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Do you think that only mathematicians and software engineers can work in artificial intelligence? Well, you are wrong if you do it. Many people who are successful in learning data and AI are of no technological origin.
Yes, you can go to artificial intelligence, even if you started your career in marketing, psychology, law, design and so on.
Here are five practical ways to do it.
1. Become a person in your team
You don’t need permission to start using AI in your team. Well, in most cases not. One of the problems may be sharing the company’s data using AI tools. Nevertheless, be the one who discovers these tools, will get acquainted with them and will probably bring greater performance to the team.
Do you know how the Excel Master or God SQL is in every team? You can be this person for artificial intelligence. The idea is to start with a tiny age, for example:
2. Learn the technical foundations
You don’t have to start the machine learning model immediately. Start from scratch what machine learning and AI are. Familiarize yourself with basic terminology and tools.
Here is a review of the technology you should know.
Here are the tools that you can read.
Resources for further information:
3. Set as a translator AI
Ai does not exist in a vacuum; This is a solution to actual problems. If we talk about business problems, specialist knowledge in the field for machine learning and artificial intelligence is needed to provide appropriate solutions. Guess who provides such knowledge? Correct. You!
Exploit this knowledge to set up as an AI translator, a bridge between technology and stakeholders outside technology. You can:
- Translate business problems into data problems
- I know how Ai suits them
- Point defects in the assumptions of the machine learning model
- Explain the results of the model for non -technical stakeholders
In this way, you start by understanding some aspects of machine learning modeling, e.g. Matrix of confusion and accuracyto real influence. From this high understanding of artificial intelligence, you can slowly go to building real models if it’s your goal.
4. Start with tools without code or low code
Before building some less intricate machine learning models, you don’t have to work on proficiency in Python. Today, there are many tools that allow you to build an AI project without or low code, using their drag and drop their interfaces.
They will also support to set up as a translator. These tools + your domain knowledge can show that you:
- Understand the problem with the real world
- Can identify the AI solution
- Exploit this AI solution to solve the problem
Here are some tools that will prove useful.
| Category | Tool | What can you do |
|---|---|---|
| AI builders without code | Lobe. | Training of painting classifiers with a dragging and dropping interface. |
| Teaching machine | Build straightforward classification models in the browser. | |
| Monkeylearn | Create non -standard NLP models for sentiments, subject or intentions. | |
| Of course AI/Zames | Send CSV and run binary classification or regression. | |
| AI Builders with a low code | Knight | Build ML work flows with visual nodes (low code, good for tabular data). |
| Computer robot | Send data, select models and implement with minimal coding. | |
| Microsoft Azure Ml Designer | Build and implement machine learning models using dragging and dropping modules for data preparation, training and evaluation. | |
| Original and productive tools powered by artificial intelligence | Runway ML | Remove video backgrounds, generate images from the text. |
| Durable | Build the target page for the company in a few seconds. | |
| Jasper AI | Write a copy of the advertisement, product descriptions, blog lucks. | |
| You have | Automatic signatures, remove the background of the image. | |
| The concept of AI | Summary notes, drag the content, separate key points. | |
| Description | Edit podcasts or movies such as a text document. | |
| Chatgpt | Brainstorm ideas, summarize reports, a sketch of content. |
5. Turn to the roles of AI-ADJECEN
A great beginning of AI’s turnover is the transition to roles that require some AI knowledge, but do not require building a real model. Such items are:
- Project managers – for coordination between interested parties and machine learning engineers/data scientists
- Technical writers – to document work flows and write user guides
- Product designers – to understand how users interact with AI systems
- Political analysts – in order to determine risk, such as honesty and explanation in AI systems
All these positions will also give you the opportunity to learn as time passes. It can be a solid basis for switching to actually building models, like AI is becoming more and more part of many professional roles.
Application
Data scientists and machine learning engineers are not the only positions that work in artificial intelligence. Many people from non -technical origin also do.
While passing, do not write back what you already know as useless. Find the intersection between machine learning and domain knowledge and start from that moment. Then, when you learn more about artificial intelligence, you can decide if you want to build real machine learning models or remain a bridge between technical and non -technical stakeholders.
Nate Rosidi He is a scientist of data and in the product strategy. He is also an analytical teacher and the founder of Stratascratch, platforms support scientists to prepare for interviews with real questions from the highest companies. Nate writes about the latest trends on the career market, gives intelligence advice, divides data projects and includes everything SQL.