OpenAI’s ChatGPT, Dalle-E and other tools quickly gained popularity in the business and content creation worlds. What is generative AI? How does it work, and why has it become such a controversial and hot topic?
gen AI, as it is commonly known, is a branch within artificial intelligence where computer algorithms are used to produce outputs which mimic human material. This includes text, images, graphics, audio, computer code and other media.
Using training data that includes illustrations of the intended outcome, gen AI algorithms create knowledge. Gen-AI can produce new material that shares traits with original input data. This is done by examining patterns and structures within the training data. In this way, Gen AI can produce information that appears genuine and human.
How Gen AI is implemented
Gen AI’s foundation is machine learning techniques based upon neural networks. These are the inner-workings of the brain. During training, large volumes of data feed the algorithms in the model. This serves as its learning base. This method can include text, code or images.
After gathering training data, AI models examine correlations and patterns to determine the basic principles of the content. As it continues to learn, the AI model adjusts settings in order to better mimic material created by humans. As it creates more material, the AI model’s outputs become more complex and persuasive.
Consider ChatGPT or Dalle-E 2 examples of gen-AI applications that may rely upon an input prompt in order to create a desired result, depending on their application.
Some of the most notable examples of gen AI tools are:
- ChatGPT: ChatGPT was created by OpenAI and is an AI model of language that can respond to cues in order to produce text that sounds like human speech.
- Dalle-E 2: OpenAI has developed a second generation AI model which uses textual cues to create visual content.
- Google Bard Launched to compete with ChatGPT. Google Bard This is a Gen-AI chatbot that has been trained using the PaLM Large Language Model.
- GitHub Copilot: GitHub Copilot, developed by GitHub and OpenAI is an AI-powered coding application that offers code completions to users of programming environments such as Visual Studio and JetBrains.
- Midjourney: Midjourney was created by an independent San Francisco research lab. It is similar to Dalle E 2. It uses context and language cues to create a photorealistic image.
Gen AI Examples in Use
Though gen-AI is still in its early stages, it has established itself across several sectors and applications.
Gen AI can create text, graphics and music as part of the content creation process. This helps marketers, journalists and artists in their creative process. Artificial intelligence-driven virtual assistants and chatbots can provide better, more personalized assistance, reduce response times, and ease the burden on customer service agents.
Gen AI is used in the following applications:
- Medical Research Gen AI is being used in medicine to accelerate the development of new medicines and reduce research costs.
- Marketing: Advertisers are using gen AI for targeted campaigns, and modifying the material to meet customer interests.
- Environment: Climate scientists use gen AI models to simulate climate change and forecast weather patterns.
- Finance: Financial experts use Gen AI to analyze patterns in the market and predict stock market development.
- Education: Some instructors use gen AI to create customized learning materials, evaluations and learning assessments for each student.
The Limitations of Gen AI
We need to tackle several issues raised by Gen AI. One significant concern is its potential to disseminate false, harmful, or sensitive information that could cause serious harm to individuals and companies — and perhaps endanger national security.
The policy makers have taken note of these threats. In April, the European Union introduced new copyright regulations for generation AI. This mandates that businesses must declare any copyrighted content used to develop these technologies.
The spread of automation by generative AI may have an adverse impact on the workplace, leading to possible job displacement. Moreover, gen AI can unintentionally amplify any biases that are present in training data. This could lead to undesirable results, which reinforce negative ideas and preconceptions. This phenomenon is usually an unnoticed consequence.
Since their debuts, ChatGPT and Bing AI have received criticism for producing incorrect or harmful outputs. As gen AI evolves, these concerns will need to be addressed. This is especially true given the difficulty of examining carefully the sources used to train AI models.
The Apathy of Some AI Companies is Scary
Various reasons may explain why some tech companies are indifferent to the dangers of gen AI.
They may first prioritize short-term profit and competitive advantage, over long-term ethics concerns.
They may not be aware of or understand the risks that gen AI poses.
Third, certain companies might view government regulation as inadequate or delayed and overlook the threat.
A too optimistic view of AI’s abilities may minimize the dangers and ignore the need to mitigate the risks.
As I wrote previously, I have witnessed a shockingly dismissive approach from senior leaders at several tech firms about the risks of misinformation with AI, especially with deep fake videos and images.
However, it’s important to also highlight the positive uses of AI. Take for example, AI’s capability to power image background remover functions. This feature, widely used in graphic design and photo editing, remarkably increases productivity by automating menial tasks. It permits designers to focus on creativity and strategic thinking. Responsible use and regulation of such AI tools can propel us towards a future where technology genuinely augments human creativity.
AI can also be used to imitate the voices of family members in order to extract money. The silicon ingredient companies are happy to leave the AI labeling up to the app or device provider. They know that the disclosure of AI-generated content will be ignored or minimized.
Some of these companies are concerned about the risks, but they’ve pushed it off by saying that “internal committees”, which are still deciding on their exact policy positions, will be addressing this issue. This hasn’t prevented many of these firms from launching their silicon solutions on the market without having explicit policies to detect deep counterfeits.
Seven AI leaders agree to voluntary standards
The White House announced last week that seven major artificial intelligence players have agreed to voluntary standards for open and responsible research.
Seven companies committed to testing their AI systems for security both internally and publicly before they made them public. They will share information and prioritize security investments. They will also create tools to help people recognise AI-generated material. They will also develop plans to address the most pressing social issues.
This is a positive step, but the list does not include the world’s most prominent silicon companies.
Closing thoughts
To protect the public from deep-fake images and videos, a multifaceted approach is necessary.
- The focus of technological advancements should be on the development of robust detection tools that can identify sophisticated manipulations.
- Campaigns of public education should be carried out to inform the general population about the dangers and existence of deep-fakes.
- It is crucial that tech companies, governments and researchers work together to develop standards and regulations on responsible AI.
- Media literacy and critical thinking can help individuals distinguish between real and fake content.
Combining these efforts will help protect the society from the negative impact of fakes.
As a final step, all Silicon companies should offer and create the digital watermarking software that allows consumers to use their smartphone apps to scan an image or a video to determine whether it was AI-generated. American silicon companies must take a leading role in this area and not let the app or device developers shoulder it.
The conventional watermarking method is not sufficient as it can easily be removed or cropped. A watermark is not foolproof but it can be effective. digital A watermarking method could alert users with a reasonable confidence level that there is, for example, an 80% chance that an image was made with AI. This would be a step in the right directions.
Unfortunately, the public will not demand this common sense safeguard, whether it is government ordered or self-regulated. They won’t do so until something terrible happens, such as someone getting injured or killed, due to gen AI. I hope that I am wrong, however, I believe this will be true, due to the competing dynamics, and the “gold-rush” mentality at work.
