How is artificial intelligence being applied
Augmented intelligence vs. artificial intelligence
Some industry experts believe the term artificial intelligence is too closelylinked to popular culture, and this has caused the general public to haveimprobable expectations about how AI will change the workplace and life ingeneral. Some researchers and marketers hope the label augmented intelligence,which has a more neutral connotation, will help people understand that mostimplementations of AI will be weak and simply improve products and services.The concept of the technological singularity — a future ruled by anartificial superintelligence that far surpasses the human brain’s ability tounderstand it or how it is shaping our reality — remains within the realm ofscience fiction.
Ethical use of artificial intelligence
While AI tools present a range of new functionality for businesses, the use ofartificial intelligence also raises ethical questions because, for better orworse, an AI system will reinforce what it has already learned.This can be problematic because machine learning algorithms, which underpinmany of the most advanced AI tools, are only as smart as the data they aregiven in training. Because a human being selects what data is used to train anAI program, the potential for machine learning bias is inherent and must bemonitored closely.Anyone looking to use machine learning as part of real-world, in-productionsystems needs to factor ethics into their AI training processes and strive toavoid bias. This is especially true when using AI algorithms that areinherently unexplainable in deep learning and generative adversarial network(GAN) applications.Explainability is a potential stumbling block to using AI in industries thatoperate under strict regulatory compliance requirements. For example,financial institutions in the United States operate under regulations thatrequire them to explain their credit-issuing decisions. When a decision torefuse credit is made by AI programming, however, it can be difficult toexplain how the decision was arrived at because the AI tools used to make suchdecisions operate by teasing out subtle correlations between thousands ofvariables. When the decision-making process cannot be explained, the programmay be referred to as black box AI.
Four types of artificial intelligence
Arend Hintze, an assistant professor of integrative biology and computerscience and engineering at Michigan State University, explained in a 2016article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentientsystems, which do not yet exist. The categories are as follows: * Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones. * Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way. * Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams. * Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
Cognitive computing and AI
The terms AI and cognitive computing are sometimes used interchangeably, but,generally speaking, the label AI is used in reference to machines that replacehuman intelligence by simulating how we sense, learn, process and react toinformation in the environment.The label cognitive computing is used in reference to products and servicesthat mimic and augment human thought processes
Examples of AI technology
AI is incorporated into a variety of different types of technology. Here aresix examples: * Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA’s tactical bots to pass along intelligence from AI and respond to process changes. * Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms: * Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets. * Unsupervised learning. Data sets aren’t labeled and are sorted according to similarities or differences. * Reinforcement learning. Data sets aren’t labeled but, after performing an action or several actions, the AI system is given feedback. * Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn’t bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision. * Natural language processing. This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it’s junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition. * Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings. * Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.
What exactly is artificial intelligence?
Sometimes called machine intelligence, artificial intelligence is thesimulation of human intelligence processes by machines. These processesinclude learning, reasoning and self-correction. Expressed differently,artificial intelligence makes it possible for machines to learn fromexperience, adjust to new inputs and perform human-like tasks.Although the term was first coined in 1956, artificial intelligence has becomemuch more popular in recent years thanks to developments in increased datavolumes, advanced algorithms, and improvements in computing power and storage.
How is artificial intelligence being applied?
To date, AI has found application across a wide range of industries — mostnotably: * Healthcare: In this field, AI applications are able to provide faster and more accurate diagnosis through mining patient data and other available data sources, as well as acting as personal healthcare assistants, reminding patients to take their pills, exercise or eat healthier. * Education: AI in education offers the opportunity to automate the large number of repetitive tasks, such as grading, allowing educators more time for higher-value activities. AI is also able to provide additional support to students, ensuring they stay on track, even replacing the need for teachers in certain instances. * Finance: Within finance, AI techniques are being used to identify potentially fraudulent transactions, as well as adopting faster and more accurate credit scoring. * Manufacturing: An application of AI within manufacturing is its ability to quickly and accurately analyse IoT data as it’s received from connected devices, thus allowing the forecasting of expected load and demand using recurrent networks. * Law: Artificial intelligence in this sector is able to automate the often overwhelming task of sorting through documents to advance legal proceedings. * Automotive: Within the automotive industry, a well-known example of AI has been the evolution of self-driving cars through a combination of computer vision, image recognition and deep learning.The value in AI is in its ability to automate repetitive learning anddiscovery through data analysis, whilst achieving incredible accuracy andadapting through progressive learning.
Artificial Intelligence Questions: Categories
Because it’s a broad area of computer science, AI questions will keep poppingup in various job interview scenarios. To make it easier for you to navigatethrough this space, we have curated a list of questions about artificialintelligence and divided them into multiple categories.So whether you’re hoping to move up the data science career ladder or lookingto start your first machine learning internship, make sure that you brush upon these AI interview questions and answers so you can walk into your nextinterview oozing confidence.
Artificial Intelligence Questions: Introduction to AI
If your AI interview is for an internship, there’s a good chance that theinterviewer will try to break the ice and make you feel more comfortable byasking some “simple” general interest questions.These types of questions usually cover the basics, so even if they soundstraightforward, you have to make sure that you don’t get stumped (seeminglysimple questions require that your answer be delivered easily and flawlessly).However, it can quickly get more involved, so you have to be ready forwhatever they throw at you.Related: The Most Common Machine Learning Terms, Explained
1. What is artificial intelligence?
AI can be described as an area of computer science that simulates humanintelligence in machines. It’s about smart algorithms making decisions basedon the available data.Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic humanintelligence at lightning speed (and with a reduced rate of error).More reading: What is AI? Everything you need to know about ArtificialIntelligence
Artificial Intelligence Questions: Statistics
AI, ML, and data science all have a great deal of overlap, so it’s crucial tocover all bases before your AI interview. However, it’s important to note thatthese fields aren’t interchangeable. Although everything is relative, AIproduces actions, ML produces predictions, and data science produces insights.So what kind of potential data science-related AI questions should you beprepared for? Let’s take a look.
Artificial Intelligence Questions: Programming
AI interview questions are bound to enter the sphere of programming soonerrather than later. So let’s dive right into it with the following AI questionsand answers.