What is ChatGPT, DALL-E, and generative AI?

Overall, the applications of generative AI are vast and varied, and it has the potential to transform many different industries and fields. As the technology continues to advance, it will be interesting to see the new and innovative ways in which it is used in the future. In addition to generating visual content, generative AI can also be used to create music and audio. This can range from original songs and compositions to human-like voice audio for use in voiceovers or assistive technologies.

You can leverage generative AI for marketing and sales campaigns to create personalized content without compromising users’ privacy. Speech-to-speech conversion is an impactful feature of most generative AI models. This can be useful for various applications, such as language translation and interpretation.

Successful generative AI examples worth noting

Generative AI has the potential to revolutionize any field where creation and innovation are key. Knowledge work and creative labor, two of the categories that generative AI seeks to improve, collectively employ billions of people. Generative artificial intelligence has the potential to make these workers at least 10 percent more efficient and/or creative. This means that they become not just quicker and more efficient but also more capable than they were before.

The field has already led to an 82-page book of DALL-E 2 image prompts, and a prompt marketplace in which for a small fee one can buy other users’ prompts. Most users of these systems will need to try several different prompts before achieving the desired outcome. But once a generative model is trained, it can be “fine-tuned” for a particular content domain with much less data. This has led to specialized models of BERT — for biomedical content (BioBERT), legal content (Legal-BERT), and French text (CamemBERT) — and GPT-3 for a wide variety of specific purposes. Snowflake has made it clear that it expects synthetic data generated by AI to play an important role in its business going forward.

> Gaming Applications

Another concern, referred to as “technological singularity,” is that AI will become sentient and surpass the intelligence of humans. The ability for generative AI to work across types of media (text-to-image or audio-to-text, for example) has opened up many creative and lucrative possibilities. No doubt as businesses and industries continue to integrate this technology into their research and workflows, many more use cases will continue to emerge.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Currently, there’s a wide range of generative AI tools on the market, from ChatGPT to Google’s Bard. For busy educators, generative AI holds promise for simplifying tedious daily tasks such as building lesson plans, outlining assignments, generating rubrics, building tests and more. And many healthcare organizations are currently Yakov Livshits implementing generative AI in various ways. For example, generative AI can be used by physicians to develop custom care plans for patients that will improve health outcomes. Whether ChatGPT or Bing AI, generative AI tools have many use cases across critical industries such as education, finance and advertising.

The fact that it generally works so well seems to be a product of the enormous amount of data it was trained on. The training data for these systems includes enormous amounts of poorly written, poorly constructed and factually inaccurate prose. Also, in response to a request for information on a subject, such programs seem willing and able to make up answers, complete with fabricated supporting articles (hallucitations) from real or made-up authorities. This leads to well-justified hesitation to use AI in life-or-death or other high-stakes situations, including complex application coding. Midjourney is an AI image generator that can create realistic images based on detailed text inputs. Manufacturers can utilize it to generate prototypes, quick mockups, and visualizations without the necessity of physical samples.

Using synthetic data in this way can help to tackle those problems (note – I will not say it solves them entirely) as datasets can be created in line with whatever level of representation or inclusiveness is needed. This is data created by machines and closely resembles real-world data that can be used for many of the same purposes. Think about a dataset comprising thousands of human faces, for example – as used to train facial recognition algorithms. You have to find and photograph thousands of people and then get their permission to store and use their data. Then, myriad checks and balances must be followed to ensure your data isn’t harmfully biased.

#35 AI-generated product descriptions

Snowflake is one of the world’s biggest “data-as-a-service” companies that, in addition to their analytics services, also offers a data marketplace covering thousands of topics, including healthcare, finance and retail. You can use the Google Cloud console or the Vertex AI API to query a model
with different parameter values to test and compare their results. Recognizing the unique capabilities of these different forms of AI allows us to harness their full potential as we continue on this exciting journey. In other words, traditional AI excels at pattern recognition, while generative AI excels at pattern creation. Traditional AI can analyze data and tell you what it sees, but generative AI can use that same data to create something entirely new. AI prompt engineering is the key to limitless worlds, but you should be careful; when you want to use the AI tool, you can get errors like ChatGPT is at capacity right now or “Too many requests in 1 hour try again later” error.

The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input. Auditors can interact with the model to discuss the organization’s activities, control systems, and business environment. ChatGPT, for examples, can assist auditors assess risk levels identify priority areas for more investigation, and get insights into potential hazards. Generative AI models can generate thousands of potential scenarios from historical trends and data.