What is Artificial Intelligence?
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
- Speech recognition
- Self-driving cars
Additionally, several technologies enable and support AI:
Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks require big data plus compute power.
The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.
Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
APIs, or application programming interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.
Where to start on your artificial intelligence career path?
Since the goal of AI is to provide software that can reason on input and explain output. AI will provide human-like interactions with software and offer decision support for specific tasks.
Laying the foundation in your Artificial Intelligence career
We advise students to do the following:
Step 1: Break any mindset barriers.
Believe you can practice and apply machine learning in the real world with real-life examples. It all starts with an idea and you can place the idea into motion using machine learning. It is possible. You must learn to expand your mind into the machine world.
If this is difficult for you, then ask yourself the following:
- What is holding you back from your Machine Learning goals?
- How to think about machine learning problems and how can a mentor help me break my barriers?
Find a positive machine learning meetup group and stick with them.
Step 2: Have a Process.
Use a process to work through machine learning problems.
- The process would look similar to this:
- Find and understand the problem.
- Prepare a data set.
- Check the algorithm changes.
- Improve the results of the data.
- Show the results to the stakeholders.
Step 3: Pick a programming tool.
Select a tool for your level and map it onto your process.
- Beginners should use Weka Workbench.
- Intermediate: Python Ecosystem.
Step 4: Practice your skills.
Select datasets to work on and practice them using the process.
- Practice Machine Learning with small in-memory data. You can also look at and practice real-life machine learning data sets. The key is to practice these small data sets so you can move to larger more complex data sets.
Step 5: Build a solid portfolio of your success.
After you have practiced your skills demonstrate to employers that you know what machine learning techniques you can create.
The most common certifications include:
- Certified Associate in Python Programming Course Training
- Beginner: Intro to deep learning course training
- Intermediate: AI with python
- Advanced: Certified Blockchain professional
The chart below shows the hands-on IT training certifications required for an AI/ML/Blockchain skills.
Other specific technical skills you need will vary based on the area you choose to focus on.
- Supervised learning – this is the process of teaching a machine large amounts of datasets.
- Unsupervised learning – In this scenario algorithms try to identify patterns in data, looking for similarities that can be used to categorize that data.
- Reinforcement learning – the system attempts to maximize a reward based on its input data, basically going through a process of trial and error until it arrives at the best possible outcome.
Companies that are hiring Artificial Intelligence Scientists
Every industry has a high demand for AI capabilities – from self-driving cars, automated answering services, patent searches, risk notification, and medical research. According to the Brookings Institute, a non-profit research group, Artificial intelligence will disrupt the future of work. They say we shouldn’t fight the growth of artificial intelligence but it must be managed properly by government policy enforcers. Some report that a fifth of occupations we know of today will be wiped out within 15 years all because of AI and automation.
AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier.
AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI.
AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.
Artificial Intelligence enhances the speed, precision, and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.
Networking is very important
We have a free Digital Natives Meetup group that has over 2,400 members. This group is a great place to meet other IT professionals and network. These free events could help you get your foot in the door. We have Meetups every month that cover the current topics and interests in the quickly changing machine learning/ artificial intelligence field. We have guest speakers that are full-time professionals who show cutting-edge technologies and will share their passions concerning the IT field with you.
We have a very popular Digital Natives Meetup and we cover these topics in-depth including launching your career in machine learning.
Practice, practice and practice your soft skills
There is no better way to get better at your skills than to practice. You can start with many free tools to get your feet wet. It is important to keep the drive to want to learn more and keep your skills up to date with the current trends.
Some places you can practice your tech skills:
● Intellectual Point’s Rapid Test Prep
● ITProTV Labs
No cost and no obligation mentoring.