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It’s amazing how quickly technology has evolved over the years and continues to evolve , and AI is no exception . It's really exciting to see how it can help us in various ways . There is no denying that many of us have benefited from it, knowingly or not, over the past decade .
No matter where you are on your AI journey, there are many myths circulating about the role AI will play in our future working lives. Still, no matter what role you play in the tech stack, the hopeful reality is that AI might make our lives easier and our jobs more productive.
In this article, we'll explore some topics related to AI and our future .
1. Myth: AI will take over all technical jobs.
Reality: AI may automate some of the tedious tasks in technology, but AI cannot replace the creativity, intuition, and problem-solving skills of human developers.
More importantly today and in the future, technical job roles will be leveraged to assist and reduce developer work. AI will help automate tedious and repetitive tasks such as code review, testing, and debugging, which can minimize the time developers spend on these tasks while freeing them to focus on more meaningful and innovative work. Overall, this leads to faster development cycles and higher quality software.
And, the development of AI itself requires the input of humans, including data scientists, machine learning engineers, and software developers. Artificial intelligence is a tool that can augment human capabilities and help them work more efficiently and productively.
There is no doubt that jobs will shift as usual. But these AI technologies will complement many jobs and create entirely new jobs that we cannot imagine today.
2. Misconception: Only data science experts can use AI.
Reality: While it's useful to know data science, you can use pre-trained models and even use AI-powered experiences without any knowledge of ML.
The myth that one needs a deep understanding of data science to take advantage of AI can be intimidating to those unfamiliar with the field. While knowing the basics of data science is certainly helpful, leveraging AI is unnecessary in many cases.
An example is pretrained models , which have been trained on large amounts of data and are ready for a specific task, such as classifying images or translating languages. These pre-trained models can be accessed through an API and used to power experiences or applications without requiring any knowledge of data science or machine learning.
Another example is AI experiences , such as voice assistants or chatbots, which use natural language processing to understand and respond to user input. These experiences are often powered by pre-trained models and can be integrated into applications without any knowledge of machine learning.
However, it is important to note that while AI can be used without an understanding of data science, having a basic understanding of the field makes it easier to understand the limitations and potential bias of AI solutions.
3. Myth: Training custom AI models is too expensive and resource intensive.
Reality: You can customize a pretrained base model
We all know that training machine learning models can be very resource intensive. It requires a lot of data, computing power, and time, which is a barrier for people who want to train their own models but don't have the resources.
There are various ways to customize the already trained base model. This might be a good option for someone who wants to use machine learning but doesn't have the resources to train their own models. These super models are pre-trained on massive amounts of data and can be fine-tuned to perform specific tasks or cater to specific industries. By fine-tuning a pretrained model, you can take advantage of the original training while tailoring the model to your specific needs.
Another option is to use a cloud-based machine learning platform that provides a scalable infrastructure and pre-built tools and frameworks for model development. These platforms can help reduce the computational burden of training your own models and provide access to pretrained models and APIs.
4. Misconception: AI is just another hyped technology trend.
Reality: don't fall behind!
AI has already proven to have an impact on many industries and will likely continue to do so in the future. AI is a disruptive technology that is already transforming businesses and industries by enabling automation, improving decision making and unlocking new insights from data. AI-driven solutions are being used in healthcare, finance, manufacturing, transportation, and many other fields, and the use of AI applications is only expected to grow.
Beyond the potential benefits, there are potential risks of AI, such as job losses, bias and privacy concerns.
Even for those who are not in technical fields, artificial intelligence can provide new job opportunities. Emerging roles like the prompt engineer will become increasingly important with the ability of users to create "good prompts" that are clear, concise, and easy to understand . It should also be specific enough to yield the desired output, but not so specific as to limit the creativity of the language model.
Waiting for AI to show up is not a practical or sensible approach. Instead, individuals and businesses should stay informed about the latest developments in AI and explore potential applications in their respective fields. Beyond the career-related benefits of AI, it can improve your personal life by providing commute optimization, home automation, and even personal finance advice to help you save money.
5. Misconception: No-code/low-code AI platforms are only for non-technical users.
Reality: No-code/low-code platforms help bridge the gap between technical and non-technical users
One of the greatest benefits of no-code/low-code AI platforms is that they enable anyone to build AI applications — think chatbots or specialized search — regardless of their technical skills. These platforms can help bridge the gap between technical and non-technical users by empowering them to participate in the software development process. Non-technical users can create simple applications using a visual interface and pre-built components, while technical users can customize these applications and integrate them with other systems.
Additionally, no-code/low-code platforms are also useful for technical users, especially those who wish to focus on higher-level tasks rather than getting bogged down in coding details. For example, a data scientist might use a no-code/low-code platform to quickly prototype a machine learning model without writing code from scratch.
No-code/low-code platforms are powerful and can be used for a wide range of applications, from simple forms and workflows to more complex applications that require data integration, machine learning, and other advanced capabilities. This makes them an invaluable tool for organizations of all sizes and industries to benefit from AI without having to hire expensive AI developers, allowing both technical and non-technical users to contribute to the software development process, streamlining business processes and accelerate innovation.
Conclusion: AI still needs a human touch
Artificial intelligence is a powerful tool that can be used to increase the efficiency of many different technical and non-technical tasks. However, it is important to remember that it is no substitute for human creativity and ingenuity. AI can help us generate ideas, but how we use them is up to us.
For example, we actually use AI to help us develop and write this blog, including brainstorming where to start and how to structure our content. This allows us to write faster and keep our thoughts organized, but AI doesn’t (and can’t) capture our creativity or unique perspective, which is needed to make content relevant and appeal to the right audience. All in all, the reality is that helping AI do its job better still depends on humans.
Interested in learning more about AI? Follow Google Cloud on Twitter and join us for an upcoming Twitter space on June 1st to discuss all these AI myths and more!