For others, AI and machine learning provoke anxiety, as they contemplate the existential threat that intelligent machines could pose to humanity Some people are excited about the rise of artificial intelligence and machine learning because of the possibilities they present. Others are anxious about them because they might pose a threat to humanity.
Some people are afraid of the rise of artificial intelligence.
Some people are concerned about whether they will still have a job in the future, as digital skills are becoming more and more important in the world of marketing.
This article will show marketers how to improve their skills so that they can be more valuable in the digital age.
There are eight essential skills – digital and soft skills – that will help you survive the rise of artificial intelligence.
1. Data analysis
Contrary to popular belief, data analysts are not always the first ones to be laid off. In fact, learning how to analyze data can be a useful skill to add to your resume.
Data is very valuable and has even been compared to oil. According to Forbes, the revenue from Big Data will be more than $100 billion by 2027.
Data is only useful if it is “drilled” by humans.
It is only through proper data analysis that businesses can understand consumer data and gain actionable insights from it.
Nowadays, many companies are using data-driven marketing methods, and more are joining the trend every day. Although artificial intelligence (AI) and machine learning can collect and arrange data, businesses will still need employees who are good at data analysis to make use of the information effectively.
2. Domain knowledge
If you want to have a career in artificial intelligence, you need to know about neural networks, machine learning, and deep learning.
If you have an understanding of the industry you are working in, you will be able to identify the risks and challenges that need to be addressed.
Developing innovative technologies can help address societal challenges.
If you have knowledge about AI, you can use it to create technologies and services that improve people’s lives and help businesses.
3. Programming languages
You need to be able to write code that is based on your specific needs and uses.
- Python: Python is widely used in AI and machine learning due to its simplicity, code reliability, and faster execution. It will help you write complex algorithms and requires minimal code. It comes with many pre-made libraries for advanced computing and scientific computation.
- Java: Java is also used extensively in AI for implementing mappers and reducers, intelligence programming, genetic programming, search algorithms, neural networks, ML solutions, and more.
- R: You need R for statistical computation, numerical analysis, machine learning, neural networks, and more. R allows you to collect and organize data sets, apply ML and statistical functions, and use matrix transformations and linear algebra for data processing.
- C++: The good old C++ is used in AI to enable procedural programming and manipulating hardware resources. You can use it to develop operating systems, browsers, and video games. Its flexibility and object-oriented functions make it highly useful in AI.
It would be beneficial if you had a comprehensive understanding of computer architecture, data structures, optimization algorithms, graphs, trees, and more.
If you can speak more than one language, it can give you an advantage as you can contribute more to organizations that place an emphasis on multilingualism in their workforce.
Frameworks and libraries
You must know more than just programming languages to be a good programmer. Understanding frameworks and libraries is also important to write quality code quickly.
Some of the most useful tools for AI are TensorFlow, SciPy, NumPy, Scikit-learn, Apache Spark, PyTorch and more.
- TensorFlow is an open-source machine learning platform with a comprehensive and flexible set of tools, community resources, and libraries to help researchers develop sophisticated ML-powered applications with ease.
- SciPy is an open-source Python library used for solving scientific and mathematical issues. It helps users manipulate and visualize data using various commands.
- NumPy is a Python-based package used for scientific computing and advanced mathematical operations while handling massive data sets.
- Scikit-learn is a powerful Python library for machine learning and has lots of ML and statistical modeling tools.
4. Content creation
Content is still the most important aspect of a website, and robots are not able to write good copy.
Can robots write news stories? Actually, they can to a degree. The New York Times recently discussed “The Robot Reporter,” showing how automated tech is already more than capable of bashing out a few thousand words while you’re busy chatting at the coffee machine.
So, what can a hard-working writer do to compete against others?
And they certainly don’t just dabble in search engine optimization Typically, smart content marketers have more than one skill. They aren’t just writers, or just social media marketers, or just people who dabble in search engine optimization.
The modern content marketer is someone who has worked in a variety of freelance roles and has gained a lot of knowledge and skills from those experiences.
If you’re agile, by the time AI tightens its grip on the world, you will have an array of content capabilities, such as:
- Blog writing
- Sales writing
- Social media marketing
- Email marketing
- Image creation
- Video production
The more skills you have, the more useful you will be.
One of the most important digital skills to have now is in cybersecurity. With more and more companies moving to the cloud, the value of data online is becoming more and more valuable.
That being said, try wrapping your mind around these projections:
- The cloud computing market is going to be worth over $400 billion by 2020. (IT Proportal)
- By the year 2022, over $1.3 trillion in IT spending will be affected by the cloud. (Forbes)
- 93% of companies think the cloud will be used for half of all enterprise transactions in the years ahead. (Gartner)
This text discusses the implications of increased connectivity for businesses and individuals. businesses stand to benefit greatly from increased connectivity, but people with malicious intent can also use it to their advantage.
In 2018, major data breaches occurred at Google, Facebook, and Quora, which resulted in the loss of money and the personal information of millions of people.
It is no surprise that cybersecurity experts are highly sought after in today’s climate. Not only are they responsible for deterring cybercriminals, but they also play a role in protecting company assets and personal data from technological advances.
The founder of QuadrigaCX recently died suddenly, taking the company’s recovery passwords with him to his grave.
He quickly changed his cryptocurrency exchange into a place that was like a Shakespearean crypt, locking investors out of $190 million with no way to get it back.
It is hoped that there will not be a revolt by robots similar to that in the film ‘I, Robot’, but it is clear that we will also need security experts to protect us from computers.
The untrusting protagonist in “I, Robot” was right when he said that computers don’t have a conscience or heart, they’re “just lights and clockwork.”
They do whatever they have been programmed to do.
Nowadays, it’s important for companies to have a good reputation and to be responsible citizens.
Consequently, many companies are opting to engage in social responsibility activities. Nowadays, more and more people are taking notice of the state of our planet and how we treat each other. Therefore, businesses have to show that they are socially responsible and can be trusted before consumers will want to support them. Many brands are now choosing to participate in social responsibility programs as a result.
Some people are asking for more rules about how artificial intelligence can be used because they worry that it might not be fair or responsible. The European Union has said that AI systems must be able to show why they made certain decisions, so that people can understand how they work.
It’s important for companies to have systems that are secure and private, without discriminating against users. It’s great to be able to use AI to improve your digital skills, but you need to know when to stop.
It is important to know when to use emotional intelligence and empathy.
Here’s a common misconception:
Some people think that artificial intelligence (AI) and machine learning will eventually replace the need for personal interaction. However, this is not true.
The reality is that communication will be more crucial than before during the rise of AI. Research from Harvard Business Review explored the inner workings of 1,500 companies that are currently using AI and found that communication is a key success factor.
HBR found that the best performers were those companies that worked together with machines, making themselves better through collaborative intelligence.
Even if your company starts using an AI chatbot for customer service, you will still need to be available to talk to people to maintain the human touch.
If your customer wants to speak to a human and your business does not offer that option, you are at risk of creating a barrier between customers and your business.
By 2020, Gartner predicts that as many as 85% of all customer service interactions will be conducted by chatbots.
Even though AI is slowly becoming more advanced, we can’t expect it to take over every job. It takes time for AI to learn and become effective, so people will still be needed in customer service for now.
Although AI is very beneficial for marketers, it still has some limitations. It is not able to replicate a genuine human response that includes emotions and empathy for a customer’s specific situation.
8. Mathematical knowledge
AI professionals tend to work a lot with algorithms and applied mathematics. Because of this, it’s important to have strong analytical and problem-solving skills, as well as mathematical knowledge so you can solve AI problems in an efficient way.
It’s desirable to have mathematical skills like linear algebra, statistics, probability, graphs, and optimization techniques so that you can solve problems and create algorithms based on requirements.
- Linear algebra: Linear and abstract algebra form the basis of many parts of AI like machine learning and computer vision. It involves matrices, vectors, tensors, and others.
- Statistics: Statistical is another vital subject that you need to master if you want a bright career in AI and ML. It involves data collection, interpretation, and analysis. It coincides with data science, but you need statistical skills to understand the patterns.
- Probability: Probability is an essential part of artificial intelligence. Hence, you must possess sound knowledge of probability and probability distribution to have a smooth start in AI. It can be used on the discriminative and generative models, support vector machines, etc.
- Graph: Knowing how to look at graphs and understand what it conveys is needed in AI. It is an integral part of AI, and you will be constantly exposed to analyze data by looking at different graphs.
9. Machine learning
Machine Learning is a branch of artificial intelligence that deals with algorithms that learn from data and improve themselves over time.
ML algorithms train on data to create models that can make decisions and predictions.
In order to create artificial intelligence, it is necessary to have knowledge of machine learning. Machine learning is what allows a computer to understand how to behaviour intelligently.
This is done by teaching the machine to recognize patterns and implement this knowledge in real world situations.
ML can be used for a variety of purposes, including improving computer vision, filtering emails, assisting in medicine, and more. It also helps in making predictions more accurately with the help of computers.
Some examples of everyday ML are search engine suggestions.
10. Deep learning
Deep learning is a branch of machine learning that allows computers to mimic how humans gain specific knowledge. This process includes predictive analytics and statistics and capitalizes on layers to extract deeper features from data, such as images or sound.
With higher layers, detailing would be more nuanced.
Deep learning involves using algorithms that are arranged in a hierarchy of increasing abstraction and complexity. Each algorithm transforms its input in a non-linear way.
The artificial intelligence will take the information that it has learned and use it to create a model or an output. It will keep doing this until it has reached the desired level of accuracy.
This means that data will need to go through multiple processing stages to be accurate and refined.
Deep learning can be used in a variety of fields and can be very helpful for data scientists when it comes to collecting, interpreting, and analyzing large amounts of data quickly and easily.
Some applications that can be used with this technology are face recognition, speech recognition, and virtual assistants. This technology can also be used to help driverless cars see.
11. Big data and distributed computing
Predictive and data analysis in AI requires a lot of data, which needs powerful computers to process it. Using one system to do all the computing may not be enough.
Concepts such as Big Data and distributed computing can be helpful.
- Big Data is a technology that involves extracting, managing, and analyzing an enormous amount of data efficiently. This data needs high computation resources and offers excellent statistical power. It is used in user behavior analysis, predictive analytics, and other analytical needs involving large data sets.
- Distributed computing is a branch of computer science involving distributed systems whose components are situated on various networked computers coordinating and communicating their actions by exchanging messages. Its applications are multiplayer online video games, peer-to-peer (P2P) applications, etc.
Ready to embrace AI
The debate on whether artificial intelligence will take away or create jobs is ongoing, but time is moving on and the digital landscape is continuing to change.
There is no doubt that artificial intelligence systems will play a major role in global industry and business in the future, so there is no point in worrying about the inevitable.
Instead, why not do something about it?
Although AI has a lot of potential, it still has limitations and it may be a while before it reaches its full potential. Even when it does, there will still be a need for humans to work alongside computers and robots.
Marketers should continuously expand their knowledge and learn new digital skills. Coding and data analysis are just some of the ways marketers can become digital leaders.
The people who will be most successful in the future are those who are right now undergoing their own personal digital transformation.