Where to start a career in Artificial Intelligence?

Updated on : January 21, 2022 by Angel Harding



Where to start a career in Artificial Intelligence?

Right now, pursuing a career in AI almost always means doing ML research (and deep learning in particular). There are several ways to do it, all of them are difficult and fun in their own way.

Just because interest in deep learning has evolved only in the last decade, very few universities offer up-to-date courses on it, like the Stanford CS231n. At the same time, you will always be asked not only to have a good CS experience in general, but also to be familiar with the algorithms and techniques applied to neural networks. Getting that kind of education is not a prerequisite, but if you have a

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Right now, pursuing a career in AI almost always means doing ML research (and deep learning in particular). There are several ways to do it, all of them are difficult and fun in their own way.

Just because interest in deep learning has evolved only in the last decade, very few universities offer up-to-date courses on it, like the Stanford CS231n. At the same time, you will always be asked not only to have a good CS experience in general, but also to be familiar with the algorithms and techniques applied to neural networks. Getting that kind of education is not a prerequisite, but if you have the opportunity to take similar courses, chances are good they will be taught by active researchers or developers, so that kind of collaboration can go a long way.

The first path is the academy. Between pros and cons:

Pros:

  1. Deep learning deep learning (ha!). Really, if you are doing a PhD, you will learn the ins and outs of at least one type of technology. If you work hard and are fortunate enough to have strong collaborators and advisers, you can publish an article on a conference like NIPS that will give you just about any position you want if you don't have any red flags.
  2. A very solid mathematical foundation. You will understand many things faster and more deeply than many of your colleagues.

Cons:

  1. The requirements for your education can range from "very high" to "shit, what do these symbols mean?", Depending on the field of study and the spectrum of problems the lab is working on. I was asked about the integral traders cores right away at the beginning of my interview.
  2. Your career can be very slow. The PhD is a long-term commitment and while others build things, you try to invent an algorithm to govern them all. Maybe you will.

If that's not your preferred way, you can think about starting AI. Everything I say on this topic should be taken with a grain of salt: I know a little about ANNs, but have not yet become familiar with the opening scene.

Pros:

  1. You can start hacking and building whatever you want to build.
  2. Nobody tells you what to do ... for some time.

Cons:

  1. You can fail. There is a very, very good chance that it will. It's the same as with all other startups, except AI is freaking out and people rarely invest in it unless you have a great plan, a great product, a great team, and are as smart as Demis Hassabis. .
  2. Building AI products is really difficult.
  3. It is tremendously difficult.
  4. Deep learning is not a magic wand.
  5. Did I mention that creating artificial intelligence products is very difficult?

When you are inexperienced, the most effective investment in your future that you can make is gaining tons of knowledge and experience. Combining it with a lot of financial headaches and the emotional roller coaster of startup life can be a bit difficult, so interning in the industry in a more comfortable way to learn from the best.

Pros:

  1. Easier than getting to a big graduate school.
  2. You will keep building real things

Cons:

  1. It is still very competitive and if you are too old or do not have relevant skills like basic ML, scientific thinking and computer background, that will be a disadvantage.
  2. You can't choose what to do

The first scam will be an even bigger disadvantage if you want to run a startup, so ...

The entire industry is in the process of figuring it all out and it is still unknown when this will happen. Not very soon, but you must act now.

Forget corporate technologies (for now). Forget competitive programming, hardware, piracy, whatever the other kids think is cool. Read Goodfellow, Bengio, Courville "Deep Learning", you have just the right amount of math. Get some general ML experience, implement a few simple things. It will be helpful if you have to go through the interview. Get a special skill, like experience with Theano or TensorFlow, to have a real experience with heavy computing and make your CV stand out. Find your passion among topics of interest to AI, it can be computer vision, speech recognition, NLP or whatever floats on your boat.

As for the language, Python is great. Take out tons of code, put it on Github, then you'll replace it with better code. It may contain a model for your future NIPS role. Who knows. But do it now. If there is one thing that I would change in the past, it is the time spent on coding.

Job opportunities in Artificial Intelligence have grown exponentially in recent years. It looks more promising than any other job available these days. To establish a career in AI, you must possess relevant technical skills. AI career opportunities are present due to the wide applications in different fields.

Let's look at some of the career opportunities in AI

Business Intelligence Developer

The primary responsibility is to consider business acumen alongside AI. They verify different business trends by evaluating complicated data sets. A business intelligence developer also helps

Keep reading

Job opportunities in Artificial Intelligence have grown exponentially in recent years. It looks more promising than any other job available these days. To establish a career in AI, you must possess relevant technical skills. AI career opportunities are present due to the wide applications in different fields.

Let's look at some of the career opportunities in AI

Business Intelligence Developer

The primary responsibility is to consider business acumen alongside AI. They verify different business trends by evaluating complicated data sets. A business intelligence developer also helps increase a company's profits by nurturing, developing, and preparing business intelligence solutions.

Two factors they recognize are profitability and business efficiency. They also help streamline workflow and different processes throughout the organization. He who knows data and sets and computer programming can acquire this position.

Machine learning engineer

They are involved in building and maintaining self-executing software that facilitates machine learning initiatives. Machine learning engineers are in continuous demand from companies. They have extraordinary data management features and work with large amounts of data. They work in the fields of fraud prevention, risk management, image and voice recognition, and consumer awareness.

AI data analyst

Its main function is to perform data cleansing, data interpretation and data mining. By deleting the data, the data necessary to carry out the interpretation of the data is collected. Inferences are made by an AI data analyst with the help of statistical methods and tools. A complete understanding of MS Excel and an understanding of regression is essential to acquire this position.

AI Engineer

AI engineers are problem solvers who test, apply, and develop different models of artificial intelligence. They manage the structure of AI effectively. They make use of understanding neural networks and machine learning algorithms to develop useful AI models.

Big Data Engineer

The role of a Big Data Engineer is to create an ecosystem for business systems to interact efficiently. The main task is to effectively build and manage big data of an organization and also carry out the function of obtaining results from big data in a robust way. In Spark and Hadoop systems, a big data engineer is responsible for establishing, managing, and preparing a big data environment.

Data scientist

They help collect data from multiple sources in order to evaluate it for constructive inferences. The inferences were helpful in addressing various business-related problems. Depending on past and present information, different data patterns, data scientists make various predictions. A company is positively affected by the work done by data scientists. You must be equipped with modern tools like Hadoop, Spark, Pig, or Hive while pursuing this career option. You should also be comfortable using programming languages ​​such as SQL, Scala, or Python.

Scientific researcher

Research Scientists strive to conduct comprehensive research on artificial intelligence and machine learning applications. A research scientist is one who has gained experience in the fields of statistics, machine learning, applied mathematics, and deep learning. You will benefit from knowledge of parallel computing, graphical modeling, computer perception, and distributed computing.

With so many opportunities available, you can take advantage of the career opportunities available in AI. We wish you all the best :)

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