What Companies Can Learn From The AI Gold Rush

Since its release in November 2022, ChatGPT 3.5 has sparked interest among users and tech investors. AI and Large Language Models (LLMs) sprang into popularity, with millions of users rushing to utilize the chatbot, forever changing the world.

The current Artificial Intelligence (AI) boom is palpable, and despite how important AI has become, the negative sides of the AI boom are increasingly becoming apparent.

Whether it’s about copyrights, bias, ethics, privacy, security, or job impact, the repercussions of AI development are felt all around the world. That is why the EU’s desire to address ethical and moral concerns by regulating technology through the AI Act is both urgent and relevant.

Simultaneously, perhaps every major organization on the planet has explored how to effectively integrate Artificial Intelligence into their websites, goods, and services to maximize productivity, optimize customer pleasure, and eventually raise sales.

Take Account of AI’s Potential Risks

The AI boom, like the gold rush, resulted in a large number of people getting on board to prevent missing out on the opportunity. However, the use of AI in businesses should not be approached in the same way as the ‘Wild West’ of the gold rush. Instead, it should be accompanied by a clear warning, similar to nicotine advertising, because disregarding AI risks and side effects could have catastrophic repercussions in extreme instances.

The most common dangers range from development departments unintentionally sharing designs or lines of code with public LLMs to changing customer expectations about how organizations use AI and data in the process.

In 2016, Microsoft‘s Tay sent out over 95,000 tweets over 16 hours, many of which were racist and misogynistic, causing significant harm.

According to a Cohesity survey, more than 78% of consumers are concerned about AI’s unfettered or uncontrolled usage of their data.

AI has already been implemented in many organizations without anybody establishing guidelines for its use or keeping an eye on compliance. This is similar to what happened at the onset of cloud computing when the rush to use the technology caused many businesses to lose important data and money.

How To Tame AI

Any company that wants to employ AI responsibly in the upcoming year needs to establish stringent AI policies, manage access, and regulate its proliferation internally to avoid all of these issues.

Amazon and the financial behemoth JPMC are among the many companies that have recently placed restrictions on their employees’ use of ChatGPT to maintain a high level of control before the floodgates open. They intend to gradually restore appropriate access as soon as usage policies and technical controls are in place.

Additionally, it is critical that businesses specify exactly what data their AI projects may access and how they can handle it. A good way to regulate this in a scalable way is with traditional role-based access controls that connect roles and tasks with data sources. Access to the data sources is restricted to those who have the required privileges.

Data sovereignty and other regional restrictions should also be strictly enforced.

Regulating AI Training

It is currently impossible or unheard-of to track precisely the kind of training that AI models were given and this may cause issues in the future. There could be moral, ethical, and legal repercussions for this blind spot. Future AI decisions that are lethal will have negative effects in at least one of those domains, or the worst-case scenario, all of them. How the AI models were trained to get the lethal result will be of interest to a strict court.

Enforce Transparency in AI Training

Documenting the training process of artificial intelligence and classifying the input data are essential. Companies will be able to enhance the quality of the learning process and increase consumer transparency as a result.

It is necessary to ensure proper control of what AI can access for its training. It is also appropriate for AI developers to approach this responsibly, using only data that has been approved and making sure that the AI and its human element have the right level of access to the data and cannot alter it inappropriately or access data that they are not permitted to see.

The process of artificial intelligence training is still unclear; it involves intricate mathematical procedures and, most importantly, takes a long period. Tesla has been training its artificial intelligence to drive itself in actual traffic conditions for years. However, how can years of learning be preserved from loss and inaccurate input? How can you safeguard that knowledge from rivals or threat actors that could seek to negatively influence behavior? How can you prevent your intellectual property from being illegally incorporated into AI training? The latter is best illustrated by the lawsuit the New York Times filed against Microsoft and OpenAI for using NYT articles to train GPT LLMs without permission. This brings us full round to the topic of handling AI responsibly and responsibly.

Presently, no startup has figured out how to make the AI engine remember which bits and bytes were altered throughout the learning process when new data was entered. So it is not possible to immediately reset the AI engine to a previous state if someone supplied it wrong data, such as legally protected content. This will require a special technique, the like of which has been developed in other areas of IT. In certain areas of software development, you can take system-wide snapshots and, in case of an emergency, go back to a previous version. You then lose the information entered between the time the snapshot was taken and the time issues were discovered. But not all of the information is lost though.

Conclusion

Countries and businesses must take into account the risks posed by unregulated AI development in the current AI boom and establish a solid framework to ensure that AI is generally used ethically rather than maliciously.