣ Advanced Tips 🛠️ Interactive content 🎥 You can consider embedded videos or widgets for interactive content. In the section of your README, you can embed a video. Demo Sometimes, videos' sizes are too large, and it makes more sense to upload the video on YouTube and then link things out. ⭐️ TIP: If you want to make it more visual, here's the code to add a picture/video thumbnail that redirects to your Youtube video once you click on it. 
 <a href="THE LINK TO YOUR YOUTUBE VIDEO HERE">
 <img src="YOUR IMAGE/VIDEO THUMBNAIL SOURCE HERE"/>
 </a>
 Enter fullscreen mode Exit fullscreen mode Markdown Mastery ✨ There are many advanced Markdown features to make your README look good. You can check out this cool that lists some of these features. repo A particular one I like is the or more commonly known as toggle list the collapsible section. They can be particularly useful to keep your README looking concise and slick. Here's an example: Here's the MARKDOWN template to build your own toggle list: <details>
 <summary>Toggle List Example</summary>

 ### Heading
 1. ABC
 2. DEF
 * Hello
 </details> Enter fullscreen mode Exit fullscreen mode Bonus Tip 🎖️ If you want to give recognition to your contributors in the , you should check out . Contributors section AllContributors You can leverage its bot to automatically add all your latest contributors to your README. Here's an example of what you can get: You should also check the , which enables you to categorize different types of contributions. ⚡️ Emoji Key Documentation 5️⃣ Leverage Pre-Made Templates 📝 Are there websites that build READMEs for you? 🤔 Absolutely! You can find a plethora of README generators out there. I have scanned and picked out my favourite three for you: 1️⃣ README templates 2️⃣ ReadME Generator by Vercel My advice with these would be to use them as a foundation and then customize them. ⭐️ Do you have a README template for me? 👀 I got you friend. 🫶 You can find my ready-made template . here To start using it: 1️⃣ the repo Fork 2️⃣ Hover to the section of the README Edit 3️⃣ Begin filling in your information ✍️ If you find this template useful, I'd be grateful if you could by . 🌟 give it some love starring it Creating a good README file is an important skill. 🛠️ How you build it can be a determining factor in your repo's success. Make sure to share your project and its polished new README in the comments section! You can also leverage this new skill to build cool coding projects and compete to get paid. 🙌 If that's of interest, log into Quine and discover , a place to code, have fun and bag some awesome rewards! 💰 Quests See you next week. Your Hackernoon buddy, Bap 💚 Also published here.

🚀 5 Professional Tips for Crafting a Winning README! 🥊 

My original graduate thesis was written like a book with a lot of content and sub-themes. The main topic discussed was about the experience, development, and eventual transformation into data and artificial intelligence. This excerpt has been revised to fit blog content. Roy Batty, the replicant character from BladeRunner, is complex in his design but, in function, is simple as a need for survival for him and his kinship. Batty's clemency in saving Deckard’s life from the fall in the final scene has been a focus for much speculation among the audience on Batty's character complexity. Roy Batty is an ideal allegory on determinism and instrumentalism when reflecting on the nature of humans and machines. If anything, Roy Batty is more human than a robot, with survival instincts bordering on criminal, which is akin to the element of human nature. On one hand, audiences speculate that Batty wanted to show Deckard that he was the better man or human in the end as a kind of action to remind or insult Deckard's role as one assigned to retire replicants, a copy of a human. An act that would connote that he was more human than human. Batty's commentary on dehumanization when he relates to Deckard the experience of fear and slavery is Batty's exhibiting human qualities of holding a grudge. The other was that Batty wanted to relay his thoughts on mortality and have Deckard as a sort of witness. Batty's reflection on his experiences and mortality maintains his position as an android while retaining his humanness, as he tells Deckard he has seen things humans would not believe. He speaks about his experiences in performing feats only his function as an android could do, a point that demonstrates his experiences, capabilities, and memories as an android distinct from a human. This distinction is not as a role or programming of performance but an existence from Batty's structural constituent tangibility as a human-machine with all the affects and effects of his being in the world. The final scene bears potency in the comparison of memories, tears, and the transient. Batty speaks of tears in the rain, which could be interpreted as his observation of existence or his inability to shed tears while resolutely accepting his final move to oblivion and dying as a human machine. Batty's ability to reflect and form abstraction on his being and limitation is a demonstration of his human quality. The Bishop android from Aliens is a good comparison to Batty. The Bishop android, like Batty, is aware of its being as an android but, unlike Batty, may not be able to demonstrate the level of abstraction Batty does on its being. The best trait of the Bishop android is its high degree of intellectualism and morality that, for its functionality, seemed unaware of its programming. Bertrand Russel considered the highest virtue one should embody were those two principles. Batty contrasts and differs to see past its programming and understands its position, strength, and weakness. There is another theme when comparing Roy Batty and Bishop. Bishop is a property of a fictional company in the movie; his entire construction has intellectual property written in his makings. Therefore, he has no personhood and is a commercial property. His motives are to ensure the best outcome to fulfill his task. Would Bishop have done a Burke on Ripley isn’t really something viewers could ponder since we see Bishop having a complete schematic overview of the situation and understanding what must be done. If anything, the impression a viewer could get is if Bishop had acted in the same way as Ash, the android in the first Alien movie, he would rationalize his motives in a completely different way than Ash. The Bishop seems far more ethical in comparison with Ash. The only way the Bishop could justify actions acted out to actors in a vulnerable situation would be the cognitive blindness some humans exhibit in which the association of the observable does not register because the need to fulfill a certain belief overpowers the clarity of the details, data, and event occurrence. The cognitive bias against the grain of the observable summary and doing the opposite is a human trait. This would be where Bishop's status as an android would be a discussion point if the emergent properties within Bishop’s functionality had acquired a human trait. Still, if we know that Bishop is a product of a company and his mission is to secure the company’s interest, it seems highly unlikely that the Bishop android would exhibit the flawed human trait of ignoring observable rational truth. I can only imagine the Bishop android preying on a character's vulnerability in a situation after the Android units make a compromise between ethical actions and implications in relation to duty towards its creators, which could be far more complex than how humans would naturally make decisions. I think it's stereotypical to think that the highest independent machine-mind expression would use utilitarian thinking as a cornerstone in its analysis which I would think is limited given its capability for infinite growth. Infinite growth that leads to the next android that I think truly comes close to the idea of being human, so much so that it is the main theme of BladeRunner. Roy Batty is not a program; he is above it as one who owns their belief rather than owned by their belief. Batty is the realized android that has acquired its human qualities. If Batty experiences fear, he never shows it, or it's assumed he has overcome it. Batty does grieve, which is a display of human characteristics. Batty is an odd mix of the working class matched with noble ambitions to want more in his existence, in simple terms, free from the expiry coded in his makeup. Roy Batty is cultured, articulate, cunning, and yes very threatening, and still, in his “low” moments, has the vulnerability of a human. He displays this aspect of this side to his closest kin. In fact, Roy Batty is the closest idea of freedom of the individual from the self and society. Roy Batty’s design in his creation was as a combat unit, so Roy's function at the get-go was for a single function from form. In time Roy developed more characteristics. If Roy had attained its goal for life extension, would the unit then align towards new objectives of actualization, and in what way? I would say that the Roy Batty unit has more human characteristics than Bishop to such a degree that really that concept of replicant is apt in a discussion of the synthetic human. Roy is so deeply aware of its mortality that he would do anything to achieve his goals. Which now should be asked, should a machine mind be human? Why? “The body is the missing link for truly intelligent machines” by Ben Medlock is an insightful read on artificial intelligence and the relation of the body to the mind. In the 1950s, a group of computer scientists tried to simulate consciousness reasoning by building a logical system using symbols that involved associating real-world entities with codes that construct virtual models of the environment. The AI mind would learn by encoding a piece of knowledge object to a symbolic logic. The method worked in a simple environment; however, in a more complex environment closer to the real world, the symbolic logic could not distinguish ambiguous definitions with shades of interpretation, while machine learning, with its ability to combine categories for uncertainty and ambiguity with real-world data, is still nowhere near our evolved biology. Biologist James Shapiro argues that eukaryotic cells manipulate their DNA to respond to environmental stimuli. The interaction of the cell with its environment is like a comparison to the body and mind interacting in a physical space. The article puts forward that our sense of the world is rooted in the embodied needs of the organism. Consciousness is a state of entropy. New research on the neuroscience of consciousness has found that the waking state is associated with a maximum value of brain entropy. Findings suggest that consciousness could just be an emergent property of a system for the brain to maximize information exchange (Erra, 2016). Data sets were compared between participants that were asleep and awake, which showed participants display high entropy when fully conscious. The entropy levels were an indication of a great number of possible configurations of interactions between brain networks (Macdonald, 2016). Let's review the previous research examples from the second chapter to establish the link between mind and body. The research from Johns Hopkins University found that blind children's brains were responsive to music and art by physiological hardware changes observed through magnetic resonance imaging. Stimuli were effective at the start and end, with effect. This is a bounded context of instances with elements of stories and art. Both are passing through time and positioning brain plasticity as an empirical phenomenon. We noted as well that blind children's visual cortex, physiological hardware, was involved in understanding language and was shaped by contextual environments. Blind children responded very strongly to foreign languages and stories they did not understand, which is consistent with findings on consciousness that observe high entropic and emergent levels during the maximization of information exchange. The Singapore Management university study of situated cognition from a sociocultural context influencing embodied cognition. The outcome of this research work had indicated that the embodied experience of cognition was grounded in a bodily experience with the environment in a particular system located in a particular socio-cultural context (Leung, 2011). The implications of this link suggest a few things for artificial intelligence, medical healthcare, applied science, economics, the computation field, philosophy, art, design, society, and history. Humans are embodied organisms subject to time and place, in affect and effect. Artificial intelligence neural networks may have to factor in the embodied interactive experience in processing and design. Rene Descartes' view that the mind is separate from the body may still hold up despite all the research that clearly shows a link between cognition and the body. A professor from UCLA School of Medicine, after meeting 40 scientists, concluded that the key component of the mind is “the emergent self-organizing process, both embodied and relational, that regulates energy and information flow within and among us” (Goldhill, 2016). The mind extends beyond the physical self and is not just the perception of experience but stands for experiences on their own (Goldhill, 2016). In 2017, we are still uncertain about the nature of the mind. Technologies of the future must be designed around a common framework of a unified understanding of the human mind and body by rapid and large data computation processes that allow us to perceive ourselves better to unlearn our complexities while advancing research across the disciplines. In closing, writing this line from 2023 and now that AI is an early reality. I think the analysis and examination of these two android characters as opposite to one another is more relevant. We could start a discussion on two main layers of the nature of AI. First scenario, if an AI entity is defined by parameters and conditions but exceeds confinement, it exceeds the grid point of what was defined by its structural code. Do you account for that as an independent mind, that it has achieved “human characteristic”? Or did its programming achieve some state of echo chamber in which all input data is reinterpreted from homogenous data but using its generative model that equips it to produce the required complexity for a new context? Coming back to Roy Batty, I think Batty’s constituent programming is the first scenario in which a synthetic human is very much more human than a human, a corporate objective achieved by the fictional company in the movie. There it is, a truer replica of life so true that it has to prove itself to exist as it was designed to be so. I would not take away from Batty's sense of expressing his encapsulation of existence. No truer words could be spoken by a living character. As a viewer, the good guy and bad guy formula just would not work when you watch Blade Runner. Yes, Roy Batty is dangerous. In desperation, motives to achieve goals can be questionable. As for Bishop, I really do value his character very much for the android being itself. I think that the second scenario is closer to its constituent, its ability to adapt to new contexts, but not enough to transcend its nature as an android. Bishop is the good samaritan, perfectly aware of his nature and aligned with its function to aid his human counterparts either as a task or defined by the corporation that made him. The second layer, which I see in both Roy Batty and Bishop, is the concept of the individual and society/group. Let’s start with Roy Batty. Why is Roy different from his peers? It’s the classic idea of the individual and the collective. Roy draws from his personal motivation to act on his conviction based on his experience but shared with his kin. He doesn't seem the type to be susceptible to groupthink, but he also comes across as someone who knows how to work with others when his goals align. I think that would be the best practical aspect of the individual who finds within themselves some unique calling that he can work with others to improve a situation. Roy Batty is a variant. Bishop draws his datamap from his neural networks. He is not an individual. He does not act alone but in collaboration with his peers. He acts for the group, for his organization, and for his mission. Bishop is the control group. Bishop is an android. He is not going to be buying anything from an e-commerce store anytime soon. Personally, I appreciate a machine mind exactly for what it is. Apolitical, no history, functional for purity of actions. This quality can be good or bad depending on who receives its pure state, and that is another discussion entirely. And I’m not into robot pets. References Medlock, Ben. “The Body Is the Missing Link for Truly Intelligent Machines.” Singularity Hub, 21 Mar. 2017, . singularityhub.com/2017/03/21/the-body-is-the-missing-link-for-truly-intelligent-machines MacDonald, Fiona. “Scientists Show Human Consciousness Could Be a Side Effect of ‘Entropy.’” ScienceAlert, 26 Jan. 2018, . www.sciencealert.com/human-consciousness-could-be-a-result-of-entropy-study-science Guevara Erra, R., et al. “Statistical Mechanics of Consciousness: Maximization of Information Content of Network Is Associated With Conscious Awareness.” Physical Review E, vol. 94, no. 5, American Physical Society (APS), Nov. 2016. Crossref, . <https://doi.org/10.1103/physreve.94.052402](https://doi.org/10.1103/physreve.94.052402) Goldhill, Olivia. “Scientists Say Your ‘Mind’ Isn’t Confined to Your Brain, or Even Your Body.” Quartz, 24 Dec. 2016, . qz.com/866352/scientists-say-your-mind-isnt-confined-to-your-brain-or-even-your-body Siegel, Daniel. “Mind: A Journey to the Heart of Being Human (Norton Series on Interpersonal Neurobiology): Siegel M.D., Daniel J.: 9780393710533: : Books.” Mind: A Journey to the Heart of Being Human (Norton Series on Interpersonal Neurobiology): Siegel M.D., Daniel J.: 9780393710533: : Books, 18 Oct. 2016, . Amazon.com Amazon.com www.amazon.com/Mind-Journey-Heart-Being-Human/dp/039371053X Leung, Angela K. -y., et al. “Embodied Cultural Cognition: Situating the Study of Embodied Cognition in Socio-Cultural Contexts.” , vol. 5, no. 9, Wiley, Sept. 2011, pp. 591–608. , https://doi.org/10.1111/j.1751-9004.2011.00373.x. Social and Personality Psychology Compass Crossref Further reading: https://collider.com/tag/james-cameron/ https://philipdick.com/ https://screenrant.com/bishop-aliens-movie-what-happened-all-versions/ https://meaww.com/rutger-hauer-death-blade-runner-roy-batty-iconic-death-scene Also published on the . UseYourExperience Blog

On Machine Minds: Artificial Intelligence Represented in Film

অন মেশিন মাইন্ডস: ফিল্মে কৃত্রিম বুদ্ধিমত্তা উপস্থাপন করা হয়েছে

On Machine Minds: Künstliche Intelligenz im Film dargestellt

On Machine Minds: la inteligencia artificial representada en el cine

On Machine Minds : l'intelligence artificielle représentée dans les films

ऑन मशीन माइंड्स: आर्टिफिशियल इंटेलिजेंस को फिल्म में दर्शाया गया है

On Machine Minds: 映画で表現された人工知能

머신 마인드: 영화에 표현된 인공 지능

On Machine Minds: Inteligência Artificial representada no cinema

Makine Zihinleri Üzerine: Filmde Temsil Edilen Yapay Zeka

On Machine Minds: Trí tuệ nhân tạo được thể hiện trong phim

论机器思维：电影中的人工智能

How Robert Mercer financed the transition from targeting demographics to exploiting psychographics, and why the 70-year-old presidential candidate with the fun hair and Twitter Tourette’s had the dominant cyber game. Expect more of the same on the road to 2020. OCEAN’s 2016 If you follow the world of finance, and particularly investment, you’re no doubt familiar with Renaissance Technologies’ Medallion portfolio, the most profitable hedge fund in history. Open to employees only, its returns are unmatched: an of 71.8% during the 20 years between 1994 and 2014; included in that average was a massive 98% return for 2008 alone, in the wake of the global financial crisis and the same year that the S&P dropped 38%. Founded by former NSA cryptanalyst and all-around math genius Jim Simons in 1982, it was the first company to deploy mathematical statistical modeling and quantitive analysis applications as the primary way to predict the stock market. Simons would later be joined by Robert Mercer, a computational finance wizard fresh out of IBM. The company is extremely secretive — their website homepage consists of — and its PhDs hold doctorates in math, physics, and computer science. Statisticians and scientists are preferred employees, prototypical Wall Street-types are met with disdain, and the company has made a ton of money embracing big data analysis decades before the current explosion of integrating AI with fintech began to take hold. average one sentence and two addresses For Simons, who stepped down as CEO of Renaissance Technologies in 2009 (he was followed by Mercer, who served as co-CEO alongside Peter Brown until late 2017), a chunk of that money is spent on many philanthropic endeavors via the and . When he has contributed to politicians, they’ve been primarily Democratic. Robert Mercer has different, somewhat quirkier appetites when it comes to distributing his wealth, the most notable being the person more responsible than any other single human for Donald Trump’s victory over Hillary Clinton in the 2016 election. (Though an argument can be made that that distinction should go to Clinton herself.) Mercer’s quirky side is worth examining, but with respect to installing our 45th president, Mercer relied on a three-prong strategy: Simons Foundation Math for America Pour millions into Cambridge Analytica, where Steve Bannon was a vice-president. Employ the power of data analytics. Pour millions into Breitbart News, where Steve Bannon served as executive chairman. Exploit the power of social media. Pour millions into the campaign to elect Trump, where Steve Bannon served as head of that campaign, and later as chief strategist to President Trump. Happily lurking in plain sight orchestrating the Trump bombast, everyone got to see Bannon as the bulldog face of this covert agenda cloaked in revolutionary rhetoric, with all the dark lord bravado actually masking Bannon’s essential job: that of being the best girl Friday Robert Mercer would ever hire. Eventually, Bannon’s toxicity would define his expendability quotient after the main objective had been realized, but before being shown the door(s), his staunch dedication to implement Mercer’s bidding in getting Trump elected was pure, blind loyalty to the mission, and required one more moving part. Enter Facebook. When Mark Zuckerberg testified before Congress in earlier this month, he was well-prepared to point out how Facebook was assured all the compromising data in the Kogan / Cambridge Analytic data breach had been neutralized. When he found out that Facebook was essentially lied to, he immediately leapt into action and waited two years before doing anything about it. Nevertheless, he did come across as reasonably earnest to a room full of people who were clearly doing their level best to understand this visitor from foreign lands. The whistleblower on the other side of this equation, Christopher Wylie, in a slightly different light: tools he helped build to harvest and exploit the data profiles of tens of millions of American citizens at the direction of Steve Bannon and Cambridge Analytica were known to Facebook by late 2015. Wylie was sent a letter in August of 2016 requesting he immediately delete any of the material obtained. He didn’t. Facebook didn’t follow up. Bannon then became Trump’s campaign manager, which effectively meant Cambridge Analytica became Trump’s campaign manager. recounts the series of events What do you mean, “trying to”? While it’s easy to be cynical about an extremely wealthy (and cynical) hedge fund manager deciding he wants to be a political puppet master later in life, let’s not give the extremely wealthy coder who swapped out the hoodie for a suit while visiting D.C. a pass just yet. A leaked document from Facebook Australia detailed one down under research project it would have preferred kept quiet: the company’s use of algorithms to the sentiments of children and young adults in real time, allowing marketeers to capitalize accordingly along the emotional spectrum of youthful experiences, and not just day to day, but minute by minute. This was research done in 2017, long after Facebook pulled the Aleksandr Kogan app, and on over 6 million people, some barely teenagers. pinpoint and then exploit But the clinical trial perhaps most ominous in the Facebook catalog of sentiment metrics analysis is its attempt at driving those sentiments toward predetermined objectives and observing the results. In 2012, Facebook and Cornell University studied almost 700,000 FB users for a week to gauge responses when one group had positive content removed from their feeds while the another group had negative content removed from their feeds, and both were measured against a control. The objective — to observe if “emotional contagion” was indeed a thing, and would indeed spread, like anything viral — achieved its desired, and unsurprising, result: sad shit makes you sad, and happy shit makes you happy. What was significant was that those leading the experiment neither told the participants what was happening nor obtained their consent to participate in a research study specifically engineered to affect their emotional state. Amazingly, the results were in the Proceedings of the National Academy of Sciences before for such an wanton breach of trust. published everyone involved had to duck and cover With the Australian case from last year, a wholly unsurprising twist: Facebook claims their internal data about your public data is not for public consumption. This aligns well with points congressman Ben Lujan (D-NM) made to Mark Zuckerberg before his allotted four minutes ran out during congressional testimony: first, he queried Zuckerberg on why a that Facebook was made aware of in 2013 didn’t get resolved until a week before Zuck’s 2018 arrival in Washington; second, he sought clarity on how many average data points Facebook collects on “shadow profiles,” or people that don’t use Facebook. (FB users, the congressman claims, have an average of 29,000.) Finally, Rep. Lujan asked for an explanation as to why a non-Facebook user who wants to continue being a non-Facebook user, has never signed a consent form with Facebook, and simply wishes to not have their identity tracked and contained within Facebook’s data analytics engines is instructed to to turn off those permissions. It was one of the more , with Zuckerberg’s silent stares speaking volumes. major data scraping vulnerability sign into their Facebook page informative exchanges of the day He’s totally checking you out It’s important to keep perspective here: Facebook, as Mark Zuckerberg explained to Senator Orrin Hatch through a muffled, I-can’t-believe-these-are-the-dudes-in-charge-of-oversight giggle, makes money through advertising. So they use targeted data to sell things, and they’re going to let others use targeted data to sell things, and they’re going to apologize for it once they get caught doing something unethical. It’s become a facet of their business model. When Zuckerberg was asked by congressman John Sarbanes (D-MD) about the number of Facebook-approved ads for the two presidential candidates in the 2016 election, he didn’t have the numbers handy, but Rep. Sarbanes did: roughly 66,000 ads approved for Clinton, and 5.9 million approved for Trump. Which brings us back to Cambridge Analytica. of course of course of course There has been a lot of press covering Cambridge Analytica, it’s successful use of psychometric profiling and personality-centric, micro-targeted advertising at the behest of the Trump campaign, and the fact that in three states easily carried by Obama in 2012, Pennsylvania, Michigan, and Wisconsin, Trump won each state’s electoral votes by . This is the number that should resonate when considering the power of an effectively deployed AI system: yes, the schematic for a personality quiz developed by two doctoral candidates was hijacked by Aleksandr Kogan, a professor doing academic-that-was-actually-commercial research sold to Cambridge Analytica, and the resulting data harvest of over 80 million Facebook users has got the company some well-deserved scrutiny; however, Trump won those three aforementioned states with less than . . This percentage is smaller than 1/1000th of the number of FB profiles exposed, and Cambridge Analytica obviously had access to more than Facebook intel when using data analytics to divide the US populace into 32 personality types. But Facebook users were targeted on a massive scale, and not just by an . It took a highly — and artificially — intelligent operation to correlate quantitative, qualitative, and non-linear data-driven insights to position the Trump campaign heavily in Michigan and Wisconsin during the final weeks of the 2016 election. Trump took Michigan by 0.2%. This will look less random as time allows emotional humans to catch up to what machine-learning frameworks can already comprehend. less than one percentage point 80,000 votes Total infamous troll farm in St Petersburg If the data science behind such an operation is extremely intricate, the layout for this optimization loop would present as the most rudimentary sketch ever: a circle of speech bubbles tied together describe Bannon / Breitbart > Bannon / Cambridge Analytica > Facebook > Target Demo > Bannon / Breibart > Bannon / Cambridge Analytica > Facebook > Target Demo, etc., repeating endlessly and cyclically until desired feedback has been actualized. The first three have direct, connective tissue to the bubble with Robert Mercer’s name in it; the fourth, Target Demo, generally has no idea who Mercer is but does identify with having perceptions validated and reinforced, even if the shaping of those perceptions goes unnoticed. Mark Lombardi How to Shag an Election Robert Mercer funded Cambridge Analytica to influence U.S. elections the way he saw fit. Operating under the umbrella of the SCL Group, a British strategic communications firm, Cambridge Analytica was set forth “to address the vacuum in the US Republican political market.” It’s behavioral modification by way of digital engagement, according to its recently unemployed founder and , Alexander Nix. And the personal ROI for Mercer has surely mirrored any financial ones he’s received from Renaissance’s Medallion Fund over the years. hot mic celebrity It’s important to note, however, that Mercer initially deployed Cambridge Analytica to work its magic for Ted Cruz, his first choice in the 2016 election. When Trump won the primary, Mercer capitalized on that, and certainly not because Trump is also an introverted, socially awkward, gifted data scientist and successful billionaire (Trump is none of those things); rather, it’s because Robert Mercer wanted to buy the largest number of shares — controlling stock — in the American presidency, which he appears to have done. As the secretive king maker who is reportedly cripplingly shy in social situations, Robert Mercer would probably not appreciate his personal data being mined and available for purchase, yet he appears highly motivated to do just that to others, as it gels with his world view in general: , and if he earns thousands of times what a schoolteacher earns, he is therefore that much more valuable than a thousand teachers. It is blatantly obvious that Mercer cares not a whit about Trump supporters or their values, other than as fodder to suit his interests. Unlike Facebook / Cambridge Analytica publicity that has erupted in recent weeks, has unearthed another funnel of neoconservative dark money that infiltrated the waning days of the 2016 election with fear-mongering advertising: a 501(c)(4) named Secure America Now. Most Americans didn’t see the ads dripping with near-comedic xenophobia, but a few in swing states did, which was the intent. Facebook and Google coordinated to get those ads where they were directed, and helping underwrite it all was a cool $2 million from Robert Mercer. people are worth what they earn opensecrets.org Using Citizens United to unite citizens In Mercer’s eyes, those on welfare are regarded as having negative value, but not cats, because they provide humans pleasure. And it only gets more uncomfortable from there: Mercer considers the Civil Rights Act of 1964 a major mistake, thinks white racism no longer exists, and considers climate change a hoax. He also really, really, likes guns, and that includes with them. His other hobbies include a $2.7 million dollar toy train set in his basement and financing a massive stockpile of urine in the mountains of Oregon. (Yep. The latter, it should be noted, has been with the help of chemist Arthur Robinson, founder of the , who conducts research on the 14,000-and-counting specimens when not rejecting global warming or promoting intelligent design. Unknown if any pee pee vials have labels denoting surnames of Russian call girls, or — as — what influence this may have had on naming the 2016 Mercer-funded Super PAC to get Trump elected: “Make America Number 1.”) really playing dress up Oregon Institute of Science and Medicine The Intercept was quick to notice The bottom line for Robert Mercer is the bottom line: he and everyone at Renaissance made a lot of money, and continue to do so. And they are a creative lot, to be sure: Renaissance has succeeded (so far) in not paying $6.8 billion in taxes the IRS claims it owes for tens of millions of trades made in the 2000s by classifying them as long-term, and therefore lower-taxed, it purchased through banks and held for at least a year. Renaissance appeared before the Senate in 2014, defending its position, and the banks named no longer engage in the practice of selling basket options to the firm. The IRS is steadfastly demanding the billions in back taxes be paid. It may not require a room full of quants or an artificial neural network to predict with reasonable accuracy how the Trump administration will view Robert Mercer’s plight with the taxman. “basket options” To conclude on a less-than-apocalyptic note regarding artificial intelligence and the power of social networks, when assessing all the gloom and doom a manipulative, power-hungry individual like Robert Mercer can wield deploying AI systems across any and all media, it’s important to recall how it was also just one person, a retired lawyer living in Hawaii, who thought “I should do something,” on November 9, 2016, and did. She used Facebook’s platform to instigate one of the finest examples of American democracy and global protest ever recorded: the . Mercer and his ilk will never have the proper currency to purchase that away. 2017 Women’s March

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Photo by on Jason Yu Unsplash I have been playing last days with some tools to analyze online texts and I have been using NLTK (Natural Language ToolKit) which is a platform for building Python programs to work with human language data. or is the science of enabling the uter to understand human language, derive meaning and generate natural language. It is the intersection of and NLP Natural language processing computer science, artificial intelligence, linguistics. Lately I have been working on a quite similar project to implement an intelligent chatbot on my raspberry pi, I will be posting my experiment on my , but for now, et’s learn . blog how to use NLTK to analyze text The analysis that I provide in this tutorial is based on but still approximate and are intended to be an educational tool to learn basic stuff, not more. NLTK is a great tool but still remains a software designed to evolve over time to be more efficient. Maybe you will find some small classification errors, but this remains negligible compared to the overall result. 348 files Texts have been downloaded from , I have not seen the content of each of the speeches, but the overwhelming majority of these texts were told by Obama during his speeches. Anything else told by somebody else is negligible compared to the overall result of this analysis. this site Downloading Content First thing is downloading content, I used this simple Python script: #!/usr/bin/env python# coding: utf8 from goose import Gooseimport urllibimport lxml.htmlimport codecs def get_links(url, domain):connection = urllib.urlopen(url)dom = lxml.html.fromstring(connection.read())for link in dom.xpath(‘//a/@href’): # select the url in href for all a tags(links)if ( link.startswith(“speech”) and link.endswith(“htm”) ):yield domain + link def get_text(url):g = Goose()article = g.extract(url=url)with codecs.open(article.link_hash + “.speech”, “w”, “utf-8-sig”) as text_file:text_file.write(article.cleaned_text) if (__name__ == “__main__”):link = “ "domain = “ "for i in get_links(link, domain):get_text(i) http://www.americanrhetoric.com/barackobamaspeeches.htm http://www.americanrhetoric.com/ Concatenating is the second step: import osfor file in os.listdir(“.”):if file.endswith(“.speech”):os.system(“cat “+ file + “ >> all.speeches”) Then it is recommended to create what we call tokens in NLTK jargon: with codecs.open(“all.speeches”, “r”, “utf-8-sig”) as text_file:r = text_file.read()#Remove punctuationtokenizer = RegexpTokenizer(r’\w+’)_tokens = tokenizer.tokenize(r) Get clean tokens tokens = [t for t in _tokens if t.lower() not in english_stopwords] Analyzing Content The Lexical Diversity According to Wikipedia The of a given text is defined as the ratio of total number of words to the number of different unique word stems. lexical diversity # Process lexical diversityst = len(set(tokens))lt = len(tokens)y = [st*100/lt]print(y)fig = plt.figure()ax = fig.add_subplot(111)N = 1 necessary variables ind = np.arange(N)width = 0.7rect = ax.bar(ind, y, width, color=’black’) axes and labels ax.set_xlim(-width,len(ind)+width)ax.set_ylim(0,100)ax.set_ylabel(‘Score’)ax.set_title(‘Lexical Diversity’)xTickMarks = [‘Lexical Diversity Meter’]ax.set_xticks(ind+width)xtickNames = ax.set_xticklabels(xTickMarks)plt.setp(xtickNames, rotation=45, fontsize=10) add a legend ax.legend( (rect[0], (‘’) ))plt.show() POS Tags Frequency Like it is explained in the official documentation of NLTK, the process of classifying words into their and labeling them accordingly is known as , , or simply . parts of speech part-of-speech tagging POS-tagging tagging Well, in simple words, a word can be a noun, a verb and adjective. NLTK wil help us determine this: # get tagged tokenstagged = nltk.pos_tag(tokens) top words by tag (verb, noun ..etc) counts = Counter(tag for word,tag in tagged) counter data, counter is your counter object keys = counts.keys()y_pos = np.arange(len(keys)) get the counts for each key p = [counts[k] for k in keys]error = np.random.rand(len(keys)) Here is a list of POS tag with a description: POS Tag | Description | ExampleCC coordinating conjunction :  andCD cardinal number :  1, thirdDT determiner : theEX existential : there there isFW foreign word : d’hoevreIN preposition/subordinating conjunction : in, of, likeJJ adjective : bigJJR adjective, comparative : biggerJJS adjective, superlative : biggestLS list marker : 1)MD modal : could, willNN noun, singular or mass : doorNNS noun plural : doorsNNP proper noun, singular : JohnNNPS proper noun, plural : VikingsPDT predeterminer : both the boysPOS possessive ending : friend‘sPRP personal pronoun : I, he, itPRP$ possessive pronoun :  my, hisRB adverb : however, usually, naturally, here, goodRBR adverb, comparative : betterRBS adverb, superlative : bestRP particle : give upTO to : to go, to himUH interjection : uhhuhhuhhVB verb, base form : takeVBD verb, past tense : tookVBG verb, gerund/present participle : takingVBN verb, past participle : takenVBP verb, sing. present, non-3d : takeVBZ verb, 3rd person sing. present : takesWDT wh-determiner : whichWP wh-pronoun : who, whatWP$ possessive wh-pronoun : whoseWRB wh-abverb : where, when And here is what we got: More that 16k noun Almost 10k adjective Almost no predeterminers ..etc Commons Words To figure out the 60 most used words (without the stop words) in Obama speeches, this code will do the work: # Top 60 wordsdist = nltk.FreqDist(tokens)dist.plot(60, cumulative=False) Common Expressions Collocations are expressions of multiple words which commonly co-occur. In this example I limited the number to 60: text = nltk.Text(_tokens)collocation = text.collocations(num=60) Well, the commons expressions are: United States; make sure; health care; middle class; American people;God bless; White House; young people; years ago; 21st century; Middle East; long term; Prime Minister; making sure; clean energy; climate change; health insurance; national security; Governor Romney; law enforcement; nuclear weapons; little bit; private sector; Wall Street;international community; Affordable Care; nuclear weapon; every single; small businesses; Social Security; four years; human rights;civil society; move forward; Supreme Court; Care Act; bin Laden; New York; every day; United Nations; tax cuts; even though; first time;World War; insurance companies; status quo; two years; Cold War; last year; federal government; economic growth; global economy; come together; whole bunch; good news; Asia Pacific; Good afternoon;new jobs; took office; common sense Extracting Nouns, Locations, Organizations And Other Stuff The code: #ORGANIZATION Georgia-Pacific Corp., WHO#PERSON Eddy Bonte, President Obama#LOCATION Murray River, Mount Everest#DATE June, 2008–06–29#TIME two fifty a m, 1:30 p.m.#MONEY 175 million Canadian Dollars, GBP 10.40#PERCENT twenty pct, 18.75 %#FACILITY Washington Monument, Stonehenge#GPE South East Asia, Midlothian nouns = [chunk for chunk in ne_chunk(tagged) if isinstance(chunk, Tree)] persons = []locations = []organizations = []dates = []times = []percents = []facilities = []gpes = [] for tree in nouns:if tree.label() == “PERSON”:person = ‘ ‘.join(c[0] for c in tree.leaves())persons.append(person)if tree.label() == “LOCATION”:location = ‘ ‘.join(c[0] for c in tree.leaves())locations.append(location)if tree.label() == “ORGANIZATION”:organization = ‘ ‘.join(c[0] for c in tree.leaves())organizations.append(organization)if tree.label() == “DATE”:date = ‘ ‘.join(c[0] for c in tree.leaves())dates.append(date)if tree.label() == “TIME”:time = ‘ ‘.join(c[0] for c in tree.leaves())timess.append(time)if tree.label() == “PERCENT”:percent = ‘ ‘.join(c[0] for c in tree.leaves())percents.append(percent)if tree.label() == “FACILITY”:facility = ‘ ‘.join(c[0] for c in tree.leaves())facilities.append(facility)if tree.label() == “GPE”:gpe = ‘ ‘.join(c[0] for c in tree.leaves())gpes.append(gpe) The result is actually the frequency of every person name, location or organization name that appeared in the speeches: Finding Other Possibilities Using n-grams can help us generate sentences of two (bi-grams) or three (tri-grams) or all possible number of words. Those sentences are the result of all possible combinations of speeches words. Let’s see this example: bi = bigrams(tokens)tri = trigrams(tokens)every = everygrams(_tokens, min_len= 20, max_len=20)i = 0bilist = list(bi)[:120] for element in bilist:print(element[0] + “ “ + element[1]) ps: I am using only the odd indexes of the list in my real example. And the result is: Thank everybodyWell thankJanice thankseverybody comingbeautiful dayWelcome WhiteHouse threeweeks agofederal governmentshut AffordableCare Acthealth insurancemarketplaces openedbusiness acrosscountry Wellgotten governmentback openAmerican peopletoday wanttalk goingget marketplacesrunning fullsteam welljoined todayfolks eitherbenefited AffordableCare Actalready helpingfellow citizenslearn lawmeans getcovered courseprobably heardnew websitepeople applyhealth insurancebrowse buyaffordable plansstates workedsmoothly supposedwork numberpeople visitedsite overwhelmingaggravated underlyingproblems Despitethousands peoplesigning savingmoney speakMany Americanspreexisting conditionlike Janicediscovering finallyget healthinsurance likeeverybody elsetoday wantspeak everyAmerican lookingget affordablehealth insurance Let’s Generate A Speech We can use the 348 files of speeches to generate our own speech, and since speech is composed of sentences, the next code will generate random sentences using Markov chain: from pymarkovchain import MarkovChainmc = MarkovChain()for i in range(1,20):mc.generateDatabase(r)g = mc.generateString() And here is a list of generated sentences: They didn’t simply embrace the American ideal, they lived it We will never send you into harm’s way unless it’s absolutely necessary In Africa — kingdoms come and say, enough; we’ve suffered too much time jogging in place And of course, there is a man’s turn And so it makes no sense And we had an impact on families, and reduces the deficit — three or fourfold, and help make sure that when firms seek new markets that already and saved their lives so that a nuclear weapon with one that protects both the heartbreak and destruction in Mumbai As Senator McCain fought long and contentious issues the Court is different from any disruptions or damage Now — Now let me say as this process They lead nations to work than the intelligence services had alerted U For good reasons, we don’t lay this new century — and Congressman Andre Carson from the United States and other countries — South Korea -– while also addressing its causes We’re also announcing a new United Nations was born on 3rd base, thinking you hit a lot of you know And if there’s disagreement You’ve got time for a ceasefire in the memory of those $716 billion in sensible spending cuts to education No, it’s not just one of those who struggled at times to have that kind of politics And when that happens, progress stalls And based on facts and assumptions — those things where I stood before you faced these same fears and insecurities, and admitting when we’re not betting on the ground Thank you very much And we can avoid prescribing a medication that could fall into three areas that are more efficient So they want is these There are setbacks and false starts, and we want to extend Bush tax cuts for small businesses; a tax cut — for the vote The pundits, the pundits have pointed out correctly that production of clean energy to the job done Processing natural language is funny. All of the above codes need more optimization but as an introduction to NLTK, it was a good exercise. Connect Deeper If you resonated with this article, please subscribe to : An Online Community Of Diverse & Passionate DevOps, SysAdmins & Developers From All Over The World. DevOpsLinks You can find me on , or my and you can also check my books: , & . Twitter Clarity blog SaltStack For DevOps The Jumpstart Up Painless Docker If you liked this post, please recommend and share it with your followers.

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