AI ML DL Artificial Intelligence Machine Learning and Deep Learning
Artificial Intelligence (AI) and its subsets Machine Learning (ML) and Deep Learning (DL) are playing a major role in Data Science. Data Science is a comprehensive process that involves preprocessing, analysis, visualization and prediction. Lets deep dive into AI and its subsets.
Artificial Intelligence (AI) is a branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is mainly divided into three categories as below
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI).
Narrow AI sometimes referred to as ‘Weak AI’, performs a single task in a particular way at its best. For example, an automated coffee machine robs which performs a well-defined sequence of actions to make coffee.
Whereas AGI, which is also referred to as ‘Strong AI’ performs a wide range of tasks that involve thinking and reasoning like a human.
Some example is Google Assist, Alexa, Chatbots which uses Natural Language Processing (NPL). Artificial Super Intelligence (ASI) is the advanced version that outperforms human capabilities. It can perform creative activities like art, decision making and emotional relationships.
Now let’s look at Machine Learning (ML). It is a subset of AI that involves the modelling algorithms that helps to make predictions based on the recognition of complex data patterns and sets.
Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered. Different methods of machine learning are
- supervised learning (Weak AI – Task-driven)
- non-supervised learning (Strong AI – Data Driven)
- semi-supervised learning (Strong AI -cost-effective)
- reinforced machine learning. (Strong AI – learn from mistakes)
Supervised machine learning uses historical data to understand behaviour and formulate future forecasts. Here the system consists of a designated dataset.
It is labelled with parameters for the input and the output. And as the new data comes the ML algorithm analysis the new data and gives the exact output on the basis of the fixed parameters. Supervised learning can perform classification or regression tasks.
Examples of classification tasks are image classification, face recognition, email spam classification, identify fraud detection, etc. and for regression tasks are weather forecasting, population growth prediction, etc.
Unsupervised machine learning does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly.
They use techniques such as clustering or dimensionality reduction. Clustering involves grouping data points with similar metric.
It is data-driven and some examples of clustering are movie recommendation for the user in Netflix, customer segmentation, buying habits, etc. Some dimensionality reduction examples are feature elicitation, big data visualization.
Semi-supervised machine learning works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive.
Reinforcement learning is fairly different when compared to supervised and unsupervised learning. It can be defined as a process of trial and error finally delivering results. t is achieved by the principle of iterative improvement cycle (to learn by past mistakes). Reinforcement learning has also been used to teach agents autonomous driving within simulated environments. Q-learning is an example of reinforcement learning algorithms.
Moving ahead to Deep Learning (DL), it is a subset of machine learning where you build algorithms that follow a layered architecture. DL uses multiple layers to progressively extract higher-level features from the raw input.
For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.
DL is generally referred to a deep artificial neural network and these are the algorithm sets that are extremely accurate for the problems like sound recognition, image recognition, natural language processing, etc.
To summarize Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is deep learning. Thanks to AI as it is capable of solving harder and harder problems (like detecting cancer better than oncologists) better than humans can.
Reference – https://towardsdatascience.com/understanding-the-difference-between-ai-ml-and-dl-cceb63252a6c?gi=9c15bafcd7d3
Absolutely everyone is psyched about synthetic intelligence. Great strides have been built in engineering and in the system of equipment studying. Even so, at this early phase in its enhancement, we may want to suppress our enthusiasm to some degree.
Already the price of AI can be observed in a broad variety of trades such as internet marketing and income, organization operation, insurance plan, banking and finance, and much more. In brief, it is a suitable way to conduct a wide variety of business routines from managing human money and examining people’s efficiency by way of recruitment and extra. Its potential runs via the thread of the total small business Eco construction. It is extra than clear presently that the price of AI to the full financial system can be really worth trillions of bucks.
Sometimes we may well neglect that AI is even now an act in progress. Due to its infancy, there are still limitations to the know-how that will have to be triumph over in advance of we are indeed in the brave new globe of AI.
In the latest podcast posted by the McKinsey Worldwide Institute, an organization that analyzes the worldwide economic system, Michael Chui, chairman of the firm, and James Manyika, director, mentioned what the limitations are on AI and what is getting performed to relieve them.
Manyika noted that the limits of AI are “purely technical.” He determined them as to how to demonstrate what the algorithm is executing? Why is it creating the selections, results, and forecasts that it does? Then there are practical limitations involving the info as very well as its use.
He stated that in the system of discovering, we are providing computer facts to not only system them, but also coach them. “We are training them,” he mentioned. They are experienced by supplying them labeled data. Educating equipment to discover objects in a photograph or to acknowledge a variance in an information stream that may perhaps indicate that equipment is heading to breakdown is executed by feeding them a good deal of labeled info that indicates that in this batch of info the equipment is about to crack and in that selection of data the equipment is not about to split and the computer figures out if a piece of equipment is about to break.
Chui determined five restrictions to AI that have to be overcome. He stated that now people are labeling the facts. For example, people today are likely via pics of website traffic and tracing out the cars and trucks and the lane markers to create labeled info that self-driving autos can use to produce the algorithm necessary to drive the autos.
Manyika mentioned that he is aware of students who go to a community library to label artwork so that algorithms can be established that the computer system utilizes to make forecasts. For instance, in the United Kingdom, groups of people are identifying pictures of distinct breeds of dogs, making use of labeled facts that are used to create algorithms so that the personal computer can detect the data and know what it is.
This method is remaining employed for health care reasons, he pointed out. People are labeling images of different varieties of tumors so that when a computer scans them, it can recognize what a tumor is and what form of tumor it is.
The issue is that too much sum of knowledge is essential to instruct the personal computer. The challenge is to create a way for the computer system to go as a result of the labeled knowledge more quickly.
Instruments that are now becoming used to do that incorporate generative adversarial networks (GAN). The applications use two networks — 1 generates the correct matters and the other distinguishes no matter if the computer is building the correct detail. The two networks contend towards each individual other to permit the laptop to do the appropriate issue. This system will allow a laptop or computer to produce artwork in the fashion of an individual artist or create architecture in the fashion of other matters that have been noticed.
Manyika pointed out persons are currently experimenting with other procedures of machine mastering. For illustration, he said that researchers at Microsoft Analysis Lab are creating in-stream labeling, an approach that labels the information through use. In other words and phrases, the laptop or computer is striving to interpret the knowledge dependent on how it is being made use of. Despite the fact that in-stream labeling has been about for a while, it has just lately made main strides. Even now, according to Manyika, labeling knowledge is a limitation that wants more development.
A different limitation to AI is not plenty of information. To overcome the issue, companies that develop AI are attaining data for many decades. To try and slice down the amount of time to assemble knowledge, organizations are turning to simulated environments. Producing a simulated natural environment in just a computer permits you to run much more trials so that the computer can understand a lot of extra factors more quickly.
Then there is the issue of outlining why the computer system determined what it did. Regarded as explainability, the challenge offers rules and regulators who might look into an algorithm’s decision. For case in point, if an individual has been let out of jail on bond and a person else wasn’t. An individual is going to want to know why. One particular could test explain the determination, but it certainly will be hard.
Chui defined that there is a strategy becoming made that can present the explanation. Called LIME, which stands for locally interpretable product-agnostic rationalization. It will involve hunting at sections of a model and inputs and seeing whether or not. That alters the end result. For case in point. If you are looking at a photo and hoping to establish it. If the merchandise in the photograph is a pickup truck or a car. Then if the windscreen of the truck or the back of the auto is improved. Then does either one particular of people variations make a variation? That reveals that the design is concentrating on the back of the motor vehicle. Or the windscreen of the truck to make a choice. What’s going on is that there are experiments being done. On the product to ascertain what will make a big difference.
Last but not least, biased details are also a limitation of AI. If the data likely into the laptop is biased, then the result is also biased. For illustration, we know that some communities are subject to additional police presence than other communities.
If the personal computer is to figure out whether a large selection of law enforcement in a group boundaries criminal offense and the information arrives from the community with large law enforcement presence and a neighborhood with very little if any police existence, then the computer’s choice is based mostly on more details from the neighborhood with police and no if any information from the community that does not have police. The oversampled community can lead to a skewed conclusion. So reliance on AI may possibly result in a reliance on inherent bias in the details. The challenge, therefore, is to figure out a way to “de-bias” the info.
So, as we can see the probability of AI, we also have to identify its limits. Never fret AI researchers are doing work feverishly on the difficulties. Some factors that ended up thought of limitations on AI. A number of decades in the past are not now since of its fast growth. That is why you want to constantly examine with AI researchers what is feasible right now.
