Generative AI is here: How tools like ChatGPT could change your business Guest blog by McKinsey QuantumBlack

Investing in research and development to improve existing generative models, create new models, and discover new applications for generative AI. They create and optimize generative models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Recurrent Neural Networks (RNNs) for a variety of applications such as image and text generation, music composition, and more. We believe that AI technology has the potential to change the world of design, and our post is a great starting point for designers who want to explore the power of AI tools in their future projects. One thing is certain, Generative AI is set to transform industries and has already drastically changed the landscape of Big Tech. These speakers provide their expertise on the latest development in Generative AI and share their insights and predictions for the future. When applied judiciously, generative tools can enhance ideation, drafting, editing, data analysis, and more to unlock unprecedented efficiency, personalisation, and insight.

Projects such as these often require us to create 3D landscapes, these could be the surfaces of alien planets, or Earth millions or billions of years ago. We’re interested in how we can use generative AI to generate 2D “height maps” that can be used in the creation of 3D landscapes. Maps are 2D images that are used to represent different aspects of a scene, object or effect. Different kinds of maps are used for different purposes, such as creating 3D geometry, defining surface details, creating procedural effects, and particle simulations.

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We’re exploring the development of a toolkit of text-to-image generative AI processes designed to generate maps that can be used in many different applications across our VFX work. While 2D image generation tools have so far been used primarily to create photographic, painterly or cartoon images, there hasn’t been much focus on how these tools could be used to create images that can be used in the production of assets for VFX. Several natural language processing AI models have come to prominence in recent months, such as generative AIs like ChatGPT. These models demonstrate a huge step forward in accessible AI which will develop substantially and quickly; likely growing to become something we use frequently in our everyday lives. These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter. We have seen several notable cases that have exposed flaws in the way training data is being extracted.

This output can easily be incorporated into automated marketing or video content, automating the process of creating narration and voiceovers. It is a conversational interface for OpenAI’s GPT-3 large language model, which has recently been made available to the public as a free research preview. In response to text prompts such as questions or instructions, it will output text in any form, including prose, poetry, and even computer code.

Copyright issues surrounding generative AI

Gone are the days when the healthcare industry could spend years before getting a drug to address emergencies or pandemics. Today, healthcare providers can use generative AI and computer simulations to create safer and more effective drugs. Those working genrative ai in the advertising, podcasting, marketing, or education sectors can tell you how happy they are with this application of generative AI. For instance, a university professor can easily convert lecture notes to speech for students within minutes.

However, in principle, this would be similar to the process used in relation to inaccurate data held on due diligence databases such as WorldCheck (see this article for further information on this process). Article 4 of UK GDPR makes clear that collecting personal data, storing it temporarily, hosting such information and / or making it available to internet users amounts to processing of that data. It is the data controller who is responsible for compliance with the data protection principles under the GDPR.

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Although the last few years have seen amazing advances in the AI tools and applications that are available for business, it’s very clear that we’re still in the early stages of the journey. Many of the future developments we will see are likely to arise from combining generative, predictive, and prescriptive elements of AI. Organizations have been using predictive AI for some time now, but as Bonaci notes, ”What makes predictive AI even more powerful, is the ability to leverage real-time data to power in-the-moment experiences and recommendations for customers. For example, not only does Netflix make recommendations on streaming content you may want to watch based on your viewing behavior, they’re also predicting what artwork you’ll be most drawn to and personalizing tv and movie title covers in real time. The way this is most commonly achieved in business today is through a process known as machine learning (ML). This refers to algorithms trained on data that are capable of making decisions based on what they know, and getting better as they learn more.

Most people will have first come across the term in science fiction, where, for over 100 years, we’ve been entertained by stories involving intelligent robots – sometimes friendly, sometimes not so. To help me understand the differences and explore some use cases for these categories I was joined on my podcast by Davor Bonaci, CTO and Executive Vice President of DataStax. Bonaci has been with DataStax since the start of the year when it acquired Kaskada, the ML business he co-founded. Before we start, it should be noted that there is often a lot of overlap – specific AI implementations may well fit into more than one of these categories.

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At City, a working group is reviewing how and when students can make the best use of generative AI tools and advising staff on how to incorporate AI, ethically and educationally, into assessments. Tools are likely to produce comprehensive outputs on subjects that are widely written on, so if you are looking for assistance with more specialised or niche areas, or cutting edge research, the information may not be so well developed. Make the most of our two-decade experience of developing software products to drive the revolution happening right now.

• Rather than rely on expensive manual video production, generative AI enables marketers to efficiently scale high-quality video content.
• As explored throughout this guide, generative AI looks like it will be a seminal shift in how marketing content is produced by the immense potential it has to transform content creation, strategy, and marketing operations.
• The FCA’s CEO, Nikhil Rathi, recently delivered a speech on the FCA’s emerging regulatory approach to big tech and artificial intelligence.
• As is the norm everywhere from social media to travel planning, using an app often means giving the company behind it the rights to everything you put in, and sometimes everything they can learn about you and then some.

Personal information may also be used to improve OpenAI’s services and to develop new programs and services. In short, it has access to everything you do on DALL-E or ChatGPT, and you’re trusting OpenAI not to do anything shady with it (and to effectively protect its servers against hacking attempts). To eliminate bias, the data should accurately reflect the diversity of the intended customer base and target audience. Also, if you’re planning to use customer data to train AI, customers must be informed about how the data will be used.

Can I use generative Artificial Intelligence (AI) tools (e.g., ChatGPT, Bard, Quillbot, Grammarly, etc.) in my assessed work?

Content marketing is the practice of creating and distributing valuable, relevant, and consistent content to attract and engage a specific target audience. Instead of directly promoting a product or service, content marketing focuses on providing valuable information and insights that are helpful to the target audience. They will use generative AI to design and create new products that meet their customers’ requirements and specific tastes. Additionally, Meta has made some AI-powered updates to Meta Advantage, a suite of automation products that leverage AI and machine learning to optimise campaign results and personalise ads. Meta Platforms, the owner of Facebook and Instagram, has announced that it is testing new artificial intelligence (AI) tools to help create better ads and improve campaign results. Generative AI is one amongst many leaps forward in technology which will have significant implications on the way we work.

Such statements certainly look like an attempt by OpenAI to push the responsibility for incorrect answers provided by ChatGPT back onto users (or at least to absolve itself of liability). These caveats and warnings are likely to be noted by courts when assessing defences pleaded by individuals who have re-published inaccurate information originally produced by ChatGPT. OpenAI may also seek to rely genrative ai on defences available to intermediaries in defamation claims. However, OpenAI and ChatGPT do not appear to fall neatly into some of the more common defences relied upon by hosts of user-generated content, as OpenAI ultimately establishes the algorithms behind ChatGPT and has gathered the data used by ChatGPT. It goes beyond being a passive host or even editor of content that is defamatory.

A working group will be established to explore the development, opportunities and implications these models and details on the work of this group will be published on this webpage. AI can generate a large number of potential business names in a short amount of time, giving you a list of unique and creative names that you might not come up with otherwise. Additionally, be aware that may save the information you enter. It’s best to avoid entering any commercially sensitive or proprietary information in your prompts (these are the questions or tasks you ask an AI tool to help with). It’s worth putting some guardrails in place right at the start of your journey with these tools, or indeed deciding not to deal with them at all, based on how your data is collected and processed. Here’s what you need to look out for and the ways in which you can get some control back.