AI for the masses: The mainstreaming of Artificial Intelligence

As we enter the new year, it’s clear that 2023 is shaping up to be a pivotal one for the world of artificial intelligence (AI). This technology, which has been rapidly advancing in recent years, is now poised to move from the realm of research and development and into mainstream use.

Leaders at each of the major technology companies have been talking about AI over the last few years and only up until recent with some of the major developments in the second half of 2022 is that we have begun paying attention with the introduction of end user accessible AI applications such as DALL-E 2, Midjourney and most recently the ChatGPT chatbot.

To get started on peeling back the layers of the complex Onion that AI is I would like you to understand the difference between research AI and applied AI.

  • Research AI refers to the cutting-edge work being done by scientists and engineers to push the boundaries of what is possible with AI. This often takes the form of complex mathematical models and algorithms that are designed to enable machines to “think” and “learn” in ways that are similar to humans.
  • Applied AI refers to the use of AI technologies in real-world situations, and in the business world, it is being used to improve efficiency, make better decisions, and create new products and services.

Applied AI is probably the area most of us can relate to as we can certainly see it in action today. One common use case is in banking, where Applied AI can identify fraudulent transactions with the use of Machine Learning (ML), where AI makes decisions on transactions being fraudulent or not.

Some additional AI Use Case Scenarios like facial recognition or text recognition are built on Cognitive services, also known as AI services, a set of pre-built APIs that enable developers to add AI capabilities to their applications without having to build the underlying AI models themselves, a few examples of these services are:

  • Natural language processing (NLP) is a cognitive service, which enables machines to understand human language and respond in a way that is natural and human-like. For example, a virtual assistant that uses NLP can understand and respond to voice commands, and can also understand and respond to text-based inputs such as email and chat.
  • Computer vision is another example of cognitive service which enables machines to understand and interpret images and video. This can be used in a wide range of applications, such as security systems that can detect intruders, self-driving cars that can understand and respond to their environment, and retail systems that can identify and track products.
  • Speech recognition and generation, emotion and sentiment analysis are some other cognitive services that can be used in applied AI scenarios.

Probably the most exciting areas of applied AI is generative AI this is the area that has been making the news in the past few months. This type of AI involves creating systems that can generate new content, such as text, images, or music. This has the potential to revolutionize industries such as media, entertainment, and advertising. Some examples of generative AI in action include ChatGPT a language model that can generate human-like text to generate for example a new storyline for a TV show or movie, Midjourney a tool that can generate images with simple text prompts, and GitHub Copilot which can help developers write code.

The buzz around generative AI is real and the next killer app is just around the corner to assist us with creative tasks.  However, we have to keep in mind AI cannot create original content or ideas on its own it relies on pre-existing data and patterns to make predictions and decisions. It can generate new content based on existing patterns, but it cannot create something truly original without human input or supervision. Additionally, AI lacks the ability to understand context and meaning in the same way that humans do, which can affect the quality and relevance of its output.

Another limitation of AI in replacing human creativity is that it is not capable of understanding emotions, intentions, and values, which are essential for creating meaningful and impactful content. Humans have a unique ability to understand the nuances of human behavior and emotions, which allows us to create content that resonates with people on a deeper level.

Instead of replacing human creativity, AI can be used to help creators and business users make their life a bit easier. For example, AI can assist in tasks such as research, data analysis, and content generation, which can save time and increase productivity. Additionally, AI can provide new perspectives and insights that can inspire new ideas and approaches.

If you’re looking to start learning about AI, here are a few recommendations to consider:

For more information on Generative AI take a look at Base10’s blog post that includes a visual map of some of the biggest players in the Generative AI space.

Learning about AI will help you to gain the knowledge and skills needed to understand the potential of AI in business scenarios and its impact on different industries. Businesses are increasingly recognizing the potential of AI to improve efficiency, make better decisions, and create new products and services, and those with the knowledge and skills to harness this technology will be well-positioned for success in the years to come.

In conclusion, 2023 is shaping up to be a big year for AI, as more and more companies and organizations begin to realize the potential of this technology. Whether you’re a researcher working on the cutting edge of AI or a business looking to implement AI-powered solutions, now is the time to get onboard with this transformative technology. With the right approach, AI can help us to achieve new heights of innovation and efficiency, and open up a world of new possibilities for all of us.

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