ChatGPT: generative AI perspectives in society and business

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ChatGPT platform is gaining momentum: during last months, artificial intelligence (AI) has entered a period of “generational change”. Presently, it is being capable of carrying on sophisticated dialogue with users and generating seemingly original – although degenerative – content. These Chat GPT and other AI’s capabilities can be employed in numerous socio-economic spheres and in corporate activities. 

    During last weeks and even a couple of years, the artificial intelligence entered a period of “generational change”; until now, machines have never been able to exhibit behavior indistinguishable from humans. However, new generative AI models are capable of carrying on sophisticated conversations with users, and also generate seemingly original –though degenerative – content. Advantageous use of new competitive technologies – particularly for businesses – the participants have – first of all – to understand the generative AI’s potentials.
Most businesses already acquainted with the ChatGPT platform: the artificial intelligence research company (called OpenAI and backed by Microsoft) released this free service at the end of 2022; it has quickly captured the public imagination by holding human-like conversations, writing essays on numerous topics, generating computer-based text analysis, processing huge files of data, etc. What is also amazing, this GPT can even help aspirants to pass parts of the US national medical license exam and the bar application. While spending some time “chatting” with this online transformer, one has an impression of opening new and profound applications.
Generative AI is a set of algorithms, capable of generating seemingly new and realistic content (e.g. texts, images and/or audio) from the processing data. The most powerful generative AI algorithms are built on top of foundation models that are trained on a vast quantity of unlabeled data in a self-supervised way to identify underlying patterns for a wide range of tasks.
For example, GPT-3.5 (see more below), a foundation model trained on large volumes of text, can be adapted for answering questions, text summarization and analysis. Some other multi-modal (i.e. text-to-image) GPTs model can be adapted to create images, expand images beyond their original size and/or create variations of existing paintings.
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GPT-LaMDA: types of transformative text models
There are already exist several elaborated “performants” providing generating texts:
• GPT-3, or Generative Pretrained Transformer-3, is an autoregressive model pre-trained on a large corpus of text to generate high-quality natural language text. GPT-3 is designed to be flexible and can be fine-tuned for a variety of language tasks, such as language translation, summarization, and question answering.
• LaMDA, or Language Model for Dialogue Applications, is a pre-trained transformer language model to generate high-quality natural language text, similar to GPT. However, LaMDA was trained on dialogue with the goal of picking up nuances of open-ended conversation.
• LLaMA is a smaller natural language processing model compared to GPT-4 and LaMDA, with the goal of being as a “performant”. While also being an autoregressive language model based on transformers, LLaMA is trained on more tokens to improve performance with lower numbers of parameters.
With a new trend towards some generative AI models, which actively use both large amounts of internet data and – unfortunately- copyrighted materials, comes an issue of responsible AI’s practices as a vital organizational imperative. As companies, employees, and customers become more familiar with applications based on AI technology, and as generative AI models become more capable and versatile, a whole new level of applications will soon emerge.
The new set of applied generative AI systems (such as ChatGPT recently) has the potential of transforming several business and even industrial sectors.
Hence, there is a great need for modern corporate leaders to be ready for the contemporary trends and strategies in generative AIs.

Generative AI for business
Generative AI has massive implications for business and corporate activities: many companies have been already active in practically using generative AI’s facilities.
Quite often, companies are developing and fine-tuning so-called “custom-generative AI models” to be applied by specific corporate needs.
For example, most often beneficial sides of utilizing generative AI can include:= expanding labor productivity, = personalizing customer experience, = accelerating R&D through generative design, and = creating new business models and accommodating already existing.
Other usages can include, e.g. a) analysing consumer marketing strategies (for example, GPTs can personalize experiences, content, and product recommendations); b) financial sectors (it can generate personalized investment recommendations, analyze market data and test different scenarios to propose new trading strategies.
Specific GPT’s application can be in bio-pharmacy: here GPTs can generate data on millions of candidate molecules for a certain disease, test their application subsequently and finally significantly speeding up research cycles.
Given that the pace the technology is advancing, business leaders in most industrial sectors should consider using generative AIs in the production systems within a year or so; i.e. starting internal innovation already now. Companies that don’t embrace the disruptive power of generative AI would suffer enormous disadvantages with competitors in expenses and innovation. However, incorporating generative AI into products, systems and processes will require rethinking customers’ preferences, fostering new skills and managing significant changes in labor forces and employment. One thing is clear: the process will be complicated for the corporate sector: hence, it’s high time to start “experimenting” in using new technologies in business strategy while watching closely technology evolution.
With the modern ChatGPTs and other generative models able to “communicate” with humans in a conversational mode and execute digitally numerous tasks, the generative AI can really become a part of the labor force. Though GPTs will not replace all workers – “handcraft” is still quite spread in business and manufacturing- many applications will augment rather than replace productivity of humans. Experts acknowledge that “this will require a rethinking of business models and transforming ways of working to embed the AI into business; companies that don’t embrace the disruptive change in labor productivity will find themselves at catastrophic cost and innovation disadvantages”.
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Global approach to AI
Most advanced generative AL’s international assessments can be found in the OECD AI principles, which are actually the first intergovernmental standard on AI. Since their adoption in 2019 with the OECD Council Recommendation on Artificial Intelligence, adhering countries have worked to ensure that AI systems are used in the following spheres: a) benefiting people and the planet; b) respect democratic values and human rights; c) being transparent and explainable; d) being robust, secure and safe; e) holding AI actors accountable for their proper functioning.
Presently some 55 countries around the world have reported national AI strategies to steer trustworthy AI development and deployment, according to the OECD’s AI policy observatory. Over 890 related policy initiatives across 69 jurisdictions have also been recorded in the policy hub in April 2023: these include policies that accompany labour market transitions, foster R&D, and strengthen AI skills across different social groups.
Ethics frameworks, guidelines, codes of conduct, standards, and algorithmic impact assessments are being piloted at both national and international levels, by both the public and private sectors. The majority of national initiatives are still predominately non-binding, although different countries are putting into place regulatory frameworks to address high-risk AI systems or impacts. AI-related regulation raises new challenges for international interoperability, requiring international action to harmonise key definitions and align technical implementation.
For example, among the Baltic States, Estonia has adopted 9 initiatives, Latvia – 4 and Estonia -3. In May 2018 the Nordic and Baltic States adopted a common declaration on AI; and in February 2020, the Latvian government adopted a national AI strategy.
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    Although AI employment has been small and mostly concentrated in a few high-skill technical occupations, recent empirical analysis shows that the demand for AI skills has spread across a larger set of occupations such as mechanical engineers, product managers, and language and communication professionals. Recent evidence also indicates that jobs requiring AI skills demand high-level cognitive skills such as creative problem solving. This means that upcoming shifts in AI-related jobs will likely be associated with a contextual increase in the demand for high-level cognitive skills (as opposed to routine tasks), and associated with higher wages.
With the exponential advances in general AI’s applications over the last six months, the expert opinion is such that there are going to be rapid changes, it will be crucial to cultivate robust and diverse AI generative capacities, and that the AI actors shall be accountable for the proper functioning and trustworthiness of AI systems.
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    The world’s most famous chatbot applications have been closely followed by the European privacy watchdogs: e.g. Italy imposed a temporary ban at the end of March on the grounds that it could violate Europe’s privacy rulebook; although at the end of Aril abandoned it. It can be seen as just the start of further socially controlled measures over generative AIs.
It seems that generative AI systems shall be prepared to see some surveillance over the “cutting-edge technologies” with certain problems for governments and social media over imposed risks ranging from data protection to misinformation, cybercrime, fraud, etc.

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