1. Artificial Intelligence History
Artificial intelligence is a term that was coined in 1956 by John McCarthy but has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. Over the years, Hollywood producers have been making AI movies and science fiction novels depicting Artificial Intelligence as human-like robots that take over the world but conversely AI engineers have made it evolve to provide many specific benefits in every industry including healthcare and more.
2. What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. This technology makes computers “smart”. They work on their own without being encoded with commands. In practice, the term “artificial intelligence” means a program which mimics human cognition. Basically human aspects like learning and problem-solving can be done by computers, though the ideal intelligent machine is a flexible agent which perceives its environment and takes actions to maximize its chance of success at some goal or objective.
3. Categories of Artificial Intelligence
Artificial Intelligence can be categorized as either weak or strong.
- Weak AI: also known as narrow AI, is a system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI.
- Strong AI: also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously. AI chatbots function in this manner.
4. Components of Artificial Intelligence
As the hype around Artificial Intelligence has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.
AI as a service (AIaaS)
Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings or providing access to artificial intelligence as a service (AIaaS) platforms. AIaaS allows individuals and companies to experiment with AI for various business purposes and sample multiple platforms before making a commitment. Popular AI cloud offerings include the following:
- Amazon AI
- IBM Watson Assistant
- Microsoft Cognitive Services
- Google AI
6. Types of Artificial Intelligence
With the recent rapid growth in AI Technology, engineers now design machines capable of understanding verbal commands, distinguish pictures, drive cars and play games better than we do. The classification of AI basically lies on its memory, cognitive or mind functionality.
A. Reactive machines
This is the most basic type of Artificial Intelligence systems are purely reactive and have the ability neither to form memories nor to use past experiences to inform current decisions. An example is Deep Blue; IBM’s chess-playing supercomputer capable of identifying pieces on a chessboard, anticipating next moves but doesn’t keep stock of past moves.
B. 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.
C. Theory of the 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 Artificial Intelligence will be able to infer human intentions and predict behaviour, a necessary skill for AI systems to become integral members of human teams.
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.
7. How Artificial Intelligence Works
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:
7.a. Branches of Artificial Intelligence
- Machine learning: automates analytical model building. Machine learning uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
- Neural network: is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit.
- Deep learning: uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
- Cognitive computing: is a subfield of AI that strives for natural, human-like interactions with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.
- Computer vision: relies on pattern recognition and deep learning to recognize what’s in a picture or video
- Natural language processing (NLP): is the ability of computers to analyze, understand and generate human language, including speech.
Additionally, several technologies enable and support AI:
- Graphical processing units: are key to AI because they provide the heavy compute power that’s required for iterative processing.
- The Internet of Things: generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.
- Advanced algorithms: are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
- AI automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue.
- AI adds intelligence to existing products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
- AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online.
- AI analyzes more and deeper data using neural networks that have many hidden layers.
9. Applications of Artificial Intelligence
Every industry has a high demand for AI products – especially question answering systems that can be used for legal assistance, patent searches, risk notification and medical research. Other uses include:
- Health Care: AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier.
- Retail: AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI.
- Manufacturing: AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.
- Banking: In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.
- Business: machine learning algorithms are being integrated into analytics and customer relationship management (CRM) platforms to uncover information on how to better serve customers.
- Education: AI can automate grading, giving educators more time. It can assess students and adapt to their needs, helping them work at their own pace.
- Law: Law firms are using machine learning to describe data and predict outcomes, computer vision to classify and extract information from documents.
- Security: By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations.
10. How to Become an AI Engineer
To aim at Artificial Intelligence jobs, you need to have analytical skills and the ability to solve problems with cost-effective and efficient solutions. Together with insight into technological innovations that translate to state-of-the-art programs, below is a list of skillset acquisition that will enable you to excel as an AI engineer;
- Earn a bachelor’s degree first in computer science, finance, economics or a related field
- Be proficient with some programming languages like python, C++, Java, Deep Learning & neural networks, etc.
- Business Skills to help with creative thinking
- Personal traits like analytical thinking, patience, attention to detail, etc.
- Certification Courses in Data Science, Machine Learning or Artificial Intelligence
Depending upon their level of expertise, AI Engineers in general:
- Study and transform Data Science Prototypes
- Research and Implement Appropriate ML algorithms and tools
- Develop Machine Learning Applications according to Requirements
- Working with Electric Engineers and Robotics Team
- Select appropriate Datasets and Data Representation Methods
- Run Machine Learning / AI Tests and Experiments
- Train and retrain systems When Necessary
- Keep Abreast of Developments in the Field
12. Salaries and Companies hiring AI engineers
According to Indeed, the Average Salary of an Artificial Intelligence Engineer is around $110,000 per Annum, with a minimum of $105,244 and a maximum of $144,611.
Companies that hire top AI talent range from startups like Argo AI to tech giants like IBM, Uber, Facebook, amazon, Microsoft, etc.
13. Challenges using artificial intelligence
Artificial intelligence is going to change every industry, but we have to understand its limits.
The principle limitation of Artificial Intelligence is that it learns from the data. That means any inaccuracies in the data will be reflected in the results.
Today’s AI systems are trained to do a clearly defined task. The system that detects fraud cannot drive a car or give you legal advice hence no flexibility as with humans.
In conclusion, present AI techniques are geared towards understanding memory, learning and the ability to base decisions on past experiences. But however, AI engineers are pushing the growth of the industry towards creating machines that are self-aware