Artificial Intelligence (AI): its definition - the functionality available with it and its operation
In the 1950s, the field's founders,Hyman Minsky (Hyman Minsky)andJoseph McCarthy, described (Joseph McCarthy)Artificial intelligence is any task performed by a machine that was previously viewed as requiring human intelligence.This is obviously a somewhat loose definition, That's why you'll sometimes see arguments about whether something is truly artificial intelligence or not.
Modern definitions of what it means to create intelligence are more specific.
Said Francois Chollet, researcher Artificial Intelligence at Google and founder of the Machine Learning Software Library (Keras), intelligence is related to a system's ability to adapt and improvise in a new environment, to generalize its knowledge and apply it to unfamiliar scenarios. :
"Intelligence is the ability with which you acquire new skills in tasks for which you were not prepared before. It is not considered a skill in itself, nor what you can do, but rather how well and efficiently you learn new things."
Under the above definition, modern AI-powered systems, such as virtual assistants, can be described as having demonstrated"narrow AI"; It is the ability to generalize their training when performing a limited set of tasks, such as speech recognition or computer vision.
AI systems typically exhibit at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, movement, and manipulation (and to a lesser extent: social intelligence and creativity).
Artificial intelligence (AI) promises to deliver some of the most amazing innovations of this century. Self-driving cars, robotic personal assistants, and automated disease diagnosis are all the result of the emerging artificial intelligence revolution that will reshape the way we live and work. With the demand for talented engineers doubling in the past few years, there are unlimited opportunities for professionals who want to work within and develop artificial intelligence.
While jobs designing and improving AI applications are on the rise, some analysts expect these efforts to significantly disrupt the economy. This is because AI systems can process limitless amounts of data, and humans - the millions of people in today's job market - are simply not up to the task.
A recent reportfrom the McKinsey Global Institute indicates that about a third of the American workforce will face the risk of being laid off by 2030. Workers in industries Data-intensive professionals are particularly at risk, including financial and administrative professionals, legal support staff, marketing content writers, and IT workers.
Although it is unclear what jobs might be lost and how many new ones would be created.The World Economic Forum expects artificial intelligence to lead to a net increase of 58 million jobs globally. .
But beyond the impact the new AI economy will have on the workforce of the future, college students and young professionals will benefit from entering this burgeoning field. However, breaking into the field of artificial intelligence is not as simple as learning computer science or obtaining a university degree. But it requires initiative, courage and knowledge. In fact, >50% of senior AI professionals report a skills gap, a "talent crisis" Genuine, according toErnst & Young.
Learn Artificial Intelligence: Intelligence is required
AI has a high learning curve, but for motivated students, what they stand to gain from the AI market far outweighs the amount of time and effort they invest while learning. Success in this field typically requires a bachelor's degree in computer science or a related discipline such as mathematics. Higher positions may require a master's or doctoral degree, although a college degree is no longer a demanding requirement by major employers such as Apple. And “Google”. In general, your success will depend largely on factors that have nothing to do with formal education.
Dan Ayoub, General Manager of Mixed Reality Education at Microsoft, said:
“Curiosity, confidence, and persistence are important traits for every student thinking about breaking into an emerging field, and artificial intelligence is no exception.”
As an expert in the field of artificial intelligence, he talked about how to get a job in this field:
"Unlike careers whose path was set decades ago, AI is still in its infancy, which means you may have to forge your own path and get creative."
This means that there is no standard degree or curriculum for AI. Some universities may not offer a specific set of courses for a major or specialization in AI, while those with dedicated AI programs may have unique approaches to the specialization.
Artificial Intelligence courses and curricula
Computer Science courses (in addition to learningthe basics of data science, machine learning, and Java ) is a good starting point. There are a number of new undergraduate and graduate programs emerging every day that are designed to prepare a person to specialize in artificial intelligence.
As we note below, AI consists of several overlapping disciplines. Understanding statistical methods, for example, is as important as having a background in computer science. In addition to the topics listed here, it may be useful to take interdisciplinary courses in areas such as cognitive science to provide a conceptual framework for AI applications.
A sample of basic materials in the artificial intelligence curriculum
Mathematics and statistics
- linear algebra.
- Calculus.
- Matrices and linear transformations.
- Integration and approximation.
- Modern decline.
- probability theory.
- Bayesian networks.
- Probabilistic graphical models.
Computer Science
- Computer systems and programming.
- Principles of necessity calculation.
- Principles of functional programming.
- Data science basics.
- Parallel and sequential data structures and algorithms.
- Logical programming and arithmetic logic.
- Software development.
Specialized materials
- Machine learning, deep learning and reinforcement learning.
- Information theory, inference and learning algorithms.
- Neural networks for machine learning.
- Artificial intelligence representation and problem solving.
- Natural language processing.
- Computer vision and image analysis.
Once you've mastered some of the basics, find the subfields of AI that interest you most and shape your curriculum accordingly. The following list shows more specialist subjects that you may take as electives during your degree; These topics are also worth exploring at any stage of your career.
Additional classes may be available to teach students specific applications of AI in fields such asbiology, healthcare, and neuroscience.
A sample of specialized AI material collections
Machine learning
- Deep reinforcement learning and control.
- Applied machine learning.
- Machine learning for text extraction.
- Advanced data analysis.
Decision-making robots
- Neural computation.
- Independent agents.
- Cognitive robots.
- Strategic thinking for artificial intelligence.
- Robot movement and dynamics.
Linguistics
- Information retrieval and search engines.
- Speech processing.
- Computational visualization.
- Computational photography.
- Vision sensors.
But there is interaction
- Human-centered systems design.
- Human-robot interaction.
- Automated manipulation.
- Safe and interactive robots.
Whether you are a university student or already working, it is important to define your AI study curriculum in advance. As Ayoub explained,
"Schools like Carnegie Mellon, Stanford, and MIT are just a few that have designed paths for those who want to work in artificial intelligence, but there are many others.
There are also Supplemental programs can help a mid-career person retrain to move into a career in AI."
For example, Microsoft recently announceda track for Microsoft Professionals, which is part of a larger effort that includes a developer-focused AI school . Available online to anyone, the programs provide job-ready skills and real-world experience for engineers and others looking to improve their AI and data science skills through a series of online courses featuring hands-on labs and expert instructors.
When it comes to the best jobs of the future, few industries stand out as much as artificial intelligence. A 2019 Gartner reportshowed that enterprise applications of AI have grown 270% in four years, creating demand that exceeds current supply. For qualified job candidates.
This is great news for professionals looking for machine learning jobs and related careers in artificial intelligence. The number of industries using artificial intelligence is increasing to the point where no major organization will be affected by this rapidly developing technological revolution.
Artificial intelligence jobs and salaries
Industry analysts and technologists are closely monitoring trends in artificial intelligence (AI). Even students planning a career in this dynamic field are closely monitoring developments
According to the survey, here are the 10 highest-paying AI jobs and their average salaries in the US, according to Indeed:
1. Director of analytics
An Analytics Manager is the person primarily responsible for managing the data analytics and data warehousing departments, overseeing all activities and ensuring alignment with the company's vision and goals. The Analytics Manager directs the management, development, and integration of data analytics and business intelligence needed to support the mission, vision, strategies, and business objectives.
In addition, he organizes and brings together the manpower, technology, processes, and financial resources needed to meet the company's current and future analytics needs. a>
The Analytics Manager realizes that information and data are one of the company's most important assets, so he works to apply data and information in order to provide optimal performance. The Analytics Manager is also a member of the company's Executive Committee and has a role in Participate within various committee meetings as necessary to influence data capabilities and competencies within the company.
Average salary: $140,837 annually
2. Principal scientist
The principal scientist is responsible for planning and conducting experiments and investigations. They often work from a laboratory (they may also work at universities, government laboratories, pharmaceutical companies, research organizations, chemical companies, or environmental agencies). Principal scientists conduct experiments in areas such as medical research, earth sciences, biological research, chemistry research, or pharmacology.
A scientist's main ultimate goal: to provide new information or explain the causes of various phenomena.
Successful principal scientists have excellent communication skills, research and report writing skills, analytical skills, and relevant technical skills. It is also preferable to have in-depth knowledge of the legal and regulatory laws in their field of work.
Between 2018 and 2028, experts expect demand for (major global) to grow by 8% and provide 10,600 jobs across the United States alone.
Average Salary: $138,271 Per Year
3. Machine learning engineer
In practical terms, the job of a machine learning engineer is close to that of a data scientist. Both positions work with vast amounts of information, and their work requires exceptional data management skills and the ability to perform complex modeling on dynamic data sets.
But that's where the similarity ends. Data professionals produce insights, usually in the form of charts or reports that are presented to a human audience. On the other hand,machine learning engineers design self-running software to automate predictive models. Each time the program performs an operation, it uses those results to perform future operations with a greater degree of accuracy. This is how he "learns" Software/hardware
A well-known example of machine learning is the recommendation algorithm for Netflix and other consumer-driven services. Every time a user watches a video or searches for a product, these sites add more data points to their algorithm. As the volume of data increases, the algorithm's recommendations to the user about other content become more accurate (all without any kind of human intervention).
Machine learning is closely related to artificial intelligence, and includes deep learning (DL). This subfield uses artificial neural networks to “think” And solve complex problems with multi-layer (deep) datasets. Some common examples of deep learning include virtual assistants, translation apps, chatbots, and driverless cars. Over time, these techniques will become more accurate and practical.
Average Salary: $134,449 Per Year
4. Computer vision engineer
A computer vision engineer spends his time researching biological vision, developing machine learning, deep learning, and artificial intelligence. While some computer vision engineers undoubtedly work hard to research and study these topics for the purpose of advancing technology, the vast majority of computer vision engineering jobs involve work in electronics, e-commerce, and aerospace applications.
This does not mean that these jobs do not require research and study to make improvements to computer vision systems, but the majority of these computer science professionals are not working full-time to solve the real problem of computer vision and artificial general intelligence. Rather, the majority of computer vision engineer jobs focus on developing applications, improving computer vision systems, and writing algorithms.
Average salary: $134,346 annually
5. Data scientist
In simple terms, a data scientist's job is to analyze data—and I mean data, of course!—to gain actionable insights.
Data scientist tasks include:
- Identify data analysis problems that provide the organization's greatest opportunities.
- Identify the correct data sets and variables.
- Collect large sets of structured and unstructured data from various sources.
- Filter and verify data to ensure accuracy, completeness, and uniformity.
- Develop models and algorithms and apply them to extract stores Big Data.
- Analyze data to identify patterns and trends.
- Interpreting data to discover solutions and opportunities.
- Communicate results to stakeholders using visual presentations and other media.
In general, a data scientist is someone who knows how to extract meaning from data and interpret it, which requires tools and methods from statistics and machine learning, which is one of the tasks that artificial intelligence will probably not be able to fill on a daily basis, because a data scientist spends a lot of time in the process of collecting data. Organizing and managing it. This process requires persistence, statistics, and software engineering skills (skills that are also necessary to understand biases in data and to debug code output).
Average salary: $130,503 annually
6. Data engineer
Data engineers build data pipelines that transform raw, unstructured data into formats that data scientists can use for analysis. They are responsible for creating and maintaining the analytics infrastructure that relates to (almost) all functions that handle data. This includes architectures such as databases, servers, and large-scale processing systems.
Data engineers are expected to:
- Create and maintain optimal data collection plan structure.
- Collect large, complex data sets that meet business requirements.
- Identify, design and implement internal process improvements.
- Improve data delivery and redesign infrastructure to increase scalability.
- Design the infrastructure required to optimally extract, transform and load data from a variety of data sources using SQL and AWS technologies.
- Build analytics tools that use data plans to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
- Work with internal and external stakeholders to assist with data-related technical issues and support data infrastructure needs.
- Create data tools for analysis team members and data scientists.
Average salary: $125,999 annually
7. Algorithm engineer/developer
An algorithm engineer is generally responsible for developing algorithms, which are technical pieces of computer code that produce specific results in many different fields.
Some experts may call these "high-tech programmers" because algorithms are often the most technical and complex parts of web/technology projects. An algorithm developer often works from a specific problem or goal, and builds specific algorithms to address the problem or achieve specific results.
One way to understand what algorithm engineers do is to compare them with other web programmers or computer programmers who work primarily on interfaces and other user-oriented work. Web/software designers sometimes don't focus on any of the technical functionality of the product. While algorithm developers always focus on functional code that allows the display of "intelligence" Technology.
The tasks of an algorithm engineer include:
- Create cost-effective and scalable systems, and develop innovative algorithmic solutions.
- Test innovative ideas and work in a creative environment.
- Evaluating, maintaining and updating new and old systems.
- Develop an algorithm system that records all processes and can be maintained by the team.
- Manage the design, development and deployment of a scalable, real-time system.
- Research algorithm improvements and perform data processing.
- Assist the project team in communicating and implementing project schedules.
- Creating improved algorithms for finger detection systems in mobile phones and laptops.
- Designing and developing algorithms and programs to correct visual vision.
- Design, implement and maintain GDSII software layouts and graphical interfaces.
- Design and implementation of video enhancement algorithms.
- Research and analyze video processing technology and develop proposals for video algorithms.
Average salary: $104,112 annually
8. Computer scientist
GASUP scientists typically work with a wide range of computing technology concepts and tools, either to find ways to innovate how we use existing technologies to develop breakthroughs in entirely new computing technology.Examples of the type of breakthroughs can include What they've been working on recently are things like machine learning, artificial intelligence, and the Internet of Things.
In addition to working on creating practical digital tools with the goal of patenting them and later selling them, or using them in a variety of different computer solutions, computer scientists also work academically on virtual solutions and innovations, creating algorithms that can later be applied to computer programs. /span>As such, the job description of a computer scientist can change from place to place.
Computer scientist tasks often include things like: Exploring a range of problems and challenges in computing in order to develop theories and tools to solve them. They often work with scientists on complex problems that require a lot of computing power, or to create new computing languages, algorithms, and tools to make digital technology systems more efficient.
Many computer scientists work on virtual experiments, creating, running, and testing new solutions, and then analyzing and reporting the results of those experiments.
Average salary: $97,850 annually
9. Statistician
In short, a statistician applies statistical methods and models to real-world problems. It collects, analyzes and interprets data to assist in many decision-making processes within companies.
According to the US Bureau of Labor Statistics, the job outlook for this field is very positive. He hopes that overall employment for mathematicians and statisticians will grow by 30% between 2018-2028 (which is almost five times the growth for all professions!).
In general, a statistician often works to interpret data in a way that can inform organizational and business strategies; For example, by understanding changes in consumer behavior and purchasing trends.
On the other hand, in the public sector, analytics often focus on promoting the public good; For example, by collecting and analyzing environmental, demographic, or health data.
A statistician's duties include:
- Collect, analyze and interpret data.
- Identify trends and relationships in data.
- Designing data collection processes.
- Communicating results to stakeholders and decision makers.
- Providing advice on organizational and business strategy.
- Assistance in decision making.
Average salary: $83,731 annually
10. Research engineer
The field of engineering research can vary greatly depending on the engineering discipline itself. However, a research engineer typically works within the research and development department of an organization, government agency, or academic institution. In general, research engineers develop products, processes, or technology for their employer. To achieve this, they collect relevant information, data or samples, then analyze their research and conduct tests to create optimal and innovative solutions.
Some of the industries or fields in which research engineers work include (medical or health care - transportation - military - computer hardware and software - industrial and commercial product development - energy/oil, gas, renewable energy, mining, etc.)
A research engineer's duties vary by field. However, some general or shared responsibilities include:
- Conduct research to identify solutions to industry problems.
- Develop concepts for existing or new products, processes, or equipment relevant to their industry.
- Design industry-relevant products, equipment, or technology based on concepts and ensure functionality.
- Build product and system prototypes for testing purposes.
- Use specialized equipment to analyze and test processes, equipment, or materials.
- Use statistical processes to evaluate data.
- Managing and leading members of design or research teams.
- Coordinate schedules, resources, and tasks to achieve project objectives as project leader.
- Preparing reports summarizing tests and their results.
- Writing research and grant proposals.
Average salary: $71,600 annually
