Why Advanced Technologies now?
There has been a recent rapid rise in interest in advanced technologies, of which AI has been surrounded by a high level of hype. Most of these advanced technologies are not new, but the current emergence is made possible based on the latest technological advancements. Until recently, only government entities and research institutions had access to the computational power needed to use AI and machine learning processes. Following Moore’s Law, the computing power available has grown immensely, while costs at the same time have been decreasing, which has allowed for the emergence of AI and other advanced technology to be used on a much broader scale. Other technological advances which also have allowed for this emergence, includes GPU’s, cloud storage, and access to big data.
Artificial intelligence is a sub-field of computer science advanced technology with the goal of enabling computers to perform tasks that are normally done by humans, both complex decision making and simple reoccurring tasks. The development of systems that can carry out tasks based on intelligent decisions has been increasing rapidly in the recent years.
- Strong vs. Weak AI
- Narrow vs. General AI
- General Applications
Strong AI has the aim of building a system/machine that genuinely simulates human reasoning. This system’s intellectual ability would be indistinguishable from a human with consciousness, sentience and mind. On a lower level, weak AI is non-sentient AI that is focused on one narrow task. Weak AI systems have no genuine intelligence or self-awareness. These systems are already a present day reality.
Another distinction that can be made between Advanced technology AI systems is narrow vs. general. Narrow AI are systems that are designed for a specific task, such as spam filters or self-driving cars. General AI is a system that is designed for the ability to reason in general. These systems would be able to do both filter spam and drive a car.
- Speech recognition
- Self-driving cars
- Customer service
- Medical diagnosis
- Fraud detection
- Robot navigation
Machine learning is a type of data analysis that gives the computer the ability to learn without being explicitly programmed. Also known as predictive analytics or predictive modeling, it is a subfield within artificial intelligence advanced technology. Generally, algorithms are constructed to learn from and make predictions based on data. Using sample inputs to build a model, the machine learning algorithms overcome following strictly static program instructions by making data driven predictions and decisions.
- Supervised vs. Unsupervised Learning
- Common Machine Learning Algorithms
- Model Selection
- Applications for Machine Learning
Two of the primary types of learning within machine learning are supervised and unsupervised learning. Supervised learning is when the computer is given a training data set as input and then the desired outputs. A model is trained until it achieves the desired level of accuracy on the training data. With unsupervised learning, unlabeled data is given to the learning algorithm. This method is used to find patterns or data clusters that are unknown to the user.
- Supervised Regression
- Simple and multiple linear regression
- Decision tree or forest regression
- Artificial Neural networks
- Ordinal regression
- Poisson regression
- Nearest neighbor methods
- Supervised Two-class and Multi-class Classification
- Logistic regression and multinomial regression
- Decision tree, forest and jungles
- Support vector machine
- Perceptron methods
- Bayesian classifiers
- One versus all multiclass
- K-means clustering
- Hierarchical clustering
- Anomaly Detection
- Support vector machine (one class)
- Principle component analysis
Selecting the correct model is imperative to get the most out of your data and analysis. Generally, one would begin with a simple model and then increase the complexity when necessary. Some things to take into account when choosing a model include interpretability, simplicity, accuracy, speed and scalability.
- Fraud detection
- Image detection
- Customer segmentation
- Recommender systems
- Natural language processing
- User behavior analytics
- Speech and handwriting recognition
Data science is an interdisciplinary field about scientific methods, processes and systems used to extract knowledge from data. It draws from many fields including mathematics, statistics, information science and computer science. Advanced technology like Data science is used to analyze massive amounts of data and extract lots of knowledge and value from it. With recent technology progress, data is everywhere and is found in exponentially increasing quantities. Data science can put this data into new business value for organizations.
- Big Data
- Data Mining
Big data is a term that describes the large volume of data that comes into a business every day. These data sets are so large and complex that traditional tools are inadequate to deal with it. Using data science methods to analyze this data can have real business value for many companies.
Data mining is a process that is used to turn raw data into useful information. It involves the analysis of data for relationships that have not previously been discovered. While the term mining implies extraction of data, the focus of data mining is actually on the extraction of patterns and knowledge from large amounts of data.
- Targeted Advertising on digital platforms
- Search engines
- Recommender systems
- Image and speech recognition
- Fraud and risk detection
- Delivery logistics
Robotics is a branch of engineering and computer science that deals with the design, construction, operation and use of robots. Additionally, it encompasses the computer systems for their control, sensory feedback and information processing. Robotics can be substituted for humans working in dangerous environments, repetitive process and other labour intensive processes. AI, machine learning and data science are all fundamental parts of the advancement of robotics and advanced technology.
- Up and Coming Trends
- Fields within Robotics
- Smarter Learning: Robots are learning more efficiently and quickly. Deep learning has made a big impact on image, speech and video content recognition.
- Knowledge Sharing: Robots will begin to teach each other so that two robots with completely different roles can teach each other what they know.
- Personal Robots: Home helpers and companions will soon be common place with robots becoming cheaper and software becoming more capable.
- Drones: Increasingly smart and autonomous drones will fill the skies. Check out 2021.AI’s Dronuts here.
- Factory robots
- Military robots: such as bomb defusing robots
- Medical robots: able to perform low-invasive surgery
- Household robots
- Agricultural robots
- Autonomous robots
- Cloud robotics
- Cognitive robotics
- Multi-agent systems
- Soft robotics