Your resume should be exceptional. It should grab attention, compel someone to keep reading and make them believe you’d be perfect for the role they have available. Recruiters receive thousands of applications for every posting, so standing out is imperative if you’re going to land an interview. You don’t need ChatGPT to create your entire resume, but it can certainly take it to the next level and keep you ahead of the competition.
Creating an exceptional resume is considerate. It helps the recruiter out. It frees the busy entrepreneur from wondering if you’ll be a good fit. Of course, it’s not enough to have a great resume, you have to show up and do the work to ratify your claims. Your resume gets your foot in the door and it’s up to you to prove you’re worth keeping. Here’s how to improve that all-important document to make sure it doesn’t let you down.
5 ways you can utilize ChatGPT to improve your resume
1. Improve the formatting and structure
Even to the most seasoned hiring manager, a big block of text can seem daunting. If your resume contains lengthy paragraphs, it’s unengaging and unappealing and something needs to change.
Use ChatGPT to optimize the format and structure of your resume. Paste in the content and ask for suggestions on organizing the sections, improving the visual layout, or making it more reader-friendly. If it’s friendly to the reader, it’s helpful to your job search. Don’t miss this step out.
Here’s an example prompt: “Please review my resume and suggest any improvements to the formatting and structure. I want it to be visually appealing and easy to read.” Then paste your words and see what suggestions the large language model makes.
2. Enhance the content
It’s highly likely you’re too familiar with your resume. It’s been dug out of your files haphazardly every time you want to apply for a role. Perhaps you wrote it years ago and have only made minor changes. Overlooking the basics will cost you recruitment success, so be prepared for a complete overhaul.
Ask ChatGPT to enhance its content. Ask it to help you refine the descriptions of your past roles, highlight your key accomplishments in a better way, and provide recommendations for incorporating relevant keywords and phrases.
Use this prompt: “Can you review my work experience section and suggest ways to make it more impactful? I want to highlight my achievements and emphasize the skills that are most valuable to employers.” And provide details of the type of role you are after. When you have a response, ask it to refine the content to sound like you, and replace those tired sections with fresh ones.
Level up your resume using ChatGPT in a few minutes. getty
3. Refine the skills and qualifications
You know that your skills and qualifications are relevant for the role, that’s why you’re going for it in the first place. But you’re one resume in a pile of hundreds. You cannot expect the recruiter to connect the dots. Make it easy for them by spelling it out.
ChatGPT can help you here. Ask it to refine the skills and qualifications section of your resume. Ask it for help articulating and prioritizing your skills, to make sure they align with the requirements of the position.
Use this prompt: “I have a long list of skills, but I’m not sure which ones to prioritize. Can you help me choose the most relevant skills for the position I’m targeting? My skills are [list of skills] and the position requires [requirements of role]” ChatGPT’s strength is language, and that’s what needed here. The language of your experience should match the language of their requirements. Make that happen with this simple prompt.
4. Make the summary more compelling
Many roles require a covering letter, or at least an opening statement. Don’t let these components detract from what is now a solid resume. Your opening statement or covering letter should be compelling and concise. It should capture your professional identity and career aspirations and help the recruiter or business owner picture you in the role.
Use ChatGPT for feedback and suggestions on what you have so far. You might already be there with the content, but need it delivered in a slightly better way. Here’s where ChatGPT reworks your words to be reader-friendly and easy to digest, for frictionless application to the role of your dreams.
Use the prompt: “Could you please review my summary statement and make it more captivating? I want it to immediately grab the attention of hiring managers.” Then paste your summary, see what comes out, and give further direction to adjust the tone up and down. Consider the formality, the friendliness and how direct you want to be. Match the vibe of the company you’re applying to for maximum points.
5. Proofread and spot errors
For an entrepreneur or hiring manager with a high attention to detail, one spelling error could cost you an interview. They see a misplaced apostrophe or typo and assume that you don’t care. They assume you overlook details and that the role isn’t important to you. They project this mistake forward, and predict that you’ll show the same lack of care in the role. They’ll throw your application in the bin and never email you back.
Enlist ChatGPT as your proofreading assistant to stop this from happening. It can find grammatical errors, spelling mistakes, and language improvements. It can provide suggestions for clearer wording, concise phrasing, and enhance the overall readability of your documentation.
Here’s the prompt: “Please proofread my resume and suggest any improvements to grammar, spelling, or language usage. I want to make sure it’s error-free and professionally written.” Then paste your resume and see what it finds. Breathe a sigh of relief when the LLM spots errors you would have overlooked.
Use ChatGPT to help secure your next role
These five steps will help get you through the recruitment process, then it’s up to you to prove you’re the real deal. By leveraging ChatGPT’s capabilities and using really good prompts, you can get valuable insights and recommendations to refine and improve your CV or resume, increasing your chances of making a positive impression on potential employers and landing your dream gig.
Founder of Coachvox.ai – we make AI coaches. Forbes 30 under 30 class of 2017. Post-exit entrepreneur and author of Ten Year Career. Competitive powerlifter and digital nomad.
Major brands are paying for ads on these sites and funding the latest wave of clickbait, according to a new report.
This article is from The Technocrat, MIT Technology Review’s weekly tech policy newsletter about power, politics, and Silicon Valley. To receive it in your inbox every Friday, sign up here.
We’ve heard a lot about AI risks in the era of large language models like ChatGPT (including from me!)—risks such as prolific mis- and disinformation and the erosion of privacy. Back in April, my colleague Melissa Heikkilä also predicted that these new AI models would soon flood the internet with spam and scams. Today’s story explains that this new wave has already arrived, and it’s incentivized by ad money.
People are using AI to quickly spin up junk websites in order to capture some of the programmatic advertising money that’s sloshing around online, according to a new report by NewsGuard, exclusively shared with MIT Technology Review. That means that blue chip advertisers and major brands are essentially funding the next wave of content farms, likely without their knowledge.
NewsGuard, which rates the quality of websites, found over 140 major brands advertising on sites using AI-generated text that it considers “unreliable”, and the ads they found come from some of the most recognized companies in the world.Ninety percent of the ads from major brands were served through Google’s ad technology,despite the company’s own policies that prohibit sites from placing Google-served ads on pages with “spammy automatically generated content.”
The ploy works because programmatic advertising allows companies to buy ad spots on the internet without human oversight: algorithms bid on placements to optimize the number of relevant eyeballs likely to see that ad. Even before generative AI entered the scene, around 21% of ad impressions were taking place on junk “made for advertising” websites, wasting about $13 billion each year.
NewsGuard identifies these websites by using AI to check whether they contain text that matches the standard error messages from large language models like ChatGPT. Those flagged are then reviewed by human researchers.
Most of the websites’ creators are completely anonymous, and some sites even feature fake, AI-generated creator bios and photos.
As Lorenzo Arvanitis, a researcher at NewsGuard, told me, “This is just kind of the name of the game on the internet.” Often, perfectly well-meaning companies end up paying for junk—and sometimes inaccurate, misleading, or fake—content because they are so keen to compete for online user attention. (There’s been some good stuff written about this before.)
The big story here is that generative AI is being used to supercharge this whole ploy, and it’s likely that this phenomenon is “going to become even more pervasive as these language models become more advanced and accessible,” according to Arvanitis.
And though we can expect it to be used by malign actors in disinformation campaigns, we shouldn’t overlook the less dramatic but perhaps more likely consequence of generative AI: huge amounts of wasted money and resources.
What else I’m reading
Chuck Schumer, the Senate majority leader in the US Congress, unveiled a plan for AI regulation in a speech last Wednesday, saying that innovation ought to be the “North Star” in legislation. President Biden also met with some AI experts in San Francisco last week, in another signal that regulatory action could be around the corner, but I’m not holding my breath.
Political campaigns are using generative AI, setting off alarm bells about disinformation, according to this great overview from the New York Times. “Political experts worry that artificial intelligence, when misused, could have a corrosive effect on the democratic process,” reporters Tiffany Hsu and Steven Lee Myers write.
Last week, Meta’s oversight board issued binding recommendations about how the company moderates content around war. The company will have to provide additional information about why material is left up or taken down, and preserve anything that documents human rights abuses. Meta has to share that documentation with authorities, when appropriate as well. Alexa Koenig, the executive director of the Human Rights Centre, wrote a sharp analysis for Tech Policy Press explaining why this is actually a pretty big deal.
What I learned this week
The science about the relationship between social media and mental health for teens is still pretty complicated. A few weeks ago, Kaitlyn Tiffany at the Atlantic wrote a really in-depth feature, surveying the existing, and sometimes conflicting, research in the field. Teens are indeed experiencing a sharp increase in mental-health issues in the United States, and social media is often considered a contributing factor to the crisis.
The science, however, is not as clear or illuminating as we might hope, and just exactly how and when social media is damaging is not yet well established in the research. Tiffany writes that “a decade of work and hundreds of studies have produced a mixture of results, in part because they’ve used a mixture of methods and in part because they’re trying to get at something elusive and complicated.” Importantly, “social media’s effects seem to depend a lot on the person using it.”
Tailoring prompts for ChatGPT means increasingly the effectiveness of the chatbot’s responses. Here are the best tried and tested prompts to bookmark.
ChatGPT has taken the world by storm since its release, with millions of users flocking to utilise its services at an unprecedented rate.
However, while some users have found the artificial intelligence (AI) chatbot to be a useful tool, others have been less than impressed, citing issues and limitations with their interactions with it.
One key factor to consider is the way in which users communicate with it. Simple commands may not always suffice, with users needing to employ more nuanced prompts to achieve their desired outcomes.
To help users make the most of ChatGPT’s capabilities, experts on social media platforms such as Twitter have been sharing valuable insights and strategies for effective communication with the chatbot.
Why is getting prompts right so important?
ChatGPT has been facing criticism for its inability to perform specific tasks accurately and its tendencies to lie and hallucinate. However, the secret to mastering ChatGPT and getting desired outcomes is choosing the correct prompts for it.
By using specific prompts, users can navigate the chatbot more effectively and achieve more personalised responses, unlocking the full potential of ChatGPT.
The importance of tailoring perfect prompts is so valuable that companies are recruiting experts who can communicate with chatbots effectively and a new job, AI prompt engineering, has emerged in the market with a salary range of up to $300 000 (€275 346).
Euronews Next has compiled a list of the five most useful prompts and put them to the test.
Prompt 1: Simplifying complex notions
Prompt: Hey ChatGPT. I want to learn about (insert specific topic). Explain (insert specific topic) in simple terms. Explain to me like I’m 11 years old.
ChatGPT explains blockchain for an 11 years oldChatGPT
ChatGPT’s ability to provide clarity, use simple language and provide explanations are top tier. When asked to explain blockchain in a way an 11-year-old understands, its oversimplification of complex notions helps users to understand things outside of their expertise and with no prior knowledge of technical terms required.
Prompt 2: Generate the perfect marketing plan
Prompt: I want you to act as an advertiser. You will create a campaign to promote a product or service of your choice. You will choose a target audience, develop key messages and slogans, select the media channels for promotion, and decide on any additional activities needed to reach your goals. My first suggestion request is, “I need help creating an advertising campaign for (insert description of service or product)”
ChatGPT use for marketing campaignsChatGPT
ChatGPT has access to the Internet’s database. It knows what people like, what appeals to them the most, what advertisements work well for companies and the marketing strategies to build a successful brand in any domain.
With ChatGPT on hand, the time when the success of marketing strategies is left in doubt or is a question of mere luck appears to be coming to an end.
So much so that individuals are using ChatGPT to build a whole company from scratch. Perhaps the interesting part of this development is that it is working, and by following simple step-by-step guides from the chatbot, users have been able to launch businesses and generate profit.
Prompt 3: Take advantage of expert consulting
Prompt: I will provide you with an argument or opinion of mine. I want you to criticise it as if you were <person>
Person: (insert expert name)
Argument: (insert desired topic)
ChatGPT use for expert opinion from Elon MuskChatGPT
No one is better at providing money-making advice than the richest man in the world. Thanks to successful people’s presence online like billionaire Elon Musk, ChatGPT is able to easily mimic their thinking process and personify them to provide relevant and helpful advice to users.
Prompt 4: Job interview simulations
Prompt: Simulate a job interview for (insert specific role). Context: I am looking for this job and you are the interviewer. You will ask me appropriate questions as if we were in an interview. I will respond. Only ask the following question once I have responded.
Simulating job interviews using ChatGPTChatGPT
Provide the chatbot with enough context about the job you’re interviewing for and let it do its magic. This is a great way to practice your interview responses and get an overall idea of what questions you might get asked.
As you provide the chatbot with more and more information when responding, it will tailor its questions more effectively.
Prompt 5: Make ChatGPT write like you
Prompt: [Insert Text]
Write about (insert text topic) as the above author would write.
ChatGPT mimics writing style based on writing sampleChatGPT
One of the many complaints people have about chatGPT is its inability to provide content tailored to each user. This leaves many complaining about the dullness of the responses and how in some cases it can easily be guessed that an AI wrote the piece.
However, when using the correct prompt, ChatGPT is capable of mimicking one’s own writing style and providing personalised responses.
Recently OpenAI rolled out a new feature to its ChatGPT AI in the form of a new code interpreter which can be usefully wide variety of different tasks. If you are interested in learning more about the new ChatGPT Code Interpreter this quick guide will provide an overview as well as a few examples to get you started.
Watch the video embedded below, kindly created by the App Of The Day YouTube channel to learn 10 ChatGPT Code Interpreter tips and tricks you can use to help improve your productivity, research or data analysis.
In a nutshell, the ChatGPT Code Interpreter is a multi-faceted tool that streamlines a range of tasks. From data interpretation and visualization to Python code analysis and optimization, this tool has the potential to revolutionize your digital tasks. Give it a try and experience the difference.
1. Diving into Data Interpretation
The Code Interpreter is more than just a basic AI tool. It goes beyond the mundane, digging deep into diverse datasets to extract meaningful trends. This function can be utilized to understand your data, whether it’s a personal dataset or test data obtained from online platforms like Kaggle.
2. Creating Compelling Visualizations
Ever needed to represent your data visually but felt overwhelmed by the process? The Code Interpreter comes in handy by automatically generating engaging visual representations such as graphs and diagrams from your data. This feature takes away the guesswork, leaving you with clear, impactful visuals.
3. Processing Images
Have a stack of images that need editing? Look no further than the Code Interpreter. This versatile feature can crop, transform, and even create color palettes from images. Now, you can efficiently handle your image editing needs, all within ChatGPT.
4. Handling File Conversions
File conversions can be a hassle, especially when dealing with different formats. Here’s where the Code Interpreter shines. It simplifies file conversion tasks, such as transforming a song into an MP3 or a JPEG into a PNG.
5. Crafting QR Codes
A QR code is a quick way to direct people to specific online content. Whether you’re running a digital campaign or want to share a web page quickly, the Code Interpreter can create QR codes for you with minimal fuss.
6. Financial Data Analysis
If you’re seeking to understand financial data like stock prices, the Code Interpreter can be your best bet. This tool does the heavy lifting, helping you make sense of complex financial data without the need for an economics degree.
7. Data-based Predictions
While not an oracle, the Code Interpreter does an impressive job of making predictions based on datasets. Although the accuracy may vary, it can be a handy tool for forecasting trends, guiding decision-making processes.
8. Interacting with Personal Data
The Code Interpreter isn’t just a data viewer; it’s an interactive tool. It can work with your personal datasets, helping you understand and visualize your data more effectively. This can be a game-changer for personal or professional projects requiring data analysis.
9. Optimizing Python Code
If you’re a Python enthusiast or just dabbling in the language, the Code Interpreter can become your new best friend. It can help you analyze, optimize, and improve your Python code. Its detailed analysis allows you to understand your code’s functionality and purpose better, paving the way for improved coding skills.
10. Crafting Presentations from Datasets
Imagine having an AI assistant help you design a compelling presentation using a dataset. Sounds too good to be true? With the Code Interpreter, it’s a reality. This feature can use datasets to suggest content and design full presentations, saving you precious time.
If you would like to learn more about the latest ChatGPT Code Interpreter and how you can use it check out our previous article. Or visit the official OpenAI website and developer documentation if you would like to implement the new features into your AI application or service.
At Cannes Lions, the year’s biggest ad event, you couldn’t escape talk of ChatGPT or Midjourney, even at the yacht parties.
“If you were branding this Cannes, it would be the AI Cannes,” Meta ad executive, Nicola Mendelsohn, told me last week. We were sitting in a glass-walled cabana on the French Riviera, steps away from the shimmering blue Mediterranean Sea.
The Cannes she was referring to isn’t the one you’ve probably heard of — the film festival — but rather Cannes Lions, a similarly swanky festival celebrating advertising instead of cinema.
Every June, thousands of advertising professionals fly in for a bonanza of events. While the festival’s official programming happens at the Palais des Festivals et des Congrès convention center, the real networking happens at beachside business meetings, yacht deck happy hours, and celebrity-studded after-parties. The hot-ticket items this year were Spotify’s invite-only concerts by Florence and the Machine and the Foo Fighters, consulting agency MediaLink’s and iHeartMedia’s exclusive Lizzo performance, and TikTok’s end-of-week closing party. On the iHeartMedia yacht, Paris Hilton DJ’ed to a crowd so packed that the party was shut down by the cops.
But it’s not all rosé and champagne: Cannes Lions is a high-stakes hustling opportunity for power brokers at tech companies, ad agencies, and consumer brands — think Nike, Unilever, and Coca-Cola — to check in on multimillion-dollar advertising deals in the second half of the year, and plan new ones for the year ahead.
This year, the festival came on the tail end of a particularly rough time for the tech and advertising world. Digital ad spending slowed down significantly in 2022 compared to years prior, primarily due to rising inflation, an unsteady global economy, and policy changes that made it harder to track users’ browsing habits. That decline contributed to mass layoffs and budget cuts across the media industry. Although conditions are improving a bit, it’s unlikely spending will return to the levels it reached in the early pandemic, and the latest forecasts show continued advertising spending cuts. Given the economic uncertainty, some companies were sending fewer staffers to the festival and cutting back on their presence.
But everyone wants a reason to party and make deals at Cannes Lions. Since advertising funds so many of the free online services we rely on — everything from Facebook to Google to media publishers, including Vox — the industry’s success or failure has massive effects on the average consumer. And in the past year, the advertising industry has desperately needed something to be optimistic about.
Luckily for those looking for a vibe shift, AI had officially entered the chat.
The Carlton Hotel where TikTok had its press preview on June 19, 2023, in Cannes, France. Olivier Anrigo/Getty Images for TikTok
For a week in June, the developing technology was the talk of the beach in the south of France. And while I’m used to nonstop AI hype back home in Silicon Valley, I was not expecting to experience so much of it in Cannes. The streets were plastered with billboards; panels and late-night party chatter were all about AI. Google demoed new tools, Meta announced an upcoming AI assistant that will help advertisers make ads, and Microsoft hosted back-to-back days of AI-themed programming at a beachside venue decorated with images of AI-generated sea creatures.
There was so much AI talk at Cannes Lions this year that, at times, people sounded sick of talking about it. “I’m trying to find the AI superpowered yacht,” I heard one attendee say in jest as he sat on the deck of a luxury vessel, drink in hand.
Jokes and some healthy cynicism aside, the questions everyone seemed to be asking hint at some pretty serious shifts for the media business. Will AI fundamentally change the way we create and consume advertising? Will it be able to lift digital advertising out of its slump? And will it ultimately enhance or replace the human creativity that goes into making ads? Will it save (or destroy) journalism?
AI isn’t new, but it’s the saviour the ad industry needs right now
Six years ago, one of the world’s largest advertising agencies, Publicis Groupe, was widely ridiculed for cutting its marketing presence at Cannes so that it could instead invest money into developing a new AI business assistant, called Marcel. Clients and competing ad firms alike dismissed the idea that AI was a worthwhile endeavour for an agency in the business of human creativity.
“At the time, it was panned by everybody, but now it looks pretty smart,” Jem Ripley, the US CEO of digital experience for Publicis, told me in the hotel lobby of the Le Majestic hotel, a hot spot for executive meetings at the conference. To rub it in a little, this year, Publicis launched a hate-to-say “I told you so” billboard campaign around Cannes reminding people how prescient they’d been with developing the AI-powered Marcel platform.
Even before they became hot buzzwords in the industry, automation and AI were powering advertising behind the scenes for years. The two biggest digital advertising platforms, Google and Meta, have long used AI technologies to develop the automated software that determines the price they charge for an ad, who they show the ad to, and even what lines of marketing copy are most effective to use. As users, we don’t see it day-to-day, but that technology is core to many tech companies’ businesses.
Paris Hilton performed a DJ set during the iHeartMedia After Party on the iHeart Yacht, The Dionea, during the Cannes Lions Festival on June 20, 2023, in Cannes, France. Adam Berry/Getty Images for iHeartMedia
On the consumer side of things, apps like TikTok, Instagram, and YouTube all build AI into the underlying algorithms that decide what content you see, based on what the tech thinks you’re interested in. Think about how TikTok predicts what funny videos you want to see next or how Google ranks your search results; all of it uses AI.
“Everybody wants this to be the year of AI, which I think to some degree it is,” said Blake Chandlee, TikTok’s president of global business solutions, sitting with me in his company’s Cannes outpost inside the swanky Carlton Hotel. “AI is not new. This concept of large language models, it’s been around for years. … What’s new is ChatGPT and some of the bots and the applications of the technology.”
Just as everyone from artists to writers has learned the value of AI from apps like ChatGPT, Midjourney, and Bard, advertising companies are now realizing what these tools can do for them. That mainstream adoption, combined with the fact that marketers are looking to cut costs in this uncertain economic climate, means that AI is exploding in the ad industry at this moment.
I chatted with everyone from creative directors at the top of the totem pole to rank-and-file copywriters at the festival last week, and almost everyone I spoke with said they had experimented with AI tools in their day-to-day duties. And not because their boss told them to, but because they thought it could save them time writing an email, sketching an ad mock-up, or brainstorming an ad concept. Some of them were also worried that it could one day replace their jobs — more on that later — but for now, they were having fun with it.
“I think this year is particularly exciting because it’s sort of like the iceberg breaking through the surface,” said Vidhya Srinivasan, vice president and general manager for Google Ads, in an interview at Google’s beach outpost last Wednesday. “And so I think it’s more personal, and it’s much more tangible for people now. And that brings about a different kind of energy.”
What the AI future of advertising will look like
Standing onstage in a grand theatre at the Palais du Festival, Robert Wong, vice president of Google Creative Lab, touted the AI tools his company has starting to put in the hands of advertisers.
In one demonstration, Wong showed how a client can upload a single image of a company logo — a colourful Google “G” icon, in his demo — into Google’s systems and immediately get back a bunch of high-quality 3D images in the same branded style, from a Google dog cartoon to a Google-branded glass of rosé, which was fitting for the venue.
A waitress serves drinks to visitors arriving for a guided meditation by British podcaster and author Jay Shetty aboard the iHeart Yacht, The Dionea, during the Cannes Lions Festival on June 20, 2023. Adam Berry/Getty Images for iHeartMedia
While this quick demo may not seem dramatic compared to some of the splashy generative AI creations we’ve seen lately, like the Pope in a puffer jacket, it was met with “oohs” and “ahhs” from the audience of advertising professionals. That’s because for designers, work like that could take days or weeks. In just a few keystrokes, this new Google tool could give them limitless iterations of a design to experiment with.
“Day-to-day, what I see is designers literally doing sketches in a matter of seconds versus hours. And not one, but like 10,” said Wong in a press conference after the presentation. “And that’s just the beginning. I think we don’t even know what these tools might be in the future.”
Meta also made some AI announcements at the conference, including that it’s working on an AI-powered assistant that can help advertisers create ads. With its so-called AI Sandbox, the company in May released a slew of advertising tools that let advertisers use quick text prompts to come up with AI-generated advertising copy, create different visual backgrounds for their ads, or resize their images. For now, the program is only open to a small group of beta testers, but it’s expanding to more users later this year.
In the long run, the cost savings for brands using generative AI for advertising could be “substantial,” according to Mendelsohn, Meta’s global head of business group.
“It gets better as we train the machines,” she said during our interview at Meta Beach. “And then you think about the reduction not just in cost, but in the impacts on climate. People are not having to travel to be able to do shooting in different ways, or even the reusing of back catalogue of ads and things in the past.”
As the tech giants build out tools for their advertising customers, some are already experimenting with open source generative AI software with some impressive results.
For example, some major household brands are already starting to use AI to create high-production-value commercial videos.
In October, Coca-Cola enlisted the AI image creation tool Stable Diffusion to help create a video that was shortlisted for an award at the festival. The ad, called “Coca-Cola Masterpiece,” used AI in addition to traditional methods, like CGI, to create complex animations under a tight deadline. The two-minute spot shows characters popping out of the art in a gallery to toss a classic Coca-Cola bottle in and out of famous paintings, like a Warhol and a van Gogh; the bottle takes on the visual style of the work of art when it enters each picture. It’s an incredibly complex animation process that took only eight weeks, according to visual effects company Electric Theatre Collective, which Coca-Cola commissioned. Without the help of AI, the company told Digiday, it could have taken five times longer.
“We wanted to use technology to get the kind of perfection we needed, the kind of quality we needed, in a short time,” Pratik Thakar, Coca-Cola’s global head of generative AI, said on a panel hosted by Microsoft.
Generative AI holds promise for creating new kinds of audio advertising, too. Spotify, for instance, is exploring whether it can train AI on specific people’s voices so that it can one day generate original audio ads from scratch.
“Can we start to get to a place where — I use Morgan Freeman as a canonical example — if you go and license the IP for his voice, can we use machines to help scale that even further?” said Lee Brown, global head of advertising for Spotify, which has been growing its ad business in recent years. “So is there an opportunity here for us? I think there’s a lot of potential there.”
Spotify’s villa party at Cannes Lions. Antony Jones/Getty Images for Spotify
Some of these more sophisticated generative AI tools are still just possibilities for the ad industry at the moment. In the meantime, both Google and Bing are doing something a bit simpler: putting ads inside the conversations people are having with their AI chatbot assistants (Search Generative Experience and BingAI, respectively). The companies say this helps advertisers show users ads that are more relevant to people than what they’d see in a regular search.
The idea is that when you’re researching something like how to plan a trip to Greece, a chatbot would have more context about what you’re looking for — somewhere near the beach that’s kid-friendly in June, for instance — based on a series of follow-up questions you’re having with the bot rather than just through a single search query.
“From a marketer’s point of view, it’s interesting because you have a deeper insight into the user’s intent, because they’re in the conversation where you have more context about what they’re doing,” said Google’s Srinivasan.
Google’s presentation on new generative AI tools it’s rolling out for advertisers. Google
In other words, with generative AI search engines, people ask detailed follow-up questions and actually talk to the bots. Jennifer Creegan, general Manager of global marketing and operations for Microsoft advertising, said in a panel last Wednesday that people’s search queries are three times longer in BingAI because of this back and forth. This leads people to click on an advertiser link, she added, and buy something more quickly.
“The best thing about all of this is this is not something I’m showing you in PowerPoint at Cannes to talk about the future,” Creegan said. “This is real. This is in the wild today. People are using it.”
The concerns about AI and ads
Even though new advancements in AI and advertising are real and in the wild, human judgment still needs to play a role in how it all works. Advertisers aren’t ready to fully hand over the reins to the robots to make their ads.
SNL’s dinner party at Cannes Lions 2023. Fred Jagueneau/NBCUniversal via Getty Images
First of all, AI doesn’t replace taste. That means humans still need to review all the draft AI marketing copy or artwork manually. That’s because big companies are still cautious about protecting their brands, and it’s up to the people at the ad firms they hire to make judgment calls.
“At the end of the day, there’s still a healthy concern — I think rightfully so — from our clients about what is going out there,” said Publicis executive Ripley.
Another reservation major brands have around AI is that it could use other people’s creative work that it scrapes from the web, which could open them up to copyright infringement lawsuits. Publicis recently joined C2PA, a standard that watermarks images created by generative AI and can attach proper copyright information to it so that artists get credit for their work.
Advertisers are also worried about brand safety. Given how AI chatbots have a propensity to generate incorrect information, also known as “hallucinations,” or occasionally veer off into emotionally loaded conversations, advertisers need to make sure that the quality of AI-generated ads is up to par.
“For every hour you put into generative AI as a business driver, you need to put an hour into governance,” said Lou Paskalis, a long time ad executive who’s now chief strategy officer of Ad Fontes Media. “You need to make sure you don’t create a monster.”
All this raises some red flags for the workers in the ad industry. After all, if generative AI can reduce the number of people it takes to, say, produce a video or sketch an animation, the technology could wipe out a swath of jobs, particularly those on the creative side.
Among many advertising executives at Cannes Lions this year, there was an acknowledgment that AI will fundamentally change the kind of work people do. Despite tech companies’ optimism that AI will enhance and not replace human creativity, many said the new technology will get rid of some jobs while creating other new ones. One common refrain from ad execs was that the more creative your work is, the harder it will be to replace.
In the words of Coca-Cola’s Thakar, “Five-out-of-10” level creative advertising work is “free now.” He said, “So we need to figure it out … if you are really doing nine-out-of-10 work, then definitely there is always a demand.”
Florence Welch of Florence and the Machine performs onstage during Cannes Lions at Spotify Beach on June 20, 2023, in Cannes, France. Dave Benett/Getty Images for Spotify
Other executives compared AI to the invention of photography, which didn’t entirely replace painters as some thought it would. like Google’s SVP of research, technology, and society, James Manyika.
“AI and art are not at odds,” Manyika said in a keynote introducing Google’s new advertising tools. “AI doesn’t replace human creativity. It enhances, enables, and liberates it.”
Ultimately, it doesn’t seem as though any of the concerns about AI stealing or replacing people’s work are stopping advertisers from jumping on the AI bandwagon. This embrace of the new technology could be a boon to the struggling ad industry. And that, in turn, could benefit consumers who rely on free services propped up by advertising.
But like every other industry AI is impacting, the rise of AI-powered ads will force us to decide what still needs a human touch and what we’re happy to leave to the bots to handle.
Feature Image Credit: At Cannes Lions advertising festival in 2023, AI dominated the conversation.Paige Vickers/Vox
Shirin Ghaffary is a senior Vox correspondent covering the social media industry. Previously, Ghaffary worked at BuzzFeed News, the San Francisco Chronicle, and TechCrunch.
Sometimes you need some help but you’re not sure who to ask. Your entrepreneur friends are busy, you’re not booked in with your coach for another week, and you’re not convinced your best friend from school will understand your business challenge. How can you get those nudges in the right direction without having to wait?
More often than not, we don’t need to be taught, we need to be guided. The best business coaches know that their entrepreneur clients probably have the answer, they just haven’t asked the questions that retrieve it.
What if an AI model could be trained to ask those questions? What if you could, confidentially, tell an AI model your problems and it could guide you through to solutions, directing its responses and encouraging you to think hard, consider pros and cons and discard options in favour of the way forward that’s right for you?
As AI coaching gets more advanced, test it out with ChatGPT. While it won’t give you a business coach based on the work of a real business owner that inspires you, it can be trained to hold space and encourage you to think for yourself.
Prompting a large language model (LLM) to coach you
Configured with the right words, you can hold a back and forth conversation with ChatGPT or another LLM, as if you were chatting with a real person. Set it up by this starter prompt:
“Hi there! I’m seeking guidance as I navigate my business journey, and I’d love to engage in a conversation with you as my business coach. My business is [briefly describe your business or business idea], and I’m facing some challenges in [mention specific areas or issues]. I believe your expertise can help me gain clarity, develop effective strategies, and overcome obstacles. Can we engage in a back-and-forth conversation where I can share more details about my business, and you can ask questions, confront my thinking and find the root cause of some of my challenges?”
Set the scene by completing the square brackets, then send the prompt and wait for a response. ChatGPT will say it’s ready to begin the conversation, then you can open your floodgates and chat away.
It’s a good idea not to disclose very specific information that can identify you or your business. Keep it anonymous or keep it vague, but give enough detail for ChatGPT’s questions to be useful. Once you’re on a roll you can paste the responses into your files or make a note of the next steps for taking action.
Turn ChatGPT into your personal AI business coach with this powerful prompt. getty
Will AI replace business coaches?
For artificial intelligence to replace coaches, it has to be welcomed by clients. Some are up for giving it a go, but others aren’t sure about the effectiveness of an AI coach and think they will miss nuances like body language and tone.
Regardless of whether coaches are safe from artificial intelligence, they can use it themselves. They can utilize AI-powered coaching to keep logs of their client conversations so a model can suggest new questions, lines of enquiry, or spot patterns they didn’t see. They can expand their content into different formats in a few clicks, they can get ideas for how to attract new clients and how to coach existing clients more effectively.
Even if you’re not convinced that AI coaching can replace in-person coaching, consider that it could supercharge personal development compared with journaling or introspection. At the moment, you write your thoughts and questions into your journal but it doesn’t talk back. You ponder your next move and challenges in your head, but might not reach any conclusions. A back and forth conversation, instead of journaling or pondering, could lead to better breakthroughs on a grander scale.
You’re not asking ChatGPT to solve your problems, you’re asking it to ask you questions so you can solve them yourself.
Use this simple yet powerful prompt to configure ChatGPT to become your AI business coach and see if the practice works for you. While it might not replace the work your real coach can do, it might tide you over until your next session. Use the language model to your advantage and unlock your next level.
Founder of Coachvox.ai – we make AI coaches. Forbes 30 under 30 class of 2017. Post-exit entrepreneur and author of Ten Year Career. Competitive powerlifter and digital nomad.
It’s been almost six months since OpenAI dropped the ChatGPT bomb and effectively reinvigorated AI adoption across a multitude of verticals, sectors, industries and users. For the first time in years, we are seeing a large-scale commercial adoption of generative AI technology—and new applications of LLMO models are introduced by the day.
While individual uses have comprised some of the most common examples of how AI has been transforming our day-to-day workflows, the enterprise-level application of the technology is relatively overlooked, even though it holds immense potential.
Due to transformative strides on an individual consumer level, AU holds immense promise on an enterprise growth and process level—from enhancing creativity and productivity to streamlining business processes and decision making, generative AI can reform how organizations operate today.
In this article, I will explore five potential generative AI use cases for the enterprise sector which stand to unlock new opportunities and help drive innovation.
1. Creative Innovation And Branding
Generative AI opens new doors for enterprises to reach new levels of creativity and innovation—from branding and marketing efforts to content creation and internal communication or data visualization. With the help of such tools as MidJourney, Stable Diffusion, Dall-E and ChatGPT, among others, companies can produce a high-quality and high volume of content, both visual and textual.
Leaders should seek solutions that allow the brand to push boundaries when it comes to branding, brand storytelling, content creation (photo, video, text), A/B testing, blog writing, headline generation and a variety of other assets that can help push the boundaries of any brand’s online and offline presence.
Generative AI is also a powerful lever to pull for user feedback, persona development, value proposition exploration and developing customized marketing campaigns and experiences that stem directly from customer needs and asks.
2. Decisions And Predictions Based Upon Deeper Foresight
Forecasting, future-proofing and planning are the cornerstones of successful enterprise management. In other words, leaders must constantly strive for efficient and sustainable growth built upon data-driven and well-informed decision making.
Generative AI offers a significant boost in this area by providing organizations with advanced data analysis and predictive analytics capabilities. By analysing large volumes of structured and unstructured data, generative AI can provide immediate and accurate insights, aiding decision makers in formulating strategies, optimizing processes and predicting industry trends.
Generative AI isn’t just a powerful source of predictive analytics. Its progressive capabilities also include simulations of potential scenarios if provided with all variables to account for—which creates a clear scenario to aid in data-driven decision making, risk mitigation and opportunity discovery.
3. Streamlined Workflows, Operations And Processes
Generative AI’s ability to analyse data and identify patterns stands as a powerful solution for optimizing workflows, identifying inefficiencies and building processes that are significantly more streamlined and automated. The application of this includes but is not limited to supply chain management, resource allocation and workflow automation—with generative AI transforming labour-intensive tasks into efficient and accurate processes.
In other words, generative AI is also able to assume more administrative or time-consuming, repetitive tasks that would otherwise prove to be a waste of time for employees who strive to drive more impact and plan strategically on a managerial level. This can not only save time and reduce costs but also enhance overall productivity, enabling employees to focus on more strategic and value-driven/high-involvement activities.
4. Personalized Customer Experiences
Building on the major branding, storytelling and marketing benefits of generative AI, the technology also opens doors to new ways of delivering exceptional customer experiences. Generative AI provides brands and organizations with the tools to personalize their interactions with customers on a whole new level—from more powerful chatbots that proactively react to the customer needs to providing unique personalized recommendations to customers based on their preferences and previous activity.
By analysing large amounts of customer data, generative AI can generate individualized product/service recommendations, highly targeted ads and customized user interfaces fit for every user’s personal activity patterns. This is a direct path to higher customer satisfaction, loyalty and engagement while ensuring higher consistency in conversions and bottom-line impact.
5. Accelerated Research And Development
Research and development (R&D) is largely where innovation happens within the enterprise sector. Improving this sector is like throwing a brand new lifeline to any enterprise business—and generative AI has the power to significantly accelerate the R&D process by assisting in the ideation, prototyping and testing phases.
The simulation capabilities generative AI offers can allow for the mapping and exploration of a variety of formulas, outcomes, products and prototypes while significantly shortening time to market and product launches. This can allow businesses to stay ahead of the competition, adapt to rapidly changing markets and deliver cutting-edge solutions to their customers.
Revolution In The Enterprise Sector
Generative AI holds tremendous potential to provide organizations with unprecedented capabilities to innovate, streamline internal and external workflows and deliver custom-tailored experiences to their customers. From empowering creative positioning and branding initiatives to improving data-backed decision making, generative AI holds the potential to drive enterprise to new heights of success.
As the enterprise sector continues to adopt generative AI, leaders must build out strategic initiatives that invest in the necessary infrastructure, talent and resources to remain ahead of the curve and leverage the technology in full capacity. Close partnerships with AI experts, data scientists and researchers are only one of the many ways to efficiently incorporate generative AI into existing business processes and systems.
Gary Fowler is a serial AI entrepreneur with numerous startups and an IPO. He is CEO and cofounder of GSDVS.com and Yva.ai. Read Gary Fowler’s full executive profile here.
If you are interested in learning more about ChatGPT and artificial intelligence put together a quick introductory list of 100 ChatGPT terms explained in just a few sentences. Allowing you to easily grasp its application and research it more thoroughly if required. Here are some terms that are often used in discussions, papers and documentation relating to ChatGPT and similar AI models. Don’t forget to bookmark this glossary of terms or link to it for future reference.
100 ChatGPT terms explained :
Natural Language Processing (NLP): This is the field of study that focuses on the interaction between computers and humans through natural language. The goal of NLP is to read, decipher, understand, and make sense of human language in a valuable way.
Artificial Intelligence (AI): AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
Machine Learning (ML): ML is a type of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Transformers: This is a type of ML model introduced in a paper titled “Attention is All You Need”. Transformers have been particularly effective in NLP tasks, and the GPT models (including ChatGPT) are based on the Transformer architecture.
Attention Mechanism: In the context of ML, attention mechanisms help models focus on specific aspects of the input data. They are a key part of Transformer models.
Fine-tuning: This is a process of taking a pre-trained model (like GPT) and training it further on a specific task. In the case of ChatGPT, it’s fine-tuned on a dataset of conversations.
Tokenization: In NLP, tokenization is the process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens.
Sequence-to-Sequence Models: These are types of ML models that transform an input sequence into an output sequence. ChatGPT can be viewed as a kind of sequence-to-sequence model, where the input sequence is a conversation history and the output sequence is the model’s response.
Function Calling: In the context of programming, a function call is the process of invoking a function that has been previously defined. In the context of AI like ChatGPT, function calling can refer to using the model’s “generate” or “complete” functions to produce a response.
API: An API, or Application Programming Interface, is a set of rules and protocols for building and interacting with software applications. OpenAI provides an API that developers can use to interact with ChatGPT.
Prompt Engineering: This refers to the practice of crafting effective prompts to get the desired output from language models like GPT.
Context Window: This refers to the number of recent tokens (input and output) that the model considers when generating a response.
Deep Learning: This is a subfield of ML that focuses on algorithms inspired by the structure and function of the brain, called artificial neural networks.
Neural Networks: In AI, these are computing systems with interconnected nodes, inspired by biological neural networks, which constitute the brain of living beings.
BERT (Bidirectional Encoder Representations from Transformers): This is a Transformer-based machine learning technique for NLP tasks developed by Google. Unlike GPT, BERT is bidirectional, making it ideal for tasks that require understanding context from both the left and the right of a word.
Supervised Learning: This is a type of machine learning where the model is trained on a labelled dataset, i.e., a dataset where the correct output is known.
Unsupervised Learning: In contrast to supervised learning, unsupervised learning involves training a model on a dataset where the correct output is not known.
Semi-Supervised Learning: This is a machine learning approach where a small amount of the data is labelled, and the large majority is unlabelled. This approach combines aspects of both supervised and unsupervised learning.
Reinforcement Learning: This is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent receives rewards or penalties for the actions it takes, and it learns to maximize the total reward over time.
Generative Models: These are models that can generate new data instances that resemble the training data. ChatGPT is an example of a generative model.
Discriminative Models: In contrast to generative models, discriminative models learn the boundary between classes in the training data. They are typically used for classification tasks.
Backpropagation: This is a method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights in the network.
Loss Function: In ML, this is a method of evaluating how well a specific algorithm models the given data. If the predictions deviate too much from the actual results, loss function would cough up a very large number. It’s used during the training phase to update the weights.
Overfitting: This happens when a statistical model or ML algorithm captures the noise of the data. It occurs when the model is too complex relative to the amount and noise of the training data.
Underfitting: This is the opposite of overfitting. It occurs when the model is too simple to capture the underlying structure of the data.
Regularization: This is a technique used to prevent overfitting by adding a penalty term to the loss function.
Hyperparameters: These are the parameters of the learning algorithm itself, not derived through training, that need to be set before training starts.
Epoch: One complete pass through the entire training dataset.
Batch Size: The number of training examples in one forward/backward pass (one epoch consists of multiple batches).
Learning Rate: This is a hyperparameter that determines the step size at each iteration while moving toward a minimum of a loss function.
Activation Function: In a neural network, the activation function determines whether a neuron should be activated or not by calculating the weighted sum and adding bias.
ReLU (Rectified Linear Unit): This is a type of activation function that is used in the hidden layers of a neural network. It outputs the input directly if it is positive, else, it will output zero.
Sigmoid Function: This is an activation function that maps34. Softmax Function: This is an activation function used in the output layer of a neural network for multi-class classification problems. It converts a vector of numbers into a vector of probabilities, where the probabilities sum up to one.
Bias and Variance: Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm. Variance is error due to too much complexity in the learning algorithm.
Bias Node: In neural networks, a bias node is an additional neuron added to each pre-output layer that stores the value of one.
Gradient Descent: This is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient.
Stochastic Gradient Descent (SGD): This is a variant of gradient descent, where instead of using the entire data set to compute the gradient at each step, you use only one example.
Adam Optimizer: Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models.
Data Augmentation: This is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data.
Transfer Learning: This is a research problem in ML that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
Multilayer Perceptron (MLP): This is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer.
Convolutional Neural Networks (CNNs): These are deep learning algorithms that can process structured grid data like an image, and are used in image recognition and processing.
Recurrent Neural Networks (RNNs): These are a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows them to use their internal state (memory) to process sequences of inputs.
Long Short-Term Memory (LSTM): This is a special kind of RNN, capable of learning long-term dependencies, and is used in deep learning because of its promising performance.
Encoder-Decoder Structure: This is a type of neural network design pattern. In an encoder-decoder structure, the encoder processes the input data and the decoder takes the output of the encoder and produces the final output.
Word Embedding: This is the collective name for a set of language modelling and feature learning techniques in NLP where words or phrases from the vocabulary are mapped to vectors of real numbers.
Embedding Layer: This is a layer in a neural network that turns positive integers (indexes) into dense vectors of fixed size, typically used to find word embeddings.
Beam Search: This is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set.
Temperature (in the context of AI models): This is a parameter in language models like GPT-3 that controls the randomness of predictions by scaling the logits before applying softmax.
Autoregressive Models: This is a type of random process where future values are a linear function of its past values, plus some noise term. ChatGPT is an example of an autoregressive model.
Zero-Shot Learning: This refers to the ability of a machine learning model to understand and act upon tasks that it has not seen during training.
One-Shot Learning: This is a concept in machine learning where the learning algorithm is required to classify objects based on a single example of each new class.
Few-Shot Learning: This55. Language Model: A type of model used in NLP that can predict the next word in a sequence given the words that precede it.
Perplexity: A metric used to judge the quality of a language model. Lower perplexity values indicate better language model performance.
Named Entity Recognition (NER): An NLP task that identifies named entities in text, such as names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
Sentiment Analysis: An NLP task that determines the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions of a speaker or writer.
Dialog Systems: Systems that can converse with human users in natural language. ChatGPT is an example of a dialog system.
Seq2Seq Models: Models that convert sequences from one domain (e.g., sentences in English) to sequences in another domain (e.g., the same sentences translated to French).
Data Annotation: The process of labelling or categorizing data, often used to create training data for machine learning models.
Pre-training: The first phase in training large language models like GPT-3, where the model learns to predict the next word in a sentence. This phase is unsupervised and uses a large corpus of text.
Knowledge Distillation: A process where a smaller model is trained to reproduce the behaviour of a larger model (or an ensemble of models), with the aim of creating a model with comparable predictive performance but lower computational complexity.
Capsule Networks (CapsNets): A type of artificial neural network that can better model hierarchical relationships, and are better suited to tasks that require understanding of spatial hierarchies between features.
Bidirectional LSTM (BiLSTM): A variation of the LSTM that can improve model performance on sequence classification problems.
Attention Models: Models that can focus on specific information to improve the results of complex tasks.
Self-Attention: A method in attention models where the model checks each word in the input sequence for all the other words to better understand their impact on the sentence.
Transformer Models: Models that use self-attention mechanisms, often used in understanding the context of words in a sentence.
Generative Pre-training Transformer (GPT): A large transformer-based language model with billions of parameters, trained on a large corpus of text from the internet.
Multimodal Models: AI models that can understand inputs from different data types like text, image, sound, etc.
Datasets: Collections of data. In machine learning, datasets are used to train and test models.
Training Set: The portion of the dataset used to train a machine learning model.
Validation Set: The portion of the dataset used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters.
Test Set: The portion of the dataset used to provide an unbiased evaluation of a final model fit on the training dataset.
Cross-Validation: A resampling procedure used to evaluate machine learning models on a limited data sample.
Word2Vec: A group of related models that are used to produce word embeddings.
GloVe (Global Vectors for Word Representation): An unsupervised learning algorithm for obtaining vector representations for words.
TF-IDF (Term Frequency-Inverse Document Frequency): A numerical statistic that reflects how important a word is to a document in a collection or corpus.
Bag of Words (BoW): A representation of text that describes the occurrence of words within80. n-grams: Contiguous sequences of n items from a given sample of text or speech. When working with text, an n-gram could be a sequence of words, letters, or even sentences.
Skip-grams: A variant of n-grams where the components (words, letters) need not be consecutive in the text under consideration, but may leave gaps that are skipped over.
Levenshtein Distance: A string metric for measuring the difference between two sequences, also known as edit distance. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into the other.
Part-of-Speech Tagging (POS Tagging): The process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.
Stop Words: Commonly used words (such as “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.
Stemming: The process of reducing inflected (or sometimes derived) words to their word stem, base or root form.
Lemmatization: Similar to stemming, but takes into consideration the morphological analysis of the words. The lemma, or dictionary form of a word, is used instead of just stripping suffixes.
Word Sense Disambiguation: The ability to identify the meaning of words in context in a computational manner. This is a challenging problem in NLP because it’s difficult for a machine to understand context in the way a human can.
Syntactic Parsing: The process of analysing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar.
Semantic Analysis: The process of understanding the meaning of a text, including its literal meaning and the meaning that the speaker or writer intends to convey.
Pragmatic Analysis: Understanding the text in terms of the actions that the speaker or writer intends to perform with the text.
Topic Modelling: A type of statistical model used for discovering the abstract “topics” that occur in a collection of documents.
Latent Dirichlet Allocation (LDA): A generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
Sentiment Score: A measure used in sentiment analysis that reflects the emotional tone of a text. The score typically ranges from -1 (very negative) to +1 (very positive).
Entity Extraction: The process of identifying and classifying key elements from text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
Coreference Resolution: The task of finding all expressions that refer to the same entity in a text. It is an important step for a lot of higher level NLP tasks that involve natural language understanding such as document summarization, question answering, and information extraction.
Chatbot: A software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.
Turn-taking: In the context of conversation, turn-taking is the manner in which orderly conversation is normally carried out. In a chatbot or conversational AI, it refers to the model’s ability to understand when to respond and when to wait for more input.
Anaphora Resolution: This is a task of coreference resolution that focuses on resolving what a particular pronoun or a noun phrase refers to.
Conversational Context: The context in which a conversation is taking place. This includes the broader situation, the participants’ shared knowledge, and the rules and conventions of conversation.
Paraphrasing: The process of restating the meaning of a text using different words. This can be useful in NLP for tasks like data augmentation, or for improving the diversity of chatbot responses.
Document Summarization: The process of shortening a text document with software, in order to create a summary with the major points of the original document. It is an important application of NLP that can be used to condense large amounts of information.
Automatic Speech Recognition (ASR): Technology that converts spoken language into written text. This can be used for voice command applications, transcription services, and more.
Text-to-Speech (TTS): The process of creating synthetic speech by converting text into spoken voice output.
To learn more about ChatGPT terminology and the new artificial intelligence recently upgraded by OpenAI jump over to its official website.
One of the hot topics this year is ChatGPT, an artificial intelligence technology hailed as a turning point in our lives and work. Keep up with the progress of the world.
One of the most noteworthy artificial intelligence innovations this year is the AI-Crypto Trading Bot ATPBot, which has won the reputation of “ChatGPT in the investment world” due to its integration of artificial intelligence technology and quantitative trading. It provides traders with superior asset trading performance beyond any other bot in the industry.
With its huge data processing and analysis capabilities, ATPBot is similar to ChatGPT’s natural language understanding and processing capabilities. It represents the efficient use of artificial intelligence in quantitative trading and empowers investors.
By utilizing data and algorithms to determine trade times and prices, ATPBot minimizes emotional interference and human error. Today, let us explore ATPBot together, discover the magical ability of this trading bot, and improve the efficiency and stability of quantitative trading.
What is ATPBot?
ATPBot is a platform focused on quantitative trading strategy development and services. It develops and implements quantitative trading strategies for its users with the advantages of AI technology. ATPBot are intending to provide crypto investors with efficient and stable trading strategies.
By analyzing market data in real time and using natural language processing to extract valuable insights from news articles and other text-based data, ATPBot can quickly respond to changes in market conditions and make more profitable trades. Additionally, ATPBot uses deep learning algorithms to continually optimize its trading strategies, ensuring that they remain effective over time.
Comparing ATPBot with other trading bots
ATPBot boasts unique advantages compared to other trading bots in the market. Unlike many other trading bot platforms, which rely solely on predetermined parameters set by the trader, ATPBot adopts extensively tested and verified trading strategies. By conducting rigorous historical data analysis and market analysis, ATPBot has fine-tuned its strategies to minimize risk and losses while maximizing profits. This differs from other trading bots that have no control over the trading process and often lead to traders losing money.
Moreover, ATPBot eliminates the need for users to spend endless hours manually testing different parameters or acquiring expertise in charting and indicator operations. With ATPBot, users can rely on a reliable and mature trading bot that professionally manages their investment for an efficient and effective trading experience.
What are the advantages of ATPBot
Provide an AI strategy for 24-hour trading: Our team will develop an AI strategy for you with 24-hour trading needs. Whether trading day or night, the strategy will continuously monitor the market and make trading decisions accordingly.
Experienced Strategy Modelling Team: Our team has more than 20 years of experience and manages nearly $1 billion in capital. They will use their expertise and experience to design a strategic model for you to meet your needs.
Powerful computing power support: We will provide huge computing power support to help you determine the best strategy configuration parameters. By using high-performance computing and optimization algorithms, we can quickly and accurately find the best configuration parameters, thereby improving your trading results.
Time-saving and emotion-free trading: Our goal is to save you time and remove the influence of emotions from trading. With automated trading and AI strategies, you can let the system execute your trading decisions, avoiding emotional decisions and human errors.
Strong Profitability: Our strategies are rigorously tested and optimized to ensure their superior profitability in the market. Our actual transaction results far exceed the performance of most funds and private placements in the market, which enables you to obtain higher returns and investment income.
Why Choose ATPBot?
1. World-leading Technology: Cutting-edge algorithms that combine multiple factors are adopted to find profitable methods through complex data types.
2. Simple to Use: All strategies are ready-made that do not require tuning. All you need to begin running a profitable strategy is just a simple click.
3. Millisecond-levelTrading: Real-time market monitoring to capture signals and millisecond-level response for quick operations.
4. Ultra-low Management Fee: A permanent one-time payment to achieve a higher return on investment.
5. Security and Transparency: All transactions are processed by the third-party exchange Binance; ATPBot has no access to your funds and we are committed to providing maximum protection for your security.
6. 24/7 Trading: AI trades 24/7 automatically, and you can get profits even when you are sleeping at night.
7. 24/7 Service: One-on-one service; Fix your issues quickly.
Just like ChatGPT is your trusted writing and programming assistant, ATPBot is your exclusive investment analyst and faithful trading partner. Don’t miss out on the opportunity to revolutionize your investment experience with ATPBot.
Register the ATPBottoday to open the door to AI quant trading, and share the profits of AI technology algorithms with ATPBot.
In addition to the functions of the platform itself, ATPBot also has a professional discord community, which gathers a large number of quantitative trading researchers and practitioners. In the community, you can interact with quantitative trading enthusiasts from all over the world, sharing experiences and ideas. Not only will this improve your trading knowledge and skills, but you can also learn and get inspired by other people’s trading strategies. At the same time, our community also provides professional guidance, including guidance on market trends, market analysis and trading skills, to help you go further on the road of quantitative trading.
Disclaimer. This is a sponsored post. Cryptopolitan does not endorse and is not responsible for or liable for any content, accuracy, quality, advertising, products or other materials on this page. Readers should do their own research before taking any actions related to the company. Cryptopolitan is not responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods or services mentioned in this sponsored post.
Generative tools like ChatGPT and Stable Diffusion have got everyone talking about artificial intelligence (AI) – but where is it headed next?
It’s already clear that this exciting technology will have a big impact on the way we live and work. UK energy provider Octopus Energy has said that 44% of its customer service emails are now being answered by AI. And the CEO of software firm Freshworks has said that tasks that previously took eight to 10 weeks are now being completed in days as a consequence of adopting AI tools into its workflows.
But we’re still only at the beginning. In the coming weeks, months, and years we will see an acceleration in the pace of development of new forms of generative AI. These will be capable of carrying out an ever-growing number of tasks and augmenting our skills in all manner of ways. Some of them may seem as unbelievable to us today as the rise of ChatGPT and similar tools would have done just a few months back.
So, let’s take a look at some of the ways we can expect generative AI to evolve in the near future and some of the tasks it will be lending a hand with before too long:
Beyond ChatGPT
Text-based generative AI is already pretty impressive, particularly for research, creating first drafts, and planning. You might have had fun getting it to write stories or poems, too, but probably realized it isn’t quite Stephen King or Shakespeare yet, particularly when it comes to coming up with original ideas. Next-generation language models – beyond GPT-4 – will understand factors like psychology and the human creative process in more depth, enabling them to create written copy that’s deeper and more engaging. We will also see models iterating on the progress made by tools such as AutoGPT, which enable text-based generative AI applications to create their own prompts, allowing them to carry out more complex tasks.
As well as text, current generative AI technology is quite good at creating images based on natural language prompts, and there are even some tools that use it to generate video. However, they have some limitations due to the intensive nature of the required data processing. As this domain of generative AI becomes more advanced, it’s likely that it will become easy to create images and videos of just about anything, to the extent that it becomes difficult to distinguish generative AI content from reality. This could lead to issues such as deepfakes becoming problematic, resulting in the spread of fake news and disinformation.
Generative AI in the Metaverse
There are many predictions about how the way we interact with information and each other in the digital domain will involve. Many of these focus on immersive, 3D environments and experiences that can be explored through virtual and augmented reality (VR/AR). Generative AI will speed up the design and development of these environments, which is a time and resource-intensive process, and Meta (formerly Facebook) has indicated that this could play a part in the future of its 3D worlds platforms. Additionally, generative AI can be used to create more lifelike avatars that help to bring these environments to life, capable of more dynamic actions and interactions with other users.
Generative Audio, Music, and Voice AI
AI models are already impressively capable when it comes to generating music and mimicking human voices. In music, generative AI is likely to increasingly become an invaluable tool for songwriters and composers, creating novel compositions that can serve as inspiration or encourage musicians to approach their creative process in new ways. We are also likely to see it being used to create real-time, adaptive soundtracks – for example, in video games or even to accompany live footage of real-world events such as sports. AI voice synthesis will also improve, bringing computer-generated voices closer to the levels of expression, inflection, and emotion conveyed by a human voice. This will open new possibilities for real-time translation, audio dubbing, and automated, real-time voiceovers and narrations.
Generative Design
AI can be used by designers to assist in prototyping and creating new products of many shapes and sizes. Generative design is the term given for processes that use AI tools to do this. Tools are emerging that will allow designers to simply enter the details of the materials that will be used and the properties that the finished product must have, and the algorithms will create step-by-step instructions for engineering the finished item. Airbus engineers used tools like this to design interior partitions for the A320 passenger jet, resulting in a weight reduction of 45% over human-designed versions. In the future, we can expect many more designers to adopt these processes and AI to play a part in the creation of increasingly complex objects and systems.
Generative AI in Video Games
Generative AI has the potential to significantly impact the way video games are designed, built, and played. Designers can use it to help conceptualize and build the immersive environments that games use to challenge players. AI algorithms can be trained to generate landscapes, terrain, and architecture, freeing up time for designers to work on engaging stories, puzzles, and gameplay mechanics. It can also create dynamic content – such as non-player characters (NPCs) that behave in realistic ways and can communicate with players as if they are humans (or orcs or aliens) themselves, rather than being restricted to following scripts. Once game designers get to grips with implementing generative AI into their workflows, we can expect to see games and simulations that react to players’ interactions on the fly, with less need for scripted scenarios and challenges. This could potentially lead to games that are far more immersive and realistic than even the most advanced games available today.
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things. Why don’t you connect with Bernard on Twitter (@bernardmarr), LinkedIn (https://uk.linkedin.com/in/bernardmarr) or instagram (bernard.marr)?
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