CheckCitation/SourcePlease click:opencv
(XR Navigation Network March 07, 2024) In previous blog posts, OpenCV shared the basics of computer vision, a guide to becoming a computer vision engineer, and the different stages of computer vision research and how to publish your research, among other things!
In the following blog post, the team goes over a beginner's guide to the key AI skills you'll need in 2024:
present (sb for a job etc)
Artificial Intelligence is undoubtedly one of the latest advancements in the world of technology. As it grows and is used in a wide range of industries, from healthcare to gaming and virtual reality, the industry has created a huge demand for AI professionals. But the field of AI is not as simple as a walk in the park.
Don't worry, this article will cover the 11 AI skills you'll need to succeed in AI in 2024. So let's get started.
Skills needed for a successful career
In 2014, the global artificial intelligence market was valued at $6.3 billion. By 2024, this figure is expected to reach a staggering $305.9 billion. This can be attributed to a number of factors, such as deep learning and algorithmic breakthroughs. Combined with immense computing power, resources, and data storage, the growth of AI is not going to stop. More than 80% organizations, from SMBs to multinationals, are adopting AI in their systems, so it's crucial for those looking to enter the field to understand the basic AI skills required. Let's start with the hard skills below.
hard skill
Mastering any field requires mastering a set of hard and soft skills. The field of Artificial Intelligence is no exception. This section will cover all the hard skills required for AI mastery, so let's not waste it and get started right away.
1. Mathematics
One of the top hard skills a person needs to master is math. Why is math a necessary skill for AI? What does math have to do with AI?
Artificial Intelligence systems are mainly designed to automate most of the processes and to understand and help humans better. Artificial Intelligence systems consist of models, tools, frameworks, and logic, and they constitute mathematical topics. Concepts such as linear algebra, statistics, and differentiation are all key math topics that launch a career in AI. Let's explore each of them below.
1.1 Linear Algebra
Linear algebra is used to solve data problems and calculations in machine learning models. It is one of the most important math concepts one needs to master. Most models and data sets are represented as matrices. Linear algebra is used for data preprocessing, transformations, and evaluation. Let's look at the main use cases below.
1.1.1 Data representation
Data is a critical first step in training a model. But before that, the data needs to be converted into arrays before it can be fed into the model. Calculations are performed on these arrays, which return an output in the form of a tensor or matrix. Similarly, any problem in scaling, rotation or projection can be represented as a matrix.
1.1.2 Vector embedding
Vectors are used to organize data and contain magnitude and direction. Vector embedding involves utilizing machine learning and artificial intelligence. Here, a specific engineering model is trained to convert different types of images or text into numerical representations of vectors or matrices. Using vector embedding can greatly improve data analysis and gain insights.
1.1.3 Dimensionality reduction
This technique can be used when we want to reduce the number of features in a dataset while retaining as much information as possible. By dimensionality reduction, the high dimensional data is converted into a low dimensional space which reduces the model complexity and improves the generalization performance.
1.2 Statistics
Statistics is another mathematical concept used to discover unseen patterns from analyzing and presenting raw data. Two common statistical topics that must be mastered are listed below.
1.2.1 Inferential statistics
Inferential statistics use samples to make generalizations about larger data. We can estimate and predict future outcomes. By utilizing sample data, inferential statistics perform inferential operations to make predictions.
1.2.2 Descriptive statistics
In descriptive statistics, characteristics are described and data are presented as pure facts. Predictions are made from known data using indicators such as distributions, variances, or concentrated trends.
1.3 Differential calculus
Differentiation is the process of taking the derivative from a function. This derivative measures the change in the rate of the function. Calculus plays a crucial role when using deep learning or machine learning algorithms and models. They help algorithms gain insights from data. Simply put, they deal with the rate at which quantities change.
Differentials are used for both algorithm optimization and model functions. They measure how a function changes when the input variables change. When applied, algorithms learned from data are improved.
What is the role of differential calculus in artificial intelligence?
In Artificial Intelligence, we mainly deal with cost functions and loss functions. In order to find the correlation function, we need to find the maximum or minimum value. To do this, changes need to be made to all the parameters, which is very cumbersome, time-consuming and expensive. This is where techniques like gradient descent come in handy. They are used to analyze how the output changes when the input changes.
Math has proven to be a foundational step in your list of AI skills, helping to process data, learn patterns, and gain insights.
2. Programming
One of the first AI skills you need to master to succeed in this field is programming. Through programming, you can apply AI theories and concepts to applications. For example, it can be used as a cornerstone for building deep learning and machine learning models and training them. Another example is help in cleaning, analyzing, and manipulating data.
Some may argue that the increased complexity of AI will make programming skills less important. Such systems and algorithms have their limitations. Programmers can greatly improve the efficiency of the system. With most industries incorporating artificial intelligence into their business, the demand for skilled coders is very high. This will at the same time help you stay relevant in this competitive job market.
A large number of coding languages are involved here, the most common being C, C++, Java and Python.
2.1 Python
Python is one of the most popular programming languages used by developers. It is an interpreted language, which means that there is no need to translate into machine language instructions to run a program.Python is a general-purpose procedural language that can be used in a variety of fields and industries.
Why is Python so popular?
It is compatible with many operating systems, giving it a very high degree of flexibility. In addition, one does not need to develop complex codes.
Python greatly reduces the number of lines of code to execute and the time required for execution.
It provides a large number of pre-built libraries such as NumPy for scientific computing and SciPy for advanced computing.
2.2 C++
C++ is a general-purpose and powerful programming language for building high-performance AI systems. It is the second most popular choice among programmers, especially in areas where scalability and performance are critical.
They run models much faster than interpreted languages such as Python. Another benefit of using C++ is their ability to interact with other languages and libraries.
As a compiled language, C++ offers high performance and is suitable for building systems that require high computational power.
C++ is easier to use for performance optimization and memory usage.
Another advantage is that C++ can run on different platforms, making it easy to deploy applications in different environments.
With a wide range of libraries and frameworks, C++ is a powerful and flexible language for developing deep learning and machine learning in production.
As mentioned above, programming languages are the first fundamental step to success in AI. Let's move on to the next AI skill: frameworks and libraries.
3. Frameworks and libraries
Frameworks and libraries in AI refer to pre-built packages that provide all the basic components needed to build and run models. They typically include algorithms, data processing tools, and pre-trained models. Related frameworks are the foundation for implementing machine learning and deep learning algorithms. Frameworks eliminate the need to hand-code or code from scratch, proving cost-effective for organizations building AI applications. So why use AI frameworks?
The framework is equipped with pre-implemented algorithms, optimization techniques and utilities for data processing that can help developers solve specific problems. This simplifies the application development process.
As mentioned earlier, the framework is very cost-effective. Due to the availability of pre-built components, development costs are significantly reduced. Organizations are able to create applications in a more efficient manner and in a shorter period of time compared to traditional methods.
Frameworks can be broadly categorized into open source and commercial frameworks.
Open Source Framework: A framework released under an open source license is an open source framework. Users can use them for any purpose. They are free to use and usually include source code and licensed derivative works. Supported by an active community, one can find a wealth of troubleshooting and learning resources.
Commercial frameworks: Unlike open source frameworks, commercial frameworks are developed and licensed by specific brands. Users are limited in what they can do and additional fees may be levied. Commercial frameworks usually have dedicated support in case any problems are encountered. Since the framework in question is owned by a specific company, advanced features and optimizations can be found that are usually user-centric.
That's all there is to say about types of frameworks. Let's explore the basic frameworks and libraries below.
3.1 PyTorch
PyTorch是MetaPyTorch is an open source library developed by Meta in 2016. It is mainly used for deep learning, computer vision and natural language processing. Thanks to the developers' efforts to improve its structure, it is very easy to learn, making it very similar to traditional programming. Since most of the tasks in PyTorch can be automated, productivity can be greatly increased.PyTorch has a large community that provides a lot of support to developers and researchers.GPyTorch, Allen NLP and BoTorch are among the popular libraries.
3.2 TensorFlow
TensorFlow是GoogleTensorFlow is an open source framework developed by Google in 2015. It supports numerous classification and regression algorithms and is used for high-performance numerical computation for machine learning and deep learning. giants such as AirBnB, eBay, and Coca-Cola use TensorFlow. it provides simplifications and abstractions that can make code smaller and more efficient. tensorFlow is widely used for image recognition. There is also TensorFlow Lite, which allows deployment of models on mobile and edge devices.
3.3 MLX
MLX是appleMLX is an open source framework developed by Apple for deploying machine learning models on Apple devices. Unlike other frameworks such as PyTorch and TensorFlow, MLX offers unique features.MLX is designed specifically for Apple's M1, M2, and M3 series chips. It utilizes a neural engine and SIMD instructions to significantly improve training and inference speeds compared to other frameworks running on Apple hardware. The result: a smoother and more responsive experience on iPhone, iPad and Mac.MLX is a powerful package that offers developers superior performance and flexibility. One drawback is that, as a fairly new framework, it may not offer all the features of seasoned frameworks like TensorFlow and PyTorch.
3.4 SciKit-learn
SciKit-learn is a free and open source machine learning Python library built on top of NumPy, SciPy and Matplotlib. It provides a unified and streamlined API and comes with comprehensive documentation and tutorials, data mining and machine learning features. Once a developer understands the basic syntax of one model type, it's easy to switch to another model or algorithm.SciKit-learn provides an extensive user guide to help you quickly access resources ranging from multi-labeled algorithms to covariance estimation. It has a wide variety of use cases, from smaller prototypes to more complex deep learning tasks.
3.5 Keras
Keras is an open source advanced neural network API that runs on top of other frameworks. It is part of the TensorFlow library and allows us to define and train neural network models with a few lines of code.Keras provides a simple and consistent API that reduces the time spent running generic code. At the same time, it requires less prototyping time, which means models can be deployed in less time. Giants such as YouGov, Yelp and Netflix are using Keras.
4. Data engineering
21世纪是大数据时代。数据是推动人工智能背后创新的关键因素。它为企业提供简化其流程，并根据其业务目标做出明智决策的信息。随着物联网、social contact媒体和数字化的爆炸式增长，数据量急剧增加。但面对如此庞大的数据量，收集、分析和存储数据是相当具有挑战性的任务。这就是数据工程发挥作用的地方。它主要用于构建、安装和维护系统和管道，促进组织有效地收集、清理和处理数据。
We introduced statistics in an earlier section, and it plays an equally important role in data engineering. The related basics will help data engineers to better understand the project requirements. Statistics helps in drawing inferences from data. Data engineers can utilize statistical metrics to measure the usage of data in a database. It is good to have a basic understanding of descriptive statistics, such as calculating percentiles from collected data.
Now that we understand what data engineering is, we'll dive deeper into the role of data engineering in AI.
4.1 Data collection
As the name suggests, data collection involves gathering data from various sources and extracting insightful information from it. Where can we find data? Data can be collected from various sources such as online tracking, surveys, feedback and social media. Businesses use data collection to optimize the quality of work, make market predictions, find new customers and make profitable decisions. There are three ways of data collection.
First-party data collection: in this form of data collection, data is obtained directly from the customer. This can be done through websites, social media platforms or apps. First-party data is accurate and reliable. This form of data collection refers to customer relationship management data, subscriptions, social media data or customer feedback.
Second-party data collection is collecting data from trusted partners. This could be a business outside of the brand collecting data. This is very similar to first-party data because the data is obtained through a trusted source. Brands utilize second-party data to gain better insights and scale their business.
Third-party data collection: Here, data is collected from external sources that are not related to the business or customers. This form of data is collected from a variety of sources and then sold to a variety of brands and used primarily for marketing and sales purposes. Third-party data collection offers a wider range of audiences than the first two. However, this kind is not always collected in compliance with privacy laws.
4.2 Data integration
Data integration dates back to the 1980s. Its main purpose was to suppress differences between relational databases using business rules. Unlike today's cloud technologies, data integration back then relied more on physical infrastructure and tangible repositories. Data integration involves combining various data types from different sources into one data set. This can be used to run applications and help with business analytics. Organizations can use relevant data sets to make better decisions, drive sales, and provide a better customer experience.
Data integration is found in almost every industry, from finance to logistics. Below we explore different types of data integration methods.
Manual Data Integration: This is the most basic data integration technique. With manual data integration, we have complete control over the integration and management. Data engineers can perform data cleansing, reorganization, and manually move it to the desired destination.
Unified Data Access Integration: In this form of integration, data is displayed consistently for ease of use while keeping the data source in its original location. It is simple, provides a unified view of data, multiple systems or applications connect to a single data source, and does not require high storage space.
Application-based data integration: Here, software is utilized to locate, acquire and format data, and then integrate the data to the desired destination. This includes pre-built connections to various data sources and the ability to connect to other data sources when necessary. With application-based data integration, data transfer can be seamless and use fewer resources due to automation. It is also easy to use and does not always require specialized technical knowledge.
Universal Storage Data Integration: As data volumes increase, organizations are looking for more common storage options. Much like Unified Access Integration, information is converted to data before being replicated to a data warehouse. With ready access to data in one location, we can run business analytics tools when needed. This form of data integration provides a higher level of data integrity.
Middleware Data Integration: Here, the integration takes place between the application layer and the hardware infrastructure. Middleware data integration solutions transfer data from various applications to the database. Using middleware, networked systems can communicate better and can transfer enterprise data in a consistent manner.
5. Machine learning methods and algorithms
Computer programs that can adapt and evolve based on the data they process are machine learning algorithms. They are called training data, which is essentially mathematical data that is learned from the data provided to them. Machine learning algorithms are among the most widely used algorithms today. They are integrated into almost every kind of hardware from smartphones to sensors.
Machine learning algorithms can be categorized in different ways depending on their purpose. We will delve into each one.
5.1 Supervised learning
In supervised learning, machines learn by example. They obtain inferences from previously learned data and use labeled data to obtain new data. Algorithms recognize patterns in the data and make predictions. The algorithm makes predictions and is corrected by the developer until a high level of accuracy is achieved. Supervised learning includes:
Categorization: here the algorithm draws inferences from observations and determines to which category the new observation belongs.
Regression: in regression, understanding the relationship between various variables, where the focus is on a dependent variable and a range of other changing variables to make it useful for forecasting and prediction.
Forecasting: this is the process of making future predictions based on past and present data.
5.2 Unsupervised learning
In unsupervised learning, algorithms analyze data for patterns. Machines study the available data and infer correlations. Algorithms interpret big data and try to organize it in a structured way. Unsupervised learning includes:
Dimensionality reduction: this form of unsupervised learning reduces the number of variables needed to find the desired information.
Clustering: this involves grouping similar data sets based on defined criteria.
5.3 Semi-supervised learning
Semi-Supervised Learning (SSL) This machine learning technique utilizes a small amount of labeled data and a large amount of unlabeled data to train predictive models. With this form of learning, the cost of manual annotation can be reduced and data preparation time can be limited. Semi-supervised learning bridges the gap between supervised and unsupervised learning and solves the problems that exist between the two.SSL can be used for a range of problems from classification and regression to association and clustering. SSL can be used in a large number of applications without compromising accuracy because of the large amount of unlabeled data that exists and is relatively inexpensive.
We explore common machine learning algorithms below.
Logistic regression: logistic regression is a form of supervised learning used to predict yes or no probabilities based on prior observations. Correlation predictions are based on the relationship between one or several existing independent variables. Logistic regression proves to be crucial in data preparation activities by placing datasets into predefined containers to present information during extraction, transformation and loading.
Decision Tree: Decision tree is a supervised learning algorithm that creates a flowchart and makes decisions based on numerical predictions. Unlike other supervised learning algorithms, it can solve regression and classification problems. By learning simple decision rules, we can predict the category or value of the target variable. Decision trees are very flexible and come in various forms for business decision making applications. They use data that does not require much cleaning or normalization, while not taking much time to train new data.
Parsimonious Bayes: Parsimonious Bayes This probabilistic machine learning algorithm is used for various classification problems such as text categorization for which we train high dimensional datasets. It is a powerful predictive modeling algorithm based on Bayes theorem. It is much faster to build models and make predictions with this algorithm, but it requires a high level of expertise to develop.
Random Forest : Random Forest This well-known machine learning algorithm is used for classification and regression tasks and uses supervised learning methods. It produces good results even without hyperparameter tuning. Due to its simplicity and versatility, it is the algorithm of choice for machine learning practitioners. Random forest is a classifier that contains multiple decision trees in different subsets of a given dataset and finds the mean to optimize the accuracy of that dataset.
KNN: KNN is a simple algorithm that stores all available cases and classifies new data. It is a supervised learning classifier for prediction using proximity. It is used in classification and regression tasks but usually it is used as a classification algorithm. It can handle both categorical and numerical data which makes it flexible to handle different types of datasets and used in classification and regression tasks. Due to its simplicity and ease of implementation, it is a common choice for developers.
Machine learning algorithms are important for utilizing one's AI skills and for a successful career in AI. In this section, we covered different types of computational learning algorithms and common techniques. Let's take a look at the next AI skill: deep learning.
6. Deep learning
From large-scale language models like ChatGPT to self-driving cars like Tesla, recent advances in the field of artificial intelligence can be attributed to deep learning.
What exactly is deep learning?
Deep learning is a subfield of artificial intelligence that attempts to replicate the way the human brain works in machines by processing data. Deep learning models analyze complex patterns in text, images, and other forms of data to produce accurate insights and predictions. Deep learning algorithms need data to solve problems. In a way, it is a subfield of machine learning. But unlike machine learning, deep learning consists of multiple layers of algorithms called neural networks.
A neural network is a computational model that attempts to replicate the complex functions of the human brain. Neural networks have multiple layers of interconnected nodes to process and learn from data. By analyzing hierarchical patterns and features in data, neural networks can learn complex representations of data.
Commonly used architectures in deep learning are discussed below.
CNNs are deep learning algorithms designed for tasks such as object detection, image segmentation and object recognition. They can autonomously extract features on a large scale, eliminating the need for manual feature engineering and thus increasing efficiency.CNNs are generalized and can be applied in areas such as computer vision and NLP. CNN models like ResNet50 and VGG-16 can be adapted to new tasks with little data.
FNN, also known as deep network or multilayer perceptron MLP, which is a basic neural network in which inputs are processed in one direction.FNN is one of the earliest and at the same time one of the most successful learning algorithms.FNN consists of an input layer, an output layer, a hidden layer and neuron weights. The input neuron receives data through the hidden layer and then leaves the output neuron.
RNNs are one of the most advanced algorithms for processing temporal data such as time series and natural language. They maintain an internal state that captures previous inputs, which makes them more suitable for speech recognition and language transcription, such as Siri or Alexa.RNNs are the algorithm of choice for processing sequential data such as speech, text, audio, and video.
Deep learning contains numerous subtypes.
6.1 Computer vision
Computer Vision (Computer Vision/CV) is another area of artificial intelligence that has boomed in recent years. We can attribute this to the huge amount of usable data generated today (about 3 billion images are shared every day). Computer vision dates back to the 1950s.
What is computer vision? Computer vision is a subfield of artificial intelligence that trains machines and computers to interpret their surroundings as we do. In short, it gives machines the power of vision.
6.2 Natural Language Processing NLP
Another subfield accelerating deep learning is NLP, the ability to give machines the ability to process and understand human language. We've probably all used NLP technology in some form, such as virtual assistants like Amazon's Alexa or Samsung's Bixby. This technology is usually based on machine learning algorithms to analyze examples and make inferences based on statistics, meaning that the more data a machine receives, the more accurate the results will be.
How can NLP benefit businesses? From news stories to social media, NLP systems can analyze and process large amounts of data from different sources and provide valuable insights to evaluate a brand's performance. By streamlining the process, this technology can make data analysis more efficient.
NLP technology comes in many forms such as chatbots, auto-completion tools, and language translation. Key aspects of learning to master NLP include:
Data Cleaning
marking
Field Embedding
model development
Having strong knowledge of NLP and computer vision fundamentals can open the door to high-paying positions such as computer vision engineers, NLP engineers, NLP analysts, and more.
7 Deployment
Model deployment is the final step that links all of the above steps together. It is the process of promoting accessibility and its operation in a limited environment where they can make predictions and gain insights. Here, the model is integrated into a larger system. Relevant predictions are available to the public. This can pose a challenge for different reasons, such as testing or differences between model development and training. But with the right modeling framework, tools and processes, the described problems can be overcome.
Traditionally, models were deployed on local servers or machines, which limited their accessibility and scalability. Fast forward to today, and with cloud computing platforms like Amazon Web Services and Azure, deployments have become much more seamless. They have improved how models are deployed, resources are managed, and the complexity of how scaling and maintenance is handled.
Below we look at the core features of model deployment.
Scalability: model scalability is the ability of a model to handle large amounts of data without compromising performance or accuracy. It involves:
Scale up or down on the cloud platform as needed
It ensures optimal performance and makes it more cost-effective
Provides load balancing and auto-scaling, which is critical for handling diverse workloads and ensuring high availability
Help assess whether the system can handle increasing workloads and how adaptable it is
Reliability: This refers to the effectiveness of the model in performing the desired task with minimal errors. Reliability depends on several factors.
Redundancy is the backup of critical resources in the event of failure or unavailability
Monitoring is to assess the system during deployment and resolve any issues that arise
Testing verifies the correctness of the system before and after it is deployed
Error handling refers to how a system recovers from a failure without compromising functionality and quality
7.1 Cloud Deployment
The next step is to choose a deployment environment specific to our needs, such as cost, security and integration features. Cloud computing has come a long way in the last decade. In the initial years, cloud model deployment options were very limited.
What is cloud deployment? It is an arrangement of different variables such as ownership and accessibility of a distributed framework. As a virtual computing environment, we can choose a deployment model based on the amount of data we want to store and who controls the infrastructure.
7.2 Private Cloud
This is where companies build, operate and own their data centers. Multinationals and big brands often use private clouds for better customization and compliance requirements, but this requires investment in software and personnel. A private cloud is best suited for companies that want to have full control over their data and resources and be able to control costs. It is ideal for storing confidential data that only authorized personnel can access.
7.3 Public Cloud
A public cloud deployment involves a third-party provider that hosts infrastructure and software shared data centers. Unlike private clouds, it saves on infrastructure and staffing costs. Public clouds are easy to use and more scalable.
7.4 Hybrid Cloud
It is a type of cloud that combines private and public clouds. They facilitate the movement of data and applications between the two environments. Hybrid platforms offer more:
dexterity
surety
Deployment Options
compliancy
Choosing the right public cloud provider can be a daunting task. So, below we will cover the major vendors that are currently dominating the market:
Amazon Web Services：由亚马逊开发的AWS于2006年推出，是云计算行业的首批先驱之一。AWS在全球245个国家拥有超过200项云服务，以32%的市场份额位居榜首。可口可乐、adobe和Netflix等巨头都在使用它。
Google Cloud Platform: Launched in 2008, it started as an "application engine" and became Google Cloud Platform in 2012. Today, it has 120 cloud services, which makes it a good choice for developers. The compute engine is one of its best features and supports any operating system with customized and predefined machine types.
MicrosoftMicrosoft Azure: Launched in 2010, Azure offers traditional cloud services in 46 regions and has the second highest share of the cloud market. You can quickly deploy and manage models, deploy and manage models, and share cross workspace collaboration.
7.5 Monitoring Model Performance
Once the model is deployed, the next step is to monitor the model.
Why monitor model performance? Models typically degrade over time. Starting from deployment, the performance of models starts to slowly degrade. This is done to ensure that they perform consistently as expected. Here, we track the behavior of deployed models and make analysis and inferences from them. Next, if the model needs any updates in production, we need a real-time view to evaluate it. This can be done by validating the results.
Monitoring can be categorized as:
In operational level monitoring, there is a need to ensure that the resources used for the system are healthy and if they are not normal, they will be operated on.
Function-level monitoring is where we monitor the input layer, the model, and the output predictions.
7.6 Resource optimization
Resource optimization is a key aspect of model deployment. This is particularly useful in situations where resources are limited. One way to optimize resources is to tune the model itself. Let's explore a few ways to do this.
Simplify: One way to optimize a model might be to use a model with simpler, fewer components or operations. How do we do this? By using the features mentioned below:
Models with smaller architectures
Models with fewer layers
Models with faster activation functions
Pruning: Pruning is the process of removing unneeded parts of the model that do not contribute much to the output. It involves reducing the number of layers or connections in the model to make it smaller and faster. Common pruning techniques include Weight Pruning and Neuron Pruning.
Quantization: Model quantization is another way to make a model more optimized. This involves reducing the bit width of the values used in the model. As with previous model optimization methods, quantization reduces the memory and storage requirements of the model while allowing for faster inference.
The above summarizes the hard skills needed to get on the path to AI. But wait, there's more. I'm talking about soft skills. What exactly are soft skills? And why are they important? This will be discussed in more detail in the next section.
soft skill
Soft skills are the "non-essentials" that a person needs to possess in addition to specialized knowledge. Soft skills are inherent in all of us, they are not skills we learn from books or courses. Soft skills are the bridge between your technical skills and those of your employer or coworkers. In other words, how effectively you communicate and collaborate. According to Deloitte's survey, 92% brands say soft skills are equal in importance to hard skills. They demonstrate a person's ability to communicate internally within a company, lead a team or make decisions to improve business performance.
Below we explore the key soft skills, the strengths one must have.
8. Problem solving
Why you've been hired. To use your expertise in your field to solve problems. This is another important soft skill that requires people to identify problems, analyze them and implement solutions. This is one of the most sought-after skills, and 86% employers are looking for resumes with this skill set. Ultimately, companies are always looking for people who can solve their problems. Anyone who is a good problem solver is valuable in the job market.
9. Critical thinking
As more and more automated processes emerge, it becomes critical for leaders and experts to interpret and contextualize results and make decisions. Critical thinking helps to evaluate results and provide factual responses. Logical reasoning helps to identify any discrepancies in the system. This involves a mixture of rational thinking, separating the relevant from the irrelevant, considering the context of the information obtained, and pondering its implications. It therefore involves solving complex problems by analyzing the pros and cons of various solutions, and you need to use logic and reasoning rather than intuition.
10. Inquisitiveness
The quest for knowledge is an important component of one's career. It is a desire to explore things, ask questions, and delve deeper. Curiosity pushes people to step out of their comfort zones and explore uncharted territory in their area of expertise. While AI systems can analyze and extrapolate from large amounts of data, they lack the ability to understand or question. The more a person explores, the more innovation they can bring about.
11. Ethical decision-making
With today's massive amounts of data, AI systems can manipulate large datasets and make inferences from patterns drawn from the data. However, we cannot rely on systems to make correct or fair decisions, as they may rely on social biases. If left unattended, biases can lead to organizational discrimination as inequities are perpetuated.
This is where ethical decision-making comes into play. It articulates a person's ability to ensure that the outcome safeguards the freedom of one or more individuals and conforms to societal norms. It ensures that the systems deployed are not used in an intrusive or harmful manner.
in conclusion
In this article, we cover all the essential hard skills, such as programming and deep learning, as well as soft skills, such as critical thinking and problem solving. Hopefully, this article has given you the insights and the right mindset to help you start your journey in utilizing your AI skills. See you in the next installment.
© Copyright notes
The copyright of the article belongs to the author, please do not reprint without permission.
Related posts
No comments...
EN_US 
ZH