Specialization: AI
What are some examples of AI in use?
Some compelling examples of AI applications are: Chatbots Facial recognition Image tagging Natural language processing Sales prediction Self-driving cars Sentiment analysis https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-what-are-the-applications-of-ai
What's the most popular programming language used in AI?
The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior. Its popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like Tensorflow and VTK. There's a chance that the interviewer might keep the conversation going and ask you for more examples. If that happens, you can mention the following: Java Julia Haskell Lisp https://www.springboard.com/blog/best-programming-language-for-ai/
What is artificial intelligence?
AI can be described as an area of computer science that simulates human intelligence in machines. It's about smart algorithms making decisions based on the available data. Whether it's Amazon's Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error). https://www.zdnet.com/article/what-is-ai-everything-you-need-to-know-about-artificial-intelligence/
Why is game theory important to AI?
Game theory, developed by American mathematician Josh Nash, is essential to AI because it plays an underlying role in how these smart algorithms improve over time. At its most basic, AI is about algorithms that are deployed to find solutions to problems. Game theory is about players in opposition trying to achieve specific goals. As most aspects of life are about competition, game theory has many meaningful real-world applications. These problems tend to be dynamic. Some game theory problems are natural candidates for AI algorithms. So, whenever game theory is applied, multiple AI agents that interact with each other will only care about utility to itself. Data scientists within this space should be aware of the following games: Symmetric vs. asymmetric Perfect vs. imperfect information Cooperative vs. non-cooperative Simultaneous vs. sequential Zero-sum vs. non-zero-sum https://www.quora.com/What-is-the-connection-between-Game-Theory-and-AI
How is Google training data for self-driving cars?
If you're interested and heavily involved within this space, this question should be a no-brainer. If you know the answer, it'll demonstrate your knowledge about a variety of ML methods and how ML is applied to autonomous vehicles. But even if you don't know the answer, take a stab at it as it will show your creativity and inventive nature. Google has been using reCAPTCHA to source labeled data on storefronts and traffic signs for many years now. The company also has been using training data collected by Sebastian Thrun, CEO of the Kitty Hawk Corporation and the co-founder (and former CEO) of Udacity. Such information, although it might not seem significant, will show a potential employer that you're interested and excited about this field. https://digit.hbs.org/submission/google-x-leveraging-data-and-algorithms-for-self-driving-cars/
How would you describe ML to a non-technical person?
ML is geared toward pattern recognition. A great example of this is your Facebook newsfeed and Netflix's recommendation engine. In this scenario, ML algorithms observe patterns and learn from them. When you deploy an ML program, it will keep learning and improving with each attempt. If the interviewer prods you to provide more real-world examples, you can list the following: Amazon product recommendations Fraud detection Search ranking Spam detection Spell correction https://www.quora.com/How-do-you-explain-Machine-Learning-and-Data-Mining-to-non-Computer-Science-people
What would you say are common misconceptions about AI?
Many AI-related misconceptions are making the rounds in the age of "fake news." The most common ones are: AI will replace humans AI systems aren't safe AI will lead to significant unemployment While these types of stories are common, they're far from the truth. Even though some AI-based technology is able to complete some tasks—for example, analyzing zettabytes of data in less than a second—it still needs humans to gather the data and define the patterns for identification. So we aren't near a reality where technology has the potential to replace us or our jobs. http://knowledge.wharton.upenn.edu/article/whats-behind-the-hype-about-artificial-intelligence-separating-myth-from-reality/
What are AI neural networks?
Neural networks in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more. https://ai.googleblog.com/2019/03/exploring-neural-networks.html
What's the difference between strong AI and weak AI?
The difference between the two is just like the terms sound. Strong AI can successfully imitate human intelligence and is at the core of advanced robotics. Weak AI can only predict specific characteristics that resemble human intelligence. Alexa and Siri are excellent examples of weak AI. Strong AI Can be applied widely Extensive scope Human-level intelligence Processes data by using clustering and association Weak AI Can be great at performing some simple tasks Uses both supervised and unsupervised learning The scope can be minimal
What's TensorFlow?
TensorFlow is an open-source framework dedicated to ML. It's a comprehensive and highly adaptable ecosystem of libraries, tools, and community resources that help developers build and deploy ML-powered applications. Both AlphaGo and Google Cloud Vision were built on the Tensorflow platform. https://appliedmachinelearning.blog/2018/12/26/tensorflow-tutorial-from-scratch-building-a-deep-learning-model-on-fashion-mnist-dataset-part-1/
What's a Turing test?
The Turing test, named after Alan Turing, is a method of testing a machine's human-level intelligence. For example, in a human-versus-machine scenario, a judge will be tasked with identifying which terminal was occupied by a human and which was occupied by a computer based on individual performance. Whenever a computer can pass off as a human, it's deemed intelligent. The game has since evolved, but the premise remains the same. http://www.psych.utoronto.ca/users/reingold/courses/ai/turing.html
What's the difference between AI and ML?
AI and ML are closely related, but these terms aren't interchangeable. ML actually falls under the umbrella of AI. It demands that machines carry out tasks in the same way that humans do. The current application of ML in AI is based around the idea that we should enable access to data so machines can observe and learn for themselves. https://interestingengineering.com/whats-the-difference-between-machine-learning-and-ai
What's a random forest? Could you explain its role in AI?
A random forest is a data construct that's applied to ML projects to develop a large number of random decision trees while analyzing variables. random forest (Source) These algorithms can be leveraged to improve the way technologies analyze complex data sets. The basic premise here is that multiple weak learners can be combined to build one strong learner. This is an excellent tool for AI and ML projects because it can work with large labeled and unlabeled data sets with a large number of attributes. It can also maintain accuracy when some data is missing. As it can model the importance of attributes, it can be used for dimensionality reduction.
What are intelligent agents?
An intelligent agent is an autonomous entity that leverages sensors to understand a situation and make decisions. It can also use actuators to perform both simple and complex tasks. In the beginning, it might not be so great at performing a task, but it will improve over time. The Roomba vacuum cleaner is an excellent example of this. https://www.sciencedirect.com/science/article/pii/S0167404817302511
Can you name the properties of a good knowledge representation system?
From the perspective of systems theory, a good knowledge representation system will have the following: Acquisition efficiency to acquire and incorporate new data Inferential adequacy to derive knowledge representation structures like symbols when new knowledge is learned from old knowledge Inferential efficiency to enable the addition of data into existing knowledge structures to help the inference process Representation adequacy to represent all the knowledge required in a specific domain https://www.slideshare.net/Vishalchd11/knowledge-representation-in-ai
What's your favorite use case?
Just like research, you should be up to date on what's going on in the industry. As such, if you're asked about use cases, make sure that you have a few examples in mind that you can share. Whenever possible, bring up your personal experiences. You can also share what's happening in the industry. For example, if you're interested in the use of AI in medical images, Health IT Analytics has some interesting use cases: Detecting Fractures And Other Musculoskeletal Injuries Aiding In The Diagnosis Neurological Diseases Flagging Thoracic Complications And Conditions Screening For Common Cancers
In your opinion, how will AI impact application development?
These types of questions help the interviewer ascertain your level of interest in the field. If you're naturally passionate about AI and everything related to it, you should have some knowledge about current industry trends. So, if you have been actively following this space, you'll know all about AIOps. In the coming months, you can expect AI to be more involved in how we build applications. It has the potential to transform how we use and manage the infrastructure at a micro and macro level. Some say that DevOps will be replaced by what they are calling AIOps because it allows developers to engage in accurate root cause analysis by combining big data, ML, and visualization. AIOps can be described as a multilayered platform that can be used to automate and improve IT operations. In this scenario, developers can leverage analytics and ML to collect and process data from a variety of sources. This information can then be analyzed in real time to identify and rectify problems. https://thenewstack.io/aiops-is-devops-ready-for-an-infusion-of-artificial-intelligence/
