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At 9am on March 14, 2023, when this Apple news hit the newsfeeds, the founding team of Uptok jumped off their seats with excitement in a downtown co-working space in Chicago. Uptok went to market ...

Apple ignites a video commerce revolution helping startups like Uptok to go mainstream

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Offer 1-on-1 video sessions to shoppers with high purchase intent on your website. Answer questions, conduct demos, suggest products, and even offer personal discounts on the fly—all in one seamless experience. More face time = More conversions Whether it's a busy day or a slow span, our super-intelligent algorithm is designed to maximize your face-time with the most active shoppers. E-Commerce + real people = Magic The best digital experience is a human experience. Unmask your smile and let it inspire your shoppers. It is time to rewrite your retail equation. - Be open for business everywhere Expand your reach to new markets while making real human connections without incurring the cost of a physical store. - Your team can be anywhere Set up a sales studio or serve shoppers from home. Don't hide your smile behind a mask. Let it inspire them. - Shoppers served per salesperson multiplied Sales team will be optimized for "active" shoppers across the total market instead of being bound by the traffic patterns of one store. Visit http://uptok.com

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Meet the Winners of Startups in North and Central America

The Jungle by Upton Sinclair is part of the HackerNoon Books series. Read this book online for free on HackerNoon! Title: The Jungle Author: Upton Sinclair Release Date: June, 1994 [eBook #140] Language: English TABLE OF LINKS Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Chapter 21 Chapter 22 Chapter 23 Chapter 24 Chapter 25 Chapter 26 Chapter 27 Chapter 28 Chapter 29 Chapter 30 Chapter 31 About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Upton Sinclair. (1994). THE JUNGLE. Urbana, Illinois: Project Gutenberg. Retrieved April 2022, from https://www.gutenberg.org/files/140/140-h/140-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever.  You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html . Photo by Patrick Hendry on Unsplash

The Jungle by Upton Sinclair  - Table of Links

Is Artificial Intelligence as scary as we have been led to believe, or is it just the big tech giants that have a track record of unethical data use, that are going to continue to use AI the way they have been using our other data: to line their pockets as much as they possibly can. Even those with the most rudimentary understanding of understand that its strength lies in its ability to understand data, and if you want a Language Model or AI to be smarter or trained for a specific purpose, the key element is data. This is precisely where AI and Big tech begin their crossover, as the tech giants expectedly have the largest reserves of Cloud Data that can be used to train and develop AI Models. Artificial Intelligence Since 2022, , , and all invested billions and forged strong relationships with the most advanced AI development companies of our time, shortly after the first version of Chat GPT was released. Google Microsoft Amazon Even within such deals, the employees often find themselves in ethical dilemmas related to the use of AI in big tech. Dario Amodei left Open AI, seemingly due to safety and ethical concerns over Microsoft's involvement. Shortly after, he founded Anthropic, to turn to the other evil stepsister, taking around $ in investment from Amazon and from Google. 1.25 Billion dollars $2 Billion Given the turbulent past (and present) of Big Tech companies, privacy ethics, and their outpour of support for Artificial Intelligence, it's probable at this point to worry that the problem is not in the development of AI, but in the privacy concerns that we are all too familiar with. Examining the relationship between tech giants, privacy concerns, the capabilities of AI language models, and government regulations, it's crucial to consider the risks associated with the high potential of AI when wielded by entities with malicious intentions. The AI Revolution Language Learning Models (LLMs) or Artificial Intelligence as most people know it, is a massive combination of a bunch of Algorithms that together, are able to act autonomously to create results based on the information it has been trained on. AI is not as new of a tool as most think it is; many AI tools are used in our everyday lives. From the maps in our cars, to social media ads, to our Netflix recommendations, all of them use AI tools to learn our routine and habits, and make recommendations and guesses as to what we are likely to engage with. Used correctly, Artificial Intelligence has the power to transform how we interact with technology in our daily lives. Not only could it make our lives more convenient, but it could also transform accessibility for those who aren’t otherwise able to interact with reality as easily as most. Those who are visually impaired for example, could to narrate the world around them for better navigation. use AI AI is already being used to streamline many different processes, including E-commerce, healthcare technology, finance, agriculture, and education. Making people’s jobs easier. Being able to perform the automated tasks we as humans already do means that we don’t have to spend so much time on mundane tasks, a big part of many people's jobs, and focus on the areas where human ingenuity is paramount. For example, AI makes it easier for me to decide the better commute route to work today, given the construction on Broadway, and the traffic stop downtown, which means I can have an extra 10 min of sleep and make myself a coffee before I leave, which will make my day much better and allow me to be more productive at work. Something important to remember is that, unlike other tools that solely rely on chips to control their functions, AI is trained on large amounts of information, which it then learns and is able to recall when we feed it certain prompts. This is largely where the relationship between the development of AI and big tech begins, as Google, Amazon, and Microsoft have some of the largest stores of human data (probably including yours) which they can and are leveraging to train their AI models. It's a little bit alarming that the companies that have proved they are the least trustworthy rwith our Data are the ones who are leading the charge to develop even smarter AI. Needless to say, it seems like a recipe for disaster. And we, the consumers, have the most to lose. The Dark Side of the Artificial Moon Many of us in the tech world are cautiously optimistic about what generative AI tools hold for the future and we continue to see inspiring innovation using AI technology with great potential. Don’t get me wrong, AI can be a very good thing in our world and is currently being used to create crucial technology to help many in their everyday lives, such as but the concerns and reservations most people have about AI have to be adequately addressed when introducing such powerful technology into our daily lives. As profit-centered as they are, Big Tech Giants have a responsibility to respect the data privacy of their consumers (a responsibility they have ) and particularly with regards to AI that is trained using Data, it becomes extremely paramount to be very conscious of the kind of data you are using to train the Language Models. Some people don’t actually want their Facebook photos, Instagram stories, location history, financial data etc. to be used to train an AI model. Moreso with more sensitive data like medical, biometric, financial and location data. continually disregarded Is AI Hysteria misplaced? We have definitely seen an in hysteria surrounding the capability of AI and worries about it stealing jobs, gaining sentience, and eventually overtaking the human race, but realistically speaking what should we be scared of is Google using our data against us to maximize profit. This is something they have BEEN doing, but the potential to leverage our data against us becomes more serious when we start dealing with AI that understands and records every aspect of your life, and is accessible to the wrong people. uptake Risks of AI models being trained on Sensitive/Personal data There have already been incidents where people that data collected using Amazon Alexa has been requested and given to law enforcement without a warrant, and used against them in a criminal investigation. report There have also been multiple incidents where personal data has been used to further political agendas and potentially spread misinformation. A multitude of data breaches in which the data collected by big tech companies, falls into the wrong hands, giving a criminal access to the personal information of millions of people. Using personal data to train AI models opens a risk to potential problems with privacy violations as a result of unauthorized access in the process of data input and exchange. If not handled under scrutiny and with care, during the lengthy process of the data being used to train the AI models, it can be passed back and forth many times, with people having unregulated access to personal data. When handling such complex information in such large volumes, it’s not implausible that there will be data breaches as a result of a lapse in security measures that lead to the unauthorized release of sensitive information. The importance of training AI on a diverse set of data is because of the very real and previously experienced probability of an AI model having biases and discrimination based on the training it has received. Take facial recognition for example, an AI model may be used to detect who in a given store is stealing, and feed security over a three-year period. If the people who appear in said security footage are predominantly from a particular race, the AI model may begin to predict that anyone who is outside of that particular pool of people is more likely to steal. If the training data is not diverse and representative, the AI model may struggle to accurately generalize its learning to a diverse population. If AI models are trained on only a set of demographics, then that leaves the significant risk of potential biases in the Language Model itself, based on the bias in the data. The issue is relatively simple here; if certain groups have more personal data available, then how do we prevent biases within the AI? It may result in exclusionary outcomes for underrepresented communities within the data set. There is also the factor of a lack of consent and transparency as to whether it is disclosed to users that their data is being collected for use in training AI models. Nowadays when we are constantly bombarded with information and often experience choice paralysis, of people do not read through terms and conditions when signing up for any given application, and therefore raises the question of whether consumers actually know exactly what they are signing up for in regards to their data rights. This can play a huge role in eroding user trust, with already finding shockingly low trustworthiness, with 42% of people having low or now trust in big tech companies. 91% studies It is a very real threat that even these big tech giants are susceptible to cyber-attacks leading to massive data breaches, as has thoroughly demonstrated, and such risks are only heightened with the increase in use and transmission of large amounts of data, in order to train AI. Tech giants like Facebook have a tense history with such cyber attacks, and leaks of user data, and it leaves the consumer wondering if it is wise to introduce another point of vulnerability when the tech giants aren’t even able to handle what is already on their plates. recent history Another potential problem we could see in coming years with the proliferation of AI technology, is the re-identification of anonymized data, which again could reveal sensitive information which could put individuals at risk. Closing thoughts It makes total sense that a lot of people have many reservations about artificial intelligence and what it could bring to the labor force, (although it likely wouldn’t change anything anyway) but I think in our natural human instinct to be skeptical about anything new, we forget that the problem might have been here all along. It's not entirely the fault of the average worker, these multi-billion dollar corporations have probably put a considerable amount of effort into making sure that we don’t hate them as much as we should, but it is still quite interesting to see people demonize the tool that strikes and not the hand that wields it. Regardless of whether you have an understanding of AI or not, you still probably know about the data protection failings of these tech giants, and so it shouldn’t be a surprise when in the next 5 years we see controversy around AI being used unethically to track your every step and make money off of it. Lead image by on Unsplash Adi Goldstein

AI Isn’t the Problem, Big Tech Is

এআই সমস্যা নয়, বড় প্রযুক্তি

Nicht KI ist das Problem, sondern Big Tech

La IA no es el problema, las grandes tecnologías sí lo son

L’IA n’est pas le problème, la Big Tech l’est

एआई समस्या नहीं है, बड़ी तकनीक है

AIが問題なのではなく、ビッグテックが問題なのです

AI가 문제가 아니라 빅테크가 문제다

IA não é o problema, é a grande tecnologia

Проблема не в искусственном интеллекте, проблема в больших технологиях

Sorun Yapay Zeka Değil, Büyük Teknoloji

AI không phải là vấn đề, mà là công nghệ lớn

人工智能不是问题，大科技才是问题

When hearing about the behemoth " " podcasts may be among the last things to cross your imagination. After all, it's not hours-long, stuffed dialogues that get the viewer through a dozen various emotions that fueled the platform's unmatched popularity and catapulted it among the top apps with the most considerable penetration rates. TikTok The short-form videos condensed the whole meaning and purpose of the piece of content in a matter of sentences and hooked users by delivering information in the most straightforward and captivating ways. Through its appeal and mainstreamness, TikTok can reach more than adults monthly. It sees its popularity going nowhere anytime soon, especially now that podcasters are getting a taste of the benefits it can deliver. 1 billion TikTok has lately embraced new purposes and become a thriving ecosystem for podcasters to have a louder voice and connect with the categories of internet users they're targeting. And because of Monolith, the large-scale, real-time recommendation system embedded in TikTok's algorithm made it such a reliable platform for those desiring content tailored to their needs and preferences, podcasters or aspiring podcasters can conduct their work with more support. And as seasoned podcasters are well aware, the journey to a thriving audience involves a multifaceted strategy. TikTok, coupled with niche tools and services designed to , excels beyond compare among social media platforms. help content creators in their quest for audience growth So, how can a platform designed and aggrandized for briefly summarized content become such a haunted tool for the opposite type of video? And what are some examples of world-famous podcasters who nailed it? Gen Z looks at podcasts as a leading source of information. Gen Z unearths fresh podcasts at a speed rate incomparable with any other generation, being the first ones flocking to the platform and slowly leaving others such as heavyweight Facebook and Instagram behind. Longing for a piece of the pie represented by the booming popularity of TikTok podcast viewing, quick-on-the-uptake platforms have mobilized in their attempt to meet the changing demands of this growing watcher category through features and options similar to those of TikTok. Spotify, the most revered music platform at the moment, has long expanded beyond its initial scope of allowing independent artists in their developmental stages and giant record labels to upload content, fairly paying each contributor for their share. Spotify is now a platform where podcasters are at home, and more and more look to it to boost their content's popularity and reach. Yet, it's premature and rushed to claim that Spotify will go as far as TikTok succeeded in the podcast game and build such a kingdom. Podcast creators are migrating to TikTok for its unparalleled potential Creators can use numerous ways to promote their channels and content on TikTok. Probably one of the most straightforward and accessible methods is to take the route of comments. That section allows users to share their thoughts, which often take the form of means to make one's digital offerings acknowledged. Undoubtedly, TikTok couldn't have made it among the myriad platforms aimed at publicizing podcasts without a specifically designed feature. In light of an increasingly refined user base, the " " section is precisely what a podcaster or any other type of long-format content creator needs to grow their following and gain notoriety based on their exact type of content. This focal point of the platform wins organic growth for any type of podcaster because it recommends videos after analyzing the user interests instead of how large the account's following is. Genuine effort is at play here! Those searching for videos on human evolution shouldn't be obscured by content speculating on the world's state a century from now. For Your Feed Celebrity Memoirs Book Club – one of the most successful stories to look to With over 305k followers at the moment and a growing cohort of viewers familiar with this podcast, Celebrity Memoirs Book Club has grown from serving a small user base of several hundred to a score that doesn't stop rising. This weekly podcast began over three years ago and has built a passionate following ever since on their platform. What fueled its ascension to popularity are the topics approached, serving as a fantastic means to help those with tight schedules learn the highlights in the celebrity world. After all, who wouldn't want to skip the part where they consume endless hours of content just to extract an essential that could have otherwise been delivered more succinctly and less boringly? Not to mention the pandemic of deceptive clickbait and misleading headlines that are the bane of today's online world. Celebrity Memoirs Book Club is the channel where today's internauts repelled by lengthy memoirs can elevate their knowledge about celebrities, famous individuals, and everything noteworthy. By looking at the hosts' ways of delivering their message and having watchers glued to their displays, more podcasters aspiring to achieve tremendous fame can take their efforts to the next level. Diary of a CEO – another example of a well-done job For the business world has always been one of the hottest (and most complex) topics; having interested parties wrap their heads around it, the podcasters approaching this topic in the savviest and cleverest ways are on the path to achieving unmatched success. Diary of a CEO is where collections abounding in wisdom catch the interest of individuals of any age category. The hosts approach subjects of both failure and triumph, making viewers feel empowered and optimistic about their future. There's something inescapable about content that tackles the ins and outs of such a complex topic and shades light on the most intricate aspects, whether it's about how the or how everyone can tap into their unearthed persuasive talker. rich evade taxes The wrap-up TikTok, conceived with the exact opposite thought in mind rather than dishing out minutes-long videos, has managed to meet more than its original purpose. Now, podcasters find a great ally in its technology and potential, and users flock from different podcast-based platforms to their competitor, TikTok, for numerous reasons, whether it’s the pursuit of entertainment or a thirst for education.

TikTok's Podcast Revolution: A Surprising Ally for Creators and Audiences

– that is the key ingredient to dominating your market and thriving in today’s cutthroat and competitive business ecosystem. As the world spins at an unprecedented rate, we see new brands emerging left and right, coupled with cutting-edge technologies that introduce disruptive shifts in the marketplace. Stay ahead of the game To remain relevant and achieve the coveted top-tier status, businesses must adapt and innovate continuously. Artificial Intelligence (AI) is undeniably a game-changer in this endeavor. Today, AI gets thrown around in every corner of any given industry. From optimizing sales processes, and streamlining operations, to boosting business growth, this tech breakthrough proves to be a cornerstone of innovation that’s here to stay for good. AI is no longer a buzzword; it's a strategic imperative for those looking to thrive, rather than merely survive. Here are 10 useful ways to use AI to accelerate your business: Data-Driven Decision Making Here’s where AI excels the most: gathering, processing, and analyzing data in a millisecond to push business leaders away from guesswork and lean toward data-driven decision-making. It is wise to utilize AI-powered analytics tools in your business processes, from identifying trends, and unlocking customer preferences, to deciphering market insights. In uncovering the treasure trove of your company’s data and transforming them into actionable insights, invest in AI-driven algorithm platforms that elevate conversion rates, optimize customer success, and identify the most promising prospects. A great example is Coho AI, a software that capitalizes on product usage data, intelligent segmentation, and user journey, equipping sales, operations, product, and customer success, with powerful AI-driven insights, and streamlining their processes through intelligent automation. There's an AI for that: Be a Master Dealmaker Dealmaking has evolved from simply reading a room and deciphering your prospects’ body language to a virtual world where you can only see an inch of their frames on your screen. Remote work has certainly paved the way for faster, rapid-fire deals, but has equally introduced a new set of challenges that dealmakers need to navigate through. With the lack of face-to-face interaction, intuitive understanding, and body language cues, dealmaking in the digital age relies heavily on data and technology, making AI an invaluable ally. Invest in behavioral intelligence tools to free yourself from the burden of reading your prospect’s non-verbal cues, empowering you to focus on the core aspects of dealmaking. These tools can analyze communication patterns, identify prospects' preferences, and assess their receptivity to various propositions – so you don’t have to. One tool that makes a difference is Novacy, an AI-powered behavioral intelligence platform, that helps B2B revenue teams close more deals by understanding their prospects' underlying perceptions by using audio, text, and body language analysis. There's an AI for that: Embrace Predictive Maintenance AI has been a saving grace for businesses with physical assets, both in costs and efficiency. By predicting whether a piece of equipment is on the verge of failure or requires maintenance, AI helps prevent costly downtime, extends the lifespan of assets, and reduces maintenance expenditures, ultimately improving the bottom line. Its impact on manufacturing and logistics is multifaceted, driving benefits that extend well beyond the shop floor. *__There's an AI for that: __*Uptake, a leader in predictive maintenance solutions, offers an AI-driven platform that empowers businesses to proactively manage their physical assets. Their software uses machine learning to analyze equipment data, predict maintenance needs, and provide actionable insights. By implementing Uptake's solution, businesses can significantly reduce maintenance costs, enhance asset reliability, and optimize their operations. Improved Marketing Campaigns In the ever-evolving world of marketing, AI can revolutionize your campaigns. AI-driven marketing tools can segment your audience, predict campaign performance, and optimize ad spending in real time. By ensuring your marketing efforts reach the right audience with the right message at the right time, AI can maximize ROI and elevate the impact of your campaigns. Emarsys is a powerful AI marketing platform that enables businesses to create highly personalized and data-driven marketing campaigns. It uses AI to analyze customer behavior, predict future actions, and automate marketing processes, ensuring that each interaction with your audience is highly relevant and effective. There's an AI for that: Supply Chain Optimization The pandemic has proven that supply chain management is a complex task that requires heavy technological support. In an increasingly globalized world, AI bridges this gap by providing real-time insights into your supply chain, from sourcing materials to ensuring on-time deliveries. It optimizes inventory, reduces transportation costs, and improves supply chain efficiency, ultimately impacting your bottom line positively. Leading this new dawn for the supply chain is Coupa, a global player in the continuous supply chain design, which leverages the power of AI and comprehensive data models. Its product, transforms design and analysis from one-off projects to a consistent and repeatable process, enabling businesses to adapt quickly to changing market conditions and reduce costs. There's an AI for that: Supply Chain Modeler, Fraud Detection and Prevention In an age of increasing cyber threats and financial fraud, AI can be an invaluable ally in safeguarding your business. Machine learning algorithms can detect unusual patterns and anomalies, preventing fraudulent transactions and securing your financial assets. An AI-driven fraud detection and prevention platform, Featurespace offers adaptive behavioral analytics to help companies identify and stop fraudulent activities in real time. This proactive tool’s smart technology can protect your business and your customers from financial fraud, which is a must-have in today’s era of data leaks and cybercrimes. There's an AI for that: Personalized Product Recommendations E-commerce businesses can harness AI to deliver personalized product recommendations. By analyzing customers' browsing and purchase history, AI can propose items that are more likely to resonate with individual customers, leading to increased sales and customer satisfaction. When it comes to AI-powered product recommendations, Amazon still leads the pack. As a global e-commerce giant, this platform boasts a cutting-edge recommendation engine that has substantially contributed to its success by providing customers with what they want when they want it. There's an AI for that: Human Resources Optimization AI can streamline various HR processes, from candidate screening and onboarding to employee engagement and performance management. Automation and data-driven insights enable HR professionals to focus on strategic activities, foster a more engaging employee experience, and drive higher retention rates. Workday, an AI-driven HR software, offers tools for talent acquisition, workforce management, and employee engagement. It uses AI to streamline HR processes, improve talent sourcing, and enhance the overall employee experience, ultimately contributing to higher retention rates and a more efficient HR department. : There's an AI for that Risk Management To say that change is constant is an understatement. In the modern business world, change and uncertainty are the norm, exposing companies to risks and vulnerabilities. This makes it high time for businesses to invest in AI-powered risk management solutions where its predictive capabilities can assess market fluctuations and regulatory changes, and enable companies to develop proactive risk mitigation strategies. When it comes to leveraging AI to analyze data from a myriad of sources, forecast potential risks that are often overlooked by humans, and provide actionable insights for decision-makers, RiskWatch is a reliable tool. This comprehensive AI-powered risk assessment management software provides advanced risk assessment and protection against threats that may ruin and tarnish your brand. There's an AI for that: Environmental Sustainability Currently, environmental consciousness is at its peak. From developing sustainable products, and optimizing energy efficiency and consumption to reducing carbon footprint, modern businesses are under increasing pressure to abide by these growing eco-friendly standards. In this eco-conscious era, AI is an indispensable tool that can assist businesses in reducing their environmental impact and embracing sustainability. OneTrust offers ESG and Sustainability Cloud that empowers organizations with the ability to define their core ESG metrics, track progress towards these goals, and foster trust with key stakeholders through enhanced transparency. There's an AI for that: Conclusion As AI technologies continue to advance, their role in business acceleration will only grow. Efficient and effective leaders know that incorporating AI into various operations is paramount to adapt to the ever-changing business landscape, enabling them to stay steps ahead of the game. Embracing AI is no longer a matter of “if” – it’s a matter of “when,” and those who act now will reap its benefits fast.

10 Useful Ways to Use AI to Accelerate Your Business

TROUBLE AT HAMLEY HALL.

THE STEAM-ENGINE.

Quite a few years ago, in a elsewhere, I had mentioned the following: previous article “The huge technological progress of the last decade addressed and greatly impacted the basic human need of communication. Websites, blogs, emails, text messages, Facebook, Snapchat, Slack and smartphones on the one hand and Google on the other as the dominant intermediary, all in essence addressing our need to communicate. I would refer to this as the ‘communication era’ that has “plateaued” in terms of potential truly disruptive innovation. The next era will be the ‘knowledge era’, where Artificial Intelligence (AI) will dominate.“ Now OpenAI has gulped the entire Web to usher in this “knowledge era” which could be seen as a new technology paradigm as it introduces Natural Language Processing and Inference ubiquitously. At my company Fortuit, we are now building Generative AI infused apps that solve real world problems. Inevitably, the questions that arise are: What is ChatGPT aka Generative AI What are the business benefits of integrating Generative AI? What are the Data Privacy implications? To what extent are businesses integrating Generative AI? Let’s go. WHAT IS GENERATIVE AI (like CHATGPT)? To put the discussion into context, it is important to describe what ChatGPT is in business and in more technical terms, as even within the software industry, AI and Machine Learning expertise is not ubiquitous. Business Perspective ChatGPT is a cutting-edge Generative AI tool designed to enhance customer interactions, automate repetitive tasks, and provide valuable insights. Example use cases include market research, customer support and sales / marketing efforts, allowing businesses to streamline communication processes, handle inquiries and even generate leads. Its ability to understand and generate human-like text makes it a powerful asset for businesses seeking to improve their market position and enhance their customer engagement strategies. Businesses can leverage ChatGPT to create interactive and dynamic customer experiences, ultimately leading to increased customer satisfaction and loyalty. Technical Perspective For developers, ChatGPT is a tool that provides natural language understanding and generation capabilities to create intelligent, conversational interfaces to their applications. ChatGPT is based on the Generative Pre-trained Transformer (GPT) architecture which is a type of deep learning (ML) model designed for natural language processing tasks. This Transformer architecture, referred to as a Large Language Model (LLM), was introduced in a paper titled “Attention Is All You Need” by Google engineers Vaswani et al. in 2017. In more detail, A GPT neural network architecture (LLM) is comprised of an “Encoder” and a “Decoder”. Encoder The encoder’s job is to read and understand user input, such as a sentence or a paragraph. It uses a mechanism called “self-attention” to capture relationships between words in the input, weighing the importance of each word as regards all other words, not concerning itself with the orders of the words as such. In this way contextual information and dependencies between the input words are captured. Decoder The decoder is used for generating text by predicting the next word given the input based and the previously generated sequence of words. Example To illustrate with a simple example: Input: “What is a Chabot” Encoder: · Input becomes: [0, 1 ,2, 3] · Accordingly, the calculated “attention” scores: [0.2, 0.3, 0.4, 0.7], indicating that the word “Chabot” commands the highest interest (attention) · The scores are multiplied by each word’s “vector”, where a vector is a numeric representation of a word in a vector “space” where similar words have similar vectors indicating contextual proximity (i.e., the words are usually seen together, like “hotel” and “travel”) · The Encoder’s outputs: [0.2 * vector (What), 0.3 * vector (is), 0.4 * vector (a), 0.7 * vector (Chabot)] Decoder · The Encoder’s output is the Decoder’s input · From the Encoders output, the decoder gets the probabilities of next potential words from the overall (English or other) vocabulary · From these words, the decoder’s mechanism selects the one with the highest probability to come next, in this case this word ‘System” · So, the generated response starts to become “What is a Chabot? … [system]” · This is repeated until the desired response length is reached. No more technical stuff from here on! THE BUSINESS BENEFITS OF INTEGRATING GENERATIVE AI The critical question for businesses is what this huge new technological opportunity means and what are the benefits for a company’s workings overall through the application of Generative AI. Furthermore, how can it seamlessly and safely be integrated into each business function with a view to improving performance and efficiency, connecting and centralizing these business processes? How to streamline Generative AI Integration into a coherent, simple long term AI Strategy? Business 101 To put the above in context, lets briefly take a step back to Business 101 and see what processes make up a business. Overall, business processes can be approximately divided into three categories: Core, Support and “Long Tail” Core Processes · Production · Sales and Marketing · Order Fulfillment · Customer Support · Product Development Support Processes · Human Resources (HR) & Recruiting · Finance · IT · Procurement Long Tail (everything else) A long-tail process is a custom / ad hoc workflow in response to evolving business needs, usually reflecting a gap between systems, apps, departments, or workflows, usually addressed through manual work or an informal workflow. Examples · Contract Management · Vendor approval · New hire request · Budget requests Application of Generative AI to Business Processes A huge opportunity exists for businesses to leapfrog competition by reimagining how humans get work done with generative AI applications at their side. Generative AI can and is being applied to business processes, unlocking novel use cases and also speeding up, scaling, and improving existing ones. Some potential business benefits Of Generative AI integration are: Automated Content Creation Personalized Customer Experiences Customer Service and Support Language Translation and Localization Cost Reduction Innovation and Idea Generation Overall, implementing generative AI enhances productivity, reduces costs, improves customer satisfaction, and unlocks innovation across various business function. But, to be more specific, below are some very beneficial use cases (among many more) in reference to the business processes described above. Core Business Process: Sales and Marketing A Sales AI Assistant to augment sales performance Imagine a customer sales call where the company’s “Sales AI Assistant” suggests upselling opportunities to the salesperson in real time based on the actual content of the conversation, drawing from CRM data, market trends, current news and social media influencer data. Before the call, the AI Assistant would provide a sales pitch draft. After the call, an assessment would be automatically generated that would allow the salesperson to provide feedback to the AI assistant with a view to helping it performance get better and better. The above does not mean replacing salespeople but teaming them up with generative AI. Core Business Process: Product Development Adding value to an existing product Imagine if for every specific product a company sells an employee could be assigned to guide the customer from initial use and un-boxing to trouble shooting and to best use guidelines. Customer satisfaction would sky rocket but of course the cost would be unsustainable. Imagine for example a specific Air Fryer product that has an accompanying specific AI Assistant implemented through a mobile app with speech to text and text to speech functionality that will guide product use from day one. Consider what this means for upselling, feedback, engagement, support and loyalty. This would be like hiring thousands of support employees, essentially for free. Support Business Process: Human Resources Company Culture and standards, training and on-boarding HR assistant A generative HR AI Assistant trained on proprietary knowledge such as company policies, customer interaction guidelines, and overall company culture and governance could provide continuous guidance and support to all company employees. The same assistant could act as a company Learning Management System (LMS), and a new employee on-boarding guidance counselor. Central AI Assistant — all processes, all assistants Importantly, in conjunction to applying generative AI to the various business processes described above, a core AI Assistant could be implemented as a “centralization” vehicle connecting, improving and monitoring all business processes and sub-assistants, so improving performance and efficiency and unlocking innovation across overall business functionality. Generative AI Implementation Modes All AI Assistants will have their own custom knowledge (LLM), and could potentially access data though API’s that connect to various company databases including ERP and CRM. All assistants would be implemented only as a Chat UX app accessible through mobile or the web with distinct authentication levels. All AI assistants would be minimal in terms of design and UX features, relying only on natural language chats sessions and a great amount of knowledge (unstructured) and data (structured). Cloud implementation · Custom LLM (hosted on the cloud on a service like Hugging Face Spaces which offers a simple way to host ML apps) · ChatGPT API leveraged to complement the Custom LLM as required. · AI Assistant app implemented on the cloud on a service like Azure or AWS. · AI Assistant implemented as an IOS or Android mobile app As regards Data Privacy, OpenAI recently launched ChatGPT Enterprise to address related concerns. ChatGPT Enterprise provides: · Data encryption “at rest” and “in transit” · Data privacy through AES 256 and TLS 1.2+ · SOC 2 compliance · Customer prompts and sensitive corporate data remain untapped for OpenAI model training In-house Implementation · LLM and Apps hosted within corporate IT infrastructure · No access to Chat GPT API or other GEN AI API’s. Hugging Face has over 300,000 models readily available, including exceptionally powerful large-language models, allowing businesses to deploy their own GPT privately and locally. Hybrid Implementation Internal use AI Assistants (like the HR example above), could be implemented and made accessible only within the corporate IT infrastructure. Non-public documents such as meeting minutes, legal documents, product specifications, technical documentation, R&D reports, business procedures, and so on could be made available only to internal AI Assistants. External use Assistants that rely on public company data clearly should use 3rd Party LLM services like ChatGPT, Google Bard and/or others. In theory, In-house implementations are safer but much more expensive. But, if in-house security is not at enterprise level (like the specifications mentioned above), is it really safer? DATA PRIVACY, COMPLIANCE AND SECURITY IMPLICATIONS OF GENERATIVE AI To be clear, Data Privacy and Compliance requirements have always been an important aspect of data processing and should be a priority for all business process and IT Systems. These issues and are not something that requires prioritization due to the recent advent of Generative AI technology. From a legal perspective, to date the United States lacks comprehensive, broadly applicable privacy protections like the European Union’s General Data Protection Regulation (“GDPR”) and its equivalent regulations in the United Kingdom and Switzerland, but does have a patchwork of federal and state laws addresses data privacy here at home. For example, The Virginia Consumer Data Protection Act (“VCDPA”) regulates only collection and use of personal information of consumers (employee data is exempt from regulatory requirements). The California Privacy Rights Act (“CPRA”) requires that notice of the business’s personal data handling practices be provided prior to or at the time of initial data collection. In terms of AI specific Regulation, there are various undergoing efforts (US, UK, EU), but it is a very difficult task. Innovation is stifled by over-regulation, and even if certain countries do over-regulate, others will clearly under-regulate. Let’s briefly look at the basic requirements for some of the regulatory directives mentioned above. European Union: GDPR (General Data Protection Act) Basic Principles o Lawful, fair and transparent processing o Limitation of purpose, data and storage o Data subject rights o Consent o Personal data breaches o Privacy by Design o Data Protection Impact Assessment o Data transfers o Data Protection Officer o Awareness and training United States: California Consumer Privacy Act (CCPA) (proxy for other potential regulation) Applies to businesses: o with an annual gross revenue of over $25 million o that gather, buy, sell, or receive personal information of over 50,000 California residents, households, or devices o derive more than 50% of annual revenue from selling the personal information of California residents Basic Principles o Right to Disclosure o Right to Access o Right to Contact Information. o Right to be Forgotten. o Opt-out of Data Sales and Marketing. o Right to Fair Treatment. o Periodic Privacy Policy Updates. United Kingdom: Data Protection Act Basic Principles o Used fairly, lawfully and transparently o Used for specified, explicit purposes o Used in a way that is adequate, relevant and limited to only what is necessary o Accurate and, where necessary, kept up to date o Kept for no longer than is necessary o Handled in a way that ensures appropriate security, including protection against unlawful or unauthorized processing, access, loss, destruction or damage Approaches to Generative AI Data Privacy, Compliance, Security Sandboxing Sandboxing is strategy to keep data safe when working with AI models. Sandboxing means creating a controlled environment within which a system or application operates. So a company can have internal apps working only with an internal (sandboxed) network (lan) environment. Data Obfuscation Obfuscation means replacing sensitive data with encrypted forms, sending this version to the LLM, and process the response by replacing the replaced data with the original version. For example, sending the following to ChatGPT: “Joe Smith lives at 4607 Rodeo LN Los Angeles CA 90016–5609 USA”. What is Joe’s current address?” would be translated to “<NAME-1>” lives at4607 Rodeo LN Los Angeles CA 90016–5609 USA. What is :<NAME-1>”’s current address?” The ChatGPT response “<NAME-1> lives at 4607 Rodeo LN Los Angeles CA 90016–5609 USA.” Would be converted to “Joe Smith lives at 4607 Rodeo LN Los Angeles CA 90016–5609 USA.” before being sent to the user. Moderation and Fact Checking API In all LLM use cases, a Moderation and Fact Checking API should be deployed. This would filter out a large range of inappropriate material in AI and/or user-generated content (UGC). At the same time, it would fact check both the input and output. LLMs do not recall facts (as databases do) but instead generate text that reads like human-written text that will always sound right but in some cases may be incorrect. This is known as hallucination. To mitigate this, in addition to the Moderation and Fact Checking API a containing certified company knowledge should be included and tapped into initially by the AI Assistant. custom LLM Use of Disclaimers As regards all AI applications, in addition to recorded user terms and conditions acceptance, disclaimers should clearly indicate that the content has been generated by AI and may not always be accurate and/or taken as medical, financial (or other) advice. Transparency and Data Best Practices Transparency about how Generative AI models have been trained is important. Users must always know what data might and might not be collected about them when using generative AI. There should always be an accessible mechanism for users to request data deletion or opt-out of certain data processing activities. Data de-identification and anonymization, identification of Personal Identifying Information (PII), data loss prevention and data minimization should always be considered when developing any *(Generative) AI application. TO WHAT EXTENT ARE BUSINESSES INTEGRATING GENERATIVE AI? OpenAI’s blog post reports ChatGPT’s impressive adoption. With over 80 percent uptake in Fortune 500 companies, industry leaders such as Block, Canva, and PwC are using ChatGPT Enterprise to expedite tasks ranging from coding to communications. Overall, AI integration is already underway in a significant number of businesses today with an ever-increasing trend as reflected in a recent Forbes Advisor Study shown below. According to this study, “a significant number of businesses (53%) apply AI to improve production processes, while 51% adopt AI for process automation and 52% utilize it for search engine optimization tasks such as keyword research. Companies are also leveraging AI for data aggregation (40%), idea generation (38%) and minimizing safety risks (38%). In addition, AI is being used to streamline internal communications, plans, presentations and reports (46%). Businesses employ AI for writing code (31%) and website copy (29%) as well.” Another study by revealed that 45 percent of top-level executives mentioned that exposure to ChatGPT had motivated them to boost their investments in AI. This trend is likely to continue with the introduction of ChatGPT Enterprise. Gartner Takeaway Generative AI is a major and rare opportunity for today’s businesses to transform themselves into less rigid and less bureaucratic entities, enabling a transition to a nimbler and more productive version of themselves, more akin to smaller niche companies. Current generative AI and other AI technologies have the potential to automate work activities that absorb a substantial percent of employees’ time today, allowing focus on more creative and productive activities. The potential benefits of Generative AI are significant. Risks and safeguards must be considered and implemented. In fact, integration is inevitable. P.S. Although I hold an MSc from QMW London in Artificial Intelligence — Advanced Methods in Computer Science and have technical expertise in ML Models, I am still continuously impressed by the workings of ChatGPT — GPT-4. I asked it to read this article, check grammar and spelling, tell me how much time would be required for someone to read it and to give me some NEW Gen AI insights. It answered saying that 12 minutes of reading would be required (stating the average read time per word), indicated grammar and spelling issues, and gave some great, deep insights referring to Reskilling, Impact on education, Cultural Implications, Accessibility/Inclusivity and Environmental Impact. I said “Thanks great answer” and it responded with “if there’s anything else I can help you with, feel free to ask, oh and happy reading”. From a technical perspective that “happy reading” part at the end is impressive, reflecting the extent of ChatGPT’s contextual understanding at more than one dimension, implying that AI systems are becoming increasingly sophisticated in their ability to comprehend and respond to human input, enhancing user experiences and interactions.

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