10 Ways In Which You Skill Up For AI

Artificial Intelligence is a phenomenon exhibited by machines and deals with how to create computers that can show intelligent behavior. AI has been defined in many ways but mostly, it can be prescribed as a way of making a Computer System understand difficult tasks while carrying out complex commands.

One of the significant benefits of AI is that it can help human beings much better decisions by offering them more insights and recommendations centered around data. You can improve your AI skills as it has many other applications and is being employed in many industries including finance, manufacturing, transportation, and healthcare.

There is a rising demand for AI across all different industries. A report claims that AI is scheduled to reach $62.5 Billion in 2022 at a CAGR of 21.3% during 2021-2022. The Global AI market could also rise to $360 Billion by 2028 at a CAGR of 33.6% during the period of 2021-2028. There is no doubt that AI is playing an important role and the importance of learning AI skills will only increase in the future.

In this post, we will look at the ten ways in which you can skill up for Artificial Intelligence. In case you are from a technology background then keep reading to know more. 

What is Upskilling?

Upskilling involves different or expanded job functions. For instance, with training a web designer can grow into a career in application development or even UX design. As a matter of fact, upskilling can either occur on the job, led by an employer, or even on your own sometimes.

Learning new skills can also be the key to breaking into a new industry or a role if you look at it as an investment in your professional success. Salesforce allows you to skill up with Trailhead content on AI which further breaks down barriers to learning and also provides anyone with access to learning in-demand skills. 

AI learning content includes a primer on Einstein GPT, the first Generative AI for CRM, the role and importance of unified data as the foundation for AI, the role of AI in business, smart CRM assistants, and how to improve customer service. 

The technology trends in AI can be challenging for assessing how much investment is needed to make in the learning process. However, there have been significant changes so that AI can be taken to the next level and the time to start investing is now. 

Related Read: How are AI Chatbots Revolutionizing Customer Support?

Learn AI or Lag Behind

Generative AI is similar to the introduction of innovations such as nuclear power, the industrial revolution, the internet, and everything that has changed the way the world works. It is clear why companies have been trying to hire skilled people for integrating it into their work.

Upskilling is key to an engaged workforce which can then boost productivity. The fact is that with more digital skills, it becomes easy to keep your customers at the center of everything they do. All global workers say that businesses should prioritize AI skills in their employee development strategy.

One does not have to spend years earning a degree in AI and Trailhead offers learning modules for critical skills which then lead to solid careers in professional settings and a skilled workforce for employers.

Ways to Skill up for AI

1. Einstein GPT

Salesforce has come up with Einstein GPT which is the world’s first Generative AI CRM technology that is capable of delivering AI-generated content across service, marketing, commerce, IT interaction, sales, etc. By using Einstein GPT, Salesforce can transform all customer experiences with Generative AI.

Einstein GPT also infuses the proprietary AI models with Generative AI technology from a wide ecosystem of partners and real-time data from the Salesforce Data Cloud which ingests, harmonizes, and even unifies a company’s customer data. 

Using Einstein GPT, customers can then connect the data to OpenAI’s advanced AI models and also choose their own external model and use natural-language prompts within the Salesforce CRM for generating content that can adapt to changing customer information and needs. 

For example, Einstein GPT can also generate personalized emails for sales professionals for sending customers, generate specific responses for customer service to answer customer questions, generate targeted content for marketers for increasing campaign response rates, and auto-generate code for developers.

The world is experiencing one of the most serious technological shifts with the rise of real-time technologies and Generative AI. This comes at a time when customers are becoming more intelligent, personalized, and automated. Einstein GPT and Data Cloud can be integrated into the cloud which is another way that opens the door to the AI future for all customers.

2. Programming Skills

Being an AI aspirant should allow you to comprehend sound programming languages, particularly Python, Java, C++, JavaScript, and others. All programming languages have their own specifications to understand and employ in AI along with machine learning.

The highly demanded skills for AI are critical thinking and problem-solving. These skills are not good enough to be knowledgeable in coding languages and programming. More and more people involved in AI must look at the problems and break them down into individual components so that they can solve smaller problems. 

Python is a commonly used language in AI and machine learning because of the simplicity, code reliability, and quicker execution that it offers. It also helps in writing complex algorithms and requires minimal coding because it comes with pre-made libraries for advanced computing and scientific computation. 

Java is used for implementing mappers and reducers, genetic programming, search algorithms, neural networks, ML solutions, and more. C++ is needed for enabling procedural programming and manipulating hardware resources. It can be used for developing operating systems, browsers, and video games. It comes with flexibility and object-oriented functions which make it useful in AI.

3. Libraries and Frameworks

There is a wide range of libraries and frameworks to choose from when it comes to developing AI applications. Some of the renowned libraries and frameworks include NumPy, TensorFlow, Seaborn, etc. They are commonly used for exploring many data sets, scientific computing, numeric operations, etc. 

A highly demanded AI skill is having an intricate knowledge of different libraries and frameworks relevant to AI while understanding the platforms to write code quickly and accurately for AI applications. 

Anyone who wishes to make a start in AI or build a professional career in the field must be familiar with subject matter such as neural networks, deep learning, machine learning, and different frameworks and libraries. The stand-out applications also possess domain knowledge in the form of industry-specific insights and a naturally driven mindset.

4. Mathematics and Statistics

For creating machines that can learn more from experience, we also need to program them with an ability to understand and reason well and this is where Mathematics and Statistics come into the picture. 

Mathematics is the study of patterns and relationships in numbers while Statistics allows us to collect, analyze, and interpret data. The combination of mathematics and statistics offers us the tools required to analyze and understand data.

In addition, AI professionals also work together on algorithms and applied mathematics which is why you should always have strong analytical and problem-solving skills along with mathematical knowledge so that you can solve AI problems. Mathematical skills such as linear algebra, probability, graphs, optimization techniques, etc. are desirable. These skills can be used for solving problems and creating algorithms based on common requirements.

Professionals in the field of AI should call on the wide knowledge of various mathematical fields for creating algorithms and solving a company, industry, and other problems. Some of the top applicants and professionals are likely to excel in subjects such as Statistics and Linear Algebra. 

5. Machine Learning and Deep Learning

Machine learning and Deep Learning are quickly evolving fields in Computer Science. They involve training computers for learning from data without it being explicitly programmed. It is a discipline that gives Computers the ability to learn more from past experience and also adapt to new situations. 

On the other hand, Deep Learning is a domain of Machine Learning that uses neural networks to learn at a deeper level as neural networks involve systems of interconnected processing nodes that can learn to understand patterns of input data. 

The information on ML is required in reasoning as AI enables a PC or framework to act wisely. ML is a cycle that can help the machine to become smarter and carry out progressive errands. ML can be used in different applications including PC Vision, email sifting, and medication discourse acknowledgment, and that is only just the beginning. 

Machine learning is additionally connected with computational measurements to make precise expectations with the assistance of PCs and a portion of the ordinary instance of ML comes under the web index ideas. 

6. Natural Language Processing and Computer Vision

Natural language processing is basically a comprehensive study of how Computers can understand and process human language. It involves tasks such as understanding the true meaning of works, parsing sentences into different components, and understanding relationships between words. 

NLP can be used for a wide range of tasks such as automated translation, machine comprehension, and automated translation. On the other hand, Computer Vision is a study of how computers can work with and understand digital images. 

Computer vision generally includes identifying objects in images, recognizing facial features, and estimating the 3D geometry of objects in the images. Both Computer Vision and NLP are necessary subfields of AI with a range of applications. 

7. Data Science and Data Analysis

Data is considered to be the lifeblood of businesses that are running today. Data Science and Data Analysis are both important tools for understanding the way in which the world functions around us. They help in making much better decisions, understanding the customer needs, and tracking the progress and performance of something.

Data Science and Data Analysis are also important for businesses of all sizes and almost anyone who wishes to maximize the available data. Each day, organizations process a large amount of data and many people are beginning to understand the insights and business gains that data can offer when it is used properly. 

Data comes with a game-changing potential and that is why there is a rising demand for Data Analysts, Data Architects, Database Administrators, Business Analysts, Chief Data Officers, and other professionals. In fact, the number of Data Scientist job vacancies rises by over 50% each year. 

If you wish to become an AI Data Analyst, you will be required to have strong analytical and critical thinking skills. You will also want to understand complex data sets and identify patterns and trends. Lastly, you will also be required to communicate your findings to others in a much clearer and more concise manner.

8. Soft Skills

AI is advancing at a remarkable speed and as that happens, there is also an increasing need for soft skills. The fact of the matter is that soft skills are not easily quantified or automated and include critical thinking, communication, collaboration, and problem-solving. 

Employers have started understanding the importance of soft skills in the age of AI and to become successful in this age, employees will need to use these skills for working collaboratively with computers and other employees. 

9. System Design and Architecture

System design and architecture count as the quality attributes of the program that is being built. It also describes the elements of a system and how things can fit and work together for completing the requirements of the system. 

AI systems when developed properly without using any suitable architecture can lead to failure because of a system’s behaviour on the data and misaligned environments for different components. For developing a smooth-running AI system, important skills in system architecture are always important. 

10. Signal Processing Techniques

Signal processing techniques are mainly centered around the representation, transformation, and manipulation of signals on a computer. It comes with a wide range of application systems including digital communications, medical imaging, consumer electronics, etc. 

Hence, signal processing techniques are important for all AI professionals in achieving efficiency and productivity in digital communication.

Prepare for the Future of Work with AI

The demand for AI skills is increasing as all companies understand that this technology comes with the potential to improve operations. People who are able to use AI skills will be highly demanded and will also be able to find jobs in many sectors. 

In addition, the professionals who can develop their own AI skills will also be able to create new opportunities for themselves and their businesses. In case you are looking to upskill yourself in AI then there are many ways to make that happen. 

Salesforce offers extensive education on AI and how you can improve yourself in its applications. Follow Trailhead to learn and know more.