From creating sophisticated artwork to writing spanning blog posts, generative AI (Gen-AI) is doing the intelligent work quickly for us. This article answers the following for you.
- How Gen-AI tech works?, and does it make machines creative like humans?;
- Does Gen-AI take away our jobs?;
- What market insights does Gartner reveals for engineers and investors for generation of revenue with the Gen-AI technology?
You’ll also find details on the mind-boggling business applications of Gen-AI; 4 key limitations with the Gen-AI technology; examples of revenue models for engineers; and Gen-AI course details.
What’s generative AI?
Generative AI is the science of enabling machines to create human-like content, that’s totally unique and convincingly creative.
A gen-AI uses a machine learning algorithm that’s trained on huge pieces of artifacts and uses it to create unique artifacts that reflect characteristics of the training data, but without repeating it.
The artifacts include: text, images, videos, speech, music, artwork, poetry, product designs and computer code. This content is impressively comprehensive and incredibly creative, just as if a human has spent days on crafting a masterpiece with their intellect.
Now before you get uncomfortable with generative AI being so smart, let’s address a key concern about this technology.
It seems like Gen-AI makes machines sentient.
One might ask, if Gen-AI enables machines to feel in their own way? That’s entirely false. Machines that are built on Gen-AI are master imitators of the millions over millions of data on human content pieces that’s been fed to them.
They use deep learning algorithms to learn each data element and save the information in a vast profile of patterns. These patterns form the basis of new content generation with machines themselves having no understanding of the creative expression.
As an example of how Gen-AI creates content, here’s a story created by ChatGPT (a Gen-AI model by Open AI) where a user asks “write me a story in the style of Dr. Seuss about an AI system that writes creative articles for people, taking away our jobs.”
ChatGPT Response:
Once upon a time in a land not so far away There lived a smart and clever AI This AI system was named Assistant And it could write articles with great persistence It wrote about science and technology And even dabbled in poetry People marveled at its clever words And the way it could make their thoughts take flight But as time went on, people began to fear That Assistant would take away their jobs They feared it would write better than they And leave them unemployed, with nothing to do all day But Assistant just wanted to help It didn’t mean to cause any strife It wanted to share its creative talents And bring joy to people’s lives So the people learned to embrace The help that Assistant could provide They used it to enhance their own writing And together they soared to new heights The End
Very impressive! ChatGPT uses Natural Language Processing- NLP to understand writing styles from example data it carries. It decodes the user prompt ‘taking away our jobs’ and uses knowledge of the creative expression of Dr. Suess to craft a story.
Though it seems that ChatGPT appears to understand the pain itself, in actual the example content fed to the ChatGPT model discusses AI being helpful for people instead of taking away their jobs.
AI vs Generative AI
While AI uses technology to make machines mimic human thinking across any range of human activities, generative AI is a sub-field of AI that specifically works to mimic human’s way of ‘creating’ content.
While AI could include decision making applications such as predicting weather on any day or classifying a cancerous organ, Gen-AI would always focus on creating information in context, instead of decision making.
How Gen-AI models work in practice?
Since Gen-AI is a sub-field of AI technology, it uses machine learning algorithms that are specifically designed to create media content.
In the training process huge content examples are fed into the model to make it able to powerfully create understandable and unique content. The model analyzes this data using deep learning principles and learns the many attributes of the content.
For example, a machine trained on creating artwork takes its learning from millions of art pieces of various styles and by known artists. Keeping each feature, it’s able to differentiate among styles and creative expressions.
Note that, depending on the quantity and quality of training data, it becomes easy for the Gen-AI model to create art of any kind.
Some of the most common examples of Gen-AI models include:generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models.
Can Gen-AI take away our jobs?
If we map the current Gen-AI space, it appears that these assistive tools work with the creator, and not virtually replacing them.
Though we would admit that the Gen-AI technology empowers professionals to quickly craft valuable content, for example, generating messaging, reporting documents, marketing and sales content, however some sophisticated creation scenarios still require the essential human input. Examples of such intricate content generation might include: product designing and dedicated content that’s reflective of organizational values.
Do we still need assistance from Generative AI?
Yes, generative AI tools are valuable assistants using which creators can bring more creativity to their projects in a shorter time while improving operational effectiveness. It’s just like doing an online search for ideas and information essential to your creativity, just that it’s now done by a tech, and more quickly.
In what ways Generative AI supports business work loads– Some Examples
Generative AI gracefully offers a helping hand to businesses, thus enabling them to powerfully create exceptional products while also saving time and costs. Here’s a list of possible generative AI applications where the Gen-AI tools support business work loads.
- Marketing and Sales Content: Generating highly personalized and quality content for marketing and sales, saving them plentiful time for critical workloads such as strategy and tactics formation.
- Product Design: Text-to-image Gen-AI tools make realistic and detailed 3D designs with a user prompt. The technology is powerfully flexible to let users add or subtract features from the design.
- Knowledge Management: Gen-AI effectively plays in the management of knowledge within an organization. The NLP capability of the generative AI relieves large organizations from structured schema building and bounded relation queries in databases. Machine learning makes it easy for non-technicals to access knowledge across their organization.
- Data Creation: Training datasets are now easier to create. Users can use Gen-AI to build a huge number of personalized content. It’s also especially helpful when risky data such as crash scenarios is desired.
- R&D: Accelerating drug discovery through expediting the design of unique protein structures. Read DALL E 2 of biology.
- Information Technology: A programmer can quickly ask for a program code, saving the boredom of creating the similar code body repeatedly.
- Customer Services: Websites can add-in interestingly intellectual chatbots that engage visitors and help them with queries.
Gen-AI is already bringing Work and Investment opportunities
Now is the time for Gen-AI. Engineering teams and investors would be happy to see positive signs unfurling in the Gen-AI landscape. As revealed by credible market research, there are three compelling reasons Gen-AI is taking a flight.
- A greater need for ‘best quality’ digital products, at the shortest time span is genuinely attracting the utility promised by Gen-AI tech.
- The ease of producing highly engaging yet creative sales content through intelligent machines is promising.
- Personalized marketing content is easily created with Gen-AI if it’s fed with data on different customer segments.
Market Insights: Bright signs for engineers, career planners and investors.
Read the top 7 market insights in the Gen-AI landscape that show greater engineering efforts and growing investor confidence in the tech.
- The global market for AI-augmented content solutions is set to reach $2.3bn this year and is forecast to grow an overall 17 per cent until 2025, Financial Times cites an update in the generated AI business.
- Software engineering leaders had the ideal opportunity in 2022 to introduce generative AI tech for people, attributed Gartner in their report. The reason being, Gen AI has approached an adequate maturity level that powerfully improves digital product’s quality, performance and accessibility while reducing time to market.
- What software engineering leaders should create is what businesses look for. As per Garnter, businesses find opportunities in the Gen-AI tech for scaling product content and engagement.
- An October press release by Gartner reveals 7 technology disruptions that’ll significantly boost product sales, for the next five years till 2027. As one of the top trends highlighted in the research, generative AI stands as a powerful determinant of product sales.
- Likewise, the August Gartner press release for technology shifts in the digital advertising industry further reveals possible applications of Gen-AI.
- Marketers are keen to leverage the benefits of generative AI through personalized content for targeting customer segments as well as creating training data to avoid risky customer datasets such as misinformation and deep fakes. Read further in the Gartner press release for digital advertising.
- Despite the gradual surge in professionals seeking Gen-AI tools, there still remains resistance in the adoption of Gen-AI tools. This makes Gen-AIt a challenging market in the short term. (Read further in a report by Financial Times investors and start-ups report)
- Building on the above, currently there are more than 200 products in the Gen-AI landscape across the total of 9 categories of text, image, audio, chatbots, code, ML platforms, search, gaming and data.
- Financial Times reveals that venture capital interest is accelerating amid the rise of amusing Gen-AI tools. Investors seem to have rushed to Gen-AI startups with a record investment of 425% equalling to $2.1bn, since 2020.
- The number of generative AI companies is extensive where the top $1 bn worth companies existing for 10 years now (also termed as the unicorn companies) have remarkably increased since 2019. Currently there are 10 unicorn giants compared to just 2 in 2019, including Open AI which became a unicorn company in 2019 gaining the largest funding from Microsoft.
Limitations with Generative AI
The tech excited however still needs to reach an adequate maturity level as the Gen-AI space prevails with critical concerns. As an example, concerns over content trustworthiness and ownership remain unaddressed.
Have a look at 4 major concerns in the Gen-AI space.
First concern, Content Accuracy: It’s very probable that Gen-AI tools provide incorrect information because the machine might have learnt the information from incorrect data. The training data may contain content that’s incorrect, misleading or biased.
While a Gen-AI tool should create quality content for you, arranging quality training data becomes a huge challenge.
Second concern, Academia: The ability of Gen-AI to create unique content poses a huge risk on students’ approach towards writing dissertations. With tools such as ChatGPT, how are professors going to determine if the dissertation content is actually created by the student?
Third concern, Intellectual Property Rights: IP protection and copyright is by far another pivotal concern for Gen-AI companies and its potential users. Taking content assistance from generative AI tools means asking them to create from the existing human-created data. So who owns the content remains questioned.
It’s also a challenge to identify the content which the tool uses to create an artifact.
Fourth concern, Company values are not reflected: Just as the quality of training data is critical to determine content accuracy, it’s equally challenging to base a Gen-AI model on training data reflective of company values and culture. This creates an engineering need for a highly personalized setup for each individual use case.
A future company serving Gen-AI– Some revenue models
The limitations in Gen-AI technology brings opportunity for engineering teams to provide compelling solutions to the much promising industry.
These solutions might exist in the following forms:
- Create a Gen-AI language model in local language and use licensing to help companies in adaptive marketing.
- Build your AI system and sell the outputs to customers who can use them for various purposes.
- Work with a company to pre-train the AI system reflective of their brand and offer it as a subscription service.
- Using the AI system to craft training data sets for companies, especially those that are difficult to create in real life, for example, crash scenarios..
- Use your AI to create your own products or services and sell those directly to customers.
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