Artificial intelligence (AI) is a rapidly evolving field that has the potential to transform many aspects of our lives. The history of AI can be traced back centuries, with early examples including the creation of mechanical devices that could perform simple tasks such as calculating numbers or playing chess. However, it was not until the development of the electronic computer in the mid-20th century that AI began to be explored more seriously as a field of study.
Alan Turing's 1950 paper "Computing Machinery and Intelligence" is widely considered a seminal work in the field of AI, as it introduced the concept of the Turing Test as a way to evaluate a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
Since then, the field of AI has grown significantly, with the development of various approaches and techniques for building intelligent systems.
Some of the key areas of research in AI include machine learning, natural language processing, and robotics. Machine learning involves the use of algorithms and statistical models to enable systems to learn from data and make predictions or decisions based on that learning. Natural language processing involves the development of algorithms and systems that can understand, interpret, and generate human language. Robotics involves the design and development of robots that can perform tasks in the physical world, often with some degree of autonomy.
The advancements in AI have led to the development of many useful applications, including voice assistants, self-driving cars, and disease diagnosis systems.
The most recent milestone in AI history was achieved in 2020 when Baidu released the LinearFold AI algorithm to medical and scientific and medical teams developing a vaccine during the early stages of the SARS-CoV-2 (COVID-19) pandemic. Traditionally, the process of determining the RNA (ribonucleic acid) sequence of a virus is time-consuming and labor-intensive, requiring the use of specialized techniques and equipment. The development of the LinearFold AI algorithm made it possible to predict the RNA sequence of SARS-CoV-2 much more quickly, potentially saving valuable time in the race to develop a vaccine against the virus. The algorithm was able to predict the RNA sequence of the virus in only 27 seconds, which is 120 times faster than any other methods available.
However, the rapid pace of development in the field has also raised important ethical and societal questions, such as the potential impact of AI on employment and the need to ensure that AI systems are fair and unbiased.
This blog post is a deep dive into the world of AI and invaluable content for anyone recruiting in this field.
What are the types of AI?
Artificial intelligence can be broadly classified into weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job while Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like.
Weak artificial intelligence, also known as narrow AI or applied AI, refers to systems that are designed to perform a specific task or set of tasks. These systems are not capable of exhibiting general intelligence and are only able to perform the tasks they have been designed for. Examples of weak AI systems include voice assistants such as Apple's Siri or Amazon's Alexa, which are designed to carry out specific tasks such as answering questions or providing information. Weak AI systems are further divided into two categories:
- Reactive AI systems are those that do not have the ability to learn or adapt to new situations. These systems are only able to react to stimuli in their environment and do not have any internal memory or state.
- Limited memory AI systems, on the other hand, have the ability to learn and adapt to new situations, but their memory is limited and they do not have the ability to use past experiences to inform their decisions in the present.
Strong artificial intelligence, also known as general AI or artificial general intelligence (AGI), refers to systems that have the ability to exhibit human-like intelligence and perform a wide range of tasks. These systems are able to learn, adapt, and exhibit a high degree of intelligence and problem-solving ability. Strong AI systems can be further divided into two categories:
- Theory of mind AI systems are those that have the ability to understand and represent the mental states of other agents, including humans and other AI systems. These systems are able to understand that other agents have their own beliefs, desires, and goals, and can use this understanding to predict and influence their behavior.
- Self-aware AI systems are those that have a sense of self-awareness and are able to reflect on their own mental states and experiences. These systems are able to understand their own consciousness and are capable of self-reflection and introspection.
While self-aware AI systems are a topic of much research and speculation, they have not yet been developed and remain largely in the realm of science fiction.
What are the real world applications of Artificial Intelligence?
Google’s driverless cars and Tesla’s Autopilot features are examples of AI in the real world. Elon Musk, CEO of Tesla Motors, has suggested via Twitter that Teslas will have the ability to predict the destination that their owners want to go via learning their pattern or behavior via AI.
IBM's Watson is another example of the application of AI in healthcare. Watson is a question-answering computer system that is designed for use in the medical field, and is capable of suggesting various kinds of treatment for patients based on their medical history. Watson has been used in a number of healthcare settings to assist with diagnosis and treatment recommendations, and has been shown to be effective in improving patient outcomes.
Furthermore, here is a brief summary of some of the most common commercial business uses of AI with new ones being introduced every day!
- Banking fraud detection: AI can be used to analyze data on fraudulent and non-fraudulent transactions to predict if a new transaction is likely to be fraudulent or not. This can help banks and financial institutions to protect against fraudulent activities and improve security.
- Online customer support: AI is increasingly being used to automate customer support and voice messaging systems, providing quick and efficient responses to customer inquiries and complaints.
- Cybersecurity: Machine learning algorithms and ample sample data can be used to detect anomalies and adapt and respond to threats in real-time, helping to improve cybersecurity and protect against cyber attacks.
- Virtual assistants: Voice recognition technology is being used in virtual assistants such as Siri, Cortana, Alexa, and Google to enable users to give commands and receive information and answers in response. These systems are constantly learning and adapting based on user preferences to provide more personalized and accurate responses.
- Healthcare: AI is being used in a wide range of healthcare applications, including administration, telemedicine, assisted diagnosis, robot-assisted surgery, and vital stats monitoring. These systems are helping to improve efficiency, accuracy, and patient outcomes in the healthcare industry.
Where is AI headed?
Artificial Intelligence (AI) is a rapidly growing field that is having a significant impact on many aspects of our lives and is expected to continue to be a major driver of technological innovation in the future. The use of AI is increasing across a wide range of industries, from healthcare and transportation to finance and retail, and is expected to have a significant impact on the global economy.
As AI technologies continue to develop and become more widespread, they are likely to have an increasingly significant impact on the social setting and quality of life. This is already evident in the ways in which AI is being used to improve efficiency, accuracy, and outcomes in various sectors, as well as the potential for AI to create new sources of growth and change the way work is done. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career.
Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries.
For instance, this PWC article predicts that AI could contribute as much as $15.7 trillion to the global economy by 2035.
China and the United States are expected to be among the countries that will benefit the most from the AI boom, accounting for a large share of the global impact.
Why hire an Artificial Intelligence Engineer?
AI engineers are problem-solvers who ensure that AI systems or infrastructures are implemented properly within an organization. If you are an aspiring AI engineer or a startup that seeks to provide AI solutions to your clientele, understanding the impact of your role is your starting point in succeeding in this field. In general AI engineers establish and achieve objectives using techniques associated with AI reasoning and uncertainty.
- Applying logic, probability analysis, and machine-learning concepts to problem-solving initiatives.
- Analyzing systems to effectively monitor and control development projects.
- Using AI best practices in regards to applications in speech recognition, data processing, data mining, and robotic control.
Depending on the Industry, AI Engineers Can Have Many Specific Roles and Responsibilities, such as:
- Designing Software - AI engineers should have deep knowledge of machine learning, including model building and validation. They also decide whether a model is ready for deployment and if it needs to be retained or replaced. The ultimate goal of AI isn’t just to create machines that can learn but more importantly, to create machines that are capable of self-analysis.
- Creating & Deploying AI Algorithms - AI-based systems run based on algorithms. These intelligent algorithms, combined with iterative processing, enable the software to learn on its own. AI engineers write codes needed for an AI machine to function. Coding is the toughest part of developing an AI-based system and the engineer has to study the requirements of the product and understand what logic or algorithm should be applied before writing the code
- Build Infrastructure for Data Science - AI is widely used in data extraction and analysis. AI software engineers ensure that the environments created during the development of the product are easily replicated and managed after the final product. They are also responsible for setting up and managing the production and development of AI infrastructure
- Analyzing Data - Engineers gather data and run it against machine learning algorithms to identify common pitfalls. They collaborate with data scientists and architects, as well as business analysts to ensure that the analytics back-end corresponds to the business objectives
- Natural Language Processing - NLP is the field of study centered on improving the ability of humans and computers/machines to communicate. The goal is to improve the way machines respond to the human voice or command. Just like in machine learning, NLP makes use of big data and algorithms to work. Prominent examples of NLP are the voice assistants like Siri and Alexa.
- Image & Video Processing - With image and video processing algorithms, machines and robots can analyze what they see and react accordingly. From the engineer’s perspective, this means that machines can identify structural problems and other issues in any manufacturing or production process.
Overall, AI engineers play a crucial role in helping organizations to implement and optimize AI systems to achieve their objectives and stay competitive in an increasingly technology-driven world.
What technologies do Artificial Intelligence engineers know?
AI engineers use a wide range of tools and technologies to design, build, and maintain AI systems. Some common tools and technologies used by AI engineers include:
- Machine learning frameworks: Machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn are used by AI engineers to develop and train machine learning models. These frameworks provide a set of tools and libraries for creating and training machine learning models, including algorithms for data processing, feature extraction, and model training.
- Natural language processing libraries: Natural language processing (NLP) libraries such as NLTK, spaCy, and GPT-3 are used by AI engineers to build systems that can understand and process human language. These libraries provide tools and algorithms for tasks such as tokenization, part-of-speech tagging, and named entity recognition.
- Robotics libraries: Robotics libraries such as ROS (Robot Operating System) and Gazebo are used by AI engineers to build and control robotic systems. These libraries provide tools and algorithms for tasks such as robot simulation, motion planning, and robot control.
- Data visualization tools: Data visualization tools such as Matplotlib, Seaborn, and Plotly are used by AI engineers to visualize and analyze data. These tools allow AI engineers to create charts, plots, and other graphical representations of data.
What Programming Languages Shouldn't be Used for AI?
Some languages aren't well-suited for AI development. These are generally niche languages or languages that are too low-level.
- PHP. PHP is a scripting language that's popular for web development. However, it's not flexible or robust enough for AI development.
- Ruby. Ruby is another scripting language that's popular for web development. But unlike Python, Ruby isn't great at rapid prototyping — it will take longer to create a working AI system.
- C. C is a popular low-level language among system administrators and embedded system developers. However, it's not well-suited for AI development because it doesn't have many high-level features.
- Fortran. Fortran is chiefly popular among scientists and engineers. However, it's hard to learn and doesn't provide many quality-of-life features, making development difficult.
In general, AI engineers should be proficient in at least one programming language and should be familiar with the tools and libraries that are commonly used in the field of AI, such as machine learning frameworks, natural language processing libraries, and robotics libraries.
It is also important for AI engineers to have strong problem-solving skills and be able to think critically and creatively to solve complex AI challenges.
Why hire an AI Engineer versus using third party tools?
There are a few reasons why you might want to hire an AI engineer rather than relying on third-party tools:
- Customization: An AI engineer can help you build custom models that are tailored specifically to your business needs. This allows you to have more control over the functionality of your AI solution and to make sure it meets your specific requirements.
- Integration: An AI engineer can help you integrate your custom models with your existing systems and processes. This can be important if you need to integrate your AI solution with other tools or if you need to ensure that it fits seamlessly into your workflow.
- Maintenance: An AI engineer can help you maintain and update your custom models over time. This can be important if you need to make changes to your AI solution or if you need to ensure that it stays up to date with the latest advancements in the field.
- Expertise: An AI engineer has the expertise and knowledge necessary to build and maintain custom AI models. This can be valuable if you don't have in-house expertise in this area or if you need guidance on how to approach a particular problem.
Overall, hiring an AI engineer can be a good option if you need a custom, integrated, and well-maintained AI solution that meets your specific business needs. However, if you only need a simple, off-the-shelf AI solution, then using third-party tools may be a more cost-effective option as AI engineers are extremely costly!
How to find Artificial Intelligence Engineers?
LinkedIn remains the best place to look for Artificial Intelligence Engineers or Data Scientists, at least in the United States and Canada. Their Recruiter search has several fields you can use to search candidates, including title, location, current organization, years of experience etc. and by creating a boolean the search can be narrowed down.
To know how to create a boolean Linkedin checkout - Boolean Help
In addition to searching yourself on LinkedIn, here are some other ways to find and engage with AI Engineers:
- Job boards - AI Job Board, AI Job Hunt, AI & ML Jobs, Kaggle, Careerbuilder, Upwork, LinkedIn, Dice, Monster, Workable, SimplyHired, Glassdoor, Remoteok.io (Remote), Craigslist (freelance) and Ladders, to name a few. Job boards will typically yield more active candidates vs. the higher-quality passive candidates on LinkedIn, but they can still be a good source.
- Staffing companies - you can utilize staffing companies like Rocket, RobertHalf, Randstad and others to help you find a proficient AI Engineer.
- AI sourcing - you can use tools like Hireflow or Fetcher to source AI Engineers for you to engage.
Competing in the age of Artificial Intelligence
There are many ways in which companies can leverage the power of AI to gain a competitive advantage. Some of the ways in which Ant Financial Services Group has done this include:
- Using AI to analyze data from its mobile payments platform (Alipay) to make informed decisions about which businesses to enter and how to optimize their operations.
- Leveraging AI to automate many of its processes, including customer service, risk assessment, and fraud detection, which allows the company to serve a large number of customers with a relatively small number of employees.
- Using AI to personalize the customer experience by analyzing customer data and tailoring products and services to individual customers' needs.
- Investing in AI research and development to stay at the forefront of the field and to continuously improve the capabilities of its AI systems.
Other companies can follow a similar approach by identifying areas of their business where AI could be used to improve efficiency, reduce costs, or enhance the customer experience, and then investing in the necessary infrastructure and expertise to implement AI solutions. It's also important for companies to stay up to date on the latest developments in AI and to continuously monitor and adapt their AI strategies to stay competitive.
Here are a few additional ways that a company can leverage the power of AI:
- Automating repetitive tasks: AI can be used to automate tasks that are time-consuming or repetitive, freeing up employees to focus on more complex and value-added work.
- Enhancing decision-making: AI can help companies make more informed and accurate decisions by analyzing large amounts of data and identifying patterns and trends that may not be immediately apparent to humans.
- Improving efficiency: AI can help companies streamline their processes and optimize their operations, resulting in increased efficiency and productivity.
- Enhancing the customer experience: AI can be used to personalize the customer experience by analyzing customer data and providing recommendations or tailored content.
- Improving prediction and forecasting: AI can be used to make more accurate predictions and forecasts based on data analysis, which can help companies plan for the future and make strategic decisions.
By leveraging the power of AI in these ways, companies can gain a competitive advantage over their rivals and drive business growth.
Sample Interview questions for Artificial Intelligence Engineers
Here are some sample interview questions for AI engineers based on our experience at Rocket.
Logic & Algorithms
- Give a briefing about ANN (Artificial Neural Network) and explain its layers
- What is Markov’s Decision process?
- Define Hyperparameter
- Define reward maximization
- Define fuzzy logic and its applications?
- What is the Minimax Algorithm?
- What is a Backpropagation Algorithm?
- Name the extraction techniques for dimensionality reduction.
- What is a heuristic function, and where is it used?
- What are the eigenvalues and eigenvectors?
- What is a uniform cost search algorithm?
- Explain Alpha–Beta pruning
- Explain the relevance of Turing tests in AI
- How is Game theory important in Artificial Intelligence?
- Explain the design of a Chatbot?
- What are parametric and non-parametric models
- Explain the Hidden Markov model?
- What is an Artificial neural network? Explain some commonly used Artificial Neural networks
- Explain different ways to evaluate the performance of the ML model
- Give the steps for A* algorithm
Programming Languages & Tools
- What are different platforms for Artificial Intelligence (AI) development?
- Implement A* Algorithm search method using any of your favorite programming languages?
- Implement Depth First Search using C++ or Prolog
- Implement Recursion using C++
- Implement a Hash table using Java
Behavioral / Soft Skills
- Suppose you have to explain to a beginner how a face detection system works. How would you do that?
- “Customers who bought this also bought this”, you might have seen this when shopping on Amazon. How do you think this works?
- Suppose you know a farmer. He tells you that despite working hard in the fields, his crop yield is deteriorating. How can AI help him?
- If you are starting a new business, how will you use AI to promote your business?
Hope you found this blog post a useful start in your journey of recruiting AI engineers.
Rocket pairs talented recruiters with advanced AI to help companies hit their hiring goals and knows technology recruiting inside out. Rocket is headquartered in the heart of Silicon Valley but has recruiters all over the US & Canada serving the needs of our growing client base across engineering, product management, data science and more through a variety of offerings and solutions.